Exome sequencing of ATP1A3-negative cases of alternating hemiplegia of childhood reveals SCN2A as a novel causative gene.

Alternating hemiplegia of childhood (AHC) is a rare neurodevelopment disorder that is typically characterized by debilitating episodic attacks of hemiplegia, seizures, and intellectual disability. Over 85% of individuals with AHC have a de novo missense variant in ATP1A3 encoding the catalytic α3 subunit of neuronal Na+/K+ ATPases. The remainder of the patients are genetically unexplained. Here, we used next-generation sequencing to search for the genetic cause of 26 ATP1A3-negative index patients with a clinical presentation of AHC or an AHC-like phenotype. Three patients had affected siblings. Using targeted sequencing of exonic, intronic, and flanking regions of ATP1A3 in 22 of the 26 index patients, we found no ultra-rare variants. Using exome sequencing, we identified the likely genetic diagnosis in 9 probands (35%) in five genes, including RHOBTB2 (n = 3), ATP1A2 (n = 3), ANK3 (n = 1), SCN2A (n = 1), and CHD2 (n = 1). In follow-up investigations, two additional ATP1A3-negative individuals were found to have rare missense SCN2A variants, including one de novo likely pathogenic variant and one likely pathogenic variant for which inheritance could not be determined. Functional evaluation of the variants identified in SCN2A and ATP1A2 supports the pathogenicity of the identified variants. Our data show that genetic variants in various neurodevelopmental genes, including SCN2A, lead to AHC or AHC-like presentation. Still, the majority of ATP1A3-negative AHC or AHC-like patients remain unexplained, suggesting that other mutational mechanisms may account for the phenotype or that cases may be explained by oligo- or polygenic risk factors.

Cluster Headache Genomewide Association Study and Meta-Analysis Identifies Eight Loci and Implicates Smoking as Causal Risk Factor.

OBJECTIVE: The objective of this study was to aggregate data for the first genomewide association study meta-analysis of cluster headache, to identify genetic risk variants, and gain biological insights. METHODS: A total of 4,777 cases (3,348 men and 1,429 women) with clinically diagnosed cluster headache were recruited from 10 European and 1 East Asian cohorts. We first performed an inverse-variance genomewide association meta-analysis of 4,043 cases and 21,729 controls of European ancestry. In a secondary trans-ancestry meta-analysis, we included 734 cases and 9,846 controls of East Asian ancestry. Candidate causal genes were prioritized by 5 complementary methods: expression quantitative trait loci, transcriptome-wide association, fine-mapping of causal gene sets, genetically driven DNA methylation, and effects on protein structure. Gene set and tissue enrichment analyses, genetic correlation, genetic risk score analysis, and Mendelian randomization were part of the downstream analyses. RESULTS: The estimated single nucleotide polymorphism (SNP)-based heritability of cluster headache was 14.5%. We identified 9 independent signals in 7 genomewide significant loci in the primary meta-analysis, and one additional locus in the trans-ethnic meta-analysis. Five of the loci were previously known. The 20 genes prioritized as potentially causal for cluster headache showed enrichment to artery and brain tissue. Cluster headache was genetically correlated with cigarette smoking, risk-taking behavior, attention deficit hyperactivity disorder (ADHD), depression, and musculoskeletal pain. Mendelian randomization analysis indicated a causal effect of cigarette smoking intensity on cluster headache. Three of the identified loci were shared with migraine. INTERPRETATION: This first genomewide association study meta-analysis gives clues to the biological basis of cluster headache and indicates that smoking is a causal risk factor. ANN NEUROL 2023;94:713-726.

Genotype-phenotype correlations in SCN8A-related disorders reveal prognostic and therapeutic implications.

We report detailed functional analyses and genotype-phenotype correlations in 392 individuals carrying disease-causing variants in SCN8A, encoding the voltage-gated Na+ channel Nav1.6, with the aim of describing clinical phenotypes related to functional effects. Six different clinical subgroups were identified: Group 1, benign familial infantile epilepsy (n = 15, normal cognition, treatable seizures); Group 2, intermediate epilepsy (n = 33, mild intellectual disability, partially pharmaco-responsive); Group 3, developmental and epileptic encephalopathy (n = 177, severe intellectual disability, majority pharmaco-resistant); Group 4, generalized epilepsy (n = 20, mild to moderate intellectual disability, frequently with absence seizures); Group 5, unclassifiable epilepsy (n = 127); and Group 6, neurodevelopmental disorder without epilepsy (n = 20, mild to moderate intellectual disability). Those in Groups 1-3 presented with focal or multifocal seizures (median age of onset: 4 months) and focal epileptiform discharges, whereas the onset of seizures in patients with generalized epilepsy was later (median: 42 months) with generalized epileptiform discharges. We performed functional studies expressing missense variants in ND7/23 neuroblastoma cells and primary neuronal cultures using recombinant tetrodotoxin-insensitive human Nav1.6 channels and whole-cell patch-clamping. Two variants causing developmental and epileptic encephalopathy showed a strong gain-of-function (hyperpolarizing shift of steady-state activation, strongly increased neuronal firing rate) and one variant causing benign familial infantile epilepsy or intermediate epilepsy showed a mild gain-of-function (defective fast inactivation, less increased firing). In contrast, all three variants causing generalized epilepsy induced a loss-of-function (reduced current amplitudes, depolarizing shift of steady-state activation, reduced neuronal firing). Functional effects were known for 170 individuals. All 136 individuals carrying a functionally tested gain-of-function variant had either focal (n = 97, Groups 1-3) or unclassifiable (n = 39) epilepsy, whereas 34 individuals with a loss-of-function variant had either generalized (n = 14), no (n = 11) or unclassifiable (n = 6) epilepsy; only three had developmental and epileptic encephalopathy. Computational modelling in the gain-of-function group revealed a significant correlation between the severity of the electrophysiological and clinical phenotypes. Gain-of-function variant carriers responded significantly better to sodium channel blockers than to other anti-seizure medications, and the same applied for all individuals in Groups 1-3. In conclusion, our data reveal clear genotype-phenotype correlations between age at seizure onset, type of epilepsy and gain- or loss-of-function effects of SCN8A variants. Generalized epilepsy with absence seizures is the main epilepsy phenotype of loss-of-function variant carriers and the extent of the electrophysiological dysfunction of the gain-of-function variants is a main determinant of the severity of the clinical phenotype in focal epilepsies. Our pharmacological data indicate that sodium channel blockers present a treatment option in SCN8A-related focal epilepsy with onset in the first year of life.

Metabolic profile changes in serum of migraine patients detected using 1H-NMR spectroscopy.

BACKGROUND: Migraine is a common brain disorder but reliable diagnostic biomarkers in blood are still lacking. Our aim was to identify, using proton nuclear magnetic resonance (1H-NMR) spectroscopy, metabolites in serum that are associated with lifetime and active migraine by comparing metabolic profiles of patients and controls. METHODS: Fasting serum samples from 313 migraine patients and 1512 controls from the Erasmus Rucphen Family (ERF) study were available for 1H-NMR spectroscopy. Data was analysed using elastic net regression analysis. RESULTS: A total of 100 signals representing 49 different metabolites were detected in 289 cases (of which 150 active migraine patients) and 1360 controls. We were able to identify profiles consisting of 6 metabolites predictive for lifetime migraine status and 22 metabolites predictive for active migraine status. We estimated with subsequent regression models that after correction for age, sex, BMI and smoking, the association with the metabolite profile in active migraine remained. Several of the metabolites in this profile are involved in lipid, glucose and amino acid metabolism. CONCLUSION: This study indicates that metabolic profiles, based on serum concentrations of several metabolites, including lipids, amino acids and metabolites of glucose metabolism, can distinguish active migraine patients from controls.