Lessons from mammalian hibernators: molecular insights into striated muscle plasticity and remodeling
Bibliographic record
Abstract
Striated muscle shows an amazing ability to adapt its structural apparatus based on contractile activity, loading conditions, fuel supply, or environmental factors. Studies with mammalian hibernators have identified a variety of molecular pathways which are strategically regulated and allow animals to endure multiple stresses associated with the hibernating season. Of particular interest is the observation that hibernators show little skeletal muscle atrophy despite the profound metabolic rate depression and mechanical unloading that they experience during long weeks of torpor. Additionally, the cardiac muscle of hibernators must adjust to low temperature and reduced perfusion, while the strength of contraction increases in order to pump cold, viscous blood. Consequently, hibernators hold a wealth of knowledge as it pertains to understanding the natural capacity of myocytes to alter structural, contractile and metabolic properties in response to environmental stimuli. The present review outlines the molecular and biochemical mechanisms which play a role in muscular atrophy, hypertrophy, and remodeling. In this capacity, four main networks are highlighted: (1) antioxidant defenses, (2) the regulation of structural, contractile and metabolic proteins, (3) ubiquitin proteosomal machinery, and (4) macroautophagy pathways. Subsequently, we discuss the role of transcription factors nuclear factor (erythroid-derived 2)-like 2 (Nrf2), Myocyte enhancer factor 2 (MEF2), and Forkhead box (FOXO) and their associated posttranslational modifications as it pertains to regulating each of these networks. Finally, we propose that comparing and contrasting these concepts to data collected from model organisms able to withstand dramatic changes in muscular function without injury will allow researchers to delineate physiological versus pathological responses.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".