Enhanced suppression of polyclonal CD8+25+ regulatory T cells via exosomal arming of antigen-specific peptide/MHC complexes
Bibliographic record
Abstract
Abstract Compared with CD4+25+ regulatory T cells (Tregs), the mechanisms for natural, polyclonal CD8+25+ Treg immune suppression have been significantly less studied. We previously showed that polyclonal T cells can acquire antigen-specific targeting activity through arming with exosomal peptide-MHC (pMHC). In this study, we assessed the suppressive effect of CD8+25+ Tregs or CD8+25+ Tregs armed with ovalbumin (OVA)-specific exosomes on other immune cells and OVA-specific dendritic cell (DCOVA)-stimulated antitumor immunity. We demonstrate that CD8+25+ Tregs inhibit T cell proliferation in vitro in a cell contact-dependent fashion but independent of the expression of immunosuppressive IL-10, TGF-β, and CTLA-4. CD8+25+ Tregs anergize naïve T cells upon stimulation by up-regulating T cell anergy-associated Egr2 and down-regulating IL-2 production. Tregs also anergize DCs by preventing DC maturation through the down-regulation of Iab, CD80, CD86, and inflammatory cytokines, leading to defects in T cell stimulation. Moreover, CD8+25+ Tregs inhibit CTLs through inducing CTL death via perforin-mediated apoptosis and through reducing effector CTL cytotoxic activity via down-regulating CTL perforin-production and degranulation. In addition, we show that CD8+25+ Tregs suppress DCOVA-stimulated CTL responses in priming and effector phases and inhibit immunity against OVA-expressing CCLOVA lung cancer. Remarkably, polyclonal CD8+25+ Tregs armed with OVA-specific exosomal pMHC class-II (pMHC-II), or pMHC class-I (pMHC-I) complexes exert their enhanced inhibition of CTL responses in the priming and the effector phases, respectively. Taken together, our investigation reveals that assigning antigen specificity to nonspecific polyclonal CD8+25+ Tregs for enhanced immune suppression can be achieved through exosomal pMHC arming. This principle may have a great effect on Treg-mediated immunotherapy of autoimmune diseases.
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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 | 0.000 |
| 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".