Regulatory T-Cells Protect From Type 1 Diabetes After Induction by Coxsackievirus Infection in the Context of Transforming Growth Factor-β
Bibliographic record
Abstract
OBJECTIVE: Coxsackievirus infections have long been associated with the induction of type 1 diabetes. Infection with coxsackievirus B4 (CB4) enhances type 1 diabetes onset in NOD mice by accelerating the presentation of beta-cell antigen to autoreactive T-cells. It has been reported that a progressive defect in regulatory T-cell (Treg) function is, in part, responsible for type 1 diabetes onset in NOD mice. This defect may contribute to susceptibility to viral-induced type 1 diabetes. We asked whether the immune response after CB4 infection could be manipulated to reestablish peripheral tolerance while maintaining the immune response to virus. RESEARCH DESIGN AND METHODS: NOD mice expressing transforming growth factor-beta (TGF-beta) specifically in the beta-cells were infected with CB4, and the functional role of Tregs in disease protection was measured. Systemic treatments with TGF-beta were used to assess its therapeutic potential. RESULTS: Here, we report that Tregs induced after CB4 infection in the presence of TGF-beta prevented type 1 diabetes. The capacity to directly infect pancreatic beta-cells correlated with increased numbers of pancreatic Tregs, suggesting that presentation of beta-cell antigen is integral to induction of diabetogenic protective Tregs. Furthermore, the presence of these viral induced Tregs correlated with protection from type 1 diabetes without altering the antiviral response. Finally, when TGF-beta was administered systemically to NOD mice after infection, the incidence of type 1 diabetes was reduced, thereby signifying a potential therapeutic role for TGF-beta. CONCLUSIONS: We demonstrate manipulations of the immune response that result in Treg-mediated protection from type 1 diabetes without concomitant loss of the capacity to control viral infection.
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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) | 0.001 | 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".