The prostacyclin receptor PTGIR is a NRF2-dependent regulator of CD8+ T cell exhaustion
Bibliographic record
Abstract
CD8+ T cell exhaustion (Tex) limits immune control of cancer, but the underlying molecular drivers are unclear. In the present study, we identified the prostaglandin I2 (prostacyclin) receptor PTGIR as a cell-intrinsic regulator of T cell exhaustion. Transcriptomic profiling of terminally exhausted (Ttex) CD8+ T cells revealed increased activation of the nuclear factor erythroid 2-related factor 2 (NRF2) oxidative stress response pathway. Enhancing NRF2 activity (by conditional deletion of Kelch-like ECH-associated protein 1 (KEAP1)) boosts glutathione production in CD8+ T cells but accelerates terminal exhaustion. NRF2 upregulates PTGIR expression in CD8+ T cells. Silencing PTGIR expression enhances T cell effector function (that is, interferon-γ and granzyme production) and limits Ttex cell development in chronic infection and cancer models. Mechanistically, PTGIR signaling impairs T cell metabolism and cytokine production while inducing transcriptional features of Tex cells. These findings identify PTGIR as a NRF2-dependent immune checkpoint that regulates balance between effector and exhausted CD8+ T cell states. Targeting CD8+ T cell exhaustion is a strategy to enhance immune checkpoint inhibition and to fight cancer. Here the authors show a NRF2-dependent role for the prostaglandin I2 receptor PTGIR in controlling T cell exhaustion.
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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) | 0.001 | 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".