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      Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses.

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          Abstract

          Ocean microbes drive biogeochemical cycling on a global scale. However, this cycling is constrained by viruses that affect community composition, metabolic activity, and evolutionary trajectories. Owing to challenges with the sampling and cultivation of viruses, genome-level viral diversity remains poorly described and grossly understudied, with less than 1% of observed surface-ocean viruses known. Here we assemble complete genomes and large genomic fragments from both surface- and deep-ocean viruses sampled during the Tara Oceans and Malaspina research expeditions, and analyse the resulting 'global ocean virome' dataset to present a global map of abundant, double-stranded DNA viruses complete with genomic and ecological contexts. A total of 15,222 epipelagic and mesopelagic viral populations were identified, comprising 867 viral clusters (defined as approximately genus-level groups). This roughly triples the number of known ocean viral populations and doubles the number of candidate bacterial and archaeal virus genera, providing a near-complete sampling of epipelagic communities at both the population and viral-cluster level. We found that 38 of the 867 viral clusters were locally or globally abundant, together accounting for nearly half of the viral populations in any global ocean virome sample. While two-thirds of these clusters represent newly described viruses lacking any cultivated representative, most could be computationally linked to dominant, ecologically relevant microbial hosts. Moreover, we identified 243 viral-encoded auxiliary metabolic genes, of which only 95 were previously known. Deeper analyses of four of these auxiliary metabolic genes (dsrC, soxYZ, P-II (also known as glnB) and amoC) revealed that abundant viruses may directly manipulate sulfur and nitrogen cycling throughout the epipelagic ocean. This viral catalog and functional analyses provide a necessary foundation for the meaningful integration of viruses into ecosystem models where they act as key players in nutrient cycling and trophic networks.

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          Most cited references36

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

          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. DOI: http://dx.doi.org/10.7554/eLife.08490.001
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            Computational approaches to predict bacteriophage–host relationships

            Metagenomics has changed the face of virus discovery by enabling the accurate identification of viral genome sequences without requiring isolation of the viruses. As a result, metagenomic virus discovery leaves the first and most fundamental question about any novel virus unanswered: What host does the virus infect? The diversity of the global virosphere and the volumes of data obtained in metagenomic sequencing projects demand computational tools for virus–host prediction. We focus on bacteriophages (phages, viruses that infect bacteria), the most abundant and diverse group of viruses found in environmental metagenomes. By analyzing 820 phages with annotated hosts, we review and assess the predictive power of in silico phage–host signals. Sequence homology approaches are the most effective at identifying known phage–host pairs. Compositional and abundance-based methods contain significant signal for phage–host classification, providing opportunities for analyzing the unknowns in viral metagenomes. Together, these computational approaches further our knowledge of the interactions between phages and their hosts. Importantly, we find that all reviewed signals significantly link phages to their hosts, illustrating how current knowledge and insights about the interaction mechanisms and ecology of coevolving phages and bacteria can be exploited to predict phage–host relationships, with potential relevance for medical and industrial applications.
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              The Thaumarchaeota: an emerging view of their phylogeny and ecophysiology

              Thaumarchaeota range among the most abundant archaea on Earth. Initially classified as ‘mesophilic Crenarchaeota’, comparative genomics has recently revealed that they form a separate and deep-branching phylum within the Archaea. This novel phylum comprises in 16S rRNA gene trees not only all known archaeal ammonia oxidizers but also several clusters of environmental sequences representing microorganisms with unknown energy metabolism. Ecophysiological studies of ammonia-oxidizing Thaumarchaeota suggest adaptation to low ammonia concentrations and an autotrophic or possibly mixotrophic lifestyle. Extrapolating from the wide substrate range of copper-containing membrane-bound monooxygenases, to which the thaumarchaeal ammonia monooxygenases belong, the use of substrates other than ammonia for generating energy by some members of the Thaumarchaeota seems likely.
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                Author and article information

                Journal
                Nature
                Nature
                Springer Nature
                1476-4687
                0028-0836
                September 29 2016
                : 537
                : 7622
                Affiliations
                [1 ] Department of Microbiology, The Ohio State University, Columbus, Ohio 43210, USA.
                [2 ] Theoretical Biology and Bioinformatics, Utrecht University, 3584 CH Utrecht, The Netherlands.
                [3 ] Centre for Molecular and Biomolecular Informatics, Radboud University Medical Centre, 6525 GA Nijmegen, The Netherlands.
                [4 ] Department of Marine Biology, Federal University of Rio de Janeiro, Rio de Janeiro, CEP 21941-902, Brazil.
                [5 ] Structural and Computational Biology, European Molecular Biology Laboratory, 69117 Heidelberg, Germany.
                [6 ] Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan 48109, USA.
                [7 ] Division of Microbial Ecology, Department of Microbiology and Ecosystem Science, Research Network Chemistry Meets Microbiology, University of Vienna, A-1090 Vienna, Austria.
                [8 ] Austrian Polar Research Institute, A-1090 Vienna, Austria.
                [9 ] Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona 85721, USA.
                [10 ] Department of Marine Biology and Oceanography, Institut de Ciències del Mar (ICM), CSIC Barcelona E0800, Spain.
                [11 ] Institute of Marine Sciences (CNR-ISMAR), National Research Council, 30122 Venezia, Italy.
                [12 ] CEA - Institut de Génomique, GENOSCOPE, 91057 Evry, France.
                [13 ] PANGAEA, Data Publisher for Earth and Environmental Science, University of Bremen, 28359 Bremen, Germany.
                [14 ] MARUM, Bremen University, 28359 Bremen, Germany.
                [15 ] Directors' Research, European Molecular Biology Laboratory, 69117 Heidelberg, Germany.
                [16 ] CNRS, UMR 7144, EPEP, Station Biologique de Roscoff, 29680 Roscoff, France.
                [17 ] Sorbonne Universités, UPMC Université Paris 06, UMR 7144, Station Biologique de Roscoff, 29680 Roscoff, France.
                [18 ] Institut de Biologie de l'École Normale Supérieure, École Normale Supérieure, Paris Sciences et Lettres Research University, CNRS UMR 8197, INSERM U1024, F-75005 Paris, France.
                [19 ] CNRS, UMR 7093, Laboratoire d'océanographie de Villefranche, Observatoire Océanologique, 06230 Villefranche-sur-mer, France.
                [20 ] Sorbonne Universités, UPMC Université Paris 06, UMR 7093, Observatoire Océanologique, 06230 Villefranche-sur-mer, France.
                [21 ] Mediterranean Institute of Advanced Studies, CSIC-UiB, 21-07190 Esporles, Mallorca, Spain.
                [22 ] King Abdullah University of Science and Technology, Red Sea Research Center, Thuwal 23955-6900, Saudi Arabia.
                [23 ] Max-Delbrück-Centre for Molecular Medicine, 13092 Berlin, Germany.
                [24 ] CNRS, UMR 8030, 91057 Evry, France.
                [25 ] Université d'Evry, UMR 8030, 91057 Evry, France.
                [26 ] Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, Ohio 43210, USA.
                Article
                nature19366
                10.1038/nature19366
                27654921
                1a43c300-81d4-4b8f-9880-f51ccc32990a
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