Abstract
Knowledge of the interactome improves the understanding of disease metabolism. Biological information about interactions among genes and their protein products, computationally extracted in the context of SysBiomics, can hint at molecular causes of diseases, be essential for understanding biological systems, and provide clues for new therapeutic approaches. Quick and efficient access to this data have become critical issues for biologists. We have implemented a computational platform that integrates pathway, protein–protein interaction, differentially expressed genome and literature mining data to result in comprehensive networks for insomnia and intervention effects of Jujuboside B (JuB). The interaction data were imported into Cytoscape software, a popular bioinformatics package for biological network visualization and data integration, for screening the central nodes of the network, exploiting functional study of the central node genes, exploring the mechanism of insomnia. Results showed that seven differentially expressed genes confirmed by Cytoscape as the central nodes of the network in insomnia had interactions, forming a complicated interaction network (77 nodes, 96 edges). Among gene nodes, HBA1, LEP, MAOA, PRNP, GHRL, CLOCK and SLC6A4 were verified as the genes with maximal differential expressions. Of note, we further observed that the HBA1, LEP, SLC6A4 and MAOA were JuB target genes. The interaction network of the differentially expressed genes, especially the central nodes of this network, can provide clues to the insomnia, early diagnosis and molecular targeted therapy. Our findings demonstrate that the integration of interaction network in genomic space can not only speed the genome-wide identification of drug targets but also find new applications for the existing drugs.
Highlights
SysBiomics approach seeks to comprehend the complexity of organisms by combining many different kinds of data to create predictive models
Network-based pharmacology is emerging as an important paradigm for analysis of biological systems
We present an integrated approach to predict targets for approved anti-insomnia Jujuboside B (JuB) by exploring network pharmacology
Summary
SysBiomics approach seeks to comprehend the complexity of organisms by combining many different kinds of data (protein-protein and protein-DNA interactions, protein modifications, biochemistry, etc.) to create predictive models. In the era of SysBiomics, the focus on understanding complex organisms is shifting from studying individual genes and proteins towards the relationships between them (Gilchrist et al, 2006; Hopkins, 2008). These relationships are usually expressed in terms of various kinds of biological networks. Molecular interactions are the focus of many functional genomics studies, and they form a cornerstone of Systems biology research. Many studies have reported interesting biological findings from these networks, including the relationships between various statistical properties of a gene and its function at the molecular level based on networks (Tamble et al, 2011). Increasing complexity of functional genomics data drives the development of methods and tools for data integration and visualization (Wu, Jiang, Zhang, & Li, 2008)
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