SCN1A-deficient excitatory neuronal networks display mutation-specific phenotypes.

Contains fulltext : 300240.pdf (Publisher’s version ) (Open Access)
Dravet syndrome is a severe epileptic encephalopathy, characterized by (febrile) seizures, behavioural problems and developmental delay. Eighty per cent of patients with Dravet syndrome have a mutation in SCN1A, encoding Nav1.1. Milder clinical phenotypes, such as GEFS+ (generalized epilepsy with febrile seizures plus), can also arise from SCN1A mutations. Predicting the clinical phenotypic outcome based on the type of mutation remains challenging, even when the same mutation is inherited within one family. This clinical and genetic heterogeneity adds to the difficulties of predicting disease progression and tailoring the prescription of anti-seizure medication. Understanding the neuropathology of different SCN1A mutations may help to predict the expected clinical phenotypes and inform the selection of best-fit treatments. Initially, the loss of Na+-current in inhibitory neurons was recognized specifically to result in disinhibition and consequently seizure generation. However, the extent to which excitatory neurons contribute to the pathophysiology is currently debated and might depend on the patient clinical phenotype or the specific SCN1A mutation. To examine the genotype-phenotype correlations of SCN1A mutations in relation to excitatory neurons, we investigated a panel of patient-derived excitatory neuronal networks differentiated on multi-electrode arrays. We included patients with different clinical phenotypes, harbouring various SCN1A mutations, along with a family in which the same mutation led to febrile seizures, GEFS+ or Dravet syndrome. We hitherto describe a previously unidentified functional excitatory neuronal network phenotype in the context of epilepsy, which corresponds to seizurogenic network prediction patterns elicited by proconvulsive compounds. We found that excitatory neuronal networks were affected differently, depending on the type of SCN1A mutation, but did not segregate according to clinical severity. Specifically, loss-of-function mutat