Critical Role of CD4 Help in CD154 Blockade-Resistant Memory CD8 T Cell Activation and Allograft Rejection in Sensitized Recipients
Bibliographic record
Abstract
Allograft rejection in sensitized recipients remains the major problem in clinical organ transplantation. We have developed a donor-type skin-sensitized mouse cardiac allograft model (BALB/c-->C57BL/6) in which both rejection (<5 days) and alloreactive CD8 activation are resistant to CD154 blockade. First, we attempted to elucidate why CD154 blockade fails to protect cardiac grafts in sensitized recipients. The gene array analysis has revealed that treatment with anti-CD154 mAb (MR1) had distinctive impact on host immunity in naive vs sensitized animals. Unlike in naive counterparts, host sensitization mitigated the impact of CD154 blockade on critical immune signaling pathways. Indeed, we identified 3234 genes in cardiac grafts that were down-regulated by MR1 in naive (at least 5-fold), but remained unaffected in sensitized hosts. Moreover, MR1 treatment failed to prevent accumulation of CD4 T cells in cardiac allografts of sensitized recipients. Then, to determine the role of CD4 help in CD154 blockade-resistant immune response, we used CD4-depleting and CD4-blocking Ab, in conjunction with MR1 treatment. Our data revealed that CD154 blockade-resistant CD8 activation in sensitized mice was dependent on CD4 T cells. In the absence of CD4 help, CD154 blockade prevented differentiation of alloreactive CD8 T cells into CTL effector/memory cells and abrogated acute rejection (cardiac graft survival for >30 days), paralleled by selective target gene depression at the graft site. These results provide the rationale to probe potential synergy of adjunctive therapy targeting CD4 and CD154 to overcome graft rejection in sensitized recipients.
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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".