15-Deoxy-Δ12,14-prostaglandin J2 promotes phosphorylation of eukaryotic initiation factor 2α and activates the integrated stress response
Bibliographic record
Abstract
Stress granules (SGs) are cytoplasmic RNA–protein aggregates formed in response to inhibition of translation initiation. SGs contribute to the stress response and are implicated in a variety of diseases, including cancer and some forms of neurodegeneration. Neurodegenerative diseases often involve chronic phosphorylation of eukaryotic initiation factor 2α (eIF2α), with deletions of eIF2α kinases or treatment with eIF2α kinase inhibitors being protective in some animal models of disease. However, how and why the integrated stress response (ISR) is activated in different forms of neurodegeneration remains unclear. Because neuroinflammation is common to many neurodegenerative diseases, we hypothesized that inflammatory factors contribute to ISR activation in a cell-nonautonomous manner. Using fluorescence microscopy and immunoblotting, we show here that the endogenously produced product of inflammation, 15-deoxy-Δ 12,14 -prostaglandin J2 (15-d-PGJ2), triggers eIF2α phosphorylation, thereby activating the ISR, repressing bulk translation, and triggering SG formation. Our findings define a mechanism by which inflammation activates the ISR in a cell-nonautonomous manner and suggest that inhibition of 15-d-PGJ2 production might be a useful therapeutic strategy in some neuroinflammatory contexts.
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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".