Computational models of naturally acquired immunity to falciparum malaria

恶性疟疾自然获得性免疫力的计算模型

• 批准号: 10474820
• 负责人: ATUL J BUTTE
• 金额: $ 41.06万
• 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: 2020-04-01 至 2025-03-01
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10474820
• 关键词: Acute; Address; Adult; Affect; Africa South of the Sahara; Age; Animals; Antibodies; Antibody Response; Antiparasitic Agents; Area; Bayesian Network; Biological; Biological Assay; Blood; Cell physiology; Child; Chronic; Clinic; Clinical; Cohort Studies; Complement; Complex; Computer Models; Data; Development; Disease; Educational workshop; Evolution; Exposure to; Falciparum Malaria; Fever; Gene Expression Profiling; Genetic; Growth; Human; Immune; Immune response; Immune system; Immunity; Immunologics; Individual; Infection; Inflammatory; Inflammatory Response; Intervention; Laboratories; Learning; Malaria; Malaria Vaccines; Measurement; Modeling; Modernization; Morbidity - disease rate; Outcome; Parasites; Participant; Phenotype; Plasmodium falciparum; Process; Property; Recurrence; Research; Time; Uganda; Vaccine Design; Vaccines; Validation; Visualization; acquired immunity; adaptive immune response; age group; attenuation; base; cohort; density; epidemiologic data; experimental study; flexibility; insight; longitudinal dataset; malaria infection; mortality; pathogen; public repository; recurrent infection; response; web site

PROJECT SUMMARY/ABSTRACT Immunity to malaria is complex, involving a fine interplay between immune compartments over time. Most prior efforts to understand the development of immunity have been limited to a narrow set of measurements or reductionist animal or human challenge models that fail to capture the complexity of repeated infection in naturally exposed individuals. We propose to comprehensively evaluate and model the innate and adaptive immune response to repeated P. falciparum (Pf) infections over time. This project takes advantage of a unique malaria cohort study in Uganda, with participants seen in our clinic monthly and for all illnesses, allowing us to capture both symptomatic and asymptomatic infections. By leveraging our well-characterized cohort, detailed immunological characterization of host responses, and state-of-the-art computational models of immunity, we will 1) Comprehensively characterize the immune response to symptomatic and asymptomatic P. falciparum infections. We hypothesize that symptomatic – but not asymptomatic – infections will be characterized by an attenuation of the innate and adaptive inflammatory response. We will profile the innate and adaptive immune response to symptomatic and asymptomatic infections in children at multiple time points in the weeks following Pf infection. Data from transcriptional profiling, deep cellular phenotyping, antibody profiling, and stimulation assays will be used to build flexible computational models, capturing interactions between different compartments of the immune system and the trajectory of the immune response after a single infection. 2) Determine how the immune state evolves in response to recurrent P. falciparum infections. We hypothesize that recurrent infection will result in a shift of the immune state from one biased towards dynamic, inflammatory immune responses to one characterized by a more stable, regulatory state and the acquisition of functional antibodies. We will model the evolution of key immunological parameters identified in Aim 1, along with assays of anti-parasitic humoral and cellular function, over years of repeated infection and across ages by generating longitudinal data over a period of 2 years. This aim complements Aim 1 in providing important information to define emergent properties of the immune response from cumulative infections over longer time scales, spanning the period of immune acquisition. 3) Identify key aspects of the immune state leading to anti-parasite and anti-disease immunity to P. falciparum infection. We hypothesize that functional antibody responses will be most strongly associated with anti-parasite immunity, and that attenuation of innate responses will be most strongly associated with anti-disease immunity. Guided by findings from Aims 1 and 2, we will develop computational models to identify the key determinants of clinical immune phenotypes, obtained by evaluating the clinical outcomes of infection over the subsequent year. All models will be validated and iteratively refined using data from independent individuals, external data and laboratory-based experiments. Data and models will be made available and findable through appropriate public repositories.

项目摘要/摘要 对疟疾的免疫力是复杂的，随着时间的推移，免疫区室之间存在良好的相互作用 了解免疫发展的努力仅限于一组狭窄的测量或 还无法捕获竞争感染的动物或人类挑战模型 自然暴露的个人。 随着时间的流逝，对反复的恶性疟原虫（PF）感染的免疫反应。 在乌干达进行的疟疾队列研究，参与者在我们的诊所每月和厌食症中看到，使我们能够 通过利用我们的良好表征，详细捕获有症状的和无症状的感染。 免疫反应的免疫学表征和免疫的最新计算模型，我们 会1）全面表征对症状和无症状的免疫反应。 恶性疾病感染。 以先天性和适应性炎症反应的关注为特征。 在多个时间点儿童中对症状和无症状无症状的自适应免疫反应 在PF感染后的几周内。 分析和电台测定将用于构建灵活的计算模型，捕获互动 在免疫系统的不同隔室与免疫反应的轨迹之间 单一感染。 感染。 朝着动态，炎症免疫反应对一种以更稳定，调节状态和 功能抗体的采集。 在AIM 1中，以及反复感染和反复感染的测定法和 跨年龄通过在2年内生成纵向数据。 重要的信息，以定义累积不一分子的免疫反应的新兴特性。 更长的时间尺度，涵盖免疫获取期。 导致抗帕拉吉特和抗疾病的抗蛋白酶免疫，我们假设这一点 功能抗体反应与抗寄生虫免疫最密切相关 与抗疾病的免疫力最密切相关的先天反应。 1和2，我们将开发计算模型，以识别临床免疫表型的关键决定因素，即 通过评估后期所有模型的临床感染结果。 并使用来自独立个人，外部数据和基于实验室的数据进行迭代完善 实验。