Accurate prediction of neutralization capacity from deep mining of SARS-CoV-2 serology

摘要 本项目的目标是建立一种准确、灵敏的中和预测方法 通过抗体谱的深度挖掘来检测血清样品抗SARS-CoV-2的能力。COVID-19疫情 仍然是一个全球性的威胁，有近700万例病例和40万例死亡。在缺乏有效疫苗的情况下 抗SARS-CoV-2的免疫是SARS-CoV-2的主要保护机制 （再）感染。我们最近对恢复期血清样本的研究表明， 容量变化很大（超过100倍），只有一小部分具有高中和能力。因为病毒 中和测定固有地是低通量的，将其应用于高风险人群是不现实的， 医院工作人员及时。不幸的是，只有适度的相关性之间的 中和能力和使用标准ELISA测定的抗SARS-CoV-2抗体水平。 显然，我们仍然不知道什么类型的抗体有助于病毒中和。我们的总体 本项目中要检验的假设是，通过更深入地检查患者血清中的抗体谱 在抗原、表位和抗体类型方面，我们将能够确定定量的 病毒中和的预测标志物。为此，我们将建立SARS-CoV-2的多重检测方法 血清学，这将使我们能够深入表征抗体谱。然后我们将开发一个预测 算法，利用。我们已经组建了一个专家团队，他们在抗体方面具有真正互补的技能， 表征、病毒学和数据挖掘。我们有大量的恢复期血清样本， 这将使我们能够严格验证我们的技术。我们将尽快实现以下目标。（一） 我们将建立一种能检测15种抗体-抗原的SARS-CoV-2多重血清学检测方法 在单一反应中的相互作用。主要技术创新是引入多维流 细胞仪我们将生产多种抗原，包括刺突，受体结合结构域和核衣壳 蛋白质及其天然和设计的变体。我们将使用一个大的面板， 恢复期血清样本。(2)我们将开发一种改进的病毒中和试验，以更好地量化 中和能力(3)我们将开发一个预测算法，利用中和能力， 我们的多重检测的抗体谱。该分析将确定有助于 中和该项目的最终产品将包括一种高通量血清学检测， 比当前标准更丰富的抗体谱，并伴随有准确的预测算法。在一起， 该平台将有助于促进对SARS-CoV-2感染的基本了解， 开发针对这种可怕病原体的疫苗和疗法。

ABSTRACT The goal of this project is to establish an accurate and sensitive method for predicting the neutralization capacity against SARS-CoV-2 of serum samples by deep mining of antibody profiles. The COVID-19 pandemic remains a global threat with nearly seven million cases and 400K deaths. In the absence of effective vaccines and therapeutics, immunity against SARS-CoV-2 is a main mechanism of protection against SARS-CoV-2 (re)infection. Our recent studies of convalescent serum samples revealed that their levels of neutralization capacity vary greatly (over 100-fold) and only a small subset has high neutralization capacity. Because viral neutralization assays are inherently low throughput, it is unrealistic to apply it to a high-risk population such as hospital workers in a timely manner. Unfortunately, there is only moderate correlation between the neutralization capacity and the level of anti-SARS-CoV-2 antibody levels determined using standard ELISA. Clearly, we still do not understand what types of antibodies contribute to viral neutralization. Our overarching hypothesis to be tested in this project is that by examining the antibody profile in patient serum more deeply and quantitatively in terms of antigens, epitopes and antibody types, we will be able to identify quantitative predictive markers for viral neutralization. To this end, we will develop multiplex assay for SARS-CoV-2 serology that will enable us to deeply characterize the antibody profile. We will then develop a predictive algorithm by utilizing. We have assembled a team of experts with truly complementary skills in antibody characterization, virology and data mining. We have access to a large number of convalescent serum samples, which will enable us to critically validate our technology. We will expeditiously execute the following aims. (1) We will develop multiplex serology assay for SARS-CoV-2 that can profile up to 15 antibody-antigen interactions in a single reaction. The main technical innovation is the introduction of multi-dimensional flow cytometry. We will produce multiple antigens including Spike, receptor-binding domain and nucleocapsid protein, and their natural and designed variants. We will refine and validate the assay using a large panel of convalescent serum samples. (2) We will develop an improved viral neutralization assay to better quantify the neutralization capacity. (3) We will develop a predictive algorithm for neutralization capacity that utilizes the antibody profiles from our multiplex assay. This analysis will identify serology parameters that contribute to neutralization. The end products of this project will include a high-throughput serology assay that gives far- richer antibody profiles than the current standard accompanied with an accurate predictive algorithm. Together, this platform will help advance a fundamental understanding of SARS-CoV-2 infection as well as the development of vaccines and therapeutics against this formidable pathogen.