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 严重急性呼吸综合征-冠状病毒-2 (SARS-CoV-2) 是一种已传播的新型冠状病毒 病毒在全球范围内迅速蔓延，并造成了前所未有的全球健康和经济威胁。 表明 SARS-CoV-2 感染与 I 型和 III 型干扰素反应受损有关， 这种降低的反应可能在免疫发病机制中发挥关键作用。 开始一项 III 型干扰素聚乙二醇化 lambda 干扰素 (Lambda) 的随机临床试验，用于治疗 斯坦福大学的 SARS-CoV-2 感染患者在母研究中，有 120 名 SARS-CoV-2 感染患者。 （有症状和无症状）被随机分配接受 Lambda 组与安慰剂组，其中 评估口咽和鼻拭子中的病毒脱落情况，每日症状筛查，持续 28 天 治疗后，并在多个时间点收集外周血，包括治疗后 4、7 和 10 个月 在这项提案中，我们将利用从该试验中收集的样本，进行全面的免疫学研究。 询问和计算分析，以阐明宿主对 SARS 免疫反应的动态 在目标 1 中，我们将确定是否具有特定的免疫特征，包括内源性 IFN-λ 的产生。 和响应 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 我们还将根据 SARS-CoV-2 感染患者的症状来评估免疫轨迹的差异。 我们将广泛分析是否存在临床症状、参与者性别和年龄。 使用与我们的 U01 并行的方法进行响应，包括转录分析、细胞表型分析、 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 模拟免疫系统不同区室之间的相互作用并评估之间的关联 在目标 2 中，我们将定义 Lambda 对免疫反应以及病毒学和临床结果的影响。 适应性免疫反应，包括 SARS-CoV-2 特异性细胞免疫和体液免疫。 Lambda 治疗可缩短血清转化时间，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 Th2 型的频率 为了进行这些研究，我们将利用我们的计算免疫学 U01。 斯坦福大学和加州大学旧金山分校的研究团队，包括临床试验和细胞免疫方面的专家（Dr. Jagannathan）、抗体分析和功能（Greenhouse 博士和 Wang 博士）、传染病流行病学 和生物统计学（Rodriguez-Barraquer 博士），以及生物医学信息学和计算生物学（Butte 博士）。 通过提高我们对宿主对自然 SARS-CoV2 感染的免疫反应的了解，确定 病毒分辨率的相关性，并分析新型免疫调节药物对这种免疫力的影响，我们 结果将提供对可用于疫苗和其他设计的机制的深入了解 疗法。