Functional adaptation of glial cells at neuromuscular junctions in response to injury
Bibliographic record
Abstract
Abstract Synaptic elements from neuromuscular junctions (NMJs) undergo massive morphological and functional changes upon nerve injury. While morphological changes of NMJ‐associated glia in response to injury has been investigated, their functional properties remain elusive. Perisynaptic Schwann cells (PSCs), glial cells at the NMJ, are essential for NMJ maintenance and repair, and are involved in synaptic efficacy and plasticity. Importantly, these functions are regulated by PSCs ability to detect synaptic transmission through, notably, muscarinic (mAChRs) and purinergic receptors' activation. Using Ca2+ imaging and electrophysiological recordings of synaptic transmission at the mouse NMJ, we investigated PSC receptors activation following denervation and during reinnervation in adults and at denervated NMJs in an ALS mouse model (SOD1G37R). We observed reduced PSCs mAChR‐mediated Ca2+ responses at denervated and reinnervating NMJs. Importantly, PSC phenotypes during denervation and reinnervation were distinct than the one observed during NMJ maturation. At denervated NMJs, exogenous activation of mAChRs greatly diminished galectin‐3 expression, a glial marker of phagocytosis. PSCs Ca2+ responses at reinnervating NMJs did not correlate with the number of innervating axons or process extensions. Interestingly, we observed an extended period of reduced PSC mAChRs activation after the injury (up to 60 days), suggesting a glial memory of injury. PSCs associated with denervated NMJs in an ALS model (SOD1G37R mice) did not show any muscarinic adaptation, a phenotype incompatible with NMJ repair. Understanding functional mechanisms that underlie this glial response to injury may contribute to favor complete NMJ and motor recovery.
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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 | 0.000 |
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".