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Cross-Tissue Regulatory Gene Networks in Coronary Artery Disease

Research output: Contribution to journalArticle

  • Husain A. Talukdar
  • Hassan Foroughi Asl
  • Rajeev K. Jain
  • Raili Ermel
  • Arno Ruusalepp
  • Oscar Franzén
  • Brian A. Kidd
  • Ben Readhead
  • Chiara Giannarelli
  • Jason C. Kovacic
  • Torbjörn Ivert
  • Joel T. Dudley
  • Mete Civelek
  • Aldons J. Lusis
  • Eric E. Schadt
  • Josefin Skogsberg
  • Tom Michoel
  • Johan L M Björkegren

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Documents

Original languageEnglish
Pages (from-to)196-208
JournalCell Systems
Volume2
Issue number3
Early online date3 Mar 2016
DOIs
StatePublished - 23 Mar 2016

Abstract

Inferring molecular networks can reveal how genetic perturbations interact with environmental factors to cause common complex diseases. We analyzed genetic and gene expression data from seven tissues relevant to coronary artery disease (CAD) and identified regulatory gene networks (RGNs) and their key drivers. By integrating data from genome-wide association studies, we identified 30 CAD-causal RGNs interconnected in vascular and metabolic tissues, and we validated them with corresponding data from the Hybrid Mouse Diversity Panel. As proof of concept, by targeting the key drivers AIP, DRAP1, POLR2I, and PQBP1 in a cross-species-validated, arterial-wall RGN involving RNA-processing genes, we re-identified this RGN in THP-1 foam cells and independent data from CAD macrophages and carotid lesions. This characterization of the molecular landscape in CAD will help better define the regulation of CAD candidate genes identified by genome-wide association studies and is a first step toward achieving the goals of precision medicine. Well-established atherosclerosis risk factors and pathways are shown to operate through regulatory gene networks, active both within and across vascular and metabolic tissues, to cause coronary artery disease (CAD). Within these CAD-causal networks, the hierarchical order and connectivity patterns of both established and new genes in CAD, including so-called key disease driver genes, advance not only our global understanding of the molecular landscape in CAD but also reveal new candidate genes that may serve as suitable drug targets.

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