Temporal associations of plasma levels of the secreted phospholipase A2 family and mortality in severe COVID-19.

Previous research suggests that group IIA-secreted phospholipase A2 (sPLA2-IIA) plays a role in and predicts lethal COVID-19 disease. The current study reanalyzed a longitudinal proteomic data set to determine the temporal relationship between levels of several members of a family of sPLA2 isoforms and the severity of COVID-19 in 214 ICU patients. The levels of six secreted PLA2 isoforms, sPLA2-IIA, sPLA2-V, sPLA2-X, sPLA2-IB, sPLA2-IIC, and sPLA2-XVI, increased over the first 7 ICU days in those who succumbed to the disease but attenuated over the same time period in survivors. In contrast, a reversed pattern in sPLA2-IID and sPLA2-XIIB levels over 7 days suggests a protective role of these two isoforms. Furthermore, decision tree models demonstrated that sPLA2-IIA outperformed top-ranked cytokines and chemokines as a predictor of patient outcome. Taken together, proteomic analysis revealed temporal sPLA2 patterns that reflect the critical roles of sPLA2 isoforms in severe COVID-19 disease.

Machine learning models reveal distinct disease subgroups and improve diagnostic and prognostic accuracy for individuals with pathogenic SCN8A gain-of-function variants.

Distinguishing clinical subgroups for patients suffering with diseases characterized by a wide phenotypic spectrum is essential for developing precision therapies. Patients with gain-of-function (GOF) variants in the SCN8A gene exhibit substantial clinical heterogeneity, viewed historically as a linear spectrum ranging from mild to severe. To test for hidden clinical subgroups, we applied two machine-learning algorithms to analyze a dataset of patient features collected by the International SCN8A Patient Registry. We used two research methodologies: a supervised approach that incorporated feature severity cutoffs based on clinical conventions, and an unsupervised approach employing an entirely data-driven strategy. Both approaches found statistical support for three distinct subgroups and were validated by correlation analyses using external variables. However, distinguishing features of the three subgroups within each approach were not concordant, suggesting a more complex phenotypic landscape. The unsupervised approach yielded strong support for a model involving three partially ordered subgroups rather than a linear spectrum. Application of these machine-learning approaches may lead to improved prognosis and clinical management of individuals with SCN8A GOF variants and provide insights into the underlying mechanisms of the disease.

Sex differences in physiological response to increased neuronal excitability in a knockin mouse model of pediatric epilepsy.

BACKGROUND: Epilepsy is a common neurological disease; however, few if any of the currently marketed antiseizure medications prevent or cure epilepsy. Discovery of pathological processes in the early stages of epileptogenesis has been challenging given the common use of preclinical models that induce seizures in physiologically normal animals. Moreover, despite known sex dimorphism in neurological diseases, females are rarely included in preclinical epilepsy models. METHODS: We characterized sex differences in mice carrying a pathogenic knockin variant (p.N1768D) in the Scn8a gene that causes spontaneous tonic-clonic seizures (TCs) at ∼3 months of age and found that heterozygous females are more resilient than males in mortality and morbidity. To investigate the cellular mechanisms that underlie female resilience, we utilized blood-brain barrier (BBB) and hippocampal transcriptomic analyses in heterozygous mice before seizure onset (pre-TC) and in mice that experienced ∼20 TCs (post-TC). RESULTS: In the pre-TC latent phase, both sexes exhibited leaky BBB; however, patterns of gene expression were sexually dimorphic. Females exhibited enhanced oxidative phosphorylation and protein biogenesis, while males activated gliosis and CREB signaling. After seizure onset (chronic phase), females exhibited a metabolic switch to lipid metabolism, while males exhibited increased gliosis and BBB dysfunction and a strong activation of neuroinflammatory pathways. CONCLUSION: The results underscore the central role of oxidative stress and BBB permeability in the early stages of epileptogenesis, as well as sex dimorphism in response to increasing neuronal hyperexcitability. Our results also highlight the need to include both sexes in preclinical studies to effectively translate results of drug efficacy studies.

Greater female than male resilience to mortality and morbidity in the Scn8a mouse model of pediatric epilepsy.

AIMS: Females and males of all ages are affected by epilepsy; however, unlike many clinical studies, most preclinical research has focused on males. Genetic variants in the voltage-gated sodium channel gene, SCN8A, are associated with a broad spectrum of neurological and epileptic syndromes. Here we investigate sex differences in the natural history of the Scn8a-N1768D knockin mouse model of pediatric epilepsy. METHODS: We utilize 24/7 video to monitor juveniles and adults of both sexes to investigate variability in seizure activity (e.g. onset and frequency), mortality and morbidity, response to cannabinoids, and mode of death. We also monitor sleep architecture using a noninvasive piezoelectric method in order to identify factors that influence seizure severity and outcome. RESULTS: Both sexes had nearly 100% penetrance in seizure onset and early mortality. However, adult heterozygous (D/+) females were more resilient as exhibited by the ability to tolerate more seizures over a longer lifespan. Homozygous (D/D) juveniles did not exhibit a sex difference in overall survival. Female estrus cycle was disrupted before seizure onset, while sleep was disrupted in both sexes in association with seizure onset. Females typically died while in convulsive status epilepticus; however, a high proportion of males died while not experiencing behavioral seizures. Only juvenile and adult males benefited from cannabinoid administration. CONCLUSIONS: These results support the hypothesis that factors associated with sexual differentiation play a role in the neurobiology of epilepsy and point to the importance of including both sexes in the design of studies to identify new epilepsy therapies.

Clinical severity is correlated with age at seizure onset and biophysical properties of recurrent gain of function variants associated with SCN8A-related epilepsy.

OBJECTIVE: Genetic variants in the SCN8A gene underlie a wide spectrum of neurodevelopmental phenotypes including several distinct seizure types and a host of comorbidities. One of the major challenges facing clinicians and researchers alike is to identify genotype-phenotype (G-P) correlations that may improve prognosis, guide treatment decisions, and lead to precision medicine approaches. METHODS: We investigated G-P correlations among 270 participants harboring gain-of-function (GOF) variants enrolled in the International SCN8A Registry, a patient-driven online database. We performed correlation analyses stratifying the cohort by clinical phenotypes to identify diagnostic features that differ among patients with varying levels of clinical severity, and that differ among patients with distinct GOF variants. RESULTS: Our analyses confirm positive correlations between age at seizure onset and developmental skills acquisition (developmental quotient), rate of seizure freedom, and percentage of cohort with developmental delays, and identify negative correlations with number of current and weaned antiseizure medications. This set of features is more detrimentally affected in individuals with a priori expectations of more severe clinical phenotypes. Our analyses also reveal a significant correlation between a severity index combining clinical features of individuals with a particular highly recurrent variant and an independent electrophysiological score assigned to each variant based on in vitro testing. SIGNIFICANCE: This is one of the first studies to identify statistically significant G-P correlations for individual SCN8A variants with GOF properties. The results suggest that individual GOF variants (1) are predictive of clinical severity for individuals carrying those variants and (2) may underlie distinct clinical phenotypes of SCN8A disease, thus helping to explain the wide SCN8A-related epilepsy disease spectrum. These results also suggest that certain features present at initial diagnosis are predictive of clinical severity, and with more informed treatment plans, may serve to improve prognosis for patients with SCN8A GOF variants.

The International SCN8A Patient Registry: A Scientific Resource to Advance the Understanding and Treatment of a Rare Pediatric Neurodevelopmental Syndrome.

Genetic variants in the SCN8A gene underlie a wide spectrum of neurodevelopmental phenotypes that range from severe epileptic encephalopathy to benign familial infantile epilepsy to neurodevelopmental delays with or without seizures. A host of additional comorbidities also contribute to the phenotypic spectrum. As a result of the recent identification of the genetic etiology and the length of time it often takes to diagnose patients, little data are available on the natural history of these conditions. The International SCN8A Patient Registry was developed in 2015 to fill gaps in understanding the spectrum of the disease and its natural history, as well as the lived experiences of individuals with SCN8A syndrome. Another goal of the registry is to collect longitudinal data from participants on a regular basis. In this article, we describe the construction and structure of the International SCN8A Patient Registry, present the type of information available, and highlight particular analyses that demonstrate how registry data can provide insights into the clinical management of SCN8A syndrome.

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

Background and Objectives: Pathogenic variants at the voltage-gated sodium channel gene, SCN8A, are associated with a wide spectrum of clinical disease outcomes. A critical challenge for neurologists is to determine whether patients carry gain-of-function (GOF) or loss-of-function (LOF) variants to guide treatment decisions, yet in vitro studies to infer channel function are often not feasible in the clinic. In this study, we develop a predictive modeling approach to classify variants based on clinical features present at initial diagnosis. Methods: We performed an exhaustive search for individuals deemed to carry SCN8A GOF and LOF variants by means of in vitro studies in heterologous cell systems, or because the variant was classified as truncating, and recorded clinical features. This resulted in a total of 69 LOF variants: 34 missense and 35 truncating variants, including 9 nonsense, 13 frameshift, 6 splice site, 6 indels, and 1 large deletion. We then assembled a truth set of variants with known functional effects, excluding individuals carrying variants at other loci associated with epilepsy. We then trained a predictive model based on random forest using this truth set of 45 LOF variants and 45 GOF variants randomly selected from a set of variants tested by in vitro methods. Results: Phenotypic categories assigned to individuals correlated strongly with GOF or LOF variants. All patients with GOF variants experienced early-onset seizures (mean age at onset = 4.5 ± 3.1 months) while only 64.4% patients with LOF variants had seizures, most of which were late-onset absence seizures (mean age at onset = 40.0 ± 38.1 months). With high accuracy (95.4%), our model including 5 key clinical features classified individuals with GOF and LOF variants into 2 distinct cohorts differing in age at seizure onset, development of seizures, seizure type, intellectual disability, and developmental and epileptic encephalopathy. Discussion: The results support the hypothesis that patients with SCN8A GOF and LOF variants represent distinct clinical phenotypes. The clinical model developed in this study has great utility because it provides a rapid and highly accurate platform for predicting the functional class of patient variants during SCN8A diagnosis, which can aid in initial treatment decisions and improve prognosis.

Identification of an allele-specific transcription factor binding interaction that regulates PLA2G2A gene expression.

The secreted phospholipase A 2 (sPLA 2 ) isoform, sPLA 2 -IIA, has been implicated in a variety of diseases and conditions, including bacteremia, cardiovascular disease, COVID-19, sepsis, adult respiratory distress syndrome, and certain cancers. Given its significant role in these conditions, understanding the regulatory mechanisms impacting its levels is crucial. Genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs), including rs11573156, that are associated with circulating levels of sPLA 2 -IIA. Through Genotype-Tissue Expression (GTEx), 234 expression quantitative trait loci (eQTLs) were identified for the gene that encodes for sPLA 2 -IIA, PLA2G2A . SNP2TFBS ( https://ccg.epfl.ch/snp2tfbs/ ) was utilized to ascertain the binding affinities between transcription factors (TFs) to both the reference and alternative alleles of identified SNPs. Subsequently, ChIP-seq peaks highlighted the TF combinations that specifically bind to the SNP, rs11573156. SP1 emerged as a significant TF/SNP pair in liver cells, with rs11573156/SP1 interaction being most prominent in liver, prostate, ovary, and adipose tissues. Further analysis revealed that the upregulation of PLA2G2A transcript levels through the rs11573156 variant was affected by tissue SP1 protein levels. By leveraging an ordinary differential equation, structured upon Michaelis-Menten enzyme kinetics assumptions, we modeled the PLA2G2A transcription's dependence on SP1 protein levels, incorporating the SNP's influence. Collectively, these data strongly suggest that the binding affinity differences of SP1 for the different rs11573156 alleles can influence PLA2G2A expression. This, in turn, can modulate sPLA2-IIA levels, impacting a wide range of human diseases.

Temporal Associations of Plasma Levels of the Secreted Phospholipase A 2 Family and Mortality in Severe COVID-19.

Previous research suggests that group IIA secreted phospholipase A 2 (sPLA 2 -IIA) plays a role in and predicts severe COVID-19 disease. The current study reanalyzed a longitudinal proteomic data set to determine the temporal (days 0, 3 and 7) relationship between the levels of several members of a family of sPLA 2 isoforms and the severity of COVID-19 in 214 ICU patients. The levels of six secreted PLA 2 isoforms, sPLA 2 -IIA, sPLA 2 -V, sPLA 2 -X, sPLA 2 -IB, sPLA 2 -IIC, and sPLA 2 -XVI, increased over the first 7 ICU days in those who succumbed to the disease. sPLA 2 -IIA outperformed top ranked cytokines and chemokines as predictors of patient outcome. A decision tree corroborated these results with day 0 to day 3 kinetic changes of sPLA 2 -IIA that separated the death and severe categories from the mild category and increases from day 3 to day 7 significantly enriched the lethal category. In contrast, there was a time-dependent decrease in sPLA 2 -IID and sPLA 2 -XIIB in patients with severe or lethal disease, and these two isoforms were at higher levels in mild patients. Taken together, proteomic analysis revealed temporal sPLA 2 patterns that reflect the critical roles of sPLA 2 isoforms in severe COVID-19 disease.

Diagnoses of Exclusion in the Workup of Abdominal Complaints.

Abdominal pain is a common complaint in the emergency department, comprising 8.8% of all visits. Despite advances in medicine and imaging, 20% to 30% of patients still leave the department without a definitive diagnosis, whichhis can be both distressing for patients and unsatisfying for providers. Diagnoses of exclusion can be perilous, and their application should be carefully considered in order to not overlook more emergent complaints. However, a working knowledge of diagnoses of exclusion can guide therapeutics and specialty referrals that can ultimately provide answers and relief to a patient population often at odds with available information and expectations.

A novel nonlinear dimension reduction approach to infer population structure for low-coverage sequencing data.

BACKGROUND: Low-depth sequencing allows researchers to increase sample size at the expense of lower accuracy. To incorporate uncertainties while maintaining statistical power, we introduce MCPCA_PopGen to analyze population structure of low-depth sequencing data. RESULTS: The method optimizes the choice of nonlinear transformations of dosages to maximize the Ky Fan norm of the covariance matrix. The transformation incorporates the uncertainty in calling between heterozygotes and the common homozygotes for loci having a rare allele and is more linear when both variants are common. CONCLUSIONS: We apply MCPCA_PopGen to samples from two indigenous Siberian populations and reveal hidden population structure accurately using only a single chromosome. The MCPCA_PopGen package is available on https://github.com/yiwenstat/MCPCA_PopGen .

Variable patterns of mutation density among NaV1.1, NaV1.2 and NaV1.6 point to channel-specific functional differences associated with childhood epilepsy.

Variants implicated in childhood epilepsy have been identified in all four voltage-gated sodium channels that initiate action potentials in the central nervous system. Previous research has focused on the functional effects of particular variants within the most studied of these channels (NaV1.1, NaV1.2 and NaV1.6); however, there have been few comparative studies across channels to infer the impact of mutations in patients with epilepsy. Here we compare patterns of variation in patient and public databases to test the hypothesis that regions of known functional significance within voltage-gated sodium (NaV) channels have an increased burden of deleterious variants. We assessed mutational burden in different regions of the Nav channels by (1) performing Fisher exact tests on odds ratios to infer excess variants in domains, segments, and loops of each channel in patient databases versus public "control" databases, and (2) comparing the cumulative distribution of variant sites along DNA sequences of each gene in patient and public databases (i.e., independent of protein structure). Patient variant density was concordant among channels in regions known to play a role in channel function, with statistically significant higher patient variant density in S4-S6 and DIII-DIV and an excess of public variants in SI-S3, DI-DII, DII-DIII. On the other hand, channel-specific patterns of patient burden were found in the NaV1.6 inactivation gate and NaV1.1 S5-S6 linkers, while NaV1.2 and NaV1.6 S4-S5 linkers and S5 segments shared patient variant patterns that contrasted with those in NaV1.1. These different patterns may reflect different roles played by the NaV1.6 inactivation gate in action potential propagation, and by NaV1.1 S5-S6 linkers in loss of function and haploinsufficiency. Interestingly, NaV1.2 and NaV1.6 both lack amino acid substitutions over significantly long stretches in both the patient and public databases suggesting that new mutations in these regions may cause embryonic lethality or a non-epileptic disease phenotype.

Influence of age at seizure onset on the acquisition of neurodevelopmental skills in an SCN8A cohort.

OBJECTIVE: To characterize a cohort of patients with SCN8A-related epilepsy and to perform analyses to identify correlations involving the acquisition of neurodevelopmental skills. METHODS: We analyzed patient data (n = 91) submitted to an online registry tailored to characteristics of children with SCN8A variants. Participants provided information on the history of their child's seizures, medications, comorbidities, and developmental skills based on the Denver II items. Spearman rank tests were utilized to test for correlations among a variety of aspects of seizures, medications, and neurodevelopmental progression. RESULTS: The 91 participants carried 71 missense variants (41 newly reported) and three truncating variants. Ages at seizure onset ranged from birth to >12 months of age (mean ± SD = 5 months 21 days ± 7 months 14 days). Multiple seizure types with multimodal onset times and developmental delay were observed as general features of this cohort. We found a positive correlation between a developmental score based upon percentage of acquired skills and the age at seizure onset, current seizure freedom, and initial febrile seizures. Analyses of cohort subgroups revealed clear distinctions between patients who had a single reported variant in SCN8A and those with an additional variant reported in a gene other than SCN8A, as well as between patients with different patterns of regression before and at seizure onset. SIGNIFICANCE: This is the first study of an SCN8A patient cohort of this size and for which correlations between age at seizure onset and neurodevelopment were investigated. Our correlation studies suggest that variants of uncertain significance should be considered in assessing children with SCN8A-related disorders. This study substantially improves the characterization of this patient population and our understanding of the neurodevelopmental effects associated with seizures for SCN8A patients, and provides a clinical context at initial presentation that may be prognostic for developmental outcome.

Genomic Evidence of Local Adaptation to Climate and Diet in Indigenous Siberians.

The indigenous inhabitants of Siberia live in some of the harshest environments on earth, experiencing extended periods of severe cold temperatures, dramatic variation in photoperiod, and limited and highly variable food resources. While the successful long-term settlement of this area by humans required multiple behavioral and cultural innovations, the nature of the underlying genetic changes has generally remained elusive. In this study, we used a three-part approach to identify putative targets of positive natural selection in Siberians. We first performed selection scans on whole exome and genome-wide single nucleotide polymorphism array data from multiple Siberian populations. We then annotated candidates in the tails of the empirical distributions, focusing on candidates with evidence linking them to biological processes and phenotypes previously identified as relevant to adaptation in circumpolar groups. The top candidates were then genotyped in additional populations to determine their spatial allele frequency distributions and associations with climate variables. Our analysis reveals missense mutations in three genes involved in lipid metabolism (PLA2G2A, PLIN1, and ANGPTL8) that exhibit genomic and spatial patterns consistent with selection for cold climate and/or diet. These variants are unified by their connection to brown adipose tissue and may help to explain previously observed physiological differences in Siberians such as low serum lipid levels and increased basal metabolic rate. These results support the hypothesis that indigenous Siberians have genetically adapted to their local environment by selection on multiple genes.

The Role of Phylogenetically Conserved Elements in Shaping Patterns of Human Genomic Diversity.

Evolutionary genetic studies have shown a positive correlation between levels of nucleotide diversity and either rates of recombination or genetic distance to genes. Both positive-directional and purifying selection have been offered as the source of these correlations via genetic hitchhiking and background selection, respectively. Phylogenetically conserved elements (CEs) are short (∼100 bp), widely distributed (comprising ∼5% of genome), sequences that are often found far from genes. While the function of many CEs is unknown, CEs also are associated with reduced diversity at linked sites. Using high coverage (>80×) whole genome data from two human populations, the Yoruba and the CEU, we perform fine scale evaluations of diversity, rates of recombination, and linkage to genes. We find that the local rate of recombination has a stronger effect on levels of diversity than linkage to genes, and that these effects of recombination persist even in regions far from genes. Our whole genome modeling demonstrates that, rather than recombination or GC-biased gene conversion, selection on sites within or linked to CEs better explains the observed genomic diversity patterns. A major implication is that very few sites in the human genome are predicted to be free of the effects of selection. These sites, which we refer to as the human "neutralome," comprise only 1.2% of the autosomes and 5.1% of the X chromosome. Demographic analysis of the neutralome reveals larger population sizes and lower rates of growth for ancestral human populations than inferred by previous analyses.

Modeling SNP array ascertainment with Approximate Bayesian Computation for demographic inference.

Single nucleotide polymorphisms (SNPs) in commercial arrays have often been discovered in a small number of samples from selected populations. This ascertainment skews patterns of nucleotide diversity and affects population genetic inferences. We propose a demographic inference pipeline that explicitly models the SNP discovery protocol in an Approximate Bayesian Computation (ABC) framework. We simulated genomic regions according to a demographic model incorporating parameters for the divergence of three well-characterized HapMap populations and recreated the SNP distribution of a commercial array by varying the number of haploid samples and the allele frequency cut-off in the given regions. We then calculated summary statistics obtained from both the ascertained and genomic data and inferred ascertainment and demographic parameters. We implemented our pipeline to study the admixture process that gave rise to the present-day Mexican population. Our estimate of the time of admixture is closer to the historical dates than those in previous works which did not consider ascertainment bias. Although the use of whole genome sequences for demographic inference is becoming the norm, there are still underrepresented areas of the world from where only SNP array data are available. Our inference framework is applicable to those cases and will help with the demographic inference.

Exome Sequencing Provides Evidence of Polygenic Adaptation to a Fat-Rich Animal Diet in Indigenous Siberian Populations.

Siberia is one of the coldest environments on Earth and has great seasonal temperature variation. Long-term settlement in northern Siberia undoubtedly required biological adaptation to severe cold stress, dramatic variation in photoperiod, and limited food resources. In addition, recent archeological studies show that humans first occupied Siberia at least 45,000 years ago; yet our understanding of the demographic history of modern indigenous Siberians remains incomplete. In this study, we use whole-exome sequencing data from the Nganasans and Yakuts to infer the evolutionary history of these two indigenous Siberian populations. Recognizing the complexity of the adaptive process, we designed a model-based test to systematically search for signatures of polygenic selection. Our approach accounts for stochasticity in the demographic process and the hitchhiking effect of classic selective sweeps, as well as potential biases resulting from recombination rate and mutation rate heterogeneity. Our demographic inference shows that the Nganasans and Yakuts diverged ∼12,000-13,000 years ago from East-Asian ancestors in a process involving continuous gene flow. Our polygenic selection scan identifies seven candidate gene sets with Siberian-specific signals. Three of these gene sets are related to diet, especially to fat metabolism, consistent with the hypothesis of adaptation to a fat-rich animal diet. Additional testing rejects the effect of hitchhiking and favors a model in which selection yields small allele frequency changes at multiple unlinked genes.

Rare variants of small effect size in neuronal excitability genes influence clinical outcome in Japanese cases of SCN1A truncation-positive Dravet syndrome.

Dravet syndrome (DS) is a rare, devastating form of childhood epilepsy that is often associated with mutations in the voltage-gated sodium channel gene, SCN1A. There is considerable variability in expressivity within families, as well as among individuals carrying the same primary mutation, suggesting that clinical outcome is modulated by variants at other genes. To identify modifier gene variants that contribute to clinical outcome, we sequenced the exomes of 22 individuals at both ends of a phenotype distribution (i.e., mild and severe cognitive condition). We controlled for variation associated with different mutation types by limiting inclusion to individuals with a de novo truncation mutation resulting in SCN1A haploinsufficiency. We performed tests aimed at identifying 1) single common variants that are enriched in either phenotypic group, 2) sets of common or rare variants aggregated in and around genes associated with clinical outcome, and 3) rare variants in 237 candidate genes associated with neuronal excitability. While our power to identify enrichment of a common variant in either phenotypic group is limited as a result of the rarity of mild phenotypes in individuals with SCN1A truncation variants, our top candidates did not map to functional regions of genes, or in genes that are known to be associated with neurological pathways. In contrast, we found a statistically-significant excess of rare variants predicted to be damaging and of small effect size in genes associated with neuronal excitability in severely affected individuals. A KCNQ2 variant previously associated with benign neonatal seizures is present in 3 of 12 individuals in the severe category. To compare our results with the healthy population, we performed a similar analysis on whole exome sequencing data from 70 Japanese individuals in the 1000 genomes project. Interestingly, the frequency of rare damaging variants in the same set of neuronal excitability genes in healthy individuals is nearly as high as in severely affected individuals. Rather than a single common gene/variant modifying clinical outcome in SCN1A-related epilepsies, our results point to the cumulative effect of rare variants with little to no measurable phenotypic effect (i.e., typical genetic background) unless present in combination with a disease-causing truncation mutation in SCN1A.

Clinical implications of SCN1A missense and truncation variants in a large Japanese cohort with Dravet syndrome.

OBJECTIVE: Two major classes of SCN1A variants are associated with Dravet syndrome (DS): those that result in haploinsufficiency (truncating) and those that result in an amino acid substitution (missense). The aim of this retrospective study was to describe the first large cohort of Japanese patients with SCN1A mutation-positive DS (n = 285), and investigate the relationship between variant (type and position) and clinical expression and response to treatment. METHODS: We sequenced all exons and intron-exon boundaries of SCN1A in our cohort, investigated differences in the distribution of truncating and missense variants, tested for associations between variant type and phenotype, and compared these patterns with those of cohorts with milder epilepsy and healthy individuals. RESULTS: Unlike truncation variants, missense variants are found at higher density in the S4 voltage sensor and pore loops and at lower density in the domain I-II and II-III linkers and the first three segments of domain II. Relative to healthy individuals, there is an increased frequency of truncating (but not missense) variants in the noncoding C-terminus. The rate of cognitive decline is more rapid for patients with truncation variants regardless of age at seizure onset, whereas age at onset is a predictor of the rate of cognitive decline for patients with missense variants. SIGNIFICANCE: We found significant differences in the distribution of truncating and missense variants across the SCN1A sequence among healthy individuals, patients with DS, and those with milder forms of SCN1A-variant positive epilepsy. Testing for associations with phenotype revealed that variant type can be predictive of rate of cognitive decline. Analysis of descriptive medication data suggests that in addition to conventional drug therapy in DS, bromide, clonazepam and topiramate may reduce seizure frequency.