Department of Physics, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA.
Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA.
Mol Biol Evol. 2018 Oct 1;35(10):2390-2400. doi: 10.1093/molbev/msy131.
Viral evolutionary pathways are determined by the fitness landscape, which maps viral genotype to fitness. However, a quantitative description of the landscape and the evolutionary forces on it remain elusive. Here, we apply a biophysical fitness model based on capsid folding stability and antibody binding affinity to predict the evolutionary pathway of norovirus escaping a neutralizing antibody. The model is validated by experimental evolution in bulk culture and in a drop-based microfluidics that propagates millions of independent small viral subpopulations. We demonstrate that along the axis of binding affinity, selection for escape variants and drift due to random mutations have the same direction, an atypical case in evolution. However, along folding stability, selection and drift are opposing forces whose balance is tuned by viral population size. Our results demonstrate that predictable epistatic tradeoffs between molecular traits of viral proteins shape viral evolution.
病毒进化途径由适应度景观决定,该景观将病毒基因型映射到适应度上。然而,对景观的定量描述和作用于其上的进化力量仍然难以捉摸。在这里,我们应用基于衣壳折叠稳定性和抗体结合亲和力的生物物理适应度模型来预测诺如病毒逃避中和抗体的进化途径。该模型通过在批量培养和基于液滴的微流控系统中的实验进化得到验证,该系统传播数百万个独立的小病毒亚群。我们证明,沿着结合亲和力的轴,逃避变异的选择和由于随机突变引起的漂变具有相同的方向,这在进化中是一种非典型情况。然而,沿着折叠稳定性,选择和漂变是相反的力量,其平衡由病毒种群大小调节。我们的结果表明,病毒蛋白分子特征之间可预测的上位性权衡塑造了病毒的进化。