Whole-Exome Sequencing in NF1-Related West Syndrome Leads to the Identification of KCNC2 as a Novel Candidate Gene for Epilepsy
Bibliographic record
Abstract
Abstract Patients with neurofibromatosis type 1 (NF1) have an increased risk for West syndrome (WS), but the underlying mechanisms linking NF1 and WS are unknown. In contrast to other neurocutaneous syndromes, intracerebral abnormalities explaining the course of infantile spasms (IS) are often absent and the seizure outcome is usually favorable. Several studies have investigated a potential genotype–phenotype correlation between NF1 and seizure susceptibility, but an association was not identified. Therefore, we identified three patients with NF1-related WS (NF1-WS) in a cohort of 51 NF1 patients and performed whole-exome sequencing (WES) to identify genetic modifiers. In two NF1 patients with WS and good seizure outcome, we did not identify variants in epilepsy-related genes. However, in a single patient with NF1-WS and transition to drug-resistant epilepsy, we identified a de novo variant in KCNC2 (c.G499T, p.D167Y) coding for Kv3.2 as a previously undescribed potassium channel to be correlated to epilepsy. Electrophysiological studies of the identified KCNC2 variant demonstrated both a strong loss-of-function effect for the current amplitude and a gain-of-function effect for the channel activation recommending a complex network effect. These results suggest that systematic genetic analysis for potentially secondary genetic etiologies in NF1 patients and severe epilepsy presentations should be done.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".