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Opposing Effects on NaV1.2 Function Underlie Differences Between SCN2A Variants Observed in Individuals With Autism Spectrum Disorder or Infantile Seizures - PubMed

  • ️Sun Jan 01 2017

Opposing Effects on NaV1.2 Function Underlie Differences Between SCN2A Variants Observed in Individuals With Autism Spectrum Disorder or Infantile Seizures

Roy Ben-Shalom et al. Biol Psychiatry. 2017.

Abstract

Background: Variants in the SCN2A gene that disrupt the encoded neuronal sodium channel NaV1.2 are important risk factors for autism spectrum disorder (ASD), developmental delay, and infantile seizures. Variants observed in infantile seizures are predominantly missense, leading to a gain of function and increased neuronal excitability. How variants associated with ASD affect NaV1.2 function and neuronal excitability are unclear.

Methods: We examined the properties of 11 ASD-associated SCN2A variants in heterologous expression systems using whole-cell voltage-clamp electrophysiology and immunohistochemistry. Resultant data were incorporated into computational models of developing and mature cortical pyramidal cells that express NaV1.2.

Results: In contrast to gain of function variants that contribute to seizure, we found that all ASD-associated variants dampened or eliminated channel function. Incorporating these electrophysiological results into a compartmental model of developing excitatory neurons demonstrated that all ASD variants, regardless of their mechanism of action, resulted in deficits in neuronal excitability. Corresponding analysis of mature neurons predicted minimal change in neuronal excitability.

Conclusions: This functional characterization thus identifies SCN2A mutation and NaV1.2 dysfunction as the most frequently observed ASD risk factor detectable by exome sequencing and suggests that associated changes in neuronal excitability, particularly in developing neurons, may contribute to ASD etiology.

Keywords: Autism spectrum disorder; Epilepsy; Na(V)1.2; SCN2A; Seizure; electrophysiology.

Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

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Figures

Figure 1
Figure 1. SCN2A/NaV1.2 genotypes and phenotypes

A. Location of 19 missense SCN2A variants in BIFS (yellow) and 49 missense SCN2A variants in EE (green) in the NaV1.2 sodium channel. The size of the circle corresponds to the number of individuals with a variant at a specific residue. Variants that are observed in three or more independent families are named, with the text color corresponding to the phenotype. B. Location of 13 missense (red) and 10 PTV (blue) SCN2A variants in ASD cases in the NaV1.2 sodium channel. The size of the circle corresponds to the number of individuals with a variant at a specific residue. Variants that were functionally assessed are named, with the text color corresponding to the variant type. C. A zoomed in view of the 8 missense SCN2A variants on the pore loop observed in ASD. Six of these variants are within five amino acid residues of the ion selectivity filter. Statistical significance was calculated using a two-sided Binomial Exact Test. D. A violin plot of non-verbal IQ in 2,347 ASD cases from the Simons Simplex Collection; equivalent data are not available for the Autism Sequencing Consortium cases. The overlaid boxplot shows the median and interquartile range. The cases are divided into three groups: 2,224 with no known de novo PTV, deletion, or duplication mutations; 117 with a de novo PTV, deletion, or duplication in an ASD-associated gene or locus (2); 6 with a de novo PTV (blue) or missense (red) mutation in SCN2A. Statistical significance was calculated using a two-sided Wilcoxon Signed Rank Test. Abbreviations: SCN2A: sodium channel, voltage-gated, type II, alpha subunit; PTV: protein truncating variant; ASD: Autism Spectrum Disorder; BIFS: Benign Infantile Familial Seizures; EE: Epileptic Encephalopathy; AA: Amino Acid; IQ: Intelligence Quotient.

Figure 2
Figure 2. Electrophysiology of SCN2A variants

A. NaV1.2 activation currents from HEK293 cells transfected with wild type, C959X, and R937H plasmids. Capacitance transient blanked for clarity. Note lack of current in either variant. B. Peak current amplitudes observed during activation protocol for all mutations. Data are normalized to cell capacitance and color coded to match subsequent panels (WT, black; D12N, cyan; D82G, green; T1420M, magenta; other missense: yellow; all loss of function; red). WT/R379H denotes co-expression of the two variants. R379H (K-gluc) denotes only experiments performed with a K-based internal solution. Circles are individual cells, bars are mean±SEM. Data in yellow and red were not different than untransfected controls. Asterisk: D82G, T1420M, and WT/R379H currents were smaller than wild type (p<0.0006, Kruskal-Wallis). C. Activation and inactivation curves in wild type and D82G. D82G has depolarized activation. Circles and bars are mean±SEM at each test potential. D. Voltage at which half of current was activated or inactivated for each conducting variant, compared to wild type. Data shown as box plots, with median and quartiles within the box and 10/90th percentile as tails. Individual data points are overlaid as circles. Asterisk: p=0.0043, Kruskal-Wallis. E. Top, Exemplar currents from WT and conducting variants, scaled to peak. Bottom: inactivation tau determined from single exponential fit (10–90% of peak). Circles and bars are mean±SEM. Asterisk: p<0.01, Repeated measures ANOVA. n=13, 13, 10, 9, 6 cells for WT, D12N, D82G, and T1420N, WT/R379H respectively. F. Top, overlay of 10 consecutive trials (black) and their average (red). Bottom, average variance2 between individual trials and the average. Right, nonstationary fluctuation analysis was performed by comparing variance with current amplitude. Dots are individual time points, curve is parabolic fit for nonstationary fluctuation analysis. G. Single channel conductance and the number of channels contributing to the macroscopic current, as determined in F. Data and statistics presented as in D.

Figure 3
Figure 3. Neuronal excitability in a cortical pyramidal cell model

A. Spiking generated with 2.2 nA somatic current in ASD, BIFS, and EE variants. Data are modeled in a developing neuron expressing only NaV1.2 in the AIS and axon. Top, all conducting ASD variants, compared to WT and PTV/non-conducting missense modeled as a 50% reduction in overall NaV1.2 conductance. D82G was modeled as either an activation shift alone (D82G Va) or with a reduction in channel density (D82G Va-#). Bottom: Two BIFS and two EE variants, each compared to WT. Data are vertically offset in 100 mV increments; all traces begin at −78 mV. Plots detail the number of spikes evoked in 100 ms epoch with varying somatic current injections. PTV is from 50% reduction model. Data are color coded as in legend. B. Same as A, but in a mature pyramidal cell model, with a mix of NaV1.2 and NaV1.6 in the AIS, and NaV1.6 in the axon (see Fig. S3 in Supplement 1 for distributions). C. Spike threshold with varying levels of NaV1.2 in the AIS, from WT (100%) through different levels of compensation in cells heterozygous for PTV/non-conducting missense variants (to 50%), conducting missense ASD, BIFS, and EE variants. Colored scale bar corresponds to spike threshold of the first spike observed in each trace; dark blue are conditions in which no spikes were generated. Note different scaling for EE conditions, as these variants were excitable at/near rest.

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References

    1. Sanders SJ, Murtha MT, Gupta AR, Murdoch JD, Raubeson MJ, Willsey AJ, et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature. 2012;485:237–241. - PMC - PubMed
    1. Sanders SJ, He X, Willsey AJ, Ercan-Sencicek AG, Samocha KE, Cicek AE, et al. Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci. Neuron. 2015;87:1215–1233. - PMC - PubMed
    1. De Rubeis S, He X, Goldberg AP, Poultney CS, Samocha K, Cicek AE, et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature. 2014;515:209–215. - PMC - PubMed
    1. Iossifov I, O’Roak BJ, Sanders SJ, Ronemus M, Krumm N, Levy D, et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature. 2014;515:216–221. - PMC - PubMed
    1. Fitzgerald TW, Gerety SS, Jones WD, van Kogelenberg M, King DA, McRae J, et al. Large-scale discovery of novel genetic causes of developmental disorders. Nature. 2015;519:223. - PMC - PubMed

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