Project 1

项目摘要-项目1 该项目的目标是生成一个集成的系统级数据集， 开发模型，以预测感染的个体的疾病严重程度或长期后遗症， 拉沙病毒、埃博拉病毒或SARS-CoV-2，以及对疫苗的保护性反应。的中心假设 方案一是多个变量，包括个体的生理、代谢和免疫 致病性RNA感染后的生存或长期后遗症的发展 病毒此外，我们试图确定对疫苗的反应是否具有保护性。到 为了验证这一假设，我们收集了西非和美国独特的人群， 谁是拉沙，埃博拉病毒和COVID-19的风险，或谁幸存下来，这些疾病。临床试验 拉沙疫苗最近在我们西非的临床基地启动，我们也在研究 埃博拉疫苗的持久性以及接种疫苗预防埃博拉病毒再激活的潜力 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、拉沙和COVID-19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性感染/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途径。 在目标1中，我们将定义区分拉沙、埃博拉和COVID-19的生理和代谢属性 幸存者、非幸存者和感染后后遗症的个体， 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和系统血清学，以获得深度数据集 确定拉沙、埃博拉和COVID-19患者、幸存者、 和疫苗接种者，导致不同的结果。在目标3中，我们将描述抗冠状病毒免疫 西非人的反应，并将结果与美国的队列进行比较，目的是确定 并表征对SARS-CoV-2的潜在保护性反应。最后，在目标4中，我们将整合 异构数据类型，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体的结果。我们 我将与建模核心一起使用以下组合来集成这些异构数据类型： 先进的建模、机器学习工具和相关技术，以识别预测性生物特征 这些信息包括：性别、年龄和种族差异的个性化治疗; 急性感染个体和具有长期病毒综合征（如PASC）的个体;以及潜在靶点 用于先进疗法和改进的疫苗。

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.