Plasticity of mouse enteric synapses mediated through endocannabinoid and purinergic signaling
Bibliographic record
Abstract
BACKGROUND: The enteric nervous system (ENS) possesses extensive synaptic connections which integrate information and provide appropriate outputs to coordinate the activity of the gastrointestinal tract. The regulation of enteric synapses is not well understood. Cannabinoid (CB)(1) receptors inhibit the release of acetylcholine (ACh) in the ENS, but their role in the synapse is not understood. We tested the hypothesis that enteric CB(1) receptors provide inhibitory control of excitatory neurotransmission in the ENS. METHODS: Intracellular microelectrode recordings were obtained from mouse myenteric plexus neurons. Interganglionic fibers were stimulated with a concentric stimulating electrode to elicit synaptic events on to the recorded neuron. Differences between spontaneous and evoked fast synaptic transmission was examined within preparations from CB(1) deficient mice (CB(1)(-/-)) and wild-type (WT) littermate controls. KEY RESULTS: Cannabinoid receptors were colocalized on terminals expressing the vesicular ACh transporter and the synaptic protein synaptotagmin. A greater proportion of CB(1)(-/-) neurons received spontaneous fast excitatory postsynaptic potentials than neurons from WT preparations. The CB(1) agonist WIN55,212 depressed WT synapses without any effect on CB(1)(-/-) synapses. Synaptic activity in response to depolarization was markedly enhanced at CB(1)(-/-) synapses and after treatment with a CB(1) antagonist in WT preparations. Activity-dependent liberation of a retrograde purine messenger was demonstrated to facilitate synaptic transmission in CB(1)(-/-) mice. CONCLUSIONS & INFERENCES: Cannabinoid receptors inhibit transmitter release at enteric synapses and depress synaptic strength basally and in an activity-dependent manner. These actions help explain accelerated intestinal transit observed in the absence of CB(1) receptors.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".