Network Topology in Cell Signaling

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DESCRIPTION (provided by applicant): Signaling pathways are often thought of as separable modules, transmitting signals along linear tracts resulting in the regulation of discrete cell functions. Indeed, studies of animal development often consider how contributions from independent signaling pathways are processed to create distinct cell types and tissues. However, the extent to which signaling pathways actually function as independent minimally interacting "modules" is still a matter of debate. The hypothesis underpinning this grant proposal is that signaling pathways are much more interconnected than previously thought and are embedded in complex networks. To analyze the level of independence, or "modularity", of signal transduction pathways, we propose to focus on Insulin signaling in Drosophila tissue culture cells to generate a comprehensive network of connected components based on transcriptional responses associated with the reduction of those components by RNA interference (RNAi). The Insulin signaling system is an ideal case study for our purpose. Insulin resistance, the root cause of late onset diabetes, can be caused by deregulation of components that are not solely devoted to Insulin signaling. Thus, deciphering the Insulin signaling network and the nature of cross-talk between the Insulin pathway and other pathways will be critical to understanding the patho-physiology of Insulin resistance. We have chosen to restrict our analysis to the Protein Kinase and Phosphatase (PPase) components of the network, which are biochemically related and of great interest as drug targets. The network will be built by successively perturbing, using gene-specific dsRNAs, each of the 261 Kinase and PPase that are expressed in the Drosophila SL2 cell line. The transcriptional signature will be ascertained using microarray expression profiling either in the resting state or following Insulin or EGF (Spitz) stimulation. Computational network-modeling algorithms will be used to convert gene expression profiles into network connectivities. The molecular nature of novel connections will be validated in vivo and characterized using biochemical assays. Altogether, these studies will greatly advance our knowledge of signaling networks and their organization during signal transduction.