Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse.

Mouse models have been used extensively to study human coronary artery disease (CAD) or atherosclerosis and to test therapeutic targets. However, whether mouse and human share similar genetic factors and pathogenic mechanisms of atherosclerosis has not been thoroughly investigated in a data-driven manner. We conducted a cross-species comparison study to better understand atherosclerosis pathogenesis between species by leveraging multiomics data.

Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse.

Mouse models have been used extensively to study human coronary artery disease (CAD) or atherosclerosis and to test therapeutic targets. However, whether mouse and human share similar genetic factors and pathogenic mechanisms of atherosclerosis has not been thoroughly investigated in a data-driven manner. We conducted a cross-species comparison study to better understand atherosclerosis pathogenesis between species by leveraging multiomics data.

A mechanistic framework for cardiometabolic and coronary artery diseases.

Coronary atherosclerosis results from the delicate interplay of genetic and exogenous risk factors, principally taking place in metabolic organs and the arterial wall. Here we show that 224 gene-regulatory coexpression networks (GRNs) identified by integrating genetic and clinical data from patients with ( = 600) and without ( = 250) coronary artery disease (CAD) with RNA-seq data from seven disease-relevant tissues in the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study largely capture this delicate interplay, explaining >54% of CAD heritability. Within 89 cross-tissue GRNs associated with clinical severity of CAD, 374 endocrine factors facilitated inter-organ interactions, primarily along an axis from adipose tissue to the liver ( = 152).

The HDAC9-associated risk locus promotes coronary artery disease by governing TWIST1.

Genome wide association studies (GWAS) have identified thousands of single nucleotide polymorphisms (SNPs) associated with the risk of common disorders. However, since the large majority of these risk SNPs reside outside gene-coding regions, GWAS generally provide no information about causal mechanisms regarding the specific gene(s) that are affected or the tissue(s) in which these candidate gene(s) exert their effect. The 'gold standard' method for understanding causal genes and their mechanisms of action are laborious basic science studies often involving sophisticated knockin or knockout mouse lines, however, these types of studies are impractical as a high-throughput means to understand the many risk variants that cause complex diseases like coronary artery disease (CAD).

Integrative Prioritization of Causal Genes for Coronary Artery Disease.

Background: Hundreds of candidate genes have been associated with coronary artery disease (CAD) through genome-wide association studies. However, a systematic way to understand the causal mechanism(s) of these genes, and a means to prioritize them for further study, has been lacking. This represents a major roadblock for developing novel disease- and gene-specific therapies for patients with CAD.

Single-cell analysis of mosquito hemocytes identifies signatures of immune cell subtypes and cell differentiation.

Mosquito immune cells, known as hemocytes, are integral to cellular and humoral responses that limit pathogen survival and mediate immune priming. However, without reliable cell markers and genetic tools, studies of mosquito immune cells have been limited to morphological observations, leaving several aspects of their biology uncharacterized. Here, we use single-cell RNA sequencing (scRNA-seq) to characterize mosquito immune cells, demonstrating an increased complexity to previously defined prohemocyte, oenocytoid, and granulocyte subtypes.

Transcatheter mitral valve repair: an overview of current and future devices.

The field of transcatheter mitral valve repair (TMVr) for mitral regurgitation (MR) is rapidly evolving. Besides the well-established transcatheter mitral edge-to-edge repair approach, there is also growing evidence for therapeutic strategies targeting the mitral annulus and mitral valve chordae. A patient-tailored approach, careful patient selection and an experienced interventional team is crucial in order to optimise procedural and clinical outcomes.

Cis-epistasis at the LPA locus and risk of cardiovascular diseases.

Aims: Coronary artery disease (CAD) has a strong genetic predisposition. However, despite substantial discoveries made by genome-wide association studies (GWAS), a large proportion of heritability awaits identification. Non-additive genetic effects might be responsible for part of the unaccounted genetic variance.

Transcription Factor MAFF (MAF Basic Leucine Zipper Transcription Factor F) Regulates an Atherosclerosis Relevant Network Connecting Inflammation and Cholesterol Metabolism.

Background: Coronary artery disease (CAD) is a multifactorial condition with both genetic and exogenous causes. The contribution of tissue-specific functional networks to the development of atherosclerosis remains largely unclear. The aim of this study was to identify and characterize central regulators and networks leading to atherosclerosis.

An integrative multiomic network model links lipid metabolism to glucose regulation in coronary artery disease.

Elevated plasma cholesterol and type 2 diabetes (T2D) are associated with coronary artery disease (CAD). Individuals treated with cholesterol-lowering statins have increased T2D risk, while individuals with hypercholesterolemia have reduced T2D risk. We explore the relationship between lipid and glucose control by constructing network models from the STARNET study with sequencing data from seven cardiometabolic tissues obtained from CAD patients during coronary artery by-pass grafting surgery.

alona: a web server for single-cell RNA-seq analysis.

Summary: Single-cell RNA sequencing (scRNA-seq) is a technology to measure gene expression in single cells. It has enabled discovery of new cell types and established cell type atlases of tissues and organs. The widespread adoption of scRNA-seq has created a need for user-friendly software for data analysis.

Integrative analysis of loss-of-function variants in clinical and genomic data reveals novel genes associated with cardiovascular traits.

Background: Genetic loss-of-function variants (LoFs) associated with disease traits are increasingly recognized as critical evidence for the selection of therapeutic targets. We integrated the analysis of genetic and clinical data from 10,511 individuals in the Mount Sinai BioMe Biobank to identify genes with loss-of-function variants (LoFs) significantly associated with cardiovascular disease (CVD) traits, and used RNA-sequence data of seven metabolic and vascular tissues isolated from 600 CVD patients in the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study for validation. We also carried out in vitro functional studies of several candidate genes, and in vivo studies of one gene.

Characterization of Evolutionarily Conserved NatC and NatA-N-Terminal Acetyltransferase Complexes.

Protein N-terminal acetylation is a co- and posttranslational modification, conserved among eukaryotes. It determines the functional fate of many proteins including their stability, complex formation, and subcellular localization. N-terminal acetyltransferases (NATs) transfer an acetyl group to the N-termini of proteins, and the major NATs in yeast and humans are NatA, NatB, and NatC.

PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data.

Single-cell RNA sequencing is an increasingly used method to measure gene expression at the single cell level and build cell-type atlases of tissues. Hundreds of single-cell sequencing datasets have already been published. However, studies are frequently deposited as raw data, a format difficult to access for biological researchers due to the need for data processing using complex computational pipelines.

EnsembleCNV: an ensemble machine learning algorithm to identify and genotype copy number variation using SNP array data.

The associations between diseases/traits and copy number variants (CNVs) have not been systematically investigated in genome-wide association studies (GWASs), primarily due to a lack of robust and accurate tools for CNV genotyping. Herein, we propose a novel ensemble learning framework, ensembleCNV, to detect and genotype CNVs using single nucleotide polymorphism (SNP) array data. EnsembleCNV (a) identifies and eliminates batch effects at raw data level; (b) assembles individual CNV calls into CNV regions (CNVRs) from multiple existing callers with complementary strengths by a heuristic algorithm; (c) re-genotypes each CNVR with local likelihood model adjusted by global information across multiple CNVRs; (d) refines CNVR boundaries by local correlation structure in copy number intensities; (e) provides direct CNV genotyping accompanied with confidence score, directly accessible for downstream quality control and association analysis.

Five-Year Clinical and Echocardiographic Outcomes From the NOTION Randomized Clinical Trial in Patients at Lower Surgical Risk.

Background: The NOTION trial (Nordic Aortic Valve Intervention) was designed to compare transcatheter aortic valve replacement (TAVR) with surgical aortic valve replacement (SAVR) in patients ≥70 years old with isolated severe aortic valve stenosis. Clinical and echocardiographic outcomes are presented after 5 years.

Methods: Patients were enrolled at 3 Nordic centers and randomized 1:1 to TAVR using the self-expanding CoreValve prosthesis (n=145) or SAVR using any stented bioprostheses (n=135).

Functional regulatory mechanism of smooth muscle cell-restricted LMOD1 coronary artery disease locus.

Recent genome-wide association studies (GWAS) have identified multiple new loci which appear to alter coronary artery disease (CAD) risk via arterial wall-specific mechanisms. One of the annotated genes encodes LMOD1 (Leiomodin 1), a member of the actin filament nucleator family that is highly enriched in smooth muscle-containing tissues such as the artery wall. However, it is still unknown whether LMOD1 is the causal gene at this locus and also how the associated variants alter LMOD1 expression/function and CAD risk.

CD90 Identifies Adventitial Mesenchymal Progenitor Cells in Adult Human Medium- and Large-Sized Arteries.

Mesenchymal stem cells (MSCs) reportedly exist in a vascular niche occupying the outer adventitial layer. However, these cells have not been well characterized in vivo in medium- and large-sized arteries in humans, and their potential pathological role is unknown. To address this, healthy and diseased arterial tissues were obtained as surplus surgical specimens and freshly processed.

Global analysis of A-to-I RNA editing reveals association with common disease variants.

RNA editing modifies transcripts and may alter their regulation or function. In humans, the most common modification is adenosine to inosine (A-to-I). We examined the global characteristics of RNA editing in 4,301 human tissue samples.

Network analysis of coronary artery disease risk genes elucidates disease mechanisms and druggable targets.

Genome-wide association studies (GWAS) have identified over two hundred chromosomal loci that modulate risk of coronary artery disease (CAD). The genes affected by variants at these loci are largely unknown and an untapped resource to improve our understanding of CAD pathophysiology and identify potential therapeutic targets. Here, we prioritized 68 genes as the most likely causal genes at genome-wide significant loci identified by GWAS of CAD and examined their regulatory roles in 286 metabolic and vascular tissue gene-protein sub-networks ("modules").

A novel high-resolution multilocus sequence typing of Giardia intestinalis Assemblage A isolates reveals zoonotic transmission, clonal outbreaks and recombination.

Molecular epidemiology and genotyping studies of the parasitic protozoan Giardia intestinalis have proven difficult due to multiple factors, such as low discriminatory power in the commonly used genotyping loci, which has hampered molecular analyses of outbreak sources, zoonotic transmission and virulence types. Here we have focused on assemblage A Giardia and developed a high-resolution assemblage-specific multilocus sequence typing (MLST) method. Analyses of sequenced G.