Project 1

项目1

• 批准号: 10558423
• 负责人: Robert F Garry
• 金额: $ 88.45万
• 依托单位: SCRIPPS RESEARCH INSTITUTE, THE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-02-01 至 2028-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10558423
• 关键词: 2019-nCoV; Achievement; Acute; Africa; African; Antibodies; COVID-19; COVID-19 patient; COVID-19 survivors; Cessation of life; Clinical; Clinical Trials; Communicable Diseases; Coronavirus; Country; Data; Data Set; Development; Diagnosis; Disease; Disease Outbreaks; Disease Outcome; Ebola; Ebola Vaccines; Ebola virus; Europe; Goals; Hematology; Immune response; Immunize; Immunologic Factors; Immunologics; Individual; Infection; Integration Host Factors; Joints; Lassa virus; Lead; Long COVID; Machine Learning; Measurement; Metabolic; Modeling; Outcome; Pathogenicity; Pattern; Phage ImmunoPrecipitation Sequencing; Physiological; Population; Post-Acute Sequelae of SARS-CoV-2 Infection; Predictive Factor; RNA Virus Infections; RNA Viruses; Risk; SARS-CoV-2 infection; Serology; Severity of illness; Sex Differences; Sierra Leone; Survivors; Symptoms; Syndrome; System; Systems Biology; Technology; Test Result; Testing; Therapeutic; United States; Vaccination; Vaccinee; Vaccines; Variant; Viral; Viral Hemorrhagic Fevers; Virus; Virus Diseases; Virus Latency; Work; age difference; biosignature; chronic infection; clinical research site; cohort; data streams; experience; heterogenous data; high throughput technology; improved; interest; large datasets; long-term sequelae; member; metabolome; monitoring device; pathogenic virus; personalized medicine; predictive modeling; prevent; racial difference; research clinical testing; response; tool; wearable device; wireless sensor

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.

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，使 开发预测感染者疾病严重程度或长期后遗症的模型 拉沙病毒、埃博拉病毒或 SARS-CoV-2，以及对疫苗的保护性反应。中心假设为 项目1是多个变量，包括个体生理、代谢和免疫学 因素，影响致病性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 检验这个假设，我们聚集了独特的西非和美国人群 有拉沙、埃博拉和 COVID-19 风险的人或在这些疾病中幸存下来的人。临床试验 拉沙疫苗最近已在我们西非的临床基地启动，我们也在研究 埃博拉疫苗的耐久性以及疫苗接种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 用于捕获埃博拉、拉沙和 COVID-19 的整体免疫组、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， 提供诊断、治疗和管理病毒感染后持续性疾病的途径。 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 通过编译和分析，幸存者、非幸存者以及出现感染后后遗症的个人 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，用于获取深度数据集 确定拉沙、埃博拉和 COVID-19 患者、幸存者的体液免疫反应的属性， 以及导致不同结果的疫苗接种者。在目标 3 中，我们将表征抗冠状病毒免疫 西非人的反应并将结果与​​美国队列进行比较，目的是确定 并描述对 SARS-CoV-2 的潜在保护性反应。最后，在目标 4 中我们将整合 异构数据类型来研究宿主和病毒因素在确定中的重要性 对疫苗的反应以及我们感兴趣的三种病毒的不同变种的感染结果。我们 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来识别预测性生物特征 信息：跨性别、年龄和种族差异的个性化治疗；管理策略在 急性感染者和患有长期病毒综合症（如 PASC）的人；和潜在目标 先进的治疗方法和改进的疫苗。