MicroRNAs facilitate skeletal muscle maintenance and metabolic suppression in hibernating brown bears
Bibliographic record
Abstract
Hibernating brown bears, Ursus arctos, undergo extended periods of inactivity and yet these large hibernators are resilient to muscle disuse atrophy. Physiological characteristics associated with atrophy resistance in bear muscle have been examined (e.g., muscle mechanics, neural activity) but roles for molecular signaling/regulatory mechanisms in the resistance to muscle wasting in bears still require investigation. Using quantitative reverse transcription PCR (RT-qPCR), the present study characterized the responses of 36 microRNAs linked with development, metabolism, and regeneration of skeletal muscle, in the vastus lateralis of brown bears comparing winter hibernating and summer active animals. Relative levels of mRNA of selected genes (mef2a, pax7, id2, prkaa1, and mstn) implicated upstream and downstream of the microRNAs were examined. Results indicated that hibernation elicited a myogenic microRNA, or "myomiR", response via MEF2A-mediated signaling. Upregulation of MEF2A-controlled miR-1 and miR-206 and respective downregulation of pax7 and id2 mRNA are suggestive of responses that promote skeletal muscle maintenance. Increased levels of metabolic microRNAs, such as miR-27, miR-29, and miR-33, may facilitate metabolic suppression during hibernation via mechanisms that decrease glucose uptake and fatty acid oxidation. This study identified myomiR-mediated mechanisms for the promotion of muscle regeneration, suppression of ubiquitin ligases, and resistance to muscle atrophy during hibernation mediated by observed increases in miR-206, miR-221, miR-31, miR-23a, and miR-29b. This was further supported by the downregulation of myomiRs associated with a muscle injury and inflammation (miR-199a and miR-223) during hibernation. The present study provides evidence of myomiR-mediated signaling pathways that are activated during hibernation to maintain skeletal muscle functionality in brown bears.
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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".