Cutting Edge: In Vivo Trogocytosis as a Mechanism of Double Negative Regulatory T Cell-Mediated Antigen-Specific Suppression
Bibliographic record
Abstract
Recent data have demonstrated that treatment with alphabeta-TCR(+)CD3(+)CD4(-)CD8(-)NK1.1(-) double negative (DN) regulatory T cells (Tregs) inhibits autoimmune diabetes and enhances allotransplant and xenotransplant survival in an Ag-specific fashion. However, the mechanisms whereby DN Tregs suppress Ag-specific immune responses remain largely unknown. In this study, we demonstrate that murine DN Tregs acquire alloantigen in vivo via trogocytosis and express it on their cell surface. Trogocytosis requires specific interaction of MHC-peptide on APCs and Ag-specific TCR on DN Tregs, as blocking this interaction prevents DN Treg-mediated trogocytosis. Acquisition of alloantigen by DN Tregs was required for their ability to kill syngeneic CD8(+) T cells. Importantly, DN Tregs that had acquired alloantigen were cytotoxic toward Ag-specific, but not Ag-nonspecific, syngeneic CD8(+) T cells. These data provide new insight into how Tregs mediate Ag-specific T cell suppression and may enhance our ability to use DN Tregs as a therapy for transplant rejection and 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 | 0.001 | 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".