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ARTICLE
Received 5 Sep 2016 |Accepted 12 May 2017 |Published 5 Jul 2017
Marine viruses discovered via metagenomics shed
light on viral strategies throughout the oceans
Felipe H. Coutinho1,2,3, Cynthia B. Silveira1,4, Gustavo B. Gregoracci5, Cristiane C. Thompson1,
Robert A. Edwards4, Corina P.D. Brussaard6,7, Bas E. Dutilh1,2,3,* & Fabiano L. Thompson1,8,*
Marine viruses are key drivers of host diversity, population dynamics and biogeochemical
cycling and contribute to the daily flux of billions of tons of organic matter. Despite recent
advancements in metagenomics, much of their biodiversity remains uncharacterized. Here we
report a data set of 27,346 marine virome contigs that includes 44 complete genomes. These
outnumber all currently known phage genomes in marine habitats and include members of
previously uncharacterized lineages. We designed a new method for host prediction based on
co-occurrence associations that reveals these viruses infect dominant members of the marine
microbiome such as Prochlorococcus and Pelagibacter. A negative association between host
abundance and the virus-to-host ratio supports the recently proposed Piggyback-the-Winner
model of reduced phage lysis at higher host densities. An analysis of the abundance patterns
of viruses throughout the oceans revealed how marine viral communities adapt to various
seasonal, temperature and photic regimes according to targeted hosts and the diversity of
auxiliary metabolic genes.
DOI: 10.1038/ncomms15955 OPEN
1Instituto de Biologia (IB), Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro 21944970, Brazil. 2Centre for Molecular and Biomolecular
Informatics (CMBI), Radboud Institute for Molecular Life Sciences, Radboud University Medical Centre, Nijmegen 6500 HB, The Netherlands. 3Theoretical
Biology and Bioinformatics, Utrecht University (UU), Utrecht 3584 CH, The Netherlands. 4Biology Department, San Diego State University (SDSU), San
Diego, California 92182, USA. 5Departamento de Cie
ˆncias do Mar, Universidade Federal de Sa˜o Paulo (UNIFESP), Baixada Santista 11070100, Brazil.
6Department of Marine Microbiology and Biogeochemistry, NIOZ Royal Netherlands Institute for Sea Research, and University of Utrecht, PO Box 59, 1790
AB Den Burg Texel, The Netherlands. 7Department of Aquatic Microbiology, Institute for Biodiversity and Ecosystem Dynamics (IBED), University of
Amsterdam, Amsterdam 1090 GE, The Netherlands. 8Universidade Federal do Rio de Janeiro (UFRJ)/COPPE/SAGE, Rio de Janeiro 21941950, Brazil. *These
authors contributed equally to this work. Correspondence and requests for materials should be addressed to F.L.T. (email: fabianothompson1@gmail.com).
NATURE COMMUNICATIONS | 8:15955 | DOI: 10.1038/ncomms15955 | www.nature.com/naturecommunications 1
Marine viruses regulate the community composition of
their microbial hosts by selectively killing them. Viral
lysis mediates the transfer of organic matter between
live biomass and the dissolved organic carbon pool through the
viral shunt1,2. The release of organic matter via the viral shunt is
estimated to be close to 10 billion tons of carbon per day and is
considered a fundamental step in nutrient cycling that fuels the
productivity of the oceans2–5. Associations between the viral and
host abundance have been described by the Kill-the-Winner
theory that postulates that the higher the growth rate of a
microorganism, the more likely it is to be targeted by a lytic viral
infection2,6–9. This trait allows the slow-growing prokaryotes to
reach a higher abundance than the fast growers because they are
subject to fewer lytic infections8,10. The discovery that the
decrease in the virus-to-microbe ratio at a high host abundance
that is not associated with host resistance to infections has
expanded this model11,12: the recently proposed Piggyback-the-
Winner theory of virus–host interactions postulates that at a high
host abundance, viruses favour lysogenic infections and integrate
into the host genome when those are thriving instead of killing
them through a lytic cycle11,13. The influence of viruses on the
marine microbial community is not limited to killing. Viruses
that infect bacteria and archaea, known as phages, can mediate
genetic transduction. Host organisms can acquire viral genetic
material via this mechanism and vice versa. Such an exchange of
DNA may potentially result in new functional genes that are
advantageous to the fitness of the virus or add to the
diversification of the host metabolism2,14,15. Moreover, viruses
may encode auxiliary metabolic genes that can be expressed
during infection to steer central pathways of host metabolism
such as photosynthesis and nutrient acquisition towards
processes that favour the production of new viral particles2,14–18.
Metagenomics has become a powerful tool to characterize the
biological diversity of viral communities in situ, but these studies
often rely on reference databases for read annotation. The lack of
a comprehensive database of marine viral genomes leads to poor
virome (viral metagenome) read annotation19–23. Consequently,
any taxonomic or functional analysis of viromes based on
databases of currently known reference genomes (that are biased
towards cultivable organisms) tends to overlook the majority of
the community. This disadvantage hampers our capacity to
describe and quantify the diversity of viral genomes throughout
the marine ecosystem via metagenomics. Assembling viral reads
de novo to produce sample-specific reference databases has
helped to circumvent this issue24–27. Such a strategy improves
read mapping and often reveals new complete viral genomes or
genome fragments28–30.
We sought to expand the knowledge on the genetic diversity
of marine viruses by discovering new genomes through a
high-throughput culture-independent methodology. To that
end, we assembled reads from 78 previously published marine
viromes. We discovered new viral lineages derived from highly
abundant members of marine viral communities that infect
numerically dominant members of the marine microbiome. We
then characterized the newly discovered viruses in terms of the
diversity of their metabolic genes and predicted which organisms
they would infect by using both new and previously validated
computational host prediction strategies. With that information,
we investigated the distribution profile of these newly discovered
sequences across the oceans to further understand how
environmental conditions together with microbial host
abundances affect the strategies used by marine viruses to exploit
their microbial hosts. Our findings corroborate the recently
proposed Piggyback-the-Winner theory and demonstrate how
viral communities respond to the different seasonal, temperature
and photic regimes across the global ocean.
Results
Novel diversity from the virome assembly. The assembly of 78
marine viromes (Supplementary Table 1) yielded a total of
27,346 marine virome contigs (MVCs) longer than 2.5 kbp
(N50 ¼4,216) that added up to B122 Mbp of sequence data. Of
these, 44 were circular and longer than 20 kbp and putatively
represented complete viral genomes. The remaining contigs were
likely fragments of larger genomes or complete linear genomes.
Virome reads were randomly subsampled before assembly to
allow for longer contigs to be assembled by reducing the genetic
microdiversity. This approach successfully improved the assembly
quality because the longest version of the majority of contigs was
obtained from the subsampled viromes (Supplementary Fig. 1a)
with no reduction in the quality of the assembled contigs
(Supplementary Fig. 1b). Next, relative abundances of reference
viral genomes and MVCs at 121 marine sites (Supplementary
Data 1) were calculated as follows. Reads from the 78 selected
viromes plus 43 Tara oceans viromes26 were aligned to a database
containing the MVCs and the reference viral genomes (that is,
bacterial and archaeal viruses from the National Center for
Biotechnology Information (NCBI) RefSeq database, complete
marine phage genomes obtained from fosmid libraries31 and
prophages identified in bacterial genomes with VirSorter32) for a
total of 32,833 sequences. Among the reads from 121 analysed
marine viromes, 2.2 to 82.5% (average 30.4%, s.d. 17.7%) of them
could be assigned to the MVCs. Moreover, 0.06 to 15% (average
4.1%, s.d. 3.42%) of these reads were assigned to reference viral
genomes, and 10.2 to 96.7% remained unassigned (average 65.7%
s.d. 19.1%). This result provided evidence that the MVCs are
highly abundant members of viral communities that
outnumbered all currently known prokaryote viral genomes
together (Supplementary Fig. 2). The use of the new viral
database built with both MVCs and reference viral genomes
resulted in a median 6.6-fold increase in read mapping, allowing
for up to 82% of virome read annotation. A total of 175,540
proteins were predicted to be encoded by the MVCs, of
which 107,260 (61%) appeared to be novel, as no homologues
were identified when compared with the NCBI non-redundant
(NCBI-nr) database (Supplementary Data 2).
The MVCs and the reference viral genomes were subjected to
neighbour-joining clustering on the basis of their Dice distances
(see Methods). The MVCs were spread throughout the clusters,
suggesting that these newly identified viruses belonged to diverse
phylogenetic groups (Fig. 1). Furthermore, several clusters were
formed exclusively by MVCs with very long branch lengths that
evidenced the low similarity between them and the reference viral
genomes (Supplementary Data 3 and 4). This pattern shows that
these MVCs are the first members of yet uncharacterized
evolutionary viral lineages.
Phage co-occurrence network and host prediction. The
abundances of each pairwise combination of MVCs and reference
viral genomes across samples were correlated with SparCC33 to
infer a co-occurrence network (Fig. 2). All possible pairwise
correlations between the viral genome abundances were assigned
a value between 1 and þ1. We compared the distribution of
the correlation values between the reference viral genomes
according to the genus of the host they infect. Correlation
values with an absolute SparCC score o0.3 were considered too
close to zero for a reliable assessment of their signal and were
excluded from this analysis. Out of 5,108 correlations detected
between viral genomes that shared a host of the same genus, 4,971
of them were positive (B97%), while only 137 (B3%) were
negative (Supplementary Fig. 3). Driven by this observation, we
next evaluated the capacity of abundance correlations to
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms15955
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computationally predict the hosts of the MVCs. The accuracy of
this method was assessed by analysing a subset of the network
composed only of the reference viral genomes. For each reference
viral genome with a known host, we searched for the strongest
positive correlation within the network and measured how
often that correlation pointed to a virus that infected the same
host at the phylum level. This resulted in B57% accuracy if no
correlation score cutoffs were used, that is, any value between 1
and 1 was considered a host prediction, as long as the correlation
was the highest for that genome (the weakest of these correlations
was close to þ0.25). Varying the minimum correlation score
cutoff revealed that the accuracy of the host predictions could be
increased to B87% if only scores above 0.6 were considered,
although at the expense of predicting fewer hosts. This approach
could be applied to host prediction at deeper taxonomic levels
(Supplementary Fig. 4a), but with less accurate results
(Supplementary Fig. 4b). Using the þ0.6 cutoff, we were able
to assign hosts to 1,279 MVCs (Table 1 and Supplementary
Data 5), most of which were predicted to be Cyanophages
that infected Prochlorocccocus or Synechococcus and Pelagiphages,
and some were predicted to infect Flavobacterium and
Puniceispirillum. The majority of the top correlation scores
used to assign the hosts to the MVCs were greater than þ0.6
(Supplementary Fig. 3); therefore, we assumed that they were
accurate at the phylum level.
Correlation network-based host predictions for the MVCs were
complemented by four other computational strategies (Table 1 and
Supplementary Data 5). Homology matches against a database of
bacterial genomes resulted in 268 predictions. The most frequent
host predictions obtained via this approach were Sphyngopyxis
(Alphaproteobacteria), followed by Propionibacterium (Actinobac-
teria) and Synechococcus (Cyanobacteria). Homology matches
against a database of annotated Tara oceans microbial contigs
yielded 1,393 predictions. The most common host predictions were
to unclassified Alphaproteobacteria, followed by Verrucomicrobia,
Bacteroidetes and Actinobacteria. CRISPR (clustered regularly
interspaced short palindromic repeats) spacers mined from
bacterial genomes could be linked to 20 MVCs, the majority of
which were derived from Proteobacteria genomes (most often
from Xanthomonas). Through transfer RNA (tRNA) matches,
Enterobacteria phage HK97
Rhodobacter capsulatus YW1 phage
Escherichia phage ECBP1
Escherichia phage vB EcoM CBA120
Enterobacteria phage T4
Cellulophaga phage phi193
Pseudomonas phage JG044
Synechococcus phage S-SM1
Hydrogenophaga sp. PBC phage
Enterobacteria phage P88
Acinetobacter phage YMC/09/02/B1251 ABA BP
Vibrio phage pYD21-A
Pseudomonas phage MP1412
Mycobacterium phage Contagion
Sulfitobacter phage NYA-2014a
Bacillus phage BCP78
uvMED-CGR-C30B-MedDCM- OCT-S46-C80
⎪
⎪
Pelagibacter phage
uvMED-CGR-C100-MedDCM- OCT-S33-C20
⎪
Pelagiphage
Pelagibacter phage HTVC010P
Roseobacter phage SIO1
Ralstonia phage RSK1
Pelagibacter phage HTVC011P
Enterobacteria phage T7
Escherichia coil KTE53 phage
Escherichia coil HVH121 phage
Figure 1 | Clustering of the MVCs and the reference phage genomes based on the Dice distances. The MVCs (blue) form novel branches with low
similarity to reference phage genomes (red), indicating that they are members of previously unknown lineages of viral diversity. The branch lengths are
ignored to better display the clustering topology. Supplementary Data 3 displays a circular version emphasizing exact branch lengths, and Supplementary
Data 4 is a circular version that also ignores the branch lengths.
NATURE COMMUNICATIONS | DOI: 10.1038/ncomms15955 ARTICLE
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87 MVCs could be assigned to a host, most frequently to genera
that belong to either Proteobacteria or Bacteroidetes. A total of
2,755 MVCs could be assigned to a host by at least one of these five
methods (Table 1).
MVCs are ubiquitous and abundant across the oceans. The
rank abundance curve (Fig. 3a) revealed that although reference
genomes ranked first, MVCs are among the most abundant
members of marine viromes (that is, the top 500). An analysis of
the distribution patterns of MVCs across marine virome samples
according to their predicted hosts revealed that the most
prevalent (detected in 450% of the samples) and abundant
(median relative abundance 40.01%) MVCs were those
predicted to infect Cyanobacteria and Proteobacteria (Fig. 3b and
Supplementary Data 6). This trend was also observed for the
reference viral genomes, as the most abundant and prevalent ones
infected Pelagibacter (Alphaproteobacteria) or Prochlorococcus
and Synechococcus (Cyanobacteria) (Fig. 3c).
Functional content of viruses varies according to the host.We
analysed the functional content of the MVCs and the reference
viral genomes according to their infected hosts (Supplementary
Data 7). The genes involved in purine/pyrimidine metabolism
and nucleic acid biosynthesis were among the most common
traits for all viruses. Differences between the host groups were
commonly found as potential auxiliary metabolic genes and
metabolic or transcriptional regulators. Viruses that infect
Cyanobacteria typically encode proteins involved in photo-
synthesis (that is, photosystem II and plastocyanin), the pentose
phosphate pathway and genes involved in carbon, sugar and
amino acid metabolism. Moreover, transcriptional regulators and
ABC (ATP-binding cassette) transporters are included among the
genes most often identified in the genomes of the viruses that
Host
ab
Acidobacteria
Actinobacteria
Archaea
Bacteroidetes
Chlamydiae
Chloroflexi
Crenarchaeota
Cyanobacteria
Deinococcus-Thermus
Euryarchaeota
Firmicutes
Fusobacteria
Nitrospinae
Nitrospirae
Planctomycetes
Poribacteria
Proteobacteria
Spirochaetes
Synergistetes
Tenericutes
Unknown
Verrucomicrobia
Figure 2 | Viral co-occurrence networks. The large diamonds represent the reference viral genomes colour coded according to the host phylum, and the
small grey diamonds represent the MVCs. The line colours follow a gradient according to SparCC score from blue (0.6) to red (0.9). (a) The network
displaying the strongest correlations with a SparCC score 4þ0.6 between reference phage genomes only. (b) The network displaying the strongest
correlations with a SparCC score 4þ0.6 between MVCs and reference phage genomes.
Table 1 | The number of MVCs assigned to each host taxa according to the five host prediction methods.
RefSeq homology Tara homology CRISPR tRNA Network
Unclassified Proteobacteria 0 868 0 0 0
Prochlorococcus 10 0 0 0 575
Pelagibacter 0 0 0 1 461
Synechococcus 8 2 0 4 146
Sphingopyxis 136 0 0 11 0
Flavobacterium 0820159
Unclassified Verrucomicrobia 0 142 0 0 0
Unclassified Actinobacteria 0 76 0 0 0
Propionibacterium 52 0 0 2 4
Puniceispirillum 030117
Bradyrhizobium 016000
Blastomonas 015000
Unclassified Alphaproteobacteria 0 12 0 0 0
Sphingobium 73020
Acidovorax 10 0 0 1 0
Desulfovibrio 010000
Pseudomonas 32202
Burkholderia 60200
Xanthomonas 10700
Only the top 20 most frequent taxa are shown. Supplementary Data 5 details the host predictions and the scores yielded by each method per MVC.
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms15955
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infect Proteobacteria. These transporters were also commonly
found in the genomes of viruses that infect Firmicutes, but
transcriptional regulators were not as prevalent as in the previous
group. Finally, viruses infecting Actinobacteria or Bacteroidetes
often harboured proteins involved in amino acid metabolism,
while the latter also harboured several proteins involved in sugar
metabolism.
Comparison of global marine viral communities. We applied
nonmetric multidimensional scaling (NMDS) to reveal the clus-
tering patterns of marine viromes based on the abundance of
MVCs and reference viral genomes in each sample. The viromes
were separated into three data sets to avoid potential clustering
resulting from sample preparation biases34. The Pacific Ocean
viromes (POVs) that were retrieved from a broad depth gradient
across three sites in the Pacific were separated between photic and
aphotic zone samples by NMDS1 (Fig. 4a). Tara oceans viromes,
a data set of photic zone samples obtained across the global
oceans, did not cluster according to geographical location.
Therefore, the NMDS axis values were correlated with the
environmental parameters measured at the sampling sites.
Temperature yielded the strongest Spearman’s correlation
coefficient (0.89) to NMDS1, followed by Prochlorococcus cell
abundance (0.63). Thus, the Tara oceans viromes were separated
by NMDS1 into two major groups according to water
temperature (Fig. 4b). Finally, the Abrolhos samples from warm
water coral reef environments of the photic zone were separated
between summer and winter viromes (Fig. 4c).
Shifts in viral communities with environmental conditions.
The abundance profiles of the marine viromes were used to
identify viruses whose abundance differed significantly between
the sample groups identified through NMDS. The viromes were
divided into three group pairs: POV Aphotic (4500 m deep)
POV Photic (o105 m deep); Tara Cold (o23.3 °C) Tara
Warm (423.3 °C); and Abrolhos Summer Abrolhos Winter.
Supplementary Table 2 lists the groups to which each sample was
assigned. The abundance of each MVC and the reference viral
genome between the sample groups was compared using the
Mann–Whitney test, followed by correction for multiple testing
0.0025
a
0.0020
0.0015
0.0010
0.0005
0.0000
0
Median relative abundance
100 200 300
Rank
400 500
MVC
Reference
bc
1e–02 1e–02
1e–04
1e–06
0.00
Local median abundance
1e–04
1e–06
Local median abundance
0.25 0.50
Prevalence in samples
0.75 0.00 0.25 0.50
Prevalence in samples
0.75
Host
Host
Acidobacteria
Actinobacteria
Actinobacteria
Archaea
Bacteroidetes
Chlamydiae
Chloroflexi
Crenarchaeota
Cyanobacteria
Deinococcus-Thermus
Euryarchaeota
Firmicutes
Fusobacteria
Nitrospinae
Planctomycetes
Poribacteria
Proteobacteria
Spirochaetes
Synergistetes
Tenericutes
Unknown
Verrucomicrobia
Bacteroidetes
Chlamydiae
Chloroflexi
Cyanobacteria
Firmicutes
Nitrospirae
Planctomycetes
Proteobacteria
Tenericutes
Unknown
Verrucomicrobia
Figure 3 | The abundance patterns of the MVCs and the reference viral genomes across 121 marine viromes. (a) The rank abundance curve of the top
500 most abundant reference viral genomes and MVCs. (b) The xaxis shows the prevalence (percentage of samples in which an MVC was detected),
while the yaxis shows the median relative abundance of such MVCs across the 121 marine virome samples analysed. MVCs are colour coded according to
their predicted host phylum. (c) The same as in bbut displaying the prevalence and median relative abundance of the reference phage genomes, colour
coded according to the phylum of their hosts. Supplementary Data 6 displays the average abundance and prevalence of all MVCs and reference phage
genomes across the 121 samples.
NATURE COMMUNICATIONS | DOI: 10.1038/ncomms15955 ARTICLE
NATURE COMMUNICATIONS | 8:15955 | DOI: 10.1038/ncomms15955 | www.nature.com/naturecommunications 5
via the false discovery rate35. Significant changes in abundance
(that is, a corrected Pvalue of o0.05) in at least one of the sample
groups were detected for a total of 7,614 MVCs and reference
viral genomes (Supplementary Data 8).
Mann–Whitney tests revealed that the POV Photic zone had
significantly higher abundances of MVCs predicted to infect
Cyanobacteria (a total of 155 MVCs most often predicted to
infect Prochlorococcus or Synechococcus were enriched in
these samples) or Proteobacteria (219, including Pelagibacter,
Puniceispirillum and many unclassified members of this phylum).
Meanwhile, the POVs from aphotic zone samples had
significantly higher abundances of MVCs predicted to infect
Proteobacteria (13) or Actinobacteria (7) such as Vibrio and
Propionibacterium. The Tara viromes obtained from warm water
sites had significantly higher abundances of MVCs predicted to
infect Cyanobacteria (254 in total, mainly predicted to infect
Prochlorococcus or Synechococcus) or Proteobacteria (57 in total,
predicted to infect mostly unclassified members of this phylum)
and, finally, the most often enriched MVCs from cold water sites
were predicted to infect Proteobacteria (250, mostly unclassified
followed by Pelagibacter,Puniceispirillum) and Bacteroidetes
(27, most often Flavobacterium) (Fig. 5a).
The reference viral genomes corroborated the enrichment
trends observed for the MVCs (Fig. 5b). The reference viral
genomes that targeted Cyanobacteria or Alphaproteobacteria
(for example, Pelagibacter and Puniceispirillum) were enriched in
POVs from the photic zone, while the aphotic zone samples were
enriched for viruses that infected chemoheterotrophic bacteria
such as Propionibacterium and Escherichia. The cyanophages
were the most common reference viral genomes enriched at warm
water Tara viromes. In contrast, Pelagiphages and other viruses
that infect chemoheterotrophic bacteria were enriched at cold
water Tara viromes.
The viromes were also compared according to their functional
profiles, that is, the relative abundances of KEGG (Kyoto
Encyclopedia of Genes and Genomes) orthologues (KOs) in each
sample. A total of 297 KOs present in the MVCs or the reference
viral genomes showed significant (that is, a corrected Pvalue of
o0.05) differences in abundance between the sample groups
tested (Supplementary Data 9). When compared with their photic
counterparts, the POVs from the aphotic zone samples were
characterized by the enrichment of KOs including those involved
in nucleic acid metabolism pathways (for example, purine and
pyrimidine metabolism and DNA replication) and ABC
transporters. Moreover, a comparison of cold water against warm
water Tara viromes revealed that the latter were characterized by
the enrichment of KOs including those involved in carbon
metabolism, photosynthesis, lipopolysaccharide biosynthesis and
the pentose phosphate pathway (Fig. 5c).
The virus/host ratio and host abundance correlate negatively.
We compared the relative abundance of the viral genomes with
that of their microbial hosts in paired viromes and metagenomes
from the Tara oceans data set. The virus/host ratio (VHR, defined
as the viral genome abundance divided by the host genome
abundance) was negatively correlated with the host abundance at
the levels of genus (Fig. 6a and Supplementary Table 3, reference
viruses only) or phylum (Fig. 6b and Supplementary Table 4,
reference viruses and MVCs with host prediction).
Discussion
The MVCs included novel viral genomes and genome fragments.
These sequences were divergent from previously known viral
genomes as evidenced by their very long branch lengths
(Supplementary Data 4). This result draws attention to the major
gap in our knowledge regarding the diversity of marine viruses. In
this study, we closed that gap by discovering new marine viruses
without the use of culture- and isolation-based approaches to
directly obtain complete viral genomes from marine viromes. The
discovery of the MVCs and other viruses via metagenomics
helps to characterize new viral lineages that were overlooked
by culture-dependent methods29,31,36,37. These new genomes will
improve our understanding of the processes of viral
diversification and evolution. Additionally, including the MVCs
in the reference database allowed for a more comprehensive
characterization of marine viral communities via metagenomics.
A co-occurrence network analysis was applied to investigate
the associations between microorganisms. When organisms use
the same resources and respond similarly to environmental
factors, their abundances are expected to be positively
correlated38–40. Viruses depend on a host to successfully
replicate. Therefore, the virus and host abundance across spatial
and temporal gradients are generally associated12,41–44. Viruses
that target the same organism compete for a host when present
at the same site simultaneously. Positive correlations were
dominant among viruses that targeted hosts of the same genus
(Supplementary Fig. 3). The observed strong positive correlation
trend between competitors allows co-occurrence networks to be
used as a new host prediction method. Negative correlations
0.4
abc
0.4
0.6
0.4
0.2
–0.2
0.0
0.2
Zone
Aphotic
Photic
30
Temperature
Season
Summe
r
Winter
20
10
0
0.0
–0.2
0.2
0.0
–0.2
–0.4 –0.2 0.0 0.2 –0.4 –0.2 0.0 0.2 –0.4 –0.2 0.0
NMDS1
NMDS1
NMDS1
NMDS2
NMDS2
NMDS2
0.2
Figure 4 | Virome nonmetric multidimensional scaling. The Manhattan distances were calculated based on the viral genome relative abundances and
used as the input for a NMDS analysis. (a) POVs from photic (light blue) and aphotic (dark blue) zones. (b) Tara oceans viromes from warm (light green)
and cold (dark green) waters. (c) Abrolhos viromes from summer (light red) and winter (dark red) seasons.
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms15955
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between viruses that shared the same host were also detected
(Supplementary Fig. 3). Because this type of association was very
rare within the network, they were not used for host prediction
but they could have resulted from the competitive exclusion
between viruses that shared the same host and thus also have
potential to be used for host prediction. Co-occurrence between
viral and bacterial abundance has been suggested as a host
prediction method, but with a low predictive capacity45. To the
best of our knowledge, this is the first time that virus–virus
abundance associations were used for host affiliation. The method
performed well for host prediction from the phylum to the genus
level (Supplementary Fig. 4) and yielded nearly 50% of all of our
host predictions (Table 1). Furthermore, this approach was not
dependent on the detection of exchanges of genetic material
between viruses and their hosts as required by homology matches
and CRISPR.
An analysis of paired viral and microbial Tara oceans
metagenomes24,26 indicated a reduction in the VHR towards
higher host abundances (Fig. 6). Assuming an increase in
sequence abundance proportional to the cell and viral particles
abundance in the environment, we predict a decrease in the
specific host/virus pairs ratio with an increased host abundance.
200
a
c
b
30
Group
Photic
Aphotic
Warm
Cold
Photic
Aphotic
Warm
Cold
Group
20
10
0
Host
100
No. of enriched MVCs
No. of enriched phages
0
10.0
7.5
Enriched KOs
5.0
2.5
0.0
Proteobacteria
Purine metabolism
Pyrimidine metabolism
Amino sugar and nucleotide sugar metabolism
DNA replication
Mismatch repair
Carbon metabolism
ABC transporters
Phosphotransferase system (PTS)
Porphyrin and chlorophyll metabolism
Nucleotide excision repair
Glycine, serine and threonine metabolism
Fructose and mannose metabolism
Folate biosynthesis
RNA polymerase
Lipopolysaccharide biosynthesis
Glycolysis / Gluconeogenesis
Epstein–Barr virus infection
Base excision repair
Two-component system
Huntington’s disease
Homologus recombination
Starch and sucrose metabolism
RNA degradation
Photosynthesis
Glutathione metabolism
Galactose metabolism
Viral carcinogenesis
Pentose phosphate pathway
Pentose and glucuronate interconversions
One carbon pool by folate
Cyanobacteria
Propionibacterium
Pelagibacter
Escherichia
Bacillus
Clostridium
Acinetobacter
Staphylococcus
Salmonella
Pseudomonas
Enterobacteria
Vibrio
Streptococcus
Mycobacterium
Enterococcus
Verrucomicrobia
Leptospira
Lactobacillus
Klebsiella
Enterobacter
Bartonella
Xanthomonas
Serratia
Puniceispirillum
Pseudoalteromonas
Burkholderia
Alphaproteobacteria
Sulfolobus
Stenotrophomonas
Shigella
Cyanobacteria
Verrucomicrobia
Bacteroidetes
MVC predicted host
Pathwa
y
Actinobacteria
Firmicutes
Planctomycetes
Figure 5 | Variables displaying significant changes in abundance across sample groupings. The bar lengths (yaxis) are proportional to the number of
variables in a given category (xaxis) enriched in each of the tested sample groupings (that is, photic, aphotic, warm and cold) as determined by the Mann–
Whitney test (corrected Pvalue o0.05). (a) Enriched MVCs grouped according to the predicted host phylum. (b) Enriched reference viral genomes
grouped according to the known host genus. Cyanobacteria refers to viruses that infect Prochlorococcus and Synechococcus.(c) Enriched KOs grouped
according to the metabolic pathways to which they belong.
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This pattern corroborates the decrease in VHR with an increase
in microbial abundance described by the Piggyback-the-Winner
model and hypothesizes lysogeny as a more successful strategy
for viral replication at a high host density11. The negative
relationship between the host and viral abundance emerged
consistently in the majority of the ecosystems studied11,12, and
habitats with increased prokaryotic abundance were also enriched
for markers of lysogenic infection (for example, integrases or
excisionases)11. Our data corroborated the Piggyback-the-Winner
model by using a completely independent data set
and demonstrated the ubiquity of this trend for nearly all
the detected taxa of microorganisms (Supplementary Tables 3
and 4).
The pattern observed could be explained by a model in which
the viruses opt for a lysogenic infection strategy when their
microbial hosts are thriving (that is, at high abundance). Recent
findings showed that prophages are widespread in prokaryote
genomes, including those taxa that are dominant across marine
habitats (for example, Cyanobacteria, Proteobacteria, Firmicutes,
Bacteroidetes and Actinobacteria)32 and that fast-growing
bacteria are more likely to harbour prophages integrated into
their genomes46,47. Finally, the observed reduction in the ratio
between bacterial cells and viral particles at increased microbial
abundances was consistently reported across marine
ecosystems11,12. At high host densities, rather than killing their
hosts, viruses might opt to replicate integrated into their host
genomes. According to this model, whenever conditions change
and host growth is no longer favoured, the virus goes into a lytic
cycle to ensure the production of new viral particles before the
death of the host makes viral replication impossible. A total of
134 MVC proteins were annotated as integrases or excisionases
(Supplementary Data 2), providing further evidence for the
capacity of lysogenic infections among the MVCs.
Other factors can act in association with lysogenic switching
and result in the observed trend of decrease in the VHR
accompanied by an increase in microbial abundance. Although
our previous analysis detected no association between resistance
mechanisms (for example, CRISPRs) and microbial abundance11,
the dissemination of resistant strains might contribute to the
aforementioned trend. This might be the case especially for some
slow-growing marine bacteria whose genomes do not encode
prophages (for example, Pelagibacter,Puniceispirillum and
Synechococcus32). This is not proof that lysogenic viruses do
not infect these organisms, but it does suggest that for some taxa,
the negative association between VHR and host abundance might
be driven by both lysogenic switching and resistance to viral
infection.
Use of the MVCs together with reference phage genomes
allowed us to identify differences in the genomic composition of
viruses according to their infected hosts (Supplementary Data 7).
We also identified significant differences in the viral community
taxonomic and functional composition across environmental
gradients, namely photic/aphotic and warm/cold habitats (Fig. 5).
Taken together, these results clarify how the viral community
composition adapts according to the host community composition
to better exploit the host communities. The marked shift in the
community composition among these habitats was also observed in
our NMDS analysis of microbial metagenomes (cellular fraction)
across depth and temperature gradients (Supplementary Fig. 5).
Furthermore, the viruses and their hosts displayed consistent
enrichment patterns (including dominant marine taxa such as
Pelagibacter,Prochlorococcus and Synechococcus) when comparing
photic/aphotic and warm/cold samples (Supplementary Data 8
and 10). Considering these results together with the viral
dependence on the host metabolism for replication, we concluded
that the differences we identified in the viral community
composition were derived from the modulation of the metabolism
and growth rates of the microbial hosts as by environmental
conditions. Thus, the viral communities were indirectly affected by
the photic/aphotic and warm/cold water regimes48. We could not
determine the individual effect of each of the many environmental
parameters (for example, temperature, nutrients, microbial growth
rates and so on) that characterize these habitats on the modulation
of the viral and microbial community composition. Therefore, we
assumed that the observed shifts in the microbial and viral
communities were a result of their combined effects. Interestingly,
0
ab
–2
–4
–2
Log10(VHR)
0
–2
–1
–4
–3
Log10(VHR)
–1 0
Log10(microbial abundance)
1–10
Log10(microbial abundance)
1
Acholeplasma
Actinobacteria
Bacteroidetes
Chlamydiae
Chloroflexi
Crenarchaeota
Cyanobacteria
Deinococcus-Thermus
Euryarchaeota
Firmicutes
Fusobacteria
Nitrospirae
Planctomycetes
Proteobacteria
Spirochaetes
Synergistetes
Tenericutes
Atopobium
Bacillus
Bifidobacterium
Brachyspira
Burkholderia
Candidatus Pelagibacter
Candidatus Puniceispirillum
Cellulophaga
Clostridium
Coprococcus
Corynebacterium
Croceibacter
Desulfovibrio
Exiguobacterium
Flavobacterium
Fusobacterium
Haloferax
Haloquadratum
Lactobacillus
Leptospira
Leptotrichia
Leuconostoc
Magnetospirillum
Marinomonas
Methanobacterium
Mycoplasma
Myxococcus
Neisseria
Olsenella
Porphyromonas
Prevotella
Prochlorococcus
Rhodobacter
Rhodopirellula
Ruegeria
Selenomonas
Shewanella
Spirochaeta
Spiroplasma
Staphylococcus
Streptococcus
Sulfolobus
Synechococcus
Thermus
Treponema
Vibrio
Xylella
Figure 6 | Associations between the microbial host abundance and the virus–host ratio. The xaxis displays the abundances of microbial taxa and the y
axis displays VHR, calculated based on the relative abundances of microbial taxa and the viruses that infect them in the analysed Tara oceans microbial
metagenomes and viromes. (a) Microbial taxa are summed at the taxonomic levels of genus and VHR was calculated using the abundances of reference
viral genomes only. (b) Microbial abundances are summed at the taxonomic level of phylum and VHR was calculated using the abundances of both
reference viral genomes and the MVCs for which a putative host was identified.
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light emerged as a major factor that regulated the viral community
composition that could be linked not only to the differences
between the photic and aphotic habitats but also to the distinction
between the warm/cold and the summer/winter samples because
the water temperature is influenced by the degree of solar
irradiance that in turn oscillates between the seasons.
Cyanophages and Pelagiphages were found to be enriched in
photic zone viromes, while phages infecting chemoheterotrophic
bacteria (for example, Vibrio and Propionibacterium)were
enriched in aphotic zone viromes (Fig. 5a,b and Supplementary
Data 8). The abundance of organisms that rely on light-dependent
mechanisms for energy acquisition such as Cyanobacteria
and Pelagibacter was smaller in aphotic regions dominated by
chemoheterotrophic bacteria38,49–51.Thisshiftinthecomposition
of host bacterial community explains the enrichment patterns
observed for the viral fraction. In the deep ocean, light becomes
unavailable, and temperature, organic carbon availability and
primary productivity decrease, leading to lower bacterial growth
rates51,52. Those conditions likely favour viral communities that
encode auxiliary metabolic genes that modulate bacterial
metabolism towards pathways that facilitate viral replication
under conditions that tend to slow down microbial metabolism.
For example, the aphotic zone samples were enriched for several
KOs associated with ABC transporters and nucleotide synthesis
(Fig. 5c). These genes might be used in mechanisms by which viral
communities enhance bacterial nutrient uptake and nucleotide
synthesis rates to ensure the availability of building blocks required
for the synthesis of new viral particles under nutrient-deprived
conditions14,18 (Fig. 7).
Warm water samples were enriched in viruses that infected
Prochlorococcus and Synechococcus, while those that infected
Pelagibacter,Puniceispirillum,Flavobacterium and other hetero-
trophic bacteria were typically enriched in cold water habitats
(Fig. 5a,b and Supplementary Data 8). The increase in the
abundance of Cyanobacteria driven by higher temperatures
explains the enrichment of Cyanophages in warm waters26,38,53,54.
These samples were also enriched in many KOs involved in
photosynthesis, carbon metabolism and the pentose phosphate
pathway (Fig. 5c), suggesting that viral communities from
warm waters with a higher abundance of Cyanobacteria exploit
the photosynthetic microbial community by modulating
photosynthesis and carbon fixation towards pathways that favour
the synthesis of viral particles15,17. Moreover, in cold water, the
viruses tend to rely more on infecting nonphotosynthetic
organisms and modulating their heterotrophic metabolism (Fig. 7).
Metagenomics-based studies have previously investigated shifts
in the viral community composition driven by environmental
parameters, but did so through annotation independent (k-mer
based) or protein cluster-based analyses14,55,56.Usingour
improved database for virome annotation that includes the highly
abundant MVCs allowed us to corroborate and expand these
results. Unlike k-mers or protein clusters, MVCs carry associated
information regarding their sampling source, host and the
complete or partial genomes of the viruses from which they are
derived. This allows for a more comprehensive understanding of
the differences in the community composition of the sample groups
tested that in turn could be linked to the environmental conditions.
In conclusion, we have described and analysed over 27,000
MVCs, a unique data set of complete and partial marine viral
genomes derived from highly abundant members of global
marine viromes. Many of these viruses belong to completely novel
lineages. Computational host prediction, including a new accurate
approach based on viral co-abundance correlations, suggests that
most MVCs infect dominant marine bacteria including Cyano-
bacteria and Proteobacteria. We showed that for practically all
taxonomic groups, a negative association was present between the
host relative abundance and VHR, suggesting that more lysogeny
and possibly resistance occurred at higher relative host densities
and was a widespread trend among marine viruses and their
hosts. Finally, the global distribution of the MVCs revealed how
marine viral communities adapt their composition and diversity
of auxiliary metabolic genes to exploit their microbial hosts
Warm (photic)
Temperature Cold (aphotic)
Cold (photic)
Depth
Cyanophage
AMGS:
Pelagiphage
Photosynthesis/CCM/PPP
Nucleic acid metabolism
Nutrient transporters
Heterotrophic bacteria phage
Cyanobacteria
Pelagibacter
Lytic infection
Lysogenic infection
Heterotrophic bacteria
Light
Figure 7 | Conceptual model depicting viral strategies for exploiting the marine microbiome. In the warm waters of the photic zone, Cyanophages would
be enriched and display a preference for lysogenic infections. Under these same conditions, Pelagiphages and viruses infecting heterotrophic bacteria would
be depleted and prefer lytic infections. In the cold waters of the photic zone, the opposite pattern would occur: Cyanophages depleted and lytic, and
Pelagiphages and viruses infecting heterotrophic bacteria would be enriched and lysogenic infections. In the cold waters of the aphotic zone, both
Cyanophages and Pelagiphages would be depleted and lytic, while viruses infecting heterotrophic bacteria would be enriched and lysogenic. Throughout
these gradients, these viruses carry different types of auxiliary metabolic genes that help them to exploit host metabolism during infection.
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according to changes in depth, temperature and season. The
findings presented here, together with recent discoveries made on
the ecology of marine viruses based on metagenomics13,31,55–59,
shed light on the poorly explored marine viral diversity and bring
us closer to understanding the role of viruses in the function of
marine ecosystems.
Methods
Virome samples and assembly.A total of 78 previously published and
quality-controlled marine viromes (that is, post read trimming and filtered for
low-quality sequences and potential contaminants) were selected from Metavir60 in
March 2015. These viromes were obtained from marine habitats, including photic
and aphotic regions of coastal and open ocean regions, oxygen minimum zones,
coral reef systems and coral holobionts. Supplementary Table 1 describes these
viromes in terms of the number of sequences, the average sequence length and their
original publication. Virome assemblies were performed via a random subsampling
approach aimed at obtaining longer contigs by reducing the microdiversity within
the samples. Large amounts of sequencing errors or microdiversity can lead to
fragmented assemblies61,62. An analysis of the effects of the coverage depth on the
virome assembly quality revealed that viral genomes can often be oversequenced,
that is, the coverage is extremely high but so are the errors, leading to fragmented
assemblies, a phenomenon that can be avoided by using a smaller data set that has
fewer sequences but also fewer errors, consequently improving the assembly
quality61. Subsampling was expected to facilitate the assembly of sequences derived
from the most abundant members of the community at the expense of increasing
the difficulty of the assembly of the less abundant sequences. Therefore, each
member of the community should have an optimum number of reads for the best
assembly with maximum coverage and minimum error. Our assembly strategy was
designed to achieve an optimum range of reads for as many sequences as possible.
We aimed to obtain the best assemblies possible (through the use of different
subsample sizes) while avoiding the loss of diversity due to random subsampling by
repeating several assemblies for each subset. Our strategy was based on the random
selection of a subset of the reads from each sample (ranging from 1 to 100%) and
then assembling these subsets individually. Viromes containing o100,000 reads
were subsampled to 25% of the reads (repeated 20 times), 50% (10 ), 75% (10 )
and 100% (1 ). Viromes containing 100,000 to 1,000,000 reads were subsampled
to 10% (50 ), 25% (25 ), 50% (25 ), 75% (20 ) and 100% (1 ). Viromes
containing 41,000,000 reads were subsampled to 1% (75 ), 5% (50 ), 10%
(50 ), 25% (25 ), 75% (25 ) and 100% (1 ) of the data. In addition, four
cross-assemblies were performed that merged all of the reads from samples of the
Pacific Ocean Viromes, Abrolhos coral reefs, oxygen minimum zones and Indian
Ocean data sets. These merged data sets were subsampled and reassembled using
the same strategy described above according to the number of reads in each.
The assemblies were performed by IDBA_UD63 using the default parameters and
pre-correction. Contigs derived from all of the assemblies were combined, and
those o2,500 bp were re moved. BLASTn was used to dereplicate the contigs, using
an identity cutoff of 95% and a minimum alignment coverage of 40% of the shorter
sequence. The resulting database of non-redundant Marine Virome Contigs is
available at http://www.ebi.ac.uk/ena/data/view/PRJEB19352. Coding DNA
sequences were identified with Prodigal64 within Prokka65. Protein sequences were
queried against the NCBI NCBI-nr database for annotation using Diamond66,
setting a maximum e-value of 10 5and a minimum identity of 40%.
Genome comparisons.We focused our analysis on bacterial and archaeal viruses
(phages) because they are the numerically dominant members of marine viral
communities26. A database of known phage genomes was built by merging the
MVCs with a set of reference viral genomes obtained from three sources: (1) the
NCBI RefSeq database (1,609 sequences); (2) the complete marine phage genomes
obtained from fosmid libraries (208)31 and (3) prophages identified in bacterial
genomes with VirSorter (12,498)32. The database was made non-redundant by
clustering the genomes with BLASTn with a 95% identity and a 40% coverage
cutoff, resulting in a non-redundant data set of 32,833 sequences. Next, the Dice
coefficient score was used to estimate the distances between the MVCs longer than
20 kbp and the reference viral genomes to organize them into a phylogenomic
framework31. This approach was selected because it allowed for the degree of
similarity between phage genomes to be estimated without the need for multiple
alignments or the clustering of sequences into homologue groups or the use of
universal marker genes, all of which are major disadvantages for the unbiased
investigation of viral phylogeny67. Only reference viruses that had at least one
detectable homologue to MVCs as determined by tBLASTx68 searches were used
for this analysis. The Dice distance calculation was based on an all-versus-all
tBLASTx search between the viral genomes. Any hits that either scored o30%
identity, were shorter than 30 amino acids or had an e-value 40.01 were ignored.
The distances between the viral genomes or MVCs were measured as
D
A,B
¼1(2 AB/AA þBB), where AB is the summed bitscore of all hits of
genome A against genome B. AA and BB represent the summed bitscore of all hits
of genomes A and B against themselves. The obtained distance matrix was used to
cluster the genomes via neighbour joining by the BIONJ69 algorithm, and
visualized in iTOL (Interactive Tree Of Life)70.
Abundance profiles.A matrix of abundances of all of the MVCs at 121 marine
sites was calculated as follows. Reads from the 78 selected viromes plus 43 Tara
oceans viromes26 were mapped against the database of viral genomes using
Bowtie2 (ref. 71). The very-sensitive alignment option was used along with read
end trimming and multiple matching to maximize the read mapping. Ambiguous
reads that were mapped to similar regions of different genomes were counted using
a weighted score based on the ratios of the unambiguous reads assigned to each
genome as previously described72.
Network inference.An abundance matrix was used to infer correlations between
viral genome abundances across samples. The SparCC method was applied to avoid
spurious correlations that emerged from the sparse and compositional nature of the
data33. Any MVC or reference genome detected in o40% of samples was excluded
from this analysis because these have been shown to lead to spurious correlations
due to sparse counts73. SparCC was run with 10 inference and 10 exclusion
iterations. The resulting network of correlations was visualized with Cytoscape74.
Host predictions.We used multiple computational host prediction strategies to
identify potential microbial hosts infected by the MVCs45. (1) Homology matches
against bacterial and archaeal genomes: the MVCs were queried against a database
of microbial genomes obtained from NCBI through BLASTn. Only the best hits
above 80% identity across an alignment of at least 1,000 nucleotides were
considered. (2) The aforementioned database of bacterial genomes is biased
towards cultured organisms that do not necessarily represent the diversity of
prokaryotes abundant in the oceans. To circumvent this issue, we also performed
homology matches of the MVCs against the Tara oceans contigs obtained from
http://www.ebi.ac.uk/ena/about/tara-oceans-assemblies24. This data set is a large
catalogue of marine microbial sequences that, similar to our MVCs, were obtained
via culture-independent methods and from several regions of the global oceans.
First, the Tara oceans contigs were taxonomically annotated by predicting protein
sequences by Prodigal and querying them against the NCBI-nr database using
Diamond. Only the best hits of each protein with an e-value o10e 5and an
identity 430% were considered. Next, the sum of the bitscore of all hits from each
contig was calculated, and the contigs for which the total bitscore was below 1,000
were disregarded. A hierarchical classification of the remaining contigs was
performed from domain to species if 80% or more of the total bitscore was
consistently assigned to the same taxon. The contigs unclassified at the domain
level or classified as viral or eukaryotic were excluded. (3) CRISPR spacers within
the microbial genomes were identified using CRISPR Detect v.1. Those spacers
were queried against the MVCs using the BLASTn parameters described in ref. 75.
Because CRISPR spacers are very short sequences (B20–30 nucleotides), a
maximum of two mismatches/gaps was allowed to minimize the chances of
erroneous host assignments due to spurious matches. (4) tRNA matches:
transporter RNAs identified in MVCs were queried against a database of bacterial
genomes using BLASTn and only the best hits with a minimum of 90% identity
and 90% coverage were considered. (5) Abundance correlations: we developed a
new strategy for host prediction based on abundance correlations between the
MVCs and the reference phage genomes across the marine viromes. The MVCs
were assigned to a host based on the strongest positive correlation with a reference
viral genome. Only those correlations that fell within an experimentally defined
cutoff (SparCC score Zþ0.6) were considered to maximize the number of
accurate MVC host assignments (see the Results section ‘Phage co-occurrence
network and host prediction’ for further details).
Functional profiles.All proteins encoded by the MVCs and the reference phage
genomes were queried against the OM-RGC database24 via Diamond66 and
annotated according to the KOs to which their best hit was assigned (maximum
e-value of 10 5). Next, the functional profiles (that is, the KO relative abundances)
were determined for each sample by summing up the abundance of each KO
proportionally to the abundance of the genome or the MVC in which it was
encoded. For example, in a sample containing genomes A, B and C with
abundances of 1, 5 and 10, the KO abundance in that sample would be defined as
the sum of KOs encoded in A multiplied by 1, plus those encoded in B multiplied
by 5 and those encoded in C multiplied by 10.
Marine microbial community analysis.We reanalysed the microbial marine
metagenomes first to compare the effects of environmental parameters on the viral
and microbial fractions of the marine ecosystems. Second, we wanted to determine
how the viral abundances were associated with those of the microbial hosts they
infect. To that end, the microbial metagenomes (cellular fraction) that covered a
broad spatial range and gradients of environmental parameters were selected. The
Tara oceans metagenomes24 were analysed to investigate microbial community
composition across a broad spatial gradient. The South Atlantic Ocean (SAO)
metagenomes76 covered both the photic and aphotic zones within this region of the
ocean. The abundance of the bacterial and archaeal genomes in both the Tara and
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SAO metagenomes was modelled based on the nucleotide composition profile
using FOCUS with k-mer size of seven nucleotides77.
Nonmetric multidimensional scaling.Both the virome and microbial metagen-
ome samples were compared on the basis of their taxonomic composition profiles.
The distances between samples were calculated based on the Manhattan method
and used as the input for NMDS. To avoid clustering driven by sampling
preparation biases34, these analyses were performed separately for subsets of
samples that were consistent in terms of their processing methodology: POVs, Tara
oceans and Abrolhos viromes and for Tara and SAO microbial metagenomes.
Variable enrichments.The microbial metagenomes and viromes were grouped
according to their NMDS clustering patterns (Supplementary Table 2). Next, the
relative abundances of each viral genome/MVC, KO or microbial taxon found in
the metagenomes and viromes were compared between sample groups using the
Mann–Whitney test. The Pvalues were corrected for multiple testing via the false
discovery rate35, and differences in abundance that yielded a corrected Pvalue of
o0.05 were considered significant.
Data availability.All sequences assembled from the 78 marine viromes were
deposited at ENA: http://www.ebi.ac.uk/ena/data/view/PRJEB19352.
References
1. Suttle, C. A. Viruses in the sea. Nature 437, 356–361 (2005).
2. Breitbart, M. Marine viruses: truth or dare. Mar. Sci. 4, 425–448 (2012).
3. Brussaard, C. P. D. et al. Global-scale processes with a nanoscale drive: the role
of marine viruses. ISME J. 2, 575–578 (2008).
4. Suttle, C. A. Marine viruses—major players in the global ecosystem. Nat. Rev.
Microbiol. 5, 801–812 (2007).
5. Wilhelm, W. & Suttle, C. A. Viruses and nutrient cycles in the sea. Bioscience
49, 781–788 (1999).
6. Rodriguez-Valera, F. et al. Explaining microbial population genomics through
phage predation. Nat. Rev. Microbiol. 7, 828–836 (2009).
7. Parsons, R. J., Breitbart, M., Lomas, M. W. & Carlson, C. A. Ocean time-series
reveals recurring seasonal patterns of virioplankton dynamics in the
northwestern Sargasso Sea. ISME J. 6, 273–284 (2012).
8. Thingstad, T. F. Elements of a theory for the mechanisms controlling
abundance, diversity, and biogeochemical role of lytic bacterial viruses in
aquatic systems. Limnol. Oceanogr. 45, 1320–1328 (2000).
9. Fuhrman, J. A. & Schwalbach, M. Viral influence on aquatic bacterial
communities. Biol. Bull. 204, 192–195 (2003).
10. Thingstad, T. F. & Lignell, R. Theoretical models for the control of bacterial
growth rate, abundance, diversity and carbon demand. Aquat. Microb. Ecol. 13,
19–27 (1997).
11. Knowles, B. et al. Lytic to temperate switching of viral communities. Nature
531, 466–470 (2016).
12. Wigington, C. H. et al. Re-examination of the relationship between marine
virus and microbial cell abundances. Nat. Microbiol. 1, 15024 (2016).
13. Silveira, C. B. & Rohwer, F. L. Piggyback-the-Winner in host-associated
microbial communities. npj Biofilms Microbiomes 2, 1–5 (2016).
14. Hurwitz, B. L., Hallam, S. J. & Sullivan, M. B. Metabolic reprogramming by
viruses in the sunlit and dark ocean. Genome Biol. 14, R123 (2013).
15. Thompson, L. R. et al. Phage auxiliary metabolic genes and the redirection of
cyanobacterial host carbon metabolism. Proc. Natl Acad. Sci. USA 108,
E757–E764 (2011).
16. Sharon, I. et al. Comparative metagenomics of microbial traits within oceanic
viral communities. ISME J. 5, 1178–1190 (2011).
17. Puxty, R. J. et al. Viruses inhibit CO
2
fixation in the most abundant
phototrophs on earth. Curr. Biol. 26, 1585–1589 (2016).
18. Hurwitz, B. L. & U’Ren, J. M. Viral metabolic reprogramming in marine
ecosystems. Curr. Opin. Microbiol. 31, 161–168 (2016).
19. Cassman, N. et al. Oxygen minimum zones harbour novel viral communities
with low diversity. Env. Microbiol. 14, 3043–3065 (2012).
20. Angly, F. E. et al. The marine viromes of four oceanic regions. PLoS Biol. 4,
e368 (2006).
21. Gregoracci, G. B., Dos Santos Soares, A. C., Miranda, M. D., Coutinho, R. &
Thompson, F. L. Insights into the microbial and viral dynamics of a coastal
downwelling-upwelling transition. PLoS ONE 10, 1–14 (2015).
22. Brum, J. R., Hurwitz, B. L., Schofield, O., Ducklow, H. W. & Sullivan, M. B.
Seasonal time bombs: dominant temperate viruses affect Southern Ocean
microbial dynamics. ISME J. 10, 1–13 (2015).
23. Winter, C., Garcia, J. A. L., Weinbauer, M. G., DuBow, M. S. & Herndl, G. J.
Comparison of deep-water viromes from the Atlantic Ocean and the
Mediterranean Sea. PLoS ONE 9, 1–8 (2014).
24. Sunagawa, S. et al. Structure and function of the global ocean microbiome.
Science 348, 1–10 (2015).
25. Dutilh, B. E. Metagenomic ventures into outer sequence space. Bacteriophage
7081, 3–5 (2014).
26. Brum, J. R. et al. Patterns and ecological drivers of ocean viral communities.
Science 348, 1261498 (2015).
27. Qin, J. et al. A human gut microbial gene catalogue established by metagenomic
sequencing. Nature 464, 59–65 (2010).
28. Reyes, A. et al. Gut DNA viromes of Malawian twins discordant for severe
acute malnutrition. Proc. Natl Acad. Sci. USA 112, 11941–11946 (2015).
29. Dutilh, B. E. et al. A highly abundant bacteriophage discovered in the unknown
sequences of human faecal metagenomes. Nat. Commun. 5, 1–11 (2014).
30. Minot, S. & Bryson, A. Rapid evolution of the human gut virome. Proc. Natl
Acad. Sci. USA 110, 12450–12455 (2013).
31. Mizuno, C. M., Rodriguez-Valera, F., Kimes, N. E. & Ghai, R. Expanding the
marine virosphere using metagenomics. PLoS Genet. 9, e1003987 (2013).
32. Roux, S., Hallam, S. J., Woyke, T. & Sullivan, M. B. Viral dark matter and
virus-host interactions resolved from publicly available microbial genomes.
Elife 4, e08490 (2015).
33. Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey
data. PLoS Comput. Biol. 8, e1002687 (2012).
34. Solonenko, S. A. et al. Sequencing platform and library preparation choices
impact viral metagenomes. BMC Genomics 14, 320 (2013).
35. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical
and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300
(1995).
36. Mokili, J. L., Rohwer, F. & Dutilh, B. E. Metagenomics and future perspectives
in virus discovery. Curr. Opin. Virol. 2, 63–77 (2012).
37. Labonte
´,J.M.et al. Single-cell genomics-based analysis of virus–host
interactions in marine surface bacterioplankton. ISME J. 9, 2386–2399 (2015).
38. Coutinho, F. H. et al. Niche distribution and influence of environmental
parameters in marine microbial communities: a systematic review. PeerJ 3,
e1008 (2015).
39. Faust, K. et al. Microbial co-occurence relationships in the human microbiome.
PLoS Comput. Biol. 8, e1002606 (2012).
40. Gilbert, J. A. et al. The taxonomic and functional diversity of microbes at a
temperate coastal site: a ‘multi-omic’ study of seasonal and diel temporal
variation. PLoS ONE 5, e15545 (2010).
41. Fuller, N. J. et al. Genetic diversity of marine Synechococcus and co-occurring
cyanophage communities: evidence for viral control of phytoplankton. Environ.
Microbiol. 7, 499–508 (2005).
42. Sandaa, R. A. & Larsen, A. Seasonal variations in virus-host populations in
Norwegian coastal waters: focusing on the cyanophage community infecting
marine Synechococcus spp. Appl. Environ. Microbiol. 72, 4610–4618 (2006).
43. Faruque, S. M. et al. Seasonal epidemics of cholera inversely correlate with the
prevalence of environmental cholera phages. Proc. Natl Acad. Sci. USA 102,
1702–1707 (2005).
44. Needham, D. M. et al. Short-term observations of marine bacterial and viral
communities: patterns, connections and resilience. ISME J. 7, 1274–1285
(2013).
45. Edwards, R. A., McNair, K., Faust, K., Raes, J. & Dutilh, B. E. Computational
approaches to predict virus-host relationships. FEMS Microbiol. Rev. 40,
258–272 (2015).
46. Touchon, M., Bernheim, A. & Rocha, E. P. Genetic and life-history traits
associated with the distribution of prophages in bacteria. ISME J. 10,
2744–2754 (2016).
47. Lauro, F. M. et al. The genomic basis of trophic strategy in marine bacteria.
Proc. Natl Acad. Sci. USA 106, 15527–15533 (2009).
48. Mojica, K. D. A. & Brussaard, C. P. D. Factors affecting virus dynamics and
microbial host-virus interactions in marine environments. FEMS Microbiol.
Ecol. 89, 495–515 (2014).
49. Walsh, E. A. et al. Bacterial diversity and community composition from
seasurface to subseafloor. ISME J. 10, 979–989 (2015).
50. Delong, E. F. et al. Community genomics among stratified microbial
assemblages in the ocean’s interior. Science 311, 496–503 (2006).
51. Nunoura, T. et al. Hadal biosphere: insight into the microbial ecosystem
in the deepest ocean on Earth. Proc. Natl Acad. Sci. USA 112, E1230–E1236
(2015).
52. Danovaro, R. et al. Marine viruses and global climate change. FEMS Microbiol.
Rev. 35, 993–1034 (2011).
53. Fu, F.-X., Warner, M. E., Zhang, Y., Feng, Y. & Hutchins, D. A. Effects of
increased temperature and CO
2
on photosynthesis, growth, and elemental
ratios in marine Synechococcus and Prochlorococcus (Cyanobacteria).
J. Phycol. 43, 485–496 (2007).
54. Flombaum, P. et al. Present and future global distributions of the marine
Cyanobacteria Prochlorococcus and Synechococcus.Proc. Natl Acad. Sci. USA
110, 9824–9829 (2013).
55. Hurwitz, B. L., Brum, J. R. & Sullivan, M. B. Depth-stratified functional and
taxonomic niche specialization in the ‘core’ and ‘flexible’ Pacific Ocean Virome.
ISME J. 9, 472–484 (2015).
NATURE COMMUNICATIONS | DOI: 10.1038/ncomms15955 ARTICLE
NATURE COMMUNICATIONS | 8:15955 | DOI: 10.1038/ncomms15955 | www.nature.com/naturecommunications 11
56. Hurwitz, B. L., Westveld, A. H., Brum, J. R. & Sullivan, M. B. Modeling ecological
drivers in marine viral communities using comparative metagenomics and
network analyses. Proc. Natl Acad. Sci. USA 111, 10714–10719 (2014).
57. Rosenwasser, S., Ziv, C., Creveld, S. G., van & Vardi, A. Virocell metabolism:
metabolic innovations during host-virus interactions in the ocean. Trends
Microbiol. 24, 821–832 (2016).
58. Paez-Espino, D. et al. Uncovering Earth’s virome. Nature 536, 425–430 (2016).
59. Roux, S. et al. Ecogenomics and biogeochemical impacts of uncultivated
globally abundant ocean viruses. Nature 537, 589–693 (2016).
60. Roux, S. et al. Metavir: a web server dedicated to virome analysis.
Bioinformatics 27, 3074–3075 (2011).
61. Aguirre de Ca
´rcer, D. et al. Evaluation of viral genome assembly and diversity
estimation in deep metagenomes. BMC Genomics 15, 989 (2014).
62. Nagarajan, N. & Pop, M. Sequence assembly demystified. Nat. Rev. Genet. 14,
157–167 (2013).
63. Peng, Y., Leung, H. C. M., Yiu, S. M. & Chin, F. Y. L. IDBA-UD: a de novo
assembler for single-cell and metagenomic sequencing data with highly uneven
depth. Bioinformatics 28, 1420–1428 (2012).
64. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation
site identification. BMC Bioinformatics 11, 119 (2010).
65. Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30,
2068–2069 (2014).
66. Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using
DIAMOND. Nat. Methods 12, 59–60 (2015).
67. Krupovic, M. et al. Taxonomy of prokaryotic viruses: update from the ICTV
bacterial and archaeal viruses subcommittee. Arch. Virol. 161, 1095–1099 (2016).
68. Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local
alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
69. Gascuel, O. BIONJ: an improved version of the NJ algorithm based on a simple
model of sequence data. Mol. Biol. Evol. 14, 685–695 (1997).
70. Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL): an online tool for
phylogenetic tree display and annotation. Bioinformatics 23, 127–128 (2007).
71. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2.
Nat. Methods 9, 357–359 (2012).
72. Iverson, V. et al. Untangling genomes from metagenomes: revealing an
uncultured class of marine Euryarchaeota. Science 335, 587–590 (2012).
73. Weiss, S. et al. Correlation detection strategies in microbial data sets vary
widely in sensitivity and precision. ISME J. 10, 1669–1681 (2016).
74. Saito, R. et al. A travel guide to Cytoscape plugins. Nat. Methods 9, 1069–1076
(2012).
75. Biswas, A., Gagnon, J. N., Brouns, S. J. J., Fineran, P. C. & Brown, C. M.
CRISPRTarget. RNA Biol. 10, 817–827 (2013).
76. Alves Junior, N. et al. Microbial community diversity and physical-chemical
features of the Southwestern Atlantic Ocean. Arch. Microbiol. 197, 165–179
(2014).
77. Silva, G. G. Z., Cuevas, D. a, Dutilh, B. E. & Edwards, R. A. FOCUS: an
alignment-free model to identify organisms in metagenomes using non-
negative least squares. PeerJ 2, e425 (2014).
Acknowledgements
The authors acknowledge CAPES, CNPq and FAPERJ for funding. F.H.C. was supported
by the Cie
ˆncia sem fronteiras program. B.E.D. was supported by NWO Vidi grant
864.14.004.
Author contributions
F.H.C., C.B.S. and G.B.G. designed the experiments. F.H.C., C.B.S., G.B.G., B.E.D. and
F.L.T. analysed the data. All authors contributed to the writing of the manuscript.
Additional information
Supplementary Information accompanies this paper at http://www.nature.com/
naturecommunications
Competing interests: The authors declare no competing financial interests.
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How to cite this article: Coutinho, F. H. et al. Marine viruses discovered via
metagenomics shed light on viral strategies throughout the oceans. Nat. Commun.
8, 15955 doi: 10.1038/ncomms15955 (2017).
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