Center for Viral Systems Biology (CViSB)

项目摘要-病毒系统生物学中心(CViSB) 新冠肺炎疫情鲜明地提醒人们注意传染病构成的威胁，但其他优先事项 病原体，如拉萨和埃博拉病毒，在流行地区继续构成重大挑战fi。 感染拉萨、埃博拉和SARS-CoV-2病毒可能会导致不同的急性和长期后果， 从轻微或无症状的疾病到长期的后遗症或死亡。当我们建立了 病毒系统生物学中心(CViSB，cvisb.org)2018年，我们设定了一个主要目标，回答为什么。使用一个 重点关注埃博拉，拉萨，并通过范围扩大，新冠肺炎，我们通过确定 决定疾病结局的临床、免疫、遗传和病毒分子因素。 在这次更新应用中，我们将超越标准系统生物学数据的生成和分析 并使用反向翻译方法来扩展我们先前的工作，重点放在两个基础上 研究和翻译应用。我们的中心假设仍然是病毒的复杂网络 和人为因素，包括不同的临床、免疫学、遗传学、病毒学和生理学 属性在决定拉萨、埃博拉和新冠肺炎的结果和传播方面发挥着关键作用。我们的 总的目标是识别这些分子网络，并提供对 疾病严重程度和传播的病毒、宿主和环境驱动因素，以发现 人类疾病。我们将通过应用‘组学’技术、可穿戴设备和 对西方独特的患者、接种者和幸存者队列的高通量实验方法 非洲和美国。围绕两个相互关联的项目和四个核心构建，我们将完成 主要目标如下：(1)将复杂系统免疫学、宿主遗传学、生理学、 临床和代谢数据集，用于执行预测建模和DefiNe生物签名，以描述 使用西非和美国的队列研究拉萨、埃博拉和新冠肺炎的风险、严重性和结果 各州。(2)将大规模宿主病毒基因组数据与种群数据相结合， 构建多变量网络分析和动态增长的高通量实验方法 阐明分子流行病学、功能进化、获得性免疫和 拉萨病毒、埃博拉病毒和SARS-CoV-2的宿主-病原体动态。(三)做大做强 高通量实验方法、开源软件、机器学习工具和基于网络的 用于分析和可视化大规模数据集的平台，可实现实时询问和 对复杂的宿主-病原体-环境网络结构和动力学的解释。(4)我们会 继续开发和维护强大的数据库和应用程序编程接口 开源CViSB生成了系统生物学数据集，并进行了推广和培训 利用系统生物学来研究传染病。

Project Summary - Center for Viral Systems Biology (CViSB) The COVID-19 pandemic is a stark reminder of the threat posed by infectious diseases, but other priority pathogens, such as Lassa and Ebola viruses, continue to pose significant challenges in endemic areas. Infection with Lassa, Ebola, and SARS-CoV-2 viruses can lead to diverse acute and long-term outcomes, ranging from mild or asymptomatic disease to long-term sequelae or death. When we established the Center for Viral Systems Biology (CViSB, cvisb.org) in 2018, we set a main goal of answering why. With a focus on Ebola, Lassa, and via a scope expansion, COVID-19, we have achieved this goal by identifying clinical, immunological, genetic, and virus molecular factors that determine the outcome of disease. In this renewal application, we will move beyond standard systems biology data generation and analysis and use a reverse translational approach to expand on our prior work with a focus on both fundamental research and translational application. Our central hypothesis remains that complex networks of viral and human factors, including distinct clinical, immunological, genetic, virological, and physiological attributes play key roles in determining the outcome and spread of Lassa, Ebola, and COVID-19. Our overall goal is to identify these molecular networks and provide a deep system-level understanding of the virus, host, and environmental drivers of disease severity and spread to discover predictive markers of human disease. We will successfully achieve this goal by applying ‘omics’ technologies, wearables, and high-throughput experimental approaches to unique patient, vaccinee, and survivor cohorts in West Africa and the United States. Built around two interconnected projects and four cores, we will complete the following major goals: (1) we will integrate complex systems immunology, host genetic, physiological, clinical, and metabolic datasets to perform predictive modeling and define biosignatures that describe the risk, severity, and outcome of Lassa, Ebola, and COVID-19 using cohorts in West Africa and the United States. (2) We will integrate large-scale host-virus genomic datasets with population data and high-throughput experimental approaches to build multivariate network analyses and dynamic growth models to elucidate the molecular epidemiology, functional evolution, adaptive immunity, and host-pathogen dynamics of Lassa virus, Ebola virus, and SARS-CoV-2. (3) We will expand and develop high-throughput experimental methods, open-source software, machine learning tools, and web-based platforms to analyze and visualize large-scale datasets that enable real-time interrogation and interpretation of complex host-pathogen-environment network structures and dynamics. (4) We will continue to develop and maintain robust databases and application programming interfaces for open-source CViSB generated systems biology datasets and perform outreach and training to promote the use of systems biology to study infectious diseases.