Exercise training-induced extracellular miR-136-3p modulates glucose uptake and myogenesis through targeting of NRDC in human skeletal muscle
Bibliographic record
Abstract
BACKGROUND: Regular physical training induces adaptive effects across multiple organ systems, highlighting the existence of inter-organ communication networks. However, the molecular mechanisms underlying both exercise-induced adaptations and organ-to-organ signaling are not fully characterized. Circulating extracellular vesicles (EVs), including exosomes, carry molecules like microRNAs (miRNAs) that may mediate tissue crosstalk. This study aimed to identify specific exercise training-responsive miRNAs that affect skeletal muscle function. METHODS: miRNA expression profiles of serum-derived EVs were analyzed in healthy young individuals before and after 3 weeks endurance exercise training. Exercise training-responsive miRNAs were then validated for a functional role in cellular metabolic processes in human myotubes. RESULTS: We identified several exercise training-responsive miRNAs within exosome-rich EVs in serum, including miR-136-3p. In human myotubes, miR-136-3p enhanced glucose uptake and targeted the nardilysin convertase (NRDC) gene. Transfection of miR-136-3p or silencing of NRDC induced a shift towards glycolytic metabolism in mitochondria and modulated gene expressions related to myogenesis. Pancreatic islets were identified as a potential source of miR-136-3p based on in silico analysis of gene expression and a molecular analysis of conditioned media from isolated pancreatic islets. CONCLUSION: MiR-136-3p is an endurance training-responsive molecular transducer that modulates glucose metabolism and cellular proliferation in myocytes. Associated with EVs, extracellular miR-136-3p may serve as a molecular messenger to communicate islet-skeletal muscle crosstalk after exercise. Extracellular miR-136-3p may serve as a molecular messenger to communicate islet-skeletal muscle crosstalk. Our results highlight a miRNA-mediated mechanism that participates in inter-organ communication to fine tune the metabolic adaptations to exercise.
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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".