Abstract
BackgroundThe incidence of congenital heart disease (CHD) is continuously increasing among infants born alive nowadays, making it one of the leading causes of infant morbidity worldwide. Various studies suggest that both genetic and environmental factors lead to CHD, and therefore identifying its candidate genes and disease-markers has been one of the central topics in CHD research. By using the high-throughput genomic data of CHD which are available recently, network-based methods provide powerful alternatives of systematic analysis of complex diseases and identification of dysfunctional modules and candidate disease genes.ResultsIn this paper, by modeling the information flow from source disease genes to targets of differentially expressed genes via a context-specific protein-protein interaction network, we extracted dysfunctional modules which were then validated by various types of measurements and independent datasets. Network topology analysis of these modules revealed major and auxiliary pathways and cellular processes in CHD, demonstrating the biological usefulness of the identified modules. We also prioritized a list of candidate CHD genes from these modules using a guilt-by-association approach, which are well supported by various kinds of literature and experimental evidence.ConclusionsWe provided a network-based analysis to detect dysfunctional modules and disease genes of CHD by modeling the information transmission from source disease genes to targets of differentially expressed genes. Our method resulted in 12 modules from the constructed CHD subnetwork. We further identified and prioritized candidate disease genes of CHD from these dysfunctional modules. In conclusion, module analysis not only revealed several important findings with regard to the underlying molecular mechanisms of CHD, but also suggested the distinct network properties of causal disease genes which lead to identification of candidate CHD genes.
Highlights
The incidence of congenital heart disease (CHD) is continuously increasing among infants born alive nowadays, making it one of the leading causes of infant morbidity worldwide
To discover molecular pathogenesis in complex disease, considerable efforts have been made to elucidate the relations between variability in gene expression and genotype [10,11,12], and putative disease genes curated from literature research can be regarded as the source of molecular perturbations while differentially expressed genes identified from mRNA profiling can represent the responsive components of source perturbations
To capture the information flow from causal genes to target genes and to identify dysfunctional modules from these causal paths, we first identified 85 target genes which are defined as those differentially expressed (DE) in sufficient proportion of patients (Additional File 1), and connected each known causal genes of CHD with these target DE genes via shortest paths shown in Figure 1A and 1B
Summary
The incidence of congenital heart disease (CHD) is continuously increasing among infants born alive nowadays, making it one of the leading causes of infant morbidity worldwide. Various studies suggest that both genetic and environmental factors lead to CHD, and identifying its candidate genes and disease-markers has been one of the central topics in CHD research. By using the high-throughput genomic data of CHD which are available recently, network-based methods provide powerful alternatives of systematic analysis of complex diseases and identification of dysfunctional modules and candidate disease genes. Linking causal disease genes with responsive differentially expressed genes by modeling the information flow in protein interactome can better reveal dysfunctional subnetworks and help the identification of disease modules. Several recent studies used such circuit flow networks to discover causal genes and associated pathways or to analyze gene network centrality [10,11,15,16]
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