Pathophysiology of and therapeutic options for a GABRA1 variant linked to epileptic encephalopathy
Bibliographic record
Abstract
Abstract We report the identification of a de novo GABRA1 (R214C) variant in a child with epileptic encephalopathy (EE), describe its functional characterization and pathophysiology, and evaluate its potential therapeutic options. The GABRA1 (R214C) variant was identified using whole exome sequencing, and the pathogenic effect of this mutation was investigated by comparing wild-type (WT) α1 and R214C α1 GABAA receptor-expressing HEK cells. GABA-evoked currents in these cells were recorded using whole-cell, outside-out macro-patch and cell-attached single-channel patch-clamp recordings. Changes to surface and total protein expression levels of WT α1 and R214C α1 were quantified using surface biotinylation assay and western blotting, respectively. Finally, potential therapeutic options were explored by determining the effects of modulators, including diazepam, insulin, and verapamil, on channel gating and receptor trafficking of WT and R214C GABAA receptors. We found that the GABRA1 (R214C) variant decreased whole-cell GABA-evoked currents by reducing single channel open time and both surface and total GABAA receptor expression levels. The GABA-evoked currents in R214C GABAA receptors could only be partially restored with benzodiazepine (diazepam) and insulin. However, verapamil treatment for 24 h fully restored the function of R214C mutant receptors, primarily by increasing channel open time. We conclude that the GABRA1 (R214C) variant reduces channel activity and surface expression of mutant receptors, thereby contributing to the pathogenesis of genetic EE. The functional restoration by verapamil suggests that it is a potentially new therapeutic option for patients with the R214C variant and highlights the value of precision medicine in the treatment of genetic EEs.
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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 | 0.001 | 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".