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: 10266220
• 负责人: ATUL J BUTTE
• 金额: $ 58.33万
• 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: 2020-07-29 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10266220
• 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

ABSTRACT Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) is a novel coronavirus that has spread rapidly across the globe and caused unprecedented global health and economic threats. Emerging evidence suggests that SARS-CoV-2 infection is associated with an impaired Type I and Type III interferon response, and that this reduced response may play a critical role in immunopathogenesis. Our collaboration has recently begun a randomized clinical trial of a Type III interferon, pegylated-lambda interferon (Lambda) for treatment of SARS-CoV-2 infected patients at Stanford University. In the parent study, 120 SARS-CoV-2 infected patients (both symptomatic and asymptomatic) are being randomized to receive Lambda vs. placebo, with assessments for viral shedding in oropharyngeal and nasal swabs, daily symptom screening for 28 days following treatment, and peripheral blood collected at multiple timepoints, including 4, 7, and 10 months post- infection. In this proposal, we will leverage samples collected from this trial, comprehensive immunologic interrogation, and computational analysis to elucidate the dynamics of the host immune response to SARS- CoV-2. In Aim 1, we will determine whether specific immune features, including endogenous IFN-λ production and cytokine production in response to toll-like receptor (TLR) ligands, predict duration of viral shedding and/or symptoms in SARS-CoV-2 infected patients. We will also evaluate differences in immune trajectories based on the presence or absence of clinical symptoms, participant sex, and age. We will broadly profile immune responses using parallel methodology to our U01, including transcriptional profiling, cellular phenotyping, plasma cytokine levels, antibody profiling and functional assays, and build flexible computational models to model interactions between different compartments of the immune system and to assess associations between immune responses and virologic and clinical outcomes. In Aim 2, we will define the impact of Lambda on the adaptive immune response, including SARS-CoV-2 specific cellular and humoral immunity. We hypothesize that treatment with Lambda reduces time to seroconversion and is associated with improved immunologic memory to Lambda, including higher titers and duration of neutralizing antibodies and frequencies of Th2-type T follicular helper cells. To perform these studies, we will leverage our computational immunology U01 research team at Stanford and UCSF including experts in clinical trials and cellular immunity (Dr. Jagannathan), antibody profiling and function (Drs. Greenhouse and Wang), infectious diseases epidemiology and biostatistics (Dr. Rodriguez-Barraquer), and biomedical informatics and computational biology (Dr. Butte). By improving our understanding of the host immune response to natural SARS-CoV2 infection, identifying correlates of viral resolution, and analyzing the impact of a novel immunomodulatory drug on this immunity, our results will provide insight into mechanisms that can be exploited in the design of vaccines and other therapeutics.

摘要 严重急性呼吸综合征-冠状病毒-2（SARS-CoV-2）是一种新型冠状病毒， 迅速席卷地球仪，造成了前所未有的全球健康和经济威胁。新出现的证据 表明SARS-CoV-2感染与I型和III型干扰素应答受损相关， 并且这种降低的反应可能在免疫发病机制中起关键作用。我们的合作最近 开始了一项III型干扰素，聚乙二醇化λ干扰素（λ）治疗 斯坦福大学的SARS-CoV-2感染患者。在母研究中，120名SARS-CoV-2感染患者 (both有症状和无症状）随机接受Lambda与安慰剂， 评估口咽和鼻拭子中的病毒脱落，每日症状筛查，持续28天 在治疗后，在多个时间点收集外周血，包括治疗后4、7和10个月， 感染在这项提案中，我们将利用从这项试验中收集的样本， 询问，和计算分析，以阐明宿主对SARS的免疫反应的动力学- 二型冠状病毒在目标1中，我们将确定是否特异性免疫特征，包括内源性IFN-λ的产生， 和细胞因子的产生，预测病毒脱落的持续时间和/或 SARS-CoV-2感染者的症状。我们还将评估免疫轨迹的差异， 是否存在临床症状、参与者性别和年龄。我们将广泛地分析免疫 使用与我们的U 01平行的方法，包括转录谱，细胞表型， 血浆细胞因子水平，抗体分析和功能测定，并建立灵活的计算模型， 建立免疫系统不同区室之间的相互作用模型， 免疫应答以及病毒学和临床结果。在目标2中，我们将定义Lambda对 适应性免疫反应，包括SARS-CoV-2特异性细胞和体液免疫。我们假设 用Lambda治疗可以减少血清转换的时间，并与改善免疫学相关。 对Lambda的记忆，包括中和抗体的更高滴度和持续时间以及Th 2型的频率 T滤泡辅助细胞。为了进行这些研究，我们将利用我们的计算免疫学U 01 研究小组在斯坦福大学和加州大学旧金山分校，包括专家在临床试验和细胞免疫（博士。 Jagannathan），抗体谱和功能（Greenhouse和Wang博士），传染病流行病学 生物统计学（Rodriguez-Barraquer博士），生物医学信息学和计算生物学（Butte博士）。 通过提高我们对自然SARS-CoV 2感染的宿主免疫应答的理解， 相关的病毒分辨率，并分析一种新型免疫调节药物对这种免疫力的影响，我们 结果将提供深入了解机制，可以利用在疫苗的设计和其他 治疗学