Dynamic regulation of inter-organelle communication by ubiquitylation controls skeletal muscle development and disease onset
Bibliographic record
Abstract
Ubiquitin-proteasome system (UPS) dysfunction is associated with the pathology of a wide range of human diseases, including myopathies and muscular atrophy. However, the mechanistic understanding of specific components of the regulation of protein turnover during development and disease progression in skeletal muscle is unclear. Mutations in KLHL40 , an E3 ubiquitin ligase cullin3 (CUL3) substrate-specific adapter protein, result in severe congenital nemaline myopathy, but the events that initiate the pathology and the mechanism through which it becomes pervasive remain poorly understood. To characterize the KLHL40-regulated ubiquitin-modified proteome during skeletal muscle development and disease onset, we used global, quantitative mass spectrometry-based ubiquitylome and global proteome analyses of klhl40a mutant zebrafish during disease progression. Global proteomics during skeletal muscle development revealed extensive remodeling of functional modules linked with sarcomere formation, energy, biosynthetic metabolic processes, and vesicle trafficking. Combined analysis of klh40 mutant muscle proteome and ubiquitylome identified thin filament proteins, metabolic enzymes, and ER-Golgi vesicle trafficking pathway proteins regulated by ubiquitylation during muscle development. Our studies identified a role for KLHL40 as a regulator of ER-Golgi anterograde trafficking through ubiquitin-mediated protein degradation of secretion-associated Ras-related GTPase1a (Sar1a). In KLHL40-deficient muscle, defects in ER exit site vesicle formation and downstream transport of extracellular cargo proteins result in structural and functional abnormalities. Our work reveals that the muscle proteome is dynamically fine-tuned by ubiquitylation to regulate skeletal muscle development and uncovers new disease mechanisms for therapeutic development in patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".