ERRα as a Bridge Between Transcription and Function: Role in Liver Metabolism and Disease
Bibliographic record
Abstract
As transcriptional factors, nuclear receptors (NRs) function as major regulators of gene expression. In particular, dysregulation of NR activity has been shown to significantly alter metabolic homeostasis in various contexts leading to metabolic disorders and cancers. The orphan estrogen-related receptor (ERR) subfamily of NRs, comprised of ERRα, ERR, and ERR, for which a natural ligand has yet to be identified, are known as central regulators of energy metabolism. If AMP-activated protein kinase (AMPK) and mechanistic target of rapamycin (mTOR) can be viewed as sensors of the metabolic needs of a cell and responding acutely via post-translational control of proteins, then the ERRs can be regarded as downstream effectors of metabolism via transcriptional regulation of genes for a long-term and sustained adaptive response. In this review, we will focus on recent findings centered on the transcriptional roles played by ERRα in hepatocytes. Modulation of ERRα activity in both in vitro and in vivo models via genetic or pharmacological manipulation coupled with chromatin-immunoprecipitation (ChIP)-on-chip and ChIP-sequencing (ChIP-seq) studies have been fundamental in delineating the direct roles of ERRα in the control of hepatic gene expression. These studies have identified crucial roles for ERRα in lipid and carbohydrate metabolism as well as in mitochondrial function under both physiological and pathological conditions. The regulation of ERRα expression and activity via ligand-independent modes of action including coregulator binding, post-translational modifications and control of protein stability will be discussed in the context that may serve as valuable tools to modulate ERRα function as new therapeutic avenues for the treatment of hepatic metabolic dysfunction and related diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".