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基于单克隆抗体的阻断 ELISA 的开发,用于检测动物中 SARS-CoV-2 的暴露情况。

Development of monoclonal antibody-based blocking ELISA for detecting SARS-CoV-2 exposure in animals.

机构信息

Department of Pathobiology, College of Veterinary Medicine, University of Illinois at Urbana-Champaign , Urbana, Illinois, USA.

Department of Population Medicine and Diagnostic Sciences, Animal Health Diagnostic Center, College of Veterinary Medicine, Cornell University , Ithaca, New York, USA.

出版信息

mSphere. 2023 Aug 24;8(4):e0006723. doi: 10.1128/msphere.00067-23. Epub 2023 Jul 6.

Abstract

The global pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a significant threat to public health. Besides humans, SARS-CoV-2 can infect several animal species. Highly sensitive and specific diagnostic reagents and assays are urgently needed for rapid detection and implementation of strategies for prevention and control of the infection in animals. In this study, we initially developed a panel of monoclonal antibodies (mAbs) against SARS-CoV-2 nucleocapsid protein. To detect SARS-CoV-2 antibodies in a broad spectrum of animal species, an mAb-based blocking enzyme-linked immunosorbent assay (bELISA) was developed. Test validation using a set of animal serum samples with known infection status obtained an optimal percentage of inhibition cut-off value of 17.6% with diagnostic sensitivity of 97.8% and diagnostic specificity of 98.9%. The assay demonstrates high repeatability as determined by a low coefficient of variation (7.23%, 4.89%, and 3.16%) between-runs, within-run, and within-plate, respectively. Testing of samples collected over time from experimentally infected cats showed that the bELISA was able to detect seroconversion as early as 7 days post-infection. Subsequently, the bELISA was applied for testing pet animals with coronavirus disease 2019 (COVID-19)-like symptoms and specific antibody responses were detected in two dogs. The panel of mAbs generated in this study provides a valuable tool for SARS-CoV-2 diagnostics and research. The mAb-based bELISA provides a serological test in aid of COVID-19 surveillance in animals. IMPORTANCE Antibody tests are commonly used as a diagnostic tool for detecting host immune response following infection. Serology (antibody) tests complement nucleic acid assays by providing a history of virus exposure, no matter symptoms developed from infection or the infection was asymptomatic. Serology tests for COVID-19 are in high demand, especially when the vaccines become available. They are important to determine the prevalence of the viral infection in a population and identify individuals who have been infected or vaccinated. ELISA is a simple and practically reliable serological test, which allows high-throughput implementation in surveillance studies. Several COVID-19 ELISA kits are available. However, they are mostly designed for human samples and species-specific secondary antibody is required for indirect ELISA format. This paper describes the development of an all species applicable monoclonal antibody (mAb)-based blocking ELISA to facilitate the detection and surveillance of COVID-19 in animals.

摘要

严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2) 的全球大流行对公共卫生构成重大威胁。除人类外,SARS-CoV-2 还可感染多种动物物种。因此,迫切需要高度敏感和特异的诊断试剂和检测方法,以便快速检测,并实施预防和控制动物感染的策略。在这项研究中,我们最初开发了一组针对 SARS-CoV-2 核衣壳蛋白的单克隆抗体 (mAb)。为了在广泛的动物物种中检测 SARS-CoV-2 抗体,我们开发了一种基于 mAb 的阻断酶联免疫吸附试验 (bELISA)。使用一组具有已知感染状态的动物血清样本进行测试验证,获得了最佳抑制百分比截断值为 17.6%,诊断敏感性为 97.8%,诊断特异性为 98.9%。该检测方法显示出较高的重复性,其运行间、运行内和板内变异系数分别为 7.23%、4.89%和 3.16%。从感染猫身上随时间采集的样本进行检测,结果表明 bELISA 能够在感染后 7 天内检测到血清转化。随后,该 bELISA 用于检测具有 COVID-19 样症状的宠物动物,在两只狗中检测到了特异性抗体反应。本研究中生成的 mAb 组为 SARS-CoV-2 诊断和研究提供了有价值的工具。基于 mAb 的 bELISA 为动物 COVID-19 监测提供了一种血清学检测方法。

重要性:抗体检测通常用作检测宿主感染后免疫反应的诊断工具。血清学(抗体)检测通过提供病毒暴露史来补充核酸检测,无论感染是否出现症状或感染是否无症状。COVID-19 的血清学检测需求很高,特别是在疫苗可用时。它们对于确定人群中病毒感染的流行程度以及识别已感染或已接种疫苗的个体非常重要。ELISA 是一种简单且实用可靠的血清学检测方法,允许在监测研究中进行高通量实施。有几种 COVID-19 ELISA 试剂盒可用。但是,它们大多是为人类样本设计的,间接 ELISA 格式需要特异性的二级抗体。本文描述了一种适用于所有物种的基于单克隆抗体 (mAb) 的阻断 ELISA 的开发,以促进动物中 COVID-19 的检测和监测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc9d/10449516/1d59706b6950/msphere.00067-23.f001.jpg

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