How antigen specificity directs regulatory T‐cell function: self, foreign and engineered specificity
Bibliographic record
Abstract
Regulatory T cells (Tregs) are a suppressive subset of T cells that have important roles in maintaining self-tolerance and preventing immunopathology. The T-cell receptor (TCR) and its antigen specificity play a dominant role in the differentiation of cells to a Treg fate, either in the thymus or in the periphery. This review focuses on the effects of the TCR and its antigen specificity on Treg biology. The role of Tregs with specificity for self-antigen has primarily been studied in the context of autoimmune disease, although recent studies have focused on their role in steady-state conditions. The role of Tregs that are specific for pathogens, dietary antigens and allergens is much less studied, although recent data suggest a significant and previously underappreciated role for Tregs during memory responses to a wide range of foreign antigens. The development of TCR- or chimeric antigen receptor (CAR)-transduced T cells means we are now able to engineer Tregs with disease-relevant antigen specificities, paving the way for ensuring specificity with Treg-based therapies. Understanding the role that antigens play in driving the generation and function of Tregs is critical for defining the pathophysiology of many immune-mediated diseases, and developing new therapeutic interventions.
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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 | 0.002 |
| 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".