Denervation atrophy is independent from Akt and mTOR activation and is not rescued by myostatin inhibition
Bibliographic record
Abstract
The purpose of our study was to compare two acquired muscle atrophies and the use of myostatin inhibition for their treatment. Myostatin naturally inhibits skeletal muscle growth by binding to ActRIIB, a receptor on the cell surface of myofibers. Because blocking myostatin in an adult wild-type mouse induces profound muscle hypertrophy, we applied a soluble ActRIIB receptor to models of disuse (limb immobilization) and denervation (sciatic nerve resection) atrophy. We found that treatment of immobilized mice with ActRIIB prevented the loss of muscle mass observed in placebo-treated mice. Our results suggest that this protection from disuse atrophy is regulated by serum and glucocorticoid-induced kinase (SGK) rather than by Akt. Denervation atrophy, however, was not protected by ActRIIB treatment, yet resulted in an upregulation of the pro-growth factors Akt, SGK and components of the mTOR pathway. We then treated the denervated mice with the mTOR inhibitor rapamycin and found that, despite a reduction in mTOR activation, there is no alteration of the atrophy phenotype. Additionally, rapamycin prevented the denervation-induced upregulation of the mTORC2 substrates Akt and SGK. Thus, our studies show that denervation atrophy is not only independent from Akt, SGK and mTOR activation but also has a different underlying pathophysiological mechanism than disuse atrophy.
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 | 0.001 |
| 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".