Biozentrum, Universität Basel, Basel, Switzerland.
Swiss Institute of Bioinformatics, Basel, Switzerland.
Mol Biol Evol. 2021 Jun 25;38(7):2767-2777. doi: 10.1093/molbev/msab065.
Seasonal influenza viruses repeatedly infect humans in part because they rapidly change their antigenic properties and evade host immune responses, necessitating frequent updates of the vaccine composition. Accurate predictions of strains circulating in the future could therefore improve the vaccine match. Here, we studied the predictability of frequency dynamics and fixation of amino acid substitutions. Current frequency was the strongest predictor of eventual fixation, as expected in neutral evolution. Other properties, such as occurrence in previously characterized epitopes or high Local Branching Index (LBI) had little predictive power. Parallel evolution was found to be moderately predictive of fixation. Although the LBI had little power to predict frequency dynamics, it was still successful at picking strains representative of future populations. The latter is due to a tendency of the LBI to be high for consensus-like sequences that are closer to the future than the average sequence. Simulations of models of adapting populations, in contrast, show clear signals of predictability. This indicates that the evolution of influenza HA and NA, while driven by strong selection pressure to change, is poorly described by common models of directional selection such as traveling fitness waves.
季节性流感病毒反复感染人类，部分原因是它们迅速改变抗原特性，逃避宿主免疫反应，因此需要频繁更新疫苗成分。因此，对未来流行株的准确预测可以提高疫苗的匹配度。在这里，我们研究了氨基酸替换的固定和频率动态的可预测性。如中性进化中所预期的那样，当前的频率是最终固定的最强预测因子。其他特性，如以前特征化的表位中的出现或高局部分支指数（LBI），预测能力很小。平行进化被发现对固定具有中等的预测能力。尽管 LBI 对预测频率动态的能力很小，但它仍然能够挑选出代表未来种群的菌株。这是由于 LBI 倾向于对与未来比平均序列更接近的共识样序列具有较高的值。相比之下，适应种群模型的模拟显示出明显的可预测性信号。这表明，流感 HA 和 NA 的进化虽然受到强烈的改变选择压力的驱动，但不能用常见的定向选择模型（如移动适应度波）很好地描述。
