Epigenetic regulator G9a provides glucose as a sweet key to stress resistance
Bibliographic record
Abstract
The ability to adapt to acute and chronic stress is important for organisms to thrive in evolutionary niches and for cells to survive in adverse conditions. The regulatory networks that control stress responses are evolutionarily conserved, and many factors that selectively activate stress responses have been identified. Less well understood are mechanisms that guard against unnecessary induction of cytoprotective factors and that connect stress responses with cellular metabolism to control energy expenditure during stress. The work of Riahi and colleagues represents important progress in this regard because it identifies the histone methyltransferase G9a as a modulator of oxidative stress responses. G9a dampens the expression of antioxidant genes, thus preventing inappropriate energy consumption. Moreover, G9a promotes the well-paced catabolism of storage glycogen and fat during stress. The importance of energy availability during stress is further evidenced by exogenous glucose rescuing the vulnerability of the G9a mutant to oxidative stress. Prior work in multiple model systems has implicated G9a in several other adaptive responses. Therefore, its role in pacing energy consumption and in restraining excessive stress response gene expression under stress may extend to other adaptive responses across species.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.017 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".