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

项目总结/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， 这表明有效的人体免疫反应可以完全预防疾病。但 目前尚不清楚哪些免疫反应特征与疾病保护有关。为了解决这个 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患者队列的抗体免疫文库永生化到酵母展示文库中 用于全面的体外功能筛选。COVID-19患者的B细胞样本将被分离， 作为单细胞乳化用于天然抗体DNA回收，并且抗体基因将转化到酵母中 用于库规模抗体功能分析的Fab展示平台。将筛选抗体与 SARS-CoV-2刺突三聚体，一种主要中和靶，也用于抑制ACE 2与MAP结合 中和抗体在人体免疫反应中的作用。 我们还将挖掘我们的可再生抗体免疫库，以获得可能与以下因素相关的更广泛的特征： 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， 包括刺突三聚体蛋白上的多个表位（如受体结合结构域，RBD，N末端 结构域、NTD和S1区）和内部病毒蛋白（例如，核衣壳蛋白）。我们还将绘制 单个B细胞应答的分子特征（例如，亲和力、基于竞争的表位作图和差异表位作图） 结合不同的刺突蛋白构象）来全面追踪抗SARS-CoV-2的分子免疫 在一个人类群体中。我们将分析每个抗体克隆的遗传特征，以帮助阐明免疫平衡。 中和抗体与非中和抗体作为潜在的疾病相关物。 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， 确定可能区分轻度和重度SARS-CoV-2感染的关键分子特征。这些新 分子尺度的相关性和潜在的生物标志物将改善基础和临床理解， 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 疾病的严重性，并确定新的有效抗体候选药物，以治疗和预防COVID-19。

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.