Generation of alloantigen-specific T regulatory cells via a chimeric antigen receptor targeting HLA-A2
Bibliographic record
Abstract
Abstract CD4+ T regulatory cells (Tregs) are of clinical interest due to their potent immunosuppressive activity in both transplantation and autoimmunity models. Clinical efficacy in humans has been limited by aspects of Treg biology, including the difficulty of isolating sufficient numbers for potency without sacrificing purity and thus function. Enriching for antigen-specific Tregs greatly increases potency, however, antigen-specific Tregs are extremely rare. The success of chimeric antigen receptor (CAR) technology in generating antigen-specific effector T cells suggested that a similar approach could be used to generate alloantigen-specific Tregs for use in transplantation. We generated a new CAR specific for HLA-A2 (A2-CAR) and tested its application in the generation of alloantigen-specific human Tregs. Naïve Tregs were sorted from human peripheral blood, activated, and transduced with A2-CAR lentivirus. Stimulation through the A2-CAR upregulated Treg activation markers and functional molecules, and resulted in proliferation. Importantly, in vitro studies showed that A2-CAR-expressing Tregs maintained their expected phenotype and suppressive function, before, during and after HLA-A2-mediated stimulation. In vivo, A2-CAR Tregs were superior to Tregs expressing an irrelevant CAR at preventing xenogeneic graft-versus-host disease caused by HLA-A2+ T cells. Together, these data suggest that alloantigen-specific Tregs generated by the introduction of an A2-CAR may be a potent and specific immunosuppressive treatment 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 | 0.000 |
| 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".