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.

抽象的 严重的急性呼吸综合征 - 核纳病毒2（SARS-COV-2）是一种新型冠状病毒，已扩散 在全球范围内迅速造成了前所未有的全球健康和经济威胁。新兴证据 表明SARS-COV-2感染与I型和III型干扰素反应受损有关， 而且这种减少的反应可能在免疫发作中起关键作用。我们的合作最近有 开始了III型干扰素，Pegymet-Lambda干扰素（Lambda）的随机临床试验，以治疗 斯坦福大学的SARS-COV-2感染患者。在父母的研究中，有120名SARS-COV-2感染患者 （有症状和无症状）正在随机分配以接受兰伯达与安慰剂，并带有 评估口咽和鼻拭子中的病毒脱落，每天症状筛查28天 治疗后，以及在多个时间点收集的外周血，包括4、7和10个月后 感染。在此提案中，我们将利用该试验收集的样本，全面的免疫学 询问和计算分析，以阐明宿主免疫反应对SARS-的动力学 COV-2。在AIM 1中，我们将确定特定的免疫特征，包括内源性IFN-λ的产生 响应Toll样受体（TLR）配体的细胞因子产生，预测病毒脱落的持续时间和/或 SARS-COV-2感染患者的症状。我们还将评估基于免疫轨迹的差异 存在或不存在临床症状，参与性别和年龄。我们将广泛概况免疫 使用平行方法对我们的U01进行反应，包括转录分析，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 模型之间的相互作用在免疫系统的不同隔室之间，并评估 免疫反应以及病毒学和临床结果。在AIM 2中，我们将定义Lambda对 适应性免疫响应，包括SARS-COV-2特异性细胞和体液免疫学。我们假设 用lambda治疗可减少血清转化的时间，并与改善的免疫学有关 记忆到lambda，包括较高的滴度和中和抗体的持续时间和Th2型的频率 T卵泡辅助细胞。为了进行这些研究，我们将利用我们的计算免疫学U01 斯坦福大学和UCSF的研究团队包括临床试验和细胞免疫的专家（博士 Jagannathan），抗体分析和功能（Greenhouse and Wang博士），传染病流行病学 和生物统计学（Rodriguez-Barraquer博士）以及生物医学信息和计算生物学（Butte博士）。 通过提高我们对天然SARS-COV2感染的宿主免疫响应的理解，确定 病毒分辨率的相关性，并分析一种新型免疫调节药对这种免疫力的影响，我们 结果将提供有关可以在疫苗和其他设计中探索的机制的见解 疗法。