Perisynaptic Glia Discriminate Patterns of Motor Nerve Activity and Influence Plasticity at the Neuromuscular Junction
Bibliographic record
Abstract
In the nervous system, the induction of plasticity is coded by patterns of synaptic activity. Glial cells are now recognized as dynamic partners in a wide variety of brain functions, including the induction and modulation of various forms of synaptic plasticity. However, it appears that glial cells are usually activated by stereotyped, sustained neuronal activity, and little attention has been given to more subtle changes in the patterns of synaptic activation. To this end, we used the mouse neuromuscular junction as a simple and useful model to study glial modulation of synaptic plasticity. We used two patterns of motor nerve stimulation that mimic endogenous motor-neuronal activity. A continuous stimulation induced a post-tetanic potentiation and a phasic Ca(2+) response in perisynaptic Schwann cells (PSCs), glial cells at this synapse. A bursting pattern of activity induced a post-tetanic depression and oscillatory Ca(2+) responses in PSCs. The different Ca(2+) responses in PSCs indicate that they decode the pattern of synaptic activity. Furthermore, the chelation of glial Ca(2+) impaired the production of the sustained plasticity events indicating that PSCs govern the outcome of synaptic plasticity. The mechanisms involved were studied using direct photo-activation of PSCs with caged Ca(2+) that mimicked endogenous plasticity. Using specific pharmacology and transgenic knock-out animals for adenosine receptors, we showed that the sustained depression was mediated by A1 receptors while the sustained potentiation is mediated by A(2A) receptors. These results demonstrate that glial cells decode the pattern of synaptic activity and subsequently provide bidirectional feedback to synapses.
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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".