A mechanistic framework for cardiometabolic and coronary artery diseases

Abstract

yocardial infarction and stroke, the leading causes of global morbidity and mortality, are caused by atherosclerosis, which originates from inflammatory, lipid, endocrine, metabolic and hemodynamic disturbances. Indeed, multiple and parallel malfunctions in metabolic organs are responsible for the complex molecular disease processes of cardiometabolic disorders (CMDs) leading to CAD 1 . For example, the liver plays a central role in determining plasma lipid levels by regulating lipoprotein synthesis and lipoprotein remnant uptake, whereas adipose tissues and skeletal muscle (SKLM) facilitate lipolysis. Similarly, blood glucose levels depend on a delicate interplay of hepatic glucose production, insulin production in pancreatic beta cells and insulin sensitivity in peripheral glycolytic tissues. Alterations in lipid or glucose metabolism may lead to obesity, which in turn may promote the development of type 2 diabetes mellitus, hypertension, systemic inflammation 2,3 and, eventually, CAD.Thus far, the role of these and other risk factors in causing the initiation and progression of CAD have typically only been considered in isolated pathways. A systemic view 4-7 of the combined high-dimensional, multiorgan metabolic processes that perturb the biology of the arterial wall has, however, not been described. Systems studies based on integrative analyses of DNA and RNA sequencing (RNA-seq) data, unlike studies focusing on DNA alone, such as genome-wide association studies (GWAS), hold promise to go beyond studies of individual genetic risk loci and candidate genes in isolated pathways by capturing the combined impact of exogenous and genetic risk factors 8-10 . To achieve this, RNA-seq data are typically first used to infer gene coexpression modules 5 ,

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

“…Subject recruitment and tissue collection in the Stockholm-Tartu Atherosclerosis Reverse Networks Engineering Task study (STARNET) were performed as previously described [ 2 , 7 – 10 ]. Briefly, patients with angiographically proven CAD who were eligible for open-thorax surgery and control subjects without CAD were enrolled into this institutional review committee approved protocol after written informed consent at Tartu University Hospital, Estonia.…”

“…Detailed methods are provided in our prior publication [ 10 ]. In brief, R package Weighted Gene Co-expression Network Analysis (WGCNA) [ 40 ] was used to identify correlation patterns among genes across RNA-seq data.…”

“…Key driver analyses were performed using Mergeomics [ 42 ]. Note that due to limitations of GENIE3, for large networks (greater than ~3000 genes), it is not possible to infer key drivers [ 10 ].…”

“…As a solution, we recently developed a streamlined, data-driven informatics pipeline to gain tractable but reliable insights on the causal mechanisms of complex CAD genetic loci using exclusively human datasets [ 7 ]. Our pipeline leverages the STARNET study that comprises RNA sequence data from multiple tissues that were collected from living patients with advanced CAD and controls without CAD [ 2 , 7 – 10 ]. However, our pipeline is also applicable to other genetics-of-gene expression datasets such as GTEx [ 11 ].…”

Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases

“…Along with CVDs, the STARNET database is a useful resource to study cardiometabolic diseases. Koplev and colleagues used STARNET to construct co-expression-based gene networks between cardiometabolic diseases and CAD ( 184 ). Similarly, Shu et al explored shared regulatory networks between type 2 diabetes and CVD and also identified 15 KDGs, including HMGCR, CAV1, IGF1, and PCOLCE, for both diseases ( 185 ).…”