Network analysis of Signal Transduction

信号传导的网络分析

• 批准号: 8241968
• 负责人: NORBERT PERRIMON
• 金额: $ 47.92万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-15 至 2015-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8241968
• 关键词: Affinity Chromatography; Architecture; Attenuated; Benchmarking; Biochemical; Biological; Biological Assay; Biological Models; Biological Process; Categories; Cells; Code; Complex; Data; Data Set; Drosophila genus; Event; Feedback; Functional RNA; Gene Expression; Generations; Genes; Genetic Transcription; Genomics; Insulin; Insulin Signaling Pathway; Label; Lead; Light; Link; Liquid Chromatography; Mass Spectrum Analysis; Measures; Messenger RNA; Metabolic; Methodology; Methods; Metric; MicroRNAs; Molecular; Nature; Ontology; Outcome; Output; Pathway Analysis; Pathway interactions; Pattern; Phosphorylation; Play; Post-Translational Regulation; Property; Proteins; Proteome; Proteomics; RNA Interference; Receptor Protein-Tyrosine Kinases; Regulation; Regulatory Pathway; Research Design; Research Methodology; Role; Signal Pathway; Signal Transduction; Specific qualifier value; Structure; System; Systems Biology; Techniques; Time; Trans-Activators; Transcriptional Regulation; Translational Regulation; Validation; candidate validation; computerized tools; gain of function; gene interaction; in vivo; insight; insulin signaling; programs; public health relevance; response; success; tandem mass spectrometry; tissue/cell culture; transcription factor

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. 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.

描述（由申请人提供）：任何网络的一个关键特征是其架构或拓扑。该拓扑结构是网络信令特性的基础，例如跨多个时间尺度的信号集成，根据输入强度和持续时间生成不同的输出，以及自维持和/或信号衰减反馈回路。全面识别网络组件并系统分析它们之间的互连性和功能关系可以揭示一般的组织原则，这反过来又会揭示网络特性和产生各种生物反应的潜在复杂性。要理解这种网络的高度互联性，需要整合来自大规模分析技术的正交数据集。我们提出了一种策略，结合质谱（MS），RNAi，基因表达和磷酸化分析的强大的实验方法，与复杂的计算工具，提供全面的了解胰岛素信号网络的结构和组织。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传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物，然后进行无标记MS蛋白定量，来破译这些复合物的动力学。使用相互作用置信分数度量，我们将选择候选反馈调节剂，并通过测量它们在刺激后多个时间点对ERK、Akt 1、S6 K和4 E-BP磷酸化水平的影响来确定它们是作为正反馈调节剂还是负反馈调节剂。此外，我们将使用代谢标记（SILAC）和微阵列基因表达数据的全局蛋白质组学分析相结合，将正反馈和负反馈调节器表征为快（通过翻译/翻译后调节）或慢（通过转录调节）。最后，使用生化测定，我们将表征候选反馈调节剂是否直接与通路的核心组分相互作用，并验证它们在体内胰岛素信号传导中的作用。在目标2中，我们将专注于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富集的基因和生物过程，并使用计算和实验方法来识别相关的转录因子（TF）。使用计算分析，我们将表征TF-靶基因相互作用，以确定共调控基因组的顺式调控代码。通过这些研究，我们建议将特定的TF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享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，它们调节构成上述目标1中识别的慢反馈环的组分。这些结果将在组织培养细胞和体内进行验证。最后，在目标3中，我们将确定哪些miRNAs在响应胰岛素信号传导时受到差异调节，验证它们并确定它们的靶点。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全球范围内的信令网络改造提供一个基准。 公共卫生相关性：我们的研究将提供一个全面的理解的各种水平的转录和翻译调节调节受体酪氨酸激酶。这一应用的成功将为全球范围内的信令网络改造提供一个基准。