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Biogeography of marine giant viruses reveals their interplay with eukaryotes and ecological functions

Nature Ecology & Evolution volume 4, pages 1639–1649 (2020)Cite this article

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Abstract

Nucleocytoplasmic large DNA viruses (NCLDVs) are ubiquitous in marine environments and infect diverse eukaryotes. However, little is known about their biogeography and ecology in the ocean. By leveraging the Tara Oceans pole-to-pole metagenomic data set, we investigated the distribution of NCLDVs across size fractions, depths and biomes, as well as their associations with eukaryotic communities. Our analyses reveal a heterogeneous distribution of NCLDVs across oceans, and a higher proportion of unique NCLDVs in the polar biomes. The community structures of NCLDV families correlate with specific eukaryotic lineages, including many photosynthetic groups. NCLDV communities are generally distinct between surface and mesopelagic zones, but at some locations they exhibit a high similarity between the two depths. This vertical similarity correlates to surface phytoplankton biomass but not to physical mixing processes, which suggests a potential role of vertical transport in structuring mesopelagic NCLDV communities. These results underscore the importance of the interactions between NCLDVs and eukaryotes in biogeochemical processes in the ocean.

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Fig. 1: Latitudinal patterns in NCLDV community composition.
Fig. 2: Community characteristics of NCLDVs.
Fig. 3: Structural differentiation of NCLDV communities across ecological zones.
Fig. 4: Phylogenetic affiliations of environmental NCLDVs and their dispersal characteristics.
Fig. 5: Associations between NCLDVs and eukaryotic communities.
Fig. 6: Vertical linkage of NCLDV communities between the surface and mesopelagic layers.

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Data availability

The complete sequence data of the OM-RGC.v2 and the abundance profile can be downloaded from https://www.ocean-microbiome.org. All sequences of 18S rRNA gene metabarcoding have been deposited at the European Nucleotide Archive (ENA) under the BioProject IDs PRJEB6610 and PRJEB9737. Environmental metadata are archived at https://doi.org/10.1594/PANGAEA.875582. Files used for recruiting NCLDV PolB genes as well as processed abundance profiles of eukaryotes and NCLDVs with corresponding environmental data are available at the GenomeNet FTP (ftp://ftp.genome.jp/pub/db/community/tara/Biogeography/).

Code availability

Custom scripts developed for this study are available at GitHub (https://github.com/HisashiENDO/NCLDV_Biogeography).

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Acknowledgements

This work was supported by JSPS/KAKENHI (nos. 26430184, 18H02279 and 19H05667 to H.O. and nos. 19K15895 and 19H04263 to H.E.); Scientific Research on Innovative Areas from the Ministry of Education, Culture, Science, Sports and Technology (MEXT) of Japan (numbers 16H06429, 16K21723 and 16H06437 to H.O.); Kyoto University Research Coordination Alliance (funding to H.E.); and the Collaborative Research Program of the Institute for Chemical Research, Kyoto University (numbers 2019–30 and 2020–27). Computational time was provided by the SuperComputer System, Institute for Chemical Research, Kyoto University. We further thank the Tara Oceans consortium, the projects OCEANOMICS (ANR-11-BTBR-0008) and France Génomique (ANR-10-INBS-09), and the people and sponsors who supported Tara Oceans. Tara Oceans (including both the Tara Oceans and Tara Oceans Polar Circle expeditions) would not exist without the leadership of the Tara Expeditions Foundation and the continuous support of 23 institutes (https://oceans.taraexpeditions.org). This article is contribution number 108 of Tara Oceans.

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Authors and Affiliations

  1. Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan

    Hisashi Endo, Romain Blanc-Mathieu, Yanze Li & Hiroyuki Ogata

  2. Laboratoire de Physiologie Cellulaire & Végétale, CEA, CNRS, INRA, IRIG, Université Grenoble Alpes, Grenoble, France

    Romain Blanc-Mathieu

  3. Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, Zürich, Switzerland

    Guillem Salazar & Shinichi Sunagawa

  4. CNRS, UMR 7144, Station Biologique de Roscoff, Roscoff, France

    Nicolas Henry & Colomban de Vargas

  5. Sorbonne Universités, UPMC Université Paris 06, UMR 7144, Station Biologique de Roscoff, Roscoff, France

    Nicolas Henry & Colomban de Vargas

  6. Genoscope, Institut de Biologie François Jacob, CEA, Université Paris-Saclay, Évry, France

    Karine Labadie

  7. Department of Microbiology, The Ohio State University, Columbus, OH, USA

    Matthew B. Sullivan

  8. Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH, USA

    Matthew B. Sullivan

  9. Institut de Biologie de l’ENS, Département de Biologie, École Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France

    Chris Bowler

  10. Research Federation for the study of Global Ocean Systems Ecology and Evolution, FR2022/Tara Oceans, Paris, France

    Chris Bowler & Patrick Wincker

  11. Génomique Métabolique, Genoscope, Institut de Biologie François Jacob, CEA, CNRS, Université Évry, Université Paris-Saclay, Évry, France

    Patrick Wincker

  12. School of Marine Sciences, University of Maine, Orono, ME, USA

    Lee Karp-Boss

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  1. Hisashi Endo
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Contributions

H.E. and H.O. designed the study. H.E. performed most of the bioinformatics analysis. R.B.-M. and Y.L. contributed to the bioinformatics analysis. G.S., N.H., K.L., C.d.V., M.B.S., C.B., P.W., L.K.-B. and S.S. contributed to the generation of primary data. C.d.V., M.B.S., C.B., P.W., L.K.-B., S.S. and H.O. coordinated Tara Oceans. All authors contributed to the writing of the manuscript.

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Correspondence to Hiroyuki Ogata.

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Extended data

Extended Data Fig. 1 Sample-size dependence of the observed NCLDVs.

Sample-based rarefaction curves showing accumulated richness of NCLDV polB genes detected in different a, size fractions, b, depths and c, biomes. Error bars indicate ±1 standard deviation.

Extended Data Fig. 2 Phylogenetic affiliations of environmental NCLDVs.

Phylogenetic tree constructed from 905 long (≥700 amino acid) PolB sequences from the OM-RGC.v2 and 67 known NCLDV sequences. Branches of Mimiviridae and Phycodnaviridae were collapsed in the tree. Known sequences and the environmental gene sequences were colour-coded according to family-level classification.

Extended Data Fig. 3 Phylogenetic affiliations of environmental Phycodnaviridae.

Phylogenetic tree constructed from 905 long (≥700 amino acid) PolB sequences from the OM-RGC.v2 and the 67 known NCLDV sequences. Branches other than Phycodnaviridae were collapsed in the tree. Known sequences and the environmental gene sequences were colour-coded according to family-level classification. Blue branches indicate phylotypes closely related (>90% amino acid identity) to those of NCLDV MAGs having chrysophyte homologues.

Extended Data Fig. 4 Phylogenetic affiliations of environmental Mimiviridae.

Phylogenetic tree constructed from 905 long (≥700 amino acid) PolB sequences from the OM-RGC.v2 and the 67 known NCLDV sequences. Branches other than Mimiviridae were collapsed in the tree. Known sequences and the environmental gene sequences were colour-coded according to the family-level classification. Blue branches indicate phylotypes closely related (>90% amino acid identity) to those of NCLDV MAGs having chrysophyte homologues.

Extended Data Fig. 5 Latitudinal patterns in NCLDV diversity from pole to pole.

Latitudinal variations in richness and Shannon’s diversity index at each depth for a, pico- and b, femto-size fractions (left panels). Box plots are shown (right panels) to summarize variation in richness and Shannon’s index across sampling depths (centre line, median; box limits, 25%–75% quantiles; whiskers, 1.5× interquartile range). Shaded areas represent 90% confidence intervals.

Extended Data Fig. 6 Number and taxonomic composition of genes predicted in NCLDV contigs encoding chrysophyte homologues.

Most of the NCLDV MAGs having chrysophyte homologues were phylogenetically assigned to the Mimiviridae by using PolB genes in the MAGs (Extended Data Fig. 4). The two exceptions, which were closely related to Phycodnaviridae (Extended Data Fig. 3), are shown with an asterisk (*) in the figure.

Extended Data Fig. 7 Vertical linkage of NCLDV communities between the DCM and mesopelagic layers.

a, Latitudinal trend in NCLDV community similarity between two depths (with the station numbers). b-f, NCLDV vertical community similarity is plotted against the surface chlorophyll a biomass (b), NCLDV richness in the mesopelagic layer (c), sampling depth of mesopelagic seawater (d), the mixed layer depth (e) and temperature difference between epipelagic and mesopelagic samples (f). NCLDV data were derived from the pico-size fraction. Shaded areas represent 90% confidence intervals.

Extended Data Fig. 8 Comparisons of relative frequencies in NCLDV phylotypes between the surface and mesopelagic layers.

Station number and the Bray-Curtis similarities between two depths are indicated above each plot. Samples are presented in ascending order of the similarity. Each dot is colour-coded according to family-level classification.

Extended Data Fig. 9 Comparisons of relative frequencies in NCLDV phylotypes between the DCM and mesopelagic layers.

Station number and the Bray-Curtis similarities between two depths are indicated above each plot. Samples are presented in ascending order of the similarity. Each dot is colour-coded according to family-level classification.

Extended Data Fig. 10 Effects of per-sample abundance of total NCLDV gene on the diversity estimates.

Relationships between length-normalized PolB gene abundance of NCLDVs and a, the number of phylotypes, and b, Shannon’s index in the unprocessed (raw) samples. Samples containing >2,000 length-normalized read counts of PolBs were not shown in the figures. Shaded areas represent 90% confidence intervals.

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Endo, H., Blanc-Mathieu, R., Li, Y. et al. Biogeography of marine giant viruses reveals their interplay with eukaryotes and ecological functions. Nat Ecol Evol 4, 1639–1649 (2020). https://doi.org/10.1038/s41559-020-01288-w

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