Immunomodulation of Antigen Presenting Cells Promotes Natural Regulatory T Cells That Prevent Autoimmune Diabetes in NOD Mice
Bibliographic record
Abstract
Progression towards type 1 diabetes (T1D) in susceptible patients is linked to a progressive decline in the capacity of regulatory T cells (Treg) to maintain tolerance. As such, therapies aimed at redressing the failing Treg compartment have been the subject of intense investigation. Treg dysfunction in T1D has recently been linked to a reduced capacity of antigen presenting cells (APCs) to maintain Treg function rather than Treg intrinsic defects. This suggests that therapies aimed simply at addressing the failing Treg compartment are unlikely to provide long-term protection. Here, we demonstrate that modulation of the inflammatory status of CD11b+CD11c- APCs favors the upregulation of protective Tregs in a mouse model of T1D. We further demonstrate that reduced expression of the costimulatory molecule CD40 plays a role in this increased immunoregulatory capacity. Strikingly, Treg upregulation resulted exclusively from an increase in natural Tregs rather than the peripheral conversion of conventional T cells. This suggests that modulation of CD11b+ CD11c- APCs inflammatory properties favors the establishment of natural Treg responses that, unlike adaptive Treg responses, are likely to maintain tolerance to a broad range of antigens. As such, modulation of this APC subset represents a potential therapeutic avenue to reestablish peripheral tolerance and protect from autoimmune diseases such as T1D.
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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 | 0.001 | 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 | 0.001 |
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".