Maintaining skeletal muscle mass: lessons learned from hibernation
Bibliographic record
Abstract
New Findings What is the topic of this review? This report describes identification and characterization of serum‐ and glucocorticoid‐regulated kinase 1 (SGK1) as a regulator of skeletal muscle mass during hibernation, as well as in physiological and pathophysiological conditions in non‐hibernating species. What advances does it highlight? This is first demonstration that SGK1 plays a crucial role in skeletal muscle homeostasis. Overexpression of SGK1 protects mice from immobilization and starvation‐induced muscle atrophy. Serum‐ and glucocorticoid‐regulated kinase 1 should be considered as a novel therapeutic target to combat loss of muscle mass in multiple conditions. Muscle disuse and starvation are often associated with a catabolic response leading to a dramatic loss of skeletal muscle mass. Hibernating animals represent a unique situation where muscle mass is maintained despite prolonged periods of immobilization and lack of nutrition. We analysed the molecular pathways upregulated during hibernation in an obligate hibernator, the 13‐lined ground squirrel (Ictidomys tridecemlineatus). Although Akt has an established role in skeletal muscle maintenance, we found that activated Akt was decreased in skeletal muscle of hibernating squirrels. Another serine–threonine kinase, serum‐ and glucocorticoid‐regulated kinase 1 (SGK1), was upregulated during hibernation and contributed to protection from loss of muscle mass via downregulation of proteolysis and autophagy and via an increase in protein synthesis. We extended our observations to non‐hibernating animals and demonstrated that SGK1‐null mice developed muscle atrophy. These mice displayed an exaggerated response to immobilization and starvation. Furthermore, SGK1 overexpression prevented immobilization‐induced muscle atrophy. Taken together, our results identify SGK1 as a novel therapeutic target to combat skeletal muscle loss in acquired and inherited forms of muscle atrophy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".