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.

项目总结/文摘