A novel de novo HCN1 loss-of-function mutation in genetic generalized epilepsy causing increased neuronal excitability
Bibliographic record
Abstract
The causes of genetic epilepsies are unknown in the majority of patients. HCN ion channels have a widespread expression in neurons and increasing evidence demonstrates their functional involvement in human epilepsies. Among the four known isoforms, HCN1 is the most expressed in the neocortex and hippocampus and de novo HCN1 point mutations have been recently associated with early infantile epileptic encephalopathy. So far, HCN1 mutations have not been reported in patients with idiopathic epilepsy. Using a Next Generation Sequencing approach, we identified the de novo heterozygous p.Leu157Val (c.469C > G) novel mutation in HCN1 in an adult male patient affected by genetic generalized epilepsy (GGE), with normal cognitive development. Electrophysiological analysis in heterologous expression model (CHO cells) and in neurons revealed that L157V is a loss-of-function, dominant negative mutation causing reduced HCN1 contribution to net inward current and responsible for an increased neuronal firing rate and excitability, potentially predisposing to epilepsy. These data represent the first evidence that autosomal dominant missense mutations of HCN1 can also be involved in GGE, without the characteristics of epileptic encephalopathy reported previously. It will be important to include HCN1 screening in patients with GGE, in order to extend the knowledge of the genetic causes of idiopathic epilepsies, thus paving the way for the identification of innovative therapeutic strategies.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".