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A Mechanistic Framework for Cardiometabolic and Coronary Artery Diseases

Abstract

Coronary atherosclerosis results from the delicate interplay of genetic and exogenous risk factors, principally taking place in metabolic organs and the arterial wall. Here we show that 224 gene-regulatory coexpression networks (GRNs) identified by integrating genetic and clinical data from patients with ( = 600) and without ( = 250) coronary artery disease (CAD) with RNA-seq data from seven disease-relevant tissues in the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study largely capture this delicate interplay, explaining >54% of CAD heritability. Within 89 cross-tissue GRNs associated with clinical severity of CAD, 374 endocrine factors facilitated inter-organ interactions, primarily along an axis from adipose tissue to the liver ( = 152). This axis was independently replicated in genetically diverse mouse strains and by injection of recombinant forms of adipose endocrine factors (EPDR1, FCN2, FSTL3 and LBP) that markedly altered blood lipid and glucose levels in mice. Altogether, the STARNET database and the associated GRN browser (http://starnet.mssm.edu) provide a multiorgan framework for exploration of the molecular interplay between cardiometabolic disorders and CAD.

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References
1.
Deloukas P, Kanoni S, Willenborg C, Farrall M, Assimes T, Thompson J . Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet. 2012; 45(1):25-33. PMC: 3679547. DOI: 10.1038/ng.2480. View

2.
Leek J, Johnson W, Parker H, Jaffe A, Storey J . The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012; 28(6):882-3. PMC: 3307112. DOI: 10.1093/bioinformatics/bts034. View

3.
Yao D, OConnor L, Price A, Gusev A . Quantifying genetic effects on disease mediated by assayed gene expression levels. Nat Genet. 2020; 52(6):626-633. PMC: 7276299. DOI: 10.1038/s41588-020-0625-2. View

4.
Cooper G, Bahar I, Becich M, Benos P, Berg J, Espino J . The center for causal discovery of biomedical knowledge from big data. J Am Med Inform Assoc. 2015; 22(6):1132-6. PMC: 5009908. DOI: 10.1093/jamia/ocv059. View

5.
Aibar S, Gonzalez-Blas C, Moerman T, Huynh-Thu V, Imrichova H, Hulselmans G . SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017; 14(11):1083-1086. PMC: 5937676. DOI: 10.1038/nmeth.4463. View