γ‐Aminobutyric acid receptor alpha 1 subunit loss of function causes genetic generalized epilepsy by impairing inhibitory network neurodevelopment
Bibliographic record
Abstract
OBJECTIVE: In humans, mutations of the γ-aminobutyric acid receptor subunit 1 (GABRA1) cause either mild or severe generalized epilepsy. Although these epilepsy-causing mutations have been shown to disrupt the receptor activity in vitro, their in vivo consequences on brain development and activity are not known. Here, we aim at unraveling the epileptogenesis mechanisms of GABRA1 loss of function. METHODS: larval brains. RESULTS: Interestingly, mutant fish show fully penetrant seizures at juvenile stages that accurately mimic tonic-clonic generalized seizures observed in patients. Moreover, highly penetrant seizures can be induced by light stimulation, thus providing us with the first zebrafish model in which evident epileptic seizures can be induced by nonchemical agents. Our transcriptomic assay identified misregulated genes in several pathways essential for correct brain development. More specifically, we show that the early development of the brain inhibitory network is specifically affected. Although the number of GABAergic neurons is not altered, we observed a drastic reduction in the number of inhibitory synapses and a decreased complexity of the GABAergic network. This is consistent with the disruption in expression of many genes involved in axon guidance and synapse formation. SIGNIFICANCE: Together with the role of GABA in neurodevelopment, our data identify a novel aspect of epileptogenesis, suggesting that the substratum of GABRA1-deficiency epilepsy is a consequence of early brain neurodevelopmental defects, in particular at the level of inhibitory network wiring.
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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 | 0.000 |
| 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".