The Genomic Rate of Molecular Adaptation of the Human Influenza A Virus
The Genomic Rate of Molecular Adaptation of the Human Influenza A Virus
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DOI：
10.1093/molbev/msr044
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发表时间：
2011-09-01
影响因子：
10.7
通讯作者：
Pybus, Oliver G.
中科院分区：
文献类型：
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作者：
Bhatt, Samir;Holmes, Edward C.;Pybus, Oliver G.
在基因组水平上量化适应性进化是进化生物学的一个基本而又具有挑战性的方面。在这里，我们开发了一种方法，该方法扩展和推广了以前的方法，以估计快速进化的种群中基因组适应的速度，并将其应用于完整的人类甲型流感病毒基因组序列的大数据集。与以前的研究一致，我们观察到病毒血凝素(HA1)结构域1的适应性进化率特别高。然而，我们的新方法也揭示了以前未曾见过的其他病毒基因的适应。值得注意的是，我们发现病毒神经氨酸酶表面残基的适应速度(每年每个密码子)比HA1高，这表明在前者上有很强的抗体介导的选择。我们还观察到几种非结构蛋白的高适应性进化，这可能与病毒逃避T细胞和先天免疫反应有关。此外，我们的分析为人类H1N1流感经历了比H3N2更弱的抗原选择的假设提供了强有力的定量支持。除了揭示流感病毒达尔文阳性选择的动力学和决定因素外，这里介绍的方法也适用于其他可获得密集采样基因组序列的病原体，因此非常适合于解释下一代基因组测序数据。
Quantifying adaptive evolution at the genomic scale is an essential yet challenging aspect of evolutionary biology. Here, we develop a method that extends and generalizes previous approaches to estimate the rate of genomic adaptation in rapidly evolving populations and apply it to a large data set of complete human influenza A virus genome sequences. In accord with previous studies, we observe particularly high rates of adaptive evolution in domain 1 of the viral hemagglutinin (HA1). However, our novel approach also reveals previously unseen adaptation in other viral genes. Notably, we find that the rate of adaptation (per codon per year) is higher in surface residues of the viral neuraminidase than in HA1, indicating strong antibody-mediated selection on the former. We also observed high rates of adaptive evolution in several nonstructural proteins, which may relate to viral evasion of T-cell and innate immune responses. Furthermore, our analysis provides strong quantitative support for the hypothesis that human H1N1 influenza experiences weaker antigenic selection than H3N2. As well as shedding new light on the dynamics and determinants of positive Darwinian selection in influenza viruses, the approach introduced here is applicable to other pathogens for which densely sampled genome sequences are available, and hence is ideally suited to the interpretation of next-generation genome sequencing data.