Determining the Role of Skeletal Muscle microRNA‐133a in Early‐Onset Insulin Resistance
Bibliographic record
Abstract
Fetal exposure to diabetes during pregnancy increases the risk for early‐onset insulin resistance in the offspring; however, the key molecular regulators responsible for fetal metabolic programming have not been characterized in muscle tissue. We demonstrate that prenatal exposure to gestational diabetes decreased the soleus muscle expression of microRNA‐133a in offspring. Using tandem mass spectrometry techniques we identify a conserved phosphorylation motif within the MEF2 and SRF transcription factors that is targeted by PKCδ and regulates microRNA‐133a expression and mitochondrial function in response to a lipotoxic signal. Reconstitution of MEF2 function in cultured myotubes by expression of a neutralizing mutation in this identified phosphorylation motif restores microRNA‐133a expression and mitochondrial membrane potential during lipotoxicity. Furthermore, overnight exposure to palmitate reduced mitochondrial oxygen consumption and insulin‐stimulated glucose uptake, which were reversed when cells were transfected with microRNA‐133a mimicking oligonucleotides. Mechanistically, we demonstrate that microRNA‐133a regulates mitochondrial function through translational inhibition of a mitophagy and cell death modulating protein, called Nix. In addition, Nix‐and palmitate‐induced mitochondrial depolarization were attenuated by the beta2‐adrenergic agonist clenbuterol. Finally, we show that rodents exposed to gestational diabetes during fetal development display muscle diacylglycerol accumulation, concurrent with insulin‐resistance, PKCδ activation, and elevated Nix expression as young adult rats. Our data identifies a novel pathway whereby fetal exposure to diabetes reduces microRNA‐133a, alters skeletal muscle metabolism, and may predispose the offspring to early‐onset insulin resistance. Support or Funding Information Supported by Children's Hospital Foundation of Manitoba, and NSERC Canada to Joseph Gordon and CIHR to Vernon Dolinsky.
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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 | 0.000 |
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 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".