Functional correlates of clinical phenotype and severity in recurrent SCN2A variants

Abstract

In SCN2A-related disorders, there is an urgent demand to establish efficient methods for determining the gain- (GoF) or loss-of-function (LoF) character of variants, to identify suitable candidates for precision therapies. Here we classify clinical phenotypes of 179 individuals with 38 recurrent SCN2A variants as early-infantile or later-onset epilepsy, or intellectual disability/autism spectrum disorder (ID/ASD) and assess the functional impact of 13 variants using dynamic action potential clamp (DAPC) and voltage clamp. Results show that 36/38 variants are associated with only one phenotypic group (30 early-infantile, 5 later-onset, 1 ID/ASD). Unexpectedly, we revealed major differences in outcome severity between individuals with the same variant for 40% of early-infantile variants studied. DAPC was superior to voltage clamp in predicting the impact of mutations on neuronal excitability and confirmed GoF produces early-infantile phenotypes and LoF later-onset phenotypes. For one early-infantile variant, the co-expression of the α1 and β2 subunits of the Nav1.2 channel was needed to unveil functional impact, confirming the prediction of 3D molecular modeling. Neither DAPC nor voltage clamp reliably predicted phenotypic severity of early-infantile variants. Genotype, phenotypic group and DAPC are accurate predictors of the biophysical impact of SCN2A variants, but other approaches are needed to predict severity.

Distinguishing Loss-of-Function and Gain-of-Function SCN8A Variants Using a Random Forest Classification Model Trained on Clinical Features

“…2 In the case of SCN2A and SCN8A genes, primarily expressed on excitatory neurons in the brain, efficient methods are needed for determining the GOF or LOF character of variants. Biophysical studies have shown that GOF variants cause early-infantile epilepsies of variable severity, with seizure onset typically occurring in the first year of life, 3,4 while LOF variants result in later-onset epilepsies or neurodevelopmental delays and behavioral features without epilepsy. 3,5,6 The distinction between GOF and LOF variants has important implications for the treatment of these disorders.…”

“…Biophysical studies have shown that GOF variants cause early-infantile epilepsies of variable severity, with seizure onset typically occurring in the first year of life, 3,4 while LOF variants result in later-onset epilepsies or neurodevelopmental delays and behavioral features without epilepsy. 3,5,6 The distinction between GOF and LOF variants has important implications for the treatment of these disorders. Patients with GOF variants often benefit from sodium channel blockers (SCBs), while SCBs tend to aggravate symptoms of patients with LOF variants.…”

Proximity Analysis of Native Proteomes Reveals Interactomes Predictive of Phenotypic Modifiers of Autism and Related Neurodevelopmental Conditions

“…3B). It is known that mutations in Scn2a can result in either infantile epileptic encephalopathy (IEE) or ASD, depending on the nature of the mutational effect (gain-or-loss of function) 62 . Cortical neurons cultured from Scn2a +/R102Q mice exhibited a significant attenuation in neural firing activity (∼40% decrease at DIV14; p < 0.001, Fig.…”

Predicting functional effects of ion channel variants using new phenotypic machine learning methods

“…Learning with phenotypic similarity as features represents a natural extension of previous sequence-and structure-based machine learning algorithms [27,28]. Previous studies have PLOS COMPUTATIONAL BIOLOGY demonstrated that clinical features represent an important source of information that may be sufficient to clearly delineate between GOF and LOF in several channelopathies [41,42]. Here, we introduce the concept of extending semantic similarity analysis of human phenotype ontology (HPO) terms towards kernel-based machine learning methods.…”