PML is a ROS sensor activating p53 upon oxidative stress
Bibliographic record
Abstract
Promyelocytic leukemia (PML) nuclear bodies (NBs) recruit partner proteins, including p53 and its regulators, thereby controlling their abundance or function. Investigating arsenic sensitivity of acute promyelocytic leukemia, we proposed that PML oxidation promotes NB biogenesis. However, physiological links between PML and oxidative stress response in vivo remain unexplored. Here, we identify PML as a reactive oxygen species (ROS) sensor. Pml−/− cells accumulate ROS, whereas PML expression decreases ROS levels. Unexpectedly, Pml−/− embryos survive acute glutathione depletion. Moreover, Pml−/− animals are resistant to acetaminophen hepatotoxicity or fasting-induced steatosis. Molecularly, Pml−/− animals fail to properly activate oxidative stress–responsive p53 targets, whereas the NRF2 response is amplified and accelerated. Finally, in an oxidative stress–prone background, Pml−/− animals display a longevity phenotype, likely reflecting decreased basal p53 activation. Thus, similar to p53, PML exerts basal antioxidant properties but also drives oxidative stress–induced changes in cell survival/proliferation or metabolism in vivo. Through NB biogenesis, PML therefore couples ROS sensing to p53 responses, shedding a new light on the role of PML in senescence or stem cell biology.
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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 | 0.001 |
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".