An IL-27/NFIL3 signalling axis drives Tim-3 and IL-10 expression and T-cell dysfunction
Bibliographic record
Abstract
The inhibitory receptor T-cell immunoglobulin and mucin domain-3 (Tim-3) has emerged as a critical regulator of the T-cell dysfunction that develops in chronic viral infections and cancers. However, little is known regarding the signalling pathways that drive Tim-3 expression. Here, we demonstrate that interleukin (IL)-27 induces nuclear factor, interleukin 3 regulated (NFIL3), which promotes permissive chromatin remodelling of the Tim-3 locus and induces Tim-3 expression together with the immunosuppressive cytokine IL-10. We further show that the IL-27/NFIL3 signalling axis is crucial for the induction of Tim-3 in vivo. IL-27-conditioned T helper 1 cells exhibit reduced effector function and are poor mediators of intestinal inflammation. This inhibitory effect is NFIL3 dependent. In contrast, tumour-infiltrating lymphocytes from IL-27R−/− mice exhibit reduced NFIL3, less Tim-3 expression and failure to develop dysfunctional phenotype, resulting in better tumour growth control. Thus, our data identify an IL-27/NFIL3 signalling axis as a key regulator of effector T-cell responses via induction of Tim-3, IL-10 and T-cell dysfunction. Tim-3 is an inhibitory molecule that suppresses T-cell responses. Here the authors show that the cytokine IL-27, acting through the transcription factor NFIL3, induces Tim-3 in vivo, and that IL-27-conditioned Th1 cells have poor effector function.
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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) | 0.002 | 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".