Misregulation of autophagy and protein degradation systems in myopathies and muscular dystrophies
Bibliographic record
Abstract
A number of recent studies have highlighted the importance of autophagy and the ubiquitin-proteasome in the pathogenesis of muscle wasting in different types of inherited muscle disorders. Autophagy is crucial for the removal of dysfunctional organelles and protein aggregates, whereas the ubiquitin-proteasome is important for the quality control of proteins. Post-mitotic tissues, such as skeletal muscle, are particularly susceptible to aged or dysfunctional organelles and aggregation-prone proteins. Therefore, these degradation systems need to be carefully regulated in muscles. Indeed, excessive or defective activity of the autophagy lysosome or ubiquitin-proteasome leads to detrimental effects on muscle homeostasis. A growing number of studies link abnormalities in the regulation of these two pathways to myofiber degeneration and muscle weakness. Understanding the pathogenic role of these degradative systems in each inherited muscle disorder might provide novel therapeutic targets to counteract muscle wasting. In this Commentary, we will discuss the current view on the role of autophagy lysosome and ubiquitin-proteasome in the pathogenesis of myopathies and muscular dystrophies, and how alteration of these degradative systems contribute to muscle wasting in inherited muscle disorders. We will also discuss how modulating autophagy and proteasome might represent a promising strategy for counteracting muscle loss in different diseases.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".