Dendritic Cell–Dependent In Vivo Generation of Autoregulatory T Cells by Antidiabetogenic MHC Class II
Bibliographic record
Abstract
Several mechanisms have been proposed to explain how certain MHC class II molecules afford dominant resistance to autoimmune diseases like type 1 diabetes (T1D). However, it remains unclear how protective MHC types can blunt autoreactive T cell responses directed against a diverse repertoire of autoantigenic epitopes presented by disease-promoting MHCs. In this study, we show that expression of I-E on dendritic cells (DCs) of NOD mice promotes the differentiation of MHC promiscuous autoreactive CD4(+) clonotypes into antidiabetogenic autoregulatory T cells. We expressed an I-Eα(kloxP) transgene in NOD mice and used cell type-specific I-E ablation to show that I-E-expressing DCs, but not B cells, promote the generation of autoreactive CD4(+)Foxp3(+) regulatory T cells (Tregs) and their accumulation in the pancreas-draining lymph nodes. There, these Tregs suppress the presentation of β cell Ags to naive autoreactive CD4(+) and CD8(+) T cells restricted by diabetogenic MHC molecules in an I-E-independent manner. Whereas selective removal of I-E on DCs abrogated autoregulatory Treg formation and T1D protection, selective removal of I-E on B cells was inconsequential. These results explain how certain MHC class II molecules can completely suppress antigenically complex autoimmune responses in an Ag-nonspecific manner.
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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 | 0.001 |
| 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".