Aldehyde Oxidase 1 Deficiency Enhances Aerobic Exercise Performance by Promoting Skeletal Muscle Adaptation and Improving Mitochondrial Function
Bibliographic record
Abstract
Aerobic exercise has significant health benefits, including preventing chronic diseases like sarcopenia. It strongly depends on muscle fiber types, with higher oxidative fiber ratios enhancing endurance. However, the molecular mechanisms underlying aerobic exercise capacity remain incompletely understood. In this study, we identified 395 genes associated with muscle fiber types, among which 39 were linked to metabolic pathways. Notably, we focused on aldehyde oxidase 1 (AOX1), a molybdenum flavin enzyme, due to its unique non-mitochondrial localization, suggesting a potential causal role in regulating muscle metabolism. We further revealed a significant downregulation of Aox1 mRNA expression in the skeletal muscle of mice after two weeks of exercise training, indicating its involvement in exercise adaptation. To further explore this link, we generated Aox1 knockout (KO) mice and subjected them to endurance capacity tests. Aox1 KO mice exhibited significantly enhanced exercise endurance compared to wild-type (WT) controls, accompanied by a shift toward a more oxidative muscle phenotype, as indicated by an increased proportion of oxidative fibers. Mechanistically, Aox1 KO mice exhibit increased expression of PGC-1α, enhanced mitochondrial function, and increased capillary density in skeletal muscle, facilitating improved oxygen delivery and utilization during exercise. Additionally, in vitro experiments using C2C12 myotubes revealed that Aox1 knockdown alleviated starvation- and TNF-α-induced muscle atrophy, which partially mimics sarcopenia, highlighting its protective role against aging- and stress-induced muscle damage. These findings identify AOX1 as a negative regulator of aerobic exercise capacity and stress resilience, advancing our understanding of skeletal muscle adaptation and highlighting AOX1 as a potential target for improving exercise performance and mitigating sarcopenia.
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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".