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

• 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
• 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

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.

项目摘要/摘要 该项目将确定COVID-19患者的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 个人表明有效的人类免疫反应可以完全预防疾病。但是，它 尚不清楚哪些免疫反应特征与疾病的保护有关。解决这个问题 问题，在这里我们将分析COVID-19患者的综合抗体免疫反应，并确定 抗体免疫的分子特征与COVID-19症状严重程度如何相关。 首先，我们将使抗体免疫库中的抗体免疫库中的19位患者队列进入酵母显示库 用于全面的体外功能筛选。来自COVID-19患者的B细胞样本将分离出来，并 乳化为单一细胞，用于天然抗体DNA回收，抗体基因将转化为酵母 用于曲目尺度抗体功能分析的FAB显示平台。将筛选抗体以结合 SARS-COV-2尖峰三聚体，一个主要的中和靶标，也用于抑制ACE2与MAP的结合 中和人免疫反应中的抗体。 我们还将开采可再生抗体免疫库的更广泛特征，可能与 Covid-19疾病严重程度。我们将研究针对广泛的SARS-COV-2抗原和表位的抗体， 在尖峰三聚体蛋白上包括多个表位（例如受体结合结构域RBD，N端子 结构域，NTD和S1区域）和内部病毒蛋白（例如核素蛋白）。我们还将映射 单个B细胞响应的分子特征（例如亲和力，基于竞赛的表位映射和差异 与不同的尖峰蛋白构象结合）以全面跟踪抗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 作为潜在疾病相关的中和与非中和抗体的中和。 最后，我们将对每个患者人群的抗体库进行大规模数据挖掘 确定可能区分轻度和严重SARS-COV-2感染的关键分子特征。这些新 分子规模相关和潜在的生物标志物将改善基本和临床理解以促进 COVID-19预防和疗法。我们试图揭示Covid-19的关键基于免疫的生物标志物 疾病严重程度并鉴定出新的有效抗体候选药物，以治疗和预防COVID-19。