Donor-specific chimeric antigen receptor Tregs limit rejection in naive but not sensitized allograft recipients
Bibliographic record
Abstract
Cell therapy with autologous donor-specific regulatory T cells (Tregs) is a promising strategy to minimize immunosuppression in transplant recipients. Chimeric antigen receptor (CAR) technology has recently been used successfully to generate donor-specific Tregs and overcome the limitations of enrichment protocols based on repetitive stimulations with alloantigens. However, the ability of CAR-Treg therapy to control alloreactivity in immunocompetent recipients is unknown. We first analyzed the effect of donor-specific CAR Tregs on alloreactivity in naive, immunocompetent mice receiving skin allografts. Tregs expressing an irrelevant or anti-HLA-A2-specific CAR were administered to Bl/6 mice at the time of transplanting an HLA-A2+ Bl/6 skin graft. Donor-specific CAR-Tregs, but not irrelevant-CAR Tregs, significantly delayed skin rejection and diminished donor-specific antibodies (DSAs) and frequencies of DSA-secreting B cells. Donor-specific CAR-Treg–treated mice also had a weaker recall DSA response, but normal responses to an irrelevant antigen, demonstrating antigen-specific suppression. When donor-specific CAR Tregs were tested in HLA-A2-sensitized mice, they were unable to delay allograft rejection or diminish DSAs. The finding that donor-specific CAR-Tregs restrain de novo but not memory alloreactivity has important implications for their use as an adoptive cell therapy in transplantation.
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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 | 0.001 |
| 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".