Muscles in Winter: The Epigenetics of Metabolic Arrest
Bibliographic record
Abstract
The winter months are challenging for many animal species, which often enter a state of dormancy or hypometabolism to "wait out" the cold weather, food scarcity, reduced daylight, and restricted mobility that can characterize the season. To survive, many species use metabolic rate depression (MRD) to suppress nonessential metabolic processes, conserving energy and limiting tissue atrophy particularly of skeletal and cardiac muscles. Mammalian hibernation is the best recognized example of winter MRD, but some turtle species spend the winter unable to breathe air and use MRD to survive with little or no oxygen (hypoxia/anoxia), and various frogs endure the freezing of about two-thirds of their total body water as extracellular ice. These winter survival strategies are highly effective, but create physiological and metabolic challenges that require specific biochemical adaptive strategies. Gene-related processes as well as epigenetic processes can lower the risk of atrophy during prolonged inactivity and limited nutrient stores, and DNA modifications, mRNA storage, and microRNA action are enacted to maintain and preserve muscle. This review article focuses on epigenetic controls on muscle metabolism that regulate MRD to avoid muscle atrophy and support winter survival in model species of hibernating mammals, anoxia-tolerant turtles and freeze-tolerant frogs. Such research may lead to human applications including muscle-wasting disorders such as sarcopenia, or other conditions of limited mobility.
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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) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".