ePoster

Using a neural network model to understand KCNA6 p.Lys376Val variant induced loss of function in childhood epilepsy

Lordstrong Akano, Jerome Clatot, C.B. Currin, Tim P. Vogels, Ethan M. Goldberg
FENS Forum 2024(2024)
Messe Wien Exhibition & Congress Center, Vienna, Austria

Conference

FENS Forum 2024

Messe Wien Exhibition & Congress Center, Vienna, Austria

Resources

Authors & Affiliations

Lordstrong Akano, Jerome Clatot, C.B. Currin, Tim P. Vogels, Ethan M. Goldberg

Abstract

We recently identified a novel p.Lys376Val variant in KCNA6–encoding the voltage-gated potassium channel Kv1.6–that has been implicated in childhood epilepsy. Here, we aimed to identify the mechanisms by which the variant could contribute to disease.We first synthesized WT and p.Lys376Val KCNA6 and subcloned the cDNA in p.GFP-IRES plasmids. WT, p.Lys376Val and WT + p.Lys376Val (at a 50:50 ratio) were transfected in HEK-293T cells. We characterized the resulting potassium currents using standard voltage clamp techniques. When expressed alone, the KCNA6-p.Lys376Val variant led to a 50% decrease in the amplitude of the transient outward current, a -17 mV hyperpolarized shift of the activation threshold, and decelerated deactivation kinetics. However, in cells expressing both WT + p.Lys376Val variant (thus mimicking the patient’s heterozygous state) the amplitude of the transient outward currents was not significantly different from WT, although activation threshold and deactivation kinetics remained significantly altered.Next, we built a generalized integrate-and-fire neuron with an added potassium channel to model the action of Kv1.6. We successfully tuned parameters describing the kinetics of the WT and variant channel, showing a decrease in spike generation. We then used our neuron model in recurrent excitatory/inhibitory integrate-and-fire networks, expressing the variant channel in fractions of the excitatory and/or inhibitory cell populations. Preliminary results suggest that the p.Lys376Val variant of Kv1.6 channels as fitted in our models is sufficient to generate epileptiform pattern, depending on what sub-population of neurons harbor the variant channel.

Unique ID: fens-24/using-neural-network-model-understand-92d7e464