Antibody display libraries for precision screening of antibody immune responses to SARS-CoV-2

用于精确筛选针对 SARS-CoV-2 的抗体免疫反应的抗体展示文库

基本信息

批准号：
10199286
负责人：
Brandon James DeKosky
金额：
$ 25.93万
依托单位：
UNIVERSITY OF KANSAS LAWRENCE
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10199286
关键词：
2019-nCoV Acute Lung Injury Address Affinity Antibodies Antibody Repertoire Antibody Response Antibody-Dependent Enhancement Antigens Antiviral Agents B-Lymphocytes Binding COVID-19 COVID-19 pandemic Cardiovascular Diseases Cell Survival Cells Characteristics Chromosomes Clinical DNA Development Diabetes Mellitus Disease Endosomes Environment Epitope Mapping Epitopes Equilibrium Fluorescence-Activated Cell Sorting Follow-Up Studies Genes Genetic Human Immune Immune response Immunity Immunologic Markers In Vitro Individual Infection Intervention Libraries Light Link Lung diseases Maps Medical Messenger RNA Middle East Respiratory Syndrome Coronavirus Molecular Monoclonal Antibodies N-terminal Nucleocapsid Proteins Obesity Pathway interactions Patients Pharmaceutical Preparations Plasma Population Prevention therapy Procedures Property Protein Conformation Proteins Recovery Respiratory distress Risk Role SARS coronavirus Sampling Serological Severe Acute Respiratory Syndrome Severities Severity of illness Sorting - Cell Movement Speed Structure Symptoms Techniques Technology Vaccines Viral Viral Antibodies Viral Proteins Virus Yeasts antibody libraries base burden of illness cohort coronavirus disease cost data mining drug candidate drug development drug discovery experimental study high risk improved insight large scale data lung injury medical countermeasure molecular scale mortality neutralizing antibody next generation sequencing pandemic disease patient population personalized screening potential biomarker prevent receptor binding response screening single cell analysis small molecule

项目摘要

PROJECT SUMMARY/ABSTRACT This project will determine the antibody-based immune features in COVID-19 patients to accelerate the development of new medical interventions. SARS-CoV-2 causes asymptomatic or mild disease in many individuals, demonstrating that an effective human immune response can fully prevent disease. However, it remains unclear what immune response features are associated with protection from disease. To address this question, here we will analyze comprehensive antibody immune responses in COVID-19 patients and determine how the molecular features of antibody immunity correlate with COVID-19 symptom severity. First, we will immortalize antibody immune libraries from COVID-19 patient cohorts into yeast display libraries for comprehensive in vitro functional screening. B cell samples from COVID-19 patients will be isolated and emulsified as single cells for native antibody DNA recovery, and antibody genes will be transformed into a yeast Fab display platform for repertoire-scale antibody functional analyses. Antibodies will be screened for binding to the SARS-CoV-2 spike trimer, a dominant neutralization target, and also for inhibition of ACE2 binding to map neutralizing antibodies in human immune responses. We will also mine our renewable antibody immune libraries for broader features that may correlate with COVID-19 disease severity. We will investigate antibodies targeting broad SARS-CoV-2 antigens and epitopes, including multiple epitopes on the spike trimer protein (such as the receptor binding domain, RBD, the N terminal domain, NTD, and the S1 region) and internal viral proteins (e.g., nucleocapsid protein). We will also map the molecular features of single B cell responses (e.g. affinity, competition-based epitope mapping, and differential binding to different spike protein conformations) to comprehensively track anti-SARS-CoV-2 molecular immunity in a human cohort. We will analyze the genetic features of each antibody clone to help elucidate the balance of neutralizing vs. non-neutralizing antibodies as potential disease correlates. Finally, we will perform large-scale data mining of the antibody repertoires from each patient population to identify key molecular features that may distinguish mild and severe SARS-CoV-2 infections. These new molecular-scale correlates and potential biomarkers will improve basic and clinical understanding to advance COVID-19 preventions and therapies. We seek to reveal critical immune-based biomarkers of COVID-19 diseases severity and identify new potent antibody drug candidates to treat and prevent COVID-19.

项目概要/摘要该项目将确定 COVID-19 患者基于抗体的免疫特征，以加速开发新的医疗干预措施。 SARS-CoV-2 在许多人中引起无症状或轻度疾病个人，证明有效的人体免疫反应可以完全预防疾病。然而，它目前尚不清楚哪些免疫反应特征与预防疾病相关。为了解决这个问题问题，在这里我们将分析 COVID-19 患者的全面抗体免疫反应并确定抗体免疫的分子特征如何与 COVID-19 症状严重程度相关。首先，我们将把来自 COVID-19 患者群体的抗体免疫文库永久化到酵母展示文库中用于全面的体外功能筛选。来自 COVID-19 患者的 B 细胞样本将被分离并乳化为单细胞，回收天然抗体DNA，抗体基因将转化到酵母中用于全谱规模抗体功能分析的 Fab 展示平台。将筛选抗体以结合 SARS-CoV-2 刺突三聚体，一种主要中和靶点，也用于抑制 ACE2 与图谱的结合人类免疫反应中的中和抗体。我们还将挖掘我们的可再生抗体免疫库以获得更广泛的特征，这些特征可能与 COVID-19 疾病的严重程度。我们将研究针对广泛 SARS-CoV-2 抗原和表位的抗体，包括刺突三聚体蛋白上的多个表位（例如受体结合域、RBD、N末端结构域、NTD 和 S1 区域）和内部病毒蛋白（例如核衣壳蛋白）。我们还将绘制地图单个 B 细胞反应的分子特征（例如亲和力、基于竞争的表位作图和差异结合不同的刺突蛋白构象）全面追踪抗 SARS-CoV-2 分子免疫在人类队列中。我们将分析每个抗体克隆的遗传特征，以帮助阐明中和抗体与非中和抗体作为潜在疾病的相关性。最后，我们将对每个患者群体的抗体库进行大规模数据挖掘，以确定可区分轻度和重度 SARS-CoV-2 感染的关键分子特征。这些新分子尺度相关性和潜在的生物标志物将提高基础和临床理解，以推进 COVID-19 的预防和治疗。我们寻求揭示 COVID-19 的关键免疫生物标志物疾病的严重程度并确定新的有效候选抗体药物来治疗和预防 COVID-19。