Cell‐extrinsic <scp>CTLA</scp>4‐mediated regulation of dendritic cell maturation depends on <scp>STAT</scp>3
Bibliographic record
Abstract
Regulatory T (Treg) cells suppress immune responses by downregulating the expression of costimulatory molecules CD80 and CD86 on dendritic cells (DCs) through cytotoxic T lymphocyte antigen 4 (CTLA4). However, it is unclear whether inducible Treg (iTreg) cells can hamper immune responses via the same mechanism. Moreover, whether a reverse signal sent by CTLA4 alone is sufficient to prevent maturation of DCs has never been evaluated. Here, we demonstrate that stimulation of DCs with CTLA4, either expressed by inducible Treg cells or by cross-linking with CTLA4Fc fusion protein, can significantly inhibit LPS-induced CD80 and CD86 mRNA and protein expression in both mouse and human DCs. Importantly, CTLA4Fc-treated DCs had reduced ability to stimulate CD4(+) and CD8(+) T-cell proliferation and cytokine production in both syngeneic and allogeneic settings. We also investigated the molecular mechanism involved in the induction of tolerogenic DCs by CTLA4. We determined that the interaction of CTLA4 with its high affinity ligand CD80 on DCs induces STAT3 phosphorylation followed by reduction of NF-κB activity, leading to suppression of CD80 and CD86 gene transcription and protein production. Our work opens new windows for the generation of tolerogenic DCs that could ultimately be used for treating autoimmune diseases and transplant rejection.
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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) | 0.003 | 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".