The microRNA Signature in Response to Insulin Reveals Its Implication in the Transcriptional Action of Insulin in Human Skeletal Muscle and the Role of a Sterol Regulatory Element–Binding Protein-1c/Myocyte Enhancer Factor 2C Pathway
Bibliographic record
Abstract
OBJECTIVE: Factors governing microRNA expressions in response to changes of cellular environment are still largely unknown. Our aim was to determine whether insulin, the major hormone controlling whole-body energy homeostasis, is involved in the regulation of microRNA expressions in human skeletal muscle. RESEARCH DESIGN AND METHODS: We carried out comparative microRNA (miRNA) expression profiles in human skeletal muscle biopsies before and after a 3-h euglycemic-hyperinsulinemic clamp, with TaqMan low-density arrays. Then, using DNA microarrays, we determined the response to insulin of the miRNA putative target genes in order to determine their role in the transcriptional action of insulin. We further characterized the mechanism of action of insulin on two representative miRNAs, miR-1 and miR-133a, in human muscle cells. RESULTS: Insulin downregulated the expressions of 39 distinct miRNAs in human skeletal muscle. Their potential target mRNAs coded for proteins that were mainly involved in insulin signaling and ubiquitination-mediated proteolysis. Bioinformatic analysis suggested that combinations of different downregulated miRNAs worked in concert to regulate gene expressions in response to insulin. We further demonstrated that sterol regulatory element-binding protein (SREBP)-1c and myocyte enhancer factor 2C were involved in the effect of insulin on miR-1 and miR-133a expression. Interestingly, we found an impaired regulation of miRNAs by insulin in the skeletal muscle of type 2 diabetic patients, likely as consequences of altered SREBP-1c activation. CONCLUSIONS: This work demonstrates a new role of insulin in the regulation of miRNAs in human skeletal muscle and suggests a possible implication of these new modulators in insulin resistance.
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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 | 0.001 | 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".