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

深度挖掘SARS-CoV-2血清学，准确预测中和能力

• 批准号: 10195613
• 负责人: SHOHEI KOIDE
• 金额: $ 46.61万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-19 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10195613
• 关键词: 2019-nCoV; Algorithms; Antibodies; Antigens; Biological Assay; Blood specimen; COVID-19 pandemic; Cells; Cessation of life; Clinical; Data; Data Set; Development; Environment; Enzyme-Linked Immunosorbent Assay; Epitopes; Exposure to; FDA approved; Flow Cytometry; Goals; Gold; Health; Hospitals; Immune response; Immunity; Immunoglobulin A; Immunoglobulin G; Immunoglobulin M; Infection; Methods; Microspheres; Mining; Nucleocapsid Proteins; Positioning Attribute; Protein Engineering; Reaction; Reporter; Reproducibility; Sampling; Series; Serologic tests; Serum; Surface; Technology; Testing; Therapeutic; Time; Training; Transfusion; Vaccines; Variant; Viral; Viral Antigens; Viral Markers; Virus; data mining; density; design; high risk population; improved; innovation; learning strategy; multidimensional data; mutant; neutralizing antibody; pathogen; prediction algorithm; predictive marker; profiles in patients; rapid technique; receptor binding; response; skills; supervised learning; vaccine development; vaccine efficacy; virology

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.

抽象的 该项目的目的是建立一种准确而敏感的方法来预测中和 通过深化抗体曲线的深化挖掘对SARS-COV-2的能力。 19009年大流行 仍然是全球威胁，案件近700万例和40万例死亡。在没有有效疫苗的情况下 和治疗药，对SARS-COV-2的免疫力是保护SARS-COV-2的主要机制 （重新）感染。我们最近对康复血清样品的研究表明它们的中和水平 容量差异很大（超过100倍），只有一个小子集具有高中和能力。因为病毒 中和测定本质上是低吞吐量，将其应用于高危人群（例如 医院工作人员及时。不幸的是，在 使用标准ELISA确定的中和能力和抗SARS-COV-2抗体水平的水平。 显然，我们仍然不了解哪种类型的抗体会导致病毒中和。我们的总体 在该项目中要测试的假设是，通过检查患者血清中的抗体谱。 并在抗原，表位和抗体类型方面进行定量，我们将能够识别定量 病毒中和的预测标记。为此，我们将开发SARS-COV-2的多重分析 血清学将使我们能够深刻地表征抗体谱。然后，我们将发展一个预测性 利用算法。我们已经组建了一个具有真正互补技能的专家团队 表征，病毒学和数据挖掘。我们可以访问大量康复血清样品， 这将使我们能够批判性地验证我们的技术。我们将迅速执行以下目标。 （1） 我们将针对SARS-COV-2开发多重血清学测定法，该测定最多可介绍15种抗体抗原 单一反应中的相互作用。主要技术创新是引入多维流量 细胞仪。我们将产生多种抗原，包括尖峰，受体结合结构域和Nucleocapsid 蛋白质及其自然和设计的变体。我们将使用大型面板来完善和验证测定法 康复血清样品。 （2）我们将开发改进的病毒中和测定法，以更好地量化 中和能力。 （3）我们将开发一种使用中和能力的预测算法 来自我们多重测定的抗体曲线。该分析将确定有助于 中和。该项目的最终产品将包括一个高通量血清学测定法，这使得 比当前标准伴有精确预测算法的抗体曲线更丰富。一起， 该平台将有助于提高对SARS-COV-2感染以及 针对这种强大的病原体的疫苗和治疗剂的开发。

项目成果

Impaired Humoral Immunity to SARS-CoV-2 Vaccination in Non-Hodgkin Lymphoma and CLL Patients.

• DOI: 10.1101/2021.06.02.21257804
• 发表时间: 2021-06-03
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Diefenbach, Catherine;Caro, Jessica;Koide, Shohei
• 通讯作者: Koide, Shohei

A Rapid and Sensitive Microfluidics-Based Tool for Seroprevalence Immunity Assessment of COVID-19 and Vaccination-Induced Humoral Antibody Response at the Point of Care.

• DOI: 10.3390/bios12080621
• 发表时间: 2022-08-10
• 期刊: BIOSENSORS-BASEL
• 影响因子: 5.4
• 作者: Rajsri, Kritika Srinivasan;McRae, Michael P.;Simmons, Glennon W.;Christodoulides, Nicolaos J.;Matz, Hanover;Dooley, Helen;Koide, Akiko;Koide, Shohei;McDevitt, John T.
• 通讯作者: McDevitt, John T.