Functional Waning of Naturally Occurring CD4+ Regulatory T-Cells Contributes to the Onset of Autoimmune Diabetes
Bibliographic record
Abstract
OBJECTIVE: In this study, we asked whether a possible quantitative or qualitative deficiency in naturally occurring Foxp3(+)CD4(+) regulatory T-cells (nT(reg)), which display potent inhibitory effects on T-cell functions in vitro and in vivo, may predispose to the development of type 1 diabetes. RESEARCH DESIGN AND METHODS: We assessed the frequency and function of Foxp3(+) nT(reg) cells in primary and secondary lymphoid tissues in the NOD animal model of type 1 diabetes. RESULTS: We show that the cellular frequency of Foxp3(+) nT(reg) cells in primary and secondary lymphoid tissues is stable and does not decline relative to type 1 diabetes-resistant mice. We show that thymic and peripheral CD4(+)CD25(+) T-cells are fully functional in vivo. We also examined the functional impact of CD4(+)Foxp3(+) nT(reg) cells on the development of autoimmune diabetes, and we demonstrate that nT(reg) cells do not affect the initial priming or expansion of antigen-specific diabetogenic T-cells but impact their differentiation in pancreatic lymph nodes. Moreover, CD4(+)Foxp3(+) nT(reg) cells also regulate later events of diabetogenesis by preferentially localizing in the pancreatic environment where they suppress the accumulation and function of effector T-cells. Finally, we show that the nT(reg) cell functional potency and intra-pancreatic proliferative potential declines with age, in turn augmenting diabetogenic responses and disease susceptibility. CONCLUSIONS: This study demonstrates that Foxp3-expressing nT(reg) cells in NOD mice regulate diabetogenesis, but temporal alterations in nT(reg) cell function promote immune dysregulation and the onset of spontaneous 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) | 0.001 | 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) | 0.002 | 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".