Neurotrophins modulate neuron–glia interactions at a vertebrate synapse
Bibliographic record
Abstract
Neurotrophins are important modulators of synaptic function at both developing and mature synapses in the CNS and PNS. At the neuromuscular junction (NMJ), neurotrophins, as well as perisynaptic Schwann cells (PSCs) are critical for the long-term maintenance and stability of the synapse. Considering this correlation and the acute interactions that occur at the synapse between PSCs and the nerve terminal, we wondered if neurotrophins could also be involved in neuron-glia signalling. To test if neurotrophins were able to signal to PSCs we used brief applications of neurotrophin-3 (NT-3), brain-derived neurotophic factor (BDNF) or nerve growth factor (NGF; 100 ng/mL). Soleus muscles of mice were incubated with the Ca(2+) indicator Fluo-4AM and Ca(2+) responses in PSCs were elicited through nerve stimulation (50 Hz, 30 s). Our results indicate that acute application of both NT-3 and BDNF, but not NGF, increased PSC Ca(2+) responses. Investigation of the mechanisms involved in these increases revealed distinct pathways for BDNF and NT-3. BDNF increased PSC responsiveness through potentiation of ATP responses while NT-3 modulated muscarinic acetylcholine receptor signalling. Using local applications of the neurotrophins, we found that both neurotrophins were able to elicit Ca(2+) responses in PSCs where BDNF used a phospholipase C-inositol 1,4,5-triphosphate (PLC-IP(3)) mechanism, while NT-3 required extracellular Ca(2+). Our results demonstrate a neurotrophin-dependent modulation of neuron-glia signalling through differential mechanisms employed by NT-3 and BDNF. Hence, neurotrophins precisely and differentially regulate PSC functions through modulation of either purinergic or cholinergic signalling pathways.
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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".