Measurement and Prediction of Endothelial Cross-family Signaling

内皮跨家族信号传导的测量和预测

基本信息

批准号：
10672552
负责人：
Princess Izevbua Imoukhuede
金额：
$ 77.74万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2025-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10672552
关键词：
Adaptor Signaling Protein Adipocytes Adipose tissue Affinity Amino Acids Area Binding Blood Vessels Cell Proliferation Cell Survival Cell physiology Cells Computer Models Data Development Directed Molecular Evolution Disease Disease Progression Dose Effector Cell Endothelial Cells Endothelial Growth Factors Receptor Endothelium Engineering Family Fibrinogen Fibroblasts Fibrosis Goals Growth Factor Growth Factor Receptors Health Human In Vitro Inflammation Insulin Resistance KDR gene Knowledge Lead Libraries Ligands Lipids Malignant Neoplasms Measurement Measures Mediating Medicine Modeling Molecular National Heart, Lung, and Blood Institute Obesity Organoids PTK2 gene Pathogenicity Permeability Phosphorylation Platelet Activating Factor Platelet-Derived Growth Factor Platelet-Derived Growth Factor Receptor Pre-Clinical Model Process Property Proteins Receptor Activation Regulation Research Role Scheme Signal Transduction Site Structure System Systems Biology Testing Therapeutic Tissue Expansion Tissues Translating Tyrosine Validation Vascular Diseases Vascular Endothelial Growth Factors Vascularization Work angiogenesis base design experimental analysis in silico innovation kinetic model member migration molecular dynamics mutation screening novel novel strategies obesity treatment obesogenic pedagogy pre-clinical predictive test protein structure receptor receptor binding response screening simulation text searching theories

项目摘要

PROJECT SUMMARY/ABSTRACT The vascular endothelial growth factors (VEGFs) direct key signaling processes in obesity and at least 70 other diseases. However, focus on this signaling node alone has not achieved the promise of predictable angiogenic control. Current models are incomplete as other growth factors, besides VEGF, contribute to vascular disease progression, presenting a complexity that cannot be predictably regulated by targeting one node in this system. Therefore, there is a continuing need to account for the complexity of additional, multi-component signaling networks, a goal that can be achieved via data-driven, computational systems biology in close concert with experimental analysis of signaling and functional response. Toward this goal, we aim to examine a novel paradigm of network regulation called cross-family signaling, in which members from one growth factor family [e.g., platelet derived growth factors (PDGFs)] bind to and signal through members of another family (e.g., VEGFRs). We hypothesize that systematic examination of protein structure and downstream signaling within the cross-family paradigm via simulation, ligand-engineering, network quantification, and computational modeling can uncover novel mechanisms to control angiogenesis. We will test this hypothesis through three aims, sensitively quantifying receptor activation rates and functional responses of cross-family binding (e.g., proliferation, migration, and barrier function); predicting and measuring the structural properties of cross-family binding via molecular simulations and directed evolution; and developing validated deterministic models (mass-action kinetic modeling) of cross- family signaling and applying them to study and control the dynamics of cross-family signaling in human cell systems, in silico. We are primed to lead this new research because we are among the first to pursue this important theoretical paradigm, and we lead this cause to understand cell signaling via structure/function–based computational modeling. This work will catalyze a shift in perspective and innovation in the areas of cell signaling, systems biology, and predictive design of obesity- focused therapies.

项目概要/摘要血管内皮生长因子 (VEGF) 指导肥胖和至少 70 种其他疾病的关键信号传导过程。然而，仅关注这一信号传导节点并没有实现当前模型可预测的血管生成控制的希望。不完整，因为除了 VEGF 之外，其他生长因子也会导致血管疾病进展，呈现出复杂性无法通过针对该系统中的一个节点来进行可预测的监管，因此，需要持续进行核算。针对额外的多组件信号网络的复杂性，可以通过数据驱动来实现这一目标，计算系统生物学与信号传导和功能反应的实验分析密切相关。为了实现这一目标，我们的目标是研究一种称为跨家族信号传导的新型网络监管范式，其中来自一种生长因子家族的成员[例如血小板衍生生长因子 (PDGF)] 与成员结合并通过成员发出信号我们捕获了另一个家族（例如 VEGFR）的蛋白质结构和下游的系统检查。通过模拟、配体工程、网络量化和计算在跨家族范式内进行信号传导建模可以揭示控制血管生成的新机制，我们将通过三个目标来检验这一假设：敏感地量化受体激活率和跨家族结合的功能反应（例如增殖、迁移和屏障功能）；通过分子预测和测量跨家族结合的结构特性模拟和定向进化；以及开发经过验证的交叉确定性模型（质量作用动力学建模）家族信号传导，并将其应用于研究和控制人类细胞系统中跨家族信号传导的动态，硅片。我们准备好领导这项新研究，因为我们是最早追求这一重要理论范式的人之一，并且我们通过基于结构/功能的计算模型来理解细胞信号传导。促进细胞信号传导、系统生物学和肥胖预测设计领域的观点转变和创新针对性治疗。