Department of Biology, Emory University, Atlanta, GA 30322, USA.
Graduate Program in Population Biology, Ecology, and Evolution, Emory University, Atlanta, GA 30322, USA.
Viruses. 2021 Jun 23;13(7):1216. doi: 10.3390/v13071216.
Animal models are frequently used to characterize the within-host dynamics of emerging zoonotic viruses. More recent studies have also deep-sequenced longitudinal viral samples originating from experimental challenges to gain a better understanding of how these viruses may evolve in vivo and between transmission events. These studies have often identified nucleotide variants that can replicate more efficiently within hosts and also transmit more effectively between hosts. Quantifying the degree to which a mutation impacts viral fitness within a host can improve identification of variants that are of particular epidemiological concern and our ability to anticipate viral adaptation at the population level. While methods have been developed to quantify the fitness effects of mutations using observed changes in allele frequencies over the course of a host's infection, none of the existing methods account for the possibility of cellular coinfection. Here, we develop mathematical models to project variant allele frequency changes in the context of cellular coinfection and, further, integrate these models with statistical inference approaches to demonstrate how variant fitness can be estimated alongside cellular multiplicity of infection. We apply our approaches to empirical longitudinally sampled H5N1 sequence data from ferrets. Our results indicate that previous studies may have significantly underestimated the within-host fitness advantage of viral variants. These findings underscore the importance of considering the process of cellular coinfection when studying within-host viral evolutionary dynamics.
动物模型常用于描述新发人畜共患病毒在宿主内的动态。最近的研究还对来自实验性挑战的纵向病毒样本进行了深度测序，以更好地了解这些病毒在体内和传播事件之间是如何进化的。这些研究经常发现能够在宿主内更有效地复制，并且在宿主之间更有效地传播的核苷酸变异。定量突变对宿主内病毒适应性的影响程度可以提高识别具有特定流行病学关注的变异的能力，并提高我们预测群体水平病毒适应的能力。虽然已经开发了使用宿主感染过程中观察到的等位基因频率变化来量化突变的适应度效应的方法，但现有的方法都没有考虑到细胞合并感染的可能性。在这里，我们开发了数学模型来预测细胞合并感染背景下的变异等位基因频率变化，并进一步将这些模型与统计推断方法相结合，以展示如何在细胞多重感染的同时估计变异适应性。我们将我们的方法应用于来自雪貂的经验性纵向采样 H5N1 序列数据。我们的结果表明，以前的研究可能大大低估了病毒变异在宿主内的适应性优势。这些发现强调了在研究宿主内病毒进化动态时考虑细胞合并感染过程的重要性。
