The seizure threshold 2 (SZT2) gene encodes a large, highly conserved protein that is associated with epileptogenesis. In mice, Szt2 is abundantly expressed in the central nervous system. Recently, biallelic SZT2 mutations were found in 7 patients (from 5 families) presenting with epileptic encephalopathy with dysmorphic features and/or non-syndromic intellectual disabilities. In this study, we identified by whole-exome sequencing compound heterozygous SZT2 mutations in 3 patients with early-onset epileptic encephalopathies. Six novel SZT2 mutations were found, including 3 truncating, 1 splice site and 2 missense mutations. The splice-site mutation resulted in skipping of exon 20 and was associated with a premature stop codon. All individuals presented with seizures, severe developmental delay and intellectual disabilities with high variability. Brain MRIs revealed a characteristic thick and short corpus callosum or a persistent cavum septum pellucidum in each of the 2 cases. Interestingly, in the third case, born to consanguineous parents, had unexpected compound heterozygous missense mutations. She showed microcephaly despite the other case and previous ones presenting with macrocephaly, suggesting that SZT2 mutations might affect head size.
Epilepsies are common neurological disorders and genetic factors contribute to their pathogenesis. Copy number variations (CNVs) are increasingly recognized as an important etiology of many human diseases including epilepsy. Whole-exome sequencing (WES) is becoming a standard tool for detecting pathogenic mutations and has recently been applied to detecting CNVs. Here, we analyzed 294 families with epilepsy using WES, and focused on 168 families with no causative single nucleotide variants in known epilepsy-associated genes to further validate CNVs using 2 different CNV detection tools using WES data. We confirmed 18 pathogenic CNVs, and 2 deletions and 2 duplications at chr15q11.2 of clinically unknown significance. Of note, we were able to identify small CNVs less than 10 kb in size, which might be difficult to detect by conventional microarray. We revealed 2 cases with pathogenic CNVs that one of the 2 CNV detection tools failed to find, suggesting that using different CNV tools is recommended to increase diagnostic yield. Considering a relatively high discovery rate of CNVs (18 out of 168 families, 10.7%) and successful detection of CNV with <10 kb in size, CNV detection by WES may be able to surrogate, or at least complement, conventional microarray analysis.
N-methyl-d-aspartate (NMDA) receptors are glutamate-activated ion channels that are widely distributed in the central nervous system and essential for brain development and function. Dysfunction of NMDA receptors has been associated with various neurodevelopmental disorders. Recently, a de novo recurrent GRIN2D missense variant was found in two unrelated patients with developmental and epileptic encephalopathy. In this study, we identified by whole exome sequencing novel heterozygous GRIN2D missense variants in three unrelated patients with severe developmental delay and intractable epilepsy. All altered residues were highly conserved across vertebrates and among the four GluN2 subunits. Structural consideration indicated that all three variants are probably to impair GluN2D function, either by affecting intersubunit interaction or altering channel gating activity. We assessed the clinical features of our three cases and compared them to those of the two previously reported GRIN2D variant cases, and found that they all show similar clinical features. This study provides further evidence of GRIN2D variants being causal for epilepsy. Genetic diagnosis for GluN2-related disorders may be clinically useful when considering drug therapy targeting NMDA receptors.
Many algorithms to detect copy number variations (CNVs) using exome sequencing (ES) data have been reported and evaluated on their sensitivity and specificity, reproducibility, and precision. However, operational optimization of such algorithms for a better performance has not been fully addressed. ES of 1199 samples including 763 patients with different disease profiles was performed. ES data were analyzed to detect CNVs by both the eXome Hidden Markov Model (XHMM) and modified Nord's method. To efficiently detect rare CNVs, we aimed to decrease sequencing biases by analyzing, at the same time, the data of all unrelated samples sequenced in the same flow cell as a batch, and to eliminate sex effects of X-linked CNVs by analyzing female and male sequences separately. We also applied several filtering steps for more efficient CNV selection. The average number of CNVs detected in one sample was <5. This optimization together with targeted CNV analysis by Nord's method identified pathogenic/likely pathogenic CNVs in 34 patients (4.5%, 34/763). In particular, among 142 patients with epilepsy, the current protocol detected clinically relevant CNVs in 19 (13.4%) patients, whereas the previous protocol identified them in only 14 (9.9%) patients. Thus, this batch-based XHMM analysis efficiently selected rare pathogenic CNVs in genetic diseases.
Whereas large-scale statistical analyses can robustly identify disease-gene relationships, they do not accurately capture genotype-phenotype correlations or disease mechanisms. We use multiple lines of independent evidence to show that different variant types in a single gene, SATB1, cause clinically overlapping but distinct neurodevelopmental disorders. Clinical evaluation of 42 individuals carrying SATB1 variants identified overt genotype-phenotype relationships, associated with different pathophysiological mechanisms, established by functional assays. Missense variants in the CUT1 and CUT2 DNA-binding domains result in stronger chromatin binding, increased transcriptional repression, and a severe phenotype. In contrast, variants predicted to result in haploinsufficiency are associated with a milder clinical presentation. A similarly mild phenotype is observed for individuals with premature protein truncating variants that escape nonsense-mediated decay, which are transcriptionally active but mislocalized in the cell. Our results suggest that in-depth mutation-specific genotype-phenotype studies are essential to capture full disease complexity and to explain phenotypic variability.