Wang Chengmin, Lyu Nan, Deng Lingling, Wang Jing, Gu Wenwen, Ding Hua, Wu Yan, Luo Jing, Wang Liang, Lyv Xueze, Liu Xiaodong, Tao Yi, He Hongxuan
Key Lab of Animal Ecology and Conservation Biology, National Research Center for Wildlife-Borne Diseases, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.
Department of Infectious Diseases, Hangzhou Center for Disease Control and Prevention, Hangzhou, China.
Front Genet. 2018 Jun 7;9:204. doi: 10.3389/fgene.2018.00204. eCollection 2018.
The adaptive evolution of influenza virus is an important question, but predicting its evolutionary future will be more challenging. Here, we investigated the mutation characteristic of influenza virus based on the complete genome data of 2009 (H1N1) influenza A virus. By assuming that evolution proceeds via the accumulation of mutations, we analyzed the mutation networks at four different time stages and found that the network structure follows the characteristics of a scale-free network. These results will be important for epidemiology and the future control of influenza viruses. Furthermore, we predicted the predominant mutation virus strain by using the early mutation network of influenza viruses, and this result was consistent with the WHO recommendation for the candidate vaccine of influenza virus. The key contribution of this study is that we explained the biological significance of this scale-free network for influenza pandemic and provided a potential method for predicting the candidate vaccine by using the early-stage network.
流感病毒的适应性进化是一个重要问题，但预测其进化未来将更具挑战性。在此，我们基于2009年甲型（H1N1）流感病毒的全基因组数据研究了流感病毒的突变特征。通过假设进化是通过突变积累进行的，我们分析了四个不同时间阶段的突变网络，发现网络结构遵循无标度网络的特征。这些结果对于流感病毒的流行病学和未来防控将具有重要意义。此外，我们利用流感病毒的早期突变网络预测了主要的突变病毒株，这一结果与世界卫生组织对流感病毒候选疫苗的推荐一致。本研究的关键贡献在于我们解释了这种无标度网络对流感大流行的生物学意义，并提供了一种利用早期网络预测候选疫苗的潜在方法。
