Collaborative Research: RAPID: Spatial Modeling of Immune Response to Multifocal SARS-CoV-2 Viral Lung Infection

合作研究：RAPID：多灶性 SARS-CoV-2 病毒肺部感染免疫反应的空间建模

• 批准号: 2029696
• 负责人: Stephanie Forrest
• 金额: $ 7.99万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2029696&HistoricalAwards=false
• 关键词: Collaborative; Research; RAPID; Spatial; Modeling

COVID-19 recently emerged as a worldwide pandemic, causing untold human suffering and severe economic disruptions. How individual immune systems respond to a novel coronavirus, for example, why some individuals clear infection efficiently while others do not is not known. This project seeks to understand how immune cells, specifically T cells, find cells infected with virus that are dispersed in the lung. It will address how spatial distributions of infected cells and movement patterns of T cells through complex lung structures determine the course of infection. The project will develop the Spatial Immunological Model of Coronavirus (SIM-Cov), a simulation model for studying these effects and improving understanding of how the immune system controls infection by coronaviruses. The model will take computed tomography (CT) scans of an infected human lung as input, as well as biological data on how T cells interact with the virus and infected lung cells. The model will predict the course of infection in the form of visually intuitive movies showing how the infection progresses through time in different individuals. By modeling variability in individuals’ infectious rates over time, SIM-Cov will improve our understanding of why the severity of COVID-19 varies so much among individuals. The model and movies will be publicly accessible, and incorporated into educational materials for high school and college students. The project will also train two graduate and one undergraduate students in interdisciplinary research.One gap in understanding infection dynamics of the novel coronavirus SARS-CoV-2 is why the severity of infection varies so much among individuals. This project addresses that gap by incorporating the role played by spatial-temporal dynamics in within-host infections and immune control, particularly the role of T cells which are required for viral clearance. Most quantitative models of viral infection use differential equations or stochastic models and do not account for the spatial distribution of infected cells or T cell movement patterns. The project addresses this gap by developing a three-dimensional spatial model of the whole lung (SIM-Cov) that tests how spatial interactions between T cells and virus affect viral growth, load and clearance within the lungs. Ultimately, these within-host factors contribute to the rate of clearance within a single host and transmission between hosts. SIM-Cov will be parameterized and validated with empirical imaging data (CT scans of SARS-infected patients) and the emerging literature on SARS-CoV-2 and immune responses. SIM-Cov will model the lung microenvironment, including vasculature and epithelium surrounding the airways and alveolar spaces, the spatial and temporal spread of virus throughout the lung, and the spatial arrangement and movement of T cells. The project will have broad-ranging impacts for understanding coronavirus infection dynamics and educational impacts through dissemination of the model and movies produced in the project, as well as the engagement of three students in interdisciplinary research.This RAPID award is made by the Physiological Mechanisms and Biomechanics Program and the Symbiosis, Defense, and Self-recognition Program in the BIO Division of Integrative Organismal Systems, and by the Established Program to Stimulate Competitive Research (EPSCoR), using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

2019冠状病毒病最近成为全球性大流行病，给人类带来了无尽的痛苦和严重的经济破坏。个体的免疫系统如何对新型冠状病毒作出反应，例如，为什么有些人有效地清除感染，而另一些人则不知道。该项目旨在了解免疫细胞，特别是T细胞，如何找到分散在肺部的病毒感染细胞。它将解决如何感染细胞的空间分布和T细胞通过复杂的肺部结构的运动模式决定感染的过程。该项目将开发冠状病毒的空间免疫模型（SIM-Cov），这是一种模拟模型，用于研究这些影响，并提高对免疫系统如何控制冠状病毒感染的理解。该模型将采用受感染的人类肺部的计算机断层扫描（CT）作为输入，以及T细胞如何与病毒和受感染的肺细胞相互作用的生物数据。该模型将以直观的电影形式预测感染过程，展示感染如何在不同个体中随着时间的推移而发展。通过对个体感染率随时间的变化进行建模，SIM-Cov将提高我们对COVID-19严重程度在个体之间差异如此之大的原因的理解。该模型和电影将向公众开放，并纳入高中和大学生的教育材料。该项目还将培养两名研究生和一名本科生进行跨学科研究。了解新型冠状病毒SARS-CoV-2感染动力学的一个差距是为什么感染的严重程度在个体之间差异如此之大。该项目通过整合时空动态在宿主内感染和免疫控制中所起的作用，特别是病毒清除所需的T细胞的作用，来解决这一差距。大多数病毒感染的定量模型使用微分方程或随机模型，并且不考虑感染细胞的空间分布或T细胞运动模式。该项目通过开发一个全肺的三维空间模型（SIM-Cov）来解决这一差距，该模型测试T细胞和病毒之间的空间相互作用如何影响肺内的病毒生长，负荷和清除。最终，这些宿主内因素有助于单个宿主内的清除率和宿主之间的传播。SIM-Cov将被参数化，并通过经验成像数据（SARS感染患者的CT扫描）和新出现的关于SARS-CoV-2和免疫反应的文献进行验证。SIM-Cov将模拟肺微环境，包括气道和肺泡周围的血管和上皮，病毒在整个肺中的空间和时间传播，以及T细胞的空间排列和运动。该项目将通过传播项目中制作的模型和电影，以及三名学生参与跨学科研究，对理解冠状病毒感染动力学和教育影响产生广泛影响。这个RAPID奖由生理机制和生物力学计划以及综合有机系统生物部的共生，防御和自我识别计划颁发，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。