Exome sequencing implicates ancestry-related Mendelian variation at SYNE1 in childhood-onset essential hypertension.

Childhood-onset essential hypertension (COEH) is an uncommon form of hypertension that manifests in childhood or adolescence and, in the United States, disproportionately affects children of African ancestry. The etiology of COEH is unknown, but its childhood onset, low prevalence, high heritability, and skewed ancestral demography suggest the potential to identify rare genetic variation segregating in a Mendelian manner among affected individuals and thereby implicate genes important to disease pathogenesis. However, no COEH genes have been reported to date. Here, we identify recessive segregation of rare and putatively damaging missense variation in the spectrin domain of spectrin repeat containing nuclear envelope protein 1 (SYNE1), a cardiovascular candidate gene, in 3 of 16 families with early-onset COEH without an antecedent family history. By leveraging exome sequence data from an additional 48 COEH families, 1,700 in-house trios, and publicly available data sets, we demonstrate that compound heterozygous SYNE1 variation in these COEH individuals occurred more often than expected by chance and that this class of biallelic rare variation was significantly enriched among individuals of African genetic ancestry. Using in vitro shRNA knockdown of SYNE1, we show that reduced SYNE1 expression resulted in a substantial decrease in the elasticity of smooth muscle vascular cells that could be rescued by pharmacological inhibition of the downstream RhoA/Rho-associated protein kinase pathway. These results provide insights into the molecular genetics and underlying pathophysiology of COEH and suggest a role for precision therapeutics in the future.

Epigenome-wide association studies identify novel DNA methylation sites associated with PTSD: A meta-analysis of 23 military and civilian cohorts.

Background: The occurrence of post-traumatic stress disorder (PTSD) following a traumatic event is associated with biological differences that can represent the susceptibility to PTSD, the impact of trauma, or the sequelae of PTSD itself. These effects include differences in DNA methylation (DNAm), an important form of epigenetic gene regulation, at multiple CpG loci across the genome. Moreover, these effects can be shared or specific to both central and peripheral tissues. Here, we aim to identify blood DNAm differences associated with PTSD and characterize the underlying biological mechanisms by examining the extent to which they mirror associations across multiple brain regions. Methods: As the Psychiatric Genomics Consortium (PGC) PTSD Epigenetics Workgroup, we conducted the largest cross-sectional meta-analysis of epigenome-wide association studies (EWASs) of PTSD to date, involving 5077 participants (2156 PTSD cases and 2921 trauma-exposed controls) from 23 civilian and military studies. PTSD diagnosis assessments were harmonized following the standardized guidelines established by the PGC-PTSD Workgroup. DNAm was assayed from blood using either Illumina HumanMethylation450 or MethylationEPIC (850K) BeadChips. A common QC pipeline was applied. Within each cohort, DNA methylation was regressed on PTSD, sex (if applicable), age, blood cell proportions, and ancestry. An inverse variance-weighted meta-analysis was performed. We conducted replication analyses in tissue from multiple brain regions, neuronal nuclei, and a cellular model of prolonged stress. Results: We identified 11 CpG sites associated with PTSD in the overall meta-analysis (1.44e-09 < p < 5.30e-08), as well as 14 associated in analyses of specific strata (military vs civilian cohort, sex, and ancestry), including CpGs in AHRR and CDC42BPB . Many of these loci exhibit blood-brain correlation in methylation levels and cross-tissue associations with PTSD in multiple brain regions. Methylation at most CpGs correlated with their annotated gene expression levels. Conclusions: This study identifies 11 PTSD-associated CpGs, also leverages data from postmortem brain samples, GWAS, and genome-wide expression data to interpret the biology underlying these associations and prioritize genes whose regulation differs in those with PTSD.

Systems biology dissection of PTSD and MDD across brain regions, cell types, and blood.

The molecular pathology of stress-related disorders remains elusive. Our brain multiregion, multiomic study of posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) included the central nucleus of the amygdala, hippocampal dentate gyrus, and medial prefrontal cortex (mPFC). Genes and exons within the mPFC carried most disease signals replicated across two independent cohorts. Pathways pointed to immune function, neuronal and synaptic regulation, and stress hormones. Multiomic factor and gene network analyses provided the underlying genomic structure. Single nucleus RNA sequencing in dorsolateral PFC revealed dysregulated (stress-related) signals in neuronal and non-neuronal cell types. Analyses of brain-blood intersections in >50,000 UK Biobank participants were conducted along with fine-mapping of the results of PTSD and MDD genome-wide association studies to distinguish risk from disease processes. Our data suggest shared and distinct molecular pathology in both disorders and propose potential therapeutic targets and biomarkers.

A first-generation genome-wide map of correlated DNA methylation demonstrates highly coordinated and tissue-independent clustering across regulatory regions.

Genome-wide DNA methylation studies have typically focused on quantitative assessments of CpG methylation at individual loci. Although methylation states at nearby CpG sites are known to be highly correlated, suggestive of an underlying coordinated regulatory network, the extent and consistency of inter-CpG methylation correlation across the genome, including variation between individuals, disease states, and tissues, remains unknown. Here, we leverage image conversion of correlation matrices to identify correlated methylation units (CMUs) across the genome, describe their variation across tissues, and annotate their regulatory potential using 35 public Illumina BeadChip datasets spanning more than 12,000 individuals and 26 different tissues. We identified a median of 18,125 CMUs genome-wide, occurring on all chromosomes and spanning a median of ~1 kb. Notably, 50% of CMUs had evidence of long-range correlation with other proximal CMUs. Although the size and number of CMUs varied across datasets, we observed strong intra-tissue consistency among CMUs, with those in testis encompassing those seen in most other tissues. Approximately 20% of CMUs were highly conserved across normal tissues (i.e. tissue independent), with 73 loci demonstrating strong correlation with non-adjacent CMUs on the same chromosome. These loci were enriched for CTCF and transcription factor binding sites, always found within putative TADs, and associated with the B compartment of chromosome folding. Finally, we observed significantly different, but highly consistent, patterns of CMU correlation between diseased and non-diseased states. Our first-generation, genome-wide, DNA methylation map suggests a highly coordinated CMU regulatory network that is sensitive to disruptions in its architecture.

SRCP: a comprehensive pipeline for accurate annotation and quantification of circRNAs.

Here we describe a new integrative approach for accurate annotation and quantification of circRNAs named Short Read circRNA Pipeline (SRCP). Our strategy involves two steps: annotation of validated circRNAs followed by a quantification step. We show that SRCP is more sensitive than other individual pipelines and allows for more comprehensive quantification of a larger number of differentially expressed circRNAs. To facilitate the use of SRCP, we generate a comprehensive collection of validated circRNAs in five different organisms, including humans. We then utilize our approach and identify a subset of circRNAs bound to the miRNA-effector protein AGO2 in human brain samples.

Edematous severe acute malnutrition is characterized by hypomethylation of DNA.

Edematous severe acute childhood malnutrition (edematous SAM or ESAM), which includes kwashiorkor, presents with more overt multi-organ dysfunction than non-edematous SAM (NESAM). Reduced concentrations and methyl-flux of methionine in 1-carbon metabolism have been reported in acute, but not recovered, ESAM, suggesting downstream DNA methylation changes could be relevant to differences in SAM pathogenesis. Here, we assess genome-wide DNA methylation in buccal cells of 309 SAM children using the 450 K microarray. Relative to NESAM, ESAM is characterized by multiple significantly hypomethylated loci, which is not observed among SAM-recovered adults. Gene expression and methylation show both positive and negative correlation, suggesting a complex transcriptional response to SAM. Hypomethylated loci link to disorders of nutrition and metabolism, including fatty liver and diabetes, and appear to be influenced by genetic variation. Our epigenetic findings provide a potential molecular link to reported aberrant 1-carbon metabolism in ESAM and support consideration of methyl-group supplementation in ESAM.

NovoGraph: Human genome graph construction from multiple long-read de novo assemblies.

Genome graphs are emerging as an important novel approach to the analysis of high-throughput human sequencing data. By explicitly representing genetic variants and alternative haplotypes in a mappable data structure, they can enable the improved analysis of structurally variable and hyperpolymorphic regions of the genome. In most existing approaches, graphs are constructed from variant call sets derived from short-read sequencing. As long-read sequencing becomes more cost-effective and enables de novo assembly for increasing numbers of whole genomes, a method for the direct construction of a genome graph from sets of assembled human genomes would be desirable. Such assembly-based genome graphs would encompass the wide spectrum of genetic variation accessible to long-read-based de novo assembly, including large structural variants and divergent haplotypes. Here we present NovoGraph, a method for the construction of a human genome graph directly from a set of de novo assemblies. NovoGraph constructs a genome-wide multiple sequence alignment of all input contigs and creates a graph by merging the input sequences at positions that are both homologous and sequence-identical. NovoGraph outputs resulting graphs in VCF format that can be loaded into third-party genome graph toolkits. To demonstrate NovoGraph, we construct a genome graph with 23,478,835 variant sites and 30,582,795 variant alleles from de novo assemblies of seven ethnically diverse human genomes (AK1, CHM1, CHM13, HG003, HG004, HX1, NA19240). Initial evaluations show that mapping against the constructed graph reduces the average mismatch rate of reads from sample NA12878 by approximately 0.2%, albeit at a slightly increased rate of reads that remain unmapped.

Patient-Specific Quantitation of Mitral Valve Strain by Computer Analysis of Three-Dimensional Echocardiography: A Pilot Study.

BACKGROUND: A paucity of data exists on mitral valve (MV) deformation during the cardiac cycle in man. Real-time 3-dimensional (3D) echocardiography now allows dynamic volumetric imaging of the MV, thus enabling computerized modeling of MV function directly in health and disease. METHODS AND RESULTS: MV imaging using 3D transesophageal echocardiography was performed in 10 normal subjects and 10 patients with moderate-to-severe or severe organic mitral regurgitation. Using proprietary 3D software, patient-specific models of the mitral annulus and leaflets were computed at mid- and end-systole. Strain analysis of leaflet deformation was derived from these models. In normals, mean strain intensity averaged 0.11±0.02 and was higher in the posterior leaflet than in the anterior leaflet (0.13±0.03 versus 0.10±0.02; P<0.05). Mean strain intensity was higher in patients with mitral regurgitation (0.15±0.03) than in normals (0.11±0.02; P=0.05). Higher mean strain intensity was noted for the posterior leaflet in both normal and organic valves. Regional valve analysis revealed that both anterior and posterior leaflets have the highest strain concentration in the commissural zone, and the boundary zone near the annulus and at the coaptation line, with reduced strain concentration in the central leaflet zone. CONCLUSIONS: In normals, MV strain is higher in the posterior leaflet, with the highest strain at the commissures, annulus, and coaptation zones. Patients with organic mitral regurgitation have higher strain than normals. Three-dimensional echocardiography allows noninvasive and patient-specific quantitation of strain intensities because of MV deformations and has the potential to improve noninvasive characterization and follow-up of MV disease.