Development of Memory-Like Autoregulatory CD8+ T Cells Is CD4+ T Cell Dependent
Bibliographic record
Abstract
Progression of spontaneous autoimmune diabetes is associated with development of a disease-countering negative-feedback regulatory loop that involves differentiation of low-avidity autoreactive CD8(+) cells into memory-like autoregulatory T cells. Such T cells blunt diabetes progression by suppressing the presentation of both cognate and noncognate Ags to pathogenic high-avidity autoreactive CD8(+) T cells in the pancreas-draining lymph nodes. In this study, we show that development of autoregulatory CD8(+) T cell memory is CD4(+) T cell dependent. Transgenic (TG) NOD mice expressing a low-affinity autoreactive TCR were completely resistant to autoimmune diabetes, even after systemic treatment of the mice with agonistic anti-CD40 or anti-4-1BB mAbs or autoantigen-pulsed dendritic cells, strategies that dramatically accelerate diabetes development in TG NOD mice expressing a higher affinity TCR for the same autoantigenic specificity. Furthermore, whereas abrogation of RAG-2 expression, hence endogenous CD4(+) T cell and B cell development, decelerated disease progression in high-affinity TCR-TG NOD mice, it converted the low-affinity TCR into a pathogenic one. In agreement with these data, polyclonal CD4(+) T cells from prediabetic NOD mice promoted disease in high-affinity TCR-TG NOD.Rag2(-/-) mice, but inhibited it in low-affinity TCR-TG NOD.Rag2(-/-) mice. Thus, in chronic autoimmune responses, CD4(+) Th cells contribute to both promoting and suppressing pathogenic autoimmunity.
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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 | 0.000 |
| 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".