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

Abstract

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.

Genome-Wide RNAi Screen for the Discovery of Gene Function, Novel Therapeutical Targets and Agricultural Applications

“…After future filtering, S1pr2 gene was identified as one of key regulators of insulin signaling. Increased plasma insulin levels were detected in male S1pr2 -/-knockout mice, suggesting there is a potential link between S1pr2 and insulin resistance [37].…”

“…The cell-based RNAi screens have yielded tremendous amount of novel discoveries and greatly promoted our understanding on many basic biological processes, molecular functions and complexity of cellular networks. New findings from genome-wide RNAi screens have led to the identification of novel components of canonical signaling transduction pathways, such new players of ERK pathway [16] and phosphorylation networks regulating JUN NH2-terminal kinase (JNK) pathway [36]; the role of S1pr2 gene (Sphingosine-1-phosphate receptor 2) in insulin signaling [37]; novel modulators of p53 pathway [38]; and key genes that are essential for the proliferation of cancer cells [17]. Recently, an integrative approach with RNAi screen and whole genome structural analysis identified IKBKE kinase as a breast cancer oncogene [39].…”

“…Disrupted insulin signaling leads to many human diseases, such as diabetes. To facilitate the underlying mechanism of diabetes and identify novel components and modulators of the insulin signaling pathway, a RNAi screen was conducted using 3T3-L1 adipocytes [37]. About 313 obesity and diabetes related genes were selected in the RNAi screen.…”

Delineation of key regulatory elements identifies points of vulnerability in the mitogen-activated signaling network

“…Previous studies support the utility of analyzing network interactions in receptorspecific pathways, to generate insights into signaling mechanisms. Furthermore, they also emphasized the potential of this approach for identifying new drug targets (Tu et al 2009;Astsaturov et al 2010;Bandyopadhyay et al 2010).…”

Functional genomic analysis of glioblastoma multiforme through short interfering RNA screening: a paradigm for therapeutic development

“…27 Several studies have implemented this analysis on gene expression data and RNAi screening data. 6,46,69,72 For instance, Bredel et al 6 identified 3 novel MYC-interacting genes in human gliomas through functional network analysis of gene expression data with Ingenuity Pathways Analysis (IPA); Mori et al 46 reported that gene profiling and pathway analysis helped to elucidate the molecular mechanisms involved in neuroendocrine transdifferentiation of prostate cancer cells; and Tu et al 69 used an siRNA screening approach to identify a reliable set of components/modulators of the insulin signaling pathway. Thus, functional genomic analyses that leverage multiple types of information have begun to show promise in uncovering important biology not apparent from standard analysis methods.…”