Role of TGF-β in Self-Peptide Regulation of Autoimmunity
Bibliographic record
Abstract
Transforming growth factor (TGF)-β has been implicated in regulation of the immune system, including autoimmunity. We have found that TGF-β is readily produced by T cells following immunization with self-peptide epitopes that downregulate autoimmune responses in type 1 diabetes (T1D) prone nonobese diabetic (NOD) mice. These include multiple peptide epitopes derived from the islet β-cell antigens GAD65 (GAD65 p202-221, GAD65 p217-236), GAD67 (GAD67 p210-229, GAD67 p225-244), IGRP (IGRP p123-145, IGRP p195-214) and insulin B-chain (Ins. B:9-23) that protected NOD mice from T1D. Immunization of NOD mice with the self-MHC class II I-A g7 β-chain-derived peptide, I-Aβ g7 p54-76 also induced large amounts of TGF-β and also protected these mice from diabetes development. These results indicate that peptides derived from disease related self-antigens and MHC class II molecules primarily induce TGF-β producing regulatory Th3 and Tr1-like cells. TGF-β produced by these cells could enhance the differentiation of induced regulatory iTreg and iTreg17 cells to prevent induction and progression of autoimmune diseases. We therefore suggest that peripheral immune tolerance could be induced and maintained by immunization with self-peptides that induce TGF-β producing T cells.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".