├── .gitignore ├── README.md ├── data ├── annotation │ ├── brat_export.csv │ ├── doccano_export.csv │ └── ngramviewer_export.csv ├── brat │ ├── collection_01 │ │ ├── PMC2193209.ann │ │ ├── PMC2193209.txt │ │ ├── PMC2634967.ann │ │ ├── PMC2634967.txt │ │ ├── PMC2646571.ann │ │ ├── PMC2646571.txt │ │ ├── PMC2938478.ann │ │ ├── PMC2938478.txt │ │ ├── PMC3046151.ann │ │ ├── PMC3046151.txt │ │ ├── PMC3095633.ann │ │ ├── PMC3095633.txt │ │ ├── PMC3173465.ann │ │ ├── PMC3173465.txt │ │ ├── PMC3189223.ann │ │ ├── PMC3189223.txt │ │ ├── PMC3235500.ann │ │ ├── PMC3235500.txt │ │ ├── PMC3304099.ann │ │ ├── PMC3304099.txt │ │ ├── annotation.conf │ │ └── visual.conf │ └── collection_02 │ │ ├── PMC200936.ann │ │ ├── PMC200936.txt │ │ ├── PMC2193209.ann │ │ ├── PMC2193209.txt │ │ ├── PMC2196041.ann │ │ ├── PMC2196041.txt │ │ ├── PMC2587175.ann │ │ ├── PMC2587175.txt │ │ ├── PMC2634967.ann │ │ ├── PMC2634967.txt │ │ ├── PMC2646571.ann │ │ ├── PMC2646571.txt │ │ ├── PMC2772737.ann │ │ ├── PMC2772737.txt │ │ ├── PMC2783637.ann │ │ ├── PMC2783637.txt │ │ ├── PMC2805085.ann │ │ ├── PMC2805085.txt │ │ ├── PMC2938478.ann │ │ ├── PMC2938478.txt │ │ ├── PMC2983473.ann │ │ ├── PMC2983473.txt │ │ ├── PMC2989239.ann │ │ ├── PMC2989239.txt │ │ ├── PMC2996551.ann │ │ ├── PMC2996551.txt │ │ ├── PMC3046151.ann │ │ ├── PMC3046151.txt │ │ ├── PMC3064981.ann │ │ ├── PMC3064981.txt │ │ ├── PMC3067507.ann │ │ ├── PMC3067507.txt │ │ ├── PMC3092345.ann │ │ ├── PMC3092345.txt │ │ ├── PMC3095633.ann │ │ ├── PMC3095633.txt │ │ ├── PMC3173465.ann │ │ ├── PMC3173465.txt │ │ ├── PMC3189223.ann │ │ ├── PMC3189223.txt │ │ ├── PMC3204990.ann │ │ ├── PMC3204990.txt │ │ ├── PMC3228524.ann │ │ ├── PMC3228524.txt │ │ ├── PMC3235500.ann │ │ ├── PMC3235500.txt │ │ ├── PMC3246047.ann │ │ ├── PMC3246047.txt │ │ ├── PMC3249647.ann │ │ ├── PMC3249647.txt │ │ ├── PMC3304099.ann │ │ ├── PMC3304099.txt │ │ ├── PMC3317433.ann │ │ ├── PMC3317433.txt │ │ ├── PMC3321800.ann │ │ ├── PMC3321800.txt │ │ ├── PMC3323935.ann │ │ ├── PMC3323935.txt │ │ ├── PMC3639604.ann │ │ ├── PMC3639604.txt │ │ ├── PMC3650071.ann │ │ ├── PMC3650071.txt │ │ ├── PMC3711858.ann │ │ ├── PMC3711858.txt │ │ ├── PMC3750006.ann │ │ ├── PMC3750006.txt │ │ ├── PMC3787487.ann │ │ ├── PMC3787487.txt │ │ ├── PMC3791721.ann │ │ ├── PMC3791721.txt │ │ ├── PMC3842119.ann │ │ ├── PMC3842119.txt │ │ ├── PMC3850168.ann │ │ ├── PMC3850168.txt │ │ ├── PMC3854702.ann │ │ ├── PMC3854702.txt │ │ ├── PMC3855395.ann │ │ ├── PMC3855395.txt │ │ ├── PMC3926063.ann │ │ ├── PMC3926063.txt │ │ ├── PMC3927957.ann │ │ ├── PMC3927957.txt │ │ ├── PMC4007342.ann │ │ ├── PMC4007342.txt │ │ ├── PMC4023883.ann │ │ ├── PMC4023883.txt │ │ ├── PMC4056277.ann │ │ ├── PMC4056277.txt │ │ ├── PMC4084624.ann │ │ ├── PMC4084624.txt │ │ ├── PMC4100769.ann │ │ ├── PMC4100769.txt │ │ ├── PMC4151505.ann │ │ ├── PMC4151505.txt │ │ ├── PMC4159719.ann │ │ ├── PMC4159719.txt │ │ ├── PMC4168117.ann │ │ ├── PMC4168117.txt │ │ ├── PMC4214202.ann │ │ ├── PMC4214202.txt │ │ ├── PMC4224975.ann │ │ ├── PMC4224975.txt │ │ ├── PMC4233385.ann │ │ ├── PMC4233385.txt │ │ ├── PMC4241840.ann │ │ ├── PMC4241840.txt │ │ ├── PMC4291544.ann │ │ ├── PMC4291544.txt │ │ ├── PMC4337382.ann │ │ ├── PMC4337382.txt │ │ ├── PMC4385920.ann │ │ ├── PMC4385920.txt │ │ ├── PMC4418961.ann │ │ ├── PMC4418961.txt │ │ ├── PMC4423225.ann │ │ ├── PMC4423225.txt │ │ ├── PMC4426480.ann │ │ ├── PMC4426480.txt │ │ ├── PMC4451961.ann │ │ ├── PMC4451961.txt │ │ ├── PMC4474185.ann │ │ ├── PMC4474185.txt │ │ ├── PMC4552951.ann │ │ ├── PMC4552951.txt │ │ ├── PMC4592272.ann │ │ ├── PMC4592272.txt │ │ ├── PMC4628936.ann │ │ ├── PMC4628936.txt │ │ ├── PMC4649113.ann │ │ ├── PMC4649113.txt │ │ ├── PMC4710466.ann │ │ ├── PMC4710466.txt │ │ ├── PMC4720349.ann │ │ ├── PMC4720349.txt │ │ ├── PMC4851424.ann │ │ ├── PMC4851424.txt │ │ ├── PMC4856445.ann │ │ ├── PMC4856445.txt │ │ ├── PMC4905708.ann │ │ ├── PMC4905708.txt │ │ ├── PMC4959015.ann │ │ ├── PMC4959015.txt │ │ ├── PMC5020626.ann │ │ ├── PMC5020626.txt │ │ ├── PMC5052263.ann │ │ ├── PMC5052263.txt │ │ ├── PMC5112176.ann │ │ ├── PMC5112176.txt │ │ ├── PMC5118948.ann │ │ ├── PMC5118948.txt │ │ ├── PMC5191835.ann │ │ ├── PMC5191835.txt │ │ ├── PMC5206501.ann │ │ ├── PMC5206501.txt │ │ ├── PMC5257256.ann │ │ ├── PMC5257256.txt │ │ ├── PMC5290235.ann │ │ ├── PMC5290235.txt │ │ ├── PMC5293011.ann │ │ ├── PMC5293011.txt │ │ ├── PMC5342705.ann │ │ ├── PMC5342705.txt │ │ ├── PMC5343661.ann │ │ ├── PMC5343661.txt │ │ ├── PMC5417820.ann │ │ ├── PMC5417820.txt │ │ ├── PMC5429091.ann │ │ ├── PMC5429091.txt │ │ ├── PMC5464295.ann │ │ ├── PMC5464295.txt │ │ ├── PMC5519767.ann │ │ ├── PMC5519767.txt │ │ ├── PMC5520220.ann │ │ ├── PMC5520220.txt │ │ ├── PMC5578684.ann │ │ ├── PMC5578684.txt │ │ ├── PMC5591438.ann │ │ ├── PMC5591438.txt │ │ ├── annotation.conf │ │ ├── unannotated │ │ ├── PMC5611819.ann │ │ ├── PMC5611819.txt │ │ ├── PMC5611846.ann │ │ ├── PMC5611846.txt │ │ ├── PMC5648021.ann │ │ ├── PMC5648021.txt │ │ ├── PMC5727967.ann │ │ ├── PMC5727967.txt │ │ ├── PMC5749247.ann │ │ ├── PMC5749247.txt │ │ ├── PMC5833121.ann │ │ ├── PMC5833121.txt │ │ ├── PMC5876181.ann │ │ ├── PMC5876181.txt │ │ ├── PMC5923349.ann │ │ ├── PMC5923349.txt │ │ ├── PMC5983667.ann │ │ ├── PMC5983667.txt │ │ ├── PMC6092975.ann │ │ ├── PMC6092975.txt │ │ ├── PMC6122729.ann │ │ ├── PMC6122729.txt │ │ ├── PMC6130380.ann │ │ ├── PMC6130380.txt │ │ ├── PMC6141714.ann │ │ ├── PMC6141714.txt │ │ ├── PMC6157333.ann │ │ ├── PMC6157333.txt │ │ ├── PMC6197911.ann │ │ ├── PMC6197911.txt │ │ ├── PMC6274670.ann │ │ ├── PMC6274670.txt │ │ ├── PMC6282816.ann │ │ ├── PMC6282816.txt │ │ ├── PMC6290922.ann │ │ ├── PMC6290922.txt │ │ ├── PMC6372559.ann │ │ ├── PMC6372559.txt │ │ ├── PMC6373736.ann │ │ └── PMC6373736.txt │ │ └── visual.conf ├── doccano │ ├── annotations_export_01.jsonl │ ├── annotations_export_02.jsonl │ ├── annotations_import_01.jsonl │ └── annotations_import_02.jsonl ├── meta │ ├── cell_types.csv │ ├── cytokines.csv │ ├── raw │ │ ├── cell_types.manual.csv │ │ ├── cl.raw.csv │ │ ├── cytokines.cameron.csv │ │ ├── cytokines.ckr.xls │ │ ├── cytokines.manual.csv │ │ ├── filters.csv │ │ ├── pro.raw.csv.gz │ │ ├── transcription_factors.lambert.csv │ │ └── transcription_factors.manual.csv │ ├── surface_proteins.csv │ └── transcription_factors.csv ├── results │ ├── labels.csv │ ├── predictions.csv.gz │ └── tags.csv.gz └── supervision │ └── immunexpresso │ ├── data.csv │ ├── import.csv │ └── verbs.csv ├── docker ├── Dockerfile ├── README.md └── requirements.txt ├── docs ├── annotations │ └── guideline.md └── images │ ├── annotation-examples.pdf │ ├── relation_examples.png │ └── training_outline.png ├── env.sh ├── pipeline ├── 01-entrez-import.ipynb ├── 01-pmcoa-import.ipynb ├── 02-meta-import-cl.ipynb ├── 02-meta-import-pro.ipynb ├── 03-meta-cell-types.ipynb ├── 03-meta-cytokines.ipynb ├── 03-meta-surface-proteins.ipynb ├── 03-meta-transcription-factors.ipynb ├── 04-immunexpresso.ipynb ├── 05-document-loader.ipynb ├── 06-candidate-generator.ipynb ├── 07-candidate-splits.ipynb ├── 08-candidate-lfs.ipynb ├── 09-modeling-lfs.ipynb ├── 10-modeling-sgm.ipynb ├── 11-modeling-bert.ipynb ├── 11-modeling-rnn-strong.ipynb ├── 11-modeling-rnn-weak.ipynb ├── 12-analysis-scores.ipynb ├── 13-analysis-relations.ipynb ├── misc │ ├── bert │ │ ├── v01 │ │ │ ├── run_tcre_dataset_utils.py │ │ │ └── training.py │ │ └── v02 │ │ │ ├── README.md │ │ │ ├── run_dataset.py │ │ │ ├── run_example.ipynb │ │ │ └── utils_dataset.py │ ├── eda │ │ ├── eda-cl.ipynb │ │ ├── eda-meta.ipynb │ │ ├── eda-parse-tree.ipynb │ │ ├── eda-phenotype.ipynb │ │ ├── eda-pmcoa.ipynb │ │ ├── eda-pro.ipynb │ │ ├── eda-scibert.ipynb │ │ ├── eda-snorkel-schema.ipynb │ │ └── eda-word2vec.ipynb │ ├── modeling │ │ ├── label-model-training-v1.ipynb │ │ ├── label-model-training-v2.ipynb │ │ ├── label-model-training-v3-r1-analysis.ipynb │ │ ├── label-model-training-v3-r2-analysis.ipynb │ │ └── label-model-training-v3.ipynb │ ├── scripts │ │ ├── click-test.py │ │ └── missing-cand-debug.py │ ├── simulation │ │ ├── sim-f1-scores.ipynb │ │ └── sim-rnn-scenarios.ipynb │ ├── snorkel │ │ ├── crosslink-cand-doc.ipynb │ │ ├── crosslink-cand-type.ipynb │ │ └── id-set-generator.ipynb │ ├── spacy │ │ └── spacy-examples.ipynb │ └── tokenization │ │ ├── protein-tokenization-analysis.ipynb │ │ ├── protein-tokenization-dataset.ipynb │ │ └── protein-tokenization-example.ipynb └── scripts │ ├── brat-label-export.ipynb │ ├── brat-label-import.ipynb │ ├── candidate-relocation.ipynb │ ├── docanno-annotator.ipynb │ ├── move-annotation-files.ipynb │ ├── snorkel-annotator.ipynb │ └── tagging-frequency.ipynb ├── results ├── summary.ipynb └── summary.render.ipynb └── src ├── ptkn ├── __init__.py ├── protein_tokenization.py └── protein_tokenization_test.py └── tcre ├── __init__.py ├── brat.py ├── entrez ├── __init__.py └── integration.py ├── env.py ├── exec ├── __init__.py └── v1 │ ├── __init__.py │ ├── bert │ ├── README.md │ ├── __init__.py │ ├── run_dataset.py │ └── utils_dataset.py │ ├── cli.py │ ├── cli_client.py │ ├── model.py │ └── optim.py ├── integration.py ├── ix.py ├── labeling.py ├── lib.py ├── logging.py ├── meta.py ├── modeling ├── __init__.py ├── data.py ├── features.py ├── metrics.py ├── models │ ├── __init__.py │ ├── estimators.py │ └── rnn.py ├── sampling.py ├── simulation.py ├── training.py ├── utils.py └── vocab.py ├── nb ├── __init__.py └── utils.py ├── parsing.py ├── processing.py ├── query.py ├── supervision.py ├── tagging.py ├── tokenization.py └── visualization.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | 106 | # snorkel 107 | snorkel.*.db 108 | snorkel.db 109 | checkpoints/ 110 | .stats_cache 111 | viz/ 112 | dask-worker-space/ 113 | runs/ 114 | data/snorkel 115 | -------------------------------------------------------------------------------- /data/brat/collection_01/annotation.conf: -------------------------------------------------------------------------------- 1 | [entities] 2 | CELL_LINE 3 | CELL_TYPE 4 | DNA 5 | PROTEIN 6 | RNA 7 | CYTOKINE 8 | TF 9 | 10 | [relations] 11 | Induction Arg1:CYTOKINE, Arg2:CELL_TYPE 12 | Differentiation Arg1:TF, Arg2:CELL_TYPE 13 | Secretion Arg1:CELL_TYPE, Arg2:CYTOKINE 14 | 15 | [events] 16 | 17 | [attributes] 18 | -------------------------------------------------------------------------------- /data/brat/collection_01/visual.conf: -------------------------------------------------------------------------------- 1 | [drawing] 2 | CELL_TYPE bgColor:#2ca02c 3 | CELL_LINE bgColor:#bcbd22 4 | DNA bgColor:#d62728 5 | PROTEIN bgColor:#1f77b4 6 | RNA bgColor:#ff7f0e 7 | CYTOKINE bgColor:#17becf 8 | TF bgColor:#9467bd 9 | [labels] 10 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC200936.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 63 93 memory cytotoxic T lymphocytes 2 | T1 CELL_TYPE 150 157 T cells 3 | T2 CELL_TYPE 162 185 long-lived memory cells 4 | T3 CELL_TYPE 261 294 naive and early activated T cells 5 | T4 PROTEIN 303 306 GFP 6 | T5 CELL_TYPE 362 388 effector cytotoxic T cells 7 | T6 DNA 406 409 GFP 8 | T7 PROTEIN 514 517 TCR 9 | T8 PROTEIN 561 616 lymphocytic choriomeningitis virus glycoprotein peptide 10 | T9 PROTEIN 618 625 gp33-41 11 | T10 CELL_LINE 669 691 P14XT-GFP CD8+ T cells 12 | T11 PROTEIN 714 718 IL-2 13 | T12 DNA 803 806 GFP 14 | T13 PROTEIN 859 875 effector markers 15 | T14 CYTOKINE 958 963 IL-15 16 | T15 CYTOKINE 976 980 IL-2 17 | T17 CELL_TYPE 1054 1066 memory cells 18 | T18 PROTEIN 1114 1130 effector markers 19 | T19 CELL_TYPE 1391 1426 endogenously generated memory cells 20 | T20 CELL_TYPE 1480 1492 memory cells 21 | T16 CELL_TYPE 1015 1029 effector cells 22 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC200936.txt: -------------------------------------------------------------------------------- 1 | Effector differentiation is not prerequisite for generation of memory cytotoxic T lymphocytes. 2 | 3 | The lineage relationship between short-lived effector T cells and long-lived memory cells is not fully understood. We have described T-GFP mice previously, in which naive and early activated T cells express GFP uniformly, whereas cells that have differentiated into effector cytotoxic T cells selectively lose GFP expression. Here we studied antigen-specific CD8 T cell differentiation using T-GFP mice crossed to the TCR transgenic (Tg) mice P14 (specific for the lymphocytic choriomeningitis virus glycoprotein peptide, gp33-41). After activation with antigenic peptide, P14XT-GFP CD8+ T cells cultured in high-dose IL-2 developed into cells with effector phenotype and function: they were blastoid, lost GFP expression, expressed high levels of activation and effector markers, and were capable of immediate cytotoxic function. In contrast, cells cultured in IL-15 or low-dose IL-2 never developed into full-fledged effector cells. Rather, they resembled memory cells: they were smaller, were GFP+, did not express effector markers, and were incapable of immediate cytotoxicity. However, they mediated rapid-recall responses in vitro. After adoptive transfer, they survived in vivo for at least 10 weeks and mounted a secondary immune response after antigen rechallenge that was as potent as endogenously generated memory cells. In addition to providing a simple means to generate memory cells in virtually unlimited numbers, our results suggest that effector differentiation is not a prerequisite for memory cell generation. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC2587175.ann: -------------------------------------------------------------------------------- 1 | T3 CELL_TYPE 121 128 T cells 2 | T4 PROTEIN 144 182 lineage-specific transcription factors 3 | T5 PROTEIN 184 188 TH17 4 | T10 TF 484 505 nuclear receptor RORα 5 | T11 CYTOKINE 527 531 TGFβ 6 | T12 CYTOKINE 536 540 IL-6 7 | T13 TF 589 593 RORα 8 | T14 CYTOKINE 654 659 IL-17 9 | T15 CYTOKINE 664 670 IL-17F 10 | T18 TF 749 753 RORα 11 | T19 CYTOKINE 784 789 IL-17 12 | T20 TF 850 854 RORα 13 | T21 TF 859 863 RORγ 14 | T22 TF 956 960 RORα 15 | T23 TF 965 969 RORγ 16 | T25 TF 1141 1145 RORα 17 | T26 TF 1150 1154 RORγ 18 | T1 TF 71 75 RORα 19 | T2 TF 80 84 RORγ 20 | T27 CELL_TYPE 0 4 TH17 21 | R1 Differentiation Arg1:T1 Arg2:T27 22 | R2 Differentiation Arg1:T2 Arg2:T27 23 | T6 TF 278 282 RORγ 24 | T28 CELL_TYPE 316 320 TH17 25 | T7 TF 338 342 RORγ 26 | T8 CELL_TYPE 391 395 TH17 27 | R3 Differentiation Arg1:T6 Arg2:T28 28 | T9 CELL_TYPE 437 441 TH17 29 | R4 Induction Arg1:T11 Arg2:T9 30 | R5 Induction Arg1:T12 Arg2:T9 31 | T29 TF 546 551 STAT3 32 | R6 Differentiation Arg1:T29 Arg2:T9 33 | T30 CELL_TYPE 603 607 TH17 34 | R7 Differentiation Arg1:T13 Arg2:T30 35 | R8 Secretion Arg1:T30 Arg2:T14 36 | R9 Secretion Arg1:T30 Arg2:T15 37 | T16 CELL_TYPE 911 915 TH17 38 | R10 Differentiation Arg1:T21 Arg2:T16 39 | R11 Differentiation Arg1:T20 Arg2:T16 40 | T17 CELL_TYPE 986 990 TH17 41 | R12 Differentiation Arg1:T22 Arg2:T17 42 | R13 Differentiation Arg1:T23 Arg2:T17 43 | T24 CELL_TYPE 1092 1096 TH17 44 | R14 Differentiation Arg1:T25 Arg2:T24 45 | R15 Differentiation Arg1:T26 Arg2:T24 46 | R16 CKTFEnhancement Arg1:T11 Arg2:T10 47 | R17 CKTFEnhancement Arg1:T12 Arg2:T10 48 | R18 TFCKEnhancement Arg1:T13 Arg2:T14 49 | R19 TFCKEnhancement Arg1:T13 Arg2:T15 50 | R20 TFCKEnhancement Arg1:T18 Arg2:T19 51 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC2587175.txt: -------------------------------------------------------------------------------- 1 | TH17 lineage differentiation is programmed by orphan nuclear receptors RORα and RORγ. 2 | 3 | The functional differentiation of T cells is mediated by lineage-specific transcription factors. TH17 has been recently identified as a distinct TH lineage that mediates tissue inflammation. RORγ was previously shown to regulate TH17 differentiation; RORγ deficiency, however, did not completely abolish TH17 cytokine expression. Here we report that TH17 cells also highly express another related nuclear receptor RORα, which is induced by TGFβ and IL-6 in a STAT3-dependent manner. Over-expression of RORα promotes TH17 differentiation and significantly upregulates IL-17 and IL-17F expression, possibly through the CNS2 element in the IL-17-IL-17F gene locus. RORα deficiency results in reduced IL-17 expression in vitro and in vivo. Furthermore, we found that RORα and RORγ co-expression synergistically leads to greater TH17 differentiation. Double deficiencies in RORα and RORγ globally impair TH17 generation and completely protect mice against experimental autoimmune encephalomyelitis. Therefore, TH17 lineage differentiation is mediated by both RORα and RORγ. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC2772737.ann: -------------------------------------------------------------------------------- 1 | T0 DNA 0 34 Blimp-1/Prdm1 Alternative Promoter 2 | T1 PROTEIN 106 167 zinc-finger PR domain transcriptional repressor Blimp-1/Prdm1 3 | T2 DNA 371 387 mouse Prdm1 gene 4 | T3 DNA 398 426 alternative promoter regions 5 | T4 DNA 438 461 alternative first exons 6 | T5 DNA 481 487 exon 3 7 | T6 DNA 673 700 transcriptional start sites 8 | T7 DNA 827 846 NF-κB binding sites 9 | T8 PROTEIN 867 872 NF-κB 10 | T9 PROTEIN 899 904 Prdm1 11 | T10 CELL_TYPE 922 936 mutant B cells 12 | T11 PROTEIN 953 958 Prdm1 13 | T12 CELL_TYPE 1036 1060 antibody-secreting cells 14 | T13 DNA 1077 1092 distal promoter 15 | T14 DNA 1101 1116 ∼70 kb upstream 16 | T15 DNA 1219 1226 exon 1B 17 | T16 DNA 1382 1392 Prdm1 gene 18 | T17 PROTEIN 1414 1419 NF-κB 19 | T18 PROTEIN 1441 1446 Prdm1 20 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC2772737.txt: -------------------------------------------------------------------------------- 1 | Blimp-1/Prdm1 Alternative Promoter Usage during Mouse Development and Plasma Cell Differentiation▿ . 2 | 3 | The zinc-finger PR domain transcriptional repressor Blimp-1/Prdm1 plays essential roles in primordial germ cell specification, placental, heart, and forelimb development, plasma cell differentiation, and T-cell homeostasis. The present experiments demonstrate that the mouse Prdm1 gene has three alternative promoter regions. All three alternative first exons splice directly to exon 3, containing the translational start codon. To examine possible cell-type-specific functional activities in vivo, we generated targeted deletions that selectively eliminate two of these transcriptional start sites. Remarkably, mice lacking the previously described first exon develop normally and are fertile. However, this region contains NF-κB binding sites, and as shown here, NF-κB signaling is required for Prdm1 induction. Thus, mutant B cells fail to express Prdm1 in response to lipopolysaccharide stimulation and lack the ability to become antibody-secreting cells. An alternative distal promoter located ∼70 kb upstream, giving rise to transcripts strongly expressed in the yolk sac, is dispensable. Thus, the deletion of exon 1B has no noticeable effect on expression levels in the embryo or adult tissues. Collectively, these experiments provide insight into the organization of the Prdm1 gene and demonstrate that NF-κB is a key mediator of Prdm1 expression. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC2783637.ann: -------------------------------------------------------------------------------- 1 | T0 TF 0 7 Blimp-1 2 | T1 CELL_TYPE 45 71 virus-specific CD8 T cells 3 | T2 CELL_TYPE 105 126 central memory T cell 4 | T3 PROTEIN 140 148 SUMMARY 5 | T4 CELL_TYPE 195 215 effector CD8 T cells 6 | T5 CELL_TYPE 264 289 long-lived memory T cells 7 | T7 CELL_TYPE 375 412 terminally differentiated CD8 T cells 8 | T8 TF 440 447 Blimp-1 9 | T9 CELL_TYPE 487 498 memory cell 10 | T10 CELL_TYPE 513 527 effector cells 11 | T11 TF 529 536 Blimp-1 12 | T12 CELL_TYPE 579 613 terminally differentiated effector 13 | T14 CELL_TYPE 731 762 Blimp-1-/- effector CD8 T cells 14 | T15 PROTEIN 786 803 effector molecule 15 | T16 CELL_TYPE 843 865 memory precursor cells 16 | T17 PROTEIN 946 951 CD62L 17 | T18 PROTEIN 956 960 IL-2 18 | T19 TF 1051 1058 Blimp-1 19 | T20 CELL_TYPE 1154 1174 effector CD8 T cells 20 | R1 Differentiation Arg1:T0 Arg2:T1 21 | T6 TF 341 348 Blimp-1 22 | R2 Differentiation Arg1:T6 Arg2:T7 23 | R3 Differentiation Arg1:T8 Arg2:T9 24 | T21 CELL_TYPE 695 709 central memory 25 | T22 CELL_TYPE 1115 1126 memory cell 26 | R6 Differentiation Arg1:T19 Arg2:T20 27 | R7 DifferentiationNeg Arg1:T0 Arg2:T2 28 | T13 CELL_TYPE 637 640 TEM 29 | R4 TFExpression Arg1:T12 Arg2:T11 30 | R5 TFExpression Arg1:T13 Arg2:T11 31 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC2783637.txt: -------------------------------------------------------------------------------- 1 | Blimp-1 promotes terminal differentiation of virus-specific CD8 T cells and represses the acquisition of central memory T cell properties. 2 | 3 | SUMMARY 4 | During acute infections, a small population of effector CD8 T cells evades terminal differentiation and survives as long-lived memory T cells. We demonstrate that the transcriptional repressor Blimp-1 enhances the formation of terminally differentiated CD8 T cells during LCMV infection, and Blimp-1 deficiency promotes the acquisition of memory cell properties by effector cells. Blimp-1 expression is preferentially increased in terminally differentiated effector and “effector memory” (TEM) CD8 T cells, and gradually decays after infection as central memory (TCM) cells develop. Blimp-1-/- effector CD8 T cells show some reduction in effector molecule expression, but primarily develop into memory precursor cells that survive better, and more rapidly acquire several TCM attributes, including CD62L and IL-2 expression and enhanced proliferative responses. These results reveal a critical role for Blimp-1 in controlling terminal differentiation and suppressing memory cell developmental potential in effector CD8 T cells during viral infection. 5 | 6 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC2805085.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_LINE 11 76 MUC1-Specific Th1 Effector Cell Immunotherapy Induce Differential 2 | T1 CELL_LINE 96 120 TReg Cell Subpopulations 3 | T2 CELL_TYPE 216 238 autologous lymphocytes 4 | T3 CELL_TYPE 316 347 Ag-specific CD4 and CD8 T cells 5 | T4 PROTEIN 411 415 MUC1 6 | T5 PROTEIN 522 526 MUC1 7 | T6 CYTOKINE 539 543 IL-2 8 | T7 CELL_TYPE 556 578 CD4+/Th1 effector cell 9 | T8 CELL_TYPE 638 661 peripheral blood T cell 10 | T9 PROTEIN 806 830 serum CA125 tumor marker 11 | T10 CELL_TYPE 1060 1063 PBL 12 | T11 PROTEIN 1098 1102 MUC1 13 | T12 CELL_TYPE 1291 1312 CD3/CD4/CD25+ T cells 14 | T13 TF 1447 1452 Foxp3 15 | T14 PROTEIN 1457 1463 CTLA-4 16 | T15 CELL_TYPE 1645 1666 MUC1-specific T cells 17 | T16 PROTEIN 1682 1686 CCR5 18 | T17 PROTEIN 1691 1715 CCR1 chemokine receptors 19 | T20 CELL_LINE 1833 1879 autologous MUC1-stimulated CD4+ effector cells 20 | T21 CELL_LINE 1957 1993 ” CD4/CD25+ TReg cell subpopulations 21 | T22 CELL_TYPE 1523 1536 memory T cell 22 | T23 TF 1510 1515 Foxp3 23 | T18 CYTOKINE 1734 1738 CCL4 24 | T19 CELL_TYPE 1755 1758 Th1 25 | R1 Induction Arg1:T18 Arg2:T19 26 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC2805085.txt: -------------------------------------------------------------------------------- 1 | Autologous MUC1-Specific Th1 Effector Cell Immunotherapy Induce Differential Levels of Systemic TReg Cell Subpopulations That Result in Increased Ovarian Cancer Patient Survival. 2 | 3 | Adoptive T cell immunotherapy using autologous lymphocytes is a viable treatment for patients with cancer and requires participation of Ag-specific CD4 and CD8 T cells. Here, we assessed the immunotherapeutic effects of autologous MUC1 peptide-stimulated CD4+ effector cells following adoptive transfer in patients with ovarian cancer. Using MUC1 peptide and IL-2 for ex vivo CD4+/Th1 effector cell generation, we show that three monthly treatment cycles of peripheral blood T cell restimulation and intraperitoneal re-infusion selectively modulated endogenous T cell-mediated immune responses that correlated with diminished serum CA125 tumor marker levels and enhanced patient survival. One patient remains disease free, another patient survived long-term for nearly 16 months with recurrent disease and two patients expired within 3-5 months following final infusion. Although PBL from all patients showed elevated MUC1 cytolytic activity following therapy, such responses did not correlate with therapeutic efficacy. Long-term survivors showed elevated levels of systemic memory (CD45RO) and naïve (CD45RA) CD3/CD4/CD25+ T cells when compared to that of pre-treatment levels and similarly-treated short-term survivors. Such cells co-expressed different levels of Foxp3 and CTLA-4 that resulted in progressively lower systemic Foxp3/CTLA-4 memory T cell ratios that further correlated with disease-free survival. Lastly, these patients showed elevated levels of MUC1-specific T cells expressing the CCR5 and CCR1 chemokine receptors and the chemokine CCL4 associated with Th1 cell differentiation/memory. We suggest that effective immunotherapy with autologous MUC1-stimulated CD4+ effector cells induce differential levels of systemic “Ag-experienced” and “Ag-inexperienced” CD4/CD25+ TReg cell subpopulations that influence long-term tumor immunity in ovarian cancer patients. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC2983473.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 233 244 lymphocytes 2 | T1 CELL_TYPE 312 323 lymphocytes 3 | T2 CELL_TYPE 621 628 T cells 4 | T3 CELL_TYPE 727 742 resting T cells 5 | T4 CELL_TYPE 924 931 T cells 6 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC2983473.txt: -------------------------------------------------------------------------------- 1 | The metabolic life and times of a T-cell. 2 | 3 | Summary 4 | The regulation of lymphocyte homeostasis is critical for the development and formation of productive immune responses. Cell numbers must be maintained to allow sufficient numbers of lymphocytes to combat foreign pathogens but prevent the accumulation of excess lymphocytes that may increase the risk of developing autoimmunity or neoplasia. Cell extrinsic growth factors are essential to maintain homeostasis and cell survival, and it has become increasingly apparent that a key mechanism of this control is through regulation of cell metabolism. The metabolic state of T cells can have profound influences on cell growth and survival and even differentiation. In particular, resting T cells utilize an energy efficient oxidative metabolism but shift to a highly glycolytic metabolism when stimulated to grow and proliferate by pathogen encounter. After antigen clearance, T cells must return to a more quiescent oxidative metabolism to support T-cell memory. This review highlights how these metabolic changes may be intricately involved with both T-cell growth and death in the control of homeostasis and immunity. 5 | 6 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC2989239.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 37 53 cytotoxic T cell 2 | T1 CELL_TYPE 57 68 memory cell 3 | T2 CELL_TYPE 146 158 CD8+ T cells 4 | T3 CELL_TYPE 181 193 memory cells 5 | T4 CELL_TYPE 332 363 bona fide memory cell precursor 6 | T5 CELL_TYPE 596 608 memory cells 7 | T6 CELL_TYPE 634 651 memory population 8 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC2989239.txt: -------------------------------------------------------------------------------- 1 | Once a killer, always a killer: from cytotoxic T cell to memory cell. 2 | 3 | Summary 4 | The control of the differentiation pathways followed by responding CD8+ T cells to produce protective memory cells has been intensely studied. Recent developments have identified heterogeneity at the effector cytotoxic T-lymphocyte level within which a bona fide memory cell precursor has emerged. The challenge now is to identify the cellular and molecular factors that control this developmental pathway. This review considers aspects of the regulation of the induction of effectors, the transition of effectors to memory cells, and the dynamics of the memory population. 5 | 6 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC2996551.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 123 141 memory T-cell pool 2 | T1 CELL_LINE 225 237 T-cell clone 3 | T2 PROTEIN 276 279 MHC 4 | T3 PROTEIN 310 344 T-cell receptor (TCR) –antigen–MHC 5 | T4 PROTEIN 346 354 TCR-pMHC 6 | T5 CELL_TYPE 426 433 T cells 7 | T6 CELL_LINE 610 622 T-cell clone 8 | T7 CELL_TYPE 631 645 daughter cells 9 | T8 CELL_TYPE 703 724 CD4+ and CD8+ T cells 10 | T9 PROTEIN 926 934 TCR-pMHC 11 | T10 CELL_TYPE 1003 1037 T helper type 1 memory populations 12 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC2996551.txt: -------------------------------------------------------------------------------- 1 | Nature and nurture: T-cell receptor-dependent and T-cell receptor-independent differentiation cues in the selection of the memory T-cell pool. 2 | 3 | The initiation of a T-cell response begins with the interaction of an individual T-cell clone with its cognate antigen presented by MHC. Although the strength of the T-cell receptor (TCR) –antigen–MHC (TCR-pMHC) interaction plays an important and obvious role in the recruitment of T cells into the immune response, evidence in recent years has suggested that the strength of this initial interaction can influence various other aspects of the fate of an individual T-cell clone and its daughter cells. In this review, we will describe differences in the way CD4+ and CD8+ T cells incorporate antigen-driven differentiation and survival signals during the response to acute infection. Furthermore, we will discuss increasing evidence that the quality and/or quantity of the initial TCR-pMHC interaction can drive the differentiation and long-term survival of T helper type 1 memory populations. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3067507.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 45 54 Monocytes 2 | T1 CELL_TYPE 60 75 Dendritic Cells 3 | T2 PROTEIN 152 154 CT 4 | T3 CELL_TYPE 298 318 peripheral monocytes 5 | T4 CELL_TYPE 338 353 dendritic cells 6 | T5 CELL_TYPE 355 358 DCs 7 | T6 CELL_TYPE 395 404 monocytes 8 | T7 CELL_LINE 435 469 mucosal CD103− proinflammatory DCs 9 | T8 PROTEIN 503 505 CT 10 | T9 CELL_TYPE 548 557 monocytes 11 | T10 CELL_TYPE 563 566 DCs 12 | T11 CELL_LINE 582 602 CT-treated monocytes 13 | T12 PROTEIN 623 671 granulocyte-macrophage colony-stimulating factor 14 | T13 PROTEIN 673 679 GM-CSF 15 | T14 PROTEIN 685 698 interleukin 4 16 | T15 PROTEIN 700 704 IL-4 17 | T16 PROTEIN 751 767 CD14low CD1ahigh 18 | T17 CELL_TYPE 784 809 macrophage-like phenotype 19 | T18 PROTEIN 870 872 CT 20 | T19 PROTEIN 898 938 major histocompatibility complex class I 21 | T20 PROTEIN 940 945 MHC-I 22 | T21 PROTEIN 951 957 MHC-II 23 | T22 PROTEIN 962 966 CD80 24 | T23 PROTEIN 971 999 CD86 costimulatory molecules 25 | T24 PROTEIN 1038 1042 IL-6 26 | T25 PROTEIN 1048 1053 IL-10 27 | T26 PROTEIN 1077 1104 tumor necrosis factor alpha 28 | T27 PROTEIN 1106 1111 TNF-α 29 | T28 PROTEIN 1117 1122 IL-12 30 | T29 CELL_TYPE 1132 1141 monocytes 31 | T30 CELL_TYPE 1162 1165 DCs 32 | T31 PROTEIN 1184 1186 CT 33 | T32 PROTEIN 1214 1216 CT 34 | T33 CELL_TYPE 1262 1271 monocytes 35 | T34 CELL_TYPE 1381 1390 monocytes 36 | T35 PROTEIN 1445 1462 adenylate cyclase 37 | T36 PROTEIN 1597 1599 CT 38 | T37 CELL_TYPE 1611 1620 Monocytes 39 | T38 PROTEIN 1655 1657 CT 40 | T39 CELL_TYPE 1689 1708 naïve T lymphocytes 41 | T40 PROTEIN 1762 1764 CT 42 | T41 CELL_TYPE 1804 1813 monocytes 43 | T42 CELL_TYPE 1819 1822 DCs 44 | T43 CELL_TYPE 1861 1895 activated antigen-presenting cells 45 | T44 CELL_TYPE 1897 1901 APCs 46 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3067507.txt: -------------------------------------------------------------------------------- 1 | Cholera Toxin Impairs the Differentiation of Monocytes into Dendritic Cells, Inducing Professional Antigen-Presenting Myeloid Cells ▿ . 2 | 3 | Cholera toxin (CT) is a potent adjuvant for mucosal vaccination; however, its mechanism of action has not been clarified completely. It is well established that peripheral monocytes differentiate into dendritic cells (DCs) both in vitro and in vivo and that monocytes are the in vivo precursors of mucosal CD103− proinflammatory DCs. In this study, we asked whether CT had any effects on the differentiation of monocytes into DCs. We found that CT-treated monocytes, in the presence of granulocyte-macrophage colony-stimulating factor (GM-CSF) and interleukin 4 (IL-4), failed to differentiate into classical DCs (CD14low CD1ahigh) and acquired a macrophage-like phenotype (CD14high CD1alow). Cells differentiated in the presence of CT expressed high levels of major histocompatibility complex class I (MHC-I) and MHC-II and CD80 and CD86 costimulatory molecules and produced larger amounts of IL-1β, IL-6, and IL-10 but smaller amounts of tumor necrosis factor alpha (TNF-α) and IL-12 than did monocytes differentiated into DCs in the absence of CT. The enzymatic activity of CT was found to be important for the skewing of monocytes toward a macrophage-like phenotype (Ma-DCs) with enhanced antigen-presenting functions. Indeed, treatment of monocytes with scalar doses of forskolin (FSK), an activator of adenylate cyclase, induced them to differentiate in a dose-dependent manner into a population with phenotype and functions similar to those found after CT treatment. Monocytes differentiated in the presence of CT induced the differentiation of naïve T lymphocytes toward a Th2 phenotype. Interestingly, we found that CT interferes with the differentiation of monocytes into DCs in vivo and promotes the induction of activated antigen-presenting cells (APCs) following systemic immunization. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3092345.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 28 38 CD4 T cell 2 | T1 CELL_TYPE 62 87 T follicular helper cells 3 | T2 CELL_TYPE 124 148 follicular T helper cell 4 | T3 CELL_TYPE 197 219 virus-specific T cells 5 | T4 CELL_TYPE 337 364 lyse virally infected cells 6 | T5 PROTEIN 428 442 cytokines IL-2 7 | T6 PROTEIN 444 447 TNF 8 | T7 PROTEIN 453 458 IFN-γ 9 | T8 DNA 1083 1089 ∼60–80 10 | T9 PROTEIN 1112 1123 CD8 T cell– 11 | T10 CELL_TYPE 1188 1199 CD4 T cells 12 | T11 CELL_TYPE 1518 1529 CD4 T cells 13 | T12 PROTEIN 1603 1606 TCR 14 | T13 CELL_TYPE 1683 1694 CD4 T cells 15 | T14 CELL_TYPE 1727 1741 helper subsets 16 | T15 PROTEIN 1850 1860 CD4 T cell 17 | T16 CELL_TYPE 2156 2167 CD4 T cells 18 | T17 CELL_TYPE 2185 2196 CD8 T cells 19 | T18 CELL_TYPE 2369 2380 CD8 T cells 20 | T19 PROTEIN 2530 2540 CD4 T cell 21 | T20 CYTOKINE 2655 2660 IL-21 22 | T23 CYTOKINE 2815 2820 IL-21 23 | T24 CYTOKINE 2926 2931 IL-21 24 | T25 CELL_TYPE 2966 2994 germinal center (GC) B cells 25 | T27 CELL_TYPE 3175 3182 B cells 26 | T29 CELL_TYPE 3370 3381 CD4 T cells 27 | T30 CELL_TYPE 3471 3481 CD4 T cell 28 | T31 CELL_TYPE 3795 3820 T follicular helper cells 29 | T32 CELL_TYPE 3828 3839 CD4 T cells 30 | T33 CELL_TYPE 4009 4020 CD4 T cells 31 | T34 CELL_TYPE 4146 4165 memory T and B cell 32 | T35 CELL_TYPE 4223 4238 T helper subset 33 | T36 CELL_TYPE 4364 4398 CD4 T follicular helper (Tfh) cell 34 | T37 CELL_TYPE 4463 4480 CD4 T helper cell 35 | T38 CELL_TYPE 4548 4565 CD4 T helper cell 36 | T39 PROTEIN 4621 4641 LCMV-Armstrong (Arm) 37 | T40 CELL_LINE 4645 4665 LCMV-Clone 13 (Cl 13 38 | T41 PROTEIN 4668 4676 LCMV-Arm 39 | T42 CELL_TYPE 4735 4746 CD8 T cells 40 | T43 CELL_TYPE 4775 4789 memory T cells 41 | T44 CELL_LINE 4791 4801 LCMV-Cl 13 42 | T45 PROTEIN 4869 4872 CD4 43 | T46 CELL_TYPE 4877 4888 CD8 T cells 44 | T47 CELL_TYPE 4950 4960 CD4 T cell 45 | T48 CELL_TYPE 4962 4972 CD8 T cell 46 | T21 CELL_TYPE 2688 2693 CD4 T 47 | T22 CELL_TYPE 2795 2800 CD8 T 48 | T26 CELL_TYPE 3101 3106 CD4 T 49 | T28 CELL_TYPE 3187 3192 CD8 T 50 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3228524.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 20 46 STAT3 transcription factor 2 | T1 CELL_TYPE 95 114 human T cell memory 3 | T3 TF 260 265 STAT3 4 | T5 PROTEIN 389 394 STAT3 5 | T6 CELL_TYPE 551 587 central memory CD4+ and CD8+ T cells 6 | T7 CELL_LINE 618 631 Naïve T cells 7 | T8 PROTEIN 678 719 memory-related transcription factors BCL6 8 | T9 PROTEIN 724 729 SOCS3 9 | T10 TF 1061 1066 STAT3 10 | T2 TF 125 130 STAT3 11 | T12 CELL_TYPE 174 182 T helper 12 | R1 Differentiation Arg1:T2 Arg2:T12 13 | T4 CELL_TYPE 1076 1092 central memory T 14 | R2 Differentiation Arg1:T10 Arg2:T4 15 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3228524.txt: -------------------------------------------------------------------------------- 1 | A critical role for STAT3 transcription factor signaling in the development and maintenance of human T cell memory. 2 | 3 | Summary 4 | STAT3 transcription factor signaling in specific T helper cell differentiation have been well described, whereas the broader roles for STAT3 in lymphocyte memory are less clear. Patients with autosomal dominant hyper-IgE syndrome (AD-HIES) carry dominant negative STAT3 mutations and are susceptible to a variety of bacterial and fungal infections. We found that AD-HIES patients have a cell-intrinsic defect in the number of central memory CD4+ and CD8+ T cells compared to healthy controls. Naïve T cells from AD-HIES patients had lower expression of memory-related transcription factors BCL6 and SOCS3, a primary proliferation defect, and they failed to acquire central memory-like surface phenotypes in vitro. AD-HIES patients showed a decreased ability to control varicella zoster virus (VZV) and Epstein-Barr virus (EBV) latency, and T cell memory to both of these viruses was compromised. These data point to a specific role for STAT3 in human central memory T cell formation and in control of certain chronic viruses. 5 | 6 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3246047.ann: -------------------------------------------------------------------------------- 1 | T2 PROTEIN 120 149 pattern recognition receptors 2 | T3 CELL_TYPE 153 168 dendritic cells 3 | T4 CELL_TYPE 170 173 DCs 4 | T5 CELL_TYPE 179 190 macrophages 5 | T6 PROTEIN 213 222 cytokines 6 | T7 CELL_TYPE 255 267 CD4+ T cells 7 | T9 CYTOKINE 342 370 transforming growth factor-β 8 | T10 CYTOKINE 372 377 TGF-β 9 | T12 PROTEIN 470 478 cytokine 10 | T13 CYTOKINE 556 560 IL-6 11 | T15 CYTOKINE 716 720 IL-1 12 | T17 CYTOKINE 844 848 IL-6 13 | T19 CELL_TYPE 1071 1088 Th17 cell lineage 14 | T1 CELL_TYPE 60 71 T helper-17 15 | T11 CELL_TYPE 402 413 T helper-17 16 | T20 CELL_TYPE 415 419 Th17 17 | T8 CYTOKINE 303 321 interleukin (IL)-6 18 | R1 Induction Arg1:T8 Arg2:T11 19 | R2 Induction Arg1:T8 Arg2:T20 20 | R3 Induction Arg1:T9 Arg2:T11 21 | R4 Induction Arg1:T9 Arg2:T20 22 | R5 Induction Arg1:T10 Arg2:T11 23 | R6 Induction Arg1:T10 Arg2:T20 24 | T14 CELL_TYPE 587 591 Th17 25 | R7 Induction Arg1:T13 Arg2:T14 26 | T21 CELL_TYPE 670 674 Th17 27 | T16 CELL_TYPE 765 769 Th17 28 | R8 Induction Arg1:T15 Arg2:T16 29 | T22 CELL_TYPE 887 891 Th17 30 | R9 Induction Arg1:T17 Arg2:T22 31 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3246047.txt: -------------------------------------------------------------------------------- 1 | Priming microenvironments dictate cytokine requirements for T helper-17 cell lineage commitment. 2 | 3 | Summary 4 | Activation of pattern recognition receptors on dendritic cells (DCs) and macrophages leads to secretion of cytokines that control differentiation of CD4+ T cells. The current understanding is that interleukin (IL)-6 in combination with transforming growth factor-β (TGF-β) leads to generation of T helper-17 (Th17) lineage cells. Here, we have discovered that the cytokine requirements for Th17 cell polarization depend on the site of priming. While IL-6 played a critical role in Th17 cell lineage priming in the skin and mucosal tissues, it was not required for Th17 cell priming in the spleen. In contrast, IL-1 played an irreplaceable role for priming of Th17 cell lineage cells in all tissues. Importantly, we have demonstrated that IL-6 independent and dependent pathways of Th17 cell differentiation are guided by DCs residing in various tissues. These results reveal fundamental differences by which the systemic, mucosal and cutaneous immune systems guide Th17 cell lineage commitment. 5 | 6 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3249647.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 104 114 CD4 T cell 2 | T1 CELL_LINE 242 264 CD4 T cell populations 3 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3249647.txt: -------------------------------------------------------------------------------- 1 | Effector T cell plasticity: flexibility in the face of changing circumstances. 2 | 3 | As additional states of CD4 T cell differentiation are uncovered, their flexibility is also beginning to be recognized. Components that control the plasticity of CD4 T cell populations include their cellular conditions, clonality, transcriptional circuitry and chromatin modifications. Appearance of cellular flexibility may arise from truly flexible genetic programs or alternately heterogeneous populations. New tools will be needed to define the rules that allow or prohibit cellular transitions. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3317433.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 0 24 Myeloid Suppressor Cells 2 | T1 CELL_TYPE 36 76 Retinal Pigment Epithelial Cells Inhibit 3 | T2 CELL_TYPE 176 185 RPE cells 4 | T3 CELL_LINE 257 266 RPE cells 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3317433.txt: -------------------------------------------------------------------------------- 1 | Myeloid Suppressor Cells Induced by Retinal Pigment Epithelial Cells Inhibit Autoreactive T-Cell Responses That Lead to Experimental Autoimmune Uveitis. 2 | 3 | The authors show that RPE cells induce MDSC differentiation, which could be another mechanism by which RPE cells regulate immune responses in the retina. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3321800.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 72 92 Mature T Lymphocytes 2 | T1 CELL_LINE 273 308 autologous gene-modified stem cells 3 | T2 CELL_TYPE 375 389 mature T cells 4 | T3 CELL_LINE 543 555 T-cell clone 5 | T4 DNA 607 632 T-cell protooncogene LMO2 6 | T5 CELL_TYPE 649 672 CD4/CD8 double-negative 7 | T6 DNA 965 987 IL2RA and IL15RA genes 8 | T7 CELL_LINE 1091 1114 immortalized cell clone 9 | T8 PROTEIN 1126 1130 IL-2 10 | T9 PROTEIN 1174 1185 IL2RA chain 11 | T10 PROTEIN 1190 1194 LMO2 12 | T11 CELL_LINE 1224 1248 cultured primary T cells 13 | T12 CELL_TYPE 1340 1354 mature T cells 14 | T13 PROTEIN 1439 1452 IL-2 receptor 15 | T14 PROTEIN 1461 1479 protooncogene LMO2 16 | T15 CELL_TYPE 1536 1556 mature T lymphocytes 17 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3321800.txt: -------------------------------------------------------------------------------- 1 | Retroviral Insertional Mutagenesis Can Contribute to Immortalization of Mature T Lymphocytes. 2 | 3 | Several cases of T-cell leukemia caused by gammaretroviral insertional mutagenesis in children treated for x-linked severe combined immunodeficiency (SCID) by transplantation of autologous gene-modified stem cells were reported. In a comparative analysis, we recently showed that mature T cells, on the contrary, are highly resistant to transformation by gammaretroviral gene transfer. In the present study, we observed immortalization of a single T-cell clone in vitro after gammaretroviral transduction of the T-cell protooncogene LMO2. This clone was CD4/CD8 double-negative, but expressed a single rearranged T-cell receptor. The clone was able to overgrow nonmanipulated competitor T-cell populations in vitro, but no tumor formation was observed after transplantation into Rag-1 deficient recipients. The retroviral integration site (RIS) was found to be near the IL2RA and IL15RA genes. As a consequence, both receptors were constitutively upregulated on the RNA and protein level and the immortalized cell clone was highly IL-2 dependent. Ectopic expression of both, the IL2RA chain and LMO2, induced long-term growth in cultured primary T cells. This study demonstrates that insertional mutagenesis can contribute to immortalization of mature T cells, although this is a rare event. Furthermore, the results show that signaling of the IL-2 receptor and the protooncogene LMO2 can act synergistically in maligniant transformation of mature T lymphocytes. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3323935.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 120 153 melanoma differentiation antigens 2 | T1 PROTEIN 234 242 antigens 3 | T2 PROTEIN 251 265 Melan-A/MART-1 4 | T3 PROTEIN 270 275 gp100 5 | T4 CELL_TYPE 755 769 melanoma cells 6 | T5 PROTEIN 814 835 MelanA/MART-1 antigen 7 | T6 CELL_LINE 854 872 melanoma cell line 8 | T7 CYTOKINE 915 928 interleukin-2 9 | T8 CELL_TYPE 945 958 Jurkat T cell 10 | T9 PROTEIN 983 998 T-cell receptor 11 | T10 PROTEIN 1048 1070 Melan-A/MART-1 protein 12 | T11 CELL_TYPE 1696 1703 T cells 13 | R1 Secretion Arg1:T8 Arg2:T7 14 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3323935.txt: -------------------------------------------------------------------------------- 1 | A Screening Assay to Identify Agents That Enhance T-Cell Recognition of Human Melanomas. 2 | 3 | Abstract 4 | Although a series of melanoma differentiation antigens for immunotherapeutic targeting has been described, heterogeneous expression of antigens such as Melan-A/MART-1 and gp100 results from a loss of antigenic expression in many late stage tumors. Antigen loss can represent a means for tumor escape from immune recognition, and a barrier to immunotherapy. However, since antigen-negative tumor phenotypes frequently result from reversible gene regulatory events, antigen enhancement represents a potential therapeutic opportunity. Accordingly, we have developed a cell-based assay to screen for compounds with the ability to enhance T-cell recognition of melanoma cells. This assay is dependent on augmentation of MelanA/MART-1 antigen presentation by a melanoma cell line (MU89). T-cell recognition is detected as interleukin-2 production by a Jurkat T cell transduced to express a T-cell receptor specific for an HLA-A2 restricted epitope of the Melan-A/MART-1 protein. This cellular assay was used to perform a pilot screen by using 480 compounds of known biological activity. From the initial proof-of-principle primary screen, eight compounds were identified as positive hits. A panel of secondary screens, including orthogonal assays, was used to validate the primary hits and eliminate false positives, and also to measure the comparative efficacy of the identified compounds. This cell-based assay, thus, yields consistent results applicable to the screening of larger libraries of compounds that can potentially reveal novel molecules which allow better recognition of treated tumors by T cells. 5 | 6 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3639604.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 0 5 CD11a 2 | T1 PROTEIN 142 146 CD18 3 | T2 PROTEIN 148 157 integrins 4 | T3 PROTEIN 163 177 α-chains CD11a 5 | T4 PROTEIN 208 226 adhesion molecules 6 | T5 PROTEIN 288 292 CD18 7 | T6 CELL_TYPE 395 405 leukocytes 8 | T7 CELL_TYPE 429 440 CD8 T cells 9 | T8 PROTEIN 457 462 CD11a 10 | T9 PROTEIN 464 469 CD11b 11 | T10 PROTEIN 475 480 CD11c 12 | T11 CELL_TYPE 541 552 CD8 T cells 13 | T12 PROTEIN 615 627 β2 integrins 14 | T13 CELL_TYPE 701 715 CD11a-, CD11b- 15 | T14 PROTEIN 758 763 CD11b 16 | T15 DNA 767 772 CD11c 17 | T16 CELL_TYPE 808 836 antigen-specific CD8 T cells 18 | T17 CELL_TYPE 872 890 primary CD8 T cell 19 | T18 CELL_TYPE 1028 1054 short-lived effector cells 20 | T19 CELL_LINE 1056 1071 KLRG1hi CD127lo 21 | T20 PROTEIN 1083 1091 cytokine 22 | T21 PROTEIN 1096 1106 granzyme B 23 | T22 PROTEIN 1151 1156 CD11a 24 | T23 CELL_TYPE 1211 1238 CD62L+ central memory cells 25 | T24 CELL_TYPE 1254 1265 CD8 T cells 26 | T25 PROTEIN 1274 1279 CD11a 27 | T26 PROTEIN 1379 1384 CD11a 28 | T27 CELL_TYPE 1444 1463 primary CD8 T cells 29 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3639604.txt: -------------------------------------------------------------------------------- 1 | CD11a Regulates Effector CD8 T Cell Differentiation and Central Memory Development in Response to Infection with Listeria monocytogenes. 2 | 3 | β2 (CD18) integrins with α-chains CD11a, -b, -c, and -d are important adhesion molecules necessary for leukocyte migration and cellular interactions. CD18 deficiency leads to recurrent bacterial infections and poor wound healing due to reduced migration of leukocytes to inflammatory sites. CD8 T cells also upregulate CD11a, CD11b, and CD11c upon activation. However, the role these molecules play for CD8 T cells in vivo is not known. To determine the function of individual β2 integrins, we examined CD8 T cell responses to Listeria monocytogenes infection in CD11a-, CD11b-, and CD11c-deficient mice. The absence of CD11b or CD11c had no effect on the generation of antigen-specific CD8 T cells. In contrast, the magnitude of the primary CD8 T cell response in CD11a-deficient mice was significantly reduced. Moreover, the response in CD11a−/− mice exhibited reduced differentiation of short-lived effector cells (KLRG1hi CD127lo), although cytokine and granzyme B production levels were unaffected. Notably, CD11a deficiency resulted in greatly enhanced generation of CD62L+ central memory cells. Surprisingly, CD8 T cells lacking CD11a mounted a robust secondary response to infection. Taken together, these findings demonstrated that CD11a expression contributes to expansion and differentiation of primary CD8 T cells but may be dispensable for secondary responses to infection. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3711858.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 0 21 CD8+ Effector T Cells 2 | T1 CELL_TYPE 123 144 CD8+ effector T cells 3 | T2 CELL_TYPE 224 235 fetal cells 4 | T3 CELL_TYPE 350 369 memory CD8+ T cells 5 | T4 PROTEIN 377 380 TCR 6 | T5 PROTEIN 407 429 paternal MHC molecules 7 | T6 PROTEIN 517 556 fetal minor histocompatibility antigens 8 | T7 DNA 558 563 mHags 9 | T8 PROTEIN 606 609 MHC 10 | T9 PROTEIN 614 618 mHag 11 | T10 CELL_TYPE 755 766 CD8+ T cell 12 | T11 CELL_TYPE 792 814 CD8+ T cell repertoire 13 | T12 CELL_TYPE 844 856 CD8+ T cells 14 | T13 CELL_TYPE 1211 1223 CD8+ T cells 15 | T14 CELL_TYPE 1311 1343 fetal extravillous trophoblast ( 16 | T15 CELL_TYPE 1357 1369 CD8+ T cells 17 | T16 CELL_TYPE 1418 1445 virus-specific CD8+ T cells 18 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3711858.txt: -------------------------------------------------------------------------------- 1 | CD8+ Effector T Cells at the Fetal–Maternal Interface, Balancing Fetal Tolerance and Antiviral Immunity. 2 | 3 | During pregnancy CD8+ effector T cells need optimal immune regulation to prevent a detrimental response to allogeneic fetal cells while providing immune protection to infections. A significant proportion of (prospective) mothers carry naïve or memory CD8+ T cells with a TCR that can directly bind to paternal MHC molecules. In addition, a high percentage of pregnant women develop specific T cell responses to fetal minor histocompatibility antigens (mHags). Under normal conditions, fetal–maternal MHC and mHag mismatches lead to elevated lymphocyte activation but do not induce pregnancy failure. Furthermore, viral infections alter the maternal CD8+ T cell response by changing the CD8+ T cell repertoire and increasing the influx of CD8+ T cells to decidual tissue. The normally high T cell activation threshold at the fetal–maternal interface may prevent efficient clearance of viral infections. Conversely, the increased inflammatory response due to viral infections may break fetal–maternal tolerance and lead to pregnancy complications. The aim of this review is to discuss the recent studies of CD8+ T cells in pregnancy, identify potential mechanisms for antigen-specific immune recognition of fetal extravillous trophoblast (EVT) cells by CD8+ T cells, and discuss the impact of viral infections and virus-specific CD8+ T cells during pregnancy. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3750006.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 4 55 Retinoic Acid-Metabolizing Enzyme Cyp26b1 Regulates 2 | T1 CELL_TYPE 287 294 T cells 3 | T2 PROTEIN 348 360 Cyp26 family 4 | T3 PROTEIN 364 388 cytochrome P450 oxidases 5 | T4 PROTEIN 427 456 T cell-specific family member 6 | T5 CELL_TYPE 461 473 CD4+ T cells 7 | T6 CELL_TYPE 522 529 T cells 8 | T7 PROTEIN 531 538 Cyp26b1 9 | T8 CELL_TYPE 836 856 iTreg and TH17 cells 10 | T9 PROTEIN 870 883 naïve Cyp26b1 11 | T10 CELL_TYPE 888 900 CD4+ T cells 12 | T11 PROTEIN 906 910 Rag1 13 | T12 PROTEIN 1572 1592 nuclear RA receptors 14 | T13 PROTEIN 1594 1598 RARs 15 | T14 PROTEIN 1604 1624 retinoid X receptors 16 | T15 DNA 1686 1706 RA response elements 17 | T16 CELL_TYPE 1814 1829 dendritic cells 18 | T17 CELL_TYPE 1831 1834 DCs 19 | T18 CELL_TYPE 1899 1911 CD4+ T cells 20 | T22 CELL_TYPE 2228 2235 T cells 21 | T23 CELL_LINE 2371 2390 TH1 effector T cell 22 | T24 CELL_TYPE 2585 2592 T cells 23 | T25 CELL_TYPE 2707 2714 T cells 24 | T26 PROTEIN 2798 2836 cytochrome P450 family 26, subfamily b 25 | T27 CELL_TYPE 2956 2963 T cells 26 | T28 DNA 3046 3067 cytokine TGF-β1 [10]. 27 | T29 PROTEIN 3138 3162 gut-homing receptor CCR9 28 | T30 CELL_TYPE 3166 3173 T cells 29 | T31 CELL_TYPE 3313 3320 T cells 30 | T32 CELL_TYPE 3448 3475 Intestinal cell populations 31 | T33 CELL_TYPE 3486 3489 DCs 32 | T34 CELL_TYPE 3494 3510 epithelial cells 33 | T35 TF 3732 3737 Foxp3 34 | T36 CYTOKINE 3741 3746 IL-17 35 | T37 CELL_TYPE 4073 4098 Cyp26b1-deficient T cells 36 | T38 CELL_TYPE 4227 4252 Cyp26b1-deficient T cells 37 | T39 DNA 4444 4452 germline 38 | T40 CELL_TYPE 4581 4594 adult T cells 39 | T41 PROTEIN 4669 4676 Cyp26b1 40 | T42 DNA 4713 4728 Cre recombinase 41 | T43 DNA 4754 4775 Cd4 promoter/enhancer 42 | T44 PROTEIN 4789 4796 Cyp26b1 43 | T45 PROTEIN 4926 4934 Cyp26b1 44 | T46 CYTOKINE 2054 2059 TGF-β 45 | T20 CELL_TYPE 2022 2027 iTreg 46 | T19 CELL_TYPE 1992 2014 inducible regulatory T 47 | T21 CELL_TYPE 2111 2122 T helper 17 48 | T47 CELL_TYPE 2124 2128 TH17 49 | R1 Induction Arg1:T46 Arg2:T20 50 | R2 Induction Arg1:T46 Arg2:T19 51 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3787487.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 16 51 Hematopoietic Stem/Progenitor Cells 2 | T3 PROTEIN 196 200 CD19 3 | T4 CELL_TYPE 227 234 T-cells 4 | T5 CELL_TYPE 258 283 B-lineage malignant cells 5 | T6 CELL_TYPE 422 436 effector cells 6 | T7 CELL_TYPE 494 529 hematopoietic stem/progenitor cells 7 | T8 CELL_TYPE 531 535 HSPC 8 | T9 PROTEIN 586 589 CAR 9 | T10 CELL_TYPE 644 658 effector cells 10 | T11 CELL_TYPE 690 715 B-lineage malignant cells 11 | T12 CELL_TYPE 770 794 natural killer (NK) cell 12 | T13 CELL_TYPE 814 825 human HSPCs 13 | T14 CELL_LINE 902 919 CD19-specific CAR 14 | T15 CELL_LINE 1039 1071 CAR-bearing myeloid and NK cells 15 | T16 CELL_TYPE 1091 1110 CD19-positive cells 16 | T17 PROTEIN 1117 1138 second-generation CAR 17 | T18 PROTEIN 1149 1161 CD28 domains 18 | T19 CELL_TYPE 1186 1194 NK cells 19 | T20 PROTEIN 1281 1285 HSPC 20 | T21 PROTEIN 1291 1294 CAR 21 | T22 CELL_TYPE 1345 1359 effector cells 22 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3787487.txt: -------------------------------------------------------------------------------- 1 | Modification of Hematopoietic Stem/Progenitor Cells with CD19-Specific Chimeric Antigen Receptors as a Novel Approach for Cancer Immunotherapy. 2 | 3 | Abstract 4 | Chimeric antigen receptors (CARs) against CD19 have been shown to direct T-cells to specifically target B-lineage malignant cells in animal models and clinical trials, with efficient tumor cell lysis. However, in some cases, there has been insufficient persistence of effector cells, limiting clinical efficacy. We propose gene transfer to hematopoietic stem/progenitor cells (HSPC) as a novel approach to deliver the CD19-specific CAR, with potential for ensuring persistent production of effector cells of multiple lineages targeting B-lineage malignant cells. Assessments were performed using in vitro myeloid or natural killer (NK) cell differentiation of human HSPCs transduced with lentiviral vectors carrying first and second generations of CD19-specific CAR. Gene transfer did not impair hematopoietic differentiation and cell proliferation when transduced at 1–2 copies/cell. CAR-bearing myeloid and NK cells specifically lysed CD19-positive cells, with second-generation CAR including CD28 domains being more efficient in NK cells. Our results provide evidence for the feasibility and efficacy of the modification of HSPC with CAR as a strategy for generating multiple lineages of effector cells for immunotherapy against B-lineage malignancies to augment graft-versus-leukemia activity. 5 | 6 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3791721.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 33 59 inflammatory helper T-cell 2 | T1 PROTEIN 159 163 PD-1 3 | T2 PROTEIN 170 187 membrane receptor 4 | T3 CELL_TYPE 225 235 leucocytes 5 | T4 PROTEIN 245 249 PD-1 6 | T5 CELL_TYPE 357 370 T lymphocytes 7 | T6 PROTEIN 422 426 PD-1 8 | T7 PROTEIN 436 449 T-lymphocyte– 9 | T8 PROTEIN 511 515 PD-1 10 | T9 CELL_TYPE 535 554 innate immune cells 11 | T10 PROTEIN 654 658 IL-6 12 | T11 PROTEIN 662 686 proinflammatory cytokine 13 | T12 CELL_TYPE 718 758 inflammatory autoreactive helper T cells 14 | T13 PROTEIN 865 869 PD-1 15 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3791721.txt: -------------------------------------------------------------------------------- 1 | Programmed cell death 1 inhibits inflammatory helper T-cell development through controlling the innate immune response. 2 | 3 | Significance 4 | Programmed cell death 1 (PD-1) is a membrane receptor that transmits inhibitory signals on leucocytes. Lack of PD-1 in mice results in various autoimmune diseases, which have been believed to be caused by the activation of T lymphocytes without inhibition. The current study reveals that PD-1 inhibits T-lymphocyte–mediated autoimmunity through regulating macrophage function. PD-1 deficiency only in innate immune cells caused dysregulated macrophage responses against mycobacterial adjuvant and enhanced production of IL-6, a proinflammatory cytokine, leading to the development of inflammatory autoreactive helper T cells and the exacerbation of experimental autoimmune encephalomyelitis. The lymphocyte extrinsic regulation of PD-1 provides a unique perspective on the maintenance of the immune self-tolerance and the understanding of the development of autoimmune diseases. 5 | 6 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3842119.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 41 46 IL-27 2 | T1 PROTEIN 79 84 Type1 3 | T2 CELL_TYPE 111 119 NK cells 4 | T3 PROTEIN 142 168 rapamycin kinase inhibitor 5 | T4 CELL_TYPE 366 368 DC 6 | T5 PROTEIN 386 412 pro-inflammatory cytokines 7 | T6 PROTEIN 462 469 RAPA-DC 8 | T7 PROTEIN 588 624 inflammatory cytokine cocktail (ICC) 9 | T8 PROTEIN 630 635 IFN-γ 10 | T9 PROTEIN 704 739 HLA-DR and co-stimulatory molecules 11 | T10 PROTEIN 751 759 IL-12p70 12 | T11 PROTEIN 764 769 IL-27 13 | T12 PROTEIN 796 801 IL-10 14 | T13 CELL_TYPE 871 916 allogeneic peripheral blood mononuclear cells 15 | T14 PROTEIN 961 966 IFN-γ 16 | T15 CELL_TYPE 981 988 T cells 17 | T16 CELL_TYPE 1000 1008 NK cells 18 | T17 PROTEIN 1113 1118 IFN-γ 19 | T18 PROTEIN 1139 1144 IL-10 20 | T19 PROTEIN 1154 1163 cytokines 21 | T20 PROTEIN 1196 1204 IL12-p70 22 | T21 PROTEIN 1206 1211 IL-27 23 | T22 PROTEIN 1221 1226 IL-10 24 | T23 PROTEIN 1272 1281 cytokines 25 | T24 PROTEIN 1293 1298 NK-DC 26 | T25 PROTEIN 1430 1458 ICC+IFN-γ-stimulated RAPA-DC 27 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3842119.txt: -------------------------------------------------------------------------------- 1 | Rapamycin augments human DC IL-12p70 and IL-27 secretion to promote allogeneic Type1 polarization modulated by NK cells. 2 | 3 | Mammalian target of rapamycin kinase inhibitor (mTORi) rapamycin (RAPA) use in transplantation can lead to inflammatory complications in some patients. Our goal was to better understand how mTORi-exposed human monocyte-derived dendritic cells (DC) stimulated with pro-inflammatory cytokines shape T cell allo-immunity. RAPA-conditioned-DC (RAPA-DC) displayed a more immature phenotype than untreated, control (CTRL)-DC. However, subsequent exposure of RAPA-DC to an inflammatory cytokine cocktail (ICC) plus IFN-γ induced a mature Type-1 promoting phenotype, consisting of elevated HLA-DR and co-stimulatory molecules, augmented IL-12p70 and IL-27 production, but decreased IL-10 secretion compared to CTRL-DC. Co-culture of mature (m) RAPA-DC with allogeneic peripheral blood mononuclear cells resulted in significantly increased Type-1 (IFN-γ) responses by T cells. Moreover, NK cells acted as innate modulators that conveyed activating cell-to-cell contact signals in addition to helper (IFN-γ) and/or regulatory (IL-10) soluble cytokines. We conclude that production of IL12-p70, IL-27, and low IL-10 by RAPA-DC allowed us to elucidate how these cytokines as well as NK-DC interaction shapes T cell allo-immunity. Thus, lack of inhibitory NK cell function during allo-specific T cell activation by human ICC+IFN-γ-stimulated RAPA-DC may represent an unwanted effector mechanism that may underlie RAPA-induced inflammatory events in transplant patients undergoing microbial infection or allograft rejection. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3850168.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 2 19 cytokine cocktail 2 | T1 CELL_TYPE 56 86 DC-enriched anti-tumor T cells 3 | T2 CELL_TYPE 225 254 tumor-infiltrated lymphocytes 4 | T3 CELL_TYPE 258 288 genetically engineered T cells 5 | T4 RNA 440 455 total tumor RNA 6 | T5 RNA 457 462 ttRNA 7 | T6 CELL_TYPE 467 488 prime dendritic cells 8 | T7 CELL_TYPE 490 492 DC 9 | T8 CELL_TYPE 538 556 anti-tumor T cells 10 | T9 CELL_TYPE 621 628 T cells 11 | T10 PROTEIN 646 656 ttRNA–DC–T 12 | T11 CELL_LINE 729 750 vivo-expanded T cells 13 | T12 PROTEIN 758 775 cytokine cocktail 14 | T13 CELL_TYPE 860 867 T cells 15 | T14 CYTOKINE 945 949 IL-2 16 | T15 CELL_TYPE 974 984 CD4 subset 17 | T16 CYTOKINE 992 996 IL-7 18 | T17 CELL_TYPE 1061 1068 T cells 19 | T18 PROTEIN 1084 1089 CD62L 20 | T19 CYTOKINE 1119 1123 IL-2 21 | T20 CYTOKINE 1134 1139 IL-12 22 | T21 PROTEIN 1170 1175 CD62L 23 | T22 CELL_TYPE 1204 1220 effector T cells 24 | T23 CELL_TYPE 1222 1225 Tem 25 | T24 CELL_LINE 1266 1269 –DC 26 | T25 CELL_TYPE 1296 1303 T cells 27 | T26 PROTEIN 1337 1354 cytokine cocktail 28 | T27 PROTEIN 1376 1380 IL-2 29 | T28 CELL_TYPE 1399 1416 CD62Lhigh T cells 30 | T29 CELL_TYPE 1447 1458 tumor cells 31 | T30 PROTEIN 1600 1608 ttRNA–DC 32 | T31 PROTEIN 1642 1659 cytokine cocktail 33 | T32 CELL_TYPE 1687 1705 anti-tumor T cells 34 | R1 CKProliferation Arg1:T14 Arg2:T15 35 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3850168.txt: -------------------------------------------------------------------------------- 1 | A cytokine cocktail directly modulates the phenotype of DC-enriched anti-tumor T cells to convey potent anti-tumor activities in a murine model. 2 | 3 | Adoptive cell transfer (ACT) using ex vivo-expanded anti-tumor T cells such as tumor-infiltrated lymphocytes or genetically engineered T cells potently eradicates established tumors. However, these two approaches possess obvious limitations. Therefore, we established a novel methodology using total tumor RNA (ttRNA) to prime dendritic cells (DC) as a platform for the ex vivo generation of anti-tumor T cells. We evaluated the antigen-specific expansion and recognition of T cells generated by the ttRNA–DC–T platform, and directly modulated the differentiation status of these ex vivo-expanded T cells with a cytokine cocktail. Furthermore, we evaluated the persistence and in vivo anti-tumor efficacy of these T cells through murine xenograft and syngeneic tumor models. During ex vivo culture, IL-2 preferentially expanded CD4 subset, while IL-7 enabled homeostatic proliferation from the original precursors. T cells tended to lose CD62L during ex vivo culture using IL-2; however, IL-12 could maintain high levels of CD62L by increasing expression on effector T cells (Tem). In addition, we validated that OVA RNA–DC only selectively expanded T cells in an antigen-specific manner. A cytokine cocktail excluding the use of IL-2 greatly increased CD62Lhigh T cells which specifically recognized tumor cells, engrafted better in a xenograft model and exhibited superior anti-tumor activities in a syngeneic intracranial model. ACT using the ex vivo ttRNA–DC–T platform in conjunction with a cytokine cocktail generated potent CD62Lhigh anti-tumor T cells and imposes a novel T cell-based therapeutic with the potential to treat brain tumors and other cancers. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3855395.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 0 4 mTOR 2 | T1 CELL_TYPE 31 37 T cell 3 | T2 PROTEIN 114 118 mTOR 4 | T3 PROTEIN 159 175 threonine kinase 5 | T4 PROTEIN 327 331 mTOR 6 | T5 CELL_TYPE 431 438 T cells 7 | T6 CELL_TYPE 656 668 memory cells 8 | T7 PROTEIN 768 772 mTOR 9 | T8 CELL_TYPE 883 889 T cell 10 | T9 PROTEIN 948 952 mTOR 11 | T10 PROTEIN 1043 1047 mTOR 12 | T11 CELL_TYPE 1089 1096 T cells 13 | T12 PROTEIN 1206 1231 transcription factors MYC 14 | T13 PROTEIN 1236 1241 HIF1α 15 | T14 PROTEIN 1261 1265 mTOR 16 | T15 CELL_TYPE 1311 1317 T cell 17 | T16 PROTEIN 1540 1544 mTOR 18 | T17 CELL_TYPE 1655 1662 T cells 19 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3855395.txt: -------------------------------------------------------------------------------- 1 | mTOR and metabolic pathways in T cell quiescence and functional activation. 2 | 3 | The mechanistic target of rapamycin (mTOR), an evolutionally conserved serine and threonine kinase, plays a critical role in the promotion of cell growth and proliferation via integration of cellular and environmental cues. In adaptive immunity, the mTOR pathway orchestrates multiple physiological processes including the development and homeostasis of T cells under steady state, and their subsequent activation and differentiation upon antigen recognition. Associated with such fate decisions is the dynamic reprogramming of T cell metabolic pathways, as naïve, activated and memory cells are defined by distinct bioenergetic and biosynthetic activities. Emerging evidence indicates that mTOR signaling intersects with T cell metabolism at two major levels to constitute a critical control mechanism of T cell fate decisions. First, as a central environmental sensor, mTOR links immune signaling and the availability of nutrients, especially amino acids. Second, mTOR activates specific metabolic pathways in T cells such as aerobic glycolysis (also known as the “Warburg effect”) in a process dependent upon the induction of transcription factors MYC and HIF1α. Understanding how mTOR interplays with T cell metabolism to dictate T cell fates and functions will provide fundamental insights into the mechanism of immune responses and the development of novel therapeutics against immune-mediated diseases. In this review, we summarize the current advances on mTOR signaling and T cell metabolism in the control of development, homeostasis, activation and differentiation of T cells. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3926063.ann: -------------------------------------------------------------------------------- 1 | T2 CYTOKINE 201 205 IL-9 2 | T4 CELL_TYPE 326 338 CD8+ T cells 3 | T7 CELL_TYPE 509 533 tumor-reactive Tc9 cells 4 | T8 PROTEIN 617 646 type-I CD8+ cytotoxic T cells 5 | T9 CELL_TYPE 697 706 Tc9 cells 6 | T10 CYTOKINE 922 926 IL-9 7 | T11 CELL_TYPE 962 971 Tc9 cells 8 | T12 CELL_TYPE 35 38 Tc9 9 | T13 CYTOKINE 15 19 IL-9 10 | R1 Secretion Arg1:T12 Arg2:T13 11 | T1 CELL_TYPE 89 92 Tc1 12 | T3 CELL_TYPE 245 256 cytotoxic T 13 | T14 CELL_TYPE 867 870 Tc9 14 | R2 Secretion Arg1:T14 Arg2:T10 15 | T6 CELL_TYPE 458 461 Tc9 16 | T5 CYTOKINE 408 412 IL-9 17 | R3 Secretion Arg1:T6 Arg2:T5 18 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3926063.txt: -------------------------------------------------------------------------------- 1 | Tumor-specific IL-9–producing CD8+ Tc9 cells are superior effector than type-I cytotoxic Tc1 cells for adoptive immunotherapy of cancers. 2 | 3 | Significance 4 | Our laboratory has identified a critical role of IL-9 in promoting endogenous tumor-specific cytotoxic T lymphocyte response. In this study, we found that differentiation of CD8+ T cells under T helper 9-polarizing conditions induces the development of an IL-9–producing less cytolytic IL-9–skewed CD8+ T (Tc9) cell subset. Noticeably, adoptive transfer of tumor-reactive Tc9 cells elicited greater antitumor responses against large established tumors than classic type-I CD8+ cytotoxic T cells that are used in clinical protocols. Importantly, Tc9 cells have substantially enhanced persistence potential and possess the capacity to acquire/maintain effector function after transfer. Our results also revealed that Tc9-mediated therapeutic effect critically depended on IL-9 production in vivo. The ability of Tc9 cells to confer sustained antitumor responses might open an avenue for the advances of cancer immunotherapy. 5 | 6 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC3927957.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 18 34 immunoproteasome 2 | T1 PROTEIN 201 217 immunoproteasome 3 | T2 PROTEIN 241 252 proteasomes 4 | T3 CELL_TYPE 276 285 monocytes 5 | T4 CELL_TYPE 290 301 lymphocytes 6 | T5 PROTEIN 349 366 immunoproteasomes 7 | T6 PROTEIN 370 378 cytokine 8 | T7 PROTEIN 497 513 immunoproteasome 9 | T8 CELL_TYPE 747 758 lymphocytes 10 | T9 CELL_TYPE 820 849 cytokine-producing CD4+ cells 11 | T10 CELL_TYPE 1068 1072 Th17 12 | T11 CELL_TYPE 1076 1085 Th1 cells 13 | T12 PROTEIN 1162 1178 immunoproteasome 14 | T13 CELL_TYPE 2266 2286 autoreactive T cells 15 | T14 PROTEIN 2370 2380 proteasome 16 | T15 PROTEIN 2540 2554 20S proteasome 17 | T16 PROTEIN 2560 2581 barrel-shaped complex 18 | T17 PROTEIN 2688 2698 β subunits 19 | T18 PROTEIN 2773 2783 β subunits 20 | T19 PROTEIN 2795 2797 β1 21 | T20 PROTEIN 2841 2855 20S proteasome 22 | T21 PROTEIN 2896 2901 IFN-γ 23 | T22 PROTEIN 2907 2930 tumor necrosis factor-α 24 | T23 PROTEIN 2932 2937 TNF-α 25 | T24 CELL_TYPE 2954 2974 hematopoietic origin 26 | T25 PROTEIN 2980 3003 catalytic subunits LMP2 27 | T26 PROTEIN 3005 3008 β1i 28 | T27 PROTEIN 3011 3017 MECL-1 29 | T28 PROTEIN 3029 3033 LMP7 30 | T29 PROTEIN 3052 3076 constitutive subunits β1 31 | T30 PROTEIN 3133 3149 immunoproteasome 32 | T31 PROTEIN 3246 3256 proteasome 33 | T32 PROTEIN 3320 3336 immunoproteasome 34 | T33 PROTEIN 3371 3382 MHC class I 35 | T34 PROTEIN 3498 3514 immunoproteasome 36 | T35 PROTEIN 3522 3536 class I ligand 37 | T36 PROTEIN 3833 3849 immunoproteasome 38 | T37 PROTEIN 4012 4018 PR-957 39 | T38 PROTEIN 4024 4060 LMP7-selective epoxyketone inhibitor 40 | T39 PROTEIN 4068 4084 immunoproteasome 41 | T40 PROTEIN 4094 4102 cytokine 42 | T41 CELL_TYPE 4117 4136 activated monocytes 43 | T42 CELL_TYPE 4140 4147 T cells 44 | T43 CELL_TYPE 4393 4411 naïve CD4+ T cells 45 | T45 CELL_TYPE 4736 4756 autoreactive T cells 46 | T46 PROTEIN 4770 4774 LMP7 47 | T47 CELL_TYPE 4801 4813 immune cells 48 | T48 CELL_TYPE 4868 4871 Th1 49 | T49 PROTEIN 4947 4951 LMP7 50 | T44 CYTOKINE 4415 4420 IL-17 51 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4007342.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 22 36 co-stimulatory 2 | T1 PROTEIN 38 42 OX40 3 | T2 PROTEIN 63 69 CTLA-4 4 | T3 CELL_TYPE 104 111 T cells 5 | T4 PROTEIN 175 223 TNF receptor family co-stimulatory molecule OX40 6 | T5 PROTEIN 225 230 CD134 7 | T6 PROTEIN 258 261 mAb 8 | T7 CELL_LINE 382 411 FoxP3+CD4+ regulatory T cells 9 | T8 CELL_TYPE 413 417 Treg 10 | T9 PROTEIN 467 494 checkpoint inhibitor CTLA-4 11 | T10 CELL_TYPE 520 527 T cells 12 | T11 PROTEIN 785 791 CTLA-4 13 | T12 PROTEIN 966 969 CD4 14 | T13 CELL_TYPE 1209 1212 Th1 15 | T14 CYTOKINE 1214 1218 IL-2 16 | T15 CYTOKINE 1220 1224 IFNγ 17 | T16 CYTOKINE 1250 1254 IL-4 18 | T17 CYTOKINE 1256 1260 IL-5 19 | T18 CYTOKINE 1266 1271 IL-13 20 | T19 TF 1306 1311 T-bet 21 | T20 TF 1316 1322 Gata-3 22 | T21 CYTOKINE 1348 1352 IL-4 23 | T23 CELL_TYPE 1245 1248 Th2 24 | T24 CELL_TYPE 1376 1379 Th2 25 | T22 CELL_TYPE 1412 1415 Th1 26 | T25 CELL_TYPE 1424 1438 effector CD8 T 27 | R1 Induction Arg1:T21 Arg2:T24 28 | R2 TFExpression Arg1:T23 Arg2:T19 29 | R3 TFExpression Arg1:T13 Arg2:T20 30 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4007342.txt: -------------------------------------------------------------------------------- 1 | Combined targeting of co-stimulatory (OX40) and co-inhibitory (CTLA-4) pathways elicits potent effector T cells capable of driving robust antitumor immunity. 2 | 3 | Ligation of the TNF receptor family co-stimulatory molecule OX40 (CD134) with an agonist anti-OX40 mAb enhances antitumor immunity by augmenting T cell differentiation as well as turning off the suppressive activity of the FoxP3+CD4+ regulatory T cells (Treg). In addition, antibody-mediated blockade of the checkpoint inhibitor CTLA-4 releases the “brakes” on T cells to augment tumor immunotherapy. However, monotherapy with these agents have limited therapeutic benefit particularly against poorly immunogenic murine tumors. Therefore, we examined whether the administration of agonist anti-OX40 therapy in the presence of CTLA-4 blockade would enhance tumor immunotherapy. Combined anti-OX40/anti-CTLA-4 immunotherapy significantly enhanced tumor regression and the survival of tumor-bearing hosts in a CD4 and CD8 T cell-dependent manner. Mechanistic studies revealed that the combination immunotherapy directed the expansion of effector T-bethigh/Eomeshigh granzyme B+ CD8 T cells. Dual immunotherapy also induced among distinct populations of Th1 (IL-2, IFNγ) and, surprisingly, Th2 (IL-4, IL-5, and IL-13) CD4 T cells exhibiting increased T-bet and Gata-3 expression. Furthermore, IL-4 blockade inhibited the Th2 response, while maintaining the Th1 CD4 and effector CD8 T cells that enhanced tumor-free survival. These data demonstrate that refining the global T cell response during combination immunotherapy can further enhance the therapeutic efficacy of these agents. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4056277.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 0 15 Human monocytes 2 | T1 PROTEIN 54 57 A2A 3 | T2 PROTEIN 62 85 A2B adenosine receptors 4 | T3 PROTEIN 114 130 immunoregulatory 5 | T4 PROTEIN 216 235 adenosine receptors 6 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4056277.txt: -------------------------------------------------------------------------------- 1 | Human monocytes respond to extracellular cAMP through A2A and A2B adenosine receptors. 2 | 3 | Extracellular cAMP exerts immunoregulatory functions as an intracellular second messenger, and an extracellular mediator on the adenosine receptors pathway. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4084624.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 53 79 intestinal dendritic cells 2 | T1 PROTEIN 122 129 SUMMARY 3 | T2 CELL_TYPE 236 280 Lamina propria (LP) T helper 17 (Th17) cells 4 | T3 PROTEIN 376 379 SFB 5 | T4 PROTEIN 440 452 SFB antigens 6 | T5 CELL_TYPE 456 482 intestinal dendritic cells 7 | T6 CELL_TYPE 484 487 DCs 8 | T7 PROTEIN 539 544 MHCII 9 | T8 CELL_TYPE 548 560 CD11c+ cells 10 | T9 CELL_LINE 638 660 SFB-induced Th17 cells 11 | T10 PROTEIN 672 675 SFB 12 | T11 PROTEIN 706 709 SFB 13 | T12 CELL_TYPE 729 739 Th17 cells 14 | T13 CELL_TYPE 867 879 innate cells 15 | T14 PROTEIN 913 918 MHCII 16 | T15 CELL_TYPE 941 962 innate lymphoid cells 17 | T16 CELL_TYPE 964 968 ILCs 18 | T17 PROTEIN 997 1000 SFB 19 | T18 CELL_TYPE 1080 1083 DCs 20 | T19 CELL_TYPE 1088 1092 ILCs 21 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4084624.txt: -------------------------------------------------------------------------------- 1 | Segmented filamentous bacteria antigens presented by intestinal dendritic cells drive mucosal Th17 cell differentiation. 2 | 3 | SUMMARY 4 | How commensal microbiota contributes to immune cell homeostasis at barrier surfaces is poorly understood. Lamina propria (LP) T helper 17 (Th17) cells participate in mucosal protection and are induced by commensal segmented filamentous bacteria (SFB). Here we show that MHCII-dependent antigen presentation of SFB antigens by intestinal dendritic cells (DCs) is crucial for Th17 cell induction. Expression of MHCII on CD11c+ cells was necessary and sufficient for SFB-induced Th17 cell differentiation. Most SFB-induced Th17 cells recognized SFB in an MHCII-dependent manner. SFB primed and induced Th17 cells locally in the LP and Th17 cell induction occurred normally in mice lacking secondary lymphoid organs. The importance of other innate cells was unveiled by the finding that MHCII deficiency in group 3 innate lymphoid cells (ILCs) resulted in an increase in SFB independent Th17 cell differentiation. Our results outline the complex role of DCs and ILCs in the regulation of intestinal Th17 cell homeostasis 5 | . 6 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4100769.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 697 722 Autophagy proteins LC3-II 2 | T1 PROTEIN 727 731 ATG7 3 | T2 CELL_TYPE 833 858 CD4+ and CD8+ splenocytes 4 | T3 CELL_TYPE 957 980 CD4+ and CD8+ after CLP 5 | T4 PROTEIN 1005 1009 Atg7 6 | T5 CELL_TYPE 1013 1026 T lymphocytes 7 | T6 CELL_TYPE 1104 1125 CD4+ and CD8+ T cells 8 | T7 PROTEIN 1193 1201 cytokine 9 | T8 PROTEIN 1216 1228 Th1/Th2/Th17 10 | T9 CELL_TYPE 1232 1244 CD4+ T cells 11 | T10 CELL_TYPE 1270 1281 macrophages 12 | T11 CELL_TYPE 1432 1445 T lymphocytes 13 | T12 CELL_TYPE 2040 2050 lymphocyte 14 | T13 CELL_TYPE 2200 2211 lymphocytes 15 | T14 PROTEIN 2220 2245 proinflammatory cytokines 16 | T15 CELL_TYPE 2259 2270 macrophages 17 | T16 CELL_TYPE 2280 2291 lymphocytes 18 | T17 CELL_TYPE 2407 2417 lymphocyte 19 | T18 DNA 2743 2766 autophagy-related genes 20 | T19 PROTEIN 2769 2773 Atg7 21 | T20 CELL_TYPE 2788 2815 CD4+ and CD8+ T lymphocytes 22 | T21 PROTEIN 2955 2978 T cell antigen receptor 23 | T22 PROTEIN 3046 3064 cytosolic antigens 24 | T23 PROTEIN 3083 3099 MHC II molecules 25 | T24 PROTEIN 3365 3387 Streptococcus pyogenes 26 | T25 PROTEIN 3445 3464 Toll-like receptors 27 | T26 CELL_TYPE 3498 3526 plasmacytoid dendritic cells 28 | T27 PROTEIN 3969 3973 ATG7 29 | T28 PROTEIN 4126 4130 Atg7 30 | T29 CELL_TYPE 4152 4165 T lymphocytes 31 | T30 PROTEIN 4507 4527 E-Da Hospital/I-Shou 32 | T31 PROTEIN 4563 4575 IACUC-100010 33 | T32 PROTEIN 4650 4658 BioLASCO 34 | T33 PROTEIN 4815 4822 Atg7f/f 35 | T34 PROTEIN 4945 4954 Bunkyo-ku 36 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4151505.ann: -------------------------------------------------------------------------------- 1 | T1 CELL_TYPE 127 158 terminally differentiated cells 2 | T3 DNA 519 523 CNS2 3 | T7 TF 846 851 Foxp3 4 | T10 DNA 976 980 CNS2 5 | T12 DNA 1029 1041 cis-elements 6 | T13 TF 14 19 Foxp3 7 | T14 CELL_TYPE 46 58 regulatory T 8 | T15 CELL_TYPE 344 356 Regulatory T 9 | T16 CELL_TYPE 358 362 Treg 10 | T2 TF 409 414 Foxp3 11 | R1 Differentiation Arg1:T2 Arg2:T16 12 | R2 Differentiation Arg1:T2 Arg2:T15 13 | T4 TF 537 542 Foxp3 14 | T5 CELL_TYPE 600 605 Tregs 15 | T6 CELL_TYPE 820 825 Tregs 16 | T8 TF 985 990 Foxp3 17 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4151505.txt: -------------------------------------------------------------------------------- 1 | Function of a Foxp3 cis-element in protecting regulatory T cell identity. 2 | 3 | The homeostasis of multicellular organisms requires terminally differentiated cells to preserve their lineage specificity. However, it is unclear if mechanisms exist to actively protect cell identity in response to environmental cues that confer functional plasticity. Regulatory T (Treg) cells, specified by the transcription factor Foxp3, are indispensable for immune system homeostasis. Here, we report that conserved non-coding sequence 2 (CNS2), a CpG-rich Foxp3 intronic cis-element specifically demethylated in mature Tregs, helps maintain immune homeostasis and limit autoimmune disease development by protecting Treg identity in response to signals that shape mature Treg functions and drive their initial differentiation. In activated Tregs, CNS2 helps protect Foxp3 expression from destabilizing cytokine conditions by sensing TCR/NFAT activation, which facilitates the interaction between CNS2 and Foxp3 promoter. Thus, epigenetically marked cis-elements can protect cell identity by sensing key environmental cues central to both cell identity formation and functional plasticity without interfering with initial cell differentiation. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4159719.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 18 44 Innate-like Lymphoid Cells 2 | T1 CELL_TYPE 320 341 innate lymphoid cells 3 | T2 CELL_TYPE 455 478 adaptive lymphoid cells 4 | T3 PROTEIN 771 782 ID proteins 5 | T4 PROTEIN 837 848 ID proteins 6 | T5 PROTEIN 880 911 E protein transcription factors 7 | T6 CELL_TYPE 1095 1113 innate lymphocytes 8 | T7 CELL_TYPE 1125 1145 natural killer cells 9 | T8 CELL_TYPE 1174 1195 innate lymphoid cells 10 | T9 PROTEIN 1197 1201 ILC1 11 | T10 PROTEIN 1203 1207 ILC2 12 | T11 PROTEIN 1213 1217 ILC3 13 | T12 CELL_TYPE 1224 1247 innate-like lymphocytes 14 | T13 CELL_TYPE 1259 1281 natural killer T cells 15 | T14 PROTEIN 1334 1345 ID proteins 16 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4159719.txt: -------------------------------------------------------------------------------- 1 | ID’ing Innate and Innate-like Lymphoid Cells. 2 | 3 | Summary 4 | The immune system can be divided into innate and adaptive components that differ in their rate and mode of cellular activation, with innate immune cells being the first responders to invading pathogens. Recent advances in the identification and characterization of innate lymphoid cells have revealed reiterative developmental programs that result in cells with effector fates that parallel those of adaptive lymphoid cells and are tailored to effectively eliminate a broad spectrum of pathogenic challenges. However, activation of these cells can also be associated with pathologies such as autoimmune disease. One major distinction between innate and adaptive immune system cells is the constitutive expression of ID proteins in the former and inducible expression in the latter. ID proteins function as antagonists of the E protein transcription factors that play critical roles in lymphoid specification as well as B and T-lymphocyte development. In this review, we examine the transcriptional mechanisms controlling the development of innate lymphocytes, including natural killer cells and the recently identified innate lymphoid cells (ILC1, ILC2, and ILC3), and innate-like lymphocytes, including natural killer T cells, with an emphasis on the known requirements for the ID proteins. 5 | 6 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4214202.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 66 71 IFN-I 2 | T1 PROTEIN 127 135 Cytokine 3 | T2 PROTEIN 170 188 Type I interferons 4 | T3 PROTEIN 190 195 IFN-I 5 | T4 PROTEIN 234 243 cytokines 6 | T5 PROTEIN 296 301 IFN-I 7 | T6 PROTEIN 365 370 IFN-I 8 | T7 PROTEIN 451 469 effector molecules 9 | T8 PROTEIN 512 517 IFN-I 10 | T9 PROTEIN 580 585 IFN-I 11 | T10 PROTEIN 861 866 IFN-I 12 | T11 PROTEIN 964 969 IFN-I 13 | T12 PROTEIN 1059 1064 IFN-I 14 | T13 PROTEIN 1069 1076 IFN-III 15 | T14 DNA 1104 1137 hepatitis C virus (HCV) infection 16 | T15 PROTEIN 1214 1219 IFN-I 17 | T16 PROTEIN 1308 1313 IFN-I 18 | T17 PROTEIN 1356 1361 IFN-I 19 | T18 PROTEIN 1398 1403 IFN-I 20 | T19 PROTEIN 1548 1557 cytokines 21 | T20 PROTEIN 1702 1707 IFN-I 22 | T21 PROTEIN 1783 1788 IFN-I 23 | T22 PROTEIN 1890 1895 IFN-I 24 | T23 PROTEIN 1912 1921 cytokines 25 | T24 PROTEIN 2002 2007 IFN-I 26 | T25 PROTEIN 2084 2089 IFN-I 27 | T26 PROTEIN 2231 2236 IFN-I 28 | T27 PROTEIN 2567 2572 IFN-I 29 | T28 PROTEIN 2713 2726 type III IFNs 30 | T29 DNA 2766 2775 IFN-I/III 31 | T30 PROTEIN 2777 2781 IFNs 32 | T31 PROTEIN 2893 2896 IFN 33 | T32 PROTEIN 2988 2997 IFN-α/β/λ 34 | T33 PROTEIN 3038 3042 IFNs 35 | T34 PROTEIN 3126 3131 IFN-I 36 | T35 PROTEIN 3150 3155 IFN-ε 37 | T36 CELL_LINE 3258 3261 DCs 38 | T37 CELL_TYPE 3286 3308 mononuclear phagocytes 39 | T38 CELL_TYPE 3396 3415 naïve T lymphocytes 40 | T39 CELL_TYPE 3417 3420 DCs 41 | T40 CELL_TYPE 3425 3439 sentinel cells 42 | T41 CELL_TYPE 3613 3634 effector immune cells 43 | T42 CELL_TYPE 3659 3669 DC subsets 44 | T43 PROTEIN 3713 3748 innate immune recognition receptors 45 | T44 PROTEIN 3750 3755 I2R2s 46 | T45 CELL_TYPE 3797 3817 monocyte-derived DCs 47 | T46 CELL_LINE 3819 3824 MoDCs 48 | T47 CELL_TYPE 3827 3843 Langerhans cells 49 | T48 CELL_TYPE 3845 3855 CD11b+ DCs 50 | T49 CELL_TYPE 3857 3866 XCR1+ DCs 51 | T50 CELL_TYPE 3872 3888 plasmacytoid DCs 52 | T51 CELL_TYPE 3890 3894 pDCs 53 | T52 PROTEIN 3979 3983 IFNs 54 | T53 CELL_TYPE 3988 3991 DCs 55 | T54 PROTEIN 4056 4065 cytokines 56 | T55 PROTEIN 4147 4150 IFN 57 | T56 DNA 4203 4223 I2R2s and downstream 58 | T57 PROTEIN 4259 4264 IFN-I 59 | T58 PROTEIN 4424 4429 IFN-I 60 | T59 PROTEIN 4760 4765 IFN-I 61 | T60 PROTEIN 4822 4827 IFN-I 62 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4224975.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 433 459 antibody producing B cells 2 | T1 CELL_TYPE 464 496 influenza-specific T lymphocytes 3 | T2 CELL_TYPE 562 575 T lymphocytes 4 | T3 PROTEIN 609 619 antibodies 5 | T4 CELL_TYPE 1027 1044 activated T cells 6 | T5 CELL_TYPE 1176 1183 T cells 7 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4224975.txt: -------------------------------------------------------------------------------- 1 | The Effector T Cell Response to Influenza Infection. 2 | 3 | Influenza virus infection induces a potent initial innate immune response, which serves to limit the extent of viral replication and virus spread. However, efficient (and eventual) viral clearance within the respiratory tract requires the subsequent activation, rapid proliferation, recruitment, and expression of effector activities by the adaptive immune system, consisting of antibody producing B cells and influenza-specific T lymphocytes with diverse functions. The ensuing effector activities of these T lymphocytes ultimately determine (along with antibodies) the capacity of the host to eliminate the viruses and the extent of tissue damage. In this review, we describe this effector T cell response to influenza virus infection. Based on information largely obtained in experimental settings (i.e., murine models), we will illustrate the factors regulating the induction of adaptive immune T cell responses to influenza, the effector activities displayed by these activated T cells, the mechanisms underlying the expression of these effector mechanisms, and the control of the activation/differentiation of these T cells, in situ, in the infected lungs. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4233385.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 31 39 cytokine 2 | T1 CELL_LINE 54 85 calcitriol-primed human B cells 3 | T2 PROTEIN 273 287 immunoglobulin 4 | T3 CELL_TYPE 404 429 calcitriol-primed B cells 5 | T4 CELL_TYPE 471 484 Human B cells 6 | T5 PROTEIN 506 515 anti-CD40 7 | T6 PROTEIN 520 538 interleukin (IL)-4 8 | T7 CELL_TYPE 635 642 B cells 9 | T8 CELL_TYPE 665 688 autologous CD4+ T cells 10 | T9 CELL_TYPE 694 717 B cell phenotype T cell 11 | T10 PROTEIN 751 759 cytokine 12 | T11 CELL_TYPE 791 804 Naive T cells 13 | T12 CELL_TYPE 822 853 calcitriol-primed naive B cells 14 | T13 PROTEIN 882 917 nuclear factor of activated T cells 15 | T14 PROTEIN 919 932 cytoplasmic 2 16 | T15 PROTEIN 934 940 NFATc2 17 | T16 PROTEIN 957 965 cytokine 18 | T17 PROTEIN 997 1001 CD86 19 | T18 CELL_TYPE 1016 1023 B cells 20 | T19 PROTEIN 1153 1173 anti-CD28 antibodies 21 | T20 CELL_TYPE 1198 1223 calcitriol-primed B cells 22 | T21 CELL_TYPE 1265 1272 T cells 23 | T22 CELL_TYPE 1406 1431 calcitriol-primed B cells 24 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4233385.txt: -------------------------------------------------------------------------------- 1 | Impaired T cell activation and cytokine production by calcitriol-primed human B cells. 2 | 3 | The biologically active form of vitamin D3, 1, 25-dihydroxyvitamin D3 (calcitriol), is a potent modulator of the immune response. We have shown previously that calcitriol modulates the immunoglobulin response in vitro and in vivo in mice and humans. To analyse the underlying molecular mechanisms we studied whether calcitriol-primed B cells modulate T cell activation and function. Human B cells were stimulated with anti-CD40 and interleukin (IL)-4 in the presence of increasing concentrations of calcitriol. After removal of calcitriol, primed B cells were co-cultured with autologous CD4+ T cells; the B cell phenotype T cell activation and their consecutive cytokine production were also assessed. Naive T cells co-cultured with calcitriol-primed naive B cells showed a reduced expansion, nuclear factor of activated T cells, cytoplasmic 2 (NFATc2) expression and cytokine production upon restimulation. CD86 expression on B cells after calcitriol priming was identified as an underlying mechanism, as T cell activation and expansion was rescued by activating anti-CD28 antibodies. Our data indicate that calcitriol-primed B cells display an impaired capacity to activate T cells. Taken together, we identified a novel B cell-dependent vitamin D immune regulatory mechanism, namely by decreased co-stimulation of calcitriol-primed B cells. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4241840.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 22 35 T Lymphocytes 2 | T1 CELL_TYPE 352 365 T lymphocytes 3 | T2 CELL_TYPE 367 397 tumor-infiltrating lymphocytes 4 | T3 CELL_TYPE 399 403 TILs 5 | T4 CELL_TYPE 491 495 TILs 6 | T5 CELL_TYPE 547 551 TILs 7 | T6 CELL_TYPE 627 640 T lymphocytes 8 | T7 CELL_TYPE 671 675 TILs 9 | T9 CYTOKINE 932 937 IFN-γ 10 | T10 CYTOKINE 942 947 TNF-α 11 | T11 CELL_TYPE 1047 1065 Th1-like phenotype 12 | T12 CELL_TYPE 1082 1085 Th2 13 | T13 CELL_TYPE 1087 1091 Th17 14 | T15 CELL_TYPE 1252 1265 T lymphocytes 15 | T16 CELL_TYPE 1432 1467 tumor-infiltrating γδ T lymphocytes 16 | T17 CELL_TYPE 1564 1599 tumor-infiltrating γδ T lymphocytes 17 | T18 CELL_TYPE 1785 1798 T lymphocytes 18 | T19 CELL_TYPE 2058 2065 T cells 19 | T20 PROTEIN 2082 2097 T cell receptor 20 | T21 PROTEIN 2099 2102 TCR 21 | T22 CELL_TYPE 2118 2132 effector cells 22 | T23 CELL_TYPE 2442 2449 T cells 23 | T24 CELL_TYPE 2470 2482 CD3+ T cells 24 | T25 CELL_TYPE 2702 2709 T cells 25 | T26 DNA 2725 2733 Vδ2 gene 26 | T27 PROTEIN 2761 2770 Vγ9 chain 27 | T28 CELL_TYPE 2787 2801 Vγ9Vδ2 T cells 28 | T29 CELL_TYPE 2836 2853 T cell population 29 | T30 CELL_TYPE 2898 2905 T cells 30 | T31 DNA 2929 2937 Vδ1 gene 31 | T32 DNA 2974 2985 Vγ elements 32 | T33 CELL_TYPE 3001 3008 T cells 33 | T34 PROTEIN 3023 3057 MHC class I-related molecules MICA 34 | T35 PROTEIN 3059 3063 MICB 35 | T36 DNA 3069 3074 ULBPs 36 | T37 CELL_TYPE 3099 3115 epithelial cells 37 | T38 CELL_TYPE 3210 3250 epithelial and hematopoietic tumor cells 38 | T39 PROTEIN 3287 3296 MICA/MICB 39 | T40 PROTEIN 3301 3306 ULBPs 40 | T41 PROTEIN 3338 3345 Vδ1 TCR 41 | T42 CELL_TYPE 3370 3381 Vδ1 T cells 42 | T43 CELL_TYPE 3414 3442 natural killer (NK) receptor 43 | T44 PROTEIN 3444 3449 NKG2D 44 | T45 CELL_TYPE 3502 3509 T cells 45 | T46 CELL_TYPE 3511 3525 Vγ9Vδ2 T cells 46 | T47 PROTEIN 3624 3627 MHC 47 | T48 CELL_TYPE 3800 3816 eukaryotic cells 48 | T49 PROTEIN 3921 3927 Vγ9Vδ2 49 | T50 PROTEIN 4134 4137 TCR 50 | T51 PROTEIN 4171 4186 small molecules 51 | T52 PROTEIN 4188 4192 PAgs 52 | T53 CELL_TYPE 4215 4229 Vγ9Vδ2 T cells 53 | T54 CELL_TYPE 4274 4283 monocytes 54 | T55 CELL_TYPE 4291 4306 dendritic cells 55 | T56 CELL_TYPE 4308 4311 DCs 56 | T57 PROTEIN 4375 4378 TCR 57 | T58 PROTEIN 4474 4484 Vγ9Vδ2 TCR 58 | T59 PROTEIN 4524 4545 TCR-binding molecules 59 | T60 PROTEIN 4547 4549 8) 60 | T61 PROTEIN 4598 4601 PAg 61 | T62 PROTEIN 4620 4629 F1-ATPase 62 | T63 PROTEIN 4656 4666 Vγ9Vδ2 TCR 63 | T64 PROTEIN 4686 4691 ApppI 64 | T65 PROTEIN 4720 4727 IPP (9) 65 | T66 PROTEIN 4817 4833 butyrophilin 3A1 66 | T67 PROTEIN 4844 4849 10–12 67 | T68 CELL_TYPE 4931 4942 human cells 68 | T8 CELL_TYPE 819 823 γδ T 69 | R1 Secretion Arg1:T8 Arg2:T9 70 | R2 Secretion Arg1:T8 Arg2:T10 71 | T14 CELL_TYPE 1097 1101 Treg 72 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4291544.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 8 26 C-C Motif Ligand 2 2 | T1 PROTEIN 31 51 C-C Motif Receptor 2 3 | T2 PROTEIN 233 238 pGVHD 4 | T3 PROTEIN 394 399 pGVHD 5 | T4 CELL_TYPE 439 477 donor-derived antigen-presenting cells 6 | T5 CELL_TYPE 479 483 APCs 7 | T6 PROTEIN 497 560 C-C motif ligand 2 (CCL2)–C-C motif receptor 2 (CCR2) chemokine 8 | T7 PROTEIN 583 595 C57BL/6(H2b) 9 | T8 PROTEIN 657 679 wild-type (WT) C57BL/6 10 | T9 CELL_TYPE 947 964 pulmonary T cells 11 | T10 PROTEIN 1027 1031 CCL2 12 | T11 PROTEIN 1033 1037 CCR2 13 | T12 PROTEIN 1043 1068 Type-1 T-helper cytokines 14 | T13 CELL_TYPE 1083 1092 monocytes 15 | T14 CELL_TYPE 1097 1129 monocyte-derived dendritic cells 16 | T15 CELL_TYPE 1131 1136 moDCs 17 | T16 PROTEIN 1152 1155 Syn 18 | T17 CELL_TYPE 1242 1250 lung DCs 19 | T18 CELL_TYPE 1278 1289 CD4 T cells 20 | T19 CELL_TYPE 1334 1342 lung DCs 21 | T20 CELL_TYPE 1347 1352 moDCs 22 | T21 CELL_TYPE 1373 1380 T cells 23 | T22 CYTOKINE 1382 1386 CCL2 24 | T23 CYTOKINE 1390 1394 CCR2 25 | T24 PROTEIN 1421 1426 pGVHD 26 | T25 CYTOKINE 1478 1482 IL-5 27 | T27 PROTEIN 1525 1557 hematopoietic donor-derived CCL2 28 | T28 PROTEIN 1562 1566 CCR2 29 | T29 CELL_TYPE 1591 1595 APCs 30 | T30 PROTEIN 1671 1676 pGVHD 31 | T31 PROTEIN 1748 1752 IL-5 32 | T32 PROTEIN 1757 1772 IL-13 cytokines 33 | T33 CELL_TYPE 1805 1808 APC 34 | T34 CELL_TYPE 1935 1938 HCT 35 | T26 CYTOKINE 1486 1491 IL-13 36 | R1 CKCKEnhancementNeg Arg1:T23 Arg2:T25 37 | R2 CKCKEnhancementNeg Arg1:T23 Arg2:T26 38 | R3 CKCKEnhancementNeg Arg1:T22 Arg2:T25 39 | R4 CKCKEnhancementNeg Arg1:T22 Arg2:T26 40 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4291544.txt: -------------------------------------------------------------------------------- 1 | Role of C-C Motif Ligand 2 and C-C Motif Receptor 2 in Murine Pulmonary Graft-versus-Host Disease after Lipopolysaccharide Inhalations. 2 | 3 | Environmental exposures are a potential trigger of chronic pulmonary graft-versus-host disease (pGVHD) after successful recovery from hematopoietic cell transplant (HCT). We hypothesized that inhalations of LPS, a prototypic environmental stimulus, trigger pGVHD via increased pulmonary recruitment of donor-derived antigen-presenting cells (APCs) through the C-C motif ligand 2 (CCL2)–C-C motif receptor 2 (CCR2) chemokine axis. B10.BR(H2k) and C57BL/6(H2b) mice underwent allogeneic (Allo) or syngeneic (Syn) HCT with wild-type (WT) C57BL/6, CCL2−/−, or CCR2−/− donors. After 4 weeks, recipient mice received daily inhaled LPS for 5 days and were killed at multiple time points. Allo mice exposed to repeated inhaled LPS developed prominent lymphocytic bronchiolitis, similar to human pGVHD. The increase in pulmonary T cells in Allo mice after LPS exposures was accompanied by increased CCL2, CCR2, and Type-1 T-helper cytokines as well as by monocytes and monocyte-derived dendritic cells (moDCs) compared with Syn and nontransplanted controls. Using CCL2−/− donors leads to a significant decrease in lung DCs but to only mildly reduced CD4 T cells. Using CCR2−/− donors significantly reduces lung DCs and moDCs but does not change T cells. CCL2 or CCR2 deficiency does not alter pGVHD pathology but increases airway hyperreactivity and IL-5 or IL-13 cytokines. Our results show that hematopoietic donor-derived CCL2 and CCR2 regulate recruitment of APCs to the Allo lung after LPS exposure. Although they do not alter pathologic pGVHD, their absence is associated with increased airway hyperreactivity and IL-5 and IL-13 cytokines. These results suggest that the APC changes that result from CCL2–CCR2 blockade may have unexpected effects on T cell differentiation and physiologic outcomes in HCT. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4337382.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 0 46 Tertiary Lymphoid Structure-Associated B Cells 2 | T1 CELL_TYPE 192 214 tumor-specific T cells 3 | T2 CELL_TYPE 219 226 B cells 4 | T3 CELL_TYPE 280 310 tumor-infiltrating lymphocytes 5 | T4 CELL_TYPE 417 448 tumor-infiltrating immune cells 6 | T5 CELL_TYPE 898 925 effector and memory T cells 7 | T6 CELL_TYPE 930 937 B cells 8 | T7 CELL_TYPE 1322 1333 TLS B cells 9 | T8 CELL_TYPE 1366 1390 antigen-presenting cells 10 | T9 CELL_TYPE 1395 1442 tumor antigen-specific antibody-secreting cells 11 | T10 CELL_TYPE 1642 1653 TLS B cells 12 | T11 CELL_TYPE 1758 1769 tumor cells 13 | T12 CELL_TYPE 1779 1792 stromal cells 14 | T13 CELL_TYPE 1896 1913 sense tumor cells 15 | T14 CELL_TYPE 2052 2059 T cells 16 | T15 CELL_TYPE 2061 2068 B cells 17 | T16 PROTEIN 2074 2084 antibodies 18 | T17 PROTEIN 2098 2112 tumor antigens 19 | T18 CELL_TYPE 2194 2200 T-cell 20 | T19 CELL_TYPE 2204 2218 B-cell subsets 21 | T20 CELL_TYPE 2653 2676 anti-tumor immune cells 22 | T21 PROTEIN 2703 2717 tumor antigens 23 | T22 DNA 2753 2763 tumor site 24 | T23 PROTEIN 3110 3115 MALTs 25 | T24 PROTEIN 3190 3195 NALTs 26 | T25 DNA 3238 3243 BALTs 27 | T26 DNA 3485 3490 BALTs 28 | T27 CELL_TYPE 3514 3518 SLOs 29 | T28 PROTEIN 4592 4605 tumor antigen 30 | T29 CELL_TYPE 4743 4755 immune cells 31 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4385920.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 296 312 effector T cells 2 | T1 PROTEIN 513 521 cytokine 3 | T2 PROTEIN 561 590 multiple inhibitory receptors 4 | T3 PROTEIN 600 623 programmed cell death-1 5 | T4 PROTEIN 625 629 PD-1 6 | T5 PROTEIN 632 660 lymphocyte activation gene-3 7 | T6 CELL_TYPE 662 705 cytotoxic T lymphocyte-associated antigen-4 8 | T7 PROTEIN 710 715 CD244 9 | T8 CELL_TYPE 725 746 CD4+ and CD8+ T cells 10 | T9 CELL_TYPE 929 946 exhausted T cells 11 | T10 PROTEIN 965 985 inhibitory receptors 12 | T11 CELL_TYPE 1402 1427 HIV-specific CD8+ T cells 13 | T12 PROTEIN 1431 1435 PD-1 14 | T13 PROTEIN 1556 1560 PD-1 15 | T14 CELL_TYPE 1751 1757 T-cell 16 | T15 PROTEIN 1790 1797 Blimp-1 17 | T16 PROTEIN 1799 1804 T-bet 18 | T17 PROTEIN 1809 1814 NFAT2 19 | T18 CELL_TYPE 1837 1854 exhausted T cells 20 | T19 CELL_TYPE 1949 1956 T cells 21 | T20 CELL_TYPE 2491 2508 exhausted T cells 22 | T21 CELL_TYPE 2667 2684 exhausted T cells 23 | T22 CELL_TYPE 2928 2948 CD8+ and CD4+ T-cell 24 | T23 CELL_TYPE 3002 3019 exhausted T cells 25 | T24 CELL_TYPE 3161 3168 T cells 26 | T25 CELL_TYPE 3260 3277 exhausted T cells 27 | T26 CELL_TYPE 3377 3399 CD8+ exhausted T cells 28 | T27 CELL_TYPE 3443 3465 CD4+ exhausted T cells 29 | T28 CELL_TYPE 3505 3527 CD4+ exhausted T cells 30 | T29 CELL_TYPE 3642 3659 exhausted T cells 31 | T30 PROTEIN 3795 3805 antibodies 32 | T31 CELL_TYPE 3850 3867 exhausted T cells 33 | T32 CELL_TYPE 3914 3931 exhausted T cells 34 | T33 CELL_TYPE 4249 4291 multiepitope-specific CD4+ and CD8+ T-cell 35 | T34 CELL_TYPE 4303 4315 CD4+ T cells 36 | T35 PROTEIN 4356 4381 HBV core antigen epitopes 37 | T37 CYTOKINE 4436 4441 IFN-γ 38 | T38 CYTOKINE 4447 4470 tumor necrosis factor-α 39 | T39 CYTOKINE 4472 4477 TNF-α 40 | T40 CELL_TYPE 4537 4569 CD8+ T-cell responses.1, 2, 3, 4 41 | T41 CELL_TYPE 4598 4623 HBV-specific CD8+ T cells 42 | T42 CELL_TYPE 4694 4706 CD8+ T cells 43 | T43 PROTEIN 4767 4772 HBV.5 44 | T36 CELL_TYPE 4394 4398 Th-1 45 | T44 CYTOKINE 4422 4434 interferon-γ 46 | R1 Secretion Arg1:T36 Arg2:T44 47 | R2 Secretion Arg1:T36 Arg2:T37 48 | R3 Secretion Arg1:T36 Arg2:T38 49 | R4 Secretion Arg1:T36 Arg2:T39 50 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4418961.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_LINE 0 32 Myeloid-derived Suppressor Cells 2 | T1 CELL_TYPE 128 161 myeloid-derived suppressive cells 3 | T2 CELL_TYPE 163 168 MDSCs 4 | T3 CELL_TYPE 325 330 MDSCs 5 | T4 CELL_TYPE 461 466 MDSCs 6 | T5 CELL_TYPE 742 747 MDSCs 7 | T6 CELL_TYPE 933 938 MDSCs 8 | T7 CELL_TYPE 1043 1048 MDSCs 9 | T8 CELL_TYPE 1112 1130 naïve CD4+ T cells 10 | T9 CELL_TYPE 1175 1180 MDSCs 11 | T10 CELL_TYPE 1373 1378 MDSCs 12 | T11 PROTEIN 1423 1445 inflammatory cytokines 13 | T12 CYTOKINE 1460 1465 TNF-α 14 | T13 CELL_TYPE 1483 1494 human MDSCs 15 | T14 CELL_TYPE 1548 1553 MDSCs 16 | T15 CELL_LINE 1690 1707 IL-17A/Th17 cells 17 | T16 CYTOKINE 1453 1458 IL-1β 18 | R1 Secretion Arg1:T10 Arg2:T16 19 | R2 Secretion Arg1:T10 Arg2:T12 20 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4418961.txt: -------------------------------------------------------------------------------- 1 | Myeloid-derived Suppressor Cells Have a Proinflammatory Role in the Pathogenesis of Autoimmune Arthritis. 2 | 3 | 4 | Objectives 5 | Although myeloid-derived suppressive cells (MDSCs) have been linked to T-cell tolerance, their role in autoimmune rheumatoid arthritis (RA) remains elusive. Here we investigate the potential association of MDSCs with the disease pathogenesis using a preclinical model of RA and specimen collected from RA patients. 6 | 7 | 8 | Methods 9 | The frequency of MDSCs in blood, lymphoid tissues, inflamed paws, or synovial fluid and their association with disease severity, tissue inflammation, and the levels of pathogenic T-helper (Th) 17 cells was examined in arthritic mice or in patients with RA (n=35) and osteoarthritis (OA, n=15). The MDSCs in arthritic mice were also characterized for their phenotype, inflammation status, T-cell suppressive activity, and their capacity of pro-Th17 cell differentiation. The involvement of MDSCs in the disease pathology and a Th17 response was examined by adoptive transfer or antibody depletion of MDSCs in arthritic mice or by co-culturing mouse or human MDSCs with naïve CD4+ T cells under Th17-polarizing conditions. 10 | 11 | 12 | Results 13 | MDSCs significantly expanded in arthritic mice and in RA patients, which correlated positively with disease severity and an inflammatory Th17 response. While displaying T-cell suppressive activity, MDSCs from arthritic mice produced high levels of inflammatory cytokines (e.g., IL-1β, TNF-α). Both mouse and human MDSCs promoted Th17 cell polarization ex vivo. Transfer of MDSCs facilitated disease progression, whereas their elimination in arthritic mice ameliorates disease symptoms concomitant with reduction of IL-17A/Th17 cells. 14 | 15 | 16 | Conclusions 17 | Our studies suggest that proinflammatory MDSCs with their capacity to drive Th17 cell differentiation may be a critical pathogenic factor in autoimmune arthritis. 18 | 19 | 20 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4426480.ann: -------------------------------------------------------------------------------- 1 | T0 DNA 8 15 miR-99a 2 | T1 CELL_TYPE 151 165 T (Treg) cells 3 | T2 CELL_TYPE 233 245 CD4+ T cells 4 | T3 CELL_TYPE 309 319 Treg cells 5 | T4 DNA 401 424 T-cell-expressed miRNAs 6 | T5 CELL_TYPE 428 452 naive mouse CD4+ T cells 7 | T6 DNA 675 682 miR-99a 8 | T7 DNA 687 694 miR-10b 9 | T8 CELL_TYPE 728 732 Treg 10 | T9 DNA 826 833 miR-99a 11 | T10 PROTEIN 891 917 Th17-promoting factor mTOR 12 | T11 DNA 952 959 miR-99a 13 | T12 DNA 1105 1112 miR-99a 14 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4426480.txt: -------------------------------------------------------------------------------- 1 | Induced miR-99a expression represses Mtor cooperatively with miR-150 to promote regulatory T-cell differentiation. 2 | 3 | Peripheral induction of regulatory T (Treg) cells provides essential protection from inappropriate immune responses. CD4+ T cells that lack endogenous miRNAs are impaired to differentiate into Treg cells, but the relevant miRNAs are unknown. We performed an overexpression screen with T-cell-expressed miRNAs in naive mouse CD4+ T cells undergoing Treg differentiation. Among 130 candidates, the screen identified 29 miRNAs with a negative and 10 miRNAs with a positive effect. Testing reciprocal Th17 differentiation revealed specific functions for miR-100, miR-99a and miR-10b, since all of these promoted the Treg and inhibited the Th17 program without impacting on viability, proliferation and activation. miR-99a cooperated with miR-150 to repress the expression of the Th17-promoting factor mTOR. The comparably low expression of miR-99a was strongly increased by the Treg cell inducer “retinoic acid”, and the abundantly expressed miR-150 could only repress Mtor in the presence of miR-99a. Our data suggest that induction of Treg cell differentiation is regulated by a miRNA network, which involves cooperation of constitutively expressed as well as inducible miRNAs. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4451961.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 248 261 T lymphocytes 2 | T1 PROTEIN 607 619 UC3-10A6 mAb 3 | T2 CELL_TYPE 655 686 Lung infiltrating T lymphocytes 4 | T3 PROTEIN 688 693 IL-17 5 | T4 CELL_TYPE 874 887 T lymphocytes 6 | T5 CELL_TYPE 921 934 T lymphocytes 7 | T6 PROTEIN 1050 1054 CCL2 8 | T7 PROTEIN 1056 1060 CCL3 9 | T8 PROTEIN 1065 1069 CCL5 10 | T9 PROTEIN 1157 1161 CD25 11 | T10 CELL_TYPE 1165 1182 Vγ4 T lymphocytes 12 | T11 CELL_TYPE 1230 1237 T cells 13 | T12 CELL_TYPE 1274 1285 Vγ4 T cells 14 | T16 CYTOKINE 1460 1465 IL-17 15 | T17 CYTOKINE 1485 1490 IFN-γ 16 | T18 PROTEIN 1546 1558 anti-Vγ4 mAb 17 | T19 PROTEIN 1581 1586 IL-17 18 | T20 PROTEIN 1627 1639 anti-Vγ4 mAb 19 | T21 CYTOKINE 1903 1908 IL-17 20 | T22 DNA 2074 2103 doi:10.1186/s12865-015-0098-8 21 | T23 PROTEIN 2604 2612 cytokine 22 | T24 CELL_TYPE 2647 2667 activated leukocytes 23 | T25 CELL_TYPE 2694 2707 T lymphocytes 24 | T26 CELL_TYPE 2718 2731 T lymphocytes 25 | T27 CELL_TYPE 2736 2762 unconventional lymphocytes 26 | T28 CELL_TYPE 2845 2858 T lymphocytes 27 | T29 PROTEIN 2947 2980 Vγ and Vδ gene repertoire [9, 10] 28 | T30 CELL_LINE 2986 3009 Vγ4 T lymphocyte subset 29 | T31 CELL_TYPE 3184 3191 T cells 30 | T32 CELL_TYPE 3302 3308 T cell 31 | T33 CELL_TYPE 3384 3397 T lymphocytes 32 | T34 PROTEIN 3439 3458 chemokine receptors 33 | T35 PROTEIN 3519 3521 CC 34 | T36 PROTEIN 3526 3541 CXC subfamilies 35 | T37 PROTEIN 3636 3654 microbial antigens 36 | T38 PROTEIN 3659 3683 innate surface receptors 37 | T43 CYTOKINE 3911 3916 IL-17 38 | T44 CELL_TYPE 3962 3975 T lymphocytes 39 | T46 CYTOKINE 4166 4171 IL-17 40 | T49 CELL_TYPE 4313 4326 T lymphocytes 41 | T51 CYTOKINE 4594 4599 IL-17 42 | T52 CELL_TYPE 4615 4628 T lymphocytes 43 | T53 CELL_TYPE 4812 4824 T lymphocyte 44 | T14 CYTOKINE 1338 1343 IL-17 45 | T13 CELL_TYPE 1291 1296 Vγ4 T 46 | R1 Secretion Arg1:T13 Arg2:T14 47 | T54 CELL_TYPE 1403 1406 Vγ1 48 | T15 CELL_TYPE 1850 1855 Vγ4 T 49 | R2 Secretion Arg1:T15 Arg2:T21 50 | T41 CELL_TYPE 3825 3829 γδ T 51 | T39 CYTOKINE 3730 3737 (IFN)-γ 52 | T55 CYTOKINE 3754 3761 (IL)-17 53 | R3 Secretion Arg1:T41 Arg2:T55 54 | R4 Secretion Arg1:T41 Arg2:T39 55 | T40 CELL_TYPE 3845 3850 Vγ4 T 56 | R5 Secretion Arg1:T40 Arg2:T43 57 | T42 CELL_TYPE 4081 4085 γδ T 58 | R6 Secretion Arg1:T42 Arg2:T46 59 | T45 CELL_TYPE 4487 4492 Vγ4 T 60 | R7 Secretion Arg1:T45 Arg2:T51 61 | R8 Secretion Arg1:T13 Arg2:T16 62 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4474185.ann: -------------------------------------------------------------------------------- 1 | T0 TF 30 34 RORγ 2 | T2 TF 143 176 RAR-related orphan receptor gamma 3 | T4 TF 205 210 RORγt 4 | T6 CELL_TYPE 372 381 monocytes 5 | T7 PROTEIN 404 408 RORγ 6 | T8 DNA 484 488 RORγ 7 | T9 PROTEIN 526 530 IL17 8 | T10 CELL_LINE 545 554 EL4 cells 9 | T11 PROTEIN 613 637 proinflammatory cytokine 10 | T12 CELL_LINE 667 681 RAW264.7 cells 11 | T14 CELL_TYPE 1202 1212 TH17 cells 12 | T15 PROTEIN 1269 1291 inflammatory cytokines 13 | T16 PROTEIN 1318 1339 inflammatory cytokine 14 | T17 CELL_LINE 1369 1383 RAW264.7 cells 15 | T1 TF 137 141 RORγ 16 | T3 CELL_TYPE 234 238 TH17 17 | T5 CYTOKINE 316 320 IL17 18 | R1 Differentiation Arg1:T4 Arg2:T3 19 | R2 Differentiation Arg1:T2 Arg2:T3 20 | R3 Differentiation Arg1:T1 Arg2:T3 21 | R4 Secretion Arg1:T3 Arg2:T5 22 | T13 CELL_TYPE 1574 1577 TH1 23 | T18 CYTOKINE 1665 1669 IFNγ 24 | R5 Secretion Arg1:T13 Arg2:T18 25 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4474185.txt: -------------------------------------------------------------------------------- 1 | Pharmacological repression of RORγ is therapeutic in the collagen-induced arthritis experimental model. 2 | 3 | 4 | Objective 5 | The nuclear receptor RORγ (RAR-related orphan receptor gamma; T cell specific isoform is RORγt) is a key regulator of TH17 cell differentiation controlling the production of the inflammatory cytokine IL17. Further it has been shown that LPS stimulation of monocytes leads to induction of RORγ. Previously we have shown that the potent and selective inverse agonist of RORγ, SR2211 was effective at suppressing IL17 production in EL4 cells. Further we demonstrate here that SR2211 treatment blocks proinflammatory cytokine expression in LPS stimulated RAW264.7 cells. Based on these findings SR2211 was administered to collagen-induced arthritis (CIA) mice to evaluate the ability of the compound to reduce joint inflammation. 6 | 7 | 8 | Methods 9 | Collagen was injected into the tail of DBA mice followed by a second boost inoculation 21 days later. Three days prior to the boost inoculation, SR2211 was administered into these mice twice daily for 15 days. Thymus, spleen and lymph node (DLN) were harvested and TH17 cell differentiation and DLN stimulation were performed. 10 | 11 | 12 | Results 13 | Treatment of TH17 cells with SR2211 suppressed the expression and production of inflammatory cytokines. Likewise, SR2211 reduced inflammatory cytokine production in LPS stimulated RAW264.7 cells. CIA mice administered SR2211 twice daily for 15 days exhibited statistically significant reduction in joint inflammation as compared to mice receiving only vehicle. Interestingly, systemic TH1 cell activation was detected in SR2211 treated CIA mice as indicated by an increase in IFNγ. 14 | 15 | 16 | Conclusions 17 | These findings support targeting RORγ to therapeutically repress inflammatory T cell function and macrophage activation in rheumatoid arthritis. Compounds such as SR2211 have potential utility for the treatment of inflammatory disease. 18 | 19 | 20 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4592272.ann: -------------------------------------------------------------------------------- 1 | T0 CYTOKINE 30 34 IL-2 2 | T1 CELL_TYPE 165 188 pathogen-infected cells 3 | T2 CELL_TYPE 270 281 CD8 T cells 4 | T5 CYTOKINE 963 967 IL-2 5 | T9 CYTOKINE 1187 1191 IL-2 6 | T10 CYTOKINE 1241 1245 IL-2 7 | T12 CYTOKINE 1329 1333 IL-2 8 | T13 CELL_TYPE 1622 1633 CD8 T cells 9 | T14 CELL_TYPE 1800 1818 Memory CD8 T cells 10 | T15 CELL_TYPE 1854 1865 CD8 T cells 11 | T16 PROTEIN 1901 1904 Ags 12 | T17 CELL_TYPE 2017 2035 memory CD8 T cells 13 | T18 PROTEIN 2195 2197 Ag 14 | T19 CELL_TYPE 2246 2279 primary memory (1° M) CD8 T cells 15 | T20 CELL_TYPE 2306 2323 naïve CD8 T cells 16 | T21 CELL_TYPE 2400 2418 memory CD8 T cells 17 | T22 CELL_TYPE 2649 2660 CD8 T cells 18 | T23 PROTEIN 2821 2839 signal 3 cytokines 19 | T24 CELL_TYPE 3028 3045 naïve CD8 T cells 20 | T25 CELL_TYPE 3111 3113 DC 21 | T26 PROTEIN 3297 3299 Ag 22 | T27 CELL_LINE 3400 3423 Ag-specific CD8 T cells 23 | T28 CELL_TYPE 3441 3453 memory cells 24 | T29 CELL_TYPE 3479 3496 naive CD8 T cells 25 | T30 CELL_TYPE 3526 3557 pre-existing memory CD8 T cells 26 | T31 PROTEIN 3574 3576 Ag 27 | T32 CELL_TYPE 3691 3702 CD8 T cells 28 | T33 CELL_TYPE 3748 3765 memory CD8 T cell 29 | T34 PROTEIN 3796 3806 cognate Ag 30 | T35 CELL_TYPE 4033 4051 memory CD8 T cells 31 | T36 CELL_TYPE 4167 4185 memory CD8 T cells 32 | T37 CELL_TYPE 4332 4355 Ag-specific CD8 T cells 33 | T38 CELL_TYPE 4447 4465 memory CD8 T cells 34 | T39 CELL_TYPE 4842 4859 naïve CD8 T cells 35 | T40 CELL_TYPE 64 69 CD8 T 36 | T4 CELL_TYPE 854 859 CD8 T 37 | T3 CELL_TYPE 907 912 CD8 T 38 | R1 Secretion Arg1:T3 Arg2:T5 39 | T6 CYTOKINE 1114 1118 IL-2 40 | T7 CELL_TYPE 1150 1155 CD8 T 41 | T8 CELL_TYPE 1273 1278 CD8 T 42 | T11 CELL_TYPE 1385 1390 CD8 T 43 | R2 Induction Arg1:T12 Arg2:T11 44 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4628936.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 285 294 cytokines 2 | T1 PROTEIN 306 325 metabolic mediators 3 | T2 CELL_TYPE 695 706 macrophages 4 | T3 CELL_TYPE 708 740 myeloid-derived suppressor cells 5 | T4 CELL_TYPE 742 760 T regulatory cells 6 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4628936.txt: -------------------------------------------------------------------------------- 1 | Hypoxia: a key player in antitumor immune response. A Review in the Theme: Cellular Responses to Hypoxia. 2 | 3 | The tumor microenvironment is a complex system, playing an important role in tumor development and progression. Besides cellular stromal components, extracellular matrix fibers, cytokines, and other metabolic mediators are also involved. In this review we outline the potential role of hypoxia, a major feature of most solid tumors, within the tumor microenvironment and how it contributes to immune resistance and immune suppression/tolerance and can be detrimental to antitumor effector cell functions. We also outline how hypoxic stress influences immunosuppressive pathways involving macrophages, myeloid-derived suppressor cells, T regulatory cells, and immune checkpoints and how it may confer tumor resistance. Finally, we discuss how microenvironmental hypoxia poses both obstacles and opportunities for new therapeutic immune interventions. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4649113.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 77 99 CD4 T helper (Th) cell 2 | T1 CELL_TYPE 130 144 T cell subsets 3 | T2 CELL_TYPE 241 254 naïve T cells 4 | T3 PROTEIN 340 349 cytokines 5 | T4 CELL_LINE 463 501 transdifferentiated T cell populations 6 | T5 PROTEIN 517 548 signature transcription factors 7 | T6 PROTEIN 553 562 cytokines 8 | T7 CELL_TYPE 635 651 T helper subsets 9 | T8 CYTOKINE 916 923 (IL)-17 10 | T14 CELL_TYPE 944 948 Th17 11 | R1 Secretion Arg1:T14 Arg2:T8 12 | T9 CYTOKINE 1209 1221 interferon-γ 13 | T10 CELL_TYPE 1250 1253 Th1 14 | T15 CYTOKINE 1162 1167 IL-17 15 | R2 Secretion Arg1:T10 Arg2:T9 16 | T11 TF 1322 1337 forkhead box p3 17 | T12 CELL_TYPE 1375 1387 regulatory T 18 | R3 Differentiation Arg1:T11 Arg2:T12 19 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4649113.txt: -------------------------------------------------------------------------------- 1 | Th17 plasticity and its changes associated with inflammatory bowel disease. 2 | 3 | CD4 T helper (Th) cell differentiation into distinct T cell subsets is critical to the normal function of the immune system. Until recently, the paradigm held that naïve T cells differentiated into distinct subsets under the guidance of environmental cues (e.g., cytokines) and that once polarized, these cells were committed to a particular functional state. However, the existence of transdifferentiated T cell populations, which express signature transcription factors and cytokines associated with more than one Th subset, challenges the immutability of T helper subsets and suggests that plasticity is a feature of multifaceted immune responses. How this process impacts immune dysregulation in diseases such as inflammatory bowel diseases (IBD) and the machinery that underlies this process is far from fully understood. Interleukin (IL)-17 secreting helper T (Th17) cells have been heavily implicated in tissue-specific immune pathology including murine models of IBD, human Crohn’s disease and ulcerative colitis. Plasticity within this subset is suggested by the existence of IL-17 secreting cells, which, can also secrete interferon-γ, the signature cytokine for Th1 cells or, can co-express the anti-inflammatory transcription factor forkhead box p3, a signature transcription factor of regulatory T cells. In this review we mainly discuss evidence for Th17 plasticity, mechanisms, which govern it, and highlight the potential to therapeutically target this process in human IBD. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4710466.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 0 28 Common Gamma Chain Cytokines 2 | T1 PROTEIN 101 110 cytokines 3 | T2 PROTEIN 119 123 IL-2 4 | T3 PROTEIN 125 129 IL-4 5 | T4 PROTEIN 131 135 IL-7 6 | T5 PROTEIN 137 141 IL-9 7 | T6 PROTEIN 143 148 IL-15 8 | T7 PROTEIN 154 159 IL-21 9 | T8 CELL_TYPE 230 241 lymphocytes 10 | T9 PROTEIN 329 338 cytokines 11 | T10 PROTEIN 401 413 γC cytokines 12 | T11 PROTEIN 473 477 IL-2 13 | T12 PROTEIN 479 483 IL-7 14 | T13 PROTEIN 485 490 IL-15 15 | T14 PROTEIN 496 501 IL-21 16 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4710466.txt: -------------------------------------------------------------------------------- 1 | Common Gamma Chain Cytokines in Combinatorial Immune Strategies against Cancer. 2 | 3 | Common γ chain (γC) cytokines, namely IL-2, IL-4, IL-7, IL-9, IL-15, and IL-21 are important for the proliferation, differentiation, and survival of lymphocytes that display antitumor activity, thus stimulating considerable interest for the use of cytokines in cancer immunotherapy. In this review, we will focus on the γC cytokines that demonstrate the greatest potential for immunotherapy, IL-2, IL-7, IL-15, and IL-21. We will briefly cover their biological function, potential applications in cancer therapy, and update on their use in combinatorial immune strategies for eradicating tumors and hematopoietic malignancies. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4720349.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 0 5 IFT20 2 | T1 PROTEIN 15 18 LAT 3 | T2 CELL_TYPE 161 168 T cells 4 | T3 CELL_TYPE 173 197 antigen-presenting cells 5 | T4 PROTEIN 268 273 IFT20 6 | T5 PROTEIN 275 310 intraflagellar transport protein 20 7 | T6 PROTEIN 490 495 IFT20 8 | T7 PROTEIN 497 512 T-cell receptor 9 | T8 PROTEIN 561 582 signaling adaptor LAT 10 | T9 CELL_TYPE 640 651 CD4+ T-cell 11 | T10 CELL_TYPE 772 779 T cells 12 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4720349.txt: -------------------------------------------------------------------------------- 1 | IFT20 controls LAT recruitment to the immune synapse and T-cell activation in vivo. 2 | 3 | Significance 4 | The immune synapse (IS), the site of cell–cell contact between T cells and antigen-presenting cells, plays a crucial role in the mounting of an immune response. Although IFT20 (intraflagellar transport protein 20), a component of the intraflagellar transport system, regulates polarized traffic to the IS, its role in T-cell activation in vivo is unknown. Here we show that in the absence of IFT20, T-cell receptor (TCR)-mediated signaling and recruitment of the signaling adaptor LAT to the immune synapse are impaired, leading to defective CD4+ T-cell activation and proliferation. IFT20-defective mice fail to mount effective antigen-specific T-cell responses, and their T cells do not induce disease in a T-cell-adoptive transfer model of colitis. 5 | 6 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4851424.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 0 4 mTOR 2 | T1 PROTEIN 200 204 mTOR 3 | T2 PROTEIN 699 707 cytokine 4 | T3 PROTEIN 927 953 pro-inflammatory cytokines 5 | T4 PROTEIN 968 988 interleukin (IL)-17A 6 | T5 PROTEIN 996 1000 IL-6 7 | T6 PROTEIN 1070 1103 anti-inflammatory cytokines IL-10 8 | T7 CELL_TYPE 1165 1177 CD4+ T cells 9 | T8 CELL_TYPE 1182 1194 CD8+ T cells 10 | T9 CELL_TYPE 1320 1341 T helper 1(TH1) cells 11 | T10 CELL_TYPE 1346 1356 TH17 cells 12 | T11 CELL_TYPE 1382 1396 T (Treg) cells 13 | T12 CELL_TYPE 1404 1418 lamina propria 14 | T13 CELL_TYPE 1521 1542 CD4+ and CD8+ T cells 15 | T14 CELL_LINE 1570 1584 TH1/TH17 cells 16 | T15 CELL_TYPE 1597 1607 Treg cells 17 | T16 CELL_TYPE 2309 2327 regulatory T cells 18 | T17 CELL_TYPE 2329 2334 Tregs 19 | T18 CELL_TYPE 2365 2379 helper T cells 20 | T19 CELL_TYPE 2431 2441 TH17 cells 21 | T20 CELL_TYPE 2682 2696 effector cells 22 | T21 CELL_TYPE 2712 2730 regulatory T cells 23 | T22 CELL_TYPE 2883 2903 CD4+CD45RBhi T cells 24 | T23 CELL_TYPE 3035 3045 Treg cells 25 | T24 CELL_TYPE 3151 3169 pathogenic T cells 26 | T25 CELL_TYPE 3605 3612 T cells 27 | T26 CELL_TYPE 3648 3655 T cells 28 | T27 CELL_TYPE 3685 3714 antigen-specific CD4+ T cells 29 | T28 PROTEIN 3942 3946 mTOR 30 | T29 PROTEIN 3953 3967 protein kinase 31 | T30 PROTEIN 4113 4117 mTOR 32 | T31 CELL_TYPE 4184 4203 CD4+ or CD8+ T cell 33 | T32 CELL_TYPE 4258 4268 Treg cells 34 | T33 PROTEIN 4532 4561 interleukin-10(IL-10)–/–model 35 | T34 CELL_TYPE 4594 4606 CD4+ T cells 36 | T35 PROTEIN 4642 4647 IFN-γ 37 | T36 PROTEIN 4687 4710 multi-protein complexes 38 | T37 PROTEIN 4712 4718 mTORC1 39 | T38 PROTEIN 4723 4729 mTORC2 40 | T39 PROTEIN 4792 4796 mTOR 41 | T40 PROTEIN 4972 4995 mTOR-containing complex 42 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4856445.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 60 78 CD8 T cell lineage 2 | T1 CELL_TYPE 82 93 CD4 T cells 3 | T2 CELL_TYPE 215 228 T lymphocytes 4 | T3 CELL_TYPE 355 362 T cells 5 | T4 PROTEIN 421 424 CD4 6 | T5 CELL_TYPE 429 440 CD8 T cells 7 | T6 CELL_TYPE 461 472 CD8 T cells 8 | T7 PROTEIN 486 489 MHC 9 | T12 CELL_TYPE 842 857 CD4 populations 10 | T13 CELL_TYPE 874 877 MLN 11 | T14 PROTEIN 893 897 LILP 12 | T10 CELL_TYPE 661 665 Treg 13 | T15 CELL_TYPE 647 659 regulatory T 14 | T16 TF 640 645 Foxp3 15 | R1 TFExpression Arg1:T15 Arg2:T16 16 | R2 TFExpression Arg1:T10 Arg2:T16 17 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4856445.txt: -------------------------------------------------------------------------------- 1 | Gut microbiota amplifies host-intrinsic conversion from the CD8 T cell lineage to CD4 T cells for induction of mucosal immune tolerance. 2 | 3 | Abstract 4 | Microbiota has been shown to promote tolerogenic differentiation of T lymphocytes. It remains unclear to what extent microbiota triggers de novo re-programming or amplify pre-existing plasticity intrinsic to T cells. In a study with mouse models to track the clonal fate of CD4 and CD8 T cells, we discovered that CD8 T cells converted to MHC class I-restricted CD4 T cells without regard to selfness of their antigen specificity. In mesenteric lymph nodes (MLN), CD8 T cells converted to CD4+Foxp3+ regulatory T (Treg) cells which were enriched in the large intestine lamina propria (LILP) and suppressed chemical- or immune-mediated inflammatory damage. In germ-free conditions, the converted CD4 populations were present in MLN, but absent in LILP. Therefore, an intrinsic plasticity in the host was amplified by the gut microbiota, leading to selfless tolerance induction in the intestinal mucosa. The findings may be relevant to HIV infection, cancer and autoimmune disorders. 5 | 6 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4905708.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 29 59 B7-CD28 ligand-receptor family 2 | T1 PROTEIN 177 191 B7-CD28 family 3 | T2 PROTEIN 1012 1026 B7-CD28 family 4 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4905708.txt: -------------------------------------------------------------------------------- 1 | Coinhibitory pathways in the B7-CD28 ligand-receptor family. 2 | 3 | Immune responses need to be controlled for optimal protective immunity and tolerance. Coinhibitory pathways in the B7-CD28 family provide critical inhibitory signals that regulate immune homeostasis and defense, and protect tissue integrity. These coinhibitory signals limit the strength and duration of immune responses, thereby curbing immune-mediated tissue damage, regulating resolution of inflammation, and maintaining tolerance to prevent autoimmunity. Tumors and microbes that cause chronic infections can exploit these coinhibitory pathways to establish an immunosuppressive microenvironment, hindering their eradication. Advances in understanding T cell coinhibitory pathways have stimulated a new era of immunotherapy with effective drugs to treat cancer, autoimmune and infectious diseases and transplant rejection. In this review we discuss the current knowledge of the mechanisms underlying the coinhibitory functions of pathways in the B7-CD28 family, the diverse functional consequences of these inhibitory signals on immune responses, and the overlapping and unique functions of these key immunoregulatory pathways. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC4959015.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 123 141 CD8+ memory T cell 2 | T1 CELL_TYPE 189 208 CD8+ memory T cells 3 | T2 CELL_TYPE 298 316 memory CD8+ T cell 4 | T3 CELL_TYPE 390 408 naïve CD8+ T cells 5 | T4 CELL_TYPE 796 826 less-differentiated stem cells 6 | T5 PROTEIN 1054 1076 cell fate determinants 7 | T6 CELL_TYPE 1082 1096 daughter cells 8 | T7 CELL_TYPE 1296 1308 parent cells 9 | T8 CELL_TYPE 1350 1364 daughter cells 10 | T9 PROTEIN 1583 1612 extracellular matrix proteins 11 | T10 PROTEIN 1614 1624 chemokines 12 | T11 PROTEIN 1630 1639 cytokines 13 | T12 CELL_TYPE 1916 1926 Stem cells 14 | T13 PROTEIN 2048 2057 Integrins 15 | T14 CELL_TYPE 2088 2097 stem cell 16 | T15 PROTEIN 2356 2365 integrins 17 | T16 PROTEIN 2399 2409 β1 subunit 18 | T17 CELL_TYPE 2451 2461 stem cells 19 | T18 CELL_TYPE 2478 2492 daughter cells 20 | T19 PROTEIN 2558 2561 Gui 21 | T20 PROTEIN 2566 2571 Homer 22 | T21 CELL_TYPE 2879 2908 myeloid and lymphoid lineages 23 | T22 PROTEIN 3024 3032 cytokine 24 | T23 PROTEIN 3037 3046 chemokine 25 | T24 CELL_TYPE 3123 3145 undifferentiated cells 26 | T25 CELL_TYPE 3233 3253 differentiated cells 27 | T26 CELL_TYPE 3350 3363 stromal cells 28 | T27 PROTEIN 3375 3383 integrin 29 | T28 PROTEIN 3385 3394 chemokine 30 | T29 PROTEIN 3400 3408 cytokine 31 | T30 CELL_TYPE 3467 3480 stromal cells 32 | T31 CELL_TYPE 3686 3703 multipotent cells 33 | T32 CELL_TYPE 3830 3849 low-frequency cells 34 | T33 CELL_TYPE 4215 4227 immune cells 35 | T34 PROTEIN 4328 4350 von Andrian and Mempel 36 | T35 CELL_TYPE 4476 4487 macrophages 37 | T36 PROTEIN 4509 4524 soluble factors 38 | T37 CELL_TYPE 4858 4865 B cells 39 | T38 CELL_TYPE 4867 4897 CD4+ follicular T helper cells 40 | T39 CELL_TYPE 4903 4929 follicular dendritic cells 41 | T40 CELL_TYPE 4931 4935 FDCs 42 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC5112176.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 4 26 TNF-family ligand TL1A 2 | T1 PROTEIN 35 47 receptor DR3 3 | T2 PROTEIN 147 151 IL-9 4 | T4 CYTOKINE 203 210 Tnfsf15 5 | T6 CELL_TYPE 244 262 innate lymphocytes 6 | T7 CELL_TYPE 264 268 ILC2 7 | T8 PROTEIN 282 294 receptor DR3 8 | T9 PROTEIN 296 304 Tnfrsf25 9 | T10 CELL_TYPE 558 562 ILC2 10 | T11 PROTEIN 590 593 DR3 11 | T13 CYTOKINE 694 699 IL-13 12 | T14 CYTOKINE 704 708 IL-4 13 | T15 CYTOKINE 744 748 TL1A 14 | T18 CYTOKINE 866 870 TL1A 15 | T20 CYTOKINE 928 932 IL-9 16 | T21 PROTEIN 960 963 DR3 17 | T22 CYTOKINE 992 996 TL1A 18 | T23 CYTOKINE 1037 1041 IL-2 19 | T25 CYTOKINE 1135 1139 IL-4 20 | T26 TF 1144 1149 STAT6 21 | T27 CYTOKINE 1189 1193 TL1A 22 | T28 CYTOKINE 1230 1234 TL1A 23 | T29 PROTEIN 1253 1256 DR3 24 | T31 CYTOKINE 1306 1310 IL-9 25 | T33 CYTOKINE 1483 1487 TL1A 26 | T34 CYTOKINE 1551 1555 IL-9 27 | T3 CYTOKINE 197 201 TL1A 28 | T5 CELL_TYPE 550 553 Th2 29 | T35 CELL_TYPE 644 647 Th2 30 | R1 Secretion Arg1:T35 Arg2:T13 31 | R2 Secretion Arg1:T35 Arg2:T14 32 | T12 CELL_TYPE 934 937 Th9 33 | R3 Induction Arg1:T18 Arg2:T12 34 | R4 Secretion Arg1:T12 Arg2:T20 35 | T16 CELL_TYPE 1006 1009 Th9 36 | T17 TF 1046 1051 STAT5 37 | R5 Differentiation Arg1:T17 Arg2:T16 38 | R6 Induction Arg1:T23 Arg2:T16 39 | R7 Induction Arg1:T22 Arg2:T16 40 | T19 CYTOKINE 1102 1106 OX40 41 | T24 CELL_TYPE 1123 1126 Th9 42 | R8 Induction Arg1:T19 Arg2:T24 43 | R9 Induction Arg1:T25 Arg2:T24 44 | R10 Differentiation Arg1:T26 Arg2:T24 45 | T36 CELL_TYPE 1151 1154 Th9 46 | R11 Induction Arg1:T27 Arg2:T36 47 | T37 CYTOKINE 0 3 The 48 | R12 Secretion Arg1:T36 Arg2:T31 49 | T30 CYTOKINE 1389 1393 TL1A 50 | T32 CELL_TYPE 1444 1447 Th9 51 | R13 Induction Arg1:T30 Arg2:T32 52 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC5112176.txt: -------------------------------------------------------------------------------- 1 | The TNF-family ligand TL1A and its receptor DR3 promote T-cell mediated allergic immunopathology by enhancing differentiation and pathogenicity of IL-9 producing T cells1. 2 | 3 | The TNF family cytokine TL1A (Tnfsf15) costimulates T cells and type 2 innate lymphocytes (ILC2) through its receptor DR3 (Tnfrsf25). DR3-deficient mice have reduced T cell accumulation at the site of inflammation, and reduced ILC2-dependent immune responses in a number of models of autoimmune and allergic diseases. In allergic lung disease models, immunopathology and local Th2 and ILC2 accumulation is reduced in DR3 deficient mice despite normal systemic priming of Th2 responses and generation of T cells secreting IL-13 and IL-4, prompting the question of whether TL1A promotes the development of other T cell subsets that secrete cytokines to drive allergic disease. Here we find that TL1A potently promotes generation of murine T cells producing IL-9 (Th9) by signaling through DR3 in a cell-intrinsic manner. TL1A enhances Th9 differentiation through an IL-2 and STAT5-dependent mechanism, unlike the TNF-family member OX40, which promotes Th9 through IL-4 and STAT6. Th9 differentiated in the presence of TL1A are more pathogenic, and endogenous TL1A signaling through DR3 on T cells is required for maximal pathology and IL-9 production in allergic lung inflammation. Taken together, these data identify TL1A-DR3 interactions as a novel pathway that promotes Th9 differentiation and pathogenicity. TL1A may be a potential therapeutic target in diseases dependent on IL-9. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC5118948.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 31 35 IL-4 2 | T1 PROTEIN 40 45 IL-13 3 | T2 PROTEIN 568 585 interleukin(IL)-4 4 | T3 PROTEIN 590 595 IL-13 5 | T4 PROTEIN 607 616 cytokines 6 | T5 PROTEIN 730 734 IL-4 7 | T6 PROTEIN 739 744 IL-13 8 | T7 PROTEIN 898 907 cytokines 9 | T8 PROTEIN 1054 1058 IL-4 10 | T9 PROTEIN 1063 1068 IL-13 11 | T10 CELL_TYPE 1072 1084 CD4+ T cells 12 | T11 CELL_TYPE 1089 1108 innate immune cells 13 | T12 PROTEIN 1218 1222 IL-4 14 | T13 PROTEIN 1227 1232 IL-13 15 | T14 CELL_TYPE 1244 1256 immune cells 16 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC5118948.txt: -------------------------------------------------------------------------------- 1 | The differential expression of IL-4 and IL-13 and its impact on type-2 Immunity. 2 | 3 | Allergic disease represents a significant global health burden, and disease incidence continues to rise in urban areas of the world. As such, a better understanding of the basic immune mechanisms underlying disease pathology are likely the key to developing therapeutic interventions to both prevent disease onset as well as to ameliorate disease morbidity in those individuals already suffering from a disorder linked to type-2 inflammation. Two factors central to type-2 immunity are interleukin(IL)-4 and IL-13. These two cytokines have been linked to virtually all of the major disease hallmarks associated with type-2 inflammation. Therefore, IL-4 and IL-13 and their regulatory pathways represent ideal targets to suppress disease. However, despite sharing many common regulatory pathways and receptors, these cytokines perform very distinct functions during a type-2 immune response. This review summarizes the literature surrounding the function and expression of IL-4 and IL-13 in CD4+ T cells and innate immune cells. It highlights recent in vivo findings regarding the differential expression and non-canonical regulation of IL-4 and IL-13 in various immune cells, which likely play an underappreciated and important role in type-2 allergic immunity. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC5191835.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 454 491 primary chicken erythroid progenitors 2 | T1 PROTEIN 2262 2279 master regulators 3 | T2 CELL_TYPE 2751 2771 embryonic stem cells 4 | T3 PROTEIN 2773 2776 2–8 5 | T4 CELL_TYPE 2808 2830 pluripotent stem cells 6 | T5 CELL_TYPE 2966 2990 hematopoietic stem cells 7 | T6 DNA 3732 3741 chromatin 8 | T7 CELL_TYPE 4314 4335 differentiating cells 9 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC5257256.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 127 146 proliferating cells 2 | T1 PROTEIN 291 295 Otto 3 | T2 CELL_TYPE 342 354 cancer cells 4 | T3 CELL_LINE 811 830 mouse T cell blasts 5 | T4 CYTOKINE 1048 1063 IL-2 receptor-α 6 | T5 CYTOKINE 1065 1069 CD25 7 | T7 CELL_TYPE 1649 1663 dividing cells 8 | T8 CELL_TYPE 1672 1679 T cells 9 | T9 PROTEIN 2126 2130 Otto 10 | T10 CELL_TYPE 2159 2171 cancer cells 11 | T11 CELL_TYPE 2880 2894 dividing cells 12 | T12 PROTEIN 2960 2962 Le 13 | T13 CELL_TYPE 3219 3226 T cells 14 | T14 CELL_LINE 3647 3731 pro-inflammatory T helper 17 (TH17) over anti-inflammatory T regulatory (Treg) cells 15 | T15 PROTEIN 3877 3882 GAPDH 16 | T16 PROTEIN 3884 3923 α-ketoglutarate and phosphoenolpyruvate 17 | T17 DNA 3940 3944 IFNγ 18 | T18 PROTEIN 4464 4473 galectins 19 | T19 PROTEIN 4553 4579 multiple surface receptors 20 | T20 PROTEIN 4785 4788 Lau 21 | T21 CELL_TYPE 4895 4902 T cells 22 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC5290235.ann: -------------------------------------------------------------------------------- 1 | T0 RNA 112 127 non‐coding RNAs 2 | T1 CELL_TYPE 131 138 T cells 3 | T2 DNA 166 172 ncRNAs 4 | T3 RNA 185 194 microRNAs 5 | T4 DNA 196 202 miRNAs 6 | T5 RNA 208 228 long non‐coding RNAs 7 | T6 DNA 230 237 lncRNAs 8 | T7 PROTEIN 244 257 RNA molecules 9 | T8 DNA 310 317 lncRNAs 10 | T9 CELL_TYPE 672 679 T cells 11 | T10 CELL_TYPE 757 764 T cells 12 | T11 PROTEIN 849 857 cytokine 13 | T12 PROTEIN 862 871 chemokine 14 | T13 PROTEIN 1043 1050 lncRNAs 15 | T14 DNA 1216 1222 miRNAs 16 | T15 DNA 1227 1234 lncRNAs 17 | T16 CELL_TYPE 1238 1245 T cells 18 | T17 DNA 1382 1389 lncRNAs 19 | T18 CELL_TYPE 1393 1400 T cells 20 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC5290235.txt: -------------------------------------------------------------------------------- 1 | Immunopathogenesis of systemic lupus erythematosus and rheumatoid arthritis: the role of aberrant expression of non‐coding RNAs in T cells. 2 | 3 | Summary 4 | Non‐coding RNAs (ncRNAs), including microRNAs (miRNAs) and long non‐coding RNAs (lncRNAs), are RNA molecules that do not translate into protein. Both miRNAs and lncRNAs are known to regulate gene expression and to play an essential role in T cell differentiation and function. Both systemic lupus erythematosus (SLE), a prototypic systemic autoimmune disease, and rheumatoid arthritis (RA), a representative disease of inflammatory arthritis, are characterized by a complex dysfunction in the innate and adaptive immunity. T cells play a central role in cell‐mediated immune response and multiple defects in T cells from patients with SLE and RA have been observed. Abnormality in T cell signalling, cytokine and chemokine production, T cell activation and apoptosis, T cell differentiation and DNA methylation that are associated closely with the aberrant expression of a number of miRNAs and lncRNAs have been implicated in the immunopathogenesis of SLE and RA. This review aims to provide an overview of the current state of research on the abnormal expression of miRNAs and lncRNAs in T cells and their roles in the immunopathogenesis of SLE and RA. In addition, by comparing the differences in aberrant expression of miRNAs and lncRNAs in T cells between patients with SLE and RA, controversial areas are highlighted that warrant further investigation. 5 | 6 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC5293011.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 92 115 memory T helper 2 cells 2 | T3 TF 322 326 Bcl6 3 | T5 TF 414 418 Bcl6 4 | T9 CYTOKINE 565 570 IL-33 5 | T14 CYTOKINE 726 731 IL-33 6 | T15 TF 806 810 Bcl6 7 | T17 TF 883 887 Bcl6 8 | T4 TF 331 334 TH2 9 | T1 TF 188 192 Bcl6 10 | T2 CELL_TYPE 216 219 TH2 11 | T19 CELL_TYPE 204 214 T helper 2 12 | T6 CELL_TYPE 460 463 TH2 13 | T7 CYTOKINE 520 523 Il4 14 | T8 CYTOKINE 505 518 Interkeukin 4 15 | T10 TF 582 586 Bcl6 16 | T11 CELL_TYPE 677 680 TH2 17 | T12 CELL_TYPE 703 706 TH2 18 | R1 Induction Arg1:T14 Arg2:T12 19 | T13 CELL_TYPE 857 860 TH2 20 | T16 CELL_TYPE 891 894 TH2 21 | R2 TFCKEnhancementNeg Arg1:T5 Arg2:T8 22 | R3 TFCKEnhancementNeg Arg1:T5 Arg2:T7 23 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC5293011.txt: -------------------------------------------------------------------------------- 1 | Development of chronic allergic responses by dampening Bcl6-mediated suppressor activity in memory T helper 2 cells. 2 | 3 | Significance 4 | It has been suggested that the transcriptional repressor Bcl6 suppresses T helper 2 (TH2) immune responses underlying allergic diseases. However, the molecular role of B-cell CLL/lymphoma 6 (Bcl6) in TH2 cells is incompletely understood in pathophysiological settings. We found that Bcl6 suppressed cytokine production in memory TH2 cells through binding to intron 2 of the Interkeukin 4 (Il4) locus using murine models. Furthermore, IL-33 controlled Bcl6 function at the chromatin level and consequently, augmented cytokine production in memory TH2 cells. Therefore, pro-TH2 cytokines, such as IL-33, play a role in chronic allergic diseases via the functional breakdown of Bcl6. This study identifies a relationship between TH2-promoting factors and Bcl6 in TH2 cells, which may lead to therapeutic strategies against chronic allergic diseases. 5 | 6 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC5343661.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_LINE 51 74 dendritic cell cultures 2 | T1 CELL_TYPE 107 145 human monocyte-derived dendritic cells 3 | T2 CELL_TYPE 147 150 DCs 4 | T3 PROTEIN 258 263 IL-12 5 | T4 PROTEIN 322 327 IL-10 6 | T5 CELL_LINE 445 481 murine bone marrow-derived DC models 7 | T6 CELL_TYPE 584 586 DC 8 | T7 CELL_TYPE 682 711 antigen-specific CD4+ T cells 9 | T8 CELL_TYPE 857 860 DCs 10 | T9 CELL_TYPE 1003 1014 osteoclasts 11 | T10 CELL_TYPE 1177 1179 DC 12 | T11 CELL_TYPE 1231 1246 Dendritic cells 13 | T12 CELL_TYPE 1248 1251 DCs 14 | T13 CELL_TYPE 1288 1312 antigen presenting cells 15 | T14 CELL_TYPE 1544 1547 DCs 16 | T15 PROTEIN 1756 1765 cytokines 17 | T16 PROTEIN 1975 2001 tumour-associated antigens 18 | T17 CELL_TYPE 2051 2089 antigen-specific CD4+ and CD8+ T cells 19 | T18 CELL_TYPE 2157 2159 DC 20 | T19 DNA 2367 2375 cohorts1 21 | T20 CELL_TYPE 2733 2735 DC 22 | T21 CELL_TYPE 2800 2802 DC 23 | T22 CELL_TYPE 2964 2966 DC 24 | T26 PROTEIN 3156 3170 tumor tissue69 25 | T27 CELL_TYPE 3244 3247 DCs 26 | T28 CELL_TYPE 3265 3268 DCs 27 | T29 CELL_TYPE 3331 3333 DC 28 | T30 CELL_TYPE 3421 3424 DCs 29 | T31 CELL_TYPE 3476 3478 DC 30 | T32 PROTEIN 3509 3531 gliobastoma patients13 31 | T33 PROTEIN 3579 3602 DC cell surface markers 32 | T34 CELL_TYPE 3718 3728 CD1a+CD14− 33 | T35 CELL_LINE 3733 3757 CD1a−CD14low populations 34 | T36 CELL_TYPE 3773 3788 blood monocytes 35 | T37 PROTEIN 3804 3810 GM-CSF 36 | T38 PROTEIN 3815 3819 IL-4 37 | T39 CELL_TYPE 3828 3859 CD34+ hematopoietic progenitors 38 | T40 PROTEIN 3874 3880 GM-CSF 39 | T41 PROTEIN 3885 3891 Flt3-L 40 | T42 CELL_TYPE 3907 3917 DC subsets 41 | T43 CELL_TYPE 3986 4007 CD1a+CD14− population 42 | T44 CELL_TYPE 4054 4076 cytotoxic T lymphocyte 43 | T45 PROTEIN 4118 4142 CD1a−CD14low counterpart 44 | T46 CELL_TYPE 4161 4175 CD1a+/CD1a− DC 45 | T47 CELL_TYPE 4308 4310 DC 46 | T48 CELL_LINE 4419 4430 DC cultures 47 | T49 CELL_TYPE 4478 4480 DC 48 | T52 CYTOKINE 4709 4714 IL-10 49 | T53 CELL_TYPE 4788 4790 DC 50 | T23 CYTOKINE 3003 3008 IL-12 51 | T24 CELL_TYPE 3097 3100 TH1 52 | T25 CELL_TYPE 3123 3135 regulatory T 53 | T54 CELL_TYPE 4632 4635 TH1 54 | R1 Secretion Arg1:T54 Arg2:T52 55 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC5417820.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 24 39 N-Glycoproteome 2 | T1 PROTEIN 769 848 N-linked cell-surface glycoproteomes of 15 standard laboratory human cell lines 3 | T2 CELL_TYPE 859 889 primary lymphocytic cell types 4 | T3 CELL_TYPE 1038 1051 Primary cells 5 | T4 PROTEIN 1085 1100 surface markers 6 | T5 CELL_LINE 1170 1186 model cell lines 7 | T6 PROTEIN 1448 1471 plasma membrane protein 8 | T7 CELL_LINE 1515 1551 monocytic suspension cell line THP-1 9 | T8 CELL_TYPE 1557 1587 macrophage-like adherent cells 10 | T9 PROTEIN 1624 1640 membrane protein 11 | T10 PROTEIN 1942 1960 macrophage markers 12 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC5417820.txt: -------------------------------------------------------------------------------- 1 | Monitoring Cell-surface N-Glycoproteome Dynamics by Quantitative Proteomics Reveals Mechanistic Insights into Macrophage Differentiation* 2 | 3 | The plasma membrane proteome plays a crucial role in inter- and intracellular signaling, cell survival, and cell identity. As such, it is a prominent target for pharmacological intervention. The relatively low abundance of this subproteome in conjunction with challenging extractability and solubility still hampers its comprehensive analysis. Here, we combined a chemical glycoprotein-tagging strategy with mass spectrometry to enable comprehensive analysis of the cell-surface glycoproteome. To benchmark this workflow and to provide guidance for cell line selection for functional experiments, we generated an inventory of the N-linked cell-surface glycoproteomes of 15 standard laboratory human cell lines and three primary lymphocytic cell types. On average, about 900 plasma membrane and secreted proteins were identified per experiment, including more than 300 transporters and ion channels. Primary cells displayed distinct expression of surface markers and transporters underpinning the importance of carefully validating model cell lines selected for the study of cell surface-mediated processes. To monitor dynamic changes of the cell-surface proteome in a highly multiplexed experiment, we employed an isobaric mass tag-based chemical labeling strategy. This enabled the time-resolved analysis of plasma membrane protein presentation during differentiation of the monocytic suspension cell line THP-1 into macrophage-like adherent cells. Time-dependent changes observed in membrane protein presentation reflect functional remodeling during the phenotypic transition in three distinct phases: rapid surface presentation and secretion of proteins from intracellular pools concurrent with rapid internalization of no longer needed proteins and finally delayed presentation of newly synthesized macrophage markers. Perturbation of this process using marketed receptor tyrosine kinase inhibitors revealed dasatinib to severely compromise macrophage differentiation due to an off-target activity. This finding suggests that dynamic processes can be highly vulnerable to drug treatment and should be monitored more rigorously to identify adverse drug effects. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC5429091.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 0 22 Germinal center B cell 2 | T1 PROTEIN 161 165 CD40 3 | T2 CELL_TYPE 179 199 germinal center (GC) 4 | T3 CELL_TYPE 280 309 Peri-follicular GC precursors 5 | T4 DNA 349 353 BCL6 6 | T5 PROTEIN 378 404 transcription factors RelB 7 | T6 PROTEIN 409 413 IRF4 8 | T7 CELL_TYPE 477 490 GC precursors 9 | T8 PROTEIN 662 666 CD40 10 | T9 CELL_TYPE 686 699 GC precursors 11 | T10 CELL_TYPE 719 728 GC B cell 12 | T11 CELL_TYPE 822 834 CD4+ T cells 13 | T12 PROTEIN 880 884 CD40 14 | T13 CELL_TYPE 898 905 B cells 15 | T14 CELL_TYPE 959 969 GC B cells 16 | T15 CELL_TYPE 1067 1073 T cell 17 | T17 CELL_TYPE 1198 1214 B cell follicles 18 | T18 CELL_TYPE 1330 1340 GC B cells 19 | T19 CELL_TYPE 1371 1396 T follicular helper cells 20 | T20 CELL_TYPE 1398 1401 Tfh 21 | T21 CELL_TYPE 1416 1426 GC B cells 22 | T22 CELL_TYPE 1520 1534 memory B cells 23 | T23 CELL_TYPE 1539 1575 long-term high-affinity plasma cells 24 | T24 CELL_TYPE 1656 1682 GC founder/precursor cells 25 | T25 PROTEIN 1811 1826 B cell receptor 26 | T26 PROTEIN 1828 1831 BCR 27 | T27 CELL_TYPE 1844 1872 activated follicular B cells 28 | T28 PROTEIN 1903 1931 interfollicular (IF) regions 29 | T29 CELL_TYPE 1975 1988 B and T cells 30 | T30 CELL_TYPE 2142 2152 GC lineage 31 | T31 PROTEIN 2301 2305 BCL6 32 | T32 CELL_TYPE 2333 2350 activated B cells 33 | T33 CELL_TYPE 2513 2533 GC committed B cells 34 | T34 CELL_TYPE 2641 2658 activated B cells 35 | T35 CELL_TYPE 2679 2691 ASC lineages 36 | T36 PROTEIN 2920 2923 BCR 37 | T37 PROTEIN 2941 2949 O'Connor 38 | T38 PROTEIN 2964 2968 Paus 39 | T39 CELL_TYPE 3042 3050 Th cells 40 | T40 CELL_TYPE 3099 3113 B cell lineage 41 | T41 PROTEIN 3219 3249 transcriptional repressor BCL6 42 | T42 PROTEIN 3394 3420 transcription factors IRF4 43 | T43 PROTEIN 3425 3431 Blimp1 44 | T44 PROTEIN 3518 3522 IRF4 45 | T45 PROTEIN 3539 3545 Blimp1 46 | T46 PROTEIN 3573 3577 Bcl6 47 | T47 PROTEIN 3635 3647 Bcl6 protein 48 | T48 PROTEIN 3797 3801 CD40 49 | T49 PROTEIN 3859 3862 Foy 50 | T50 PROTEIN 3878 3882 CD40 51 | T51 PROTEIN 3913 3917 NFκB 52 | T52 PROTEIN 3977 3999 heterodimeric RelB/p52 53 | T53 PROTEIN 4090 4094 CD40 54 | T54 PROTEIN 4208 4212 CD40 55 | T55 CELL_TYPE 4238 4248 GC B cells 56 | T56 PROTEIN 4289 4293 CD40 57 | T57 CELL_TYPE 4327 4336 GC B cell 58 | T58 PROTEIN 4527 4531 IRF4 59 | T59 PROTEIN 4535 4555 transcription factor 60 | T60 PROTEIN 4583 4587 IRF4 61 | T61 CELL_TYPE 4644 4654 GC B cells 62 | T62 CELL_TYPE 4659 4663 ASCs 63 | T63 PROTEIN 4781 4785 IRF4 64 | T64 PROTEIN 4861 4865 IRF4 65 | T67 CYTOKINE 4955 4960 IL-21 66 | T16 CELL_TYPE 4944 4947 Tfh 67 | R1 Secretion Arg1:T16 Arg2:T67 68 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC5520220.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 53 57 PI3K 2 | T1 PROTEIN 93 114 Th1/Th2/Th17 cytokine 3 | T2 PROTEIN 256 259 AHR 4 | T3 CELL_TYPE 288 301 T-lymphocytes 5 | T4 PROTEIN 399 408 cytokines 6 | T5 PROTEIN 414 439 phosphoinositide 3-kinase 7 | T6 PROTEIN 441 445 PI3K 8 | T7 CELL_TYPE 632 638 T cell 9 | T8 PROTEIN 845 854 ovalbumin 10 | T9 PROTEIN 856 859 OVA 11 | T10 CELL_TYPE 1083 1118 bronchoalveolar lavage fluid (BALF) 12 | T11 PROTEIN 1188 1191 IgE 13 | T12 PROTEIN 1231 1234 AHR 14 | T13 CELL_TYPE 1351 1371 Th17 cell population 15 | T15 CYTOKINE 1440 1445 IFN-γ 16 | T16 PROTEIN 1454 1458 PI3K 17 | T17 PROTEIN 1507 1523 Notch 1 receptor 18 | T18 PROTEIN 1532 1554 ligands Jagged 1 and 2 19 | T19 PROTEIN 1881 1884 AHR 20 | T20 CELL_TYPE 1914 1927 T lymphocytes 21 | T21 PROTEIN 1991 2000 cytokines 22 | T23 CYTOKINE 2021 2025 IL-4 23 | T24 CYTOKINE 2027 2031 IL-5 24 | T25 CYTOKINE 2033 2038 IL-13 25 | T26 PROTEIN 2047 2079 allergen-specific immunoglobulin 26 | T27 CELL_TYPE 2137 2147 mast cells 27 | T28 CYTOKINE 2153 2158 IFN-γ 28 | T30 PROTEIN 2246 2250 IL-4 29 | T31 PROTEIN 2254 2258 IgG1 30 | T32 PROTEIN 2263 2266 IgE 31 | T33 PROTEIN 2276 2281 IFN-γ 32 | T34 PROTEIN 2301 2305 IgG2 33 | T35 PROTEIN 2335 2351 Th1/Th2 cytokine 34 | T36 CELL_TYPE 2443 2456 T lymphocytes 35 | T39 CYTOKINE 2621 2626 IL-17 36 | T40 CYTOKINE 2628 2632 IL-6 37 | T41 CYTOKINE 2634 2639 TNF-α 38 | T42 CYTOKINE 2645 2650 IL-22 39 | T43 PROTEIN 2652 2657 IL-17 40 | T44 CELL_TYPE 2713 2724 macrophages 41 | T45 CELL_TYPE 2781 2791 Th17 cells 42 | T46 CELL_TYPE 2850 2860 Th17 cells 43 | T47 PROTEIN 2927 2931 PI3K 44 | T48 PROTEIN 3035 3039 PI3K 45 | T49 PROTEIN 3054 3074 effector protein Akt 46 | T50 PROTEIN 3159 3163 mTOR 47 | T51 PROTEIN 3169 3196 glycogen synthase kinase 3β 48 | T52 PROTEIN 3198 3203 GSK3β 49 | T53 PROTEIN 3250 3261 phosphatase 50 | T54 PROTEIN 3266 3280 tensin homolog 51 | T55 PROTEIN 3282 3286 PTEN 52 | T56 PROTEIN 3293 3297 PI3K 53 | T57 PROTEIN 3384 3386 8) 54 | T58 PROTEIN 3403 3407 PI3K 55 | T59 CELL_TYPE 3422 3429 T cells 56 | T62 PROTEIN 4543 4547 PI3K 57 | T63 PROTEIN 4934 4960 pro-inflammatory cytokines 58 | T64 PROTEIN 4964 4976 interleukins 59 | T14 CELL_TYPE 1414 1418 Th17 60 | T65 CELL_TYPE 1406 1409 Th2 61 | T22 CELL_TYPE 2006 2009 Th2 62 | R1 Secretion Arg1:T22 Arg2:T23 63 | R2 Secretion Arg1:T22 Arg2:T24 64 | R3 Secretion Arg1:T22 Arg2:T25 65 | T29 CELL_TYPE 2194 2197 Th2 66 | T66 CELL_TYPE 2172 2175 Th1 67 | R4 Secretion Arg1:T66 Arg2:T28 68 | T37 CELL_TYPE 2573 2577 Th17 69 | R5 Secretion Arg1:T37 Arg2:T39 70 | R6 Secretion Arg1:T37 Arg2:T40 71 | R7 Secretion Arg1:T37 Arg2:T41 72 | R8 Secretion Arg1:T37 Arg2:T42 73 | T38 TF 4338 4378 retinoic acid-related orphan receptor γt 74 | T60 CELL_TYPE 4417 4421 Th17 75 | R9 Differentiation Arg1:T38 Arg2:T60 76 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC5591438.ann: -------------------------------------------------------------------------------- 1 | T2 PROTEIN 161 189 master transcription factors 2 | T3 PROTEIN 191 194 TFs 3 | T4 CELL_TYPE 239 253 T cell subsets 4 | T5 PROTEIN 274 300 transcriptional regulators 5 | T6 CELL_TYPE 378 393 T cell lineages 6 | T9 TF 539 542 MSC 7 | T10 TF 632 635 MSC 8 | T11 TF 644 649 Foxp3 9 | T12 CYTOKINE 704 709 TGF-β 10 | T13 CELL_TYPE 718 723 iTreg 11 | T14 TF 752 755 MSC 12 | T15 TF 804 809 GATA3 13 | T18 CELL_TYPE 934 937 TH2 14 | T19 CELL_LINE 956 1015 Msc−/− mice spontaneously develop gut and lung inflammation 15 | T20 TF 1049 1052 MSC 16 | T21 TF 1062 1067 Foxp3 17 | T22 TF 25 33 musculin 18 | T1 CELL_TYPE 77 92 peripheral Treg 19 | T23 CELL_TYPE 118 121 TH2 20 | R1 Differentiation Arg1:T22 Arg2:T1 21 | T7 CYTOKINE 431 459 Transforming growth factor-β 22 | T24 CYTOKINE 461 466 TGF-β 23 | T25 CELL_TYPE 468 488 induced regulatory T 24 | T26 CELL_TYPE 495 500 iTreg 25 | R2 Induction Arg1:T24 Arg2:T25 26 | R3 Induction Arg1:T24 Arg2:T26 27 | R4 Induction Arg1:T7 Arg2:T25 28 | R5 Induction Arg1:T7 Arg2:T26 29 | T8 TF 529 537 Musculin 30 | T27 CELL_TYPE 560 565 iTreg 31 | T28 CELL_TYPE 595 598 TH2 32 | R6 Differentiation Arg1:T9 Arg2:T27 33 | R7 Differentiation Arg1:T8 Arg2:T27 34 | T29 CELL_TYPE 673 676 TH2 35 | R8 Differentiation Arg1:T11 Arg2:T29 36 | R9 Differentiation Arg1:T10 Arg2:T29 37 | R10 Induction Arg1:T12 Arg2:T13 38 | T16 CELL_TYPE 813 816 TH2 39 | T17 CELL_TYPE 877 880 Th2 40 | R11 DifferentiationNeutral Arg1:T15 Arg2:T16 41 | R12 DifferentiationNeutral Arg1:T14 Arg2:T16 42 | T30 CELL_TYPE 902 908 iTregs 43 | T31 TF 888 891 MSC 44 | T32 CELL_TYPE 1120 1126 iTregs 45 | T33 CELL_TYPE 1160 1163 TH2 46 | R13 Differentiation Arg1:T21 Arg2:T32 47 | R14 Differentiation Arg1:T20 Arg2:T32 48 | R15 DifferentiationNeg Arg1:T9 Arg2:T28 49 | R16 DifferentiationNeg Arg1:T8 Arg2:T28 50 | R17 DifferentiationNeg Arg1:T22 Arg2:T23 51 | R18 DifferentiationNeutral Arg1:T31 Arg2:T30 52 | R19 DifferentiationNeg Arg1:T20 Arg2:T33 53 | -------------------------------------------------------------------------------- /data/brat/collection_02/PMC5591438.txt: -------------------------------------------------------------------------------- 1 | The transcription factor musculin promotes the unidirectional development of peripheral Treg cells by suppressing the TH2 transcriptional program. 2 | 3 | Although the master transcription factors (TFs) are the key to the development of specific T cell subsets, whether additional transcriptional regulators are induced by the same stimuli that dominantly repress development of other T cell lineages has not been fully elucidated. Using Transforming growth factor-β (TGF-β) induced regulatory T cell (iTreg) system, we identify the TF Musculin (MSC) as critical for iTreg development by repression of TH2 transcriptional program. Loss of MSC reduces Foxp3 expression and induces TH2 differentiation even under TGF-β induced iTreg differentiation conditions. MSC mediates this effect by interrupting binding of GATA3 to TH2 locus and reducing intrachromosomal interactions within the Th2 locus. MSC-deficient iTregs are not able to suppress TH2 responses and the Msc−/− mice spontaneously develop gut and lung inflammation with age. Our data indicate that MSC enforces Foxp3 expression and promotes unidirectional induction of iTregs by repressing development of the TH2 developmental program. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/annotation.conf: -------------------------------------------------------------------------------- 1 | [entities] 2 | CELL_LINE 3 | CELL_TYPE 4 | DNA 5 | PROTEIN 6 | RNA 7 | CYTOKINE 8 | TF 9 | 10 | [relations] 11 | Induction Arg1:CYTOKINE, Arg2:CELL_TYPE 12 | InductionNeg Arg1:CYTOKINE, Arg2:CELL_TYPE 13 | InductionNeutral Arg1:CYTOKINE, Arg2:CELL_TYPE 14 | Differentiation Arg1:TF, Arg2:CELL_TYPE 15 | DifferentiationNeg Arg1:TF, Arg2:CELL_TYPE 16 | DifferentiationNeutral Arg1:TF, Arg2:CELL_TYPE 17 | Secretion Arg1:CELL_TYPE, Arg2:CYTOKINE 18 | SecretionNeg Arg1:CELL_TYPE, Arg2:CYTOKINE 19 | SecretionNeutral Arg1:CELL_TYPE, Arg2:CYTOKINE 20 | CKProliferation Arg1:CYTOKINE, Arg2:CELL_TYPE 21 | CKProliferationNeg Arg1:CYTOKINE, Arg2:CELL_TYPE 22 | TFProliferation Arg1:TF, Arg2:CELL_TYPE 23 | TFProliferationNeg Arg1:TF, Arg2:CELL_TYPE 24 | CKTFEnhancement Arg1:CYTOKINE, Arg2:TF 25 | TFCKEnhancement Arg1:TF, Arg2:CYTOKINE 26 | CKTFEnhancementNeg Arg1:CYTOKINE, Arg2:TF 27 | TFCKEnhancementNeg Arg1:TF, Arg2:CYTOKINE 28 | CKCKEnhancement Arg1:CYTOKINE, Arg2:CYTOKINE 29 | CKCKEnhancementNeg Arg1:CYTOKINE, Arg2:CYTOKINE 30 | TFTFEnhancement Arg1:TF, Arg2:TF 31 | TFTFEnhancementNeg Arg1:TF, Arg2:TF 32 | TFExpression Arg1:CELL_TYPE, Arg2:TF 33 | TFExpressionNeg Arg1:CELL_TYPE, Arg2:TF 34 | 35 | [events] 36 | 37 | [attributes] 38 | -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC5611819.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 0 7 MiR-150 2 | T1 CELL_TYPE 18 35 memory CD8 T cell 3 | T2 PROTEIN 56 61 c-Myb 4 | T3 CELL_TYPE 158 168 CD8 T cell 5 | T4 CELL_TYPE 249 259 CD8 T cell 6 | T5 CELL_TYPE 373 384 CD8 T cells 7 | T6 CELL_TYPE 428 464 miR-150-deficient memory CD8 T cells 8 | T7 CELL_TYPE 544 561 memory CD8 T cell 9 | T8 PROTEIN 586 612 transcription factor c-Myb 10 | T9 PROTEIN 646 651 c-Myb 11 | T10 PROTEIN 698 703 c-Myb 12 | T11 PROTEIN 712 717 Bcl-2 13 | T12 PROTEIN 722 728 Bcl-xL 14 | T13 CELL_TYPE 800 817 memory CD8 T cell 15 | T14 PROTEIN 857 878 non-repressible c-Myb 16 | T15 CELL_TYPE 891 908 memory CD8 T cell 17 | T16 CELL_TYPE 1012 1030 memory CD8 T cells -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC5611819.txt: -------------------------------------------------------------------------------- 1 | MiR-150 regulates memory CD8 T cell differentiation via c-Myb. 2 | 3 | Summary 4 | MicroRNAs play an important role in T cell responses. However, how microRNAs regulate CD8 T cell memory remains poorly defined. Here, we found that miR-150 negatively regulates CD8 T cell memory in vivo. Genetic deletion of miR-150 disrupted the balance between memory precursor and terminal effector CD8 T cells following acute viral infection. Moreover, miR-150-deficient memory CD8 T cells were more protective upon rechallenge. A key circuit whereby miR-150 repressed memory CD8 T cell development through the transcription factor c-Myb was identified. Without miR-150, c-Myb was upregulated and anti-apoptotic targets of c-Myb such as Bcl-2 and Bcl-xL were also increased suggesting a miR-150-c-Myb survival circuit during memory CD8 T cell development. Indeed, overexpression of non-repressible c-Myb rescued the memory CD8 T cell defects caused by overexpression of miR-150. Overall, these results identify a key role for miR-150 in memory CD8 T cells through a c-Myb-controlled enhanced survival circuit. 5 | 6 | -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC5611846.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 114 132 engineered T cells 2 | T1 PROTEIN 144 169 chimeric antigen receptor 3 | T2 PROTEIN 171 174 CAR 4 | T3 CELL_TYPE 608 615 T cells 5 | T4 CELL_TYPE 626 646 lymphoid progenitors 6 | T5 CELL_LINE 660 710 pluripotent stem cell (iPSC)-derived T lymphocytes -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC5611846.txt: -------------------------------------------------------------------------------- 1 | New Cell Sources for T Cell Engineering and Adoptive Immunotherapy. 2 | 3 | The promising clinical results obtained with engineered T cells, including chimeric antigen receptor (CAR) therapy, call for further advancements to facilitate and broaden their applicability. One potentially beneficial innovation is to exploit new T cell sources that reduce the need for autologous cell manufacturing and enable cell transfer across histocompatibility barriers. Here we review emerging T cell engineering approaches that utilize alternative T cell sources, which include virus-specific or T cell receptor-less allogeneic T cells, expanded lymphoid progenitors, and induced pluripotent stem cell (iPSC)-derived T lymphocytes. The latter offer the prospect for true off-the-shelf, genetically enhanced, histocompatible cell therapy products. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC5648021.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 63 75 Immune cells 2 | T1 CELL_TYPE 661 673 immune cells -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC5648021.txt: -------------------------------------------------------------------------------- 1 | Metabolic Instruction of Immunity. 2 | 3 | Choices have consequences. Immune cells survey and migrate throughout the body and sometimes take residence in niche environments with distinct communities of cells, extracellular matrix, and nutrients that may differ from those in which they matured. Imbedded in immune cell physiology are metabolic pathways and metabolites that not only provide energy and substrates for growth and survival, but also instruct effector functions, differentiation, and gene expression. This review of immunometabolism will reference the most recent literature to cover the choices that environments impose on the metabolism and function of immune cells and highlight their consequences during homeostasis and disease. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC5727967.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 9 17 cytokine 2 | T1 PROTEIN 360 368 cytokine 3 | T2 CELL_TYPE 379 392 T-lymphocytes 4 | T3 PROTEIN 819 822  17 5 | T4 CELL_TYPE 925 959 Peripheral blood mononuclear cells 6 | T5 CELL_TYPE 1200 1217 IL-1b + CD4 cells 7 | T6 PROTEIN 1353 1357 CD4+ 8 | T7 PROTEIN 1358 1364 IL-1β+ 9 | T8 PROTEIN 1369 1372 CD4 10 | T9 CELL_LINE 1374 1393 IL-1β+ CD49d+ cells 11 | T10 CELL_TYPE 1522 1531 CD4 cells 12 | T11 PROTEIN 1680 1684 IL-6 13 | T12 PROTEIN 1806 1810 IL-6 14 | T13 PROTEIN 1949 1953 IL-6 15 | T14 PROTEIN 2036 2041 TNF-α 16 | T15 PROTEIN 2180 2183 CD4 17 | T16 PROTEIN 2185 2191 IL-1β+ 18 | T17 PROTEIN 2196 2199 CD4 19 | T18 CELL_LINE 2201 2220 IL-1β+ CD49d+ cells 20 | T19 PROTEIN 3060 3078 predictive factors 21 | T20 CELL_TYPE 3257 3266 Microglia 22 | T21 CELL_TYPE 3271 3281 astrocytes 23 | T22 PROTEIN 3311 3337 pro-inflammatory cytokines 24 | T23 PROTEIN 3342 3352 chemokines 25 | T24 CELL_TYPE 3415 3435 peripheral monocytes 26 | T25 PROTEIN 3736 3762 pro-inflammatory cytokines 27 | T26 CELL_TYPE 3899 3912 T lymphocytes 28 | T27 CELL_TYPE 3914 3927 T lymphocytes 29 | T28 CELL_TYPE 4008 4015 T cells 30 | T29 PROTEIN 4057 4065 perforin 31 | T30 PROTEIN 4070 4080 granzyme B 32 | T31 PROTEIN 4205 4241 pro- and anti-inflammatory cytokines 33 | T32 CELL_TYPE 4547 4564 neural precursors 34 | T33 PROTEIN 4652 4678 pro-inflammatory cytokines 35 | T34 PROTEIN 4698 4703 IFN-γ 36 | T35 CELL_TYPE 4719 4735 oligodendrocytes 37 | T36 PROTEIN 4753 4775 inflammatory cytokines -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC5749247.ann: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hammerlab/t-cell-relation-extraction/1375d454c1c6bc68a35c9c6870d26b6b8948ee25/data/brat/collection_02/unannotated/PMC5749247.ann -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC5749247.txt: -------------------------------------------------------------------------------- 1 | Immunology of Gut Bone Signaling. 2 | 3 | In recent years a link between the gastro-intestinal tract and bone health has started to gain significant attention. Dysbiosis of the intestinal microbiota has been linked to the pathology of a number of diseases which are associated with bone loss. In addition modulation of the intestinal microbiota with probiotic bacteria has revealed to have both beneficial local and systemic effects. In the present chapter we discuss the intestinal and bone immune systems, explore how intestinal disease affects the immune system and examine how these pathologic changes could adversely impact bone health. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC5833121.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 89 102 T lymphocytes 2 | T1 PROTEIN 563 578 T cell receptor 3 | T2 CELL_TYPE 645 659 T cell subsets 4 | T3 PROTEIN 1096 1128 Nrf2-Keap1-Cul3 trimeric complex 5 | T4 PROTEIN 1402 1429 oxygen-containing molecules 6 | T5 PROTEIN 2479 2494 T cell receptor 7 | T6 PROTEIN 2496 2499 TCR 8 | T7 CELL_TYPE 2864 2871 T cells 9 | T8 CELL_TYPE 3009 3016 T cells 10 | T9 CELL_TYPE 3373 3380 T cells 11 | T10 PROTEIN 3411 3417 OXPHOS 12 | T11 PROTEIN 3523 3526 TCR 13 | T12 PROTEIN 3662 3668 OXPHOS 14 | T13 PROTEIN 3800 3820 redox coenzymes NADH 15 | T14 PROTEIN 3833 3860 flavin adenine dinucleotide 16 | T15 PROTEIN 3862 3867 FADH2 17 | T16 PROTEIN 3914 3938 electron transport chain 18 | T17 PROTEIN 3961 3965 NADH 19 | T18 PROTEIN 3989 3998 complex I 20 | T19 PROTEIN 4032 4042 complex II 21 | T20 PROTEIN 4114 4123 complex I 22 | T21 PROTEIN 4155 4166 complex III 23 | T22 PROTEIN 4171 4181 complex IV 24 | T23 PROTEIN 4201 4213 cytochrome C 25 | T24 PROTEIN 4417 4429 ATP synthase 26 | T25 PROTEIN 4800 4821 superoxide dismutases 27 | T26 PROTEIN 4823 4827 SOD1 28 | T27 PROTEIN 4832 4836 SOD2 29 | T28 PROTEIN 4898 4909 complex III 30 | T29 PROTEIN 4936 4947 complex III -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC5876181.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 0 4 IL-2 2 | T1 PROTEIN 9 14 IL-15 3 | T2 CELL_TYPE 47 88 Foxp3-expressing regulatory T lymphocytes 4 | T3 CELL_TYPE 120 133 T lymphocytes 5 | T4 CELL_TYPE 135 139 Treg 6 | T5 PROTEIN 156 182 transcription factor Foxp3 7 | T6 CELL_TYPE 291 295 Treg 8 | T7 CELL_TYPE 382 396 T-cell lineage 9 | T8 CELL_TYPE 402 426 common T cell precursors 10 | T9 PROTEIN 505 520 T cell receptor 11 | T10 PROTEIN 539 563 co-stimulatory molecules 12 | T11 PROTEIN 581 599 cytokine-receptors 13 | T12 PROTEIN 622 631 cytokines 14 | T13 CELL_TYPE 668 672 Treg 15 | T14 PROTEIN 717 730 interleukin-2 16 | T15 PROTEIN 732 736 IL-2 17 | T16 PROTEIN 742 747 IL-15 18 | T17 PROTEIN 794 799 TGF-β 19 | T18 CELL_TYPE 1035 1048 T lymphocytes 20 | T19 CELL_TYPE 1283 1287 Treg 21 | T20 PROTEIN 1298 1303 Foxp3 22 | T21 PROTEIN 1366 1408 forkhead/winged helix transcription factor 23 | T22 CELL_TYPE 1714 1727 T lymphocytes 24 | T23 CELL_TYPE 1743 1747 Treg 25 | T24 PROTEIN 1809 1841 cell-surface expressed molecules 26 | T25 PROTEIN 1857 1889 major histocompatibility complex 27 | T26 PROTEIN 1891 1894 MHC 28 | T27 CELL_TYPE 1946 1979 autospecific conventional T cells 29 | T28 CELL_TYPE 1981 1986 Tconv 30 | T29 PROTEIN 2013 2029 immune-responses 31 | T30 CELL_TYPE 2127 2131 Treg 32 | T31 PROTEIN 2223 2231 antigens 33 | T32 CELL_TYPE 2447 2464 T cell precursors 34 | T33 PROTEIN 2479 2492 self-antigens 35 | T34 CELL_TYPE 2506 2526 thymic stromal cells 36 | T35 CELL_TYPE 2643 2659 Tconv precursors 37 | T36 CELL_TYPE 2714 2718 Treg 38 | T37 CELL_TYPE 2782 2810 peripheral (lymphoid) organs 39 | T38 PROTEIN 2835 2851 immune-responses 40 | T39 CELL_TYPE 2942 2946 Treg 41 | T40 CELL_TYPE 3045 3057 colonic Treg 42 | T41 PROTEIN 3066 3082 T cell receptors 43 | T42 PROTEIN 3096 3099 TCR 44 | T43 CELL_TYPE 3125 3136 thymic Treg 45 | T44 CELL_TYPE 3388 3399 thymic Treg 46 | T45 PROTEIN 3540 3543 TCR 47 | T46 PROTEIN 3545 3553 cytokine 48 | T47 CELL_TYPE 4059 4083 Hematopoietic precursors 49 | T48 CELL_TYPE 4166 4176 thymocytes 50 | T49 CELL_TYPE 4326 4336 Thymocytes 51 | T50 PROTEIN 4411 4414 TCR 52 | T51 PROTEIN 4507 4510 TCR 53 | T52 CELL_TYPE 4668 4675 T cells 54 | T53 CELL_TYPE 4787 4797 thymocytes 55 | T54 PROTEIN 4808 4811 TCR 56 | T55 PROTEIN 4840 4861 peptide/MHC complexes 57 | T56 CELL_TYPE 4875 4907 cortical thymic epithelial cells 58 | T57 CELL_TYPE 4909 4913 cTEC -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC5923349.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 126 141 mammalian cells 2 | T1 PROTEIN 175 204 Hippo pathway core components 3 | T2 PROTEIN 214 221 LATS1/2 4 | T3 PROTEIN 223 226 YAP 5 | T4 PROTEIN 231 234 TAZ 6 | T5 CELL_TYPE 551 588 pathogen-infected or neoplastic cells 7 | T6 CELL_TYPE 615 627 immune cells 8 | T7 CELL_TYPE 838 848 leukocytes 9 | T8 PROTEIN 1001 1017 secreted factors 10 | T9 CELL_TYPE 1286 1298 immune cells 11 | T10 CELL_TYPE 1300 1313 healthy cells 12 | T11 CELL_TYPE 1315 1338 pathogen-infected cells 13 | T12 CELL_TYPE 1346 1362 neoplastic cells 14 | T13 PROTEIN 1411 1416 NF-κB 15 | T14 PROTEIN 1418 1436 Toll-like receptor 16 | T15 PROTEIN 1438 1441 TLR 17 | T16 PROTEIN 1444 1454 interferon 18 | T17 PROTEIN 1456 1459 IFN 19 | T18 PROTEIN 1465 1473 JAK/STAT 20 | T19 PROTEIN 2063 2081 5,6,7,8,9,10,11,12 21 | T20 PROTEIN 2156 2191 Hippo (Hpo) serine/threonine kinase 22 | T21 PROTEIN 2193 2196 Hpo 23 | T22 PROTEIN 2218 2249 Salvador (Sav) scaffold protein 24 | T23 PROTEIN 2288 2303 adaptor protein 25 | T24 PROTEIN 2334 2363 serine/threonine kinase Warts 26 | T25 PROTEIN 2487 2490 Yki 27 | T26 PROTEIN 2546 2549 Yki 28 | T27 DNA 2583 2586 Yki 29 | T28 PROTEIN 2630 2633 Yki 30 | T29 PROTEIN 2690 2721 Scalloped transcription factors 31 | T30 PROTEIN 2810 2815 Diap1 32 | T31 PROTEIN 3038 3073 20-like kinase 1/2 (MST1/2) kinases 33 | T32 PROTEIN 3089 3114 Salvador family WW domain 34 | T33 PROTEIN 3126 3135 protein 1 35 | T34 PROTEIN 3137 3141 SAV1 36 | T35 PROTEIN 3155 3193 binder kinase activator-like 1A and 1B 37 | T36 DNA 3195 3202 MOB1A/B 38 | T37 PROTEIN 3220 3224 MOB1 39 | T38 PROTEIN 3249 3270 tumour suppressor 1/2 40 | T39 PROTEIN 3272 3279 LATS1/2 41 | T40 PROTEIN 3282 3289 LATS1/2 42 | T41 PROTEIN 3317 3339 Yes-associated protein 43 | T42 PROTEIN 3341 3344 YAP 44 | T43 PROTEIN 3370 3416 WW domain-containing transcription regulator 1 45 | T44 PROTEIN 3418 3421 TAZ 46 | T45 PROTEIN 3474 3477 YAP 47 | T46 PROTEIN 3482 3485 TAZ 48 | T47 PROTEIN 3552 3573 transcription factors 49 | T48 PROTEIN 3581 3600 TEAD family members 50 | T49 PROTEIN 3883 3909 G protein-coupled receptor 51 | T50 PROTEIN 3911 3915 GPCR 52 | T51 PROTEIN 3928 3952 receptor tyrosine kinase 53 | T52 PROTEIN 3954 3957 RTK 54 | T53 PROTEIN 3992 3997 actin 55 | T54 PROTEIN 4128 4139 27,28,29,30 56 | T55 DNA 4163 4186 downstream gene targets 57 | T56 PROTEIN 4200 4203 YAP 58 | T57 PROTEIN 4208 4211 TAZ 59 | T58 PROTEIN 4219 4223 CTGF 60 | T59 PROTEIN 4228 4233 CYR61 61 | T60 PROTEIN 4311 4334 Hippo pathway effectors 62 | T61 PROTEIN 4336 4344 31,32,33 63 | T62 CELL_TYPE 4670 4693 pathogen-infected cells 64 | T63 CELL_TYPE 4741 4753 immune cells 65 | T64 CELL_TYPE 4873 4889 neoplastic cells -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC6092975.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 4 30 Anti-Inflammatory Mediator 2 | T1 CELL_TYPE 109 119 Th Subsets 3 | T2 PROTEIN 412 425 self-antigens 4 | T3 CELL_TYPE 499 520 T helper (Th) subsets 5 | T4 PROTEIN 553 556 VIP 6 | T5 CELL_TYPE 854 865 lymphocytes 7 | T6 CELL_TYPE 867 895 fibroblast-like synoviocytes 8 | T7 PROTEIN 897 900 FLS 9 | T8 CELL_TYPE 906 917 macrophages 10 | T9 PROTEIN 994 997 PRR 11 | T10 PROTEIN 1007 1026 toll-like receptors 12 | T11 PROTEIN 1028 1032 TLRs 13 | T12 PROTEIN 1037 1040 FLS 14 | T13 CELL_TYPE 1281 1291 Th subsets 15 | T14 CELL_TYPE 1321 1324 Th1 16 | T15 CELL_TYPE 1329 1341 Th17 subsets 17 | T16 CELL_TYPE 1355 1358 Th2 18 | T17 CELL_TYPE 1427 1442 memory Th cells 19 | T18 PROTEIN 1453 1456 VIP 20 | T19 PROTEIN 1548 1551 VIP 21 | T20 PROTEIN 1878 1898 aetiological factors 22 | T21 PROTEIN 1971 1984 self-antigens 23 | T22 CELL_TYPE 2122 2132 Th subsets 24 | T23 PROTEIN 2332 2353 Mycoplasma fermentans 25 | T24 PROTEIN 2601 2615 autoantibodies 26 | T25 PROTEIN 2624 2660 anticitrullinated protein antibodies 27 | T26 PROTEIN 2662 2666 ACPA 28 | T27 PROTEIN 2671 2688 rheumatoid factor 29 | T28 PROTEIN 2859 2863 PRRs 30 | T29 CELL_TYPE 3207 3222 apoptotic cells 31 | T30 PROTEIN 3285 3289 PRRs 32 | T31 PROTEIN 3358 3383 proinflammatory cytokines 33 | T32 PROTEIN 3392 3442 tumor necrosis factor (TNF)α, interleukin- (IL-) 6 34 | T33 PROTEIN 3454 3459 IL-12 35 | T34 CELL_TYPE 3471 3483 innate cells 36 | T35 CELL_TYPE 3492 3507 dendritic cells 37 | T36 CELL_TYPE 3509 3512 DCs 38 | T37 CELL_TYPE 3517 3528 macrophages 39 | T38 CELL_TYPE 3884 3898 Th lymphocytes 40 | T39 PROTEIN 3935 3944 cytokines 41 | T40 CELL_TYPE 4106 4120 T helper cells 42 | T41 CELL_TYPE 4144 4147 Th1 43 | T42 CELL_LINE 4149 4152 Th2 44 | T43 CELL_TYPE 4154 4158 Th17 45 | T44 CELL_TYPE 4165 4169 Th22 46 | T45 CELL_TYPE 4171 4192 follicular helper T ( 47 | T46 CELL_TYPE 4192 4195 Tfh 48 | T47 CELL_TYPE 4213 4221 T (Treg) 49 | T48 CELL_TYPE 4365 4379 T cell subsets 50 | T49 CELL_TYPE 4412 4420 Th cells 51 | T50 CELL_TYPE 4422 4426 Th17 52 | T51 CELL_TYPE 4588 4603 memory Th cells 53 | T52 CELL_TYPE 4683 4691 Th cells -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC6122729.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 242 270 fibroblastic reticular cells 2 | T1 CELL_TYPE 272 276 FRCs 3 | T2 CELL_TYPE 349 359 human FRCs 4 | T3 CELL_TYPE 402 406 FRCs 5 | T4 CELL_TYPE 441 464 human peripheral T-cell 6 | T5 CELL_TYPE 522 550 Human tonsil and lymph node– 7 | T6 CELL_TYPE 558 562 FRCs 8 | T7 CELL_TYPE 611 632 pre-activated T cells 9 | T8 CELL_TYPE 709 713 FRCs 10 | T9 PROTEIN 793 820 indoleamine-2,3-dioxygenase 11 | T10 PROTEIN 822 843 adenosine 2A Receptor 12 | T11 PROTEIN 867 907 transforming growth factor beta receptor 13 | T12 PROTEIN 909 914 TGFβR 14 | T13 CELL_TYPE 1059 1063 FRCs 15 | T14 CELL_TYPE 1065 1072 T cells 16 | T15 CELL_TYPE 1107 1111 FRCs 17 | T16 PROTEIN 1131 1156 chimeric antigen receptor 18 | T17 PROTEIN 1158 1161 CAR 19 | T18 CELL_TYPE 1163 1170 T cells 20 | T19 CELL_TYPE 1201 1208 T cells 21 | T20 CELL_TYPE 1256 1260 FRCs 22 | T21 CELL_TYPE 1383 1396 Human T cells 23 | T22 CELL_TYPE 1594 1598 FRCs 24 | T23 CELL_LINE 1718 1729 CAR T cells 25 | T24 CELL_TYPE 1922 1926 FRCs 26 | T25 CELL_TYPE 1931 1944 Stromal cells 27 | T26 CELL_TYPE 2050 2060 leukocytes 28 | T27 CELL_TYPE 2154 2182 Fibroblastic reticular cells 29 | T28 CELL_TYPE 2184 2188 FRCs 30 | T29 PROTEIN 2326 2355 chemokine C-C motif ligand 19 31 | T30 PROTEIN 2357 2362 CCL19 32 | T31 PROTEIN 2368 2397 chemokine C-C motif ligand 21 33 | T32 PROTEIN 2399 2404 CCL21 34 | T33 CELL_TYPE 2415 2422 T cells 35 | T34 CELL_TYPE 2427 2442 dendritic cells 36 | T35 PROTEIN 2474 2505 chemokine C-X-C motif ligand 13 37 | T36 PROTEIN 2507 2513 CXCL13 38 | T37 CELL_TYPE 2524 2531 B cells 39 | T38 PROTEIN 2617 2647 survival factors interleukin 7 40 | T39 PROTEIN 2649 2653 IL-7 41 | T40 PROTEIN 2659 2683 B cell activating factor 42 | T41 PROTEIN 2685 2689 BAFF 43 | T42 CELL_TYPE 2756 2760 FRCs 44 | T43 CELL_TYPE 2804 2811 T cells 45 | T44 PROTEIN 2848 2864 cyclooxygenase-2 46 | T45 PROTEIN 2897 2901 PGE2 47 | T46 PROTEIN 3115 3131 interferon gamma 48 | T47 DNA 3133 3137 IFNγ 49 | T48 PROTEIN 3308 3318 podoplanin 50 | T49 CELL_TYPE 3346 3356 mouse FRCs 51 | T50 PROTEIN 3467 3472 CCL21 52 | T51 CELL_TYPE 3538 3548 human FRCs 53 | T52 DNA 3592 3596 IFNγ 54 | T53 CELL_TYPE 3724 3728 FRCs 55 | T54 CELL_TYPE 3732 3745 human T cells 56 | T55 PROTEIN 3820 3824 COX2 57 | T56 CELL_TYPE 3857 3867 human FRCs 58 | T57 CELL_TYPE 3981 3985 FRCs 59 | T58 PROTEIN 4205 4209 1,12 60 | T59 CELL_TYPE 4225 4241 virally infected 61 | T60 CELL_TYPE 4339 4349 human FRCs 62 | T61 CELL_TYPE 4418 4437 naïve human T cells 63 | T62 PROTEIN 4571 4598 indoleamine-2,3-dioxygenase 64 | T63 PROTEIN 4600 4603 IDO 65 | T64 PROTEIN 4606 4610 COX1 66 | T65 PROTEIN 4658 4679 adenosine 2A receptor 67 | T66 PROTEIN 4681 4685 A2AR 68 | T67 PROTEIN 4692 4723 transforming growth factor beta 69 | T68 PROTEIN 4725 4729 TGFβ -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC6130380.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 87 101 infected cells 2 | T1 CELL_TYPE 241 268 intestinal epithelial cells 3 | T2 PROTEIN 506 523 PGE2 receptor EP4 4 | T3 CELL_TYPE 562 580 colonic Th17 cells -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC6130380.txt: -------------------------------------------------------------------------------- 1 | Intestinal host defense outcome is dictated by PGE2 production during efferocytosis of infected cells. 2 | 3 | Significance 4 | Citrobacter rodentium infection, a murine colitis model to study human intestinal diseases, causes exacerbated apoptosis of intestinal epithelial cells and triggers Th17 immune responses. Here, we identified a heretofore unknown role for the bioactive lipid mediator prostaglandin E2 (PGE2) in the inhibition of Th17 cell differentiation during intestinal C. rodentium infection. When the PGE2 receptor EP4 was antagonized, we detected enhanced colonic Th17 cells, increased expression of antimicrobial peptides, and decreased bacterial numbers in the colon. These results suggest that pharmacological intervention of the PGE2 signaling may be an important target to enhance Th17 actions and improve intestinal host defense. 5 | 6 | -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC6141714.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_LINE 11 39 Human Mesenchymal Stem Cells 2 | T1 CELL_TYPE 480 508 human mesenchymal stem cells 3 | T2 CELL_TYPE 510 515 hMSCs 4 | T3 CELL_TYPE 1629 1642 CD19+ B cells 5 | T4 CELL_TYPE 1644 1652 CD4+ Th1 6 | T5 CELL_TYPE 1657 1667 Th17 cells 7 | T6 CELL_TYPE 1669 1681 CD8+ T cells 8 | T7 CELL_TYPE 1683 1691 NK cells 9 | T8 CELL_TYPE 1693 1704 macrophages 10 | T9 CELL_TYPE 1706 1715 monocytes 11 | T10 CELL_TYPE 1721 1732 neutrophils 12 | T11 CELL_TYPE 1763 1768 Bregs 13 | T12 CELL_TYPE 1773 1784 Tregs cells 14 | T13 CELL_TYPE 1829 1834 hMSCs 15 | T14 PROTEIN 1881 1907 pro-inflammatory cytokines 16 | T15 PROTEIN 1916 1921 IFN-γ 17 | T16 PROTEIN 1923 1928 TNF-α 18 | T17 PROTEIN 1930 1934 IL-1 19 | T18 PROTEIN 1936 1940 IL-2 20 | T19 PROTEIN 1942 1947 IL-12 21 | T20 PROTEIN 1953 1958 IL-17 22 | T21 PROTEIN 1995 2021 immunoregulatory cytokines 23 | T22 PROTEIN 2030 2034 IL-4 24 | T23 PROTEIN 2036 2041 IL-10 25 | T24 PROTEIN 2047 2052 IL-13 26 | T25 CELL_TYPE 2194 2199 hMSCs 27 | T26 PROTEIN 2465 2478 self-antigens 28 | T27 CELL_TYPE 2605 2618 foreign cells 29 | T28 CELL_TYPE 3289 3316 human mesenchymalstem cells 30 | T29 CELL_TYPE 3318 3323 hMSCs 31 | T30 PROTEIN 3421 3425 Joly 32 | T31 CELL_TYPE 4331 4359 human mesenchymal stem cells 33 | T32 CELL_TYPE 4361 4366 hMSCs 34 | T33 CELL_LINE 4599 4609 cell lines 35 | T34 CELL_TYPE 4621 4633 chondrocytes 36 | T35 CELL_TYPE 4635 4645 adipocytes 37 | T36 CELL_TYPE 4650 4661 osteoblasts 38 | T37 CELL_TYPE 4707 4717 stem cells 39 | T38 CELL_TYPE 4855 4869 umbilical cord -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC6157333.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 356 360 M.tb 2 | T1 PROTEIN 465 469 M.tb 3 | T2 CELL_TYPE 598 614 host macrophages 4 | T3 CELL_TYPE 616 619 MΦs 5 | T4 PROTEIN 631 645 M.tb effectors 6 | T5 PROTEIN 683 702 pro-inflammatory M1 7 | T6 PROTEIN 811 817 ESAT-6 8 | T7 PROTEIN 921 927 ESAT-6 9 | T8 CELL_TYPE 995 1010 human monocytes 10 | T9 PROTEIN 1016 1022 ESAT-6 11 | T10 CELL_LINE 1065 1071 M1 MΦs 12 | T11 PROTEIN 1082 1088 ESAT-6 13 | T12 PROTEIN 1185 1216 pro-inflammatory cytokines IL-6 14 | T13 PROTEIN 1218 1223 IL-12 15 | T14 PROTEIN 1229 1234 TNF-α 16 | T15 DNA 1263 1291 M1 transcriptional signature 17 | T16 PROTEIN 1322 1328 ESAT-6 18 | T17 PROTEIN 1419 1442 pro-inflammatory M1 MΦs 19 | T18 PROTEIN 1444 1450 ESAT-6 20 | T19 PROTEIN 1471 1476 IL-12 21 | T20 PROTEIN 1519 1524 IL-10 22 | T21 PROTEIN 1589 1595 ESAT-6 23 | T22 PROTEIN 1622 1655 M1 MΦ cell surface molecules CD80 24 | T23 PROTEIN 1660 1664 CD86 25 | T24 PROTEIN 1666 1692 transcription factors IRF5 26 | T25 PROTEIN 1697 1702 c-MAF 27 | T26 PROTEIN 1704 1719 cytokines IL-12 28 | T27 PROTEIN 1721 1726 IL-10 29 | T28 PROTEIN 1732 1736 IL-6 30 | T29 PROTEIN 1749 1766 chemokines CXCL10 31 | T30 PROTEIN 1771 1776 CXCL1 32 | T31 PROTEIN 1808 1814 ESAT-6 33 | T32 PROTEIN 1853 1857 M.tb 34 | T33 CELL_LINE 2045 2047 M1 35 | T34 PROTEIN 2051 2053 M2 36 | T35 PROTEIN 2163 2167 M.tb 37 | T36 PROTEIN 2220 2226 ESAT-6 38 | T37 PROTEIN 2325 2329 M.tb 39 | T38 PROTEIN 2763 2767 M.tb 40 | T39 PROTEIN 3157 3161 M.tb 41 | T40 CELL_TYPE 3206 3244 mycobacterial antigen-presenting cells 42 | T41 PROTEIN 3599 3607 cytokine 43 | T42 PROTEIN 3838 3869 “alternatively activated M2” MΦ 44 | T43 CELL_LINE 4008 4010 M1 45 | T44 PROTEIN 4015 4020 M2 MΦ 46 | T45 CELL_TYPE 4219 4231 cancer cells 47 | T46 CELL_TYPE 4386 4395 monocytes 48 | T47 CELL_LINE 4401 4407 M1 MΦs 49 | T48 PROTEIN 4430 4436 GM-CSF 50 | T49 PROTEIN 4498 4503 IFN-γ 51 | T50 PROTEIN 4533 4551 microbial products 52 | T51 PROTEIN 4589 4615 pro-inflammatory cytokines 53 | T52 CELL_LINE 4760 4766 M1 MΦs 54 | T53 PROTEIN 4821 4853 pro-inflammatory cytokines IL-12 55 | T54 PROTEIN 4862 4867 IL-23 56 | T55 PROTEIN 4869 4873 IL-6 57 | T56 PROTEIN 4879 4884 TNF-α 58 | T57 DNA 4964 4979 M1 MΦ signature -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC6197911.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 15 30 T‐cell receptor 2 | T1 PROTEIN 42 50 cytokine 3 | T2 PROTEIN 220 238 effector cytokines 4 | T3 PROTEIN 327 339 interferon‐γ 5 | T4 PROTEIN 387 394 T‐cells 6 | T5 PROTEIN 516 531 T‐cell receptor 7 | T6 PROTEIN 533 536 TCR 8 | T7 PROTEIN 550 567 cytokine‐receptor 9 | T8 PROTEIN 579 627 KN6 γδTCR‐transduced Rag2 10 | −/− T‐cell progenitors 11 | T9 CELL_TYPE 645 658 stromal cells 12 | T10 PROTEIN 679 682 TCR 13 | T11 PROTEIN 687 700 Notch ligands 14 | T12 PROTEIN 730 739 cytokines 15 | T13 PROTEIN 886 889 TCR 16 | T14 CELL_LINE 932 942 γδ17 cells 17 | T15 PROTEIN 962 967 IL‐1β 18 | T16 PROTEIN 969 974 IL‐21 19 | T17 PROTEIN 979 984 IL‐23 20 | T18 PROTEIN 1001 1010 cytokines 21 | T19 CELL_LINE 1077 1087 γδ17 cells 22 | T20 PROTEIN 1159 1162 TCR 23 | T21 PROTEIN 1164 1192 Notch and cytokine receptors 24 | T22 DNA 1239 1243 IFNγ 25 | T23 PROTEIN 2112 2134 inflammatory cytokines 26 | T24 DNA 2318 2322 IFNγ 27 | T25 PROTEIN 2645 2672 γδ17 cell differentiation.8 28 | T26 PROTEIN 2727 2734 T‐cells 29 | T27 PROTEIN 2755 2775 ligand‐dependent TCR 30 | T28 CELL_TYPE 2817 2836 antigen naïve cells 31 | T29 PROTEIN 2862 2876 γδ17 lineage.9 32 | T30 PROTEIN 2990 2993 TCR 33 | T31 CELL_LINE 3025 3050 γδ17 cells.10, 11, 12, 13 34 | T32 PROTEIN 3140 3143 TCR 35 | T33 PROTEIN 3172 3175 TCR 36 | T34 PROTEIN 3255 3258 Id3 37 | T35 PROTEIN 3299 3308 signal.14 38 | T36 PROTEIN 3324 3327 ERK 39 | T37 PROTEIN 3383 3396 γδ17 cells,15 40 | T38 PROTEIN 3401 3404 Id3 41 | T39 PROTEIN 3442 3445 HEB 42 | T40 PROTEIN 3469 3473 γδ17 43 | T41 PROTEIN 3507 3520 γδTCR ligands 44 | T42 PROTEIN 3565 3570 γδTCR 45 | T43 PROTEIN 3717 3721 γδ17 46 | T44 PROTEIN 3756 3765 cytokines 47 | T45 PROTEIN 3831 3835 γδ17 48 | T46 CELL_TYPE 3870 3874 Th17 49 | T47 DNA 3898 3905 IL‐6.17 50 | T48 PROTEIN 3930 3935 IL‐23 51 | T49 PROTEIN 4018 4023 IL‐1β 52 | T50 PROTEIN 4028 4033 IL‐23 53 | T51 PROTEIN 4074 4085 innate‐like 54 | T52 PROTEIN 4215 4220 IL‐23 55 | T53 PROTEIN 4260 4269 IL‐23R+.8 56 | T54 PROTEIN 4359 4366 T‐cells 57 | T55 PROTEIN 4387 4403 IL‐21−/− mice.19 58 | T56 PROTEIN 4466 4475 cytokines 59 | T57 CELL_LINE 4501 4511 γδ17 cells 60 | T58 PROTEIN 4627 4631 γδ17 61 | T59 PROTEIN 4685 4691 Notch1 62 | T60 PROTEIN 4800 4804 Rorc 63 | T61 PROTEIN 4806 4811 RORγt 64 | T62 PROTEIN 4817 4823 IL‐23R 65 | T63 CELL_LINE 4894 4924 Th17 cells.20, 21, 22 The main 66 | T64 CELL_LINE 4967 4979 γδ17 T‐cells -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC6274670.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 105 132 Treg and Activated Th Cells 2 | T1 CELL_TYPE 657 675 regulatory T cells 3 | T2 CELL_TYPE 677 682 Tregs 4 | T3 CELL_TYPE 746 787 resting and activated T helper (Th) cells 5 | T4 CELL_TYPE 792 797 Tregs 6 | T5 PROTEIN 1003 1011 cytokine 7 | T6 CELL_TYPE 1157 1172 Th cell subsets 8 | T7 CELL_TYPE 1477 1485 Th cells 9 | T8 CELL_TYPE 1589 1604 Th cell subsets 10 | T9 CELL_TYPE 2263 2270 T cells 11 | T10 CELL_TYPE 2331 2338 T cells 12 | T11 CELL_TYPE 2589 2597 Th cells 13 | T12 CELL_TYPE 2599 2621 CD4-expressing T cells 14 | T13 CELL_TYPE 2871 2891 T helper (Th) subset 15 | T14 CELL_TYPE 2913 2916 Th1 16 | T15 CELL_TYPE 2920 2923 Th2 17 | T16 CELL_TYPE 3158 3161 Th1 18 | T17 CELL_TYPE 3198 3204 T cell 19 | T18 PROTEIN 4451 4466 T cell receptor 20 | T19 PROTEIN 4468 4471 TCR 21 | T20 PROTEIN 4479 4497 cytokine receptors 22 | T21 PROTEIN 4514 4533 Toll-like receptors 23 | T22 PROTEIN 4535 4539 TLRs 24 | T23 PROTEIN 4598 4619 intracellular kinases 25 | T24 PROTEIN 4621 4629 31,32,33 26 | T25 PROTEIN 4635 4647 phosphatases 27 | T26 PROTEIN 4649 4654 34,35 28 | T27 PROTEIN 4676 4726 signal transducers and activators of transcription 29 | T28 PROTEIN 4728 4732 STAT 30 | T29 PROTEIN 4738 4770 mitogen-activated protein kinase 31 | T30 PROTEIN 4772 4776 MAPK 32 | T31 PROTEIN 4816 4836 transcription factor -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC6282816.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 28 32 ICOS 2 | T1 CELL_TYPE 69 85 Human TH17 Cells 3 | T2 PROTEIN 185 194 cytokines 4 | T3 PROTEIN 262 285 costimulatory molecules 5 | T4 PROTEIN 358 380 inducible costimulator 6 | T5 PROTEIN 382 386 ICOS 7 | T6 CELL_TYPE 442 458 human TH17 cells 8 | T7 CELL_TYPE 460 476 Human cord blood 9 | T8 CELL_TYPE 499 516 CD161+CD4+T cells 10 | T9 PROTEIN 571 575 ICOS 11 | T10 CELL_TYPE 614 624 TH17 cells 12 | T11 PROTEIN 633 637 ICOS 13 | T12 PROTEIN 649 653 ICOS 14 | T13 PROTEIN 674 679 c-MAF 15 | T14 PROTEIN 681 686 RORC2 16 | T15 PROTEIN 692 697 T-bet 17 | T16 PROTEIN 759 773 interleukin-21 18 | T17 PROTEIN 775 780 IL-21 19 | T18 PROTEIN 783 788 IL-17 20 | T19 PROTEIN 808 813 IFN-γ 21 | T20 PROTEIN 851 855 CD28 22 | T21 PROTEIN 869 873 CD28 23 | T22 PROTEIN 893 897 ICOS 24 | T23 PROTEIN 923 928 RORC2 25 | T24 PROTEIN 978 1003 aryl hydrocarbon receptor 26 | T25 PROTEIN 1039 1044 IL-17 27 | T26 PROTEIN 1072 1077 IL-22 28 | T27 PROTEIN 1114 1118 ICOS 29 | T28 PROTEIN 1130 1134 ICOS 30 | T29 CELL_TYPE 1168 1194 IL-17+IFN-γ+ human T cells 31 | T30 PROTEIN 1343 1347 CD28 32 | T31 CELL_LINE 1382 1401 ICOS-expanded cells 33 | T32 PROTEIN 1509 1513 ICOS 34 | T33 CELL_TYPE 1561 1577 human TH17 cells -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC6282816.txt: -------------------------------------------------------------------------------- 1 | The Inducible Costimulator (ICOS) Is Critical for the Development of Human TH17 Cells. 2 | 3 | Human T helper 17 (TH17) cells regulate host defense, autoimmunity, and tumor immunity. Although cytokines that control human TH17 cell development have been identified, the costimulatory molecules important for TH17 cell generation are unknown. Here, we found that the inducible costimulator (ICOS) was critical for the differentiation and expansion of human TH17 cells. Human cord blood contained a subset of CD161+CD4+T cells that were recent emigrants from the thymus, expressed ICOS constitutively, and were imprinted as TH17 cells through ICOS signaling. ICOS stimulation induced c-MAF, RORC2, and T-bet expression in these cells, leading to increased secretion of interleukin-21 (IL-21), IL-17, and interferon-γ (IFN-γ) compared with cells stimulated with CD28. Conversely, CD28 ligation abrogated ICOS costimulation, dampening RORC2 expression while promoting the expression of the aryl hydrocarbon receptor, which led to reduced secretion of IL-17 and enhanced production of IL-22 compared with cells stimulated with ICOS. Moreover, ICOS promoted the robust expansion of IL-17+IFN-γ+ human T cells, and the antitumor activity of these cells after adoptive transfer into mice bearing large human tumors was superior to that of cells expanded with CD28. The therapeutic effectiveness of ICOS-expanded cells was associated with enhanced functionality and engraftment in vivo. These findings reveal a vital role for ICOS signaling in the generation and maintenance of human TH17 cells and suggest that components of this pathway could be therapeutically targeted to treat cancer or chronic infection and, conversely, that interruption of this pathway may have utility in multiple sclerosis and other autoimmune syndromes. These findings have provided the rationale for designing new clinical trials for tumor immunotherapy. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC6290922.ann: -------------------------------------------------------------------------------- 1 | T0 PROTEIN 0 5 T-bet 2 | T1 PROTEIN 99 126 T cell transcription factor 3 | T2 CELL_TYPE 138 161 T helper 1 cell lineage 4 | T3 PROTEIN 174 179 T-bet 5 | T4 PROTEIN 282 287 T-bet 6 | T5 CELL_TYPE 439 451 immune cells 7 | T6 CELL_TYPE 561 582 adaptive immune cells 8 | T7 PROTEIN 668 673 T-bet -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC6290922.txt: -------------------------------------------------------------------------------- 1 | T-bet: a bridge between innate and adaptive immunity. 2 | 3 | Originally described over a decade ago as a T cell transcription factor regulating T helper 1 cell lineage commitment, T-bet is now recognized as having an important role in many cells of the adaptive and innate immune system. T-bet has a fundamental role in coordinating type 1 immune responses by controlling a network of genetic programmes that regulate the development of certain immune cells and the effector functions of others. Many of these transcriptional networks are conserved across innate and adaptive immune cells and these shared mechanisms highlight the biological functions that are regulated by T-bet. 4 | 5 | -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC6372559.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 26 33 T Cells 2 | T1 CELL_TYPE 661 668 T cells 3 | T2 CELL_TYPE 788 807 T helper (Th) cells 4 | T3 CELL_TYPE 820 834 T (Treg) cells 5 | T4 CELL_TYPE 840 862 cytotoxic T (Tc) cells 6 | T5 CELL_TYPE 883 904 innate T-cell subsets 7 | T6 CELL_TYPE 947 961 T-cell subsets 8 | T7 CELL_TYPE 1140 1143 Th1 9 | T8 CELL_LINE 1145 1148 Th2 10 | T9 CELL_TYPE 1150 1154 Th17 11 | T10 CELL_TYPE 1160 1170 Th22 cells 12 | T11 CELL_TYPE 1220 1223 Th2 13 | T12 CELL_TYPE 1225 1229 Th22 14 | T13 CELL_TYPE 1235 1245 Treg cells 15 | T14 CELL_TYPE 1291 1294 Th1 16 | T15 CELL_TYPE 1296 1300 Th17 17 | T16 CELL_TYPE 1306 1314 Tc cells 18 | T17 CELL_TYPE 1366 1370 Th22 19 | T18 CELL_TYPE 1375 1385 Treg cells 20 | T19 CELL_TYPE 1438 1442 Th17 21 | T20 CELL_TYPE 1447 1455 Tc cells 22 | T21 CELL_TYPE 1554 1575 innate T-cell subsets 23 | T22 PROTEIN 1882 1886 NAFL 24 | T23 PROTEIN 1922 1926 NASH 25 | T24 PROTEIN 2155 2159 NASH 26 | T25 PROTEIN 2249 2253 NASH 27 | T26 PROTEIN 2285 2289 NAFL 28 | T27 PROTEIN 2383 2387 NASH 29 | T28 PROTEIN 3205 3209 NASH -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC6373736.ann: -------------------------------------------------------------------------------- 1 | T0 CELL_TYPE 67 85 progenitor T cells 2 | T1 CELL_TYPE 173 191 progenitor T cells 3 | T2 CELL_TYPE 283 309 SCID-X1 progenitor T cells -------------------------------------------------------------------------------- /data/brat/collection_02/unannotated/PMC6373736.txt: -------------------------------------------------------------------------------- 1 | Baboon envelope LVs efficiently transduced human adult, fetal, and progenitor T cells and corrected SCID-X1 T-cell deficiency. 2 | 3 | Key Points 4 | 5 | 6 | 7 | BaEV-LVs efficiently transduce progenitor T cells, providing accelerated T-cell reconstitution in vivo. 8 | 9 | 10 | BaEV-LVs efficiently correct human SCID-X1 progenitor T cells. 11 | 12 | 13 | 14 | 15 | -------------------------------------------------------------------------------- /data/brat/collection_02/visual.conf: -------------------------------------------------------------------------------- 1 | [drawing] 2 | CELL_TYPE bgColor:#2ca02c 3 | CELL_LINE bgColor:#bcbd22 4 | DNA bgColor:#d62728 5 | PROTEIN bgColor:#1f77b4 6 | RNA bgColor:#ff7f0e 7 | CYTOKINE bgColor:#17becf 8 | TF bgColor:#9467bd 9 | [labels] 10 | -------------------------------------------------------------------------------- /data/meta/raw/cytokines.ckr.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hammerlab/t-cell-relation-extraction/1375d454c1c6bc68a35c9c6870d26b6b8948ee25/data/meta/raw/cytokines.ckr.xls -------------------------------------------------------------------------------- /data/meta/raw/cytokines.manual.csv: -------------------------------------------------------------------------------- 1 | sym,lbl,spid 2 | IL-23,IL-23,1 3 | IL23,IL-23,1 4 | IL-35,IL-35,1 5 | IL35,IL-35,1 6 | IL-32,IL-32,1 7 | IL32,IL-32,1 8 | IL-32α,IL-32,1 9 | IL-32β,IL-32,1 10 | IL-32δ,IL-32,1 11 | IL-32γ,IL-32,1 12 | IL-12α,IL-12α,1 13 | IL12α,IL-12α,1 14 | IL-12 p35,IL-12α,1 15 | IL-12p35,IL-12α,1 16 | IL12p35,IL-12α,1 17 | IL-1RA,IL-1RA,1 18 | IL1RA,IL-1RA,1 19 | IL1RN,IL-1RA,1 20 | IL-1RN,IL-1RA,1 21 | IL-28,IL-28,1 22 | IL28,IL-28,1 23 | IL-28A,IL-28A,1 24 | IL28A,IL-28A,1 25 | IFNL2,IL-28A,1 26 | IL-28B,IL-28B,1 27 | IL28B,IL-28B,1 28 | IFNL3,IL-28B,1 29 | IL-1,IL-1,1 30 | IL1,IL-1,1 31 | TGF-β,TGF-β,1 32 | TGF,TGF-β,1 33 | TGFβ,TGF-β,1 34 | TGF-B,TGF-β,1 35 | TGFB,TGF-β,1 36 | TGFB1,TGF-β,1 37 | transforming growth factor β,TGF-β,1 38 | transforming growth factor b,TGF-β,1 39 | transforming growth factor beta,TGF-β,1 40 | TNF-α,TNF-α,1 41 | TNF,TNF-α,1 42 | TNF-β,TNF-β,1 43 | LT-α,TNF-β,1 44 | LTA,TNF-β,1 45 | IFN-1,IFN-1,1 46 | IFN1,IFN-1,1 47 | type I interferons,IFN-1,1 48 | type 1 interferons,IFN-1,1 49 | type I interferon,IFN-1,1 50 | type 1 interferon,IFN-1,1 51 | IFN-α1,IFN-α1,1 52 | IFN-α2,IFN-α2,1 53 | IFN-α6,IFN-α6,1 54 | IFN-β1,IFN-β1,1 55 | CCL6,CCL6,1 56 | CCL9,CCL9,1 57 | CXCL4,CXCL4,1 58 | PF4,CXCL4,1 59 | PF-4,CXCL4,1 60 | platelet factor 4,CXCL4,1 61 | SCYB4,CXCL4,1 62 | CXCL7,CXCL7,1 63 | PPBP,CXCL7,1 64 | pro-platelet basic protein,CXCL7,1 65 | B-TG1,CXCL7,1 66 | β-TG,CXCL7,1 67 | CD40LG,CD40LG,1 68 | CSF1,CSF1,1 69 | CSF-1,CSF1,1 70 | Monocyte-CSF,CSF1,1 71 | M-CSF,CSF1,1 72 | MCSF,CSF1,1 73 | CSF2,CSF2,1 74 | CSF-2,CSF2,1 75 | CSF3,CSF3,1 76 | CSF-3,CSF3,1 77 | EBI3,EBI3,1 78 | EGF,EGF,1 79 | epidermal growth factor,EGF,1 80 | EPO,EPO,1 81 | erythropoietin,EPO,1 82 | MVCD2,EPO,1 83 | ECYT5,EPO,1 84 | hematopoietin,EPO,1 85 | hemopoietin,EPO,1 86 | FGF1,FGF1,1 87 | acidic fibroblast growth factor,FGF1,1 88 | fibroblast growth factor 1,FGF1,1 89 | HBGF1,FGF1,1 90 | HBGF-1,FGF1,1 91 | aFGF,FGF1,1 92 | FGF2,FGF2,1 93 | FGF-2,FGF2,1 94 | FGFB,FGF2,1 95 | HBGF-2,FGF2,1 96 | fibroblast growth factor 2,FGF2,1 97 | basic fibroblast growth factor,FGF2,1 98 | bFGF,FGF2,1 99 | FGF-β,FGF2,1 100 | FGFβ,FGF2,1 101 | GDF15,GDF15,1 102 | GDF-15,GDF15,1 103 | MIF,MIF,1 104 | MMIF,MIF,1 105 | macrophage migration inhibitory factor,MIF,1 106 | SPP1,SPP1,1 107 | APRIL,APRIL,1 108 | TNFSF13,APRIL,1 109 | TNFSF13B,TNFSF13B,1 110 | CD258,CD258,1 111 | TNFSF14,CD258,1 112 | VEGI,VEGI,1 113 | vascular endothelial growth inhibitor,VEGI,1 114 | TNFSF15,VEGI,1 115 | OX40L,OX40L,1 116 | TNFSF4,OX40L,1 117 | CD252,OX40L,1 118 | CD153,CD153,1 119 | TNFSF8,CD153,1 120 | 4-1BB,4-1BB,1 121 | TNFSF9,4-1BB,1 122 | VEGI,VEGI,1 123 | TL1A,VEGI,1 124 | OX40,OX40,1 125 | CD134,OX40,1 -------------------------------------------------------------------------------- /data/meta/raw/filters.csv: -------------------------------------------------------------------------------- 1 | sym,table 2 | MINOR,transcription_factors 3 | GENESIS,transcription_factors 4 | PRISM,transcription_factors 5 | CD4,cytokines 6 | CD4 molecule,cytokines 7 | CD4mut,cytokines 8 | T-cell surface glycoprotein CD4,cytokines 9 | T-cell differentiation antigen L3T4,cytokines 10 | T-cell surface antigen T4/Leu-3,cytokines 11 | CD25,cytokines 12 | kit,cytokines 13 | light,cytokines 14 | rank,cytokines 15 | TR1,cytokines 16 | killer,cytokines 17 | CVID,cytokines 18 | Tc1,cytokines 19 | pilot,cytokines 20 | DP,cell_types 21 | thymic,cell_types 22 | EMT,cell_types 23 | TIL,all 24 | -------------------------------------------------------------------------------- /data/meta/raw/pro.raw.csv.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hammerlab/t-cell-relation-extraction/1375d454c1c6bc68a35c9c6870d26b6b8948ee25/data/meta/raw/pro.raw.csv.gz -------------------------------------------------------------------------------- /data/meta/raw/transcription_factors.manual.csv: -------------------------------------------------------------------------------- 1 | sym,lbl,spid 2 | RORC,RORC,1 3 | RORγ,RORC,1 4 | RORγt,RORC,1 5 | RORγ1,RORC,1 6 | RORγ2,RORC,1 7 | RORG,RORC,1 8 | RZR-GAMMA,RORC,1 9 | RZRG,RORC,1 10 | RAR-related orphan receptor gamma,RORC,1 11 | RAR related orphan receptor C,RORC,1 12 | retinoic acid-related orphan receptor,RORC,1 13 | retinoic acid-related orphan receptor γ,RORC,1 14 | retinoic acid-related orphan receptor γt,RORC,1 15 | FOXP3,FOXP3,1 16 | FoxP3,FOXP3,1 17 | Foxp3,FOXP3,1 18 | foxP3,FOXP3,1 19 | foxp3,FOXP3,1 -------------------------------------------------------------------------------- /data/results/labels.csv: -------------------------------------------------------------------------------- 1 | type|src|tgt 2 | cell_type|regulatory T|Treg 3 | cell_type|CD4-positive, CD25-positive, alpha-beta regulatory T|Treg 4 | cell_type|CD8-positive, CD25-positive, alpha-beta regulatory T|Treg 5 | cell_type|CD8-positive, CXCR3-positive, alpha-beta regulatory T|Treg 6 | cell_type|CD8-positive, CD28-negative, alpha-beta regulatory T|Treg 7 | cell_type|CD8-positive, alpha-beta regulatory T|Treg 8 | cell_type|natural T-regulatory|nTreg 9 | cell_type|induced T-regulatory|iTreg 10 | cell_type|effector CD8-positive, alpha-beta T|TEFF 11 | cell_type|effector CD4-positive, alpha-beta T|TEFF 12 | cell_type|effector T|TEFF 13 | cell_type|naive thymus-derived CD8-positive, alpha-beta T|TN 14 | cell_type|naive thymus-derived CD8-positive, alpha-beta T|TN 15 | cell_type|naive thymus-derived CD4-positive, alpha-beta T|TN 16 | cell_type|naive T|TN 17 | cell_type|effector memory CD8-positive, alpha-beta T cell, terminally differentiated|TEMRA 18 | cell_type|effector memory CD4-positive, alpha-beta T|TEM 19 | cell_type|effector memory CD8-positive, alpha-beta T|TEM 20 | cell_type|central memory CD4-positive, alpha-beta T|TCM 21 | cell_type|memory T|TMEM 22 | cell_type|CD4-positive, alpha-beta memory T|TMEM 23 | cell_type|T-helper 0|Th0 24 | cell_type|T-helper 1|Th1 25 | cell_type|T-helper 2|Th2 26 | cell_type|T-helper 3|Th3 27 | cell_type|T-helper 9|Th9 28 | cell_type|T-helper 17|Th17 29 | cell_type|T-helper 22|Th22 30 | cell_type|central memory CD8-positive, alpha-beta T|TCM 31 | cell_type|activated CD8-positive, alpha-beta T cell, human|activated T 32 | cell_type|activated CD4-positive, alpha-beta T cell, human|activated T 33 | cell_type|gamma-delta T|γδT 34 | cell_type|CD8-positive, alpha-beta cytotoxic T|Tc 35 | cell_type|cytotoxic T|Tc 36 | cell_type|CD4-positive, alpha-beta thymocyte|Thymocyte 37 | cell_type|CD8-positive, alpha-beta thymocyte|Thymocyte 38 | cell_type|double-positive, alpha-beta thymocyte|Thymocyte 39 | cell_type|DN2 thymocyte|Thymocyte 40 | cell_type|DN3 thymocyte|Thymocyte 41 | cell_type|DN2a thymocyte|Thymocyte 42 | cell_type|DN4 thymocyte|Thymocyte 43 | cell_type|double negative thymocyte|Thymocyte 44 | cell_type|immature single positive thymocyte|Thymocyte 45 | cell_type|fetal thymocyte|Thymocyte 46 | cell_type|intraepithelial|IEL 47 | cell_type|thymocyte|Thymocyte 48 | cell_type|T follicular helper|Tfh 49 | cell_type|dendritic epidermal T|DETC 50 | cell_type|helper T|Th 51 | cell_type|CD4-positive helper T|Th 52 | cell_type|alpha-beta T|αβT 53 | cell_type|CD4-positive, alpha-beta T|αβT 54 | cell_type|CD8-positive, alpha-beta T|αβT -------------------------------------------------------------------------------- /data/results/predictions.csv.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hammerlab/t-cell-relation-extraction/1375d454c1c6bc68a35c9c6870d26b6b8948ee25/data/results/predictions.csv.gz -------------------------------------------------------------------------------- /data/results/tags.csv.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hammerlab/t-cell-relation-extraction/1375d454c1c6bc68a35c9c6870d26b6b8948ee25/data/results/tags.csv.gz -------------------------------------------------------------------------------- /docker/README.md: -------------------------------------------------------------------------------- 1 | # Docker Instructions 2 | 3 | Download and install [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) for GPU 4 | utilization (or Docker CE otherwise) 5 | 6 | ## Build Instructions 7 | 8 | ```bash 9 | cd docker 10 | nvidia-docker build -t t-cell-relation-extraction -f Dockerfile . 11 | 12 | # To build using a forked snorkel repo: 13 | nvidia-docker build -t t-cell-relation-extraction \ 14 | --build-arg SNORKEL_REPO_URL=https://github.com/eric-czech/snorkel.git#egg=snorkel \ 15 | -f Dockerfile . 16 | ``` 17 | 18 | ## Run Instructions 19 | 20 | ```bash 21 | # Remote (w/ GPU) 22 | export TCRE_DATA_DIR=/data/disk2/nlp/20190311-pubmed-tcell-relation 23 | export TCRE_REPO_DIR=/home/eczech/repos/t-cell-relation-extraction 24 | nvidia-docker run --rm -ti -p 8888:8888 -p 6006:6006 \ 25 | -v $TCRE_REPO_DIR:/lab/repos/t-cell-relation-extraction \ 26 | -v $TCRE_DATA_DIR:/lab/data \ 27 | t-cell-relation-extraction 28 | 29 | # Local (Mac w/ no GPU) 30 | export TCRE_DATA_DIR=/Users/eczech/data/research/hammer/nlp/20190311-pubmed-tcell-relation 31 | export TCRE_REPO_DIR=/Users/eczech/repos/hammer/t-cell-relation-extraction 32 | docker run --rm -ti -p 8888:8888 -p 6006:6006 \ 33 | -v $TCRE_REPO_DIR:/lab/repos/t-cell-relation-extraction \ 34 | -v $TCRE_DATA_DIR:/lab/data \ 35 | t-cell-relation-extraction 36 | ``` 37 | -------------------------------------------------------------------------------- /docker/requirements.txt: -------------------------------------------------------------------------------- 1 | numpy==1.16.1 2 | pandas==0.24.2 3 | scipy==1.2.1 4 | six==1.12.0 5 | future==0.17.1 6 | sqlalchemy==1.3.3 7 | tika==1.19 8 | tqdm==4.32.1 9 | spacy==2.1.3 10 | scispacy==0.2.2 11 | torch==1.1.0 12 | py4j==0.10.8.1 13 | python-dotenv==0.10.1 14 | scikit-learn==0.21.2 15 | matplotlib==3.0.2 16 | seaborn==0.9.0 17 | plotnine==0.5.1 18 | umap-learn==0.3.9 19 | pronto==0.12.2 20 | mygene==3.1.0 21 | xlrd==1.2.0 22 | lxml==4.3.4 23 | pyarrow==0.13.0 24 | tables==3.5.2 25 | interlap==0.2.6 26 | unidecode==1.1.1 27 | pytorch-ignite==0.2.0 28 | torchtext==0.3.1 29 | gensim==3.7.3 30 | Click==7.0 31 | dill==0.3.0 32 | biopython==1.74 33 | beautifulsoup4==4.8.0 34 | dask==2.1.0 35 | distributed==2.1.0 36 | ipywidgets==7.5.1 37 | xgboost==0.90 38 | treelib==1.5.5 39 | tensorboardX==1.8 40 | pytorch-transformers==1.2.0 41 | plotly 42 | jupyterlab 43 | -------------------------------------------------------------------------------- /docs/images/annotation-examples.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hammerlab/t-cell-relation-extraction/1375d454c1c6bc68a35c9c6870d26b6b8948ee25/docs/images/annotation-examples.pdf -------------------------------------------------------------------------------- /docs/images/relation_examples.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hammerlab/t-cell-relation-extraction/1375d454c1c6bc68a35c9c6870d26b6b8948ee25/docs/images/relation_examples.png -------------------------------------------------------------------------------- /docs/images/training_outline.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hammerlab/t-cell-relation-extraction/1375d454c1c6bc68a35c9c6870d26b6b8948ee25/docs/images/training_outline.png -------------------------------------------------------------------------------- /env.sh: -------------------------------------------------------------------------------- 1 | export META_DATA_DIR=${REPO_DATA_DIR}/meta 2 | export MODEL_DATA_DIR=${REPO_DATA_DIR}/model 3 | export SUPERVISION_DATA_DIR=${REPO_DATA_DIR}/supervision 4 | export IMPORT_DATA_DIR_01=${DATA_DIR}/articles/import/20190314 5 | export IMPORT_DATA_DIR_02=${DATA_DIR}/articles/import/20190501 6 | export IMPORT_DATA_DIR_03=${DATA_DIR}/articles/import/20190621 7 | export RESULTS_DATA_DIR=${DATA_DIR}/results 8 | export W2V_MODEL_01=${DATA_DIR}/word2vec/PubMed-and-PMC-w2v.bin 9 | export SCIBERT_UNCASED_MODEL_01=${DATA_DIR}/scibert/scibert_scivocab_uncased 10 | export SCIBERT_CASED_MODEL_01=${DATA_DIR}/scibert/scibert_scivocab_cased 11 | 12 | # Must be set in environment externally # 13 | # export DATA_DIR=/Users/eczech/data/research/hammer/nlp/20190311-pubmed-tcell-relation 14 | # export REPO_DATA_DIR=/Users/eczech/repos/hammer/t-cell-relation-extraction/data 15 | # export DATA_DIR=/lab/data 16 | # export REPO_DATA_DIR=/lab/repos/t-cell-relation-extraction/data -------------------------------------------------------------------------------- /pipeline/misc/bert/v02/README.md: -------------------------------------------------------------------------------- 1 | ### Execution 2 | 3 | ``` 4 | python run_dataset.py --task_name imdb --do_train --do_eval \ 5 | --do_lower_case --data_dir $IMDB_DIR/ \ 6 | --model_type bert --model_name_or_path bert-base-uncased \ 7 | --max_seq_length 128 --learning_rate 2e-5 --num_train_epochs 3.0 \ 8 | --output_dir /tmp/imdb_output/ 9 | ``` 10 | 11 | ### Compatibility Notes 12 | 13 | 14 | New configs for pytorch-transformers: https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json 15 | 16 | ``` 17 | { 18 | "attention_probs_dropout_prob": 0.1, 19 | "hidden_act": "gelu", 20 | "hidden_dropout_prob": 0.1, 21 | "hidden_size": 768, 22 | "initializer_range": 0.02, 23 | "intermediate_size": 3072, 24 | "max_position_embeddings": 512, 25 | "num_attention_heads": 12, 26 | "num_hidden_layers": 12, 27 | "type_vocab_size": 2, 28 | "vocab_size": 30522 29 | } 30 | ``` 31 | 32 | Config with scibert named bert_config.json that must be renamed to config.json: 33 | 34 | ``` 35 | { 36 | "attention_probs_dropout_prob": 0.1, 37 | "hidden_act": "gelu", 38 | "hidden_dropout_prob": 0.1, 39 | "hidden_size": 768, 40 | "initializer_range": 0.02, 41 | "intermediate_size": 3072, 42 | "max_position_embeddings": 512, 43 | "num_attention_heads": 12, 44 | "num_hidden_layers": 12, 45 | "type_vocab_size": 2, 46 | "vocab_size": 31090 47 | } 48 | ``` 49 | 50 | ### Pytorch-transformers Notes 51 | 52 | Default cache location is ```/root/.cache/torch/pytorch_transformers``` and the logic for setting it is in [file_utils.py](https://github.com/huggingface/pytorch-transformers/blob/ed717635ff5c2bd5dfa8fd0266f309e314a3e44f/pytorch_transformers/file_utils.py#L42) 53 | 54 | The need to rename config file is also mentioned in https://pypi.org/project/spacy-pytorch-transformers/ (with no other modifications, so presumably this is all that's needed to make pytorch-transformers work with scibert exports) 55 | 56 | 57 | ### Fine Tuning Notes 58 | 59 | BERT authors recommend hyperparameter settings for fine tuning: https://mccormickml.com/2019/07/22/BERT-fine-tuning/ -------------------------------------------------------------------------------- /pipeline/misc/scripts/click-test.py: -------------------------------------------------------------------------------- 1 | 2 | import click 3 | 4 | PARAMS = {} 5 | 6 | 7 | class Client(object): 8 | 9 | def __init__(self, require_options=True, exceptions=None): 10 | self.require_options = require_options 11 | self.exceptions = exceptions 12 | 13 | def cmd(self, **kwargs): 14 | # cmd(dict(cli=dict(cli_opt=True), train=dict(train_opt='yes'))) 15 | cmd = [__file__] 16 | for fn_name, opts in kwargs.items(): 17 | if fn_name != 'cli': 18 | cmd.append(fn_name) 19 | if self.require_options: 20 | missing = set(PARAMS[fn_name]) - set(list(opts.keys())) - set(list(self.exceptions or [])) 21 | if missing: 22 | raise ValueError(f'Missing required options {missing} for command {fn_name}') 23 | for k, v in opts.items(): 24 | cmd.append('--{}={}'.format(k.replace('_', '-'), v)) 25 | return ' '.join(cmd) 26 | 27 | 28 | class param(object): 29 | 30 | def __init__(self, *args, **kwargs): 31 | self.param = args[0].replace('--', '').replace('-', '_') 32 | self.click_fn = click.option(*args, **kwargs) 33 | 34 | def __call__(self, f): 35 | self.fn_name = f.__name__ 36 | print(self.param, self.fn_name) 37 | if self.fn_name not in PARAMS: 38 | PARAMS[self.fn_name] = [] 39 | PARAMS[self.fn_name].append(self.param) 40 | return self.click_fn(f) 41 | 42 | @click.group(invoke_without_command=True) 43 | @param('--cli-opt', default='cli', required=True) 44 | @click.pass_context 45 | def cli(ctx, cli_opt): 46 | print('In cli: cli_opt=', cli_opt) 47 | 48 | 49 | @cli.command() 50 | @param('--train-opt', default='train', required=True) 51 | @click.pass_context 52 | def train(ctx, train_opt): 53 | print('In train: train_opt=', train_opt) 54 | print('PARAMS = ', PARAMS) 55 | 56 | if __name__ == '__main__': 57 | cli(obj={}) -------------------------------------------------------------------------------- /pipeline/misc/scripts/missing-cand-debug.py: -------------------------------------------------------------------------------- 1 | import os 2 | import pandas as pd 3 | os.environ['SNORKELDB'] = 'sqlite:////lab/repos/t-cell-relation-extraction/data/snorkel/snorkel.bkp_20190720.db' 4 | 5 | from snorkel import SnorkelSession 6 | from snorkel.models import Document 7 | session = SnorkelSession() 8 | 9 | doc = session.query(Document).filter(Document.name == 'PMC4785102').one() 10 | sents = doc.sentences 11 | pd.set_option('display.max_rows', 10000) 12 | df = pd.concat([ 13 | pd.DataFrame(list(zip(sent.entity_types, sent.abs_char_offsets, sent.words))) 14 | for sent in sents[:15] 15 | ]) 16 | df.to_csv('/tmp/tokens.csv', index=False) 17 | print(df) -------------------------------------------------------------------------------- /src/ptkn/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hammerlab/t-cell-relation-extraction/1375d454c1c6bc68a35c9c6870d26b6b8948ee25/src/ptkn/__init__.py -------------------------------------------------------------------------------- /src/ptkn/protein_tokenization_test.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | from ptkn.protein_tokenization import ProteinTokenizer, ProteinToken 3 | 4 | VOCAB1 = [ 5 | 'CD4', 'CD45RA', 'CD45', 'CD45RO', 'CD62L', 'CCR7', 'CD127', 6 | 'CD27', 'CD28', 'CD122', 'CD8a', 'CD8', 'CD3', 'Thy1', '4-1BB', 7 | 'CCR7', 'RORgt', 'CD95', 'CD122' 8 | ] 9 | 10 | CASES = [ 11 | # Test lots of in-vocab (mixed with a few adjacent OOV) strings and different sign expressions 12 | ('CD4+CD45RA+CD45RO-4-1BB-CD62L+++CCR7loCD127posCD27positiveCD28hiCD95+CD122+', [ 13 | 'CD4+x', 'CD45RA+x', 'CD45RO-x', '4-1BB-x', 'CD62L+x', 'CCR7-x', 14 | 'CD127+x', 'CD27+x', 'CD28=o', 'CD95=o', 'CD122=o' 15 | ], ['CD95', 'CD28', 'CD122']), 16 | 17 | # Test partial OOV with punctuation 18 | ('CD4+CD45RO/RBbright', ['CD4+x', 'CD45RO=x', '/RB=o']), 19 | 20 | # Test OOV on either end 21 | ('CD3CD4PBMC', ['CD3=o', 'CD4=x', 'PBMC=o'], ['CD3']), 22 | 23 | # Test partial OOV (i.e. hierarchical match) 24 | ('Thy1.1+OT-1+CD8+', ['Thy1=x', '.1=o','OT=o', '1=o', 'CD8+x']), 25 | 26 | # Test sign at start and terms with no sign 27 | ('+CD95CD4CD8negative', ['+o', 'CD95=x', 'CD4=x', 'CD8-x']), 28 | 29 | # Test sign only 30 | ('+', ['+o']), 31 | 32 | # Test sign only with conflict 33 | ('++-negative', ['=o']), 34 | 35 | # Test term with conflicting sign 36 | ('CD4+-', ['CD4=x']), 37 | 38 | # Test single term only 39 | ('CD4', ['CD4=x']), 40 | ('CD4neg', ['CD4-x']) 41 | ] 42 | 43 | SIGN_CHARS = {1: '+', 0: '=', -1: '-'} 44 | META_CHARS = {True: 'x', False: 'o'} 45 | 46 | class ProteinTokenizationTest(unittest.TestCase): 47 | 48 | def test_cases(self): 49 | for c in CASES: 50 | v = list(set(VOCAB1) - set(c[2] if len(c) > 2 else [])) 51 | v = {k: dict(name=k) for k in v} 52 | tokens = ProteinTokenizer(v).tokenize(c[0]) 53 | 54 | expected = c[1] 55 | actual = [] 56 | for t in tokens: 57 | sign_char = SIGN_CHARS[t.sign_value] 58 | meta_char = META_CHARS[t.metadata is not None] 59 | actual.append((t.token_text or '') + sign_char + meta_char) 60 | self.assertEquals(actual, c[1], f'Actual not equal expected for case {c[0]}') 61 | -------------------------------------------------------------------------------- /src/tcre/__init__.py: -------------------------------------------------------------------------------- 1 | import os.path as osp 2 | src_dir = osp.abspath(osp.dirname(__file__)) 3 | -------------------------------------------------------------------------------- /src/tcre/entrez/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hammerlab/t-cell-relation-extraction/1375d454c1c6bc68a35c9c6870d26b6b8948ee25/src/tcre/entrez/__init__.py -------------------------------------------------------------------------------- /src/tcre/env.py: -------------------------------------------------------------------------------- 1 | """ Environment variables common to all tasks (typically directory locations) 2 | 3 | This is useful for importing all variables often used at a CLI into 4 | any python script namespace. E.g.: 5 | 6 | from tcre.env import * 7 | print(DATA_DIR) 8 | """ 9 | import os 10 | import sys 11 | import os.path as osp 12 | from dotenv import dotenv_values 13 | 14 | TCRE_SEED = int(os.getenv('TCRE_SEED', 3832)) 15 | 16 | # Root environment variables that should always be set externally 17 | DEFAULT_ENV_VARS = ['DATA_DIR', 'REPO_DATA_DIR', 'REPO_DIR'] 18 | 19 | 20 | def _get_env_vars(default_vars=DEFAULT_ENV_VARS): 21 | pkg_dir = osp.abspath(osp.dirname(__file__)) 22 | path = osp.normpath(osp.join(pkg_dir, '..', '..', 'env.sh')) 23 | if not osp.exists(path): 24 | raise ValueError(f'Environment variable script not found (path = {path})') 25 | return {**dotenv_values(path), **{v: os.getenv(v) for v in default_vars}} 26 | 27 | 28 | def _set_env_vars(env_vars): 29 | module = sys.modules[__name__] 30 | for k, v in env_vars.items(): 31 | setattr(module, k, v) 32 | 33 | 34 | # Read environment variables from bash-friendly script and set them as globals 35 | # on this module (this is useful for syncing python environment with auxiliary bash script environment) 36 | _set_env_vars(_get_env_vars()) 37 | 38 | -------------------------------------------------------------------------------- /src/tcre/exec/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hammerlab/t-cell-relation-extraction/1375d454c1c6bc68a35c9c6870d26b6b8948ee25/src/tcre/exec/__init__.py -------------------------------------------------------------------------------- /src/tcre/exec/v1/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hammerlab/t-cell-relation-extraction/1375d454c1c6bc68a35c9c6870d26b6b8948ee25/src/tcre/exec/v1/__init__.py -------------------------------------------------------------------------------- /src/tcre/exec/v1/bert/README.md: -------------------------------------------------------------------------------- 1 | ### BERT Modeling 2 | 3 | All utilities are employed to run existing scripts rather than coding directly with the pytorch-transformers classes. 4 | 5 | See: 6 | 7 | - [Running Pytorch-Transformers on Custom Datasets](https://medium.com/dsnet/running-pytorch-transformers-on-custom-datasets-717fd9e10fe2) 8 | - [pytorch-transformers-extensions](https://github.com/nikhilno1/nlp_projects/tree/f5e4ae159970b6fd613d2c2181265db336acc934/pytorch-transformers-extensions) (slight abstractions for running pytorch-transformers on custom datasets) 9 | - [pytorch-transformers](https://github.com/huggingface/pytorch-transformers) 10 | 11 | ``` 12 | python run_dataset.py --task_name imdb --do_train --do_eval \ 13 | --do_lower_case --data_dir $IMDB_DIR/ \ 14 | --model_type bert --model_name_or_path bert-base-uncased \ 15 | --max_seq_length 128 --learning_rate 2e-5 --num_train_epochs 3.0 \ 16 | --output_dir /tmp/imdb_output/ 17 | ``` 18 | 19 | ### Compatibility Notes 20 | 21 | 22 | New configs for pytorch-transformers: https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json 23 | 24 | ``` 25 | { 26 | "attention_probs_dropout_prob": 0.1, 27 | "hidden_act": "gelu", 28 | "hidden_dropout_prob": 0.1, 29 | "hidden_size": 768, 30 | "initializer_range": 0.02, 31 | "intermediate_size": 3072, 32 | "max_position_embeddings": 512, 33 | "num_attention_heads": 12, 34 | "num_hidden_layers": 12, 35 | "type_vocab_size": 2, 36 | "vocab_size": 30522 37 | } 38 | ``` 39 | 40 | Config with scibert named bert_config.json that must be renamed to config.json: 41 | 42 | ``` 43 | { 44 | "attention_probs_dropout_prob": 0.1, 45 | "hidden_act": "gelu", 46 | "hidden_dropout_prob": 0.1, 47 | "hidden_size": 768, 48 | "initializer_range": 0.02, 49 | "intermediate_size": 3072, 50 | "max_position_embeddings": 512, 51 | "num_attention_heads": 12, 52 | "num_hidden_layers": 12, 53 | "type_vocab_size": 2, 54 | "vocab_size": 31090 55 | } 56 | ``` 57 | 58 | ### Pytorch-transformers Notes 59 | 60 | Default cache location is ```/root/.cache/torch/pytorch_transformers``` and the logic for setting it is in [file_utils.py](https://github.com/huggingface/pytorch-transformers/blob/ed717635ff5c2bd5dfa8fd0266f309e314a3e44f/pytorch_transformers/file_utils.py#L42) 61 | 62 | The need to rename config file is also mentioned in https://pypi.org/project/spacy-pytorch-transformers/ (with no other modifications, so presumably this is all that's needed to make pytorch-transformers work with scibert exports) 63 | 64 | 65 | ### Fine Tuning Notes 66 | 67 | BERT authors recommend hyperparameter settings for fine tuning: https://mccormickml.com/2019/07/22/BERT-fine-tuning/ -------------------------------------------------------------------------------- /src/tcre/exec/v1/cli_client.py: -------------------------------------------------------------------------------- 1 | from tcre.exec.v1 import cli 2 | import os 3 | 4 | 5 | def get_default_client(): 6 | """Get CLI client that will not enforce setting of less important options (log level, balancing, seed, etc)""" 7 | return Client(require_options=True, exceptions=[ 8 | 'log_level', 'seed', 'vocab_limit', 'use_lower', 'save_keys', 9 | 'log_iter_interval', 'log_epoch_interval', 'balance', 'batch_size', 10 | 'simulation_strategy', 'swap_list' 11 | ]) 12 | 13 | 14 | class Client(object): 15 | 16 | def __init__(self, require_options=True, exceptions=None): 17 | """CLI wrapper client 18 | 19 | Example: 20 | 21 | from tcre.exec.v1 import cli_client 22 | client = cli_client.Client(require_options=True, exceptions=['log_level']) 23 | # Get executable command 24 | client.cmd(cli=dict(relation_class='test'), train=dict(dims=100)) 25 | # Run and get return code for command 26 | client.run(cli=dict(relation_class='test'), train=dict(dims=100)) 27 | 28 | """ 29 | self.require_options = require_options 30 | self.exceptions = exceptions 31 | 32 | def cmd(self, executable='python', **kwargs): 33 | """Get runnable command""" 34 | cmd = [executable, cli.__file__] 35 | for fn_name, opts in kwargs.items(): 36 | if fn_name != 'cli': 37 | cmd.append(fn_name) 38 | if self.require_options: 39 | missing = set(cli.PARAMS[fn_name]) - set(list(opts.keys())) - set(list(self.exceptions or [])) 40 | if missing: 41 | raise ValueError(f'Missing required options {missing} for command {fn_name}') 42 | for k, v in opts.items(): 43 | cmd.append('--{}={}'.format(k.replace('_', '-'), v)) 44 | return ' '.join(cmd) 45 | 46 | def run(self, raise_on_nonzero=True, **kwargs): 47 | """Run command for given options 48 | 49 | Returns: 50 | rc: Return code from os.system call 51 | """ 52 | return self.execute(self.cmd(**kwargs), raise_on_nonzero=raise_on_nonzero) 53 | 54 | @classmethod 55 | def execute(cls, cmd, raise_on_nonzero=True): 56 | rc = os.system(cmd) 57 | if raise_on_nonzero and rc != 0: 58 | raise ValueError(f'Return code {rc} != 0 for command: {cmd}') 59 | return rc 60 | -------------------------------------------------------------------------------- /src/tcre/ix.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | 3 | 4 | class IXDB(object): 5 | """Model class for determining whether or not relations exist in iX database""" 6 | 7 | def __init__(self, data_file_path, min_papers=None): 8 | self.data_file_path = data_file_path 9 | self.min_papers = min_papers 10 | self.df = None 11 | 12 | def initialize(self): 13 | df = pd.read_csv(self.data_file_path) 14 | 15 | # Filter to records with known cell/cytokine mappings for internal IDS 16 | df = df[df['cell_ref_id'].notnull() & df['cytokine_ref_id'].notnull()] 17 | 18 | # Also filter to records with a minimum number of publications 19 | if self.min_papers is not None: 20 | df = df[df['num_papers'] >= self.min_papers] 21 | 22 | df = df.set_index(['cell_ref_id', 'cytokine_ref_id']).sort_index() 23 | self.df = df 24 | return self 25 | 26 | def is_relation(self, ct_id, ck_id, actor, category): 27 | if (ct_id, ck_id) in self.df.index: 28 | df = self.df.loc[(ct_id, ck_id)] 29 | df = df[(df['actor'] == actor) & (df['category'] == category)] 30 | return len(df) > 0 31 | return None 32 | 33 | def is_candidate_relation(self, cand, actor, category): 34 | # Split ids saved as ":" to do lookup on preferred id 35 | ct_id, ck_id = cand.immune_cell_type_cid, cand.cytokine_cid 36 | ct_id, ck_id = ct_id.split(':')[1], ck_id.split(':')[1] 37 | return self.is_relation(ct_id, ck_id, actor, category) 38 | 39 | -------------------------------------------------------------------------------- /src/tcre/lib.py: -------------------------------------------------------------------------------- 1 | import unicodedata 2 | import pandas as pd 3 | import os.path as osp 4 | import os 5 | from tcre.env import * 6 | 7 | SPECIES_HUMAN_ID = 1 8 | SPECIES_MOUSE_ID = 2 9 | 10 | CELL_TYPES = 'cell_types' 11 | CYTOKINES = 'cytokines' 12 | TRANSCRIPTION_FACTORS = 'transcription_factors' 13 | SURFACE_PROTEINS = 'surface_proteins' 14 | FILTERS = 'filters' 15 | 16 | 17 | def fix_jupyter_spacy_config(): 18 | # Work-around for https://github.com/explosion/spaCy/issues/3208 19 | from IPython.core.getipython import get_ipython 20 | ip = get_ipython() 21 | ip.config['IPKernelApp']['parent_appname'] = 'notebook' 22 | 23 | 24 | def get_entity_meta_data(table, enabled_only=True): 25 | path = osp.join(META_DATA_DIR, f'{table}.csv') 26 | df = pd.read_csv(path) 27 | # Disabled records are kept for provenance but generally ignored otherwise 28 | if enabled_only: 29 | df = df[df['enabled'] == True] 30 | return df 31 | 32 | 33 | def get_entity_meta_filters(table=None): 34 | path = osp.join(META_DATA_DIR, 'raw', f'{FILTERS}.csv') 35 | df = pd.read_csv(path) 36 | if table is not None: 37 | df = df[(df['table'] == table) | (df['table'] == 'all')] 38 | return df 39 | 40 | 41 | class IntervalMergingDict(object): 42 | """Dictionary for interval keys with configurable overlap merging""" 43 | 44 | def __init__(self, merge_fn): 45 | from interlap import InterLap 46 | self._intervals = InterLap() 47 | self._result = {} 48 | self.merge_fn = merge_fn 49 | 50 | def add(self, start, end, data=None): 51 | # Add the new interval and associated data 52 | self._intervals.add((start, end, data)) 53 | 54 | # Pull a list of all intervals that overlap (including itself) 55 | vals = list(self._intervals.find((start, end))) 56 | assert len(vals) > 0, 'Expecting at least one interval result' 57 | 58 | # Merge intervals if there are more than one 59 | res = vals[0] if len(vals) == 1 else self.merge_fn(vals) 60 | if not isinstance(res, tuple) or len(res) != 3: 61 | raise ValueError('Merged results should be 3-item tuples, not {}'.format(res)) 62 | 63 | # Delete all overlapping ranges in final result before adding merged value 64 | for e in vals: 65 | if e[:2] in self._result: 66 | del self._result[e[:2]] 67 | self._result[res[:2]] = res[2] 68 | 69 | def keys(self): 70 | return self._result.keys() 71 | 72 | def values(self): 73 | return self._result.values() 74 | 75 | def items(self): 76 | return zip(self.keys(), self.values()) 77 | 78 | -------------------------------------------------------------------------------- /src/tcre/logging.py: -------------------------------------------------------------------------------- 1 | import logging 2 | console = logging.StreamHandler() 3 | console.setFormatter(logging.Formatter('%(asctime)s:%(levelname)s:%(name)s: %(message)s')) 4 | logger = logging.getLogger() 5 | logger.setLevel(logging.INFO) 6 | logger.addHandler(console) -------------------------------------------------------------------------------- /src/tcre/modeling/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hammerlab/t-cell-relation-extraction/1375d454c1c6bc68a35c9c6870d26b6b8948ee25/src/tcre/modeling/__init__.py -------------------------------------------------------------------------------- /src/tcre/modeling/data.py: -------------------------------------------------------------------------------- 1 | from torchtext.data import Field, Dataset, Example 2 | import pandas as pd 3 | 4 | 5 | class DataFrameDataset(Dataset): 6 | """Class for using pandas DataFrames as a datasource""" 7 | 8 | def __init__(self, examples, fields, filter_pred=None): 9 | """ 10 | Create a dataset from a pandas dataframe of examples and Fields 11 | Arguments: 12 | examples pd.DataFrame: DataFrame of examples 13 | fields {str: Field}: The Fields to use in this tuple. The 14 | string is a field name, and the Field is the associated field. 15 | filter_pred (callable or None): use only exanples for which 16 | filter_pred(example) is true, or use all examples if None. 17 | Default is None 18 | """ 19 | self.examples = examples.apply(SeriesExample.fromSeries, args=(fields,), axis=1).tolist() 20 | if filter_pred is not None: 21 | self.examples = filter(filter_pred, self.examples) 22 | self.fields = dict(fields) 23 | # Unpack field tuples 24 | for n, f in list(self.fields.items()): 25 | if isinstance(n, tuple): 26 | self.fields.update(zip(n, f)) 27 | del self.fields[n] 28 | 29 | 30 | class SeriesExample(Example): 31 | """Class to convert a pandas Series to an Example""" 32 | 33 | @classmethod 34 | def fromSeries(cls, data, fields): 35 | return cls.fromdict(data.to_dict(), fields) 36 | 37 | @classmethod 38 | def fromdict(cls, data, fields): 39 | ex = cls() 40 | 41 | for key, field in fields.items(): 42 | if key not in data: 43 | raise ValueError("Specified key {} was not found in " 44 | "the input data".format(key)) 45 | if field is not None: 46 | setattr(ex, key, field.preprocess(data[key])) 47 | else: 48 | setattr(ex, key, data[key]) 49 | 50 | return ex 51 | -------------------------------------------------------------------------------- /src/tcre/modeling/metrics.py: -------------------------------------------------------------------------------- 1 | from ignite.metrics import MetricsLambda, Metric 2 | import pandas as pd 3 | 4 | 5 | def F1(r, p): 6 | return 2 * (p * r) / (p + r + 1e-20) 7 | 8 | 9 | def get_f1_metric(precision, recall): 10 | return MetricsLambda(F1, recall, precision) 11 | 12 | 13 | class PredictionAggregator(Metric): 14 | """Aggregate predictions across batches (useful for not repeating prediction steps)""" 15 | 16 | def __init__(self, output_transform=lambda x: {'y_pred': x[0], 'y': x[1], 'id': x[2]}): 17 | super().__init__(lambda x: x) 18 | self.predictions = [] 19 | self.output_transform = output_transform 20 | 21 | def reset(self): 22 | self.predictions = [] 23 | 24 | def update(self, output): 25 | preds = {k: v.cpu().numpy() for k, v in self.output_transform(output).items()} 26 | self.predictions.append(preds) 27 | 28 | def compute(self): 29 | return pd.concat([pd.DataFrame(p) for p in self.predictions]) 30 | 31 | -------------------------------------------------------------------------------- /src/tcre/modeling/models/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hammerlab/t-cell-relation-extraction/1375d454c1c6bc68a35c9c6870d26b6b8948ee25/src/tcre/modeling/models/__init__.py -------------------------------------------------------------------------------- /src/tcre/modeling/simulation.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | def _get_primary_entity_indices(r): 5 | # Expect tag sequence like "O, E:primary:immune_cell_type, , O, E:primary:cytokine, E:primary:cytokine, O, O" 6 | # Find primary entity tags noting that they may be repeated if they span multiple tokens 7 | tags = list(set([t for t in r['tags'] if t.startswith('E:primary')])) 8 | if len(tags) != 2: 9 | raise ValueError(f'Failed to find two primary tags for record:\n{r}') 10 | 11 | # Find first index of each distinct tag 12 | itag = [r['tags'].index(t) for t in tags] 13 | return itag 14 | 15 | 16 | def _label_by_dist(r): 17 | # Return probability based on abs distance (prob goes up when distance goes down) 18 | itag = _get_primary_entity_indices(r) 19 | dist = abs(itag[1] - itag[0]) 20 | dist = np.clip(dist, 0, 32) / 32. 21 | logit = 5*(dist - .5) 22 | prob = 1. / (1 + np.exp(-logit)) 23 | return 1 - prob 24 | 25 | 26 | def _label_by_secondary_marking(r): 27 | ptag = _get_primary_entity_indices(r) 28 | 29 | # Get indices of secondary entities (if any) 30 | stags = [i for i, t in enumerate(r['tags']) if t.startswith('E:secondary')] 31 | 32 | # Return 0 if a secondary exists between primaries, otherwise 1 33 | for i in stags: 34 | if ptag[0] <= i <= ptag[1]: 35 | return 0. 36 | return 1. 37 | 38 | 39 | def _rs(): 40 | return np.random.RandomState(1) 41 | 42 | 43 | def _get_random_labels(n): 44 | return _rs().choice([0., 1.], size=n, replace=True) 45 | 46 | 47 | LABEL_SIM_FNS = { 48 | 'position-based': _label_by_dist, 49 | 'secondary-marking': _label_by_secondary_marking 50 | } 51 | 52 | 53 | def get_simulated_labels(df, strategy): 54 | if strategy == 'random': 55 | return _get_random_labels(len(df)) 56 | if strategy.startswith('random-'): 57 | # Use the strategy suffix to get permuted labels (to preserve class balance) 58 | fn = strategy.replace('random-', '') 59 | if fn in LABEL_SIM_FNS: 60 | y = df.apply(LABEL_SIM_FNS[fn], axis=1).values 61 | _rs().shuffle(y) 62 | return y 63 | if strategy in LABEL_SIM_FNS: 64 | return df.apply(LABEL_SIM_FNS[strategy], axis=1).values 65 | raise ValueError(f'Simulation strategy "{strategy}" invalid') 66 | 67 | 68 | -------------------------------------------------------------------------------- /src/tcre/modeling/vocab.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import numpy as np 3 | from torchtext.vocab import Vocab 4 | from collections import defaultdict, Counter 5 | import pandas.core.common as com 6 | 7 | 8 | class W2VVocab(Vocab): 9 | 10 | def __init__(self, model, specials=None, random_state=None): 11 | """Build pretrained w2v vocab 12 | 13 | Args: 14 | model: Gensim model (e.g. KeyedVectors.load_word2vec_format(W2V_MODEL_01, binary=True, limit=50000)) 15 | specials: Extra tokens to add with randomized vectors ("" is always added first) 16 | random_state: Random state used to initialized random vectors 17 | """ 18 | super().__init__(Counter()) 19 | 20 | # Remove any specials already present in the vocab and prepend pad token 21 | assert '' not in model.vocab 22 | specials = list(sorted(specials)) if specials else [] 23 | specials = [v for v in specials if v not in model.vocab] 24 | specials = [''] + specials 25 | 26 | self.itos = specials + list(model.vocab.keys()) 27 | 28 | # Use zero vector for unk as well as pad 29 | def get_unk_index(): 30 | return 0 31 | self.stoi = defaultdict(get_unk_index) 32 | self.stoi.update({w: i for i, w in enumerate(self.itos)}) 33 | 34 | if len(self.itos) != len(self.stoi): 35 | raise ValueError( 36 | f'Vocab has repeated words (possibly due to unicode normalization) ' 37 | f'(len(itos) = {len(itos)}, len(stoi) = {len(stoi)}') 38 | 39 | # Add single zero vector for pad token 40 | tensors = [torch.FloatTensor(np.zeros((1, model.vectors.shape[1])))] 41 | 42 | # Add random vectors for other specials 43 | if len(specials) > 1: 44 | rs = com.random_state(random_state) 45 | tensors.append(torch.FloatTensor(rs.normal(size=(len(specials)-1, model.vectors.shape[1])))) 46 | 47 | # Add remaining vectors 48 | tensors.append(torch.FloatTensor(model.vectors)) 49 | 50 | # Concatenate vectors and ensure there are as many as there are words 51 | self.vectors = torch.cat(tensors, dim=0) 52 | if len(self.itos) != len(self.vectors): 53 | raise AssertionError( 54 | f'W2V vocab has unequal vector and vocab size ' 55 | f'(len(vocab) = {len(self.itos)}, len(vectors) = {len(self.vectors)})' 56 | ) 57 | -------------------------------------------------------------------------------- /src/tcre/nb/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hammerlab/t-cell-relation-extraction/1375d454c1c6bc68a35c9c6870d26b6b8948ee25/src/tcre/nb/__init__.py -------------------------------------------------------------------------------- /src/tcre/nb/utils.py: -------------------------------------------------------------------------------- 1 | """Utilities for programmatic notebook manipulation""" 2 | import copy 3 | import nbformat 4 | from nbconvert import HTMLExporter 5 | 6 | nbs = {} 7 | FORCE_NB_RELOAD = False 8 | 9 | 10 | def _true(*_): 11 | return True 12 | 13 | 14 | def get_nb(path, force=None): 15 | global nbs 16 | force = FORCE_NB_RELOAD if force is None else force 17 | if path not in nbs or force: 18 | with open(path, 'r') as fd: 19 | nb = nbformat.read(fd, as_version=4) 20 | nbs[path] = nb 21 | return nbs[path] 22 | 23 | 24 | def get_cell_nb(nb, predicate): 25 | predicate = predicate or _true 26 | nb = copy.deepcopy(nb) 27 | nb['cells'] = [c for c in nb['cells'] if predicate(c)] 28 | return nb 29 | 30 | 31 | def to_html(nb): 32 | html_exporter = HTMLExporter() 33 | html_exporter.template_file = 'basic' 34 | (body, resources) = html_exporter.from_notebook_node(nb) 35 | return body, resources 36 | 37 | 38 | def get_tag_html(path, name, prefix): 39 | tag = prefix + '.' + name 40 | 41 | def predicate(c): 42 | if 'metadata' in c and 'tags' in c['metadata']: 43 | for t in c['metadata']['tags']: 44 | if t == tag: 45 | return True 46 | return False 47 | nb = get_cell_nb(get_nb(path), predicate) 48 | if len(nb['cells']) == 0: 49 | raise ValueError(f'Failed to find cell with tag "{tag}"') 50 | return to_html(nb) 51 | -------------------------------------------------------------------------------- /src/tcre/query.py: -------------------------------------------------------------------------------- 1 | """Query utilities for efficient snorkel entity linking""" 2 | import pandas as pd 3 | 4 | 5 | class DocToCand(object): 6 | 7 | QUERY_TEMPLATE = ( 8 | 'SELECT D.id AS doc_id, C.id AS sentence_id, A.id AS cand_id ' 9 | 'FROM {} A ' 10 | 'INNER JOIN span B ON A.immune_cell_type_id = B.id ' 11 | 'INNER JOIN sentence C ON B.sentence_id = C.id ' 12 | 'INNER JOIN document D ON C.document_id = D.id' 13 | ) 14 | 15 | @classmethod 16 | def _get_query(cls, cand_class): 17 | if 'immune_cell_type' not in cand_class.entity_types: 18 | raise ValueError( 19 | 'Candidate class {} does not have required entity type "immune_cell_type"'.format(cand_class.field)) 20 | return DocToCand.QUERY_TEMPLATE.format(cand_class.field) 21 | 22 | @classmethod 23 | def _run_query(cls, con, query): 24 | rs = con.execute(query) 25 | return pd.DataFrame([r for r in rs], columns=rs.keys()) 26 | 27 | @classmethod 28 | def all(cls, session, classes): 29 | con = session.connection() 30 | df = pd.concat([ 31 | DocToCand._run_query(con, DocToCand._get_query(classes[c])).assign(cand_type=classes[c].field) 32 | for c in classes 33 | ]) 34 | return df 35 | -------------------------------------------------------------------------------- /src/tcre/tokenization.py: -------------------------------------------------------------------------------- 1 | """ Expression signature tokenization utility functions 2 | 3 | This module can be used to initialize a ProteinTokenizer instance to decompose strings 4 | like CD4+IL-17-IFN-γhi into the more tractable form [CD4+, IL-17-, IFN-γ+] 5 | """ 6 | from tcre import lib 7 | from ptkn.protein_tokenization import ProteinTokenizer 8 | 9 | DEFAULT_SYN_BLACKLIST = ["ifi", "dif", "esp", "tc1", "til"] 10 | 11 | 12 | def load_protein_tokenizer(syn_blacklist=DEFAULT_SYN_BLACKLIST): 13 | """ Create tokenizer using combined transcription factor, cytokine, and surface protein vocabulary 14 | 15 | Args: 16 | syn_blacklist: List of synonyms to be ignored globally (i.e. across entity types) 17 | Returns: 18 | ProteinTokenizer instance 19 | """ 20 | df_pr = lib.get_entity_meta_data(lib.SURFACE_PROTEINS) 21 | df_tf = lib.get_entity_meta_data(lib.TRANSCRIPTION_FACTORS) 22 | pm_tf = df_tf.set_index('id')[['lbl']].to_dict(orient='index') 23 | df_ck = lib.get_entity_meta_data(lib.CYTOKINES) 24 | pm_ck = df_ck.set_index('id')[['lbl']].to_dict(orient='index') 25 | vocab = { 26 | **{r['syn']: (r['label'], r['extid'], r['pref_lbl'], r['pref_id'], 'pr') 27 | for i, r in df_pr.iterrows()}, 28 | **{r['sym']: (r['lbl'], r['id'], pm_tf.get(r['prefid'], {}).get('lbl'), r['prefid'], 'tf') 29 | for i, r in df_tf.iterrows()}, 30 | **{r['sym']: (r['lbl'], r['id'], pm_ck.get(r['prefid'], {}).get('lbl'), r['prefid'], 'ck') 31 | for i, r in df_ck.iterrows()} 32 | } 33 | for syn in syn_blacklist: 34 | if syn in vocab: 35 | del vocab[syn] 36 | 37 | return ProteinTokenizer(vocab) 38 | -------------------------------------------------------------------------------- /src/tcre/visualization.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | 3 | DEFAULT_COLORS = ['red', 'blue', 'green'] 4 | 5 | 6 | def candidate_html(c, colors=None): 7 | """Get HTML visualization of a candidate""" 8 | if colors is None: 9 | colors = {} 10 | sent = c.get_parent() 11 | words = [w for w in sent.words] 12 | types = c.get_parent().entity_types 13 | cids = c.get_parent().entity_cids 14 | for ctx in c.get_contexts(): 15 | w1, w2 = ctx.get_word_range() 16 | cid = cids[w1] 17 | color = colors.get(types[w1], 'gray') 18 | words[w1] = f'
{words[w1]}' 19 | words[w2] = f'{words[w2]}
' 20 | return ' '.join(words) 21 | 22 | 23 | def candidate_df(candidates, colors=None): 24 | 25 | if colors is None: 26 | # Get entity types for candidate and map to colors arbitrarily 27 | types = list(set([t for c in candidates for t in c.__class__.__argnames__])) 28 | colors = { 29 | t: DEFAULT_COLORS[i % len(DEFAULT_COLORS)] 30 | for i, t in enumerate(types) 31 | } 32 | 33 | df = pd.DataFrame([ 34 | dict( 35 | id=c.id, 36 | type=c.type, 37 | split=c.split, 38 | e1=c.get_contexts()[0].get_span(), 39 | e2=c.get_contexts()[1].get_span(), 40 | text=candidate_html(c, colors=colors) 41 | ) 42 | for c in candidates 43 | ]) 44 | return df[['id', 'type', 'split', 'e1', 'e2', 'text']] 45 | 46 | 47 | def candidate_html_table(candidates, colors=None): 48 | return candidate_df(candidates, colors=colors).to_html(escape=False) 49 | --------------------------------------------------------------------------------