Regulatory T Cells Are Critical to Tolerance Induction in Presensitized Mouse Transplant Recipients Through Targeting Memory T Cells
Bibliographic record
Abstract
Memory T cells are a significant barrier to induction of transplant tolerance. However, reliable means to target alloreactive memory T cells have remained elusive. In this study, presensitization of BALB/c mice with C57BL/6 skin grafts generated a large number of OX40(+)CD44(hi)effector/memory T cells and resulted in rapid rejection of donor heart allografts. Recognizing that anti-OX40L monoclonal antibody (mAb) (alpha-OX40L) monotherapy prolonged graft survival through inhibition and apoptosis of memory T cells in presensitized recipients, alpha-OX40L was added to the combined treatment protocol of LF15-0195 (LF) and anti-CD45RB (alpha-CD45RB) mAb-a protocol that induced heart allograft tolerance in non-presensitized recipients but failed to induce tolerance in presensitized recipients. Interestingly, this triple therapy restored donor-specific heart allograft tolerance in our presensitized model that was associated with induction of tolerogenic dendritic cells and CD4(+)CD25(+)Foxp3(+) T regulatory cells (Tregs). Of note, CD25(+) T cell depletion in triple therapy recipients prevented establishment of allograft tolerance. In addition, adoptive transfer of donor-primed effector/memory T cells into tolerant recipients markedly reduced levels of Tregs and broke tolerance. Our findings indicated that targeting memory T cells, by blocking OX40 costimulation in presensitized recipients was very important to expansion of Tregs, which proved critical to development of tolerance.
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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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".