Dravet syndrome is caused by mutations in the SCN1A gene, which encodes for the voltage-gated sodium channel Nav1.1 – a protein that participates in conduction of nerve signals. Thousands of different SCN1A mutations have been reported, but only a small number of these have been characterized functionally. This is unfortunate, because some SCN1A mutations are associated with disorders other than Dravet syndrome, and many are benign. Even within the Dravet syndrome cases, it would be very helpful to be able to predict the future severity of the disease in infancy, based on the particular mutation present, since the full clinical diagnosis now takes until toddler age. In the current study, the authors survey the functional SCN1A work performed to date, with particular attention to any electrophysiological measurements, and examine the correlation to clinical phenotypes.

They begin at the simplest level – can just knowing the particular mutation predict clinical outcome? Mutations that cause functional protein to not be made at all reliably correlate to Dravet syndrome. Mutations that result in produced protein, but in a modified form, are more uncertain. There are so many of these, versus so few individuals per mutation, that no correlations can be made. Trying to predict the outcome via computer modeling, calculating how the protein would function given a particular mutation, was not successful.

Adding data from electrophysiology measurements helped a little. These are lab experiments on isolated cells that carry the mutated protein. It was found that retention of some degree of sodium channel function, measured as residual whole‐cell current, correlated with milder clinical phenotypes.  Lack of any whole-cell current was associated with Dravet syndrome. Still, there was a significant degree of functional variation, so it is not yet possible to neatly categorize the various mutations. The authors also mention that trying to express SCN1A mutations in mice and observing the resulting phenotypes will always carry uncertainty when extrapolating the results to humans.

The authors conclude that detailed electrophysiological knowledge has the potential to impact clinical management, and toward this goal we should continue to gather and analyze such data, and extend the types of measurements taken – ultimately we will be able to extract clear correlations.

Brunklaus, A et al. SCN1A variants from bench to bedside – improved clinical prediction from functional characterization. Hum Mutat. 2019 Nov 28 [Epub ahead of print]. DOI: 10.1002/humu.23943