Premature satellite cell activation before injury accelerates myogenesis and disrupts neuromuscular junction maturation in regenerating muscle
Bibliographic record
Abstract
Satellite cell (SC) activation, mediated by nitric oxide (NO), is essential to myogenic repair, whereas myotube function requires innervation. Semaphorin (Sema) 3A, a neuro-chemorepellent, is thought to regulate axon guidance to neuromuscular junctions (NMJs) during myotube differentiation. We tested whether “premature” SC activation (SC activation before injury) by a NO donor (isosorbide dinitrate) would disrupt early myogenesis and/or NMJs. Adult muscle was examined during regeneration in two models of injury: myotoxic cardiotoxin (CTX) and traumatic crush (CR) ( n = 4–5/group). Premature SC activation was confirmed by increased DNA synthesis by SCs immediately in pretreated mice after CTX injury. Myotubes grew faster after CTX than after CR; growth was accelerated by pretreatment. NMJ maturation, classified by silver histochemistry (neurites) and acetylcholinesterase (AchE), and α-bungarotoxin staining (Ach receptors, AchRs) were delayed by pretreatment, consistent with a day 6 rise in the denervation marker γ-AchR. With pretreatment, S100B from terminal Schwann cells (TSCs) increased 10- to 20-fold at days 0 and 10 after CTX and doubled 6 days after CR. Premature SC activation disrupted motoneuritogenesis 8–10 days post-CTX, as pretreatment reduced colocalization of pre- and postsynaptic NMJ features and increased Sema3A-65. Premature SC activation before injury both accelerated myogenic repair and disrupted NMJ remodeling and maturation, possibly by reducing Sema3A neuro-repulsion and altering S100B. This interpretation extends the model of Sema3A-mediated motoneuritogenesis during muscle regeneration. Manipulating the timing and type of Sema3A by brief NO effects on SCs suggests an important role for TSCs and Sema3A-65 processing in axon guidance and NMJ restoration during muscle repair.
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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 | 0.001 |
| 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".