HLA-A2 Chimeric antigen receptor regulatory T cells promote allograft acceptance in mice
Bibliographic record
Abstract
Abstract Purpose: Regulatory T cells (Tregs) are immune regulators essential for achieving stable allograft acceptance. We tested whether HLA-A2 CAR-Tregs have the therapeutic potential to prolong allograft survival through mediating linked-suppression and infectious tolerance. Methods:Donor hearts expressing BALB/c, HLA-A2 and 2W-OVA antigens (C57BL/6.A2 X BALB/c.2W-OVA.F1) were transplanted into C57BL/6 mice and received 250µg aCD154 ± 1X106 A2 CAR-Tregs on the day of transplant (HTx). 1) Graft survival was monitored. 2) CAR-Tregs in the peripheral circulation, spleen and lymph nodes (SLO) were monitored (Thy1.1+FoxP3+). 3) 2W-specific Tregs and Tconvs quantified at ~D-45 post-HTx. 4) Donor specific IgG was quantified. Results: 1)HLA.A2-CAR Tregs + 250µg aCD154 significantly prolonged allograft survival compared to controls with 250µg aCD154 alone (Mean: >56 vs 26 days). 2)FoxP3+CAR-Tregs were detected in the circulation at D10-40 post-HTx, and in the SLO at >D45. 3) In the SLO, CAR-Tregs mediated linked suppression by preventing 2W-specific Tconvs expansion, and infectious tolerance by expanding of 2W-specific Tregs (7-15-fold). 4)A2.CAR-Tregs significantly decreased anti-A2 and anti-donor MHCI & MHCII IgG production. Conclusion: HLA-A2.CAR-Tregs synergized with aCD154, mediated linked suppression and infectious T cell tolerance and significantly prolonged allograft survival. Remarkably, HLA-A2.CAR-Tregs suppressed donor MHC-specific IgG responses and anti-HLA-A2IgG.
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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) | 0.000 | 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) | 0.002 | 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".