Autophagic flux and oxidative capacity of skeletal muscles during acute starvation
Bibliographic record
Abstract
Autophagy is an important proteolytic pathway in skeletal muscles. The roles of muscle fiber type composition and oxidative capacity remain unknown in relation to autophagy. The diaphragm (DIA) is a fast-twitch muscle fiber with high oxidative capacity, the tibialis anterior (TA) muscle is a fast-twitch muscle fiber with low oxidative capacity, and the soleus muscle (SOL) is a slow-twitch muscle with high oxidative capacity. We hypothesized that oxidative capacity is a major determinant of autophagy in skeletal muscles. Following acute (24 h) starvation of adult C57/Bl6 mice, each muscle was assessed for autophagy and compared with controls. Autophagy was measured by monitoring autophagic flux following leupeptin (20 mg/kg) or colchicine (0.4 mg/kg/day) injection. Oxidative capacity was measured by monitoring citrate synthase activity. In control mice, autophagic flux values were significantly greater in the TA than in the DIA and SOL. In acutely starved mice, autophagic flux increased, most markedly in the TA, and several key autophagy-related genes were significantly induced. In both control and starved mice, there was a negative linear correlation of autophagic flux with citrate synthase activity. Starvation significantly induced AMPK phosphorylation and inhibited AKT and RPS6KB1 phosphorylation, again most markedly in the TA. Starvation induced Foxo1, Foxo3, and Foxo4 expression and attenuated the phosphorylation of their gene products. We conclude that both basal and starvation-induced autophagic flux are greater in skeletal muscles with low oxidative capacity as compared with those with high oxidative capacity and that this difference is mediated through selective activation of the AMPK pathway and inhibition of the AKT-MTOR pathways.
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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 | 0.000 | 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) | 0.001 | 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 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".