Intrinsic dysfunction in muscle stem cells lacking dystrophin begins during secondary myogenesis
Bibliographic record
Abstract
Loss of dystrophin causes Duchenne Muscular Dystrophy (DMD), a neuromuscular disease characterized by muscle fragility and muscle stem cell (MuSC) impairment. Conventional understanding is that DMD manifests after birth from cumulative muscle damage. Here, examination of mdx mouse embryos lacking dystrophin reveals no impairment of the primary myogenic program. By contrast, histological and single cell RNA-sequencing analysis during secondary myogenesis uncovers an increase in the proportion of fetal (f) MuSCs and a marked reduction in myogenic progenitors and myocytes, leading to fewer smaller-caliber myofibers. Wild type fMuSCs express full-length dystrophin that interacts with MARK2, whereas mdx fMuSCs downregulate MARK2 and NUMB, exhibiting reduced PARD3 polarization. Strikingly, deletion of the Numb Associated Kinase, AAK1, rescues polarization of NUMB and myogenic progenitor generation in mdx fetal muscle. Together, our results elucidate an acute disease pathology during DMD fetal development and the potential for therapeutic intervention by targeting AAK1. In mdx mouse fetuses, the lack of dystrophin markedly impairs secondary myogenesis due to reduced muscle stem cell polarity. AAK1 deletion restores polarity and rescues secondary myogenesis, revealing fetal onset in Duchenne muscular dystrophy.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".