Uncovering the dynamics of human pathogens using high throughput multiplexed serological tools

使用高通量多重血清学工具揭示人类病原体的动态

• 批准号: 10457487
• 负责人: Isabel Rodríguez-Barraquer
• 金额: $ 40.37万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10457487
• 关键词: Antibody Repertoire; Antibody Response; Antigens; Birth; Cohort Studies; Collaborations; Communicable Diseases; Country; Cross-Sectional Studies; Data; Data Analyses; Disease; Epidemiology; Evolution; Goals; Immune; Immunologics; Individual; Infection; Intervention; Methodology; Methods; Modeling; Performance; Population; Population Study; Predisposition; Recording of previous events; Research; Research Personnel; Resources; Risk; Sampling; Serology; Serology test; Serum; Software Tools; Technology; Time; Translating; Validation; Work; analytical method; disorder control; human pathogen; insight; multiple data sources; new technology; open source; pathogen; programs; response; simulation; tool; transmission process

PROJECT SUMMARY Understanding the distribution and dynamics of human pathogens is fundamental to control infectious diseases by guiding where and when to target resources. Until now, however, efforts to understand the dynamics of most pathogens have been conducted in isolation, focusing on one specific disease at a time. Over the past years, high throughput multiplexed serological assays have become available, allowing to simultaneously quantify antibody responses against 100s and even 1000s of antigens, from a single sample. These novel technologies offer unprecedented opportunities to (1) study the dynamics of multiple pathogens simultaneously, (2) characterize immune profiles of populations, and (3) evaluate hypotheses around immunological interactions within and between pathogen groups. Numerous methodological and computational challenges remain in terms of how to make meaningful epidemiological inferences from these new tools. We have been developing methods to analyze and estimate key transmission parameters from multiple sources of data, with a focus on serological assays. This proposal will expand these lines of work, developing tools and analytic methods necessary to translate the promise of high throughput serology into mechanistic insights about pathogen dynamics. We will leverage existing collaborations with researchers at the forefront of these technologies, as well as detailed population studies (including longitudinal birth cohorts and cross-sectional studies) that have collected and characterized serum samples to apply and evaluate these platforms in over 10 countries. We aim to develop and validate a suite of robust computational and analytical pipelines with the goal of (1) reconstructing the longitudinal evolution of antibody repertoires in individuals within and across pathogen groups as a function of infection history, and (2) characterizing transmission histories and immune profiles of populations. In developing our analytical pipelines, we will perform extensive simulation studies to evaluate the performance of different models under multiple assumptions of how the data is generated. We will then perform data analysis and inference to reconstruct transmission histories, susceptibility profiles of populations to multiple pathogens, estimate attack rates, and characterize longitudinal evolution of antibody responses. This will be the most comprehensive assessment, and validation, of population and individual serological responses across pathogen groups, performed to date. Through these projects, we will also develop a suite of open-source software/tools to disseminate our methods and allow other researchers to analyze their data and gain insight into immune landscapes population risk profiles. This project will create tools that use high-throughput multiplexed serological data to answer fundamental questions on the dynamics and interactions of different pathogen groups and provide insights that can be used by policymakers to guide infectious disease control efforts.

项目总结 了解人类病原体的分布和动态是控制传染病的基础 通过指导在哪里以及何时将资源作为目标来防治疾病。然而，到目前为止，人们努力理解 大多数病原体的动态都是在隔离状态下进行的，集中在一种特定的疾病上。 一次来一次。 在过去的几年里，高通量的多重血清学分析已经成为可能，允许 为了同时量化针对100s甚至1000s抗原的抗体反应，来自 单一样本。这些新技术提供了前所未有的机会(1)研究动态 多个病原体的同时，(2)表征人群的免疫特征，以及(3) 评估围绕病原体组内和病原组之间的免疫相互作用的假说。 在如何使有意义的 来自这些新工具的流行病学推论。 我们一直在开发分析和估计关键传输参数的方法 数据来源多种多样，重点是血清学分析。这项提议将扩大这些线 工作，开发必要的工具和分析方法，将高吞吐量的承诺转化为 血清学对病原体动态的机械性洞察。我们将利用现有的协作 处于这些技术前沿的研究人员，以及详细的人口研究 (包括纵向出生队列和横断面研究)，收集和表征 在10多个国家和地区应用和评估这些平台的血清样本。我们的目标是开发和 验证一套健壮的计算和分析管道，目标是(1)重建 病原菌组内和病原组间抗体谱系的纵向演变 感染史的作用，以及(2)表征传播史和免疫图谱 人口。在开发我们的分析管道时，我们将进行广泛的模拟研究，以 在多种数据假设下评估不同模型的性能 已生成。然后我们将执行数据分析和推断以重建传输历史， 人群对多种病原体的易感性概况，估计发病率，并确定 抗体反应的纵向进化。这将是最全面的评估， 对病原体群体和个体的血清学反应进行验证，以 约会。通过这些项目，我们还将开发一套开源软件/工具，以 传播我们的方法，并允许其他研究人员分析他们的数据并深入了解 免疫景观人口风险概况。 该项目将创建工具，使用高通量多路传输的血清学数据来回答 关于不同病原菌群的动态和相互作用的基本问题，并提供 可供政策制定者用来指导传染病控制工作的见解。