Human and mouse muscle transcriptomic analyses identify insulin receptor mRNA downregulation in hyperinsulinemia‐associated insulin resistance
Bibliographic record
Abstract
Hyperinsulinemia is commonly viewed as a compensatory response to insulin resistance, yet studies have demonstrated that chronically elevated insulin may also drive insulin resistance. The molecular mechanisms underpinning this potentially cyclic process remain poorly defined, especially on a transcriptome-wide level. Transcriptomic meta-analysis in >450 human samples demonstrated that fasting insulin reliably and negatively correlated with INSR mRNA in skeletal muscle. To establish causality and study the direct effects of prolonged exposure to excess insulin in muscle cells, we incubated C2C12 myotubes with elevated insulin for 16 h, followed by 6 h of serum starvation, and established that acute AKT and ERK signaling were attenuated in this model of in vitro hyperinsulinemia. Global RNA-sequencing of cells both before and after nutrient withdrawal highlighted genes in the insulin receptor (INSR) signaling, FOXO signaling, and glucose metabolism pathways indicative of 'hyperinsulinemia' and 'starvation' programs. Consistently, we observed that hyperinsulinemia led to a substantial reduction in Insr gene expression, and subsequently a reduced surface INSR and total INSR protein, both in vitro and in vivo. Bioinformatic modeling combined with RNAi identified SIN3A as a negative regulator of Insr mRNA (and JUND, MAX, and MXI as positive regulators of Irs2 mRNA). Together, our analysis identifies mechanisms which may explain the cyclic processes underlying hyperinsulinemia-induced insulin resistance in muscle, a process directly relevant to the etiology and disease progression of type 2 diabetes.
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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) | 0.000 | 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) | 0.000 | 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".