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：
10612345
负责人：
Gaudenz Danuser
金额：
$ 51.49万
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/10612345
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 Cytoskeleton 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 Machine Learning Malignant Neoplasms Maps Marketing Mathematics Membrane Methods Modeling Modification Molecular Molecular Conformation Morphogenesis Neighborhoods 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 cancer therapy cell behavior cell motility computer science control theory data integration design econometrics guanine analog high dimensionality image processing imaging approach imaging science 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GTPases参与了广泛的基本细胞行为。由于GTPase“激活”的空间和临时控制，特异性是可能的。鸟嘌呤交换因子（GEFS）生成具有精确时序和本地化的激活的，GTP结合的GTPase，而专门的与粘合分子，膜结构域和其他局部结构的相互作用指定GEF-GTPase 互动。 GEF/GTPase电路很复杂，具有局部反馈，多个GEF控制一个 GTPase，反之亦然。要剖析此空间调节的电路，需要成像，并进行新的可以从成像数据中剖析因果关系的分析技术。遵循par的意图 19-158（生物工程研究补助金），我们提出了一个多学科合作组织化学，蛋白质工程，成像和计算机科学以燃油促进信号转导成像和分析。作为生物学测试床，我们将探讨GEF-GTPase相互作用在细胞中的作用突出，单细胞迁移和集体迁移。我们将开发一种可概括的GEF方法生物传感器，并使我们经过验证的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-GTPase相互作用是异质且复杂的，因此需要多重成像对于量化它们的相对动力学。但是，使用时细胞行为的扰动尤其是有问题的同一细胞中的两个生物传感器。因此，我们将开发新的生物传感器设计，以大大减少细胞扰动。即使是重叠分子激活的最精确的成像也没有揭示因果关系关系。因此，我们将采用Granger因果关系推断的框架，该框架最初是设计用于金融市场分析，从成像中提取因果关系和反馈互动数据。将需要多个步骤将现有的格兰杰休闲概念转化为分析在空间和临时分布的分子过程中。最重要的是，我们将对多元时间序列模型中的Granger热量推断将捕获空间关系，我们将将高维统计回归的原理与从控制理论到估计的方法相结合信息在变量之间由强烈反馈耦合。我们还将开发一本小说聚类方法在高维特征空间和在细胞轮廓的欧几里得空间中，以识别信号微区。最后，测试并确认我们的假设，我们将与GTPase一起使用GEFS的新型照片激活和可抑制的类似物生物传感器控制一种蛋白质，同时观察另一种蛋白质。该研究计划将与减少扰动，生物传感器/光学多遗传多路复用能力以及图像分析/建模阐明非线性，空间临时控制信号的网络拓扑所需的方法途径。所有工具将有效部署到社区。