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.

Figure 1.

Selecting genes for the insulin resistance siRNA screen. (A) Global view of how 313 genes were selected for screening. Genes from multiple sources were considered and filtered based on whether their protein products could be targeted by small molecules. (B) Distribution of sources from which the 313 genes were selected. For example, 177 of 313 genes were selected because they were supported as causal for diabetes/obesity in an experimental cross population. (C) Distribution of the 313 genes with respect to five function categories: (1) G protein–coupled receptor (GPCR), (2) protease, (3) ion channel, (4) kinase/protease, and (5) other function. The numbers of genes in each category are provided after category names, where (+) stands for a positive siRNA hit and (−) denotes a negative siRNA hit.

Figure 2.

Flow diagram for the PEXA network reconstruction process. This reconstruction process consists of four steps: (1) perturbing genes of interest using siRNA to identify those that produce the desired phenotype (referred to here as the hit list); (2) querying through all the pathways in the KEGG database with the hit list to identify seeding paths; (3) expanding the seeding paths using PPI data to obtain a more coherent network relating to the biological process of interest (insulin signaling in this case); and (4) pruning the network obtained in step 3 to enhance the biological coherence of the network with respect to the biological processes of interest. In the hypothetical networks depicted in this figure, the orange nodes correspond to genes in the siRNA hit list, while the blue nodes are supported by the KEGG and/or PPI data as operating in the same part of the network as the hit list genes.

Figure 3.

Using PEXA to construct the insulin resistance networks. Red nodes represent genes in the siRNA hit list, green nodes represent genes that were screened but that are not in the siRNA hit list, and blue nodes represent genes that were not screened. (A) The siRNA hits (red nodes) serve as seeds for building up the seeding paths based on pathways represented in the KEGG database. (B) The seeding paths depicted in A are expanded and joined together using PPI data to form a single network. (C) After pruning we obtain a core network of genes enriched for siRNA hits. Larger sized nodes represent genes in the KEGG insulin signaling pathway, while the smaller sized yellow nodes represent small molecules in the KEGG database. The red edges represent interactions between S1pr2 (gold node) and its neighbors. We arbitrarily selected a few nodes and labeled them using a large font size to indicate the interdependency among the three plotted networks.

Figure 4.

PEXA pruning step. Two types of “superficial” nodes were identified for pruning: (A) nodes that are not siRNA hits and that have only one connection to the network via an interaction with another non-siRNA hit gene, and (B) nodes that are part of a network component comprised solely of non-siRNA hit genes and connected to the network via a non-siRNA hit gene. In the example networks depicted, blue nodes represent the superficial nodes, orange nodes represent siRNA hits, and green nodes represent non-siRNA hit nodes incorporated from either the KEGG database or PPI data. The superficial nodes and subnetworks containing no siRNA hits to which they connect are removed as part of the pruning process.

Figure 5.

Phenotypic differences between S1pr2^−/− and S1pr2^+/+ mice. Blood samples were collected from four groups of mice: male S1pr2^+/+, male S1pr2^−/−, female S1pr2^+/+, and female S1pr2^−/−. Mice were on a standard chow diet after weaning until 11 wk of age and then were switched to a high-fat diet until 21 wk of age. Results shown are for blood samples collected at 21 wk of age after a 4-h fast: (A) plasma insulin levels, (B) plasma free fatty acid levels, and (C) plasma glucose levels.