Department of Biology, Loyola University of Chicago, Chicago, Illinois, USA
Department of Computer Science, Loyola University of Chicago, Chicago, Illinois, USA.
mBio. 2018 Mar 20;9(2):e01870-17. doi: 10.1128/mBio.01870-17.
Bacteriophages are the most abundant and diverse biological entities on the planet, and new phage genomes are being discovered at a rapid pace. As more phage genomes are published, new methods are needed for placing these genomes in an ecological and evolutionary context. Phages are difficult to study by phylogenetic methods, because they exchange genes regularly, and no single gene is conserved across all phages. Here, we demonstrate how gene-level networks can provide a high-resolution view of phage genetic diversity and offer a novel perspective on virus ecology. We focus our analyses on virus host range and show how network topology corresponds to host relatedness, how to find groups of genes with the strongest host-specific signatures, and how this perspective can complement phage host prediction tools. We discuss extensions of gene network analysis to predicting the emergence of phages on new hosts, as well as applications to features of phage biology beyond host range. Bacteriophages (phages) are viruses that infect bacteria, and they are critical drivers of bacterial evolution and community structure. It is generally difficult to study phages by using tree-based methods, because gene exchange is common, and no single gene is shared among all phages. Instead, networks offer a means to compare phages while placing them in a broader ecological and evolutionary context. In this work, we build a network that summarizes gene sharing across phages and test how a key constraint on phage ecology, host range, corresponds to the structure of the network. We find that the network reflects the relatedness among phage hosts, and phages with genes that are closer in the network are likelier to infect similar hosts. This approach can also be used to identify genes that affect host range, and we discuss possible extensions to analyze other aspects of viral ecology.
噬菌体是地球上最丰富和最多样化的生物实体，新的噬菌体基因组正在快速被发现。随着越来越多的噬菌体基因组被公布，需要新的方法将这些基因组置于生态和进化背景下。由于噬菌体经常交换基因，因此通过系统发育方法很难对其进行研究，并且没有单个基因在所有噬菌体中都保守。在这里，我们展示了基因水平网络如何提供噬菌体遗传多样性的高分辨率视图，并为病毒生态学提供了新的视角。我们将分析重点放在病毒宿主范围上，并展示网络拓扑结构如何对应宿主亲缘关系，如何找到具有最强宿主特异性特征的基因群，以及这种观点如何补充噬菌体宿主预测工具。我们讨论了将基因网络分析扩展到预测噬菌体在新宿主上的出现，以及将其应用于宿主范围以外的噬菌体生物学特征。噬菌体（phages）是感染细菌的病毒，它们是细菌进化和群落结构的关键驱动因素。通常很难通过基于树的方法来研究噬菌体，因为基因交换很常见，并且没有单个基因在所有噬菌体中共享。相反，网络提供了一种比较噬菌体的方法，同时将它们置于更广泛的生态和进化背景下。在这项工作中，我们构建了一个网络，总结了噬菌体之间基因共享的情况，并测试了噬菌体生态学的一个关键限制因素，即宿主范围，与网络结构的对应关系。我们发现，网络反映了噬菌体宿主之间的亲缘关系，网络中基因越接近的噬菌体更有可能感染相似的宿主。这种方法还可以用于识别影响宿主范围的基因，我们讨论了分析其他病毒生态学方面的可能扩展。
