Insulin B peptide-MHC class II-specific chimeric antigen receptor-Tregs prevent autoimmune diabetes in mice
Bibliographic record
Abstract
Adoptive immunotherapy with Tregs is a promising approach for preventing or treating type 1 diabetes. Islet antigen-specific Tregs have more potent therapeutic effects than polyclonal cells, but their low frequency is a barrier for clinical application. To generate Tregs that recognize islet antigens, we engineered a chimeric antigen receptor (CAR) derived from a monoclonal antibody with specificity for the insulin B chain 10-23 peptide presented in the context of the IAg7 MHC class II allele present in NOD mice. Peptide specificity of the resulting InsB-g7 CAR was confirmed by tetramer staining and T cell proliferation in response to recombinant or islet-derived peptide. The InsB-g7 CAR redirected NOD Treg specificity such that insulin B 10-23-peptide stimulation enhanced suppressive function, measured via reduction of proliferation and IL-2 production by BDC2.5 T cells and CD80 and CD86 expression on dendritic cells. Cotransfer of InsB-g7 CAR Tregs prevented adoptive transfer diabetes by BDC2.5 T cells in immunodeficient NOD mice. In WT NOD mice, InsB-g7 CAR Tregs prevented spontaneous diabetes. These results show that engineering Treg specificity for islet antigens using a T cell receptor-like CAR is a promising therapeutic approach for the prevention of autoimmune diabetes.
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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) | 0.001 | 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 | 0.001 | 0.002 |
| 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".