The factor inhibiting HIF regulates T cell differentiation and anti-tumour efficacy
Bibliographic record
Abstract
T cells must adapt to variations in tissue microenvironments; these adaptations include the degree of oxygen availability. The hypoxia-inducible factor (HIF) transcription factors control much of this adaptation, and thus regulate many aspects of T cell activation and function. The HIFs are in turn regulated by oxygen-dependent hydroxylases: both the prolyl hydroxylases (PHDs) which interact with the VHL tumour suppressor and control HIF turnover, and the asparaginyl hydroxylase known as the Factor inhibiting HIF (FIH), which modulates HIF transcriptional activity. To determine the role of this latter factor in T cell function, we generated T cell-specific FIH knockout mice. We found that FIH regulates T cell fate and function in a HIF-dependent manner and show that the effects of FIH activity occur predominantly at physiological oxygen concentrations. T cell-specific loss of FIH boosts T cell cytotoxicity, augments T cell expansion in vivo, and improves anti-tumour immunotherapy in mice. Specifically inhibiting FIH in T cells may therefore represent a promising strategy for cancer immunotherapy.
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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 | 0.000 |
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".