A New Algorithm for Analysis of Within-host Viral Evolution

点击翻译按钮获取中文摘要

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.