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A mechanistic framework for cardiometabolic and coronary artery diseases

Authors :
Simon Koplev
Marcus Seldin
Katyayani Sukhavasi
Raili Ermel
Shichao Pang
Lingyao Zeng
Sean Bankier
Antonio Di Narzo
Haoxiang Cheng
Vamsidhar Meda
Angela Ma
Husain Talukdar
Ariella Cohain
Letizia Amadori
Carmen Argmann
Sander M. Houten
Oscar Franzén
Giuseppe Mocci
Omar A. Meelu
Kiyotake Ishikawa
Carl Whatling
Anamika Jain
Rajeev Kumar Jain
Li-Ming Gan
Chiara Giannarelli
Panos Roussos
Ke Hao
Heribert Schunkert
Tom Michoel
Arno Ruusalepp
Eric E. Schadt
Jason C. Kovacic
Aldon J. Lusis
Johan L. M. Björkegren
Source :
Nat Cardiovasc Res
Publication Year :
2022

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 (n = 600) and without (n = 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 (n = 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.

Subjects

Subjects :
Article

Details

Language :
English
Database :
OpenAIRE
Journal :
Nat Cardiovasc Res
Accession number :
edsair.doi.dedup.....83a526ab2830c2578f0d53873d3cae4e