Identifying Coronavirus B-cell Epitopes Associated with COVID-19 Illness Severity

识别与 COVID-19 疾病严重程度相关的冠状病毒 B 细胞表位

• 批准号: 10170660
• 负责人: James E. Gern
• 金额: $ 40.6万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-21 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10170660
• 关键词: 2019-nCoV; Allergic; Antibodies; Antibody Diversity; Antibody Response; Asthma; B cell repertoire; B-Lymphocyte Epitopes; Binding; Biological Assay; Birth; Blood specimen; COVID-19; Child; Childhood; Cohort Studies; Common Epitope; Coronavirus; Country; Data; Development; Disease Outbreaks; Enrollment; Epitopes; Family; Farming environment; Future; Human; Hypersensitivity; Immune; Immune system; Individual; Infant; Infection; Laboratories; Life; Lung diseases; Machine Learning; Microbe; Monitor; Morbidity - disease rate; Nose; Pattern; Peripheral Blood Mononuclear Cell; Population; Predisposition; Prevention; Proteome; Rhinovirus; Serological; Serum; Severities; Severity of illness; Societies; Specimen; Symptoms; Testing; Vaccine Design; Viral; Viral Antibodies; Viral Structural Proteins; Virus; Wisconsin; burden of illness; cohort; cost; cross reactivity; immunogenic; infection risk; new technology; novel coronavirus; parent grant; public health emergency; respiratory; respiratory virus; response; study population; surveillance study

Identifying Coronavirus B-cell Epitopes Associated with COVID-19 Infection and Illness The new coronavirus outbreak that begin in December 2019 has created a global public health emergency. This has led to an intense search to identify factors that contribute to the susceptibility and severity of illness. We recently developed an array to identify antibody-binding epitopes for rhinoviruses. Data from these arrays can be combined with information about viral protein structure to identify highly immunogenic regions for respiratory viruses. We propose to expand this array to include linear epitopes that represent the entire proteome of SARS- CoV-2 and all other common coronaviruses that infect humans (OC43, NL63, etc.). The study population will include children from the COAST, WISC and URECA birth cohort studies who are also participating in the HEROS SARS-CoV-2 surveillance study. As part of routine cohort activities, these children undergo serial sampling of blood and nasal secretions that we can analyze using the array to determine individual patterns of antiviral antibody epitope recognition. We hypothesize that the pattern and quantity of antibody specific for epitopes of common coronaviruses contributes to the susceptibility to SARS-CoV-2 infection and illness. We propose three specific aims that will utilize sera obtained from children before and after HEROS-confirmed infection with SARS-CoV-2. First, in specimens obtained pre-infection we will use the array to identify patterns of antibody epitope recognition to common childhood coronaviruses, assess cross-reactivity with SARS-CoV-2, and determine whether cross-reactivity is associated with protection against infection or illness. In the second aim, we will determine whether the diversity of antibody responses to common respiratory viruses is associated with a reduced risk of infection or illness. Finally, in the third aim we will describe antibody binding patterns before and after known COVID-19 cases to identify candidate regions that are immunogenic and neutralizing. To accomplish this aim, we will perform micro-neutralization assays (available in the BSL3 laboratory of Dr. Kristen Bernard, UW Madison) on convalescent sera or nasal secretions from children who developed symptomatic infection. This information will be analyzed together with pre- and post-infection array data using machine learning approaches to identify neutralizing epitopes. Identifying patterns of serologic responses that are cross- protective could help to identify susceptible individuals in the population and direct the design of vaccines to current and future viruses.

识别与 COVID-19 感染和疾病相关的冠状病毒 B 细胞表位 2019 年 12 月开始的新型冠状病毒爆发已造成全球公共卫生紧急事件。这 导致人们进行了深入的研究，以确定导致疾病易感性和严重程度的因素。我们 最近开发了一种阵列来识别鼻病毒的抗体结合表位。来自这些数组的数据可以 与病毒蛋白结构信息相结合，识别呼吸道病毒的高免疫原性区域 病毒。我们建议扩展该阵列以包括代表 SARS 整个蛋白质组的线性表位 CoV-2 和所有其他感染人类的​​常见冠状病毒（OC43、NL63 等）。研究人群将 包括来自 COAST、WISC 和 URECA 出生队列研究的儿童，他们也参与了 HEROS SARS-CoV-2 监测研究。作为常规队列活动的一部分，这些孩子经历了一系列的 对血液和鼻腔分泌物进行采样，我们可以使用阵列进行分析以确定个体的模式 抗病毒抗体表位识别。我们假设特异性抗体的模式和数量 常见冠状病毒的表位导致对 SARS-CoV-2 感染和疾病的易感性。我们 提出三个具体目标，将利用从 HEROS 确认前后儿童获得的血清 感染 SARS-CoV-2。首先，在感染前获得的样本中，我们将使用阵列来识别模式 抗体表位识别常见儿童冠状病毒，评估与 SARS-CoV-2 的交叉反应性， 并确定交叉反应是否与预防感染或疾病有关。在第二个 目的，我们将确定对常见呼吸道病毒的抗体反应的多样性是否相关 降低感染或患病的风险。最后，在第三个目标中，我们将在之前描述抗体结合模式 并在已知的 COVID-19 病例后确定具有免疫原性和中和作用的候选区域。到 为了实现这一目标，我们将进行微量中和测定（可在 Kristen 博士的 BSL3 实验室进行） 伯纳德，威斯康星大学麦迪逊分校）对出现症状的儿童的恢复期血清或鼻分泌物进行了研究 感染。该信息将与感染前和感染后阵列数据一起使用机器进行分析 识别中和表位的学习方法。识别跨血清学反应的模式 保护性可能有助于识别人群中的易感个体，并指导疫苗的设计 当前和未来的病毒。