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: 2030037
• 负责人: Melanie Moses
• 金额: $ 12万
• 依托单位: University of New Mexico
• 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 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2030037&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.

最近，COVID-19成为一场全球大流行，造成了难以估量的人类痛苦和严重的经济中断。例如，个体免疫系统如何对新型冠状病毒做出反应，为什么有些人能有效清除感染，而另一些人却不能，这些都尚不清楚。该项目旨在了解免疫细胞，特别是T细胞如何发现分散在肺部的病毒感染细胞。它将讨论感染细胞的空间分布和T细胞通过复杂肺结构的运动模式如何决定感染的过程。该项目将开发冠状病毒空间免疫学模型（SIM-Cov），这是一个模拟模型，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。该模型将以受感染人体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肺细胞相互作用的生物学数据。该模型将以视觉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时间发展。通过模拟个体感染率随时间的变化，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理解。该模型和电影将向公众开放，并被纳入高中和大学生的教材中。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感染动力学的一个空白是，为什么感染的严重程度在个体之间差异如此之大。该项目通过整合宿主内感染和免疫控制中时空动力学的作用，特别是清除病毒所需的T细胞的作用，解决了这一差距。大多数病毒感染的定量模型使用微分方程或随机模型，不考虑感染细胞的空间分布或T细胞的运动模式。该项目通过开发全肺的三维空间模型（SIM-Cov）来解决这一空白，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病毒的生长、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）和关于SARS-CoV-2和免疫反应的新文献进行验证。SIM-Cov将模拟肺微环境，包括气道和肺泡间隙周围的脉管系统和上皮，病毒在整个肺部的空间和时间传播，以及T细胞的空间排列和运动。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，该项目将对了解冠状病毒感染动态和教育影响产生广泛影响。该RAPID奖由生理机制和生物力学项目、综合有机体系统生物学部的共生、防御和自我识别项目以及刺激竞争研究的既定计划（EPSCoR）颁发，使用的资金来自冠状病毒援助、救济和经济安全（CARES）法案。该奖项反映了美国国家科学基金会的法定使命，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。

项目成果

Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection.

• DOI: 10.1371/journal.pcbi.1009735
• 发表时间: 2021-12
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Moses ME;Hofmeyr S;Cannon JL;Andrews A;Gridley R;Hinga M;Leyba K;Pribisova A;Surjadidjaja V;Tasnim H;Forrest S
• 通讯作者: Forrest S