A computational framework for influenza antigenic cartography.
A computational framework for influenza antigenic cartography.
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DOI：
10.1371/journal.pcbi.1000949
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发表时间：
2010-10-07
影响因子：
4.3
通讯作者：
Wan XF
中科院分区：
文献类型：
--
作者：
Cai Z;Zhang T;Wan XF
流感病毒在世界各地造成大量生命损失，并继续构成巨大的公共卫生挑战。基于血凝抑制（HI）试验的抗原鉴定是流感疫苗株选择的常规程序之一。然而，HI检测只是一个粗略的实验，反映了检测抗原（病毒）和参比抗血清（抗体）之间的抗原相关性。此外，抗原表征通常基于多个HI数据集。多个数据集的组合导致一个不完整的HI矩阵，其中有许多未观察到的条目。本文提出了一种新的基于不完全矩阵构建流感抗原制图的计算框架，我们称之为矩阵补全-多维尺度（MC-MDS）。在该方法中，我们首先使用低秩矩阵补全技术重建病毒和抗体的HI矩阵，然后使用多维尺度生成二维抗原制图。此外，对于具有群体免疫效应的流感流感表（如人类流感病毒），我们提出了一个时间模型，以减少群体免疫引起的流感流感表固有的时间偏差。通过将我们的方法应用于1968年至2003年分离的H3N2甲型流感病毒的HI数据集，我们确定了11个抗原变异簇，代表了这36年中所有主要的抗原漂移事件。我们的研究结果表明，完整的HI矩阵和通过MC-MDS获得的抗原制图都有助于识别流感抗原变异，从而可用于促进流感疫苗株的选择。web服务器可在http://sysbio.cvm.msstate.edu/AntigenMap上获得。流感抗原制图是地理制图的一个类比，它将流感抗原投射到二维或三维地图上，通过它我们可以可视化和测量流感抗原之间的抗原距离就像我们在地理制图中可视化和测量城市之间的地理距离一样。因此，流感抗原制图可用于识别流感抗原变异，并可用于流感疫苗株的选择。在这里，我们开发了一个新的计算框架，用于构建基于血凝抑制试验的流感抗原制图，血凝抑制试验是流感监测和疫苗株选择中的常规抗原表征方法。该方法可用于季节性流感和大流行性流感疫苗株的抗原鉴定。
Influenza viruses have been responsible for large losses of lives around the world and continue to present a great public health challenge. Antigenic characterization based on hemagglutination inhibition (HI) assay is one of the routine procedures for influenza vaccine strain selection. However, HI assay is only a crude experiment reflecting the antigenic correlations among testing antigens (viruses) and reference antisera (antibodies). Moreover, antigenic characterization is usually based on more than one HI dataset. The combination of multiple datasets results in an incomplete HI matrix with many unobserved entries. This paper proposes a new computational framework for constructing an influenza antigenic cartography from this incomplete matrix, which we refer to as Matrix Completion-Multidimensional Scaling (MC-MDS). In this approach, we first reconstruct the HI matrices with viruses and antibodies using low-rank matrix completion, and then generate the two-dimensional antigenic cartography using multidimensional scaling. Moreover, for influenza HI tables with herd immunity effect (such as those from Human influenza viruses), we propose a temporal model to reduce the inherent temporal bias of HI tables caused by herd immunity. By applying our method in HI datasets containing H3N2 influenza A viruses isolated from 1968 to 2003, we identified eleven clusters of antigenic variants, representing all major antigenic drift events in these 36 years. Our results showed that both the completed HI matrix and the antigenic cartography obtained via MC-MDS are useful in identifying influenza antigenic variants and thus can be used to facilitate influenza vaccine strain selection. The webserver is available at http://sysbio.cvm.msstate.edu/AntigenMap. Influenza antigenic cartography is an analogy of geographic cartography, and it projects influenza antigens into a two- or three-dimensional map through which we can visualize and measure the antigenic distances between influenza antigens as we visualize and measure geographic distances between the cities in a geographic cartography. Thus, influenza antigenic cartography can be utilized to identify influenza antigenic variants, and it is useful for influenza vaccine strain selection. Here we develop a new computational framework for constructing influenza antigenic cartography based on hemagglutination inhibition assay, a routine antigenic characterization method in influenza surveillance and vaccine strain selection. This method can be used for antigenic characterization in vaccine strain selection for both seasonal influenza and pandemic influenza.
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