Combinatory effects of siRNA-induced myostatin inhibition and exercise on skeletal muscle homeostasis and body composition
Bibliographic record
Abstract
Abstract Inhibition of myostatin (Mstn) stimulates skeletal muscle growth, reduces body fat, and induces a number of metabolic changes. However, it remains unexplored how exercise training modulates the response to Mstn inhibition. The aim of this study was to investigate how siRNA-mediated Mstn inhibition alone but also in combination with physical activity affects body composition and skeletal muscle homeostasis. Adult mice were treated with Mstn-targeting siRNA and subjected to a treadmill-based exercise protocol for 4 weeks. Effects on skeletal muscle and fat tissue, expression of genes, and serum concentration of proteins involved in myostatin signaling, skeletal muscle homeostasis, and lipid metabolism were investigated and compared with Mstn(-/-) mice. The combination of siRNA-mediated Mstn knockdown and exercise induced skeletal muscle hypertrophy, which was associated with an upregulation of markers for satellite cell activity. SiRNA-mediated Mstn knockdown decreased visceral fat and modulated lipid metabolism similar to effects observed in Mstn(-/-) mice. Myostatin did not regulate its own expression via an autoregulatory loop, however, Mstn knockdown resulted in a decrease in the serum concentrations of myostatin propeptide, leptin, and follistatin. The ratio of these three parameters was distinct between Mstn knockdown, exercise, and their combination. Taken together, siRNA-mediated Mstn knockdown in combination with exercise stimulated skeletal muscle hypertrophy. Each intervention or their combination induced a specific set of adaptive responses in the skeletal muscle and fat metabolism which could be identified by marker proteins in serum.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".