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从公开的微生物基因组中解析出的病毒暗物质与病毒-宿主相互作用。

Viral dark matter and virus-host interactions resolved from publicly available microbial genomes.

作者信息

Roux Simon, Hallam Steven J, Woyke Tanja, Sullivan Matthew B

机构信息

Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, United States.

Department of Microbiology and Immunology, University of British Columbia, Vancouver, Canada.

出版信息

Elife. 2015 Jul 22;4:e08490. doi: 10.7554/eLife.08490.

Abstract

The ecological importance of viruses is now widely recognized, yet our limited knowledge of viral sequence space and virus-host interactions precludes accurate prediction of their roles and impacts. In this study, we mined publicly available bacterial and archaeal genomic data sets to identify 12,498 high-confidence viral genomes linked to their microbial hosts. These data augment public data sets 10-fold, provide first viral sequences for 13 new bacterial phyla including ecologically abundant phyla, and help taxonomically identify 7-38% of 'unknown' sequence space in viromes. Genome- and network-based classification was largely consistent with accepted viral taxonomy and suggested that (i) 264 new viral genera were identified (doubling known genera) and (ii) cross-taxon genomic recombination is limited. Further analyses provided empirical data on extrachromosomal prophages and coinfection prevalences, as well as evaluation of in silico virus-host linkage predictions. Together these findings illustrate the value of mining viral signal from microbial genomes.

摘要

病毒的生态重要性如今已得到广泛认可,然而,我们对病毒序列空间和病毒-宿主相互作用的了解有限,这使得我们无法准确预测它们的作用和影响。在本研究中,我们挖掘了公开可用的细菌和古菌基因组数据集,以识别与它们的微生物宿主相关的12498个高可信度病毒基因组。这些数据将公共数据集扩充了10倍,为包括生态丰富菌门在内的13个新细菌门提供了首批病毒序列,并有助于从分类学上识别病毒群落中7%-38%的“未知”序列空间。基于基因组和网络的分类在很大程度上与公认的病毒分类法一致,并表明:(i)识别出了264个新病毒属(已知属数量翻倍),(ii)跨分类单元的基因组重组有限。进一步的分析提供了关于染色体外原噬菌体和共感染发生率的经验数据,以及对计算机模拟病毒-宿主连锁预测的评估。这些发现共同说明了从微生物基因组中挖掘病毒信号的价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bc9/4533152/f48d93db2a42/elife08490f001.jpg

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