New York Influenza Center of Excellence at David Smith Center for Immunology and Vaccine Biology, Department of Microbiology and Immunology, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA.
Center for Integrated Research Computing, University of Rochester, Rochester, NY, USA.
BMC Bioinformatics. 2018 Feb 12;19(1):51. doi: 10.1186/s12859-018-2042-4.
The ease at which influenza virus sequence data can be used to estimate antigenic relationships between strains and the existence of databases containing sequence data for hundreds of thousands influenza strains make sequence-based antigenic distance estimates an attractive approach to researchers. Antigenic mismatch between circulating strains and vaccine strains results in significantly decreased vaccine effectiveness. Furthermore, antigenic relatedness between the vaccine strain and the strains an individual was originally primed with can affect the cross-reactivity of the antibody response. Thus, understanding the antigenic relationships between influenza viruses that have circulated is important to both vaccinologists and immunologists.
Here we develop a method of mapping antigenic relationships between influenza virus stains using a sequence-based antigenic distance approach (SBM). We used a modified version of the p-all-epitope sequence-based antigenic distance calculation, which determines the antigenic relatedness between strains using influenza hemagglutinin (HA) genetic coding sequence data and provide experimental validation of the p-all-epitope calculation. We calculated the antigenic distance between 4838 H1N1 viruses isolated from infected humans between 1918 and 2016. We demonstrate, for the first time, that sequence-based antigenic distances of H1N1 Influenza viruses can be accurately represented in 2-dimenstional antigenic cartography using classic multidimensional scaling. Additionally, the model correctly predicted decreases in cross-reactive antibody levels with 87% accuracy and was highly reproducible with even when small numbers of sequences were used.
This work provides a highly accurate and precise bioinformatics tool that can be used to assess immune risk as well as design optimized vaccination strategies. SBM accurately estimated the antigenic relationship between strains using HA sequence data. Antigenic maps of H1N1 virus strains reveal that strains cluster antigenically similar to what has been reported for H3N2 viruses. Furthermore, we demonstrated that genetic variation differs across antigenic sites and discuss the implications.
流感病毒序列数据可轻松用于估算菌株之间的抗原关系,并且存在包含数十万流感病毒序列数据的数据库,这使得基于序列的抗原距离估计成为研究人员的一种有吸引力的方法。流行株与疫苗株之间的抗原不匹配会导致疫苗效力显著降低。此外,个体最初接种疫苗的疫苗株与原始株之间的抗原相关性会影响抗体反应的交叉反应性。因此,了解已传播的流感病毒之间的抗原关系对疫苗学家和免疫学家都很重要。
在这里,我们使用基于序列的抗原距离方法(SBM)开发了一种映射流感病毒株之间抗原关系的方法。我们使用了经过修改的 p-all-epitope 基于序列的抗原距离计算版本,该版本使用流感血凝素(HA)遗传编码序列数据来确定菌株之间的抗原相关性,并提供了 p-all-epitope 计算的实验验证。我们计算了 1918 年至 2016 年间从感染人类中分离出的 4838 株 H1N1 病毒之间的抗原距离。我们首次证明,使用经典多维缩放技术,可以在二维抗原图谱中准确表示 H1N1 流感病毒的基于序列的抗原距离。此外,该模型以 87%的准确性准确预测了交叉反应性抗体水平的降低,并且即使使用少量序列也具有高度可重复性。
这项工作提供了一种高度准确和精确的生物信息学工具,可用于评估免疫风险以及设计优化的疫苗接种策略。SBM 使用 HA 序列数据准确估计了菌株之间的抗原关系。H1N1 病毒株的抗原图谱表明,菌株在抗原上聚类与 H3N2 病毒的报道相似。此外,我们证明了抗原位点的遗传变异不同,并讨论了其含义。