Action of Obestatin in Skeletal Muscle Repair: Stem Cell Expansion, Muscle Growth, and Microenvironment Remodeling
Bibliographic record
Abstract
The development of therapeutic strategies for skeletal muscle diseases, such as physical injuries and myopathies, depends on the knowledge of regulatory signals that control the myogenic process. The obestatin/GPR39 system operates as an autocrine signal in the regulation of skeletal myogenesis. Using a mouse model of skeletal muscle regeneration after injury and several cellular strategies, we explored the potential use of obestatin as a therapeutic agent for the treatment of trauma-induced muscle injuries. Our results evidenced that the overexpression of the preproghrelin, and thus obestatin, and GPR39 in skeletal muscle increased regeneration after muscle injury. More importantly, the intramuscular injection of obestatin significantly enhanced muscle regeneration by simulating satellite stem cell expansion as well as myofiber hypertrophy through a kinase hierarchy. Added to the myogenic action, the obestatin administration resulted in an increased expression of vascular endothelial growth factor (VEGF)/vascular endothelial growth factor receptor 2 (VEGFR2) and the consequent microvascularization, with no effect on collagen deposition in skeletal muscle. Furthermore, the potential inhibition of myostatin during obestatin treatment might contribute to its myogenic action improving muscle growth and regeneration. Overall, our data demonstrate successful improvement of muscle regeneration, indicating obestatin is a potential therapeutic agent for skeletal muscle injury and would benefit other myopathies related to muscle regeneration.
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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) | 0.000 | 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 | 0.000 |
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".