USP19-deubiquitinating enzyme regulates levels of major myofibrillar proteins in L6 muscle cells
Bibliographic record
Abstract
The ubiquitin-proteasome system plays an important role in the degradation of myofibrillar proteins that occurs in muscle wasting. Many studies have demonstrated the importance of enzymes mediating conjugation of ubiquitin. However, little is known about the role of deubiquitinating enzymes. We previously showed that the USP19-deubiquitinating enzyme is induced in atrophying skeletal muscle (Combaret L, Adegoke OA, Bedard N, Baracos V, Attaix D, Wing SS. Am J Physiol Endocrinol Metab 288: E693-E700, 2005). To further explore the role of USP19, we used small interfering RNA (siRNA) in L6 muscle cells. Lowering USP19 by 70-90% in myotubes resulted in a 20% decrease in the rate of proteolysis and an 18% decrease in the rate of protein synthesis, with no net change in protein content. Despite the decrease in overall synthesis, there were approximately 1.5-fold increases in protein levels of myosin heavy chain (MHC), actin, and troponin T and a approximately 2.5-fold increase in tropomyosin. USP19 depletion also increased MHC and tropomyosin mRNA levels, suggesting that this effect is due to increased transcription. Consistent with this, USP19 depletion increased myogenin protein and mRNA levels approximately twofold. Lowering myogenin using siRNA prevented the increase in MHC and tropomyosin upon USP19 depletion, indicating that myogenin mediated the increase in myofibrillar proteins. Dexamethasone treatment lowered MHC and increased USP19. Depletion of USP19 reversed the dexamethasone suppression of MHC. These studies demonstrate that USP19 modulates transcription of major myofibrillar proteins and indicate that the ubiquitin system not only mediates the increased protein breakdown but is also involved in the decreased protein synthesis in atrophying skeletal muscle.
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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".