Rare genetic risk factors in common idiopathic epilepsy syndromes

Epilepsy is one of the most common neurological disorders characterized by recurrent unprovoked seizures due to increased neuronal hyperexcitability and abnormal synchronization. I have explored the genetic architecture of the most common types of idiopathic epilepsies, the idiopathic generalized epilepsies (IGEs) and the spectrum of idiopathic focal epilepsies related to rolandic epilepsies (REs). Both groups are distinguishable by the leading seizure types, age-dependent onset and electroencephalographic criteria. For IGEs a complex genetic contribution is indicated from family and twin studies, whereas the genetic basis of REs has been a matter of debate. At present, the majority of genetic factors predisposing to common idiopathic epilepsies remain elusive. The aim of the present studies was the molecular genetic dissection of genetic risk factors with strong epileptogenic effects in idiopathic epilepsies and the elucidation of their molecular pathways in epileptogenesis. Therefore, our group conducted copy number variation (CNV) analyses using high-resolution SNP arrays in two case-control cohorts comprising: i) 1582 IGE and 2795 populations controls, and ii) 308 patients with RE-spectrum epilepsies and 1512 controls of European descent. In addition, we performed candidate gene sequence analysis in 242 patients with RE-spectrum epilepsies. With regard to the low power of our study samples for genome-wide scans, our current strategies focused on a candidate gene/locus approach of genes and CNVs that have been implicated in the pathogenesis of epilepsies and related neurodevelopmental disorders, as well as candidate genes with a presumed impact on neuronal excitability. In the present cumulative thesis, I report the results of our current CNV and sequence analyses of highly plausible candidate genes based on three peer-reviewed publications and another three manuscripts that are currently in revision. We are the first to show a significant statistical association for deletions affecting the neuronal splicing regulator RBFOX1 with idiopathic epilepsies (Lal et al., 2013a). Moreover, we also detected deletions and truncating mutations in RBFOX1 as well as in the neuronal splicing regulator RBFOX3 in the RE cohort (Lal et al., 2013b). We provide genetic, functional, and neurophysiological evidence that structural exonic microdeletions affecting the GPHN gene, which codes for the synaptic scaffolding protein gephyrin, may increase neuronal excitability by an impairment of GABAergic synaptic inhibition and thereby confer susceptibility for IGE (Dejanovic et al., in revision). We also found that missense and truncating exonic mutations and microdeletions of the GRIN2A gene encoding the NMDA-receptor NR2A subunit are a major genetic risk factor in 7.5% of children with idiopathic focal epilepsies including RE-spectrum epilepsies (Lemke et al., 2013). Notably, mutations in the DEPDC5 gene, encoding DEP domain-containing protein 5, preferentially predispose to focal idiopathic epilepsies (Lal et al., in revision). Finally, screening for recurrent CNVs revealed 16p11.2 duplications as an important genetic risk factor for the spectrum of REs (Reinthaler et al., in revision), thereby further supporting the associations between 16p11.2 duplications and early onset neurodevelopmental disorders. In conclusion, our studies identified several genetic risk factors in a significant fraction of idiopathic epilepsy patients. Moreover, the present study is the first that clearly proves a genetic impact on the etiology of the common REs at the molecular genetic level. We are aware that our small samples sizes provide insufficient power for exome-wide rare-mutation or genome-wide rare-CNV association testing. Future large-scale studies are warranted to identify additional pathogenic variants to better understand the complex genetic architecture of the most common types of idiopathic epilepsies

DDGun: an untrained predictor of protein stability changes upon amino acid variants

Estimating the functional effect of single amino acid variants in proteins is fundamental for predicting the change in the thermodynamic stability, measured as the difference in the Gibbs free energy of unfolding, between the wild-type and the variant protein (ΔΔG). Here, we present the web-server of the DDGun method, which was previously developed for the ΔΔG prediction upon amino acid variants. DDGun is an untrained method based on basic features derived from evolutionary information. It is antisymmetric, as it predicts opposite ΔΔG values for direct (A → B) and reverse (B → A) single and multiple site variants. DDGun is available in two versions, one based on only sequence information and the other one based on sequence and structure information. Despite being untrained, DDGun reaches prediction performances comparable to those of trained methods. Here we make DDGun available as a web server. For the web server version, we updated the protein sequence database used for the computation of the evolutionary features, and we compiled two new data sets of protein variants to do a blind test of its performances. On these blind data sets of single and multiple site variants, DDGun confirms its prediction performance, reaching an average correlation coefficient between experimental and predicted ΔΔG of 0.45 and 0.49 for the sequence-based and structure-based versions, respectively. Besides being used for the prediction of ΔΔG, we suggest that DDGun should be adopted as a benchmark method to assess the predictive capabilities of newly developed methods. Releasing DDGun as a web-server, stand-alone program and docker image will facilitate the necessary process of method comparison to improve ΔΔG prediction

In‐Depth Extracorporeal Cardiopulmonary Resuscitation in Adult Out‐of‐Hospital Cardiac Arrest

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Duplications at 19q13.33 in patients with neurodevelopmental disorders

Objective After the recent publication of the first patients with disease-associated missense variants in the GRIN2D gene, we evaluate the effect of copy number variants (CNVs) overlapping this gene toward the presentation of neurodevelopmental disorders (NDDs). Methods We exploredClinVar (number ofCNVs = 50,794) andDECIPHER (number ofCNVs = 28,085) clinical databases of genomic variations for patients with copy number changes overlapping the GRIN2D gene at the 19q13.33 locus and evaluated their respective phenotype alongside their frequency, gene content, and expression, with publicly available reference databases. Results We identified 11 patients with microduplications at the 19q13.33 locus. The majority of CNVs arose de novo, and comparable CNVs are not present in control databases. All patients were reported to have NDDs and dysmorphic features as the most common clinical phenotype (N = 8/11), followed by seizures (N = 6/11) and intellectual disability (N = 5/11). All duplications shared a consensus region of 405 kb overlapping 13 genes. After screening for duplication tolerance in control populations, positive gene brain expression, and gene dosage sensitivity analysis, we highlight 4 genes for future evaluation: CARD8, C19orf68, KDELR1, and GRIN2D, which are promising candidates for disease causality. Furthermore, investigation of the literature especially supports GRIN2D as the best candidate gene. Conclusions Our study presents dup19q13.33 as a novel duplication syndrome locus associated with NDDs. CARD8, C19orf68, KDELR1, and GRIN2D are promising candidates for functional follow-up.Peer reviewe

Polygenic risk heterogeneity among focal epilepsies

Focal epilepsy (FE) is clinically highly heterogeneous. It has been shown recently that not only rare but also a subset of common genetic variants confer risk for FE. The relatively modest power of genetic studies in FE suggests a high genetic heterogeneity of FE when grouped as one disorder. We hypothesize that the clinical heterogeneity of FE is correlated with genetic heterogeneity on a common risk variant level. To test the hypothesis, we used an FE polygenic risk score "FE-PRS" that combines small effect sizes of thousands of common variants from the largest FE-GWAS (genome-wide association study) into a single measure. We grouped 414 individuals with FE according to common clinical features into subgroups, either by one feature at a time or by all features combined in a cluster analysis. We examined their association with FE-PRS compared to 20 435 matched population controls and observed heterogeneous FE-PRS burden among the subgroups. The highest phenotypic variance explained by FE-PRS was identified in a cluster analysis-defined FE subgroup where all individuals had unknown etiologies and psychiatric comorbidities, and the majority had early onset seizures. Our results indicate that genetic factors associated with FE have differential burden among FE subtypes. Future studies using better-powered FE-PRS might have clinical utility.Peer reviewe

Gene variant effects across sodium channelopathies predict function and guide precision therapy

Pathogenic variants in the voltage-gated sodium channel gene family lead to early onset epilepsies, neurodevelopmental disorders, skeletal muscle channelopathies, peripheral neuropathies and cardiac arrhythmias. Disease-associated variants have diverse functional effects ranging from complete loss-of-function to marked gain-of-function. Therapeutic strategy is likely to depend on functional effect. Experimental studies offer important insights into channel function but are resource intensive and only performed in a minority of cases. Given the evolutionarily conserved nature of the sodium channel genes, we investigated whether similarities in biophysical properties between different voltage-gated sodium channels can predict function and inform precision treatment across sodium channelopathies. We performed a systematic literature search identifying functionally assessed variants in any of the nine voltage-gated sodium channel genes until 28 April 2021. We included missense variants that had been electrophysiologically characterized in mammalian cells in whole-cell patch-clamp recordings. We performed an alignment of linear protein sequences of all sodium channel genes and correlated variants by their overall functional effect on biophysical properties. Of 951 identified records, 437 sodium channel-variants met our inclusion criteria and were reviewed for functional properties. Of these, 141 variants were epilepsy-associated (SCN1/2/3/8A), 79 had a neuromuscular phenotype (SCN4/9/10/11A), 149 were associated with a cardiac phenotype (SCN5/10A) and 68 (16%) were considered benign. We detected 38 missense variant pairs with an identical disease-associated variant in a different sodium channel gene. Thirty-five out of 38 of those pairs resulted in similar functional consequences, indicating up to 92% biophysical agreement between corresponding sodium channel variants (odds ratio = 11.3; 95% confidence interval = 2.8 to 66.9; P < 0.001). Pathogenic missense variants were clustered in specific functional domains, whereas population variants were significantly more frequent across non-conserved domains (odds ratio = 18.6; 95% confidence interval = 10.9-34.4; P < 0.001). Pore-loop regions were frequently associated with loss-of-function variants, whereas inactivation sites were associated with gain-of-function (odds ratio = 42.1, 95% confidence interval = 14.5-122.4; P < 0.001), whilst variants occurring in voltage-sensing regions comprised a range of gain- and loss-of-function effects. Our findings suggest that biophysical characterisation of variants in one SCN-gene can predict channel function across different SCN-genes where experimental data are not available. The collected data represent the first gain- versus loss-of-function topological map of SCN proteins indicating shared patterns of biophysical effects aiding variant analysis and guiding precision therapy. We integrated our findings into a free online webtool to facilitate functional sodium channel gene variant interpretation (http://SCN-viewer.broadinstitute.org).Peer reviewe

Neurological disorder-associated genetic variants in individuals with psychogenic nonepileptic seizures

Psychogenic nonepileptic seizures (PNES) are diagnosed in approximately 30% of patients referred to tertiary care epilepsy centers. Little is known about the molecular pathology of PNES, much less about possible underlying genetic factors. We generated whole-exome sequencing and whole-genome genotyping data to identify rare, pathogenic (P) or likely pathogenic (LP) variants in 102 individuals with PNES and 448 individuals with focal (FE) or generalized (GE) epilepsy. Variants were classified for all individuals based on the ACMG-AMP 2015 guidelines. For research purposes only, we considered genes associated with neurological or psychiatric disorders as candidate genes for PNES. We observe in this first genetic investigation of PNES that six (5.88%) individuals with PNES without coexistent epilepsy carry P/LP variants (deletions at 10q11.22-q11.23, 10q23.1-q23.2, distal 16p11.2, and 17p13.3, and nonsynonymous variants in NSD1 and GABRA5). Notably, the burden of P/LP variants among the individuals with PNES was similar and not significantly different to the burden observed in the individuals with FE (3.05%) or GE (1.82%) (PNES vs. FE vs. GE (3x2 chi (2)), P=0.30; PNES vs. epilepsy (2x2 chi (2)), P=0.14). The presence of variants in genes associated with monogenic forms of neurological and psychiatric disorders in individuals with PNES shows that genetic factors are likely to play a role in PNES or its comorbidities in a subset of individuals. Future large-scale genetic research studies are needed to further corroborate these interesting findings in PNES.Peer reviewe

Reassessment Of Lesion-Associated Gene And Variant Pathogenicity In Focal Human Epilepsies

Purpose: Increasing availability of surgically resected brain tissue from Focal Cortical Dysplasia and low-grade epilepsy-associated tumor patients fostered large-scale genetic examination. However, assessment of germline and somatic variant pathogenicity remains difficult. Methods: Here, we critically reevaluated the pathogenicity for all neuropathology-associated variants reported to date in the PubMed and ClinVar databases, including 12 disease-related genes and 88 neuropathology-associated missense variants. We (1) assessed evolutionary gene constraint using the pLI and missense z scores, (2) applied guidelines by the American College of Medical Genetics and Genomics (ACMG), and (3) predicted pathogenicity by using PolyPhen-2, CADD, and GERP. Results: Constraint analysis classified only seven out of 12 genes to be likely disease-associated, while 35 (40\%) of those 88 variants were classified as being variants of unknown significance (VUS) and 53 (60\%) as being likely pathogenic (LPII). Pathogenicity prediction yielded discrimination between neuropathology-associated variants (LPII and VUS) and rare variant scores obtained from individuals present in the Genome Aggregation Database (gnomAD). Conclusion: We conclude that several VUS are likely disease-associated and will be reclassified by future molecular evidence. In summary, interpretation of lesion-associated gene variants remains complex while the application of current ACMG guidelines including bioinformatic pathogenicity prediction will help improving interpretation and prediction

Data-driven historical characterization of epilepsy-associated genes.

Many epilepsy-associated genes have been identified over the last three decades, revealing a remarkable molecular heterogeneity with the shared outcome of recurrent seizures. Information about the genetic landscape of epilepsies is scattered throughout the literature and answering the simple question of how many genes are associated with epilepsy is not straightforward. Here, we present a computationally driven analytical review of epilepsy-associated genes using the complete scientific literature in PubMed. Based on our search criteria, we identified a total of 738 epilepsy-associated genes. We further classified these genes into two Tiers. A broad gene list of 738 epilepsy-associated genes (Tier 2) and a narrow gene list composed of 143 epilepsy-associated genes (Tier 1). Our search criteria do not reflect the degree of association. The average yearly number of identified epilepsy-associated genes between 1992 and 2021 was 4.8. However, most of these genes were only identified in the last decade (2010–2019). Ion channels represent the largest class of epilepsy-associated genes. For many of these, both gain- and loss-of-function effects have been associated with epilepsy in recent years. We identify 28 genes frequently reported with heterogenous variant effects which should be considered for variant interpretation. Overall, our study provides an updated and manually curated list of epilepsy-related genes together with additional annotations and classifications reflecting the current genetic landscape of epilepsy

MISCAST : MIssense variant to protein StruCture Analysis web SuiTe

Human genome sequencing efforts have greatly expanded, and a plethora of missense variants identified both in patients and in the general population is now publicly accessible. Interpretation of the molecular-level effect of missense variants, however, remains challenging and requires a particular investigation of amino acid substitutions in the context of protein structure and function. Answers to questions like 'Is a variant perturbing a site involved in key macromolecular interactions and/or cellular signaling?', or 'Is a variant changing an amino acid located at the protein core or part of a cluster of known pathogenic mutations in 3D?' are crucial. Motivated by these needs, we developed MISCAST (missense variant to protein structure analysis web suite; http://miscast.broadinstitute.org/). MISCAST is an interactive and user-friendly web server to visualize and analyze missense variants in protein sequence and structure space. Additionally, a comprehensive set of protein structural and functional features have been aggregated in MISCAST from multiple databases, and displayed on structures alongside the variants to provide users with the biological context of the variant location in an integrated platform. We further made the annotated data and protein structures readily downloadable from MISCAST to foster advanced offline analysis of missense variants by a wide biological community.Peer reviewe