Network Analysis of Inflammatory Genes and Their Transcriptional Regulators in Coronary Artery Disease

Abstract

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.

Integrative Analysis of Transcriptomics, Proteomics, and Metabolomics Data of White Adipose and Liver Tissue of High-Fat Diet and Rosiglitazone-Treated Insulin-Resistant Mice Identified Pathway Alterations and Molecular Hubs

“…26 Network analyzer was used to identify hub proteins, carrying the highest closeness centrality (CC), betweenness centrality (BC), and the node degree as key topological parameters. 27 Network robustness was measured as an iterative resilience in terms of removal of nodes. KEGG pathways were used to visualize pathways within the networks, and unconnected nodes were removed.…”

Roles of Thermophiles and Fungi in Bitumen Degradation in Mostly Cold Oil Sands Outcrops

“…Networks of orders were then built using positive correlation thresholds ranging from 0 to 1. A threshold of 0.6 was used to extract positively correlated orders, and the corresponding network was visualized with the Cytoscape program, version 2.8.3 (65). Shotgun metagenome sequencing and data analysis.…”

Male-specific association of the APC rs383830 T allele with the risk of coronary heart disease

“…TNF belongs to the intermediate regulators of apoptosis (Tong and Coulombe, 2006;McFerrin et al, 2012), and is a tumor suppressor gene that plays a significant role in the pathogenesis of atherosclerosis (Nair et al, 2014). Studies using mouse models have demonstrated that enhanced expression of an apoptotic gene (TNFR1) leads to accelerated atherosclerosis and reduced smooth muscle cell multiplication in the aged wild-type arteries (Zhang et al, 2010).…”