Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Lempiainen H

PROVIDER: S-EPMC5821758 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Lempiäinen Harri H Brænne Ingrid I Michoel Tom T Tragante Vinicius V Vilne Baiba B Webb Tom R TR Kyriakou Theodosios T Eichner Johannes J Zeng Lingyao L Willenborg Christina C Franzen Oscar O Ruusalepp Arno A Goel Anuj A van der Laan Sander W SW Biegert Claudia C Hamby Stephen S Talukdar Husain A HA Foroughi Asl Hassan H Pasterkamp Gerard G Watkins Hugh H Samani Nilesh J NJ Wittenberger Timo T Erdmann Jeanette J Schunkert Heribert H Asselbergs Folkert W FW Björkegren Johan L M JLM

Scientific reports 20180221 1

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 ...[more]

PMID: 29467471

Similar Datasets

Project description:Network analysis is a novel method to understand the complex pathogenesis of inflammation-driven atherosclerosis. Using this approach, we attempted to identify key inflammatory genes and their core transcriptional regulators in coronary artery disease (CAD). Initially, we obtained 124 candidate genes associated with inflammation and CAD using Polysearch and CADgene database for which protein-protein interaction network was generated using STRING 9.0 (Search Tool for the Retrieval of Interacting Genes) and visualized using Cytoscape v 2.8.3. Based on betweenness centrality (BC) and node degree as key topological parameters, we identified interleukin-6 (IL-6), vascular endothelial growth factor A (VEGFA), interleukin-1 beta (IL-1B), tumor necrosis factor (TNF) and prostaglandin-endoperoxide synthase 2 (PTGS2) as hub nodes. The backbone network constructed with these five hub genes showed 111 nodes connected via 348 edges, with IL-6 having the largest degree and highest BC. Nuclear factor kappa B1 (NFKB1), signal transducer and activator of transcription 3 (STAT3) and JUN were identified as the three core transcription factors from the regulatory network derived using MatInspector. For the purpose of validation of the hub genes, 97 test networks were constructed, which revealed the accuracy of the backbone network to be 0.7763 while the frequency of the hub nodes remained largely unaltered. Pathway enrichment analysis with ClueGO, KEGG and REACTOME showed significant enrichment of six validated CAD pathways - smooth muscle cell proliferation, acute-phase response, calcidiol 1-monooxygenase activity, toll-like receptor signaling, NOD-like receptor signaling and adipocytokine signaling pathways. Experimental verification of the above findings in 64 cases and 64 controls showed increased expression of the five candidate genes and the three transcription factors in the cases relative to the controls (p<0.05). Thus, analysis of complex networks aid in the prioritization of genes and their transcriptional regulators in complex diseases.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets