The regulatory function of lpr TCRαβ+ CD4-CD8- double negative T cells requires autocrine interferon gamma (168.24)
Bibliographic record
Abstract
Abstract Mice and humans with Fas mutations develop lymphoproliferative (lpr) disease and accumulate a large number of lymphocytes including TCRαβ+ CD4-CD8- non-NK double negative (DN) T cells. In these settings, CD4+ T cells and B cells are implicated in causing lupus-like autoimmunity; in contrast, the function of the DN T cells is largely unknown. DN T cells in other situations have been shown to secrete interferon gamma (IFNγ) and possess FasL-dependent regulatory functions. Therefore, we sought to determine whether lpr DN T cells can exert regulatory effects on syngeneic T cells and to what extent these phenomena might depend on IFNγ secretion. We found that lpr DN T cells produce IFNγ after antigen stimulation and can suppress and kill syngeneic Fas+ CD4+ T cells in responding to alloantigens. Lpr DN T cells deficient in either IFNγ or its receptor exhibited reduced suppressive and cytotoxic function in vitro and a decreased ability to suppress CD4+ T cells in vivo during graft-versus-host disease (GVHD). FasL-deficient DN T cells from gld mice also failed to suppress GVHD. These findings demonstrate that lpr DN T cells are able to suppress Fas+ syngeneic CD4+ T cells in vitro and in vivo. Expression of both FasL and the IFNγ receptor on lpr DN T cells is critical to their regulatory function. These studies have revealed a previously unrecognized IFNγ-IFNγ receptor autocrine loop that is central to DN T cell mediated immune 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) | 0.000 | 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) | 0.002 | 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".