Bibliographic record
Abstract
Abstract Insulin resistance, a condition in which a cell, tissue, or organism fails to respond appropriately to insulin, is a hallmark for the development of type 2 diabetes and a major contributor to the pathogenesis of non-alcoholic fatty liver disease. In addition to altered insulin signaling transduction, more and more research highlights dysregulated gene expression as nuclear mechanisms underlying insulin resistance. ERRα is an orphan nuclear receptor that plays a central role in the regulation of energy homeostasis. Here, we present evidence to support ERRα as a novel and potent transcriptional regulator of insulin action. By using a high-throughput insulin-based phospho-proteomic database of the mouse liver and bioinformatics analysis based on conserved kinase substrate motif, we first identified ERRα as a direct substrate of glycogen synthase kinase 3β (GSK3β). Our data demonstrate that under basal conditions, GSK3β phosphorylates ERRα at residues S19, S22, and S26. Phosphorylated ERRα is then recognized and ubiquitynated by the SCF-FBXW7 E3 ligase complex, resulting in its degradation by the proteasome. Indeed, pharmacological inhibition of GSK3β in vivo as well as liver-specific knockout of Fbxw7 both lead to the accumulation of ERRα in the liver. Insulin, by inhibiting GSK3β, leads to the stabilization of ERRα in the nucleus, resulting in the altered expression of insulin-responsive genes involved in gluconeogenesis and insulin signal transduction. Genome-wide analysis reveals that more than 40% of the insulin-regulated genes in the liver are direct ERRα targets, indicating a prominent role of ERRα in the regulation of insulin signaling. Together, our findings underscore a novel ERRα-dependent mechanism for insulin’s effects in physiology and disease, implicating the therapeutic value of targeting the GSK3β/FBXW7/ERRα axis in vivo.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".