SCN8A self-limited infantile epilepsy: Does epilepsy resolve?

SCN8A variants cause a spectrum of epilepsy phenotypes ranging from self-limited infantile epilepsy (SeLIE) to developmental and epileptic encephalopathy. SeLIE is an infantile onset focal epilepsy, occurring in developmentally normal infants, which often resolves by 3 years. Our aim was to ascertain when epilepsy resolves in SCN8A-SeLIE. We identified unpublished individuals with SCN8A-SeLIE and performed detailed phenotyping. Literature was searched for published SCN8A-SeLIE cases. Nine unpublished individuals from four families were identified (age at study = 3.5-66 years). Six had their last seizure after 3 years (range = 4-21 years); although drug-responsive and despite multiple weaning attempts (1-5), five of six remain on antiseizure medications (carbamazepine, n = 3; lamotrigine, n = 2). We identified 29 published individuals with SCN8A-SeLIE who had data on seizure progression. Of the 22 individuals aged at least 10 years, reported here or in the literature, nine of 22 (41%) had seizure offset prior to 3 years, five of 22 (23%) had seizure offset between 3 and 10 years, and eight of 22 (36%) had seizures after 10 years. Our data highlight that more than half of individuals with SCN8A-SeLIE continue to have seizures into late childhood. In contrast to SeLIE due to other etiologies, many individuals have a more persistent, albeit drug-responsive, form of epilepsy.

Leveraging multiple approaches for the detection of pathogenic deep intronic variants in developmental and epileptic encephalopathies: A case report.

About 50% of individuals with developmental and epileptic encephalopathies (DEEs) are unsolved following genetic testing. Deep intronic variants, defined as >100 bp from exon-intron junctions, contribute to disease by affecting the splicing of mRNAs in clinically relevant genes. Identifying deep intronic pathogenic variants is challenging and resource intensive, and interpretation is difficult due to limited functional annotations. We aimed to identify deep intronic variants in individuals suspected to have unsolved single gene DEEs. In a research cohort of unsolved cases of DEEs, we searched for children with a DEE syndrome predominantly caused by variants in specific genes in >80% of described cases. We identified two children with Dravet syndrome and one individual with classic lissencephaly. Multiple sequencing and bioinformatics strategies were employed to interrogate intronic regions in SCN1A and PAFAH1B1. A novel de novo deep intronic 12 kb deletion in PAFAH1B1 was identified in the individual with lissencephaly. We showed experimentally that the deletion disrupts mRNA splicing, which results in partial intron retention after exon 2 and disruption of the highly conserved LisH motif. We demonstrate that targeted interrogation of deep intronic regions using multiple genomics technologies, coupled with functional analysis, can reveal hidden causes of unsolved monogenic DEE syndromes. PLAIN LANGUAGE SUMMARY: Deep intronic variants can cause disease by affecting the splicing of mRNAs in clinically relevant genes. A deep intronic deletion that caused abnormal splicing of the PAFAH1B1 gene was identified in a patient with classic lissencephaly. Our findings reinforce that targeted interrogation of deep intronic regions and functional analysis can reveal hidden causes of unsolved epilepsy syndromes.

Diagnostic Utility of Genome-wide DNA Methylation Analysis in Genetically Unsolved Developmental and Epileptic Encephalopathies and Refinement of a CHD2 Episignature.

Sequence-based genetic testing currently identifies causative genetic variants in ∼50% of individuals with developmental and epileptic encephalopathies (DEEs). Aberrant changes in DNA methylation are implicated in various neurodevelopmental disorders but remain unstudied in DEEs. Rare epigenetic variations ("epivariants") can drive disease by modulating gene expression at single loci, whereas genome-wide DNA methylation changes can result in distinct "episignature" biomarkers for monogenic disorders in a growing number of rare diseases. Here, we interrogate the diagnostic utility of genome-wide DNA methylation array analysis on peripheral blood samples from 516 individuals with genetically unsolved DEEs who had previously undergone extensive genetic testing. We identified rare differentially methylated regions (DMRs) and explanatory episignatures to discover causative and candidate genetic etiologies in 10 individuals. We then used long-read sequencing to identify DNA variants underlying rare DMRs, including one balanced translocation, three CG-rich repeat expansions, and two copy number variants. We also identify pathogenic sequence variants associated with episignatures; some had been missed by previous exome sequencing. Although most DEE genes lack known episignatures, the increase in diagnostic yield for DNA methylation analysis in DEEs is comparable to the added yield of genome sequencing. Finally, we refine an episignature for CHD2 using an 850K methylation array which was further refined at higher CpG resolution using bisulfite sequencing to investigate potential insights into CHD2 pathophysiology. Our study demonstrates the diagnostic yield of genome-wide DNA methylation analysis to identify causal and candidate genetic causes as ∼2% (10/516) for unsolved DEE cases.

Assigning function to SNPs: Considerations when interpreting genetic variation.

Assigning function to single nucleotide polymorphisms (SNPs) to understand the mechanisms that link genetic and phenotypic variation and disease is an area of intensive research that is necessary to contribute to the continuing development of precision medicine. However, despite the apparent simplicity that is captured in the name SNP - 'single nucleotide' changes are not easy to functionally characterize. This complexity arises from multiple features of the genome including the fact that function is development and environment specific. As such, we are often fooled by our terminology and underlying assumptions that there is a single function for a SNP. Here we discuss some of what is known about SNPs, their functions and how we can go about characterizing them.

A de novo ACTB gene pathogenic variant in identical twins with phenotypic variation for hydrops and jejunal atresia.

The beta-actin gene (ACTB) encodes a ubiquitous cytoskeletal protein, essential for embryonic development in humans. De novo heterozygous missense variants in the ACTB are implicated in causing Baraitser-Winter cerebrofrontofacial syndrome (BWCFFS; MIM#243310). ACTB pathogenic variants are rarely associated with intestinal malformations. We report on a rare case of monozygotic twins presenting with proximal small bowel atresia and hydrops in one, and apple-peel bowel atresia and laryngeal dysgenesis in the other. The twin with hydrops could not be resuscitated. Intensive and surgical care was provided to the surviving twin. Rapid trio genome sequencing identified a de novo missense variant in ACTB (NM_00101.3:c.1043C>T; p.(Ser348Leu)) that guided the care plan. The identical variant subsequently was identified in the demised twin. To characterize the functional effect, the variant was recreated as a pseudoheterozygote in a haploid wild-type S. cerevisiae strain. There was an obvious growth defect of the yACT1S348L/WT pseudoheterozygote compared to a yACT1WT/WT strain when grown at 22°C but not when grown at 30°C, consistent with the yACT1 S348L variant having a functional defect that is dominant over the wild-type allele. The functional results provide supporting evidence that the Ser348Leu variant is likely to be a pathogenic variant, including being associated with intestinal malformations in BWCFFS, and can demonstrate variable expressivity within monozygotic twins.

Identifying the lungs as a susceptible site for allele-specific regulatory changes associated with type 1 diabetes risk.

Type 1 diabetes (T1D) etiology is complex. We developed a machine learning approach that ranked the tissue-specific transcription regulatory effects for T1D SNPs and estimated their relative contributions to conversion to T1D by integrating case and control genotypes (Wellcome Trust Case Control Consortium and UK Biobank) with tissue-specific expression quantitative trait loci (eQTL) data. Here we show an eQTL (rs6679677) associated with changes to AP4B1-AS1 transcript levels in lung tissue makes the largest gene regulatory contribution to the risk of T1D development. Luciferase reporter assays confirmed allele-specific enhancer activity for the rs6679677 tagged locus in lung epithelial cells (i.e. A549 cells; C > A reduces expression, p = 0.005). Our results identify tissue-specific eQTLs for SNPs associated with T1D. The strongest tissue-specific eQTL effects were in the lung and may help explain associations between respiratory infections and risk of islet autoantibody seroconversion in young children.

Untangling the genetic link between type 1 and type 2 diabetes using functional genomics.

There is evidence pointing towards shared etiological features between type 1 diabetes (T1D) and type 2 diabetes (T2D) despite both phenotypes being considered genetically distinct. However, the existence of shared genetic features for T1D and T2D remains complex and poorly defined. To better understand the link between T1D and T2D, we employed an integrated functional genomics approach involving extensive chromatin interaction data (Hi-C) and expression quantitative trait loci (eQTL) data to characterize the tissue-specific impacts of single nucleotide polymorphisms associated with T1D and T2D. We identified 195 pleiotropic genes that are modulated by tissue-specific spatial eQTLs associated with both T1D and T2D. The pleiotropic genes are enriched in inflammatory and metabolic pathways that include mitogen-activated protein kinase activity, pertussis toxin signaling, and the Parkinson's disease pathway. We identified 8 regulatory elements within the TCF7L2 locus that modulate transcript levels of genes involved in immune regulation as well as genes important in the etiology of T2D. Despite the observed gene and pathway overlaps, there was no significant genetic correlation between variant effects on T1D and T2D risk using European ancestral summary data. Collectively, our findings support the hypothesis that T1D and T2D specific genetic variants act through genetic regulatory mechanisms to alter the regulation of common genes, and genes that co-locate in biological pathways, to mediate pleiotropic effects on disease development. Crucially, a high risk genetic profile for T1D alters biological pathways that increase the risk of developing both T1D and T2D. The same is not true for genetic profiles that increase the risk of developing T2D. The conversion of information on genetic susceptibility to the protein pathways that are altered provides an important resource for repurposing or designing novel therapies for the management of diabetes.

Type 1 Diabetes Mellitus-Associated Genetic Variants Contribute to Overlapping Immune Regulatory Networks.

Type 1 diabetes (T1D) is a chronic metabolic disorder characterized by the autoimmune destruction of insulin-producing pancreatic islet beta cells in genetically predisposed individuals. Genome-wide association studies (GWAS) have identified over 60 risk regions across the human genome, marked by single nucleotide polymorphisms (SNPs), which confer genetic predisposition to T1D. There is increasing evidence that disease-associated SNPs can alter gene expression through spatial interactions that involve distal loci, in a tissue- and development-specific manner. Here, we used three-dimensional (3D) genome organization data to identify genes that physically co-localized with DNA regions that contained T1D-associated SNPs in the nucleus. Analysis of these SNP-gene pairs using the Genotype-Tissue Expression database identified a subset of SNPs that significantly affected gene expression. We identified 246 spatially regulated genes including HLA-DRB1, LAT, MICA, BTN3A2, CTLA4, CD226, NOTCH1, TRIM26, PTEN, TYK2, CTSH, and FLRT3, which exhibit tissue-specific effects in multiple tissues. We observed that the T1D-associated variants interconnect through networks that form part of the immune regulatory pathways, including immune-cell activation, cytokine signaling, and programmed cell death protein-1 (PD-1). Our results implicate T1D-associated variants in tissue and cell-type specific regulatory networks that contribute to pancreatic beta cell inflammation and destruction, adaptive immune signaling, and immune-cell proliferation and activation. A number of other regulatory changes we identified are not typically considered to be central to the pathology of T1D. Collectively, our data represent a novel resource for the hypothesis-driven development of diagnostic, prognostic, and therapeutic interventions in T1D.

The genetic architecture of type 1 diabetes mellitus.

Type 1 diabetes mellitus (T1D) is a complex autoimmune disorder characterised by loss of the insulin-producing pancreatic beta cells in genetically predisposed individuals, ultimately resulting in insulin deficiency and hyperglycaemia. T1D is most common among children and young adults, and the incidence is on the rise across the world. The aetiology of T1D is hypothesized to involve genetic and environmental factors that result in the T-cell mediated destruction of pancreatic beta cells. There is a strong genetic risk to T1D; with genome-wide association studies (GWAS) identifying over 60 susceptibility regions within the human genome which are marked by single nucleotide polymorphisms (SNPs). Here, we review what is currently known about the genetics of T1D. We argue that advancing our understanding of the aetiology and pathogenesis of T1D will require the integration of genome biology (omics-data) with GWAS data, thereby making it possible to elucidate the putative gene regulatory networks modulated by disease-associated SNPs. This approach has a potential to revolutionize clinical management of T1D in an era of precision medicine.