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.

项目总结/摘要 对疟疾的免疫是复杂的，随着时间的推移，涉及免疫区室之间的精细相互作用。大多数现有 了解免疫力发展的努力仅限于一组狭窄的测量方法， 简化的动物或人类挑战模型，未能捕捉重复感染的复杂性， 自然暴露的人。我们建议全面评估和建模的先天和适应性 随着时间的推移，对恶性疟原虫（Pf）反复感染的免疫应答。这个项目利用了一个独特的 乌干达的疟疾队列研究，参与者每月在我们的诊所就诊，所有疾病，使我们能够 捕获有症状和无症状感染者。通过利用我们的特征鲜明的队列， 宿主反应的免疫学表征和最先进的免疫计算模型，我们 1）全面表征对有症状和无症状P. 恶性疟原虫感染我们假设，有症状的-而不是无症状的-感染将是 其特征在于先天性和适应性炎症反应的减弱。我们将分析先天的 多个时间点儿童对有症状和无症状感染者的适应性免疫应答 在感染Pf后的几周内。数据来自转录谱分析、深层细胞表型分析、抗体 分析和刺激分析将用于建立灵活的计算模型，捕获相互作用 免疫系统的不同部分之间的关系以及免疫反应的轨迹， 单一感染2)确定免疫状态如何演变以应对复发性恶性疟原虫 感染.我们假设，反复感染将导致免疫状态从一个偏见的转变， 向动态的炎症免疫反应转变，以更稳定的调节状态为特征， 获得功能性抗体。我们将对确定的关键免疫学参数的演变进行建模 在目标1中，沿着抗寄生虫体液和细胞功能的测定，多年的重复感染和 通过生成2年期间的纵向数据，这一目标补充了目标1， 重要的信息，以确定从累积感染超过 更长的时间尺度，跨越免疫获得的时期。3)确定免疫状态的关键方面 导致对恶性疟原虫感染的抗寄生虫和抗病免疫。我们假设 功能性抗体应答将与抗寄生虫免疫最密切相关， 先天性反应的最强相关的是抗疾病免疫。以目标的发现为指导 1和2，我们将开发计算模型，以确定临床免疫表型的关键决定因素， 通过评估随后一年的感染临床结果获得。所有型号都将得到确认 并使用来自独立个体的数据、外部数据和基于实验室的 实验数据和模型将通过适当的公共储存库提供和查找。