Regulation of antigen‐expressing dendritic cells by double negative regulatory T cells
Bibliographic record
Abstract
TCRαβ(+) CD3(+) CD4(-) CD8(-) NK1.1(-) double negative (DN) Tregs comprise 1-3% of peripheral T lymphocytes in mice and humans. It has been demonstrated that DN Tregs can suppress allo-, xeno- and auto-immune responses in an Ag-specific fashion. However, the mechanisms by which DN Tregs regulate immune responses remain elusive. Whether DN Tregs can regulate DCs has not been investigated previously. In this study, we demonstrate that DN Tregs express a high level of CTLA4 and are able to down-regulate costimulatory molecules CD80 and CD86 expressed on Ag-expressing mature DCs (mDCs). DN Tregs from CTLA4 KO mice were not able to downregulate CD80 and CD86 expression, indicating that CTLA4 is critical for DN Treg-mediated downregulation of costimulatory molecule expression on Ag-expressing mature DCs. Furthermore, DN Tregs could kill both immature and mature allogeneic DCs, as well as Ag-loaded syngeneic DCs, in an Ag-specific manner in vitro and in vivo, mainly through the Fas-FasL pathway. These data demonstrate, for the first time, that DN Tregs are potent regulators of DCs and may have the potential to be developed as a novel immune suppression treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".