Cardiovascular diseases are leading causes for death worldwide. Genetic disposition jointly with traditional risk factors precipitates their manifestation. Whereas the implications of a positive family history for individual risk have been known for a long time, only in the past few years have genome‐wide association studies (GWAS) shed light on the underlying genetic variations. Here, we review these studies designed to increase our understanding of the pathophysiology of cardiovascular diseases, particularly coronary artery disease and myocardial infarction. We focus on the newly established pathways to exemplify the translation from the identification of risk‐related genetic variants to new preventive and therapeutic strategies for cardiovascular disease.

Aims : Mental stress substantially contributes to the initiation and progression of human disease, including cardiovascular conditions. We aim to investigate the underlying mechanisms of these contributions since they remain largely unclear. Methods and results : Here, we show in humans and mice that leucocytes deplete rapidly from the blood after a single episode of acute mental stress. Using cell-tracking experiments in animal models of acute mental stress, we found that stress exposure leads to prompt uptake of inflammatory leucocytes from the blood to distinct tissues including heart, lung, skin, and, if present, atherosclerotic plaques. Mechanistically, we found that acute stress enhances leucocyte influx into mouse atherosclerotic plaques by modulating endothelial cells. Specifically, acute stress increases adhesion molecule expression and chemokine release through locally derived norepinephrine. Either chemical or surgical disruption of norepinephrine signalling diminished stress-induced leucocyte migration into mouse atherosclerotic plaques. Conclusion : Our data show that acute mental stress rapidly amplifies inflammatory leucocyte expansion inside mouse atherosclerotic lesions and promotes plaque vulnerability.

Objective—: Genome-wide association studies have to date identified 159 significant and suggestive loci for coronary artery disease (CAD). We now report comprehensive bioinformatics analyses of sequence variation in these loci to predict candidate causal genes. Approach and Results—: All annotated genes in the loci were evaluated with respect to protein-coding single-nucleotide polymorphism and gene expression parameters. The latter included expression quantitative trait loci, tissue specificity, and miRNA binding. High priority candidate genes were further identified based on literature searches and our experimental data. We conclude that the great majority of causal variations affecting CAD risk occur in noncoding regions, with 41% affecting gene expression robustly versus 6% leading to amino acid changes. Many of these genes differed from the traditionally annotated genes, which was usually based on proximity to the lead single-nucleotide polymorphism. Indeed, we obtained evidence that genetic variants at CAD loci affect 98 genes which had not been linked to CAD previously. Conclusions—: Our results substantially revise the list of likely candidates for CAD and suggest that genome-wide association studies efforts in other diseases may benefit from similar bioinformatics analyses.

Background: A chromosomal locus at 4q32.1 has been genome-wide significantly associated with coronary artery disease risk. The locus encompasses GUCY1A3 , which encodes the α ₁ subunit of the soluble guanylyl cyclase (sGC), a key enzyme in the nitric oxide/cGMP signaling pathway. The mechanism linking common variants in this region with coronary risk is not known. Methods: Gene expression and protein expression were analyzed with quantitative polymerase chain reaction and immunoblotting, respectively. Putative allele-specific transcription factors were identified with in silico analyses and validated via allele-specific quantification of antibody-precipitated chromatin fractions. Regulatory properties of the lead risk variant region were analyzed with reporter gene assays. To assess the effect of zinc finger E box-binding homeobox 1 transcription factor (ZEB1), siRNA-mediated knockdown and overexpression experiments were performed. Association of GUCY1A3 genotype and cellular phenotypes was analyzed with vascular smooth muscle cell migration assays and platelet aggregation analyses. Results: Whole-blood GUCY1A3 mRNA levels were significantly lower in individuals homozygous for the lead (rs7692387) risk variant. Likewise, reporter gene assays demonstrated significantly lower GUCY1A3 promoter activity for constructs carrying this allele. In silico analyses located a DNase I hypersensitivity site to rs7692387 and predicted binding of the transcription factor ZEB1 rather to the nonrisk allele, which was confirmed experimentally. Knockdown of ZEB1 resulted in more profound reduction of nonrisk allele promoter activity and a significant reduction of endogenous GUCY1A3 expression. Ex vivo–studied platelets from homozygous nonrisk allele carriers displayed enhanced inhibition of ADP-induced platelet aggregation by the nitric oxide donor sodium nitroprusside and the phosphodiesterase 5 inhibitor sildenafil compared with homozygous risk allele carriers. Moreover, pharmacological stimulation of sGC led to reduced migration only in vascular smooth muscle cells homozygous for the nonrisk allele. In the Hybrid Mouse Diversity Panel, higher levels of GUCY1A3 expression correlated with less atherosclerosis in the aorta. Conclusions: Rs7692387 is located in an intronic site that modulates GUCY1A3 promoter activity. The transcription factor ZEB1 binds preferentially to the nonrisk allele, leading to an increase in GUCY1A3 expression, higher sGC levels, and higher sGC activity after stimulation. Finally, human and mouse data link augmented sGC expression to lower risk of atherosclerosis.

Recent trends within computational and data sciences show an increasing recognition and adoption of computational workflows as tools for productivity and reproducibility that also democratize access to platforms and processing know-how. As digital objects to be shared, discovered, and reused, computational workflows benefit from the FAIR principles, which stand for Findable, Accessible, Interoperable, and Reusable. The Workflows Community Initiative’s FAIR Workflows Working Group (WCI-FW), a global and open community of researchers and developers working with computational workflows across disciplines and domains, has systematically addressed the application of both FAIR data and software principles to computational workflows. We present recommendations with commentary that reflects our discussions and justifies our choices and adaptations. These are offered to workflow users and authors, workflow management system developers, and providers of workflow services as guidelines for adoption and fodder for discussion. The FAIR recommendations for workflows that we propose in this paper will maximize their value as research assets and facilitate their adoption by the wider community.

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”). The modules and genes within were scored for CAD druggability potential. The scoring enriched for targets of cardiometabolic drugs currently in clinical use and in-depth analysis of the top-scoring modules validated established and revealed novel target tissues, biological processes, and druggable targets. This study provides an unprecedented resource of tissue-defined gene–protein interactions directly affected by genetic variance in CAD risk loci.