Tregs integrate native and CAR-mediated costimulatory signals for control of allograft rejection
Bibliographic record
Abstract
Tregs expressing chimeric antigen receptors (CAR-Tregs) are a promising tool to promote transplant tolerance. The relationship between CAR structure and Treg function was studied in xenogeneic, immunodeficient mice, revealing advantages of CD28-encoding CARs. However, these models could underrepresent interactions between CAR-Tregs, antigen-presenting cells (APCs), and donor-specific Abs. We generated Tregs expressing HLA-A2-specific CARs with different costimulatory domains and compared their function in vitro and in vivo using an immunocompetent model of transplantation. In vitro, the CD28-encoding CAR had superior antigen-specific suppression, proliferation, and cytokine production. In contrast, in vivo, Tregs expressing CARs encoding CD28, ICOS, programmed cell death 1, and GITR, but not 4-1BB or OX40, all extended skin allograft survival. To reconcile in vitro and in vivo data, we analyzed effects of a CAR encoding CD3ζ but no costimulatory domain. These data revealed that exogenous costimulation from APCs can compensate for the lack of a CAR-encoded CD28 domain. Thus, Tregs expressing a CAR with or without CD28 are functionally equivalent in vivo, mediating similar extension of skin allograft survival and controlling the generation of anti-HLA-A2 alloantibodies. This study reveals a dimension of CAR-Treg biology and has important implications for the design of CARs for clinical use in Tregs.
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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) | 0.001 | 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".