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感染和接种 SARS-CoV-2 后抗体亲合力反应的动力学。

Kinetics of the SARS-CoV-2 Antibody Avidity Response Following Infection and Vaccination.

机构信息

Infectious Diseases Epidemiology and Analytics Unit, Department of Global Health, Institut Pasteur, Université Paris Cité, 75015 Paris, France.

Tallaght University Hospital, Tallaght, D24 NR0A Dublin, Ireland.

出版信息

Viruses. 2022 Jul 8;14(7):1491. doi: 10.3390/v14071491.

Abstract

Serological assays capable of measuring antibody responses induced by previous infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been critical tools in the response to the COVID-19 pandemic. In this study, we use bead-based multiplex assays to measure IgG and IgA antibodies and IgG avidity to five SARS-CoV-2 antigens (Spike (S), receptor-binding domain (RBD), Nucleocapsid (N), S subunit 2, and Membrane-Envelope fusion (ME)). These assays were performed in several cohorts of healthcare workers and nursing home residents, who were followed for up to eleven months after SARS-CoV-2 infection or up to six months after vaccination. Our results show distinct kinetic patterns of antibody quantity (IgG and IgA) and avidity. While IgG and IgA antibody levels waned over time, with IgA antibody levels waning more rapidly, avidity increased with time after infection or vaccination. These contrasting kinetic patterns allow for the estimation of time since previous SARS-CoV-2 infection. Including avidity measurements in addition to antibody levels in a classification algorithm for estimating time since infection led to a substantial improvement in accuracy, from 62% to 78%. The inclusion of antibody avidity in panels of serological assays can yield valuable information for improving serosurveillance during SARS-CoV-2 epidemics.

摘要

用于测量先前感染严重急性呼吸综合征冠状病毒 2(SARS-CoV-2)引起的抗体反应的血清学检测方法是应对 COVID-19 大流行的重要工具。在这项研究中,我们使用基于珠子的多重检测方法来测量 IgG 和 IgA 抗体以及 IgG 对五种 SARS-CoV-2 抗原(刺突(S)、受体结合域(RBD)、核衣壳(N)、S 亚单位 2 和膜-包膜融合(ME))的亲和力。这些检测方法在几批医护人员和养老院居民中进行,他们在 SARS-CoV-2 感染后最长达 11 个月或接种疫苗后最长达 6 个月进行了随访。我们的结果显示出抗体数量(IgG 和 IgA)和亲和力的明显动力学模式。虽然 IgG 和 IgA 抗体水平随时间推移而下降,IgA 抗体水平下降更快,但亲和力随感染或接种后时间的推移而增加。这些相反的动力学模式允许估计先前 SARS-CoV-2 感染的时间。在用于估计感染时间的分类算法中,除了抗体水平外,还包括亲和力测量,可将准确性从 62%提高到 78%。在血清学检测方法的面板中包含抗体亲和力可以为改善 SARS-CoV-2 流行期间的血清学监测提供有价值的信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da1f/9321390/78c15dada56b/viruses-14-01491-g001.jpg

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