CD4+ but not CD8+ memory T cells escape DN Tregs-mediated regulation via expression of Serpin Protease Inhibitor 6 (P2160)
Bibliographic record
Abstract
Abstract Memory T cell (Tm) homeostasis is poorly understood and is a critical challenge to prevent transplant rejection. At present there are no effective clinical strategies to control Tm and contradictory reports exist as to their control by regulatory T cell (Treg). We previously found that TCRab+CD3+CD4-CD8-NK1.1- (double-negative, DN) Treg could effectively suppress CD4+ and CD8+ effector T cells and thus prevent transplant rejection. We utilized this model to test whether DN Tregs could similarly suppress Tm. Interestingly DN Tregs suppressed CD8+ Tm but had no effect on CD4+ Tm cells. As well DN Tregs express very high levels of granzyme B (GrB), suggesting a potential control mechanism and indeed no suppression was observed using perforin null DN Tregs. In BALB/c (H-2d) to B6-Rag1-/- mice (H-2b) skin allograft transplantation, DN Tregs significantly reduced total CD44high CD8+ Tm in recipients and significantly prolonged graft survival. In contrast, co-transfer of DN Tregs had no effect on CD4+CD44high Tm mediated rejection. There was no effect with co-transferred CD4+ Tm. CD4+ Tm cells express higher levels of GrB inhibitory Serpin Protease Inhibitor 6 (SPI-6) mRNA and protein than CD8+ Tm. Furthermore, SPI-6 null CD4+ Tm is not resistant to DN Treg-mediated regulation. We show for the first time, that DN Treg can control CD8+ Tm and suggest that DN Treg along with targeting SPI-6 may be useful to limit the expansion of both CD4+ and CD8+ Tm cells 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) | 0.002 | 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".