Circulating exosome‐like vesicle and skeletal muscle microRNAs are altered with age and resistance training
Bibliographic record
Abstract
The age-related loss of skeletal muscle mass and functionality, known as sarcopenia, is a critical risk factor for morbidity and all-cause mortality. Resistance exercise training (RET) is the primary countermeasure to fight sarcopenia and ageing. Altered intercellular communication is a hallmark of ageing, which is not well elucidated. Circulating extracellular vesicles (EVs), including exosomes, contribute to intercellular communication by delivering microRNAs (miRNAs), which modulate post-translational modifications, and have been shown to be released following exercise. There is little evidence regarding how EVs or EV-miRNAs are altered with age or RET. Therefore, we sought to characterize circulating EVs in young and older individuals, prior to and following a 12-week resistance exercise programme. Plasma EVs were isolated using size exclusion chromatography and ultracentrifugation. We found that ageing reduced circulating expression markers of CD9, and CD81. Using late-passage human myotubes as a model for ageing in vitro, we show significantly lower secreted exosome-like vesicles (ELVs). Further, levels of circulating ELV-miRNAs associated with muscle health were lower in older individuals at baseline but increased following RET to levels comparable to young. Muscle biopsies show similar age-related reductions in miRNA expressions, with largely no effect of training. This is reflected in vitro, where aged myotubes show significantly reduced expression of endogenous and secreted muscle-specific miRNAs (myomiRs). Lastly, proteins associated with ELV and miRNA biogenesis were significantly higher in both older skeletal muscle tissues and aged human myotubes. Together we show that ageing significantly affects ELV and miRNA cargo biogenesis, and release. RET can partially normalize this altered intercellular communication. KEY POINTS: We show that ageing reduces circulating expression of exosome-like vesicle (ELV) markers, CD9 and CD81. Using late-passage human skeletal myotubes as a model of ageing, we show that secreted ELV markers are significantly reduced in vitro. We find circulating ELV miRNAs associated with skeletal muscle health are lower in older individuals but can increase following resistance exercise training (RET). In skeletal muscle, we find altered expression of miRNAs in older individuals, with no effect of RET. Late-passage myotubes also appear to have aberrant production of endogenous myomiRs with lower abundance than youthful counterparts In older skeletal muscle and late-passage myotubes, proteins involved with ELV- and miRNA biogenesis are upregulated.
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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".