Targeting ischemia-induced KCC2 hypofunction rescues refractory neonatal seizures and mitigates epileptogenesis in a mouse model
Bibliographic record
Abstract
Abstract Neonatal seizures pose a clinical challenge for their early detection, acute management, and mitigation of long-term comorbidities. A major cause of neonatal seizures is hypoxic-ischemic encephalopathy that results in seizures that are frequently refractory to the first-line anti-seizure medication phenobarbital (PB). One proposed mechanism for PB-inefficacy during neonatal seizures is the reduced expression and function of the neuron-specific K + /Cl − cotransporter 2 (KCC2), the main neuronal Cl − extruder that maintains chloride homeostasis and influences the efficacy of GABAergic inhibition. To determine if PB-refractoriness after ischemic neonatal seizures is dependent upon KCC2 hypofunction and can be rescued by KCC2 functional enhancement, we investigated the recently developed KCC2 functional enhancer CLP290 in a CD-1 mouse model of refractory ischemic neonatal seizures quantified with vEEG. We report that acute CLP290 intervention can rescue PB-resistance, KCC2 expression, and the development of epileptogenesis after ischemic neonatal seizures. KCC2 phosphorylation sites have a strong influence over KCC2 activity and seizure susceptibility in adult experimental epilepsy models. Therefore, we investigated seizure susceptibility in two different knock-in mice in which either phosphorylation of S940 or T906/T1007 was prevented. We report that KCC2 phosphorylation regulates both neonatal seizure susceptibility and CLP290-mediated KCC2 functional enhancement. Our results validate KCC2 as a clinically relevant target for refractory neonatal seizures and provide insights for future KCC2 drug development.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".