Data from: New approaches for unravelling reassortment pathways

BACKGROUND: Every year the human population encounters epidemic outbreaks of influenza, and history reveals recurring pandemics that have had devastating consequences. The current work focuses on the development of a robust algorithm for detecting influenza strains that have a composite genomic architecture. These influenza subtypes can be generated through a reassortment process, whereby a virus can inherit gene segments from two different types of influenza particles during replication. Reassortant strains are often not immediately recognised by the adaptive immune system of the hosts and hence may be the source of pandemic outbreaks. Owing to their importance in public health and their infectious ability, it is essential to identify reassortant influenza strains in order to understand the evolution of this virus and describe reassortment pathways that may be biased towards particular viral segments. Phylogenetic methods have been used traditionally to identify reassortant viruses. In many studies up to now, the assumption has been that if two phylogenetic trees differ, it is because reassortment has caused them to be different. While phylogenetic incongruence may be caused by real differences in evolutionary history, it can also be the result of phylogenetic error. Therefore, we wish to develop a method for distinguishing between topological inconsistency that is due to confounding effects and topological inconsistency that is due to reassortment. RESULTS: The current work describes the implementation of two approaches for robustly identifying reassortment events. The algorithms rest on the idea of significance of difference between phylogenetic trees or phylogenetic tree sets, and subtree pruning and regrafting operations, which mimic the effect of reassortment on tree topologies. The first method is based on a maximum likelihood (ML) framework (MLreassort) and the second implements a Bayesian approach (Breassort) for reassortment detection. We focus on reassortment events that are found by both methods. We test both methods on a simulated dataset and on a small collection of real viral data isolated in Hong Kong in 1999. CONCLUSIONS: The nature of segmented viral genomes present many challenges with respect to disease. The algorithms developed here can effectively identify reassortment events in small viral datasets and can be applied not only to influenza but also to other segmented viruses. Owing to computational demands of comparing tree topologies, further development in this area is necessary to allow their application to larger datasets.

研究背景：每年全球人群均会遭遇流感疫情暴发，而历史记载显示，曾反复出现过造成毁灭性后果的流感大流行。本研究聚焦于开发一种鲁棒性强的算法，用以检测具备复合基因组结构的流感毒株。这类流感亚型可通过基因重配（reassortment）过程产生：病毒在复制过程中可从两种不同类型的流感病毒颗粒中继承基因片段。经基因重配产生的毒株通常不会被宿主的适应性免疫系统立即识别，因此可能成为流感大流行暴发的源头。鉴于其对公共卫生的重要性以及自身的感染能力，识别流感重配毒株对于解析该病毒的演化过程、阐明可能偏向特定病毒片段的重配通路至关重要。传统上，系统发育方法被用于识别重配流感病毒。迄今为止的诸多研究均默认：若两棵系统发育树存在差异，则是基因重配导致了这种差异。尽管系统发育不一致性可能源于真实的演化历史差异，但也可能由系统发育分析误差所致。因此，本研究旨在开发一种方法，用以区分由混杂效应导致的拓扑结构不一致性，与由基因重配引发的拓扑结构不一致性。研究结果：本研究详述了两种可稳健识别基因重配事件的方法的实现流程。这两种算法的核心思路基于系统发育树或系统发育树集之间差异的显著性检验，以及模拟基因重配对树拓扑结构影响的子树修剪与重接（subtree pruning and regrafting）操作。第一种方法基于最大似然（maximum likelihood, ML）框架，命名为MLreassort；第二种方法则采用贝叶斯方法（Bayesian approach），命名为Breassort，用于基因重配检测。本研究聚焦于两种方法共同检出的基因重配事件，并分别在模拟数据集与1999年于香港分离得到的小型真实病毒数据集上对两种方法进行了测试。研究结论：分节段病毒基因组的特性给疾病防控带来了诸多挑战。本研究开发的算法可在小型病毒数据集中有效识别基因重配事件，且不仅可应用于流感病毒，还可推广至其他分节段病毒。鉴于比对树拓扑结构的计算需求较高，该领域仍需进一步发展，以支持其在更大规模数据集上的应用。