Network analysis of inflammatory genes and their transcriptional regulators in coronary artery disease

PLoS One. 2014 Apr 15;9(4):e94328. doi: 10.1371/journal.pone.0094328. eCollection 2014.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computational Biology*
  • Coronary Artery Disease / genetics*
  • Coronary Artery Disease / metabolism
  • Coronary Artery Disease / pathology
  • Gene Expression Regulation*
  • Gene Regulatory Networks*
  • Humans
  • Inflammation / genetics
  • Protein Interaction Maps
  • Signal Transduction / genetics
  • Transcription, Genetic*

Grants and funding

The authors gratefully acknowledge the support extended by the trustees of Thrombosis Research Institute, London and Bangalore, Weston Foundation, UK, Foundation Bey, Switzerland, the Tata Social Welfare Trust, India (TSWT/IG/SNB/JP/Sdm) and Department of Biotechnology, Ministry of Science and Technology, Government of India (BT/01/CDE/08/07). The sponsors did not participate in the design, conduct, sample collection analysis and interpretation of the data or in the preparation, review or approval of the manuscript.