Integrated visualization, control, and analysis of GEF – GTPase networks in living cells

活细胞中 GEF – GTPase 网络的集成可视化、控制和分析

基本信息

批准号：
10221568
负责人：
Gaudenz Danuser
金额：
$ 54.12万
依托单位：
UT SOUTHWESTERN MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-05-01 至 2026-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10221568
关键词：
Actomyosin Acute Address Adhesions Adopted Adoption Architecture Basic Science Behavior Binding Biological Biomedical Engineering Biosensor Cell Adhesion Cell Adhesion Molecules Cell physiology Cells Communities Complex Computing Methodologies Coupled Data Decision Making Disease Dyes Elements Engineering Environment Etiology Event Family Feedback Fiber Fluorescence Resonance Energy Transfer GTP Binding Guanine Guanine Nucleotide Exchange Factors Guanosine Triphosphate Phosphohydrolases Homeostasis Image Image Analysis Interdisciplinary Study Label Light Machine Learning Malignant Neoplasms Maps Mathematics Membrane Methods Modeling Modification Molecular Molecular Conformation Morphogenesis Neighborhoods Nonlinear Dynamics Oncogenic Organic Chemistry Output Pathway interactions Play Process Protein Engineering Proteins Proxy Regression Analysis Regulation Reporting Research Research Project Grants Resolution Role Route Series Signal Pathway Signal Transduction Signal Transduction Pathway Site Specific qualifier value Specificity Statistical Models Stimulus Structure System Techniques Testing Time Time Series Analysis Translating Translational Research Visualization analog base cancer therapy cell behavior cell motility computer science control theory design econometrics guanine analog high dimensionality image processing imaging approach interdisciplinary approach interest light emission mathematical methods mathematical theory migration multiplexed imaging novel optogenetics preservation response rho rho GTP-Binding Proteins single molecule spatiotemporal tool

项目摘要

A small number of Rho family GTPases participate in a broad array of fundamental cellular behaviors. Specificity is possible due to spatial and temporal control of GTPase “activation”; Guanine exchange factors (GEFs) generate activated, GTP-bound GTPases with precise timing and localization, while specialized interactions with adhesion molecules, membrane domains and other localized structures specify GEF-GTPase interactions. GEF/GTPase circuits are complex, with localized feedbacks, multiple GEFs controlling one GTPase, and vice versa. To dissect this spatiotemporally regulated circuitry requires imaging, and new analytical techniques that can dissect causal relationships from imaging data. Following the intentions of PAR- 19-158 (Bioengineering Research Grants), we propose a multidisciplinary collaboration leveraging organic chemistry, protein engineering, imaging, and computer science to fudnamentally advance signal transduction imaging and analysis. As a biological testbed we will explore the role of GEF-GTPase interactions in cell protrusion, single cell migration and collective migration. We will develop a generalizable approach to GEF biosensors, and adapt our proven GTPase biosensors to image GEF and GTPase activities in the same cell. Because GEF-GTPase interactions are heterogeneous and complex, multiplexed imaging is necessary to quantify their relative dynamics. However, perturbation of cell behavior is especially problematic when using two biosensors in the same cell. We will therefore develop new biosensor designs that greatly reduce cell perturbation. Even the most precise imaging of overlapping molecular activations has not revealed causal relationships. We will therefore adopt the framework of Granger Causality inference, which was originally devised for financial market analysis, to extract causal connections and feedback interactions from imaging data. Numerous steps will be necessary to translate the existing concepts of Granger causality to the analysis of spatially and temporally distributed molecular processes. Most importantly, we will implement a schema for Granger causality inference in multivariate time series models that will capture spatial relations, and we will combine principles of high-dimensional statistical regression with approaches from control theory to estimate information flows between variables that are coupled by strong feedbacks. We will also develop a novel clustering approach that preserves the neighborhood topology of data in a high-dimensional feature space and in the Euclidian space of the cell outline to identify signaling microdomains. Finally, to test and confirm our hypotheses, we will use new photo-activatable and photo-inhibitable analogs of GEFs together with GTPase biosensors to control one protein while observing another. This research plan will produce biosensors with reduced perturbation, biosensor/optogenetic multiplexing capabilities, and image analysis/modeling approaches necessary to shed light on the network topology of nonlinear, spatiotemporally controlled signaling pathways. All tools will efficiently deployed to the community.

少数 Rho 家族 GTP 酶参与广泛的基本细胞行为。由于鸟嘌呤交换因子的空间和时间控制，特异性是可能的； (GEF) 生成具有精确定时和定位的激活的 GTP 结合 GTPase，同时专门化与粘附分子、膜结构域和其他局部结构的相互作用指定了 GEF-GTPase GEF/GTPase 回路非常复杂，具有局部反馈，多个 GEF 控制一个回路。 GTPase，反之亦然，要剖析这种时空调节电路需要成像和新的技术。可以按照 PAR- 的意图剖析成像数据中因果关系的分析技术。 19-158（生物工程研究补助金），我们提出利用有机化学、蛋白质工程、成像和计算机科学从根本上推进信号转导作为生物测试平台，我们将探索 GEF-GTPase 相互作用在细胞中的作用。我们将开发一种通用的 GEF 方法。生物传感器，并采用我们经过验证的 GTPase 生物传感器对同一细胞中的 GEF 和 GTPase 活性进行成像。由于 GEF-GTP 酶相互作用是异质且复杂的，因此需要多重成像然而，在使用时，细胞行为的扰动尤其成问题。因此，我们将开发新的生物传感器设计，大大减少细胞数量。即使是最精确的重叠分子激活成像也没有揭示因果关系。因此，我们将采用最初的格兰杰因果推理框架。专为金融市场分析而设计，从成像中提取因果关系和反馈交互将格兰杰因果关系的现有概念转化为分析需要许多步骤。最重要的是，我们将为空间和时间分布的分子过程实现一个模式。多元时间序列模型中的格兰杰因果推理将捕获空间关系，我们将将高维统计回归原理与控制理论估计方法相结合通过强反馈耦合的变量之间的信息流我们还将开发一种新颖的方法。保留高维特征空间中数据的邻域拓扑的聚类方法在细胞轮廓的欧几里得空间中识别信号微域最后，测试和确认我们的。假设，我们将使用新的 GEF 光激活和光抑制类似物以及 GTPase 该研究计划将生产具有控制一种蛋白质同时观察另一种蛋白质的生物传感器。减少扰动、生物传感器/光遗传学复用功能以及图像分析/建模阐明非线性、时空控制信号的网络拓扑所必需的方法所有工具都将有效地部署到社区。