Antigen specificity acquisition of CD4+ regulatory T cells via acquired pMHC I complexes
Bibliographic record
Abstract
Antigen‐specific CD4 + regulatory T (Tr) cells have important roles in preventing autoimmune diseases, but they also suppress effective anti‐tumor immunity. However, the molecular mechanisms regulating their acquisition of antigen specificity, particularly with respect to CD8 + T cells, are as yet unclear. In this study, we generated CD4 + Tr cells by stimulating naïve T cells derived from ovalbumin (OVA)‐specific T cell receptor transgenic OT II mice with OVA‐pulsed dendritic cells (DC OVA ) that had been transduced with an IL‐10‐expressing adenoviral vector (DC OVA/IL‐10 ). These DC OVA/IL‐10 secreting IL‐10 induced CD4 + Tr cells that secreted IFN‐γ ‐and IL‐10, but not IL‐4. We showed that these CD4 + Tr cells acquired OVA peptide/major histocompatibility complex class I (pMHC I) following either in vitro or in vivo stimulation with DC OVA/IL‐10 . We also demonstrated that IL‐10 secretion by these pMHC I‐carrying CD4 + Tr cells was critical to their suppression of OVA‐specific CD8 + T cell responses and antitumor immunity. However, the pMHC I complexes acquired by the CD4 + Tr cells also importantly affected for their activity, enhancing their abilities to suppress OVA‐specific anti‐tumor cytotoxic T lymphocyte responses in vivo by ≈ 700% relative to analogous CD4 + Tr cells incapable of expressing pMHC I complexes (i.e., K b−/− Tr). The above conclusion has also been confirmed by the evidence that the non‐specific CD4 + 25 + Tr cells become antigen specific and contribute enhanced suppressive effect via acquired pMHC I by uptake of DC OVA ‐released exosomes carrying pMHC I. Taken together, our data suggest that CD4 + Tr cells that have acquired pMHCI complexes much more efficiently target antigen‐specific CD8 + T cells in vivo , and that this may be an important means of augmenting the antigen‐specificity of CD4 + Tr cells in general.
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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".