Physical and Genetic Interaction Landscape of the Tyrosine Kinome

酪氨酸激酶的物理和遗传相互作用景观

基本信息

批准号：
9309044
负责人：
Sourav Bandyopadhyay
金额：
$ 44.02万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-01 至 2019-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9309044
关键词：
Address Affinity Chromatography Apoptosis Atlases Behavior Binding Bioinformatics Biological Biological Assay Biology California Cancer Etiology Cell Line Cell physiology Cells Chemicals Clinical Collaborations Communities Complement Data Databases Dependency Diabetes Mellitus Disease Engineering Follow-Up Studies Genetic Genetic Epistasis Growth Human Human Genome In Vitro Knowledge Label Laboratories Libraries Malignant Neoplasms Mammalian Cell Mammals Maps Mass Spectrum Analysis Measures Mediating Mission Modeling Output Pathogenesis Pathway Analysis Phenotype Phosphorylation Site Phosphotransferases Phosphotyrosine Play Process Protein Tyrosine Kinase Protein-Protein Interaction Map Proteins Proteomics Publications Quantitative Genetics Recording of previous events Research Personnel Resources Role San Francisco Shapes Signal Pathway Signal Transduction Stimulus Techniques Translating Tyrosine Universities Validation Yeasts analog base cell growth cell type chemical genetics data integration design expression vector genetic approach in vivo innovation insight mutant network models new therapeutic target novel novel strategies public health relevance scaffold targeted biomarker tool

项目摘要

DESCRIPTION (provided by applicant): Signaling by tyrosine kinases play a major role in mammalian signal transduction and is a major component of nearly every major disease type. However, a systematic understanding of signaling pathways has remained elusive. While a number of techniques have been developed to map signaling pathways, used alone they only provide insight into one facet of signaling. We will use an integrative approach to chart the networks which control tyrosine kinase signaling at multiple levels through the use of complementary physical and genetic interaction mapping approaches. These maps will lead to network models which reflect proteins that can functionally modulate signaling and are physically associated with kinases including substrates, adaptors and regulatory subunits. To achieve this, the proposal integrates the complementary expertise of investigators at the University of California-San Francisco in high-throughput physical and genetic interaction mapping (Krogan), chemical-genetic approaches for tracing signaling pathways (Shokat) and network analysis and data integration (Bandyopadhyay). We aim to identify proteins that associate with tyrosine kinases through affinity purification-mass spectrometry (AP-MS) and kinase substrates through covalent capture-and-release in Aim 1. These data will be further characterized using a newly developed platform for quantitative genetic interaction mapping in mammalian cells, which will establish the functional relevance of these interactions by systematically identifying epistatic genetic relationships between kinases and associated proteins in Aim 2. Lastly, in Aim 3, we will unify data collected in the first two aims using network modeling to uncover detailed, mechanistic biological insights relating to mammalian tyrosine kinases. All data (raw, processed and integrated) will be made immediately available in an interactive and searchable fashion so that others can exploit the information we have collected on the tyrosine kinome.

描述（由申请人提供）：酪氨酸激酶的信号在哺乳动物信号转导中起主要作用，并且是几乎每种主要疾病类型的主要组成部分。但是，对信号通路的系统理解仍然难以捉摸。尽管已经开发了许多技术来绘制信号通路，但仅使用它们仅提供对信号传导一个方面的洞察力。我们将使用一种集成方法来绘制网络，通过使用互补的物理和遗传相互作用映射方法来控制酪氨酸激酶信号在多个级别上的网络。这些地图将导致网络模型，这些网络模型反映了可以在功能上调节信号传导的蛋白质，并与包括底物，适配器和调节亚基在内的激酶物理相关。为了实现这一目标，该提案将加州大学弗朗西斯科大学研究人员的补充专业知识整合在高通量的物理和遗传相互作用映射（Krogan），用于追踪信号通路（SHOKAT）的化学基因方法（SHOKAT）以及网络分析和数据集成（Bandyopadhyay）。 We aim to identify proteins that associate with tyrosine kinases through affinity purification-mass spectrometry (AP-MS) and kinase substrates through covalent capture-and-release in Aim 1. These data will be further characterized using a newly developed platform for quantitative genetic interaction mapping in mammalian cells, which will establish the functional relevance of these interactions by systematically identifying epistatic genetic relationships between kinases and associated proteins in AIM 2。最后，在AIM 3中，我们将使用网络建模在前两个目标中统一数据，以发现与哺乳动物酪氨酸激酶有关的详细的机械生物学见解。所有数据（原始，处理和集成）将以交互式和可搜索的方式立即提供，以便其他数据可以利用我们在酪氨酸Kinome上收集的信息。