CAR Tregs mediate linked suppression and infectious tolerance in islet transplantation
Bibliographic record
Abstract
Abstract Regulatory T cells (Tregs) have potential as a cell-based therapy to prevent or treat transplant rejection and autoimmunity. Using an HLA-A2-specific chimeric antigen receptor (A2-CAR), we previously showed that adoptive transfer of A2-CAR Tregs limited anti-HLA-A2 alloimmunity. However, it was unknown if A2-CAR Tregs could also limit immunity to autoantigens. Using a model of HLA-A2 + islet transplantation into immunodeficient non-obese diabetic mice, we investigated if A2-CAR Tregs could control diabetes induced by islet-autoreactive (BDC2.5) T cells. In mice transplanted with HLA-A2 + islets, A2-CAR Tregs reduced BDC2.5 T cell engraftment, proliferation and cytokine production, and protected mice from diabetes. Tolerance to islets was systemic, including protection of the HLA-A2 negative endogenous pancreas. In tolerant mice, a significant proportion of BDC2.5 T cells gained FOXP3 expression suggesting that long-term tolerance is maintained by de novo Treg generation. Thus, A2-CAR Tregs mediate linked suppression and infectious tolerance and have potential therapeutic use to simultaneously control both allo- and autoimmunity in islet transplantation. One Sentence Summary Alloreactive chimeric antigen receptor-engineered regulatory T cells limit diabetogenic T cell engraftment and function to prevent type 1 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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".