Apelin stimulation of the vascular skeletal muscle stem cell niche enhances endogenous repair in dystrophic mice
Bibliographic record
Abstract
Impaired skeletal muscle stem cell (MuSC) function has long been suspected to contribute to the pathogenesis of muscular dystrophy (MD). Here, we showed that defects in the endothelial cell (EC) compartment of the vascular stem cell niche in mouse models of Duchenne MD, laminin α2-related MD, and collagen VI-related myopathy were associated with inefficient mobilization of MuSCs after tissue damage. Using chemoinformatic analysis, we identified the 13-amino acid form of the peptide hormone apelin (AP-13) as a candidate for systemic stimulation of skeletal muscle ECs. Systemic administration of AP-13 using osmotic pumps generated a pro-proliferative EC-rich niche that supported MuSC function through angiocrine factors and markedly improved tissue regeneration and muscle strength in all three dystrophic mouse models. Moreover, EC-specific knockout of the apelin receptor led to regenerative defects that phenocopied key pathological features of MD, including vascular defects, fibrosis, muscle fiber necrosis, impaired MuSC function, and reduced force generation. Together, these studies provide in vivo proof of concept that enhancing endogenous skeletal muscle repair by targeting the vascular niche is a viable therapeutic avenue for MD and characterized AP-13 as a candidate for further study for the systemic treatment of MuSC dysfunction.
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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 | 0.001 | 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".