BackgroundCRISPR-Cas systems have been broadly embraced as effective tools for genome engineering applications, with most studies to date utilizing the Streptococcus pyogenes Cas9. Here we characterize and manipulate the smaller, 1053 amino acid nuclease Staphylococcus aureus Cas9.ResultsWe find that the S. aureus Cas9 recognizes an NNGRRT protospacer adjacent motif (PAM) and cleaves target DNA at high efficiency with a variety of guide RNA (gRNA) spacer lengths. When directed against genomic targets with mutually permissive NGGRRT PAMs, the S. pyogenes Cas9 and S. aureus Cas9 yield indels at comparable rates. We additionally show D10A and N580A paired nickase activity with S. aureus Cas9, and we further package it with two gRNAs in a single functional adeno-associated virus (AAV) vector. Finally, we assess comparative S. pyogenes and S. aureus Cas9 specificity using GUIDE-seq.ConclusionOur results reveal an S. aureus Cas9 that is effective for a variety of genome engineering purposes, including paired nickase approaches and all-in-one delivery of Cas9 and multiple gRNA expression cassettes with AAV vectors.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0817-8) contains supplementary material, which is available to authorized users.

Heart failure is a leading cause of cardiovascular morbidity and mortality. However, the contribution of common genetic variation to heart failure risk has not been fully elucidated, particularly in comparison to other common cardiometabolic traits. We report a multi-ancestry genome-wide association study meta-analysis of all-cause heart failure including up to 115,150 cases and 1,550,331 controls of diverse genetic ancestry, identifying 47 risk loci. We also perform multivariate genome-wide association studies that integrate heart failure with related cardiac magnetic resonance imaging endophenotypes, identifying 61 risk loci. Gene-prioritization analyses including colocalization and transcriptome-wide association studies identify known and previously unreported candidate cardiomyopathy genes and cellular processes, which we validate in gene-expression profiling of failing and healthy human hearts. Colocalization, gene expression profiling, and Mendelian randomization provide convergent evidence for the roles of BCKDHA and circulating branch-chain amino acids in heart failure and cardiac structure. Finally, proteome-wide Mendelian randomization identifies 9 circulating proteins associated with heart failure or quantitative imaging traits. These analyses highlight similarities and differences among heart failure and associated cardiovascular imaging endophenotypes, implicate common genetic variation in the pathogenesis of heart failure, and identify circulating proteins that may represent cardiomyopathy treatment targets.

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ImportanceAutism spectrum disorder (ASD) is associated with significant clinical, neuroanatomical, and genetic heterogeneity that limits precision diagnostics and treatment.ObjectiveTo assess distinct neuroanatomical dimensions of ASD using novel semisupervised machine learning methods and to test whether the dimensions can serve as endophenotypes also in non-ASD populations.Design, Setting, and ParticipantsThis cross-sectional study used imaging data from the publicly available Autism Brain Imaging Data Exchange (ABIDE) repositories as the discovery cohort. The ABIDE sample included individuals diagnosed with ASD aged between 16 and 64 years and age- and sex-match typically developing individuals. Validation cohorts included individuals with schizophrenia from the Psychosis Heterogeneity Evaluated via Dimensional Neuroimaging (PHENOM) consortium and individuals from the UK Biobank to represent the general population. The multisite discovery cohort included 16 internationally distributed imaging sites. Analyses were performed between March 2021 and March 2022.Main Outcomes and MeasuresThe trained semisupervised heterogeneity through discriminative analysis models were tested for reproducibility using extensive cross-validations. It was then applied to individuals from the PHENOM and the UK Biobank. It was hypothesized that neuroanatomical dimensions of ASD would display distinct clinical and genetic profiles and would be prominent also in non-ASD populations.ResultsHeterogeneity through discriminative analysis models trained on T1-weighted brain magnetic resonance images of 307 individuals with ASD (mean [SD] age, 25.4 [9.8] years; 273 [88.9%] male) and 362 typically developing control individuals (mean [SD] age, 25.8 [8.9] years; 309 [85.4%] male) revealed that a 3-dimensional scheme was optimal to capture the ASD neuroanatomy. The first dimension (A1: aginglike) was associated with smaller brain volume, lower cognitive function, and aging-related genetic variants (FOXO3; Z = 4.65; P = 1.62 × 10−6). The second dimension (A2: schizophrenialike) was characterized by enlarged subcortical volumes, antipsychotic medication use (Cohen d = 0.65; false discovery rate–adjusted P = .048), partially overlapping genetic, neuroanatomical characteristics to schizophrenia (n = 307), and significant genetic heritability estimates in the general population (n = 14 786; mean [SD] h2, 0.71 [0.04]; P < 1 × 10−4). The third dimension (A3: typical ASD) was distinguished by enlarged cortical volumes, high nonverbal cognitive performance, and biological pathways implicating brain development and abnormal apoptosis (mean [SD] β, 0.83 [0.02]; P = 4.22 × 10−6).Conclusions and RelevanceThis cross-sectional study discovered 3-dimensional endophenotypic representation that may elucidate the heterogeneous neurobiological underpinnings of ASD to support precision diagnostics. The significant correspondence between A2 and schizophrenia indicates a possibility of identifying common biological mechanisms across the 2 mental health diagnoses.

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Leveraging linkage disequilibrium (LD) patterns as representative of population substructure enables the discovery of additive association signals in genome-wide association studies (GWAS). Standard GWAS are well-powered to interrogate additive models; however, new approaches are required to investigate other modes of inheritance such as dominance and epistasis. Epistasis, or non-additive interaction between genes, exists across the genome but often goes undetected due to lack of statistical power. Furthermore, the adoption of LD pruning as customary in standard GWAS excludes detection of sites in LD that may underlie the genetic architecture of complex traits. We hypothesize that uncovering long-range interactions between loci with strong LD due to epistatic selection can elucidate genetic mechanisms underlying common diseases. To investigate this hypothesis, we tested for associations between 23 common diseases and 5,625,845 epistatic SNP-SNP pairs (determined by Ohta’sDstatistics) in long-range LD (> 0.25cM). We identified five significant associations across five disease phenotypes that replicated in two large genotype-phenotype datasets (UK Biobank and eMERGE). The genes that were most likely involved in the replicated associations were 1) members of highly conserved gene families with complex roles in multiple pathways, 2) essential genes, and/or 3) associated in the literature with complex traits that display variable expressivity. These results support the highly pleiotropic and conserved nature of variants in long-range under epistatic selection. Our work supports the hypothesis that epistatic interactions regulate diverse clinical mechanisms and may especially be driving factors in conditions with a wide range of phenotypic outcomes.SignificanceCurrent knowledge of genotype-phenotype relationships is largely contingent on traditional univariate approaches to genomic analysis. Yet substantial evidence supports non-additive modes of inheritance and regulation, such as epistasis, as being abundant across the genome. In this genome-wide study, we probe the biomolecular mechanisms underlying complex human diseases by testing the association of pairwise genetic interactions with disease occurrence in large-scale biobank data. Specifically, we tested intrachromosomal and interchrosomal long-range interactions between regions of the genome in high linkage disequilibrium, these regions are typically excluded from genomic analyses. The results from this study suggest that essential gene, members of highly conserved gene families, and phenotypes with variable expressivity, are particularly enriched with epistatic and pleiotropic activity.

The RNA viruses are marked by high genetic diversity, which allows them to quickly adapt to new and resistant hosts. The pathogenic turnip mosaic virus (TuMV) infects Brassicaceae plant species all over the world.Aim: To study the evolution and host expansion of a TuMV for the first time in India using molecular population genetic framework. Materials and Results: Here, we decipher the complete genome sequences of two TuMV world-B3 strains infecting yellow and black mustard in India through highthroughput RNA sequencing subjecting ribosomal RNA depleted mRNA isolated from leaves exhibiting puckering and mosaic symptoms with 100% incidence and high severity in the experimental field. The viral genomes of the two isolates were 9817 and 9829 nucleotides long. They featured two open reading frames (ORFs), one of which encoded a polyprotein comprised of 3164 amino acids and the other of which encoded a PIPO protein of 62 amino acids. Conclusions:The two TuMV strains from New Delhi region shared identity with the world-B pathotype and subpathotype world B3 showcasing its emergence first time in South Asia. In contrast, other isolates reported previously from South Asia were all Asian-BR pathotypes.Significance and Impact of the Study: According to our knowledge, this is the first instance of TuMV association with black mustard naturally. Their geographical prevalence justifies a lower degree of genetic differentiation and higher rate of gene flow calculated between the world-B and Asian-BR pathotypes. This study provides insights on population structuring, expansions and evolution, level of genetic heterogeneity and variability of worldwide prevalent isolates of TuMV which will further aid in understanding virus epidemiology and help prevent losses.

The prevalence and significance of schizophrenia-related phenotypes at the population-level are debated in the literature. Here we assess whether two recently reported neuroanatomical signatures of schizophrenia, signature 1 with widespread reduction of gray matter volume, and signature 2 with increased striatal volume, could be replicated in an independent schizophrenia sample, and investigate whether expression of these signatures can be detected at the population-level and how they relate to cognition, psychosis spectrum symptoms, and schizophrenia genetic risk. This cross-sectional study used an independent schizophrenia-control sample (n=347; age 16-57 years) for replication of imaging signatures, and then examined two independent population-level datasets: Philadelphia Neurodevelopmental Cohort [PNC; n=359 typically developing (TD) and psychosis-spectrum symptoms (PS) youth] and UK Biobank (UKBB; n=836; age 44-50 years) adults. We quantified signature expression using support-vector machine learning, and compared cognition, psychopathology, and polygenic risk between signatures. Two neuroanatomical signatures of schizophrenia were replicated. Signature 1 but not signature 2 was significantly more common in youth with PS than TD youth, whereas signature 2 frequency was similar. In both youth and adults, signature 1 had worse cognitive performance than signature 2. Compared to adults with neither signature, adults expressing signature 1 had elevated schizophrenia polygenic risk scores, but this was not seen for signature 2. We successfully replicate two neuroanatomical signatures of schizophrenia, and describe their prevalence in population-based samples of youth and adults. We further demonstrate distinct relationships of these signatures with psychosis symptoms, cognition, and genetic risk, potentially reflecting underlying neurobiological vulnerability.