PRMT5 regulates ATF4 transcript splicing and oxidative stress response
Bibliographic record
Abstract
Protein methyltransferase 5 (PRMT5) symmetrically dimethylates arginine residues leading to regulation of transcription and splicing programs. Although PRMT5 has emerged as an attractive oncology target, the molecular determinants of PRMT5 dependency in cancer remain incompletely understood. Our transcriptomic analysis identified PRMT5 regulation of the activating transcription factor 4 (ATF4) pathway in acute myelogenous leukemia (AML). PRMT5 inhibition resulted in the expression of unstable, intron-retaining ATF4 mRNA that is detained in the nucleus. Concurrently, the decrease in the spliced cytoplasmic transcript of ATF4 led to lower levels of ATF4 protein and downregulation of ATF4 target genes. Upon loss of functional PRMT5, cells with low ATF4 displayed increased oxidative stress, growth arrest, and cellular senescence. Interestingly, leukemia cells with EVI1 oncogene overexpression demonstrated dependence on PRMT5 function. EVI1 and ATF4 regulated gene signatures were inversely correlated. We show that EVI1-high AML cells have reduced ATF4 levels, elevated baseline reactive oxygen species and increased sensitivity to PRMT5 inhibition. Thus, EVI1-high cells demonstrate dependence on PRMT5 function and regulation of oxidative stress response. Overall, our findings identify the PRMT5-ATF4 axis to be safeguarding the cellular redox balance that is especially important in high oxidative stress states, such as those that occur with EVI1 overexpression.
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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".