Influence of skeletal muscle heterogeneity on autophagic signaling and response
Bibliographic record
Abstract
Skeletal muscle is a heterogeneous tissue composed of fibers with distinct contractile, metabolic, and molecular characteristics. This intrinsic heterogeneity influences how individual fibers respond to physiological stimuli, pathological stress, and cellular remodeling processes such as autophagy. Skeletal muscle autophagy is essential for maintaining proteostasis and organelle quality, particularly in high-demand tissues like skeletal muscle. However, emerging evidence indicates that autophagy is not uniformly regulated across all muscles and fibers within a skeletal muscle. Fast/glycolytic fibers, characterized by faster contractile speed and high glycolytic capacity, exhibit greater autophagic flux potentially driven by activation of energy signals, calcium, and redox-sensitive pathways. In contrast, slow/oxidative fibers, characterized by slow contractile speed and higher oxidative metabolism, show lower inducible autophagy despite elevated basal expression of autophagy-related proteins. These differences are compounded by fiber type - specific organelle architecture, recruitment patterns during activity and disuse, and substrate availability and utilization. Further, pathological conditions such as disuse, chronic disease, and myopathies often induce fiber type alterations as well as changes to organelle content and function that are closely associated with changes in autophagy signaling. Additionally, species and strain variability add another layer of complexity, complicating both the interpretation and translational relevance of autophagy studies in skeletal muscle. This review synthesizes current evidence linking skeletal muscle phenotype to autophagy regulation and highlights the need to consider skeletal muscle heterogeneity as a central variable in skeletal muscle autophagy research. A deeper understanding of skeletal muscle type/fiber-specific autophagy will improve our ability to interpret experimental findings and develop targeted interventions for skeletal muscle dysfunction.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".