Knockout of USP19 Deubiquitinating Enzyme Prevents Muscle Wasting by Modulating Insulin and Glucocorticoid Signaling
Bibliographic record
Abstract
Muscle atrophy arises because of many chronic illnesses, as well as from prolonged glucocorticoid treatment and nutrient deprivation. We previously demonstrated that the USP19 deubiquitinating enzyme plays an important role in chronic glucocorticoid- and denervation-induced muscle wasting. However, the mechanisms by which USP19 exerts its effects remain unknown. To explore this further, we fasted mice for 48 hours to try to identify early differences in the response of wild-type and USP19 knockout (KO) mice that could yield insights into the mechanisms of USP19 action. USP19 KO mice manifested less myofiber atrophy in response to fasting due to increased rates of protein synthesis. Insulin signaling was enhanced in the KO mice, as revealed by lower circulating insulin levels, increased insulin-stimulated glucose disposal and phosphorylation of Akt and S6K in muscle, and improved overall glucose tolerance. Glucocorticoid signaling, which is essential in many conditions of atrophy, was decreased in KO muscle, as revealed by decreased expression of glucocorticoid receptor (GR) target genes upon both fasting and glucocorticoid treatment. This decreased GR signaling was associated with lower GR protein levels in the USP19 KO muscle. Restoring the GR levels in USP19-deficient muscle was sufficient to abolish the protection from myofiber atrophy. Expression of GR target genes also correlated with that of USP19 in human muscle samples. Thus, USP19 modulates GR levels and in so doing may modulate both insulin and glucocorticoid signaling, two critical pathways that control protein turnover in muscle and overall glucose homeostasis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".