Transmission Bottleneck Size Estimation from Pathogen Deep-Sequencing Data, with an Application to Human Influenza A Virus
Transmission Bottleneck Size Estimation from Pathogen Deep-Sequencing Data, with an Application to Human Influenza A Virus
复制标题
DOI：
10.1128/jvi.00171-17
复制
发表时间：
2017-07-01
影响因子：
5.4
通讯作者：
Koelle, Katia
中科院分区：
文献类型：
--
作者：
Leonard, Ashley Sobel;Weissman, Daniel B.;Koelle, Katia
控制传染病传播的瓶颈描述了从供体转移到受体宿主的病原体群体的大小。瓶颈大小的准确定量对于快速进化的病原体如流感病毒特别重要，因为狭窄的瓶颈减少了转移的病毒遗传多样性的量，因此可能降低病毒适应的速率。先前的研究通过使用病原体测序数据中鉴定的变体的统计分析来估计控制病毒传播的瓶颈大小。然而，这些分析并没有考虑到受体宿主内的变异识别阈值和随机病毒复制动态。由于这些因素可以扭曲瓶颈大小的估计，我们介绍了一种新的方法来推断瓶颈的大小，占这些因素。通过使用模拟数据集，我们首先表明，我们的方法，基于β-二项式抽样，准确地恢复传输瓶颈的大小，而其他方法无法做到这一点。然后，我们将我们的方法应用于甲型流感病毒（IAV）感染的数据集，其中来自传播对的病毒深度测序数据可用。我们发现，本研究中IAV传播瓶颈大小的估计在传播对之间存在很大差异，而196个病毒粒子的平均瓶颈大小与之前对该数据集的估计一致。此外，回归分析显示，估计的瓶颈大小和供体感染的严重程度之间的正相关性，测量温度。这些结果支持的结果，从实验传输研究表明，瓶颈大小跨传输事件可以是可变的，部分受流行病学factors.IMPORTANCE的影响传输瓶颈大小描述的大小的病原体人口从捐助者转移到受体主机，并可能影响宿主种群内的病原体适应率。测序技术的最新进展使得能够从病原体遗传数据估计瓶颈大小，尽管所使用的统计方法还没有一致性。在这里，我们介绍了一种新的方法来推断瓶颈的大小，占变异识别协议和噪音在病原体复制。我们发现，不考虑这些因素会导致低估瓶颈的大小。我们将这种方法应用于现有的人类流感病毒感染的数据集，表明传输是由一个松散的，但高度可变的，传输瓶颈，其大小是正相关的感染的严重程度的捐助者。除了推进我们对流感病毒传播的理解，我们希望这项工作将提供一个标准化的统计方法来估计病毒病原体的瓶颈大小。
The bottleneck governing infectious disease transmission describes the size of the pathogen population transferred from the donor to the recipient host. Accurate quantification of the bottleneck size is particularly important for rapidly evolving pathogens such as influenza virus, as narrow bottlenecks reduce the amount of transferred viral genetic diversity and, thus, may decrease the rate of viral adaptation. Previous studies have estimated bottleneck sizes governing viral transmission by using statistical analyses of variants identified in pathogen sequencing data. These analyses, however, did not account for variant calling thresholds and stochastic viral replication dynamics within recipient hosts. Because these factors can skew bottleneck size estimates, we introduce a new method for inferring bottleneck sizes that accounts for these factors. Through the use of a simulated data set, we first show that our method, based on beta-binomial sampling, accurately recovers transmission bottleneck sizes, whereas other methods fail to do so. We then apply our method to a data set of influenza A virus (IAV) infections for which viral deepsequencing data from transmission pairs are available. We find that the IAV transmission bottleneck size estimates in this study are highly variable across transmission pairs, while the mean bottleneck size of 196 virions is consistent with a previous estimate for this data set. Furthermore, regression analysis shows a positive association between estimated bottleneck size and donor infection severity, as measured by temperature. These results support findings from experimental transmission studies showing that bottleneck sizes across transmission events can be variable and influenced in part by epidemiological factors.IMPORTANCE The transmission bottleneck size describes the size of the pathogen population transferred from the donor to the recipient host and may affect the rate of pathogen adaptation within host populations. Recent advances in sequencing technology have enabled bottleneck size estimation from pathogen genetic data, although there is not yet a consistency in the statistical methods used. Here, we introduce a new approach to infer the bottleneck size that accounts for variant identification protocols and noise during pathogen replication. We show that failing to account for these factors leads to an underestimation of bottleneck sizes. We apply this method to an existing data set of human influenza virus infections, showing that transmission is governed by a loose, but highly variable, transmission bottleneck whose size is positively associated with the severity of infection of the donor. Beyond advancing our understanding of influenza virus transmission, we hope that this work will provide a standardized statistical approach for bottleneck size estimation for viral pathogens.