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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：
10379219
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/10379219
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 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.

少量Rho家族gtpase参与广泛的基本细胞行为。