ERRα rescues histone deacetylation‐mediated down‐regulation of PGC‐1α expression in hypoxia
Bibliographic record
Abstract
PGC-1α plays a key role in cardiac metabolism by regulating downstream target genes involved in fatty acid oxidation and mitochondrial biogenesis. Our objective was to examine PGC-1α expression during cardiomyocyte hypoxia. Hypoxia (12h) decreased PGC-1α expression and promoter histone acetylation. Hypoxia-induced PGC-1α down-regulation was attenuated by a histone deacetylase inhibitor, and was accelerated by an ERRα inverse agonist. We found that ERRα regulates PGC-1α gene expression via a conserved binding site in the PGC-1α promoter. Over-expression of ERRα in isolated cardiomyocytes was sufficient to induce PGC-1α expression and rescued the loss of PGC-1α expression during hypoxia. Extending hypoxia to 24h resulted in recovery of PGC-1α expression, an effect attenuated by an AMP kinase inhibitor or exaggerated by removal of glucose. Hypoxia thus exerts a biphasic effect on PGC-1α expression: initial down-regulation due to histone deacetylation, followed by restoration via activation of AMP kinase. While ERRα was not involved in hypoxia-mediated PGC-1α down-regulation, ERRα over-expression rescued this loss, and ERRα expression was required for basal PGC-1α expression. Our results describe a novel regulatory mechanism for PGC-1α expression which may contribute to metabolic derangement during ischemia and heart failure. Supported by the Canadian Institutes of Health Research.
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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) | 0.001 | 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 | 0.001 |
| 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".