In silico model reveals the key role of GABA in KCNT1-epilepsy in infancy with migrating focal seizures.

Objective: This study was undertaken to investigate how gain of function (GOF) of slack channel due to a KCNT1 pathogenic variant induces abnormal neuronal cortical network activity and generates specific electroencephalographic (EEG) patterns of epilepsy in infancy with migrating focal seizures.

Methods: We used detailed microscopic computational models of neurons to explore the impact of GOF of slack channel (explicitly coded) on each subtype of neurons and on a cortical micronetwork. Then, we adapted a thalamocortical macroscopic model considering results obtained in detailed models and immature properties related to epileptic brain in infancy. Finally, we compared simulated EEGs resulting from the macroscopic model with interictal and ictal patterns of affected individuals using our previously reported EEG markers.

Results: The pathogenic variants of KCNT1 strongly decreased the firing rate properties of γ-aminobutyric acidergic (GABAergic) interneurons and, to a lesser extent, those of pyramidal cells. This change led to hyperexcitability with increased synchronization in a cortical micronetwork. At the macroscopic scale, introducing slack GOF effect resulted in epilepsy of infancy with migrating focal seizures (EIMFS) EEG interictal patterns. Increased excitation-to-inhibition ratio triggered seizure, but we had to add dynamic depolarizing GABA between somatostatin-positive interneurons and pyramidal cells to obtain migrating seizure. The simulated migrating seizures were close to EIMFS seizures, with similar values regarding the delay between the different ictal activities (one of the specific EEG markers of migrating focal seizures due to KCNT1 pathogenic variants).

Significance: This study illustrates the interest of biomathematical models to explore pathophysiological mechanisms bridging the gap between the functional effect of gene pathogenic variants and specific EEG phenotype. Such models can be complementary to in vitro cellular and animal models. This multiscale approach provides an in silico framework that can be further used to identify candidate innovative therapies.