An integrative multiomic network model links lipid metabolism to glucose regulation in coronary artery disease
Ariella Cohain, William Barrington, Daniel M. Jordan, Noam D. Beckmann, Carmen Argmann, Sander M. Houten, Alexander W. Charney, Raili Ermel, Katyayani Sukhavasi, Oscar Franzén, Simon Koplev, Carl Whatling, Gillian M. Belbin, Jialiang Yang, Ke Hao, Eimear E. Kenny, Zhidong Tu, Jun Zhu, Li‐Ming Gan, Ron Do, Chiara Giannarelli, Jason C. Kovacic, Arno Ruusalepp, Aldons J. Lusis, Johan Björkegren, Eric E. Schadt
Abstract
Elevated plasma cholesterol and type 2 diabetes (T2D) are associated with coronary artery disease (CAD). Individuals treated with cholesterol-lowering statins have increased T2D risk, while individuals with hypercholesterolemia have reduced T2D risk. We explore the relationship between lipid and glucose control by constructing network models from the STARNET study with sequencing data from seven cardiometabolic tissues obtained from CAD patients during coronary artery by-pass grafting surgery. By integrating gene expression, genotype, metabolomic, and clinical data, we identify a glucose and lipid determining (GLD) regulatory network showing inverse relationships with lipid and glucose traits. Master regulators of the GLD network also impact lipid and glucose levels in inverse directions. Experimental inhibition of one of the GLD network master regulators, lanosterol synthase (LSS), in mice confirms the inverse relationships to glucose and lipid levels as predicted by our model and provides mechanistic insights.