The role of USP19 deubiquitinating enzyme in muscle differentiation «in vitro»
Bibliographic record
Abstract
Muscle wasting is a significant complication of many diseases. Previous work has shown that USP19 mRNA expression is increased in muscle undergoing atrophy in rodents [1]. To further explore the role of USP19 in muscle, siRNA-mediated silencing was used in L6 muscle cells. Depletion of USP19 resulted in increased major myofibrillar protein levels [2]. These effects of silencing USP19 on myofibrillar protein expression may be due to the effects of USP19 on the differentiation of muscle cells. Therefore, in this thesis I further characterized the mechanism by which depletion of USP19 enhances expression of myofibrillar proteins. MHC and tropomyosin mRNA levels were increased upon USP19 silencing, suggesting that the observed increases in protein levels were due to increased transcription. Myogenin, a transcription factor in the myogenic regulatory factor family that regulates muscle differentiation, was increased by more than two fold at both mRNA and protein levels when USP19 was silenced and found to be responsible for mediating the increase in myofibrillar protein expression. The negative role of USP19 in muscle differentiation was confirmed as overexpressing USP19 resulted in decreased myogenin and major myofibrillar protein expression at the molecular level and decreased myotube fusion at the morphological level. The regulation of USP19 itself during muscle differentiation was then investigated. The mRNA levels were found to increase by ~ 4 fold from day 0 to day 5 of differentiation, while there was a ~ 1.5 fold increase in protein levels. USP19 is localized in both the cytosol and the nucleus but there was increased localization in the nucleus on day 4 of muscle cell differentiationd. In conclusion, USP19 negatively regulates muscle differentiation. Since new myoblast fusion and myofiber formation may occur during recovery from wasting, an inhibitor to USP19 could be a new approach to the treatment of muscle wasting.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".