Mechanism of cardiomyocyte PGC-1α gene regulation by ERRα
Bibliographic record
Abstract
Peroxisome proliferator-activated receptor (PPAR) γ coactivator 1α (PGC-1α) regulates critical genes involved in cardiac mitochondrial biogenesis and fatty acid oxidation, and its loss is associated with impaired metabolism and various cardiac pathologies. Estrogen-related receptor α (ERRα) targets many of the same genes as PGC-1α, and extensive cross talk exists between these 2 regulators. Here we report the identification of an evolutionarily conserved ERRα binding site within the PGC-1α promoter. Using luciferase reporter assays and overexpression, inhibition, or knockdown of ERRα, we show that PGC-1α expression is critically dependent upon ERRα in primary cardiomyocytes. We demonstrate that short-term hypoxia results in reduced ERRα mRNA expression, which precedes a similar loss of PGC-1α mRNA. However, chromatin immunoprecipitation reveals that despite a key role for ERRα in regulating PGC-1α in normoxic cardiomyocytes, ERRα loss is not responsible for PGC-1α loss in hypoxia. Histone deacetylase 5 (HDAC5) has previously been demonstrated to strongly inhibit expression of PGC-1α, and we show that overexpression of ERRα is sufficient to overcome this repressive effect. Our data elucidates the mechanism by which ERRα regulates cardiac PGC-1α gene expression, and suggests that ERRα may provide a means to normalize PGC-1α expression that could be useful in the development of strategies aimed at improving cardiac metabolism in disease.
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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".