Project 1

项目摘要-项目1 该项目目标是生成一个集成的系统级数据集，该数据集将支持 用来预测感染者疾病严重程度或长期后遗症的模型的发展 拉萨病毒、埃博拉病毒或SARS-CoV-2，以及对疫苗的保护性反应。的中心假说 项目1是多个变量，包括个人生理、代谢和免疫 影响fl感染后长期后遗症生存或发展的因素 病毒。此外，我们试图确定对疫苗的反应是否具有保护性。至 检验这一假设，我们已经收集了独特的西非和美国的个人队列 哪些人有感染拉萨病毒、埃博拉病毒和新冠肺炎的风险，哪些人在这些疾病中幸存下来。临床试验 拉萨疫苗最近在我们在西非的临床地点启动，我们也在研究 埃博拉疫苗的持久性和疫苗预防埃博拉病毒重新激活的潜力 幸存者。拟议的项目汇集了一套战略性组织的尖端系统 获取埃博拉、拉萨和新冠肺炎整体免疫组、抗体组和代谢组的工具 幸存者。我们将使用机器学习来识别持续感染/疾病的独特特征， 为诊断、治疗和管理病毒感染后的持续性疾病提供了一条途径。 在目标1中，我们将消除区分拉萨病毒、埃博拉病毒和新冠肺炎的生理和代谢属性 幸存者、非幸存者和感染后后遗症的个人通过汇编和分析 临床、免疫学和非传统数据，包括来自可穿戴设备的数据。在目标2中，我们将利用 高通量技术，包括PhIP-Seq、VirScan和Systems Serology，可派生深层数据集 为了确定拉萨、埃博拉和新冠肺炎患者、幸存者和 以及导致不同结果的疫苗接种者。在目标3中，我们将描述抗冠状病毒免疫 并将结果与美国的队列进行比较，目的是确定 以及表征对SARS-CoV-2的潜在保护性反应。最后，在目标4中，我们将整合 异类数据类型，以调查主机和病毒因素在确定 对疫苗的反应和感染我们感兴趣的三种病毒的不同变种的结果。我们 我将与建模核心合作，使用以下组合集成这些异类数据类型 用于识别预测性生物签名的高级建模、机器学习工具和相关技术 通知：跨性别、年龄和种族差异的个性化治疗； 急性感染者和那些有长期病毒综合症的人，如PASC；以及潜在的目标 先进的治疗方法和改良的疫苗。

Project Summary - Project 1 The objective of this project is to generate an integrated, systems-level dataset that will enable development of models that predict disease severity or long-term sequelae in individuals infected with Lassa virus, Ebola virus or SARS-CoV-2, and protective responses to vaccines. The central hypothesis of Project 1 is that multiple variables, including individual physiological, metabolic and immunological factors, influence survival or development of long-term sequelae after infection by pathogenic RNA viruses. Additionally, we seek to determine whether or not responses to vaccines will be protective. To test this hypothesis, we have assembled unique West African and United States cohorts of individuals who are at risk for Lassa, Ebola and COVID-19 or who have survived these illnesses. Clinical trials of Lassa vaccines have recently been initiated at our clinical sites in West Africa, and we are also studying the durability of Ebola vaccines and the potential of vaccination to prevent viral reactivation in Ebola survivors. The proposed project brings together a strategically organized set of cutting edge systems tools to capture the overall immunome, antibody-ome, and metabolome of Ebola, Lassa, and COVID-19 survivors. We will use machine learning to identify unique signatures of persistent infection/disease, providing a path to diagnose, treat, and manage persistent disease following viral infection. In Aim 1, we will define physiological and metabolic attributes that distinguish Lassa, Ebola and COVID-19 survivors, non-survivors, and individuals that develop post-infection sequelae by compiling and analyzing clinical, immunological and nontraditional data, including data from wearables. In Aim 2, we will utilize high-throughput technologies, including PhIP-Seq, VirScan, and Systems Serology, to derive deep datasets to identify attributes of the humoral immune responses of Lassa, Ebola and COVID-19 patients, survivors, and vaccinees that lead to different outcomes. In Aim 3, we will characterize anti-coronavirus immune responses in West Africans and compare the results to United States cohorts, with the goal of identifying and characterizing potentially protective responses to SARS-CoV-2. Finally, in Aim 4 we will integrate heterogeneous data types to investigate the importance of host and virus factors in determining responses to vaccines and outcome of infection with different variants of our three viruses of interest. We will work with the Modeling Core to integrate these heterogenous data types using a combination of advanced modeling, machine learning tools, and related technologies to identify predictive biosignatures that inform: personalized treatment across sex, age, and racial differences; management strategies in acutely infected individuals and those with long term viral syndromes such as PASC; and potential targets for advanced therapeutics and improved vaccines.