Regulation of lysosomes in skeletal muscle during exercise, disuse and aging
Bibliographic record
Abstract
Lysosomes play a critical role as a terminal organelle in autophagy flux and in regulating protein degradation, but their function and adaptability in skeletal muscle is understudied. Lysosome functions include both housekeeping and signaling functions essential for cellular homeostasis. This review focuses on the regulation of lysosomes in skeletal muscle during exercise, disuse, and aging, with a consideration of sex differences as well as the role of lysosomes in mediating the degradation of mitochondria, termed mitophagy. Exercise enhances mitophagy during elevated mitochondrial stress and energy demand. A critical response to this deviation from homeostasis is the activation of transcription factors TFEB and TFE3, which drive the expression of lysosomal and autophagic genes. Conversely, during muscle disuse, the suppression of lysosomal activity contributes to the accumulation of defective mitochondria and other cellular debris, impairing muscle function. Aging further exacerbates these effects by diminishing lysosomal efficacy, leading to the accumulation of damaged cellular components. mTORC1, a key nutrient sensor, modulates lysosomal activity by inhibiting TFEB/TFE3 translocation to the nucleus under nutrient-rich conditions, thereby suppressing autophagy. During nutrient deprivation or exercise, AMPK activation inhibits mTORC1, facilitating TFEB/TFE3 nuclear translocation and promoting lysosomal biogenesis and autophagy. TRPML1 activation by mitochondrial ROS enhances lysosomal calcium release, which is essential for autophagy and maintaining mitochondrial quality. Overall, the intricate regulation of lysosomal functions and signaling pathways in skeletal muscle is crucial for adaptation to physiological demands, and disruptions in these processes during disuse and aging underscore the ubiquitous power of exercise-induced adaptations, and also highlight the potential for targeted therapeutic interventions to preserve muscle health.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".