Dataset Information

An integrative multiomic network model links lipid metabolism to glucose regulation in coronary artery disease.

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.

SUBMITTER: Cohain AT

PROVIDER: S-EPMC7822923 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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An integrative multiomic network model links lipid metabolism to glucose regulation in coronary artery disease.

Cohain Ariella T AT Barrington William T WT Jordan Daniel M DM Beckmann Noam D ND Argmann Carmen A CA Houten Sander M SM Charney Alexander W AW Ermel Raili R Sukhavasi Katyayani K Franzen Oscar O Koplev Simon S Whatling Carl C Belbin Gillian M GM Yang Jialiang J Hao Ke K Kenny Eimear E EE Tu Zhidong Z Zhu Jun J Gan Li-Ming LM Do Ron R Giannarelli Chiara C Kovacic Jason C JC Ruusalepp Arno A Lusis Aldons J AJ Bjorkegren Johan L M JLM Schadt Eric E EE

Nature communications 20210122 1

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. B ...[more]

PMID: 33483510

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Project description:BackgroundHundreds of candidate genes have been associated with coronary artery disease (CAD) through genome-wide association studies. However, a systematic way to understand the causal mechanism(s) of these genes, and a means to prioritize them for further study, has been lacking. This represents a major roadblock for developing novel disease- and gene-specific therapies for patients with CAD. Recently, powerful integrative genomics analyses pipelines have emerged to identify and prioritize candidate causal genes by integrating tissue/cell-specific gene expression data with genome-wide association study data sets.MethodsWe aimed to develop a comprehensive integrative genomics analyses pipeline for CAD and to provide a prioritized list of causal CAD genes. To this end, we leveraged several complimentary informatics approaches to integrate summary statistics from CAD genome-wide association studies (from UK Biobank and CARDIoGRAMplusC4D) with transcriptomic and expression quantitative trait loci data from 9 cardiometabolic tissue/cell types in the STARNET study (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task).ResultsWe identified 162 unique candidate causal CAD genes, which exerted their effect from between one and up to 7 disease-relevant tissues/cell types, including the arterial wall, blood, liver, skeletal muscle, adipose, foam cells, and macrophages. When their causal effect was ranked, the top candidate causal CAD genes were CDKN2B (associated with the 9p21.3 risk locus) and PHACTR1; both exerting their causal effect in the arterial wall. A majority of candidate causal genes were represented in cross-tissue gene regulatory co-expression networks that are involved with CAD, with 22/162 being key drivers in those networks.ConclusionsWe identified and prioritized candidate causal CAD genes, also localizing their tissue(s) of causal effect. These results should serve as a resource and facilitate targeted studies to identify the functional impact of top causal CAD genes.

Dataset Information

An integrative multiomic network model links lipid metabolism to glucose regulation in coronary artery disease.

Publications

An integrative multiomic network model links lipid metabolism to glucose regulation in coronary artery disease.

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