A key feature of any network is its architecture or topology. The topology underlies network signaling properties such as integration of signals across multiple time scales, generation of distinct outputs depending on input strength and duration, and self-sustaining and/or signal attenuating feedback loops. Comprehensive identification of network components and a systematic analysis of the interconnectivity and functional relations between them could reveal general organizing principles, which in turn would shed light on network properties and the underlying complexities that generate a diverse array of biological responses. Understanding the highly interconnected nature of such networks requires integration of orthogonal datasets resulting from broad- scale analysis techniques. We propose a strategy that combines the powerful experimental methodologies of Mass Spectrometry (MS), RNAi, gene expression and phosphorylation profiling, with sophisticated computational tools, to provide a comprehensive understanding of the structure and organization of the Insulin signaling network. First, using TAP/MS (Aim 1) we will explore the complexity of the proteome organized around all currently known proteins involved in Insulin signaling. Furthermore, we will decipher the dynamics of these complexes by purifying interactors at multiple time points during pathway activation followed by label-free MS protein quantitation. Using the interaction confidence score metric we will select candidate feedback regulators and determine whether they serve as positive or negative feedback regulators by measuring their effect on the phosphorylation levels of ERK, Akt1, S6K and 4E-BP at multiple time points post stimulation. Further, we will characterize positive and negative feedback regulators as fast (via translational/posttranslational regulation) or slow (via transcriptional regulation) using a combination of global proteomic analysis by metabolic labeling (SILAC) and microarray gene expression data. Finally, using biochemical assays we will characterize whether the candidate feedback regulators interact directly with core component(s) of the pathway and validate their role in Insulin signaling in vivo.
In Aim 2 we will focus on Insulin-regulated gene transcription to gain a deeper insight into the transcriptional regulatory pathways that specify the various biological outcomes. We will analyze genes and biological processes enriched in the Insulin-induced transcriptional response and using computational and experimental approaches to identify the relevant Transcription Factors (TFs). Using computational analyses we will characterize the TF-target gene interactions to identify the cis regulatory codes for coregulated sets of genes. Through these studies we propose to link specific TFs to the regulation of specific biological processes, as it is likely that genes devoted to a particular biological function will be coregulated by a common set of trans-acting factors and a shared cis- regulatory code. Importantly, we will identify and characterize both the signaling pathways and the downstream transcriptional program(s) that regulate the components constituting slow feedback loops identified in Aim 1 above. These results will be validated both in tissue culture cells and in vivo. Finally, in Aim 3, we will identify which miRNAs are differentially regulated in response to Insulin signaling, validate them and identify their targets. These studies will allow us to identify feedback loops that are under the control of miRNAs. The success of this application will provide a benchmark for reconstructing signaling networks on a global scale.

Public Health Relevance

Our studies will provide a comprehensive understanding of the various levels of transcriptional and translational regulation regulated by a Receptor Tyrosine Kinase. The success of this application will provide a benchmark for reconstructing signaling networks on a global scale.

National Institute of Health (NIH)
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
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Biodata Management and Analysis Study Section (BDMA)
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Sechi, Salvatore
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Harvard University
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