Hypoxia-driven deSUMOylation of EXOSC10 promotes adaptive changes in the transcriptome profile
Bibliographic record
Abstract
Reduced oxygen availability (hypoxia) triggers adaptive cellular responses via hypoxia-inducible factor (HIF)-dependent transcriptional activation. Adaptation to hypoxia also involves transcription-independent processes like post-translational modifications; however, these mechanisms are poorly characterized. Investigating the involvement of protein SUMOylation in response to hypoxia, we discovered that hypoxia strongly decreases the SUMOylation of Exosome subunit 10 (EXOSC10), the catalytic subunit of the RNA exosome, in an HIF-independent manner. EXOSC10 is a multifunctional exoribonuclease enriched in the nucleolus that mediates the processing and degradation of various RNA species. We demonstrate that the ubiquitin-specific protease 36 (USP36) SUMOylates EXOSC10 and we reveal SUMO1/sentrin-specific peptidase 3 (SENP3) as the enzyme-mediating deSUMOylation of EXOSC10. Under hypoxia, EXOSC10 dissociates from USP36 and translocates from the nucleolus to the nucleoplasm concomitant with its deSUMOylation. Loss of EXOSC10 SUMOylation does not detectably affect rRNA maturation but affects the mRNA transcriptome by modulating the expression levels of hypoxia-related genes. Our data suggest that dynamic modulation of EXOSC10 SUMOylation and localization under hypoxia regulates the RNA degradation machinery to facilitate cellular adaptation to low oxygen conditions.
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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".