Zhu Heng, Hu Shaohui, Jona Ghil, Zhu Xiaowei, Kreiswirth Nate, Willey Barbara M, Mazzulli Tony, Liu Guozhen, Song Qifeng, Chen Peng, Cameron Mark, Tyler Andrea, Wang Jian, Wen Jie, Chen Weijun, Compton Susan, Snyder Michael
Molecular, Cellular, and Developmental Biology and Comparative Medicine, and Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
Proc Natl Acad Sci U S A. 2006 Mar 14;103(11):4011-6. doi: 10.1073/pnas.0510921103. Epub 2006 Mar 7.
To monitor severe acute respiratory syndrome (SARS) infection, a coronavirus protein microarray that harbors proteins from SARS coronavirus (SARS-CoV) and five additional coronaviruses was constructed. These microarrays were used to screen approximately 400 Canadian sera from the SARS outbreak, including samples from confirmed SARS-CoV cases, respiratory illness patients, and healthcare professionals. A computer algorithm that uses multiple classifiers to predict samples from SARS patients was developed and used to predict 206 sera from Chinese fever patients. The test assigned patients into two distinct groups: those with antibodies to SARS-CoV and those without. The microarray also identified patients with sera reactive against other coronavirus proteins. Our results correlated well with an indirect immunofluorescence test and demonstrated that viral infection can be monitored for many months after infection. We show that protein microarrays can serve as a rapid, sensitive, and simple tool for large-scale identification of viral-specific antibodies in sera.
为监测严重急性呼吸综合征(SARS)感染情况,构建了一种冠状病毒蛋白质微阵列,其中包含严重急性呼吸综合征冠状病毒(SARS-CoV)及另外五种冠状病毒的蛋白质。这些微阵列用于筛查来自SARS疫情期间约400份加拿大血清样本,包括确诊SARS-CoV病例、呼吸道疾病患者及医护人员的样本。开发了一种使用多个分类器预测SARS患者样本的计算机算法,并用于预测206份中国发热患者的血清。该测试将患者分为两个不同组:有SARS-CoV抗体的患者和无SARS-CoV抗体的患者。该微阵列还识别出对其他冠状病毒蛋白质血清反应阳性的患者。我们的结果与间接免疫荧光试验相关性良好,并表明感染后数月仍可监测病毒感染情况。我们表明蛋白质微阵列可作为一种快速、灵敏且简单的工具,用于大规模鉴定血清中的病毒特异性抗体。