Integrating siRNA and protein-protein interaction data to identify an expanded insulin signaling network

Rosetta Inpharmatics, a wholly owned subsidiary of Merck & Co., Inc., Seattle, Washington 98109, USA.
Genome Research (Impact Factor: 14.63). 04/2009; 19(6):1057-67. DOI: 10.1101/gr.087890.108
Source: PubMed


Insulin resistance is one of the dominant symptoms of type 2 diabetes (T2D). Although the molecular mechanisms leading to this resistance are largely unknown, experimental data support that the insulin signaling pathway is impaired in patients who are insulin resistant. To identify novel components/modulators of the insulin signaling pathway, we designed siRNAs targeting over 300 genes and tested the effects of knocking down these genes in an insulin-dependent, anti-lipolysis assay in 3T3-L1 adipocytes. For 126 genes, significant changes in free fatty acid release were observed. However, due to off-target effects (in addition to other limitations), high-throughput RNAi-based screens in cell-based systems generate significant amounts of noise. Therefore, to obtain a more reliable set of genes from the siRNA hits in our screen, we developed and applied a novel network-based approach that elucidates the mechanisms of action for the true positive siRNA hits. Our analysis results in the identification of a core network underlying the insulin signaling pathway that is more significantly enriched for genes previously associated with insulin resistance than the set of genes annotated in the KEGG database as belonging to the insulin signaling pathway. We experimentally validated one of the predictions, S1pr2, as a novel candidate gene for T2D.

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Available from: Jun Zhu, Aug 28, 2014
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    • "Building on the hypothesis that neighboring genes within an interaction network share a common biological function, other network studies seed known disease genes in functional networks combining evidence from the literature, functional annotation, genomic distances, or genetic variation data (i.e., GWAS, SNP, eQTL), to search for nearby putative genes [13-15]. Related work integrates experimental data in the interaction network, for example, significant genes from regulatory or proteomic experiments, to discover candidate genes given their proximity to query genes [16-18]. "
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