Network analysis of Signal Transduction

描述(由申请人提供)：任何网络的一个关键特征都是其架构或拓扑。该拓扑构成了网络信令属性的基础，例如跨多个时间尺度的信号集成、取决于输入强度和持续时间的不同输出的生成以及自我维持和/或信号衰减反馈环路。全面确定网络组成部分并系统地分析它们之间的相互联系和功能关系可以揭示一般的组织原则，这反过来又将揭示产生各种生物反应的网络特性和基本复杂性。要了解这种网络的高度互连性质，需要整合由大范围分析技术产生的正交数据集。我们提出了一种策略，将质谱(MS)、RNAi、基因表达和磷酸化图谱等强大的实验方法与复杂的计算工具相结合，以提供对胰岛素信号网络的结构和组织的全面了解。首先，使用TAP/MS(目标1)，我们将探索围绕所有目前已知的参与胰岛素信号转导的蛋白质组织的蛋白质组的复杂性。此外，我们将通过在途径激活过程中的多个时间点纯化相互作用，然后无标记MS蛋白定量来破译这些复合体的动力学。使用交互作用置信度得分指标，我们将选择候选反馈调节因子，并通过测量它们在刺激后多个时间点对ERK、Akt1、S6K和4E-BP磷酸化水平的影响来确定它们是作为正反馈调节因子还是负反馈调节因子。此外，我们将结合全球蛋白质组代谢标记分析(SILAC)和微阵列基因表达数据，将正反馈和负反馈调节因子定性为快速(通过翻译/翻译后调节)或缓慢(通过转录调节)。最后，我们将使用生化分析来鉴定候选反馈调节因子是否与该途径的核心成分(S)直接相互作用，并验证它们在体内胰岛素信号转导中的作用。在目标2中，我们将重点关注胰岛素调控的基因转录，以更深入地了解指定各种生物学结果的转录调控途径。我们将分析胰岛素诱导的转录反应中丰富的基因和生物过程，并使用计算和实验方法来确定相关的转录因子(TF)。利用计算分析，我们将表征转铁蛋白与靶基因的相互作用，以确定共调控基因集的顺式调控密码。通过这些研究，我们建议将特定的转录因子与特定生物过程的调节联系起来，因为致力于特定生物功能的基因可能会受到一组共同的反式作用因子和共同的顺式调节密码的共同调节。重要的是，我们将识别和表征信号通路和下游转录程序(S)，它们调节组成上述目标1中确定的慢反馈环的组件。这些结果将在组织培养细胞和体内得到验证。最后，在目标3中，我们将确定哪些miRNAs是响应胰岛素信号的差异调控的，验证它们并确定它们的靶标。这些研究将使我们能够识别在miRNAs控制下的反馈环。这一应用的成功将为在全球范围内重建信令网络提供基准。

DESCRIPTION (provided by applicant): 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.