Negative feedback control of the autoimmune response through antigen-induced differentiation of IL-10–secreting Th1 cells
Bibliographic record
Abstract
Regulation of the immune response to self- and foreign antigens is vitally important for limiting immune pathology associated with both infections and hypersensitivity conditions. Control of autoimmune conditions can be reinforced by tolerance induction with peptide epitopes, but the mechanism is not currently understood. Repetitive intranasal administration of soluble peptide induces peripheral tolerance in myelin basic protein (MBP)-specific TCR transgenic mice. This is characterized by the presence of anergic, interleukin (IL)-10-secreting CD4(+) T cells with regulatory function (IL-10 T reg cells). The differentiation pathway of peptide-induced IL-10 T reg cells was investigated. CD4(+) T cells became anergic after their second encounter with a high-affinity MBP peptide analogue. Loss of proliferative capacity correlated with a switch from the Th1-associated cytokines IL-2 and interferon (IFN)-gamma to the regulatory cytokine IL-10. Nevertheless, IL-10 T reg cells retained the capacity to produce IFN-gamma and concomitantly expressed T-bet, demonstrating their Th1 origin. IL-10 T reg cells suppressed dendritic cell maturation, prevented Th1 cell differentiation, and thereby created a negative feedback loop for Th1-driven immune pathology. These findings demonstrate that Th1 responses can be self-limiting in the context of peripheral tolerance to a self-antigen.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".