A New Algorithm for Analysis of Within-host Viral Evolution

本研究的目的是开发一种简单易用的计算方法，用于分析宿主内病毒的进化。我们的方法有两个重要的特点：非同步病毒样本可以用简单的计算算法处理，并且可以估计病毒进化过程中发生的中性和自适应进化模式。在本研究的开始阶段，我们提出了基于极大似然法的该算法的初步表述。然而，由于计算似然需要非常多的时间，并且可能的树拓扑数量随着病毒变体数量的增加呈指数增长，因此难以使用。因此，我们提出了另一种新的算法，称为基于距离的顺序链接算法，其中采用了邻居连接方法。该算法应用于单个患者感染后7年内获得的HIV-1的env基因（V3区域）的纵向数据集。结果表明，该方法可以在合理的计算时间内成功地从非同期病毒样本中重建纵向系统发育树。该方法的进一步发展将包括纳入sUPGMA的优点，这将允许估计Ne和内部样本时间；我们还想用这种方法使用密集序列的HIV-1样本来进一步验证其效率。与日本国立传染病研究所（NIID）的合作研究正在将我们的方法应用于HIV-1的pol基因的大型数据集。

The purpose of this study is to develop a computational method that would be simple and easy to use for the analysis of within-host viral evolution. Our method has two important features: noncontemporaneous viral samples can be dealt with by a simple computing algorithm, and both neutral and adaptive evolution patterns occurring during the process of viral evolution can be estimated. In the beginning stage of this research, we proposed a preliminary formulation of this algorithm that was based on the maximum likelihood method. However, it was difficult to use because the calculation of the likelihood required an extremely large amount of time and the number of possible tree topologies increased exponentially according to the increase in the number of viral variants. Therefore, we proposed another new algorithm, referred to as a distance-based sequential-linking algorithm, in which the neighbor-joining method is employed. This algorithm is applied to a longitudinal data set of the env gene (V3 region) of HIV-1 obtained over seven years after the infection of a single patient. The results suggest that this method can successfully reconstruct a longitudinal phylogenetic tree from noncontemporaneous viral samples within a reasonable calculation time.Further development of this method would include an incorporation of the merits of the sUPGMA, which would allow for an estimation of the Ne and the internal sample times ; we would also like to use densely serial samples of HIV-1 with this method for further verification of its efficiency. Collaborative research with the NIID (National Institute of Infectious Diseases) of Japan applying our method to a large data set of the pol gene of HIV-1 is in progress.