Autocrine IFNγ Controls the Regulatory Function of Lymphoproliferative Double Negative T Cells
Bibliographic record
Abstract
TCRαβ(+) CD4(-)CD8(-)NK(-) double negative T cells (DN T cells) can act as regulatory T cells to inhibit allograft rejection and autoimmunity. Their role in graft-versus-host disease and mechanisms of suppression remain elusive. In this study, we demonstrate that DN T cells can inhibit CD4(+) T cell-mediated GVHD in a semi-allogeneic model of bone marrow transplantation. Furthermore, we present evidence of a novel autocrine IFNγ signaling pathway in Fas-deficient C57BL/6.lpr (B6.lpr) DN T cells. B6.lpr DN T cells lacking IFNγ or its receptor were impaired in their ability to suppress syngeneic CD4(+) T cells responding to alloantigen stimulation both in vitro and in vivo. Autocrine IFNγ signaling was required for sustained B6.lpr DN T cell IFNγ secretion in vivo and for upregulation of surface Fas ligand expression during TCR stimulation. Fas ligand (FasL) expression by B6.lpr DN T cells permitted lysis of activated CD4(+) T cells and was required for suppression of GVHD. Collectively, our data indicate that DN T cells can inhibit GVHD and that IFNγ plays a critical autocrine role in controlling the regulatory function of B6.lpr DN T cells.
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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".