Martinez Viedma Maria Del Pilar, Kose Nurgun, Parham Leda, Balmaseda Angel, Kuan Guillermina, Lorenzana Ivette, Harris Eva, Crowe James E, Pickett Brett E
J. Craig Venter Institute, La Jolla, CA, 92137, USA.
Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
F1000Res. 2019 Nov 7;8:1875. doi: 10.12688/f1000research.20981.3. eCollection 2019.
Global outbreaks caused by emerging or re-emerging arthropod-borne viruses (arboviruses) are becoming increasingly more common. These pathogens include the mosquito-borne viruses belonging to the and genera. These viruses often cause non-specific or asymptomatic infection, which can confound viral prevalence studies. In addition, many acute phase diagnostic tests rely on the detection of viral components such as RNA or antigen. Standard serological tests are often not reliable for diagnosis after seroconversion and convalescence due to cross-reactivity among flaviviruses. In order to contribute to development efforts for mosquito-borne serodiagnostics, we incubated 137 human sera on individual custom peptide arrays that consisted of over 866 unique peptides in quadruplicate. Our bioinformatics workflow to analyze these data incorporated machine learning, statistics, and B-cell epitope prediction. Here we report the results of our peptide array data analysis, which revealed sets of peptides that have diagnostic potential for detecting past exposure to a subset of the tested human pathogens including Zika virus. These peptides were then confirmed using the well-established ELISA method. These array data, and the resulting peptides can be useful in diverse efforts including the development of new pan-flavivirus antibodies, more accurate epitope mapping, and vaccine development against these viral pathogens.
由新出现或再次出现的节肢动物传播病毒(虫媒病毒)引起的全球疫情正变得越来越普遍。这些病原体包括属于黄病毒属和瘟病毒属的蚊媒病毒。这些病毒通常会引起非特异性或无症状感染,这可能会混淆病毒流行率研究。此外,许多急性期诊断测试依赖于对病毒成分(如RNA或抗原)的检测。由于黄病毒之间存在交叉反应,标准血清学检测在血清转化和恢复期后通常对诊断不可靠。为了促进蚊媒血清学诊断的开发工作,我们将137份人类血清与由866多种独特肽组成的定制肽阵列进行了四次孵育。我们用于分析这些数据的生物信息学工作流程纳入了机器学习、统计学和B细胞表位预测。在此,我们报告肽阵列数据分析的结果,该结果揭示了一些肽组,这些肽组对于检测过去是否接触过包括寨卡病毒在内的一部分测试人类病原体具有诊断潜力。然后使用成熟的ELISA方法对这些肽进行了验证。这些阵列数据以及由此产生的肽可用于多种工作,包括开发新的泛黄病毒抗体、更准确的表位图谱绘制以及针对这些病毒病原体的疫苗开发。