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Cell Syst. 2017 Jan 25;4(1):121-128.e4. doi: 10.1016/j.cels.2016.10.016. Epub 2016 Nov 17.

Systems Genetics Approach Identifies Gene Pathways and Adamts2 as Drivers of Isoproterenol-Induced Cardiac Hypertrophy and Cardiomyopathy in Mice.

Author information

1
Department of Microbiology, Immunology and Molecular Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Departments of Anesthesiology, Physiology, and Medicine, Cardiovascular Research Laboratories, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
2
Department of Microbiology, Immunology and Molecular Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
3
Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
4
Center for Interdisciplinary Research on Complex Systems, Department of Physics, Northeastern University, Boston, MA 02115, USA.
5
Departments of Anesthesiology, Physiology, and Medicine, Cardiovascular Research Laboratories, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
6
Department of Microbiology, Immunology and Molecular Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA. Electronic address: jlusis@mednet.ucla.edu.

Abstract

We previously reported a genetic analysis of heart failure traits in a population of inbred mouse strains treated with isoproterenol to mimic catecholamine-driven cardiac hypertrophy. Here, we apply a co-expression network algorithm, wMICA, to perform a systems-level analysis of left ventricular transcriptomes from these mice. We describe the features of the overall network but focus on a module identified in treated hearts that is strongly related to cardiac hypertrophy and pathological remodeling. Using the causal modeling algorithm NEO, we identified the gene Adamts2 as a putative regulator of this module and validated the predictive value of NEO using small interfering RNA-mediated knockdown in neonatal rat ventricular myocytes. Adamts2 silencing regulated the expression of the genes residing within the module and impaired isoproterenol-induced cellular hypertrophy. Our results provide a view of higher order interactions in heart failure with potential for diagnostic and therapeutic insights.

KEYWORDS:

MICA; causal modeling; gene network; siRNA mediated knockdown; ventricular myocytes

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