Gene Coexpression Network Topology of Cardiac Development, Hypertrophy, and Failure

Abstract

Background-Network analysis techniques allow a more accurate reflection of underlying systems biology to be realized than traditional unidimensional molecular biology approaches. Using gene coexpression network analysis, we define the gene expression network topology of cardiac hypertrophy and failure and the extent of recapitulation of fetal gene expression programs in failing and hypertrophied adult myocardium. Methods and Results-We assembled all myocardial transcript data in the Gene Expression Omnibus (nϭ1617). Because hierarchical analysis revealed species had primacy over disease clustering, we focused this analysis on the most complete (murine) dataset (nϭ478). Using gene coexpression network analysis, we derived functional modules, regulatory mediators, and higher-order topological relationships between genes and identified 50 gene coexpression modules in developing myocardium that were not present in normal adult tissue. We found that known gene expression markers of myocardial adaptation were members of upregulated modules but not hub genes. We identified ZIC2 as a novel transcription factor associated with coexpression modules common to developing and failing myocardium. Of 50 fetal gene coexpression modules, 3 (6%) were reproduced in hypertrophied myocardium and 7 (14%) were reproduced in failing myocardium. One fetal module was common to both failing and hypertrophied myocardium. Conclusions-Network modeling allows systems analysis of cardiovascular development and disease. Although we did not find evidence for a global coordinated program of fetal gene expression in adult myocardial adaptation, our analysis revealed specific gene expression modules active during both development and disease and specific candidates for their regulation. (Circ Cardiovasc Genet. 2011;4:26-35.)

Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure

“…What this linear, unitary approach fails to capture are mechanisms influencing higher order phenotypes reflected in re-wiring of transcriptional partners that do not affect expression levels of specific genes. Earlier work has already led to the discovery of central genes using co-expression changes 37,38 . Here, we expanded this use of gene co-expression by exploiting not only gene interaction degree, but also integrated topological network differences and known pathway information.…”

Heart failure: a complex clinical process interpreted by systems biology approach and network medicine

“…Also, in rats with post-myocardial infarction HF, was observed a prompt induction of the fetal transcriptional gene program prior to myocardium hypertrophy (38). Dewey et al (39) used gene co-expression network analysis and determinated the gene expression network topology of cardiac hypertrophy and failure, as well as the degree of re-emergence of fetal gene expression programs in the hypertrophic and failing adult myocardium. This network analysis based on myocardial transcript data from the Gene Expression Omnibus, a publicly available repository of all microarray data, and focused on the most complete murine dataset, has disclosed specific gene expression modules triggered during both development and disease.…”

“…Often, the intramodular connectivity is calculated, in order to identify gene coexpression modules and estimate reproducibility of gene modularity between networks. Dewey et al (39) analyzed gene coexpression modules for over-representation of known transcription factor targets. In hypertrophied and failing myocardium, the transcriptional targets are assembled in nodes in a meta-network higher order topology of the transcriptome.…”

Systems-Based Approaches to Cardiovascular Biomarker Discovery

“…24 They identified a novel transcription factor, ZIC2 (transcription factor zinc finger protein 2) associated with modules common to developing and failing myocardium. In a cohort of dilated cardiomyopathy and control samples, Lin et al established dilated cardiomyopathy-specific network modules based on the combination of information from a PPI network and tissue gene expression data.…”