Antigen Specificity Acquisition of Adoptive CD4+ Regulatory T Cells via Acquired Peptide-MHC Class I Complexes
Bibliographic record
Abstract
The Ag-specific CD4(+) regulatory T (Tr) cells play an important role in immune suppression in autoimmune diseases and antitumor immunity. However, the molecular mechanism for Ag-specificity acquisition of adoptive CD4(+) Tr cells is unclear. In this study, we generated IL-10- and IFN-gamma-expressing type 1 CD4(+) Tr (Tr1) cells by stimulation of transgenic OT II mouse-derived naive CD4(+) T cells with IL-10-expressing adenovirus (AdV(IL-10))-transfected and OVA-pulsed dendritic cells (DC(OVA/IL-10)). We demonstrated that both in vitro and in vivo DC(OVA/IL-10)-stimulated CD4(+) Tr1 cells acquired OVA peptide MHC class (pMHC) I which targets CD4(+) Tr1 cells suppressive effect via an IL-10-mediated mechanism onto CD8(+) T cells, leading to an enhanced suppression of DC(OVA)-induced CD8(+) T cell responses and antitumor immunity against OVA-expressing murine B16 melanoma cells by approximately 700% relative to analogous CD4(+) Tr1 cells without acquired pMHC I. Interestingly, the nonspecific CD4(+)25(+) Tr cells can also become OVA Ag specific and more immunosuppressive in inhibition of OVA-specific CD8(+) T cell responses and antitumor immunity after uptake of DC(OVA)-released exosomal pMHC I complexes. Taken together, the Ag-specificity acquisition of CD4(+) Tr cells via acquiring DC's pMHC I may be an important mean in augmenting CD4(+) Tr cell suppression.
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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 | 0.001 |
| 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".