RT Journal
A1 Tu, Zhidong
A1 Argmann, Carmen
A1 Wong, Kenny K.
A1 Mitnaul, Lyndon J.
A1 Edwards, Stephen
A1 Sach, Iliana C.
A1 Zhu, Jun
A1 Schadt, Eric E.
T1 Integrating siRNA and protein–protein interaction data to identify an expanded insulin signaling network
JF Genome Research 
JO Genome Research 
YR 2009 
FD June 01 
VO 19 
IS 6 
SP 1057 
OP 1067 
DO 10.1101/gr.087890.108 
UL http://genome.cshlp.org/content/19/6/1057.abstract 
AB 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.