Early endocannabinoid-mediated depolarization-induced suppression of excitation delays the appearance of the epileptic phenotype in synapsin II knockout mice
Bibliographic record
Abstract
The mechanism underlying the transition from the pre-symptomatic to the symptomatic state is a crucial aspect of epileptogenesis. SYN2 is a member of a multigene family of synaptic vesicle phosphoproteins playing a fundamental role in controlling neurotransmitter release. Human SYN2 gene mutations are associated with epilepsy and autism spectrum disorder. Mice knocked out for synapsin II (SynII KO) are prone to epileptic seizures that appear after 2 months of age. However, the involvement of the endocannabinoid system, known to regulate seizure development and propagation, in the modulation of the excitatory/inhibitory balance in the epileptic hippocampal network of SynII KO mice has not been explored. In this study, we investigated the impact of endocannabinoids on glutamatergic and GABAergic synapses at hippocampal dentate gyrus granule cells in young pre-symptomatic (1-2 months old) and adult symptomatic (5-8 months old) SynII KO mice. We observed an increase in endocannabinoid-mediated depolarization-induced suppression of excitation in young SynII KO mice, compared to age-matched wild-type controls. In contrast, the endocannabinoid-mediated depolarization-induced suppression of inhibition remained unchanged in SynII KO mice at both ages. This selective alteration of excitatory synaptic transmission was accompanied by changes in hippocampal endocannabinoid levels and cannabinoid receptor type 1 distribution among glutamatergic and GABAergic synaptic terminals contacting the granule cells of the dentate gyrus. Finally, inhibition of type-1 cannabinoid receptors in young pre-symptomatic SynII KO mice induced seizures during a tail suspension test. Our results suggest that endocannabinoids contribute to maintaining network stability in a genetic mouse model of human epilepsy.
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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) | 0.001 | 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".