权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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 GTP 酶参与广泛的基本细胞行为。由于 GTP 酶“激活”的空间和时间控制，特异性是可能的；鸟嘌呤交换因子 (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 互动。 GEF/GTPase 电路很复杂，具有局部反馈，多个 GEF 控制一个 GTP酶，反之亦然。要剖析这种时空调节的电路需要成像，而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成像和分析。作为一个生物测试平台，我们将探索 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。我们将为 GEF 制定通用方法生物传感器，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，因此需要多重成像量化它们的相对动态。然而，当使用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，大大减少细胞扰动。即使是重叠分子激活的最精确成像也没有揭示因果关系关系。因此，我们将采用格兰杰因果推理的框架，该框架最初是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数据。将格兰杰因果关系的现有概念转化为分析需要许多步骤空间和时间分布的分子过程。最重要的是，我们将实现一个架构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动。我们还将开发一部小说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德空间中识别信号微域。最后，测试并确认我们的假设，我们将使用新的 GEF 光激活和光抑制类似物以及 GTPase 生物传感器控制一种蛋白质，同时观察另一种蛋白质。该研究计划将生产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途径。所有工具都将有效地部署到社区。