A systems biology framework identifies molecular underpinnings of coronary heart disease

Abstract

Objective: Genetic approaches have identified numerous loci associated with coronary heart disease (CHD). The molecular mechanisms underlying CHD gene-disease associations, however, remain unclear. We hypothesized that genetic variants with both strong and subtle effects drive gene subnetworks that in turn affect CHD.

Approach and results: We surveyed CHD-associated molecular interactions by constructing coexpression networks using whole blood gene expression profiles from 188 CHD cases and 188 age- and sex-matched controls. Twenty-four coexpression modules were identified, including 1 case-specific and 1 control-specific differential module (DM). The DMs were enriched for genes involved in B-cell activation, immune response, and ion transport. By integrating the DMs with gene expression-associated single-nucleotide polymorphisms and with results of genome-wide association studies of CHD and its risk factors, the control-specific DM was implicated as CHD causal based on its significant enrichment for both CHD and lipid expression-associated single-nucleotide polymorphisms. This causal DM was further integrated with tissue-specific Bayesian networks and protein-protein interaction networks to identify regulatory key driver genes. Multitissue key drivers (SPIB and TNFRSF13C) and tissue-specific key drivers (eg, EBF1) were identified.

Conclusions: Our network-driven integrative analysis not only identified CHD-related genes, but also defined network structure that sheds light on the molecular interactions of genes associated with CHD risk.

Keywords: coexpression network; coronary heart disease; gene expression; systems biology.

Figure 1. Systems Biology Analysis Flow Chart Depicting the Process of Identifying CHD Causal Modules and Key Drivers (KDs)

The overall analysis includes 3 steps. First, coexpression networks are constructed from CHD cases and controls separately. CHD differential modules (DMs) are then identified by comparing the network structures between cases and controls. Step 2, the DMs are then integrated with GWAS of CHD and related traits using a SNP Set Enrichment Analysis (SSEA) to identify causal DMs. Third, key regulatory genes or KDs are identified for the causal DMs based on directional networks derived from independent studies using a Key Driver Analysis (KDA) algorithm.

Figure 2. Top KDs of the CHD causal module and the associated subnetwork

The subnetwork was derived from the top 20 KDs using the tissue-specific networks from which the KDs were identified. The nodes of the largest size in the network are the top 20 KDs, and the middle-sized nodes represent other KDs and genes in the CHD causal model. Red rectangular, red circular, and green circular nodes are multi-tissue KDs, blood-specific KDs, and causal DM genes, respectively. The network graph were drawn by ProteoLens.