Estrogen-Related Receptor α Directs Peroxisome Proliferator-Activated Receptor α Signaling in the Transcriptional Control of Energy Metabolism in Cardiac and Skeletal Muscle
Bibliographic record
Abstract
Estrogen-related receptors (ERRs) are orphan nuclear receptors activated by the transcriptional coactivator peroxisome proliferator-activated receptor gamma (PPARgamma) coactivator 1alpha (PGC-1alpha), a critical regulator of cellular energy metabolism. However, metabolic target genes downstream of ERRalpha have not been well defined. To identify ERRalpha-regulated pathways in tissues with high energy demand such as the heart, gene expression profiling was performed with primary neonatal cardiac myocytes overexpressing ERRalpha. ERRalpha upregulated a subset of PGC-1alpha target genes involved in multiple energy production pathways, including cellular fatty acid transport, mitochondrial and peroxisomal fatty acid oxidation, and mitochondrial respiration. These results were validated by independent analyses in cardiac myocytes, C2C12 myotubes, and cardiac and skeletal muscle of ERRalpha-/- mice. Consistent with the gene expression results, ERRalpha increased myocyte lipid accumulation and fatty acid oxidation rates. Many of the genes regulated by ERRalpha are known targets for the nuclear receptor PPARalpha, and therefore, the interaction between these regulatory pathways was explored. ERRalpha activated PPARalpha gene expression via direct binding of ERRalpha to the PPARalpha gene promoter. Furthermore, in fibroblasts null for PPARalpha and ERRalpha, the ability of ERRalpha to activate several PPARalpha targets and to increase cellular fatty acid oxidation rates was abolished. PGC-1alpha was also shown to activate ERRalpha gene expression. We conclude that ERRalpha serves as a critical nodal point in the regulatory circuitry downstream of PGC-1alpha to direct the transcription of genes involved in mitochondrial energy-producing pathways in cardiac and skeletal muscle.
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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 | 0.001 |
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".