3-B-301 - Panx1 channels promote both anti- and pro-seizure-like activities in the zebrafish via p2rx7 receptors and ATP-signalling
Bibliographic record
Abstract
Authors: Paige Whyte-Fagundes¹, Daria Taskina¹, Nickie Safarian¹, Christiane Zoidl¹, Logan Donaldson¹, Peter Carlen² ¹York University, ²University of Toronto Abstract: The molecular determinants of excitation-inhibition imbalances promoting seizure generation in epilepsy patients are not fully understood. Experimental evidence suggests that Pannexin1 (Panx1), an ATP release channel, modulates excitability of the brain. Here, we use zebrafish larvae with Panx1a and Panx1b channels genetically knocked out or pharmacologically inhibited to evaluate the consequences of targeting Panx1 for antiepileptic drug therapies. Pentylenetetrazole was used to chemically induce seizures during in vivo recordings of local field potentials and for behavioral and molecular phenotyping. We find that loss-of-function panx1a gene mutations, or pharmacological blockade of both channels significantly reduces ictal-like events and seizure-related locomotion. Loss of panx1a also improves survival rates and transcriptome data demonstrate altered metabolic and cell signaling states. The pro- and anticonvulsant activities of both Panx1 channels affect ATP release and the purinergic receptor P2rx7. We propose that Panx1 zebrafish models offer opportunities for comprehensive studies of seizure mechanisms and for anticonvulsant drug discovery.
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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) | 0.004 | 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".