Productivity and salinity structuring of the microplankton revealed by comparative freshwater metagenomics
Identifieur interne : 000523 ( Pmc/Corpus ); précédent : 000522; suivant : 000524Productivity and salinity structuring of the microplankton revealed by comparative freshwater metagenomics
Auteurs : Alexander Eiler ; Katarzyna Zaremba-Niedzwiedzka ; Manuel Martínez-García ; Katherine D. Mcmahon ; Ramunas Stepanauskas ; Siv G E. Andersson ; Stefan BertilssonSource :
- Environmental Microbiology [ 1462-2912 ] ; 2013.
Abstract
Little is known about the diversity and structuring of freshwater microbial communities beyond the patterns revealed by tracing their distribution in the landscape with common taxonomic markers such as the ribosomal RNA. To address this gap in knowledge, metagenomes from temperate lakes were compared to selected marine metagenomes. Taxonomic analyses of rRNA genes in these freshwater metagenomes confirm the previously reported dominance of a limited subset of uncultured lineages of freshwater bacteria, whereas Archaea were rare. Diversification into marine and freshwater microbial lineages was also reflected in phylogenies of functional genes, and there were also significant differences in functional beta-diversity. The pathways and functions that accounted for these differences are involved in osmoregulation, active transport, carbohydrate and amino acid metabolism. Moreover, predicted genes orthologous to active transporters and recalcitrant organic matter degradation were more common in microbial genomes from oligotrophic versus eutrophic lakes. This comparative metagenomic analysis allowed us to formulate a general hypothesis that oceanic- compared with freshwater-dwelling microorganisms, invest more in metabolism of amino acids and that strategies of carbohydrate metabolism differ significantly between marine and freshwater microbial communities.
Url:
DOI: 10.1111/1462-2920.12301
PubMed: 24118837
PubMed Central: 4253090
Links to Exploration step
PMC:4253090Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Productivity and salinity structuring of the microplankton revealed by comparative
freshwater metagenomics</title><author><name sortKey="Eiler, Alexander" sort="Eiler, Alexander" uniqKey="Eiler A" first="Alexander" last="Eiler">Alexander Eiler</name><affiliation><nlm:aff id="au1"><institution>Department of Ecology and Genetics, Limnology, Uppsala University</institution><addr-line>Uppsala, Sweden</addr-line></nlm:aff></affiliation></author><author><name sortKey="Zaremba Niedzwiedzka, Katarzyna" sort="Zaremba Niedzwiedzka, Katarzyna" uniqKey="Zaremba Niedzwiedzka K" first="Katarzyna" last="Zaremba-Niedzwiedzka">Katarzyna Zaremba-Niedzwiedzka</name><affiliation><nlm:aff id="au2"><institution>Department of Cell and Molecular Biology, Molecular Evolution, Uppsala University</institution><addr-line>Uppsala, Sweden</addr-line></nlm:aff></affiliation></author><author><name sortKey="Martinez Garcia, Manuel" sort="Martinez Garcia, Manuel" uniqKey="Martinez Garcia M" first="Manuel" last="Martínez-García">Manuel Martínez-García</name><affiliation><nlm:aff id="au4"><institution>Single Cell Genomics Center, Bigelow Laboratory for Ocean Sciences</institution><addr-line>East Boothbay, ME, USA</addr-line></nlm:aff></affiliation><affiliation><nlm:aff id="au5"><institution>Department of Fisheries, Genetics and Microbiology, University of Alicante</institution><addr-line>Alicante, Spain</addr-line></nlm:aff></affiliation></author><author><name sortKey="Mcmahon, Katherine D" sort="Mcmahon, Katherine D" uniqKey="Mcmahon K" first="Katherine D" last="Mcmahon">Katherine D. Mcmahon</name><affiliation><nlm:aff id="au3"><institution>Departments of Civil and Environmental Engineering, and Bacteriology, University of Wisconsin-Madison</institution><addr-line>Madison, WI, USA</addr-line></nlm:aff></affiliation></author><author><name sortKey="Stepanauskas, Ramunas" sort="Stepanauskas, Ramunas" uniqKey="Stepanauskas R" first="Ramunas" last="Stepanauskas">Ramunas Stepanauskas</name><affiliation><nlm:aff id="au4"><institution>Single Cell Genomics Center, Bigelow Laboratory for Ocean Sciences</institution><addr-line>East Boothbay, ME, USA</addr-line></nlm:aff></affiliation></author><author><name sortKey="Andersson, Siv G E" sort="Andersson, Siv G E" uniqKey="Andersson S" first="Siv G E" last="Andersson">Siv G E. Andersson</name><affiliation><nlm:aff id="au2"><institution>Department of Cell and Molecular Biology, Molecular Evolution, Uppsala University</institution><addr-line>Uppsala, Sweden</addr-line></nlm:aff></affiliation></author><author><name sortKey="Bertilsson, Stefan" sort="Bertilsson, Stefan" uniqKey="Bertilsson S" first="Stefan" last="Bertilsson">Stefan Bertilsson</name><affiliation><nlm:aff id="au1"><institution>Department of Ecology and Genetics, Limnology, Uppsala University</institution><addr-line>Uppsala, Sweden</addr-line></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">24118837</idno><idno type="pmc">4253090</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4253090</idno><idno type="RBID">PMC:4253090</idno><idno type="doi">10.1111/1462-2920.12301</idno><date when="2013">2013</date><idno type="wicri:Area/Pmc/Corpus">000523</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Productivity and salinity structuring of the microplankton revealed by comparative
freshwater metagenomics</title><author><name sortKey="Eiler, Alexander" sort="Eiler, Alexander" uniqKey="Eiler A" first="Alexander" last="Eiler">Alexander Eiler</name><affiliation><nlm:aff id="au1"><institution>Department of Ecology and Genetics, Limnology, Uppsala University</institution><addr-line>Uppsala, Sweden</addr-line></nlm:aff></affiliation></author><author><name sortKey="Zaremba Niedzwiedzka, Katarzyna" sort="Zaremba Niedzwiedzka, Katarzyna" uniqKey="Zaremba Niedzwiedzka K" first="Katarzyna" last="Zaremba-Niedzwiedzka">Katarzyna Zaremba-Niedzwiedzka</name><affiliation><nlm:aff id="au2"><institution>Department of Cell and Molecular Biology, Molecular Evolution, Uppsala University</institution><addr-line>Uppsala, Sweden</addr-line></nlm:aff></affiliation></author><author><name sortKey="Martinez Garcia, Manuel" sort="Martinez Garcia, Manuel" uniqKey="Martinez Garcia M" first="Manuel" last="Martínez-García">Manuel Martínez-García</name><affiliation><nlm:aff id="au4"><institution>Single Cell Genomics Center, Bigelow Laboratory for Ocean Sciences</institution><addr-line>East Boothbay, ME, USA</addr-line></nlm:aff></affiliation><affiliation><nlm:aff id="au5"><institution>Department of Fisheries, Genetics and Microbiology, University of Alicante</institution><addr-line>Alicante, Spain</addr-line></nlm:aff></affiliation></author><author><name sortKey="Mcmahon, Katherine D" sort="Mcmahon, Katherine D" uniqKey="Mcmahon K" first="Katherine D" last="Mcmahon">Katherine D. Mcmahon</name><affiliation><nlm:aff id="au3"><institution>Departments of Civil and Environmental Engineering, and Bacteriology, University of Wisconsin-Madison</institution><addr-line>Madison, WI, USA</addr-line></nlm:aff></affiliation></author><author><name sortKey="Stepanauskas, Ramunas" sort="Stepanauskas, Ramunas" uniqKey="Stepanauskas R" first="Ramunas" last="Stepanauskas">Ramunas Stepanauskas</name><affiliation><nlm:aff id="au4"><institution>Single Cell Genomics Center, Bigelow Laboratory for Ocean Sciences</institution><addr-line>East Boothbay, ME, USA</addr-line></nlm:aff></affiliation></author><author><name sortKey="Andersson, Siv G E" sort="Andersson, Siv G E" uniqKey="Andersson S" first="Siv G E" last="Andersson">Siv G E. Andersson</name><affiliation><nlm:aff id="au2"><institution>Department of Cell and Molecular Biology, Molecular Evolution, Uppsala University</institution><addr-line>Uppsala, Sweden</addr-line></nlm:aff></affiliation></author><author><name sortKey="Bertilsson, Stefan" sort="Bertilsson, Stefan" uniqKey="Bertilsson S" first="Stefan" last="Bertilsson">Stefan Bertilsson</name><affiliation><nlm:aff id="au1"><institution>Department of Ecology and Genetics, Limnology, Uppsala University</institution><addr-line>Uppsala, Sweden</addr-line></nlm:aff></affiliation></author></analytic><series><title level="j">Environmental Microbiology</title><idno type="ISSN">1462-2912</idno><idno type="eISSN">1462-2920</idno><imprint><date when="2013">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Little is known about the diversity and structuring of freshwater microbial communities beyond
the patterns revealed by tracing their distribution in the landscape with common taxonomic markers
such as the ribosomal RNA. To address this gap in knowledge, metagenomes from temperate lakes were
compared to selected marine metagenomes. Taxonomic analyses of rRNA genes in these freshwater
metagenomes confirm the previously reported dominance of a limited subset of uncultured lineages of
freshwater bacteria, whereas Archaea were rare. Diversification into marine and freshwater microbial
lineages was also reflected in phylogenies of functional genes, and there were also significant
differences in functional beta-diversity. The pathways and functions that accounted for these
differences are involved in osmoregulation, active transport, carbohydrate and amino acid
metabolism. Moreover, predicted genes orthologous to active transporters and recalcitrant organic
matter degradation were more common in microbial genomes from oligotrophic versus eutrophic lakes.
This comparative metagenomic analysis allowed us to formulate a general hypothesis that oceanic-
compared with freshwater-dwelling microorganisms, invest more in metabolism of amino acids and that
strategies of carbohydrate metabolism differ significantly between marine and freshwater microbial
communities.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Anderson, Mj" uniqKey="Anderson M">MJ Anderson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Battin, Tj" uniqKey="Battin T">TJ Battin</name></author><author><name sortKey="Luyssaert, S" uniqKey="Luyssaert S">S Luyssaert</name></author><author><name sortKey="Kaplan, La" uniqKey="Kaplan L">LA Kaplan</name></author><author><name sortKey="Aufdenkampe, Ak" uniqKey="Aufdenkampe A">AK Aufdenkampe</name></author><author><name sortKey="Richter, A" uniqKey="Richter A">A Richter</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Beifang, N" uniqKey="Beifang N">N Beifang</name></author><author><name sortKey="Limin, F" uniqKey="Limin F">F Limin</name></author><author><name sortKey="Shulei, S" uniqKey="Shulei S">S Shulei</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bertilsson, 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Environ Microbiol</journal-id><journal-id journal-id-type="iso-abbrev">Environ. Microbiol</journal-id><journal-id journal-id-type="publisher-id">emi</journal-id><journal-title-group><journal-title>Environmental Microbiology</journal-title></journal-title-group><issn pub-type="ppub">1462-2912</issn><issn pub-type="epub">1462-2920</issn><publisher><publisher-name>BlackWell Publishing Ltd</publisher-name><publisher-loc>Oxford, UK</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">24118837</article-id><article-id pub-id-type="pmc">4253090</article-id><article-id pub-id-type="doi">10.1111/1462-2920.12301</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Articles</subject></subj-group></article-categories><title-group><article-title>Productivity and salinity structuring of the microplankton revealed by comparative
freshwater metagenomics</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Eiler</surname><given-names>Alexander</given-names></name><xref ref-type="aff" rid="au1">1</xref><xref ref-type="corresp" rid="cor1">*</xref><xref ref-type="author-notes" rid="fn1">†</xref></contrib><contrib contrib-type="author"><name><surname>Zaremba-Niedzwiedzka</surname><given-names>Katarzyna</given-names></name><xref ref-type="aff" rid="au2">2</xref><xref ref-type="author-notes" rid="fn1">†</xref></contrib><contrib contrib-type="author"><name><surname>Martínez-García</surname><given-names>Manuel</given-names></name><xref ref-type="aff" rid="au4">4</xref><xref ref-type="aff" rid="au5">5</xref></contrib><contrib contrib-type="author"><name><surname>McMahon</surname><given-names>Katherine D</given-names></name><xref ref-type="aff" rid="au3">3</xref></contrib><contrib contrib-type="author"><name><surname>Stepanauskas</surname><given-names>Ramunas</given-names></name><xref ref-type="aff" rid="au4">4</xref></contrib><contrib contrib-type="author"><name><surname>Andersson</surname><given-names>Siv G E</given-names></name><xref ref-type="aff" rid="au2">2</xref></contrib><contrib contrib-type="author"><name><surname>Bertilsson</surname><given-names>Stefan</given-names></name><xref ref-type="aff" rid="au1">1</xref></contrib><aff id="au1"><label>1</label><institution>Department of Ecology and Genetics, Limnology, Uppsala University</institution><addr-line>Uppsala, Sweden</addr-line></aff><aff id="au2"><label>2</label><institution>Department of Cell and Molecular Biology, Molecular Evolution, Uppsala University</institution><addr-line>Uppsala, Sweden</addr-line></aff><aff id="au3"><label>3</label><institution>Departments of Civil and Environmental Engineering, and Bacteriology, University of Wisconsin-Madison</institution><addr-line>Madison, WI, USA</addr-line></aff><aff id="au4"><label>4</label><institution>Single Cell Genomics Center, Bigelow Laboratory for Ocean Sciences</institution><addr-line>East Boothbay, ME, USA</addr-line></aff><aff id="au5"><label>5</label><institution>Department of Fisheries, Genetics and Microbiology, University of Alicante</institution><addr-line>Alicante, Spain</addr-line></aff></contrib-group><author-notes><corresp id="cor1"> *For correspondence. E-mail <email>alexander.eiler@ebc.uu.se</email>;
Tel. (+46) 18 471 2700; Fax (+46) 18 531134. </corresp><fn id="fn1"><label>†</label><p>These authors contributed equally.</p></fn></author-notes><pub-date pub-type="ppub"><month>9</month><year>2014</year></pub-date><pub-date pub-type="epub"><day>12</day><month>11</month><year>2013</year></pub-date><volume>16</volume><issue>9</issue><fpage>2682</fpage><lpage>2698</lpage><history><date date-type="received"><day>06</day><month>12</month><year>2012</year></date><date date-type="accepted"><day>27</day><month>9</month><year>2013</year></date></history><permissions><copyright-statement>Copyright © 2014 The Authors. <italic>Environmental Microbiology</italic> published by Society for Applied Microbiology and John Wiley
& Sons Ltd</copyright-statement><copyright-year>2014</copyright-year><license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by-nc/3.0/"><license-p>This is an open access article under the terms of the Creative Commons Attribution-NonCommercial
License, which permits use, distribution and reproduction in any medium, provided the original work
is properly cited and is not used for commercial purposes.</license-p></license></permissions><abstract><p>Little is known about the diversity and structuring of freshwater microbial communities beyond
the patterns revealed by tracing their distribution in the landscape with common taxonomic markers
such as the ribosomal RNA. To address this gap in knowledge, metagenomes from temperate lakes were
compared to selected marine metagenomes. Taxonomic analyses of rRNA genes in these freshwater
metagenomes confirm the previously reported dominance of a limited subset of uncultured lineages of
freshwater bacteria, whereas Archaea were rare. Diversification into marine and freshwater microbial
lineages was also reflected in phylogenies of functional genes, and there were also significant
differences in functional beta-diversity. The pathways and functions that accounted for these
differences are involved in osmoregulation, active transport, carbohydrate and amino acid
metabolism. Moreover, predicted genes orthologous to active transporters and recalcitrant organic
matter degradation were more common in microbial genomes from oligotrophic versus eutrophic lakes.
This comparative metagenomic analysis allowed us to formulate a general hypothesis that oceanic-
compared with freshwater-dwelling microorganisms, invest more in metabolism of amino acids and that
strategies of carbohydrate metabolism differ significantly between marine and freshwater microbial
communities.</p></abstract></article-meta></front><body><sec sec-type="intro"><title>Introduction</title><p>Lakes are systems of enhanced biological activity and are central to many biogeochemical
processes (<xref rid="b2" ref-type="bibr">Battin <italic>et al.</italic>, 2009</xref>; <xref rid="b56" ref-type="bibr">Tranvik <italic>et al.</italic>, 2009</xref>). Lakes also represent a
critical natural resource for human societies (<xref rid="b13" ref-type="bibr">Downing <italic>et
al.</italic>, 2006</xref>). Although bacteria are known to perform many critical biogeochemical
processes and thus also have the potential to modify and control water quality in these ecosystems,
we have limited understanding of their functional potential, genetic variability and community
interactions. This is partly because most abundant lake bacteria are notoriously difficult to
culture in isolation (<xref rid="b42" ref-type="bibr">Newton <italic>et al.</italic>, 2011</xref>).
The first sequenced genomes of abundant freshwater bacteria (<xref rid="b23" ref-type="bibr">Garcia
<italic>et al.</italic>, 2012</xref>; <xref rid="b28" ref-type="bibr">Hahn <italic>et al.</italic>,
2012</xref>) and recent metagenomic characterization of microorganisms from Lake Gatun (<xref rid="b51" ref-type="bibr">Rusch <italic>et al.</italic>, 2007</xref>), Lac du Bourget (<xref rid="b10" ref-type="bibr">Debroas <italic>et al.</italic>, 2009</xref>) and Lake Lanier (<xref rid="b43" ref-type="bibr">Oh <italic>et al.</italic>, 2011</xref>) have provided some first
snapshots of the functional diversity of freshwater bacterioplankton in single lake ecosystems.
These studies have corroborated findings based on 16S rRNA amplicon surveys with regards to the
composition of freshwater bacterial communities and the existence of a phylogenetically distinct
freshwater microbiota (reviewed in <xref rid="b42" ref-type="bibr">Newton <italic>et al.</italic>,
2011</xref>). Nevertheless, because of the often substantial genomic variation among even closely
related strains, it is challenging to predict community metabolism solely from taxonomic markers and
the often rather limited metabolic and functional information derived from reference isolates.</p><p>In contrast with such marker gene approaches, metagenomic analysis has the potential to summarize
the combined genetic blueprint of all organisms in a given community (<xref rid="b50" ref-type="bibr">Riesenfeld <italic>et al.</italic>, 2004</xref>). By sequencing all genetic information in
a community, the relative abundance of all represented genes can, at least in theory, be determined
and used to provide a synoptic description of the functional potential of communities under scrutiny
(i.e. <xref rid="b19" ref-type="bibr">Fierer <italic>et al.</italic>, 2007</xref>; <xref rid="b51" ref-type="bibr">Rusch <italic>et al.</italic>, 2007</xref>; <xref rid="b10" ref-type="bibr">Debroas <italic>et al.</italic>, 2009</xref>; <xref rid="b43" ref-type="bibr">Oh
<italic>et al.</italic>, 2011</xref>). By annotating and comparing multiple such data sets,
differences in the metabolic profiles across environments can furthermore be identified (<xref rid="b12" ref-type="bibr">Dinsdale <italic>et al.</italic>, 2008</xref>), and it is also possible to
identify specific genomic adaptations to life in contrasting habitats. Such metagenomic studies have
previously revealed significant relationships between the environmental conditions and the
functional composition of microbial communities in a wide range of habitats (<xref rid="b57" ref-type="bibr">Tringe <italic>et al.</italic>, 2005</xref>; <xref rid="b11" ref-type="bibr">DeLong
<italic>et al.</italic>, 2006</xref>; <xref rid="b12" ref-type="bibr">Dinsdale <italic>et
al.</italic>, 2008</xref>; <xref rid="b33" ref-type="bibr">Kunin <italic>et al.</italic>,
2008</xref>; <xref rid="b24" ref-type="bibr">Gianoulis <italic>et al.</italic>, 2012</xref>; <xref rid="b48" ref-type="bibr">Raes <italic>et al.</italic>, 2011</xref>) including a first comparison
between metagenomes from freshwater lake and marine samples (<xref rid="b43" ref-type="bibr">Oh
<italic>et al.</italic>, 2011</xref>).</p><p>Here, we use metagenomic sequence data from marine and freshwater systems to identify general
differences in functional gene profiles and the variability in metabolic profiles among lakes of
different trophic status. Comparative analyses of freshwater bacterial communities based on
taxonomic markers have previously revealed differences in bacterial community composition across
trophic gradients, where specific lineages respond either positively or negatively to high
productivity (<xref rid="b32" ref-type="bibr">Kolmonen <italic>et al.</italic>, 2011</xref>).
Microbial community structure is not only determined by environmental characteristics (<xref rid="b42" ref-type="bibr">Newton <italic>et al.</italic>, 2011</xref>) and contemporary biotic
interactions (<xref rid="b18" ref-type="bibr">Eiler <italic>et al.</italic>, 2012</xref>) but also
by a complex combination of historical factors such as dispersal limitation, past environmental
conditions and evolution (<xref rid="b41" ref-type="bibr">Martiny <italic>et al.</italic>,
2006</xref>). In comparison with oceans, inland waters are much more directly influenced by the
surrounding terrestrial landscape and coupled to inputs of organisms and chemical constituents from
the catchment. Such external influences are likely to have a profound influence on the phylogenetic
composition of bacterioplankton communities (see for example <xref rid="b35" ref-type="bibr">Lindström, 2000</xref>; <xref rid="b36" ref-type="bibr">Lindström
<italic>et al</italic>., 2005</xref>; <xref rid="b62" ref-type="bibr">Yannarell and Triplett,
2005</xref>; <xref rid="b17" ref-type="bibr">Eiler and Bertilsson, 2007</xref>; <xref rid="b42" ref-type="bibr">Newton <italic>et al.</italic>, 2011</xref>; <xref rid="b45" ref-type="bibr">Peura <italic>et al.</italic>, 2012</xref>).</p><p>To better understand factors controlling and shaping the community-level functional traits of
freshwater microplankton, nine planktonic DNA samples from seven different lakes were analysed by
pyrosequencing-enabled metagenomics. In addition, three available freshwater metagenome data sets
from National Center for Biotechnology Information-Short Read Archive (NCBI-SRA) were included in
the analysis, resulting in a combined freshwater data set from altogether 12 freshwater metagenomes.
As marine references, we used 13 marine metagenomes comprising samples from the open and coastal
ocean. One further aim was to corroborate that lake systems are not only different from marine
systems in their phylogenetic but also in their functional gene composition. By comparing lakes of
contrasting productivity, we further aimed at revealing functional differences related to nutrient
and energy acquisition as well as substrate preferences.</p></sec><sec><title>Results and discussion</title><sec><title>General description of the sampling sites and sequence data</title><p>DNA samples were collected from seven lakes, whereof two lakes were sampled twice (in Spring and
Summer) (Table <xref ref-type="table" rid="tbl1">1</xref>). These nine samples were subject
to whole-community genome shotgun 454 pyrosequencing using Titanium chemistry. An additional three
freshwater lake metagenomes and 13 marine metagenomes were obtained from public databases. The
latter included samples from open-ocean and coastal habitats (Table S1). We selected these 16
metagenomes available at the time of analysis because they were of sufficient size to be compared
with our data and processed in the most similar fashion to the nine new freshwater metagenomes with
regards to sample handling, DNA extraction, library preparation and sequencing. Still, we want to
make the reader aware that they were not processed in an identical way, which might influence the
comparison and our interpretations (<xref rid="b8" ref-type="bibr">Carrigg <italic>et al.</italic>,
2007</xref>). In addition, with the limited number of samples and shallow sequencing at hand, we can
never cover the entire functional diversity dwelling in both marine and freshwater biomes, and this
adds some uncertainties to generalizations of the major findings from this study.</p><table-wrap id="tbl1" position="float"><label>Table 1</label><caption><p>Description of lakes used in this study</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="1" colspan="1">ID</th><th align="left" rowspan="1" colspan="1">Sample location</th><th align="left" rowspan="1" colspan="1">Country</th><th align="left" rowspan="1" colspan="1">Date</th><th align="left" rowspan="1" colspan="1">Location</th><th align="left" rowspan="1" colspan="1">Sample depth</th><th align="left" rowspan="1" colspan="1">T (°C)</th><th align="left" rowspan="1" colspan="1">Size fraction (μm)</th><th align="left" rowspan="1" colspan="1">Habitat type</th><th align="left" rowspan="1" colspan="1">Tot P</th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">DamariscottaSP</td><td align="left" rowspan="1" colspan="1">Lake Damariscotta</td><td align="left" rowspan="1" colspan="1">USA</td><td align="left" rowspan="1" colspan="1">20090528</td><td align="left" rowspan="1" colspan="1">44°10′n; 69°29′w</td><td align="left" rowspan="1" colspan="1">0.5–1</td><td align="left" rowspan="1" colspan="1">12.1</td><td align="left" rowspan="1" colspan="1">> 0.2</td><td align="left" rowspan="1" colspan="1">Mesotrophic lake</td><td align="char" char="." rowspan="1" colspan="1">10</td></tr><tr><td align="left" rowspan="1" colspan="1">DamariscottaSU</td><td align="left" rowspan="1" colspan="1">Lake Damariscotta</td><td align="left" rowspan="1" colspan="1">USA</td><td align="left" rowspan="1" colspan="1">20090819</td><td align="left" rowspan="1" colspan="1">44°10′n; 69°29′w</td><td align="left" rowspan="1" colspan="1">0.5–1</td><td align="left" rowspan="1" colspan="1">12.1</td><td align="left" rowspan="1" colspan="1">> 0.2</td><td align="left" rowspan="1" colspan="1">Mesotrophic lake</td><td align="char" char="." rowspan="1" colspan="1">10</td></tr><tr><td align="left" rowspan="1" colspan="1">Ekoln</td><td align="left" rowspan="1" colspan="1">Lake Ekoln</td><td align="left" rowspan="1" colspan="1">Sweden</td><td align="left" rowspan="1" colspan="1">20070731</td><td align="left" rowspan="1" colspan="1">59°45′n; 17°36′e</td><td align="left" rowspan="1" colspan="1">0–2</td><td align="left" rowspan="1" colspan="1">19.0</td><td align="left" rowspan="1" colspan="1">0.2–100</td><td align="left" rowspan="1" colspan="1">Eutrophic lake</td><td align="char" char="." rowspan="1" colspan="1">50</td></tr><tr><td align="left" rowspan="1" colspan="1">Erken</td><td align="left" rowspan="1" colspan="1">Lake Erken</td><td align="left" rowspan="1" colspan="1">Sweden</td><td align="left" rowspan="1" colspan="1">20070620</td><td align="left" rowspan="1" colspan="1">59°25′n; 18°15′e</td><td align="left" rowspan="1" colspan="1">0–2</td><td align="left" rowspan="1" colspan="1">18.7</td><td align="left" rowspan="1" colspan="1">0.2–100</td><td align="left" rowspan="1" colspan="1">Mesotrophic lake</td><td align="char" char="." rowspan="1" colspan="1">33</td></tr><tr><td align="left" rowspan="1" colspan="1">Lanier</td><td align="left" rowspan="1" colspan="1">Lake Lanier</td><td align="left" rowspan="1" colspan="1">USA</td><td align="left" rowspan="1" colspan="1">20090827</td><td align="left" rowspan="1" colspan="1">34°12′n; 83°59′w</td><td align="left" rowspan="1" colspan="1">0–5</td><td align="left" rowspan="1" colspan="1">28.5</td><td align="left" rowspan="1" colspan="1">0.22–1.6</td><td align="left" rowspan="1" colspan="1">Mesotrophic lake</td><td align="char" char="." rowspan="1" colspan="1">30</td></tr><tr><td align="left" rowspan="1" colspan="1">MendotaSP</td><td align="left" rowspan="1" colspan="1">Lake Mendota</td><td align="left" rowspan="1" colspan="1">USA</td><td align="left" rowspan="1" colspan="1">20090512</td><td align="left" rowspan="1" colspan="1">43° 6′n; 89°24′w</td><td align="left" rowspan="1" colspan="1">0.5–1</td><td align="left" rowspan="1" colspan="1">12.68</td><td align="left" rowspan="1" colspan="1">> 0.2</td><td align="left" rowspan="1" colspan="1">Eutrophic lake</td><td align="char" char="." rowspan="1" colspan="1">118</td></tr><tr><td align="left" rowspan="1" colspan="1">MendotaSU</td><td align="left" rowspan="1" colspan="1">Lake Mendota</td><td align="left" rowspan="1" colspan="1">USA</td><td align="left" rowspan="1" colspan="1">20090823</td><td align="left" rowspan="1" colspan="1">43° 6′n; 89°24′w</td><td align="left" rowspan="1" colspan="1">0.5–1</td><td align="left" rowspan="1" colspan="1">23.07</td><td align="left" rowspan="1" colspan="1">> 0.2</td><td align="left" rowspan="1" colspan="1">Eutrophic lake</td><td align="char" char="." rowspan="1" colspan="1">100</td></tr><tr><td align="left" rowspan="1" colspan="1">Spark</td><td align="left" rowspan="1" colspan="1">Sparkling Lake</td><td align="left" rowspan="1" colspan="1">USA</td><td align="left" rowspan="1" colspan="1">20090528</td><td align="left" rowspan="1" colspan="1">46° 0′n; 89°42′w</td><td align="left" rowspan="1" colspan="1">0.5–1</td><td align="left" rowspan="1" colspan="1">13.97</td><td align="left" rowspan="1" colspan="1">> 0.2</td><td align="left" rowspan="1" colspan="1">Oligotrophic lake</td><td align="char" char="." rowspan="1" colspan="1">0.3</td></tr><tr><td align="left" rowspan="1" colspan="1">Trout</td><td align="left" rowspan="1" colspan="1">Trout Bog Lake</td><td align="left" rowspan="1" colspan="1">USA</td><td align="left" rowspan="1" colspan="1">20090528</td><td align="left" rowspan="1" colspan="1">46° 2′n; 89°41′w</td><td align="left" rowspan="1" colspan="1">0.5–1</td><td align="left" rowspan="1" colspan="1">20.71</td><td align="left" rowspan="1" colspan="1">> 0.2</td><td align="left" rowspan="1" colspan="1">Dysotrophic lake</td><td align="char" char="." rowspan="1" colspan="1">7.8</td></tr><tr><td align="left" rowspan="1" colspan="1">Vattern</td><td align="left" rowspan="1" colspan="1">Lake Vättern</td><td align="left" rowspan="1" colspan="1">Sweden</td><td align="left" rowspan="1" colspan="1">20070717</td><td align="left" rowspan="1" colspan="1">58°24′n; 14°36′e</td><td align="left" rowspan="1" colspan="1">0–2</td><td align="left" rowspan="1" colspan="1">17.0</td><td align="left" rowspan="1" colspan="1">0.2–100</td><td align="left" rowspan="1" colspan="1">Oligotrophic lake</td><td align="char" char="." rowspan="1" colspan="1">3</td></tr><tr><td align="left" rowspan="1" colspan="1">Yellowstone1</td><td align="left" rowspan="1" colspan="1">Yellowstone Lake</td><td align="left" rowspan="1" colspan="1">USA</td><td align="left" rowspan="1" colspan="1">20080916</td><td align="left" rowspan="1" colspan="1">44°28′n; 110°22′w</td><td align="left" rowspan="1" colspan="1">0–2</td><td align="left" rowspan="1" colspan="1">46</td><td align="left" rowspan="1" colspan="1">0.1–0.8</td><td align="left" rowspan="1" colspan="1">Eutrophic lake</td><td align="char" char="." rowspan="1" colspan="1">80</td></tr><tr><td align="left" rowspan="1" colspan="1">Yellowstone2</td><td align="left" rowspan="1" colspan="1">Yellowstone Lake</td><td align="left" rowspan="1" colspan="1">USA</td><td align="left" rowspan="1" colspan="1">20080915</td><td align="left" rowspan="1" colspan="1">44°28′n; 110°22′w</td><td align="left" rowspan="1" colspan="1">0–2</td><td align="left" rowspan="1" colspan="1">12.3</td><td align="left" rowspan="1" colspan="1">0.1–0.8</td><td align="left" rowspan="1" colspan="1">Eutrophic lake</td><td align="char" char="." rowspan="1" colspan="1">80</td></tr></tbody></table><table-wrap-foot><fn id="tf1-1"><p>Tot P, total phosphorus concentration (μg l<sup>−1</sup>); T,
temperature.</p></fn></table-wrap-foot></table-wrap><p>The nine lakes included in the analysis represent a wide range of trophic states, including
oligotrophic, mesotrophic and eutrophic systems (Table <xref ref-type="table" rid="tbl1">1</xref>). They range from 0.3 to 120 μg l<sup>−1</sup> in total
phosphorus (TP) and are all situated in the temperate climate zone. On average, 325 000
high-quality reads with mean length of 330 bp were obtained for the lake metagenomes and
slightly lower numbers of 280 000 sequences with mean read length of 270 bp for the
marine data sets. No quality files were available for the marine data sets, but quality filtering
(mean read quality > 21) affected the lake metagenomes very little (1–2%
for two data sets, 0% for the majority). To match the quality filtering step as best
possible, marine metagenomes had an extra upper length filter added because many long sequences were
observed to be of poor quality. Lower length limit (> 150 bp) and clustering to
remove artefacts were performed in the same way on all data sets, resulting in over 8.2 million
reads in total (Table <xref ref-type="table" rid="tbl2">2</xref>; for detail about the
removed sequences in the preprocessing steps, see Table S2). Five samples yielded much lower
total sequenced nucleotides than average (84%): marine sample from Sargasso Sea (depth
40 m, 67%) and four lake samples from Yellowstone Lake (sample 1, 79%), Lake
Mendota (spring sample, 76%), Trout-Bog Lake (75%) and Sparkling Lake (58%).
These samples were also among the most extreme outliers in terms of the eukaryotic content. To
ensure robustness of the results, the impact of including/excluding those samples from the
statistical analyses was investigated.</p><table-wrap id="tbl2" position="float"><label>Table 2</label><caption><p>Characteristics of lake and marine metagenomes</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="1" colspan="1">ID</th><th align="left" rowspan="1" colspan="1">Reference/site</th><th align="left" rowspan="1" colspan="1">Size after QC (Mb)</th><th align="left" rowspan="1" colspan="1">Reads after QC</th><th align="left" rowspan="1" colspan="1">GC content (%)</th><th align="left" rowspan="1" colspan="1">Isoelectric point</th><th align="left" rowspan="1" colspan="1">SSU rRNA genes</th><th align="left" rowspan="1" colspan="1">% Eukaryotic SSU rRNA genes</th><th align="left" rowspan="1" colspan="1">Reads with STRING hit</th><th align="left" rowspan="1" colspan="1">Reads with COG assignment</th><th align="left" rowspan="1" colspan="1">% Reads with COG assignment</th><th align="left" rowspan="1" colspan="1">Average number of single copy COGs</th><th align="left" rowspan="1" colspan="1">% Bacteria among single copy COGs</th><th align="left" rowspan="1" colspan="1">Simple EGS Mb/single copy COG</th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">DamariscottaSP</td><td align="left" rowspan="1" colspan="1"><xref rid="b40" ref-type="bibr">Martinez-Garcia <italic>et al</italic>.
2012</xref></td><td align="char" char="." rowspan="1" colspan="1">121</td><td align="char" char="." rowspan="1" colspan="1">281 625</td><td align="char" char="." rowspan="1" colspan="1">48.6</td><td align="char" char="." rowspan="1" colspan="1">9.75</td><td align="char" char="." rowspan="1" colspan="1">531 (185/0/5)</td><td align="left" rowspan="1" colspan="1">2.6</td><td align="char" char="." rowspan="1" colspan="1">149 906</td><td align="char" char="." rowspan="1" colspan="1">135 640</td><td align="left" rowspan="1" colspan="1">42</td><td align="char" char="." rowspan="1" colspan="1">78</td><td align="left" rowspan="1" colspan="1">94</td><td align="left" rowspan="1" colspan="1">1.55</td></tr><tr><td align="left" rowspan="1" colspan="1">DamariscottaSU</td><td align="left" rowspan="1" colspan="1"><xref rid="b40" ref-type="bibr">Martinez-Garcia <italic>et al</italic>.
2012</xref></td><td align="char" char="." rowspan="1" colspan="1">140</td><td align="char" char="." rowspan="1" colspan="1">323 939</td><td align="char" char="." rowspan="1" colspan="1">48.2</td><td align="char" char="." rowspan="1" colspan="1">9.82</td><td align="char" char="." rowspan="1" colspan="1">666 (200/0/27)</td><td align="left" rowspan="1" colspan="1">11.9</td><td align="char" char="." rowspan="1" colspan="1">156 281</td><td align="char" char="." rowspan="1" colspan="1">140 701</td><td align="left" rowspan="1" colspan="1">50</td><td align="char" char="." rowspan="1" colspan="1">93</td><td align="left" rowspan="1" colspan="1">93</td><td align="left" rowspan="1" colspan="1">1.51</td></tr><tr><td align="left" rowspan="1" colspan="1">Ekoln</td><td align="left" rowspan="1" colspan="1">this study</td><td align="char" char="." rowspan="1" colspan="1">115</td><td align="char" char="." rowspan="1" colspan="1">284 609</td><td align="char" char="." rowspan="1" colspan="1">46.0</td><td align="char" char="." rowspan="1" colspan="1">9.49</td><td align="char" char="." rowspan="1" colspan="1">622 (209/0/13)</td><td align="left" rowspan="1" colspan="1">5.9</td><td align="char" char="." rowspan="1" colspan="1">107 593</td><td align="char" char="." rowspan="1" colspan="1">94 783</td><td align="left" rowspan="1" colspan="1">33</td><td align="char" char="." rowspan="1" colspan="1">69</td><td align="left" rowspan="1" colspan="1">95</td><td align="left" rowspan="1" colspan="1">1.67</td></tr><tr><td align="left" rowspan="1" colspan="1">Erken</td><td align="left" rowspan="1" colspan="1">this study</td><td align="char" char="." rowspan="1" colspan="1">233</td><td align="char" char="." rowspan="1" colspan="1">554 862</td><td align="char" char="." rowspan="1" colspan="1">44.9</td><td align="char" char="." rowspan="1" colspan="1">9.41</td><td align="char" char="." rowspan="1" colspan="1">1170 (399/0/5)</td><td align="left" rowspan="1" colspan="1">1.2</td><td align="char" char="." rowspan="1" colspan="1">273 058</td><td align="char" char="." rowspan="1" colspan="1">250 931</td><td align="left" rowspan="1" colspan="1">45</td><td align="char" char="." rowspan="1" colspan="1">196</td><td align="left" rowspan="1" colspan="1">93</td><td align="left" rowspan="1" colspan="1">1.19</td></tr><tr><td align="left" rowspan="1" colspan="1">Lanier</td><td align="left" rowspan="1" colspan="1"><xref rid="b43" ref-type="bibr">Oh <italic>et al</italic>. 2011</xref></td><td align="char" char="." rowspan="1" colspan="1">449</td><td align="char" char="." rowspan="1" colspan="1">1 078 031</td><td align="char" char="." rowspan="1" colspan="1">47.1</td><td align="char" char="." rowspan="1" colspan="1">9.77</td><td align="char" char="." rowspan="1" colspan="1">1989 (714/0/20)</td><td align="left" rowspan="1" colspan="1">2.7</td><td align="char" char="." rowspan="1" colspan="1">440 459</td><td align="char" char="." rowspan="1" colspan="1">399 647</td><td align="left" rowspan="1" colspan="1">37</td><td align="char" char="." rowspan="1" colspan="1">252</td><td align="left" rowspan="1" colspan="1">93</td><td align="left" rowspan="1" colspan="1">1.78</td></tr><tr><td align="left" rowspan="1" colspan="1">MendotaSP</td><td align="left" rowspan="1" colspan="1"><xref rid="b40" ref-type="bibr">Martinez-Garcia <italic>et al</italic>.
2012</xref></td><td align="char" char="." rowspan="1" colspan="1">133</td><td align="char" char="." rowspan="1" colspan="1">319 321</td><td align="char" char="." rowspan="1" colspan="1">45.7</td><td align="char" char="." rowspan="1" colspan="1">9.52</td><td align="char" char="." rowspan="1" colspan="1">1118 (242/0/149)</td><td align="left" rowspan="1" colspan="1">38.1</td><td align="char" char="." rowspan="1" colspan="1">124 837</td><td align="char" char="." rowspan="1" colspan="1">111 654</td><td align="left" rowspan="1" colspan="1">25</td><td align="char" char="." rowspan="1" colspan="1">77</td><td align="left" rowspan="1" colspan="1">97</td><td align="left" rowspan="1" colspan="1">1.73</td></tr><tr><td align="left" rowspan="1" colspan="1">MendotaSU</td><td align="left" rowspan="1" colspan="1"><xref rid="b40" ref-type="bibr">Martinez-Garcia <italic>et al</italic>.
2012</xref></td><td align="char" char="." rowspan="1" colspan="1">192</td><td align="char" char="." rowspan="1" colspan="1">447 054</td><td align="char" char="." rowspan="1" colspan="1">47.7</td><td align="char" char="." rowspan="1" colspan="1">9.75</td><td align="char" char="." rowspan="1" colspan="1">795 (247/0/31)</td><td align="left" rowspan="1" colspan="1">11.2</td><td align="char" char="." rowspan="1" colspan="1">173 517</td><td align="char" char="." rowspan="1" colspan="1">146 222</td><td align="left" rowspan="1" colspan="1">46</td><td align="char" char="." rowspan="1" colspan="1">76</td><td align="left" rowspan="1" colspan="1">95</td><td align="left" rowspan="1" colspan="1">2.53</td></tr><tr><td align="left" rowspan="1" colspan="1">Spark</td><td align="left" rowspan="1" colspan="1"><xref rid="b40" ref-type="bibr">Martinez-Garcia <italic>et al</italic>.
2012</xref></td><td align="char" char="." rowspan="1" colspan="1">26</td><td align="char" char="." rowspan="1" colspan="1">66 160</td><td align="char" char="." rowspan="1" colspan="1">52.5</td><td align="char" char="." rowspan="1" colspan="1">10.01</td><td align="char" char="." rowspan="1" colspan="1">108 (28/0/5)</td><td align="left" rowspan="1" colspan="1">15.2</td><td align="char" char="." rowspan="1" colspan="1">22 364</td><td align="char" char="." rowspan="1" colspan="1">19 857</td><td align="left" rowspan="1" colspan="1">30</td><td align="char" char="." rowspan="1" colspan="1">8</td><td align="left" rowspan="1" colspan="1">87</td><td align="left" rowspan="1" colspan="1">3.25</td></tr><tr><td align="left" rowspan="1" colspan="1">Trout</td><td align="left" rowspan="1" colspan="1"><xref rid="b40" ref-type="bibr">Martinez-Garcia <italic>et al</italic>.
2012</xref></td><td align="char" char="." rowspan="1" colspan="1">60</td><td align="char" char="." rowspan="1" colspan="1">150 515</td><td align="char" char="." rowspan="1" colspan="1">46.5</td><td align="char" char="." rowspan="1" colspan="1">9.59</td><td align="char" char="." rowspan="1" colspan="1">335 (63/0/21)</td><td align="left" rowspan="1" colspan="1">25.0</td><td align="char" char="." rowspan="1" colspan="1">46 795</td><td align="char" char="." rowspan="1" colspan="1">41 628</td><td align="left" rowspan="1" colspan="1">28</td><td align="char" char="." rowspan="1" colspan="1">26</td><td align="left" rowspan="1" colspan="1">88</td><td align="left" rowspan="1" colspan="1">2.31</td></tr><tr><td align="left" rowspan="1" colspan="1">Vattern</td><td align="left" rowspan="1" colspan="1">this study</td><td align="char" char="." rowspan="1" colspan="1">117</td><td align="char" char="." rowspan="1" colspan="1">285 637</td><td align="char" char="." rowspan="1" colspan="1">47.4</td><td align="char" char="." rowspan="1" colspan="1">9.67</td><td align="char" char="." rowspan="1" colspan="1">540 (177/0/15)</td><td align="left" rowspan="1" colspan="1">7.8</td><td align="char" char="." rowspan="1" colspan="1">116 970</td><td align="char" char="." rowspan="1" colspan="1">103 047</td><td align="left" rowspan="1" colspan="1">36</td><td align="char" char="." rowspan="1" colspan="1">66</td><td align="left" rowspan="1" colspan="1">93</td><td align="left" rowspan="1" colspan="1">1.77</td></tr><tr><td align="left" rowspan="1" colspan="1">Yellowstone1</td><td align="left" rowspan="1" colspan="1">SRR077348</td><td align="char" char="." rowspan="1" colspan="1">181</td><td align="char" char="." rowspan="1" colspan="1">416 139</td><td align="char" char="." rowspan="1" colspan="1">43.7</td><td align="char" char="." rowspan="1" colspan="1">9.34</td><td align="char" char="." rowspan="1" colspan="1">541 (212/0/2)</td><td align="left" rowspan="1" colspan="1">0.9</td><td align="char" char="." rowspan="1" colspan="1">152 376</td><td align="char" char="." rowspan="1" colspan="1">136 972</td><td align="left" rowspan="1" colspan="1">33</td><td align="char" char="." rowspan="1" colspan="1">83</td><td align="left" rowspan="1" colspan="1">93</td><td align="left" rowspan="1" colspan="1">2.18</td></tr><tr><td align="left" rowspan="1" colspan="1">Yellowstone2</td><td align="left" rowspan="1" colspan="1">SRR078855</td><td align="char" char="." rowspan="1" colspan="1">107</td><td align="char" char="." rowspan="1" colspan="1">346 239</td><td align="char" char="." rowspan="1" colspan="1">41.4</td><td align="char" char="." rowspan="1" colspan="1">9.03</td><td align="char" char="." rowspan="1" colspan="1">754 (256/1/0)</td><td align="left" rowspan="1" colspan="1">0.0</td><td align="char" char="." rowspan="1" colspan="1">91 459</td><td align="char" char="." rowspan="1" colspan="1">86 132</td><td align="left" rowspan="1" colspan="1">25</td><td align="char" char="." rowspan="1" colspan="1">75</td><td align="left" rowspan="1" colspan="1">97</td><td align="left" rowspan="1" colspan="1">1.43</td></tr><tr><td align="left" rowspan="1" colspan="1">FRESHWATER (Mean)</td><td rowspan="1" colspan="1"></td><td align="char" char="." rowspan="1" colspan="1">156</td><td align="char" char="." rowspan="1" colspan="1">379 511</td><td align="char" char="." rowspan="1" colspan="1">46.6</td><td align="char" char="." rowspan="1" colspan="1">9.60</td><td align="char" char="." rowspan="1" colspan="1">764 (244/0/24)</td><td align="left" rowspan="1" colspan="1">10.2</td><td align="char" char="." rowspan="1" colspan="1">154 635</td><td align="char" char="." rowspan="1" colspan="1">138 935</td><td align="left" rowspan="1" colspan="1">37</td><td align="char" char="." rowspan="1" colspan="1">92</td><td align="left" rowspan="1" colspan="1">93</td><td align="left" rowspan="1" colspan="1">1.91</td></tr><tr><td align="left" rowspan="1" colspan="1">BATS0</td><td align="left" rowspan="1" colspan="1">Sargasso Sea</td><td align="char" char="." rowspan="1" colspan="1">118</td><td align="char" char="." rowspan="1" colspan="1">478 976</td><td align="char" char="." rowspan="1" colspan="1">48.0</td><td align="char" char="." rowspan="1" colspan="1">9.74</td><td align="char" char="." rowspan="1" colspan="1">1137 (431/0/13)</td><td align="left" rowspan="1" colspan="1">2.9</td><td align="char" char="." rowspan="1" colspan="1">142 979</td><td align="char" char="." rowspan="1" colspan="1">131 449</td><td align="left" rowspan="1" colspan="1">27</td><td align="char" char="." rowspan="1" colspan="1">104</td><td align="left" rowspan="1" colspan="1">97</td><td align="left" rowspan="1" colspan="1">1.14</td></tr><tr><td align="left" rowspan="1" colspan="1">BATS200</td><td align="left" rowspan="1" colspan="1">Sargasso Sea</td><td align="char" char="." rowspan="1" colspan="1">134</td><td align="char" char="." rowspan="1" colspan="1">525 891</td><td align="char" char="." rowspan="1" colspan="1">48.3</td><td align="char" char="." rowspan="1" colspan="1">9.70</td><td align="char" char="." rowspan="1" colspan="1">1049 (310/38/13)</td><td align="left" rowspan="1" colspan="1">3.6</td><td align="char" char="." rowspan="1" colspan="1">133 259</td><td align="char" char="." rowspan="1" colspan="1">121 763</td><td align="left" rowspan="1" colspan="1">23</td><td align="char" char="." rowspan="1" colspan="1">97</td><td align="left" rowspan="1" colspan="1">85</td><td align="left" rowspan="1" colspan="1">1.38</td></tr><tr><td align="left" rowspan="1" colspan="1">BATS250</td><td align="left" rowspan="1" colspan="1">Sargasso Sea</td><td align="char" char="." rowspan="1" colspan="1">115</td><td align="char" char="." rowspan="1" colspan="1">456 677</td><td align="char" char="." rowspan="1" colspan="1">46.6</td><td align="char" char="." rowspan="1" colspan="1">9.63</td><td align="char" char="." rowspan="1" colspan="1">606 (183/20/9)</td><td align="left" rowspan="1" colspan="1">4.2</td><td align="char" char="." rowspan="1" colspan="1">95 919</td><td align="char" char="." rowspan="1" colspan="1">88 658</td><td align="left" rowspan="1" colspan="1">19</td><td align="char" char="." rowspan="1" colspan="1">70</td><td align="left" rowspan="1" colspan="1">89</td><td align="left" rowspan="1" colspan="1">1.65</td></tr><tr><td align="left" rowspan="1" colspan="1">BATS40</td><td align="left" rowspan="1" colspan="1">Sargasso Sea</td><td align="char" char="." rowspan="1" colspan="1">95</td><td align="char" char="." rowspan="1" colspan="1">394 461</td><td align="char" char="." rowspan="1" colspan="1">48.1</td><td align="char" char="." rowspan="1" colspan="1">9.78</td><td align="char" char="." rowspan="1" colspan="1">675 (227/0/17)</td><td align="left" rowspan="1" colspan="1">7.0</td><td align="char" char="." rowspan="1" colspan="1">86 262</td><td align="char" char="." rowspan="1" colspan="1">79 155</td><td align="left" rowspan="1" colspan="1">20</td><td align="char" char="." rowspan="1" colspan="1">67</td><td align="left" rowspan="1" colspan="1">96</td><td align="left" rowspan="1" colspan="1">1.42</td></tr><tr><td align="left" rowspan="1" colspan="1">EqDP35155</td><td align="left" rowspan="1" colspan="1">Equatorial Pacific</td><td align="char" char="." rowspan="1" colspan="1">56</td><td align="char" char="." rowspan="1" colspan="1">219 390</td><td align="char" char="." rowspan="1" colspan="1">45.4</td><td align="char" char="." rowspan="1" colspan="1">9.70</td><td align="char" char="." rowspan="1" colspan="1">508 (164/10/3)</td><td align="left" rowspan="1" colspan="1">1.7</td><td align="char" char="." rowspan="1" colspan="1">62 135</td><td align="char" char="." rowspan="1" colspan="1">57 103</td><td align="left" rowspan="1" colspan="1">26</td><td align="char" char="." rowspan="1" colspan="1">53</td><td align="left" rowspan="1" colspan="1">91</td><td align="left" rowspan="1" colspan="1">1.05</td></tr><tr><td align="left" rowspan="1" colspan="1">NPTG35179</td><td align="left" rowspan="1" colspan="1">North Pacific Tropical Gyre</td><td align="char" char="." rowspan="1" colspan="1">45</td><td align="char" char="." rowspan="1" colspan="1">181 907</td><td align="char" char="." rowspan="1" colspan="1">44.8</td><td align="char" char="." rowspan="1" colspan="1">9.53</td><td align="char" char="." rowspan="1" colspan="1">656 (253/4/4)</td><td align="left" rowspan="1" colspan="1">1.5</td><td align="char" char="." rowspan="1" colspan="1">55 589</td><td align="char" char="." rowspan="1" colspan="1">51 145</td><td align="left" rowspan="1" colspan="1">28</td><td align="char" char="." rowspan="1" colspan="1">45</td><td align="left" rowspan="1" colspan="1">95</td><td align="left" rowspan="1" colspan="1">1.00</td></tr><tr><td align="left" rowspan="1" colspan="1">PNEq35163</td><td align="left" rowspan="1" colspan="1">Pacific North Equatorial</td><td align="char" char="." rowspan="1" colspan="1">55</td><td align="char" char="." rowspan="1" colspan="1">221 925</td><td align="char" char="." rowspan="1" colspan="1">49.8</td><td align="char" char="." rowspan="1" colspan="1">9.94</td><td align="char" char="." rowspan="1" colspan="1">790 (300/6/5)</td><td align="left" rowspan="1" colspan="1">1.6</td><td align="char" char="." rowspan="1" colspan="1">59 337</td><td align="char" char="." rowspan="1" colspan="1">53 915</td><td align="left" rowspan="1" colspan="1">24</td><td align="char" char="." rowspan="1" colspan="1">52</td><td align="left" rowspan="1" colspan="1">92</td><td align="left" rowspan="1" colspan="1">1.06</td></tr><tr><td align="left" rowspan="1" colspan="1">PNEqCc35171</td><td align="left" rowspan="1" colspan="1">Pacific North Equatorial</td><td align="char" char="." rowspan="1" colspan="1">13</td><td align="char" char="." rowspan="1" colspan="1">50 267</td><td align="char" char="." rowspan="1" colspan="1">42.5</td><td align="char" char="." rowspan="1" colspan="1">9.38</td><td align="char" char="." rowspan="1" colspan="1">101 (31/3/2)</td><td align="left" rowspan="1" colspan="1">5.6</td><td align="char" char="." rowspan="1" colspan="1">15 791</td><td align="char" char="." rowspan="1" colspan="1">14 620</td><td align="left" rowspan="1" colspan="1">29</td><td align="char" char="." rowspan="1" colspan="1">13</td><td align="left" rowspan="1" colspan="1">92</td><td align="left" rowspan="1" colspan="1">0.97</td></tr><tr><td align="left" rowspan="1" colspan="1">SPSG35131</td><td align="left" rowspan="1" colspan="1">South Pacific Subtropical Gyre</td><td align="char" char="." rowspan="1" colspan="1">36</td><td align="char" char="." rowspan="1" colspan="1">155 219</td><td align="char" char="." rowspan="1" colspan="1">47.7</td><td align="char" char="." rowspan="1" colspan="1">9.77</td><td align="char" char="." rowspan="1" colspan="1">583 (225/1/4)</td><td align="left" rowspan="1" colspan="1">1.7</td><td align="char" char="." rowspan="1" colspan="1">46 502</td><td align="char" char="." rowspan="1" colspan="1">42 726</td><td align="left" rowspan="1" colspan="1">28</td><td align="char" char="." rowspan="1" colspan="1">39</td><td align="left" rowspan="1" colspan="1">96</td><td align="left" rowspan="1" colspan="1">0.94</td></tr><tr><td align="left" rowspan="1" colspan="1">SPSG35139</td><td align="left" rowspan="1" colspan="1">South Pacific Subtropical Gyre</td><td align="char" char="." rowspan="1" colspan="1">16</td><td align="char" char="." rowspan="1" colspan="1">61 766</td><td align="char" char="." rowspan="1" colspan="1">41.9</td><td align="char" char="." rowspan="1" colspan="1">9.33</td><td align="char" char="." rowspan="1" colspan="1">169 (71/1/0)</td><td align="left" rowspan="1" colspan="1">0.0</td><td align="char" char="." rowspan="1" colspan="1">23 083</td><td align="char" char="." rowspan="1" colspan="1">21 352</td><td align="left" rowspan="1" colspan="1">35</td><td align="char" char="." rowspan="1" colspan="1">19</td><td align="left" rowspan="1" colspan="1">97</td><td align="left" rowspan="1" colspan="1">0.85</td></tr><tr><td align="left" rowspan="1" colspan="1">SPSG35147</td><td align="left" rowspan="1" colspan="1">South Pacific Subtropical Gyre</td><td align="char" char="." rowspan="1" colspan="1">21</td><td align="char" char="." rowspan="1" colspan="1">80 088</td><td align="char" char="." rowspan="1" colspan="1">43.2</td><td align="char" char="." rowspan="1" colspan="1">9.47</td><td align="char" char="." rowspan="1" colspan="1">259 (97/3/1)</td><td align="left" rowspan="1" colspan="1">1.0</td><td align="char" char="." rowspan="1" colspan="1">28 681</td><td align="char" char="." rowspan="1" colspan="1">26 504</td><td align="left" rowspan="1" colspan="1">33</td><td align="char" char="." rowspan="1" colspan="1">25</td><td align="left" rowspan="1" colspan="1">93</td><td align="left" rowspan="1" colspan="1">0.83</td></tr><tr><td align="left" rowspan="1" colspan="1">WChannelApr</td><td align="left" rowspan="1" colspan="1"><xref rid="b25" ref-type="bibr">Gilbert <italic>et al</italic>.
2010</xref></td><td align="char" char="." rowspan="1" colspan="1">102</td><td align="char" char="." rowspan="1" colspan="1">278 931</td><td align="char" char="." rowspan="1" colspan="1">39.2</td><td align="char" char="." rowspan="1" colspan="1">9.01</td><td align="char" char="." rowspan="1" colspan="1">317 (64/0/6)</td><td align="left" rowspan="1" colspan="1">8.6</td><td align="char" char="." rowspan="1" colspan="1">82 819</td><td align="char" char="." rowspan="1" colspan="1">67 968</td><td align="left" rowspan="1" colspan="1">24</td><td align="char" char="." rowspan="1" colspan="1">35</td><td align="left" rowspan="1" colspan="1">90</td><td align="left" rowspan="1" colspan="1">2.91</td></tr><tr><td align="left" rowspan="1" colspan="1">WChannelJan</td><td align="left" rowspan="1" colspan="1"><xref rid="b25" ref-type="bibr">Gilbert <italic>et al</italic>.
2010</xref></td><td align="char" char="." rowspan="1" colspan="1">208</td><td align="char" char="." rowspan="1" colspan="1">548 680</td><td align="char" char="." rowspan="1" colspan="1">38.4</td><td align="char" char="." rowspan="1" colspan="1">8.97</td><td align="char" char="." rowspan="1" colspan="1">724 (195/17/6)</td><td align="left" rowspan="1" colspan="1">2.8</td><td align="char" char="." rowspan="1" colspan="1">180 844</td><td align="char" char="." rowspan="1" colspan="1">153 475</td><td align="left" rowspan="1" colspan="1">28</td><td align="char" char="." rowspan="1" colspan="1">100</td><td align="left" rowspan="1" colspan="1">74</td><td align="left" rowspan="1" colspan="1">2.08</td></tr><tr><td align="left" rowspan="1" colspan="1">MARINE (Mean)</td><td rowspan="1" colspan="1"></td><td align="char" char="." rowspan="1" colspan="1">78</td><td align="char" char="." rowspan="1" colspan="1">281 091</td><td align="char" char="." rowspan="1" colspan="1">44.9</td><td align="char" char="." rowspan="1" colspan="1">9.53</td><td align="char" char="." rowspan="1" colspan="1">583 (196/8/6)</td><td align="left" rowspan="1" colspan="1">3.2</td><td align="char" char="." rowspan="1" colspan="1">77 938</td><td align="char" char="." rowspan="1" colspan="1">69 987</td><td align="left" rowspan="1" colspan="1">25</td><td align="char" char="." rowspan="1" colspan="1">55</td><td align="left" rowspan="1" colspan="1">91</td><td align="left" rowspan="1" colspan="1">1.33</td></tr></tbody></table><table-wrap-foot><fn><p>The isoelectric point represents the average pH at which predicted genes from a specific
metagenome carry no electric charge. In column seven, numbers in parentheses represent the number of
SSU rRNA genes annotated to Bacteria, Archaea and Eukaryota, respectively.</p></fn><fn><p>COG, clusters of orthologous groups; EGS, effective genome size; QC, quality filtering; mb,
megabases; SSU rRNA, small subunit of the ribosomal RNA.</p></fn></table-wrap-foot></table-wrap><p>In order to investigate the genomic similarity between and within freshwater and marine samples,
DNA sequences were first evaluated for features that did not a priori require any taxonomic or
functional annotation. Sequences were evaluated for Guanine and Cytosine (GC) content, isoelectric
point and amino-acid usage. The GC content of the freshwater metagenome samples was 46.6% on
the average (Table <xref ref-type="table" rid="tbl2">2</xref>), ranging from 35% to
60% for the large majority of reads in each of the individual samples (Fig. S1). This
was not significantly different to the average GC content of the marine metagenome samples (Wilcoxon
test; <italic>P</italic> = 0.406) where for example the Sargasso Sea samples
(46.6–48.6% on the average) had higher GC content than the Western English Channel
(below 40%). The isoelectric points were not significantly different (Wilcoxon test;
<italic>P</italic> = 0.624) between freshwater and marine metagenomes using
Open Reading Frames (ORFs) of at least 50 aa in length predicted from six frame translation
procedures (Table <xref ref-type="table" rid="tbl2">2</xref>). Nor did the inferred amino
acid usage differ between marine and freshwater samples [permutational multivariate analysis
of variance (PERMANOVA); <italic>P</italic> = 0.432]. Specifically, we
observed no difference in the usage of sulphur-containing amino acids, methionine and cysteine, for
which an increased cost could be expected in freshwater environments. Hence, there was no convincing
evidence for ‘elemental sparing’, which has been described as adaptive selection
pressure on amino acid usage when cellular maintenance costs for protein synthesis are assumed to
affect fitness (<xref rid="b5" ref-type="bibr">Bragg and Wagner, 2009</xref>).</p></sec><sec><title>Taxonomic composition</title><p>The microbial diversity captured in the metagenomic sequences from the 25 different metagenomes
was analysed using rRNA hidden Markov models (hmm) and tblastx against Search Tool for the Retrieval
of Interacting Genes/Proteins (STRING). Identification and analysis of rRNA genes with hmm
identified 16 743 small subunit (SSU) rRNA hits, applying an e-value cut-off of 1e-10 for a
hit (Table <xref ref-type="table" rid="tbl2">2</xref>). From these, 33% were of
bacterial origin, whereas 2.2% and 0.6% were annotated to eukaryotes and archaea,
respectively, with the rest being unclassified (64%) using the SILVA database (<xref rid="b46" ref-type="bibr">Quast <italic>et al.</italic>, 2013</xref>) in combination with the
naïve Bayesian classifier (<xref rid="b58" ref-type="bibr">Wang <italic>et al.</italic>,
2007</xref>). Two lake metagenomes (Spring sample from Lake Mendota and Trout Bog Lake) had more
than 20% of eukaryotic (18S rRNA) reads annotated as mainly algal-derived. Comparing marine
and freshwater metagenomes, archaeal 16S rRNA were more common in marine systems (on average,
3.8% of the annotated SSUs in the marine vs. 0.4% in the freshwater metagenomes) when
compared with freshwaters where the proportion of eukaryotic 18S rRNA hits was higher (on average,
3.2% of the annotated SSUs in the marine vs. 10.2% in the freshwater metagenomes).
Possible explanations are upwelling events at marine sites that may contribute Archaea to surface
communities, but also general physicochemical differences between marine and freshwaters could
select for the observed patterns. The taxonomic composition of bacteria in each individual sample
was also determined by annotating 16S rRNA genes using a custom curated freshwater database (<xref rid="b42" ref-type="bibr">Newton <italic>et al.</italic>, 2011</xref>) (Fig. <xref ref-type="fig" rid="fig01">1</xref>A). Whatever database used, Proteobacteria was the dominant
bacterial phylum in all marine metagenomes. Conversely, all but five of the lake metagenomes instead
featured Actinobacteria as the most abundant phylum. In marine environments, alpha-Proteobacteria
was the dominant class within the Proteobacteria, whereas beta-Proteobacteria were more abundant in
freshwaters. Other abundant phyla in the lakes were Verrucomicrobia, Planctomycetes, Cyanobacteria,
and Bacteroidetes. Furthermore, we observed significant differences between marine and freshwater
metagenomes in community composition analysed at the phylum level (PERMANOVA,
<italic>R</italic><sup>2</sup> = 0.34,
<italic>P</italic> < 0.001). Resolving sequences to a finer taxonomic level
(roughly comparable with genus-level) revealed a dominance of previously identified typical
freshwater bacteria in the 12 lake samples, including the freshwater SAR11 (LD12), taxa within the
Actinobacterial acI lineage (acI-B1, acI-A6, acI-C2) and the beta-Proteobacterial Polynucleobacter
(Fig. <xref ref-type="fig" rid="fig01">1</xref>A; <xref rid="b42" ref-type="bibr">Newton
<italic>et al.</italic>, 2011</xref>). Moreover, this reflects previously described patterns between
systems of different trophic status where dystrophic (humic) systems such as Trout Bog Lake are
lacking most typical freshwater taxa (<xref rid="b45" ref-type="bibr">Peura <italic>et al.</italic>,
2012</xref>).</p><fig id="fig01" position="float"><label>Fig 1</label><caption><p>Heatmap of the 20 most abundant typical freshwater taxa (A) in the metagenomics datasets as
inferred from their proportion of SSU rRNA gene sequences. Typical freshwater taxa were defined
previously using a well-curated freshwater-specific phylogeny (<xref rid="b42" ref-type="bibr">Newton <italic>et al.</italic>, 2011</xref>). (B) Barplot showing taxonomic classification of
bacterial reads into phyla based on the best hit to STRING (<xref rid="b22" ref-type="bibr">Franceschini <italic>et al.</italic>, 2013</xref>).</p></caption><graphic xlink:href="emi0016-2682-f1"></graphic></fig><p>Using the taxonomic annotations of the best tblastx hit to STRING revealed patterns highly
similar to that of the SSU rRNA taxonomy where hits to bacteria dominated (on average 92%)
over hits matching archaea (2%) and eukarya (7.6%) (Fig. S2). As for most
metagenomes, the dominant portion of the reads had no hits (on average 60% for the lake and
71% for the marine data sets) in the STRING database and could thus not be taxonomically
assigned. Still, comparing freshwater and marine metagenomes revealed that hits to the bacterial
phylum Actinobacteria were more abundant in freshwater metagenomic libraries (on average 31%)
compared with marine metagenomes where hits to Proteobacteria, especially alpha-Proteobacteria, were
dominant (on average, 38%; see Fig. <xref ref-type="fig" rid="fig01">1</xref>B), thus
corroborating observations made at the SSU rRNA level. Other prominent (sub)phyla in the freshwater
metagenomes were beta-Proteobacteria (on average 24%), Bacteroidetes (on average 10%),
Cyanobacteria (on average 21%), Verrucomicrobia/Chlamydia (on average 3%) and
Planctomycetes (on average 2%). Overall, the metagenomic comparison revealed taxonomic
distributions as expected from previous studies based on clone libraries (i.e. <xref rid="b63" ref-type="bibr">Zwart <italic>et al.</italic>, 2002</xref>; <xref rid="b16" ref-type="bibr">Eiler and
Bertilsson, 2004</xref>) and fluorescence <italic>in situ</italic> hybridization (<xref rid="b26" ref-type="bibr">Glöckner <italic>et al</italic>., 1999</xref>).</p></sec><sec><title>Comparative functional metagenomics between marine and freshwater systems</title><p>Functional assignment was made on the basis of the best tblastx cluster of orthologous genes
(COGs) hit using an E-value threshold of 1e<sup>−10</sup>. To assure the best available
taxonomic representation, the STRING database was used (<xref rid="b22" ref-type="bibr">Franceschini
<italic>et al.</italic>, 2013</xref>), as it comprises over 1000 genomes of bacteria, archaea and
eukaryota compared with 66 genomes in the original COG database. The average percentage of the reads
that could be annotated (had a COG annotation) was 37% for lake and 25% for marine
metagenomes (range 19–50% per sample). The total number of annotations (COGs) per
sample ranged from about 14 500 to almost 400 000 (Table <xref ref-type="table" rid="tbl2">2</xref>). The relative abundance of best hits assigned to each major subsystem
(orthologous gene classes, OGCs) in the marine versus freshwater system is summarized in
Table <xref ref-type="table" rid="tbl3">3</xref>, showing that ‘Amino acid transport
and metabolism’ was the dominant OGC.</p><table-wrap id="tbl3" position="float"><label>Table 3</label><caption><p>Summary statistics of each OGC and their comparison between freshwater and marine metagenomes</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" colspan="2" rowspan="1"></th><th align="left" colspan="2" rowspan="1"></th><th align="center" colspan="4" rowspan="1">Wilcoxon test</th><th align="center" colspan="6" rowspan="1">Permanova</th></tr><tr><th align="left" rowspan="2" colspan="1"></th><th align="center" colspan="2" rowspan="1">Median best<xref ref-type="table-fn" rid="tf3-1">a</xref>(all<xref ref-type="table-fn" rid="tf3-2">b</xref>)</th><th align="center" rowspan="1" colspan="1"></th><th align="center" colspan="2" rowspan="1">Best<xref ref-type="table-fn" rid="tf3-1">a</xref></th><th align="center" colspan="2" rowspan="1">All<xref ref-type="table-fn" rid="tf3-2">b</xref></th><th align="center" colspan="3" rowspan="1">Best<xref ref-type="table-fn" rid="tf3-1">a</xref></th><th align="center" colspan="3" rowspan="1">All<xref ref-type="table-fn" rid="tf3-2">b</xref></th></tr><tr><th align="left" rowspan="1" colspan="1"></th><th align="left" rowspan="1" colspan="1">Fresh</th><th align="left" rowspan="1" colspan="1">Ocean</th><th align="left" rowspan="1" colspan="1">W</th><th align="left" rowspan="1" colspan="1"><italic>P</italic> value</th><th align="left" rowspan="1" colspan="1">W</th><th align="left" rowspan="1" colspan="1"><italic>P</italic> value</th><th align="left" rowspan="1" colspan="1">F</th><th align="left" rowspan="1" colspan="1">R2</th><th align="left" rowspan="1" colspan="1"><italic>P</italic> value</th><th align="left" rowspan="1" colspan="1">F</th><th align="left" rowspan="1" colspan="1">R2</th><th align="left" rowspan="1" colspan="1"><italic>P</italic> value</th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">Translation, ribosomal structure and biogenesis</td><td align="left" rowspan="1" colspan="1">J</td><td align="char" char="." rowspan="1" colspan="1">9.4%</td><td align="char" char="." rowspan="1" colspan="1">10.4%</td><td align="char" char="." rowspan="1" colspan="1">70</td><td align="left" rowspan="1" colspan="1">0.098</td><td align="char" char="." rowspan="1" colspan="1">118</td><td align="left" rowspan="1" colspan="1">0.030</td><td align="left" rowspan="1" colspan="1">4.7</td><td align="left" rowspan="1" colspan="1">0.21</td><td align="left" rowspan="1" colspan="1">0.002</td><td align="left" rowspan="1" colspan="1">6.1</td><td align="left" rowspan="1" colspan="1">0.21</td><td align="left" rowspan="1" colspan="1">0.001</td></tr><tr><td align="left" rowspan="1" colspan="1">RNA processing and modification</td><td align="left" rowspan="1" colspan="1">A</td><td align="char" char="." rowspan="1" colspan="1">0.0%</td><td align="char" char="." rowspan="1" colspan="1">0.0%</td><td align="char" char="." rowspan="1" colspan="1">38</td><td align="left" rowspan="1" colspan="1">0.473</td><td align="char" char="." rowspan="1" colspan="1">57</td><td align="left" rowspan="1" colspan="1">0.270</td><td align="left" rowspan="1" colspan="1">8.1</td><td align="left" rowspan="1" colspan="1">0.31</td><td align="left" rowspan="1" colspan="1">0.001</td><td align="left" rowspan="1" colspan="1">10.3</td><td align="left" rowspan="1" colspan="1">0.31</td><td align="left" rowspan="1" colspan="1">0.001</td></tr><tr><td align="left" rowspan="1" colspan="1">Transcription</td><td align="left" rowspan="1" colspan="1">K</td><td align="char" char="." rowspan="1" colspan="1">3.3%</td><td align="char" char="." rowspan="1" colspan="1">3.1%</td><td align="char" char="." rowspan="1" colspan="1">9</td><td align="left" rowspan="1" colspan="1">0.002</td><td align="char" char="." rowspan="1" colspan="1">11</td><td align="left" rowspan="1" colspan="1">0.000</td><td align="left" rowspan="1" colspan="1">13.0</td><td align="left" rowspan="1" colspan="1">0.42</td><td align="left" rowspan="1" colspan="1">0.001</td><td align="left" rowspan="1" colspan="1">17.1</td><td align="left" rowspan="1" colspan="1">0.43</td><td align="left" rowspan="1" colspan="1">0.001</td></tr><tr><td align="left" rowspan="1" colspan="1">Replication, recombination and repair</td><td align="left" rowspan="1" colspan="1">L</td><td align="char" char="." rowspan="1" colspan="1">8.4% (8.6%)</td><td align="char" char="." rowspan="1" colspan="1">6.4%</td><td align="char" char="." rowspan="1" colspan="1">4</td><td align="left" rowspan="1" colspan="1">0.000</td><td align="char" char="." rowspan="1" colspan="1">6</td><td align="left" rowspan="1" colspan="1">0.000</td><td align="left" rowspan="1" colspan="1">10.2</td><td align="left" rowspan="1" colspan="1">0.36</td><td align="left" rowspan="1" colspan="1">0.001</td><td align="left" rowspan="1" colspan="1">13.8</td><td align="left" rowspan="1" colspan="1">0.38</td><td align="left" rowspan="1" colspan="1">0.001</td></tr><tr><td align="left" rowspan="1" colspan="1">Chromatin structure and dynamics</td><td align="left" rowspan="1" colspan="1">B</td><td align="char" char="." rowspan="1" colspan="1">0.0%</td><td align="char" char="." rowspan="1" colspan="1">0.0%</td><td align="char" char="." rowspan="1" colspan="1">54</td><td align="left" rowspan="1" colspan="1">0.678</td><td align="char" char="." rowspan="1" colspan="1">75</td><td align="left" rowspan="1" colspan="1">0.894</td><td align="left" rowspan="1" colspan="1">3.7</td><td align="left" rowspan="1" colspan="1">0.17</td><td align="left" rowspan="1" colspan="1">0.003</td><td align="left" rowspan="1" colspan="1">3.9</td><td align="left" rowspan="1" colspan="1">0.14</td><td align="left" rowspan="1" colspan="1">0.003</td></tr><tr><td align="left" rowspan="1" colspan="1">Cell cycle control, cell division, chromosome partitioning</td><td align="left" rowspan="1" colspan="1">D</td><td align="char" char="." rowspan="1" colspan="1">1.4% (1.3%)</td><td align="char" char="." rowspan="1" colspan="1">1.5%</td><td align="char" char="." rowspan="1" colspan="1">66</td><td align="left" rowspan="1" colspan="1">0.181</td><td align="char" char="." rowspan="1" colspan="1">107</td><td align="left" rowspan="1" colspan="1">0.123</td><td align="left" rowspan="1" colspan="1">11.4</td><td align="left" rowspan="1" colspan="1">0.39</td><td align="left" rowspan="1" colspan="1">0.002</td><td align="left" rowspan="1" colspan="1">15.1</td><td align="left" rowspan="1" colspan="1">0.40</td><td align="left" rowspan="1" colspan="1">0.001</td></tr><tr><td align="left" rowspan="1" colspan="1">Nuclear structure</td><td align="left" rowspan="1" colspan="1">Y</td><td align="char" char="." rowspan="1" colspan="1">0.0%</td><td align="char" char="." rowspan="1" colspan="1">0.0%</td><td align="char" char="." rowspan="1" colspan="1">40</td><td align="left" rowspan="1" colspan="1">0.447</td><td align="char" char="." rowspan="1" colspan="1">71</td><td align="left" rowspan="1" colspan="1">0.655</td><td align="left" rowspan="1" colspan="1">NA</td><td align="left" rowspan="1" colspan="1">NA</td><td align="left" rowspan="1" colspan="1">NA</td><td align="left" rowspan="1" colspan="1">NA</td><td align="left" rowspan="1" colspan="1">NA</td><td align="left" rowspan="1" colspan="1">NA</td></tr><tr><td align="left" rowspan="1" colspan="1">Defence mechanisms</td><td align="left" rowspan="1" colspan="1">V</td><td align="char" char="." rowspan="1" colspan="1">2.0%</td><td align="char" char="." rowspan="1" colspan="1">1.7%</td><td align="char" char="." rowspan="1" colspan="1">20</td><td align="left" rowspan="1" colspan="1">0.031</td><td align="char" char="." rowspan="1" colspan="1">25</td><td align="left" rowspan="1" colspan="1">0.003</td><td align="left" rowspan="1" colspan="1">5.7</td><td align="left" rowspan="1" colspan="1">0.24</td><td align="left" rowspan="1" colspan="1">0.005</td><td align="left" rowspan="1" colspan="1">7.9</td><td align="left" rowspan="1" colspan="1">0.26</td><td align="left" rowspan="1" colspan="1">0.001</td></tr><tr><td align="left" rowspan="1" colspan="1">Signal transduction mechanisms</td><td align="left" rowspan="1" colspan="1">T</td><td align="char" char="." rowspan="1" colspan="1">1.9%</td><td align="char" char="." rowspan="1" colspan="1">1.2%</td><td align="char" char="." rowspan="1" colspan="1">1</td><td align="left" rowspan="1" colspan="1">0.000</td><td align="char" char="." rowspan="1" colspan="1">1</td><td align="left" rowspan="1" colspan="1">0.000</td><td align="left" rowspan="1" colspan="1">12.3</td><td align="left" rowspan="1" colspan="1">0.41</td><td align="left" rowspan="1" colspan="1">0.001</td><td align="left" rowspan="1" colspan="1">16.9</td><td align="left" rowspan="1" colspan="1">0.42</td><td align="left" rowspan="1" colspan="1">0.001</td></tr><tr><td align="left" rowspan="1" colspan="1">Cell wall/membrane/envelope biogenesis</td><td align="left" rowspan="1" colspan="1">M</td><td align="char" char="." rowspan="1" colspan="1">6.3%</td><td align="char" char="." rowspan="1" colspan="1">5.3%</td><td align="char" char="." rowspan="1" colspan="1">12</td><td align="left" rowspan="1" colspan="1">0.004</td><td align="char" char="." rowspan="1" colspan="1">17</td><td align="left" rowspan="1" colspan="1">0.000</td><td align="left" rowspan="1" colspan="1">10.0</td><td align="left" rowspan="1" colspan="1">0.36</td><td align="left" rowspan="1" colspan="1">0.001</td><td align="left" rowspan="1" colspan="1">14.2</td><td align="left" rowspan="1" colspan="1">0.38</td><td align="left" rowspan="1" colspan="1">0.001</td></tr><tr><td align="left" rowspan="1" colspan="1">Cell motility</td><td align="left" rowspan="1" colspan="1">N</td><td align="char" char="." rowspan="1" colspan="1">0.2%</td><td align="char" char="." rowspan="1" colspan="1">0.2%</td><td align="char" char="." rowspan="1" colspan="1">29</td><td align="left" rowspan="1" colspan="1">0.157</td><td align="char" char="." rowspan="1" colspan="1">57</td><td align="left" rowspan="1" colspan="1">0.270</td><td align="left" rowspan="1" colspan="1">2.2</td><td align="left" rowspan="1" colspan="1">0.11</td><td align="left" rowspan="1" colspan="1">0.075</td><td align="left" rowspan="1" colspan="1">2.5</td><td align="left" rowspan="1" colspan="1">0.10</td><td align="left" rowspan="1" colspan="1">0.047</td></tr><tr><td align="left" rowspan="1" colspan="1">Cytoskeleton</td><td align="left" rowspan="1" colspan="1">Z</td><td align="char" char="." rowspan="1" colspan="1">0.1%</td><td align="char" char="." rowspan="1" colspan="1">0.3%</td><td align="char" char="." rowspan="1" colspan="1">81</td><td align="left" rowspan="1" colspan="1">0.010</td><td align="char" char="." rowspan="1" colspan="1">127</td><td align="left" rowspan="1" colspan="1">0.007</td><td align="left" rowspan="1" colspan="1">3.6</td><td align="left" rowspan="1" colspan="1">0.17</td><td align="left" rowspan="1" colspan="1">0.023</td><td align="left" rowspan="1" colspan="1">3.2</td><td align="left" rowspan="1" colspan="1">0.12</td><td align="left" rowspan="1" colspan="1">0.021</td></tr><tr><td align="left" rowspan="1" colspan="1">Extracellular structures</td><td align="left" rowspan="1" colspan="1">W</td><td align="char" char="." rowspan="1" colspan="1">0.0%</td><td align="char" char="." rowspan="1" colspan="1">0.0%</td><td align="char" char="." rowspan="1" colspan="1">48</td><td align="left" rowspan="1" colspan="1">NA</td><td align="char" char="." rowspan="1" colspan="1">78</td><td align="left" rowspan="1" colspan="1">NA</td><td align="left" rowspan="1" colspan="1">NA</td><td align="left" rowspan="1" colspan="1">NA</td><td align="left" rowspan="1" colspan="1">NA</td><td align="left" rowspan="1" colspan="1">NA</td><td align="left" rowspan="1" colspan="1">NA</td><td align="left" rowspan="1" colspan="1">NA</td></tr><tr><td align="left" rowspan="1" colspan="1">Intracellular trafficking, secretion and vesicular transport</td><td align="left" rowspan="1" colspan="1">U</td><td align="char" char="." rowspan="1" colspan="1">1.3%</td><td align="char" char="." rowspan="1" colspan="1">1.4%</td><td align="char" char="." rowspan="1" colspan="1">73</td><td align="left" rowspan="1" colspan="1">0.057</td><td align="char" char="." rowspan="1" colspan="1">114</td><td align="left" rowspan="1" colspan="1">0.052</td><td align="left" rowspan="1" colspan="1">5.3</td><td align="left" rowspan="1" colspan="1">0.23</td><td align="left" rowspan="1" colspan="1">0.002</td><td align="left" rowspan="1" colspan="1">7.6</td><td align="left" rowspan="1" colspan="1">0.25</td><td align="left" rowspan="1" colspan="1">0.001</td></tr><tr><td align="left" rowspan="1" colspan="1">Posttranslational modification, protein turnover, chaperones</td><td align="left" rowspan="1" colspan="1">O</td><td align="char" char="." rowspan="1" colspan="1">5.0%</td><td align="char" char="." rowspan="1" colspan="1">5.6%</td><td align="char" char="." rowspan="1" colspan="1">82</td><td align="left" rowspan="1" colspan="1">0.007</td><td align="char" char="." rowspan="1" colspan="1">137</td><td align="left" rowspan="1" colspan="1">0.001</td><td align="left" rowspan="1" colspan="1">10.4</td><td align="left" rowspan="1" colspan="1">0.37</td><td align="left" rowspan="1" colspan="1">0.001</td><td align="left" rowspan="1" colspan="1">13.0</td><td align="left" rowspan="1" colspan="1">0.36</td><td align="left" rowspan="1" colspan="1">0.001</td></tr><tr><td align="left" rowspan="1" colspan="1">Energy production and conversion</td><td align="left" rowspan="1" colspan="1">C</td><td align="char" char="." rowspan="1" colspan="1">9.5%</td><td align="char" char="." rowspan="1" colspan="1">11.2% (11.1%)</td><td align="char" char="." rowspan="1" colspan="1">89</td><td align="left" rowspan="1" colspan="1">0.001</td><td align="char" char="." rowspan="1" colspan="1">148</td><td align="left" rowspan="1" colspan="1">0.000</td><td align="left" rowspan="1" colspan="1">6.6</td><td align="left" rowspan="1" colspan="1">0.27</td><td align="left" rowspan="1" colspan="1">0.001</td><td align="left" rowspan="1" colspan="1">9.2</td><td align="left" rowspan="1" colspan="1">0.29</td><td align="left" rowspan="1" colspan="1">0.001</td></tr><tr><td align="left" rowspan="1" colspan="1">Carbohydrate transport and metabolism</td><td align="left" rowspan="1" colspan="1">G</td><td align="char" char="." rowspan="1" colspan="1">5.5%</td><td align="char" char="." rowspan="1" colspan="1">5.3%</td><td align="char" char="." rowspan="1" colspan="1">22</td><td align="left" rowspan="1" colspan="1">0.047</td><td align="char" char="." rowspan="1" colspan="1">48</td><td align="left" rowspan="1" colspan="1">0.110</td><td align="left" rowspan="1" colspan="1">7.2</td><td align="left" rowspan="1" colspan="1">0.28</td><td align="left" rowspan="1" colspan="1">0.001</td><td align="left" rowspan="1" colspan="1">8.8</td><td align="left" rowspan="1" colspan="1">0.28</td><td align="left" rowspan="1" colspan="1">0.001</td></tr><tr><td align="left" rowspan="1" colspan="1">Amino acid transport and metabolism</td><td align="left" rowspan="1" colspan="1">E</td><td align="char" char="." rowspan="1" colspan="1">11.7%</td><td align="char" char="." rowspan="1" colspan="1">13.7%</td><td align="char" char="." rowspan="1" colspan="1">89</td><td align="left" rowspan="1" colspan="1">0.001</td><td align="char" char="." rowspan="1" colspan="1">146</td><td align="left" rowspan="1" colspan="1">0.000</td><td align="left" rowspan="1" colspan="1">11.9</td><td align="left" rowspan="1" colspan="1">0.40</td><td align="left" rowspan="1" colspan="1">0.001</td><td align="left" rowspan="1" colspan="1">14.6</td><td align="left" rowspan="1" colspan="1">0.39</td><td align="left" rowspan="1" colspan="1">0.001</td></tr><tr><td align="left" rowspan="1" colspan="1">Nucleotide transport and metabolism</td><td align="left" rowspan="1" colspan="1">F</td><td align="char" char="." rowspan="1" colspan="1">3.8%</td><td align="char" char="." rowspan="1" colspan="1">4.2%</td><td align="char" char="." rowspan="1" colspan="1">65</td><td align="left" rowspan="1" colspan="1">0.208</td><td align="char" char="." rowspan="1" colspan="1">101</td><td align="left" rowspan="1" colspan="1">0.225</td><td align="left" rowspan="1" colspan="1">6.9</td><td align="left" rowspan="1" colspan="1">0.28</td><td align="left" rowspan="1" colspan="1">0.001</td><td align="left" rowspan="1" colspan="1">8.0</td><td align="left" rowspan="1" colspan="1">0.26</td><td align="left" rowspan="1" colspan="1">0.001</td></tr><tr><td align="left" rowspan="1" colspan="1">Coenzyme transport and metabolism</td><td align="left" rowspan="1" colspan="1">H</td><td align="char" char="." rowspan="1" colspan="1">4.1%</td><td align="char" char="." rowspan="1" colspan="1">4.9%</td><td align="char" char="." rowspan="1" colspan="1">96</td><td align="left" rowspan="1" colspan="1">0.000</td><td align="char" char="." rowspan="1" colspan="1">156</td><td align="left" rowspan="1" colspan="1">0.000</td><td align="left" rowspan="1" colspan="1">7.9</td><td align="left" rowspan="1" colspan="1">0.31</td><td align="left" rowspan="1" colspan="1">0.002</td><td align="left" rowspan="1" colspan="1">10.0</td><td align="left" rowspan="1" colspan="1">0.30</td><td align="left" rowspan="1" colspan="1">0.001</td></tr><tr><td align="left" rowspan="1" colspan="1">Lipid transport and metabolism</td><td align="left" rowspan="1" colspan="1">I</td><td align="char" char="." rowspan="1" colspan="1">4.2%</td><td align="char" char="." rowspan="1" colspan="1">4.4% (4.3%)</td><td align="char" char="." rowspan="1" colspan="1">69</td><td align="left" rowspan="1" colspan="1">0.115</td><td align="char" char="." rowspan="1" colspan="1">106</td><td align="left" rowspan="1" colspan="1">0.137</td><td align="left" rowspan="1" colspan="1">8.6</td><td align="left" rowspan="1" colspan="1">0.32</td><td align="left" rowspan="1" colspan="1">0.001</td><td align="left" rowspan="1" colspan="1">11.3</td><td align="left" rowspan="1" colspan="1">0.33</td><td align="left" rowspan="1" colspan="1">0.001</td></tr><tr><td align="left" rowspan="1" colspan="1">Inorganic ion transport and metabolism</td><td align="left" rowspan="1" colspan="1">P</td><td align="char" char="." rowspan="1" colspan="1">4.2% (4.1%)</td><td align="char" char="." rowspan="1" colspan="1">4.3%</td><td align="char" char="." rowspan="1" colspan="1">58</td><td align="left" rowspan="1" colspan="1">0.473</td><td align="char" char="." rowspan="1" colspan="1">101</td><td align="left" rowspan="1" colspan="1">0.225</td><td align="left" rowspan="1" colspan="1">13.0</td><td align="left" rowspan="1" colspan="1">0.42</td><td align="left" rowspan="1" colspan="1">0.001</td><td align="left" rowspan="1" colspan="1">16.8</td><td align="left" rowspan="1" colspan="1">0.42</td><td align="left" rowspan="1" colspan="1">0.001</td></tr><tr><td align="left" rowspan="1" colspan="1">Secondary metabolites biosynthesis, transport and catabolism</td><td align="left" rowspan="1" colspan="1">Q</td><td align="char" char="." rowspan="1" colspan="1">1.8% (1.7%)</td><td align="char" char="." rowspan="1" colspan="1">1.5%</td><td align="char" char="." rowspan="1" colspan="1">27</td><td align="left" rowspan="1" colspan="1">0.115</td><td align="char" char="." rowspan="1" colspan="1">51</td><td align="left" rowspan="1" colspan="1">0.152</td><td align="left" rowspan="1" colspan="1">10.5</td><td align="left" rowspan="1" colspan="1">0.37</td><td align="left" rowspan="1" colspan="1">0.001</td><td align="left" rowspan="1" colspan="1">13.8</td><td align="left" rowspan="1" colspan="1">0.38</td><td align="left" rowspan="1" colspan="1">0.001</td></tr><tr><td align="left" rowspan="1" colspan="1">General function prediction only</td><td align="left" rowspan="1" colspan="1">R</td><td align="char" char="." rowspan="1" colspan="1">10.0%</td><td align="char" char="." rowspan="1" colspan="1">9.2% (9.1%)</td><td align="char" char="." rowspan="1" colspan="1">26</td><td align="left" rowspan="1" colspan="1">0.098</td><td align="char" char="." rowspan="1" colspan="1">38</td><td align="left" rowspan="1" colspan="1">0.030</td><td align="left" rowspan="1" colspan="1">8.8</td><td align="left" rowspan="1" colspan="1">0.33</td><td align="left" rowspan="1" colspan="1">0.001</td><td align="left" rowspan="1" colspan="1">11.7</td><td align="left" rowspan="1" colspan="1">0.34</td><td align="left" rowspan="1" colspan="1">0.001</td></tr><tr><td align="left" rowspan="1" colspan="1">Function unknown</td><td align="left" rowspan="1" colspan="1">S</td><td align="char" char="." rowspan="1" colspan="1">5.5%</td><td align="char" char="." rowspan="1" colspan="1">3.9%</td><td align="char" char="." rowspan="1" colspan="1">6</td><td align="left" rowspan="1" colspan="1">0.000</td><td align="char" char="." rowspan="1" colspan="1">7</td><td align="left" rowspan="1" colspan="1">0.000</td><td align="left" rowspan="1" colspan="1">12.2</td><td align="left" rowspan="1" colspan="1">0.40</td><td align="left" rowspan="1" colspan="1">0.001</td><td align="left" rowspan="1" colspan="1">16.0</td><td align="left" rowspan="1" colspan="1">0.41</td><td align="left" rowspan="1" colspan="1">0.001</td></tr></tbody></table><table-wrap-foot><fn id="tf3-1"><label>a</label><p>Smaller data set of eight lake and 12 ocean samples, excluding worst quality samples (see
Table S1).</p></fn><fn id="tf3-2"><label>b</label><p>Full data set of 12 lake and 13 ocean samples (see Table S1).</p></fn><fn><p>Average and standard deviation are derived from the relative fraction of OCGs averaged over all
marine and freshwater metagenomes, respectively. <italic>P</italic>-values and W statistics from
Wilcoxon test on the contribution of ORFs to each OGC as well as results from PERMANOVA to test for
differences in functional composition between marine and freshwaters using normalized COGs from each
OGC.</p></fn><fn><p>NA, Not Assessed.</p></fn></table-wrap-foot></table-wrap><p>Counts for 35 marker COGs were used to approximate the average effective genome size in
freshwater and marine microbial communities. The estimated average effective genome size for
freshwaters (1.91) was slightly higher than for the selected marine systems (1.33,
Table <xref ref-type="table" rid="tbl2">2</xref>) (Wilcoxon test;
<italic>P</italic> < 0.003) where the latter estimates are similar to previous
estimates for marine plankton (<xref rid="b47" ref-type="bibr">Raes <italic>et al.</italic>,
2007</xref>; <xref rid="b9004" ref-type="bibr">Quaiser <italic>et al.</italic>, 2011</xref>). These
findings corroborate the widespread assumption that small and streamlined genomes are a more common
feature of bacterioplankton from oligotrophic sites (<xref rid="b9001" ref-type="bibr">Giovannoni
<italic>et al.</italic>, 2005</xref>; <xref rid="b9002" ref-type="bibr">Grote <italic>et
al.</italic>, 2012</xref>) compared with those that reside in more productive waters such as
eutrophic freshwater lakes (i.e. lakes Ekoln, Erken and Mendota; see also <xref rid="b43" ref-type="bibr">Oh <italic>et al.</italic>, 2011</xref>). Discrepancies in estimated genome sizes to
previously published estimates (Lake Lanier, our estimate 1.78 vs. published 2.2) are most likely
due to differences in databases and quality filtering used.</p><p>COGs were normalized against best hits to 35 likely essential and single copy COGs
(Table S3; <xref rid="b9" ref-type="bibr">Ciccarelli <italic>et al.</italic>, 2006</xref>;
<xref rid="b47" ref-type="bibr">Raes <italic>et al.</italic>, 2007</xref>) without taking read
length into account prior to statistical analyses. Each of these single copy COGs had, on average,
77 hits in the 25 metagenomes (range 11–279, representing averages from single metagenomes).
To assess whether or not each biome had a distinct functional profile, an ordination was conducted
using an occurrence matrix of COGs in nonmetric multidimensional scaling (metaMDS function in R;
<xref rid="b44" ref-type="bibr">Oksanen <italic>et al.</italic>, 2008</xref>). PERMANOVA (<xref rid="b1" ref-type="bibr">Anderson, 2001</xref>) corroborated the visual impression (Fig. S3)
of a significant difference in functional beta-diversity between marine and freshwater systems
(PERMANOVA; <italic>P</italic> < 0.001,
<italic>R</italic><sup>2</sup> = 0.34). These differences were maintained even
if low-quality metagenomes were excluded (PERMANOVA;
<italic>P</italic> < 0.001,
<italic>R</italic><sup>2</sup> = 0.34), or when only bacterial COGs where
analysed (PERMANOVA; <italic>P</italic> < 0.001,
<italic>R</italic><sup>2</sup> = 0.35) and when specific OGCs were analysed
separately (Table <xref ref-type="table" rid="tbl3">3</xref>). The most pronounced difference
in the composition of OGCs was observed for the OGC ‘ion transport and metabolism’ and
‘transcription’, whereas the composition within OGCs ‘Cytoskeleton’ and
‘Cell motility’ were the least separated. Moreover, we also looked for proportional
differences at the level of OGC by using Wilcoxon test. Overall, OGCs ‘energy production and
conversion’ and ‘coenzyme transport and metabolism’ were under-represented in
freshwater metagenomes, whereas core functions involved in ‘transcription’, and
‘replication, recombination and repair’ were over-represented when compared with
marine samples (Table <xref ref-type="table" rid="tbl3">3</xref>). The higher proportion of
the OGC ‘signal transduction’ in freshwater than marine metagenomes suggest that
freshwater microbial communities feature more complex interactions and cellular controls that may
involve cell-to-cell communication.</p><p>This was also reflected in a more detailed analysis based on the Wilcoxon test where all COGs
differing in resampled and normalized occurrence between marine and freshwater systems were tested.
Out of 707 COGs identified as significantly different in their prevalence between the marine and
freshwater metagenomes (<italic>P</italic> < 0.01 and false discovery rate
< 0.027), and 560 significantly different
(<italic>P</italic> < 0.01) when excluding low-quality metagenomes, limited the
list to COGs significant for both all and best data sets to 102 COGs that were over-represented in
the marine and 295 in the freshwater metagenomes (Fig. <xref ref-type="fig" rid="fig02">2</xref>, Table S4). For example, core functions belonging to ‘transcription’
such as transcriptional regulators, for example arginine repressor (bacterial) was significantly
over-represented in lakes (<italic>P</italic> < 0.001). ‘Replication,
recombination and repair’ was represented by numerous transposases, several helicases, and
the recombination repair proteins RecF and RecB, which were all significantly over-represented in
the lake metagenomes (all bacterial, <italic>P</italic> < 0.01). Other COGs
over-represented in freshwaters were related to a phosphorus starvation-inducible protein
<italic>phoH</italic> (cog1875, <italic>P</italic> < 0.001), a growth inhibitor
(cog2337, <italic>P</italic> < 0.002) and two response regulators (cog3707,
<italic>P</italic> < 0.001; cog4566,
<italic>P</italic> < 0.001). Homologues to subunits of archaeal polymerases
such as COG1311 (archaeal DNA polymerase II, SSU/DNA polymerase delta, subunit B) and COG1933
(archaeal DNA polymerase II, large subunit) were over-represented in marine metagenomes
(<italic>P</italic> < 0.005 and <italic>P</italic> < 0.001
respectively). With regards to metabolism, differences between freshwater and marine metagenomes
were limited to few key enzymes (Fig. <xref ref-type="fig" rid="fig02">2</xref>). Examples of
this is the significant over-representation of malate synthase homologues (cog2225,
<italic>P</italic> < 0.001) and isocitrate lyase (cog2224,
<italic>P</italic> < 0.002) in the marine biome, both coding for enzymes with a
central function in the glyoxylate cycle. The isocitrate lyase catalyses the cleavage of isocitrate
to succinate and glyoxylate, and the malate synthase feeds glyoxylate into the tricarboxylic acid
cycle (TCA) via oxalacetate (known as the glyoxylate shunt). This allows microorganisms to utilize
simple carbon compounds as a carbon source when complex sources such as glucose are not available.
In the absence of available carbohydrates, the glyoxylate cycle permits the synthesis of
carbohydrates needed for cell-wall assembly from lipids via acetate. In contrast, reads annotated as
being involved in carbohydrate metabolism (i.e. ‘phosphoenolpyruvate-protein kinase’
cog1080, <italic>P</italic> < 0.001; ‘Fructose-1-phosphate kinase and
related fructose-6-phosphate kinase’ cog1105,
<italic>P</italic> < 0.002) seem to be more common in freshwater as compared
with marine metagenomes, where such genes were never significantly over-represented. This included
galactose-1-phosphate uridylyltransferase (cog1085, <italic>P</italic> < 0.001)
a putative enzyme central to the Leloir pathway involved in the catalyses between galactose and
glucose. Another interesting finding was that homologues of enzymes that hydrolyse glycolipids,
glycoproteins, lactose and galactosides to monosaccharides such as alpha- (cog3345,
<italic>P</italic> < 0.001) and beta-galactosidases (cog3250,
<italic>P</italic> < 0.004) were over-represented in freshwater metagenomes.
Also, other homologues to enzymes catalysing the hydrolysis of glycosidic linkages were
over-represented in the freshwaters metagenomes, including chitinase (cog3179,
<italic>P</italic> < 0.001), glycotransferase (cog438,
<italic>P</italic> < 0.001) and glycosidase (cog2723,
<italic>P</italic> < 0.001; cog366,
<italic>P</italic> < 0.001), known to mediate the production of oligosaccharide
and monosaccharide from chitin, cellulose and hemicelluloses. This is consistent with a recent
finding that the genomes of the abundant acI-B1 taxon of freshwater Actinobacteria are enriched with
glycosidase homologues when compared with other bacterial genomes (<xref rid="b23" ref-type="bibr">Garcia <italic>et al.</italic>, 2012</xref>).</p><fig id="fig02" position="float"><label>Fig 2</label><caption><p>Heatmap of COGs showing only those that were either significantly over- (A) and under-represented
(B) in freshwater metagenomes when compared with marine metagenomes after resampling and
normalization against single-copy core COGs. Significantly over- and under-represented COGs were
identified by Wilcoxon test (<italic>P</italic> < 0.01) when testing all data
sets, as well as the best data sets only, and the subsequent estimation of false discovery rate
(q < 0.027). These lists are not exhaustive and only include well-characterized
COGs. COGs mentioned in the text are indicated. Dendograms from hierarchical cluster analysis based
on displayed COGs are shown at the top of each graph.</p></caption><graphic xlink:href="emi0016-2682-f2"></graphic></fig><p>Moreover, freshwater microbial genomes seem to harbour a higher proportion of certain putative
genes involved in transport of sugars such as xylose (cog4213,
<italic>P</italic> < 0.001; cog4214,
<italic>P</italic> < 0.001) and various polysaccharides (cog1134,
<italic>P</italic> < 0.001; cog1682,
<italic>P</italic> < 0.001; cog3833
<italic>P</italic> < 0.001) (Fig. <xref ref-type="fig" rid="fig02">2</xref>). A similar pattern was also observed for genes involved in transport of peptides (cog410,
<italic>P</italic> < 0.001; cog411,
<italic>P</italic> < 0.001; cog4177,
<italic>P</italic> < 0.002). In contrast, ORFs putatively identified as
ATP-dependent amino-acid transporters (cog2113, <italic>P</italic> < 0.00009;
cog4160, <italic>P</italic> < 0.001; cog4175,
<italic>P</italic> < 0.001; cog4176,
<italic>P</italic> < 0.002; cog4215,
<italic>P</italic> < 0.001; cog4597,
<italic>P</italic> < 0.001) were significantly over-represented in marine
metagenomes. We propose that the compositional differences in amino acid and carbohydrate metabolism
is a consequence of major differences in the overall composition of organic substrates available for
heterotrophs in the respective biomes. Freshwater systems, including the temperate systems of this
study, are highly influenced by allochthonous organic matter inputs from the catchment as well as
plant-derived polysaccharides (e.g. xylose-containing hemicellulose) inputs from the littoral zone,
whereas marine systems are less influenced by organic matter loadings from such terrestrial
surroundings and littoral fringe zones and instead rely largely on autochthonous organic matter
inputs from plankton rich in proteinaceous materials (<xref rid="b14" ref-type="bibr">Duarte and
Cebrián, 1996</xref>; <xref rid="b4" ref-type="bibr">Bertilsson and Jones, 2003</xref>).</p><p>ORFs putatively involved in acquisition of phosphate (cog573/cog581,
<italic>P</italic> < 0.003/0.006; cog1117,
<italic>P</italic> < 0.002) including phosphate uptake regulators (cog704,
<italic>P</italic> < 0.003) and sulphate (cog555,
<italic>P</italic> < 0.007; cog1118,
<italic>P</italic> < 0.001; cog1613,
<italic>P</italic> < 0.002; cog4208,
<italic>P</italic> < 0.001) were mostly over-represented in freshwater genomes.
The over-representation of exopolyphospatase (cog248,
<italic>P</italic> < 0.001) and polyphosphate kinase (cog855,
<italic>P</italic> < 0.001) homologues supports the previously recognized role
of polyphosphates as a form of phosphorus storage in freshwater environments (<xref rid="b7" ref-type="bibr">Broberg and Persson, 1988</xref>; <xref rid="b29" ref-type="bibr">Ilikchyan <italic>et
al.</italic>, 2009</xref>). We did not observe any significant differences in nitrogen metabolism
and uptake between the marine and often more productive freshwater systems. The previously inferred
reliance on potassium instead of sodium for osmoregulation was a typical feature of the freshwater
metagenomes as well as a higher representation of reads annotated as cobalt, magnesium and nickel
transporter systems (Fig. <xref ref-type="fig" rid="fig02">2</xref>A). In contrast,
homologues of zinc and manganese transporters were over-represented in marine metagenomes
(Fig. <xref ref-type="fig" rid="fig02">2</xref>B). This confirms previously reported
differences in osmoregulatory traits between freshwater and marine microorganisms inferred from
comparative metagenomics of microbial communities (<xref rid="b43" ref-type="bibr">Oh <italic>et
al.</italic>, 2011</xref>). These findings are also consistent with recent results based on
comparisons of 16S rRNA gene libraries (<xref rid="b63" ref-type="bibr">Zwart <italic>et
al.</italic>, 2002</xref>; <xref rid="b38" ref-type="bibr">Lozupone and Knight, 2007</xref>; <xref rid="b37" ref-type="bibr">Logares <italic>et al.</italic>, 2009</xref>; <xref rid="b42" ref-type="bibr">Newton <italic>et al.</italic>, 2011</xref>) where salinity was suggested to represent a
strong environmental barrier for microorganisms. Our results also point to the importance of factors
other than salinity, at least when comparing marine and freshwater environments with regards to
substrate availability and substrate acquisition. As illustrated earlier, microbial communities in
these contrasting biomes seem to have different metabolic capabilities as genes involved in amino
acid metabolism were over-represented in marine metagenomes when compared with freshwater
metagenomes, and clear differences in the strategies of carbohydrate metabolism were observed.</p></sec><sec><title>Comparing functional profiles among freshwater systems</title><p>When freshwater functional profiles were analysed by non-metric multidimensional scaling, it was
apparent that Sparkling Lake and Trout Bog Lake metagenomes were rather distinct from the others
(Fig. <xref ref-type="fig" rid="fig03">3</xref>). This can at least partly be attributed to
their high amounts of eukaryotic sequences. An additional non-exclusive explanation may be trophic
status: Trout Bog Lake was the only humic (dystrophic) system, whereas Sparkling Lake was the most
oligotrophic system in the study. Interestingly, we observed a significant correlation between the
overall functional composition and TP, a widely used proxy for ecosystem productivity (<xref rid="b52" ref-type="bibr">Schindler, 1978</xref>)
(<italic>R</italic><sup>2</sup> = 0.53,
<italic>P</italic> = 0.029; Fig. <xref ref-type="fig" rid="fig03">3</xref>). The correlation was even more significant if only bacteria were taken into account
(<italic>R</italic><sup>2</sup> = 0.52,
<italic>P</italic> = 0.018). The observation that the functional profile of one
metagenome from Yellowstone Lake was very different from the others was probably caused by the
proximity of this sample to a thermal vent and the associated higher temperature and different ion
composition. For a more detailed analysis, we relied on maximal information-based non-parametric
exploration (MINE; <xref rid="b49" ref-type="bibr">Reshef <italic>et al.</italic>, 2011</xref>)
statistics for identifying and classifying relationships between the proportion of COGs and TP. We
used a maximal information coefficient (MIC) > 0.54 (uncorrected
<italic>P</italic> < 0.05 and false discovery rate < 2.56e-07) to
identify COGs that were significantly related to productivity (TP) in the sampled lakes. A total of
183 COGs of 3335 COGs tested were identified using these criteria, whereof 34 COGs were positively
related to TP (Table S5). An inverse relationship to TP was observed for certain active
transporters of phosphonates (cog3454, cog4107) and organic compounds such as amino acids (cog559,
cog1147, cog4177). Homologues to other active transporters such as permeases (cog2998, cog4603,
cog5265) that facilitate the transport of for example nitrate and sulphate (cog619, cog659) were
negatively related with TP. The number of predicted homologues to phosphoserine phosphatase (cog560)
and serine acetyltransferase (cog1045) genes involved in amino acid metabolism was negatively
correlated with TP as were genes with a crucial role in carbohydrate degradation (cog153, cog1082,
cog3250). Other gene products that could be useful for diagnostics of metabolic processes were
carbon-monoxide dehydrogenase CoxLMS subunits (CO oxidation) that were significantly negatively
related to TP. These genes are involved in the oxidation of CO to CO<sub>2</sub> and represent an
alternative or supplementary energy source that is widespread in marine bacteria (<xref rid="b31" ref-type="bibr">King and Weber, 2007</xref>; <xref rid="b6" ref-type="bibr">Brinkhoff
<italic>et al.</italic>, 2008</xref>). CO-dehydrogenase genes were detected at higher relative
abundance in three lakes with low levels of TP: Trout Bog, Damariscotta and Vättern.</p><fig id="fig03" position="float"><label>Fig 3</label><caption><p>Non-metric multidimensional scaling plot of microbial functional diversity along a productivity
gradient (stress-value = 0.10). This plot is based on Horn–Morisita
distances from COGs lists of 12 freshwater metagenomes. Total phosphors (TP) was mapped as en
environmental variable vector onto the ordination using R function (TP) ‘envfit’.
NMDS, Non-parametric-Multi-Dimensional-Scaling.</p></caption><graphic xlink:href="emi0016-2682-f3"></graphic></fig><p>The significant relationships observed between TP and COG patterns inferred by MINE mainly
provide new genome-level confirmation of earlier empirical findings of how microbial processes such
as sugar, amino acid and phosphate acquisition strategies are structured along productivity
gradients but also identify variations in the occurrence of response regulators that allow microbes
to sense and to react to environmental stress (i.e. cog589).</p></sec><sec><title>Phylogenetic analyses of selected functional genes and the correspondence between functional
and taxonomic composition</title><p>Phylogenetic trees were constructed for a selected number of proteins including the mmoA, nirK,
pstA/B, RuBisCo and the nifH/bchL/chlL family, including Swiss-Prot references and their homologues
in the metagenomes (Fig. S4). The selected genes are involved in key biogeochemical processes
including methane oxidation, denitrification, phosphorus uptake, CO<sub>2</sub> fixation, nitrogen
fixation and the synthesis of photopigments. Obtained phylogenies were analysed to infer the
phylogenetic structuring between and within freshwater and marine sequences using PYLOCOM (<xref rid="b60" ref-type="bibr">Webb <italic>et al.</italic>, 2011</xref>). Resulting beta nearest taxon
indexes (βNTIs; see <italic>Experimental procedures</italic>) from the functional genes were
compared with βNTI derived for the 16S rRNA (Table <xref ref-type="table" rid="tbl4">4</xref>). These comparisons revealed that proteins, similar to the 16S rRNA, exhibit phylogenetic
overdispersion between biomes when compared with random phylogenetic structures. This infers that
freshwater and marine protein sequences are more different from each other than expected by chance
(<xref rid="b60" ref-type="bibr">Webb <italic>et al.</italic>, 2011</xref>). This suggests that
these key functional genes from marine and freshwater biomes are usually not closely related and
often group into distinct marine and freshwater phylogenetic clusters, similar to what has been
reported before for the 16S rRNA marker gene (<xref rid="b37" ref-type="bibr">Logares <italic>et
al.</italic>, 2009</xref>).</p><table-wrap id="tbl4" position="float"><label>Table 4</label><caption><p>Results from phylogenetic analyses estimating beta-NTI within and between marine and freshwater
sequences</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="1" colspan="1">BetaNTI</th><th rowspan="1" colspan="1"></th><th align="left" rowspan="1" colspan="1">Freshwater</th><th align="left" rowspan="1" colspan="1">Marine</th></tr></thead><tbody><tr><td align="left" rowspan="6" colspan="1">Freshwater</td><td align="left" rowspan="1" colspan="1">16S</td><td align="char" char="." rowspan="1" colspan="1">−7.775</td><td rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">bcn</td><td align="char" char="." rowspan="1" colspan="1">−1.989</td><td rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">nirK</td><td align="char" char="." rowspan="1" colspan="1">0.036</td><td rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">RuBisCo</td><td align="char" char="." rowspan="1" colspan="1">15.345</td><td rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">pstA/B</td><td align="char" char="." rowspan="1" colspan="1">−3.156</td><td rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">mmoA</td><td align="char" char="." rowspan="1" colspan="1">12.287</td><td rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="6" colspan="1">Marine</td><td align="left" rowspan="1" colspan="1">16S</td><td align="char" char="." rowspan="1" colspan="1">−27.123</td><td align="char" char="." rowspan="1" colspan="1">21.883</td></tr><tr><td rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">bcn</td><td align="char" char="." rowspan="1" colspan="1">−4.866</td><td align="char" char="." rowspan="1" colspan="1">1.754</td></tr><tr><td rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">nirK</td><td align="char" char="." rowspan="1" colspan="1">−3.736</td><td align="char" char="." rowspan="1" colspan="1">0.278</td></tr><tr><td rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">RuBisCo</td><td align="char" char="." rowspan="1" colspan="1">−232.179</td><td align="char" char="." rowspan="1" colspan="1">30.088</td></tr><tr><td rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">pstA/B</td><td align="char" char="." rowspan="1" colspan="1">−1.654</td><td align="char" char="." rowspan="1" colspan="1">3.144</td></tr><tr><td rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">mmoA</td><td align="char" char="." rowspan="1" colspan="1">−90.028</td><td align="char" char="." rowspan="1" colspan="1">22.273</td></tr></tbody></table><table-wrap-foot><fn id="tf4-1"><p>Genes annotated as 16S rRNA and related to functional genes such as mmoA, nirK, pstA/B, RuBisCo,
and the nifH/bchL/chlL family.</p></fn><fn id="tf4-2"><p>Values above +2 indicate phylogenetic clustering, whereas a NTI below −2 indicates
overdispersion.</p></fn></table-wrap-foot></table-wrap><p>To determine if 16S rRNA-derived taxonomic and functional profiles among the metagenomes were
coherent, a procrustes analysis was performed (<xref rid="b44" ref-type="bibr">Oksanen <italic>et
al.</italic>, 2008</xref>). Our results demonstrate that the known rRNA-inferred microbial community
shifts across the freshwater to marine gradient are reflected also in cohesive shifts in
community-level functions observed in the metagenomes. 16S rRNA taxonomy resolved to either
genus/typical freshwater taxa or phylum levels were significantly correlated with the functional
data based on COG annotations (<italic>R</italic> = 0.95 and
<italic>R</italic> = 0.83, respectively,
<italic>P</italic> < 0.001 using procrustes analysis). When lake data were
analysed separately, the procrustes analyses between 16S rRNA community composition (both phylum and
genus composition) and functional COG annotations revealed similarly high coefficient values
(<italic>R</italic> = 0.74 and <italic>R</italic> = 0.84,
respectively, using procrustes analysis), but because these analyses included fewer samples,
<italic>P</italic>-values increased dramatically (<italic>P</italic> < 0.033
and <italic>P</italic> < 0.11 respectively). This suggests that the taxonomic
composition as inferred by phylogenetic markers (i.e. 16S rRNA gene) and the functional potential of
communities are linked through evolutionary history. Still, it remains to be shown whether this
implies that differentiation at the fine-scale population level has only minor effects on the
overall gene content and potential subsequent ecosystem function, or instead is mainly determined by
distribution patterns of broad taxonomic groups.</p></sec></sec><sec><title>Outlook</title><p>Our metagenomic analyses of pelagic microbial communities in lakes and oceans suggest that many
core functions are shared across these two biomes. Although the functional overlap is substantial,
our analyses also point to some profound functional differences. Because of the rather shallow
coverage of the underlying genetic diversity in the metagenomes analysed here, many genes or gene
categories were not sufficiently abundant in the data set to determine with any certainty, whether
or not there were significant changes in their relative abundances across the freshwater marine
boundary or across the freshwater productivity gradient. This applies to genes associated with less
widespread metabolic processes that may nevertheless be of critical importance to carbon and
nitrogen cycling in these aquatic systems (including genes associated with N cycling, chitin
degradation and ammonia oxidation). Forthcoming deeper metagenomic sequencing will likely capture
trends also in these genes across environmental gradients and will help build a more comprehensive
understanding of how the functional capabilities of aquatic microbial communities change along
salinity and productivity gradients. Nevertheless, the present comparison of freshwater and marine
metagenomes based on whole-genome shotgun sequence data did provide functional, phylogenetic and
taxonomic trends across these gradients and will help us design biogeochemical experiments to test
metagenome-inferred predictions such as differences in substrate preferences. Examples are the
inferred prevalence towards amino acids in marine systems and difference in carbohydrate metabolism
between marine and freshwaters, and the over-representation of homologues involved in the oxidation
of recalcitrant organic matter in oligotrophic lakes compared with eutrophic lakes.</p></sec><sec sec-type="methods"><title>Experimental procedures</title><sec><title>Sample characterization and DNA extraction</title><p>For Lakes Vättern, Ekoln and Erken, integrated water samples from the upper 2 m
were collected with a rinsed 2 m Polyvinyl chloride (PVC) tube. Samples were sieved through
an autoclaved 100 μm nylon mesh prior to further processing. Samples were kept dark at
near <italic>in situ</italic> temperature and upon return to the laboratory, microbial cells from
between 0.5 and 1 l of water were collected on replicated 0.2 μm membrane
filters (Supor 200, 47 mm diameter; Gelman) by vacuum filtration followed by freezing at
−80°C until further analyses. Water temperature profiles measured on site at the time
of sampling verified that the sampling was limited to the upper mixed layer (epilimnion). TP and
dissolved organic carbon was measured using standard methods as previously described (<xref rid="b18" ref-type="bibr">Eiler <italic>et al.</italic>, 2012</xref>). Community DNA was extracted
from individual membrane filters using the FASTDNA spin kit for soil (QBiogene, Carlsbad, CA, USA)
as recommended by the manufacturer. At least three membrane filters were extracted to recover
sufficient DNA for 454 pyrosequencing. The amount and quality of recovered DNA was quantified by
spectrophotometry at 260 and 280 nm, and aqarose gel electrophoresis revealed DNA with an
average molecular weight exceeding 20 kb. All three metagenome samples were sequenced with
454 pyrosequencing with Titanium chemistry using half a chip for the Lake Erken metagenome and one
quarter of a chip for Ekoln and Vättern (separated by sample specific molecular barcodes).
Samples were collected from the epilimnia of Damariscotta Lake, Lake Mendota, Sparkling Lake, and
Trout Bog Lake, and sequenced as described elsewhere (Martinez-<xref rid="b40" ref-type="bibr">Garcia <italic>et al.</italic>, 2012</xref>). Sequences are publicly available through the European
Nucleotide Archive under project PRJEB4844.</p></sec><sec><title>Data mining</title><p>Metagenome data from Lake Lanier (<xref rid="b43" ref-type="bibr">Oh <italic>et al.</italic>,
2011</xref>) and two samples from Yellowstone Lake (T. McDermott, unpubl. data) were acquired from
SRA (fastq-files) and analysed following the quality control and annotation procedures as described
later. Fasta files for all selected marine samples were downloaded from Community
cyberinfrastructure for Advanced Microbial Ecology Research and Analysis (CAMERA) (<xref rid="b53" ref-type="bibr">Seshadri <italic>et al.</italic>, 2007</xref>). Annotations were performed
as described later.</p></sec><sec><title>Sequence annotation and functional assignment</title><p>Preprocessing was performed to bring all data sets (fasta and quality files) to the same starting
point. This procedure included the following steps: length filter (length > 150) and
quality filter (mean quality > 21) for lake metagenomes and just a length filter for
marine metagenomes (upper length filter as listed in Table S2), clustering artificial
duplicates with cd-hit-454 (<xref rid="b3" ref-type="bibr">Beifang <italic>et al.</italic>,
2010</xref>) using 97% identity threshold and 80% of the sequence in the alignment,
and finally creation of consensus sequences from the clusters with cdhit-cluster consensus ignoring
terminal gaps (-maxlen = 1). Quality-filtered data sets were used in all
analyses. Simple six-frame translation with 50 aa length threshold was used for
non-annotation-based analyses (aa usage and isoelectric point). COG annotations of the reads were
extracted from the best tblastx hit against STRING (<xref rid="b22" ref-type="bibr">Franceschini
<italic>et al.</italic>, 2013</xref>), and rRNAs were identified using hmm rRNA to obtain
annotations. An E-value threshold of 1e-10 was applied.</p><p>We also performed a second preprocessing and annotation procedure, and subsequent statistical
analyses in which results supported the main findings presented earlier. In short, a more stringent
quality filtering was performed with cutting reads when quality scores dropped below 21 and using a
length cut-off of 150 bp. Clustering artificial duplicates was performed as described
earlier. The quality-filtered data sets were then submitted to CAMERA using Rapid Analysis of
Multiple Metagenomes with a Clustering and Annotation Pipeline (RAMMCAP) (<xref rid="b53" ref-type="bibr">Seshadri <italic>et al.</italic>, 2007</xref>; <xref rid="b61" ref-type="bibr">Weizhong,
2009</xref>) with the following parameters: six-frame translation, hmm rRNA and annotation (no
clustering), which masks tRNAs and rRNAs before calling ORFs. Subsequently, Reversed Position
Specific-Basic Local Alignment Search Tool (RPS-BLAST) was performed against COG (<xref rid="b55" ref-type="bibr">Tatusov <italic>et al.</italic>, 2003</xref>). An E-value threshold of
1e-10 was applied. Fasta files for the marine data sets downloaded from CAMERA were used without any
quality filtering, except cd-hit-454 for artificial duplicate removal.</p></sec><sec><title>Statistical analyses</title><p>To ensure robustness of the statistical tests to outliers, we have compared the results using
three types of data sets: all COGs from 12 lake and 13 marine samples; all COGs from 8 lake and 12
marine samples; and only bacterial COGs from 12 lake and 13 marine samples. The smaller number of
samples for the second set resulted from excluding samples with the worst quality processing
results. Bacterial COGs are used to address the issues of varied eukaryotic content between the
samples. The abundance of individual reads matching a particular COG were normalized against the
average abundance of 35 likely essential and single copy COGs (<xref rid="b9" ref-type="bibr">Ciccarelli <italic>et al.</italic>, 2006</xref>; <xref rid="b47" ref-type="bibr">Raes <italic>et
al.</italic>, 2007</xref>) and used to generate a metabolic profile of the metagenome. This provides
a proxy for the number of genomes harbouring a specific COG in the community. Core-gene normalized
profiles were then used in statistical analyses such as metaMDS, PERMANOVA and procrustes test with
Horn-Morisita distance measure using the functions in the ‘ecodist’ and
‘vegan’ libraries in the R-package (<ext-link ext-link-type="uri" xlink:href="http://www.r-project.org">http://www.r-project.org</ext-link>; <xref rid="b27" ref-type="bibr">Goslee and Urban, 2007</xref>;
<xref rid="b44" ref-type="bibr">Oksanen <italic>et al.</italic>, 2008</xref>). PERMANOVA (<xref rid="b1" ref-type="bibr">Anderson, 2001</xref>) was used to determine significant differences
between freshwater and marine functional beta-diversity, and procrustes analysis was used to
determine correspondence between taxonomic and functional composition. To fit TP as an environmental
vector onto the ordination, we used the function ‘envfit’. The fitted vector is an
arrow that points to the direction of its most rapid change in the ordination space (direction of
the gradient), and its length is proportional to the correlation between community composition and
TP. Prior to applying the Wilcoxon test, COGs were resampled and then normalized against the
single-copy core COGs to identify over- and under-represented COGs in freshwater compared with
marine metagenomes. False discovery rate (q-value) was estimated after <xref rid="b54" ref-type="bibr">Storey (2002</xref>). MINE (<xref rid="b49" ref-type="bibr">Reshef <italic>et
al.</italic>, 2011</xref>) was used with default settings for identifying and classifying
relationships between the resampled and normalized COG abundances, and TP that was used as a proxy
of lake productivity (<xref rid="b52" ref-type="bibr">Schindler, 1978</xref>). Relationships were
defined as significant when the MIC was > 0.54 with a <italic>P</italic>-value
< 0.01 and a false discovery rate < 2.56e-07.</p></sec><sec><title>Taxonomic assignments</title><p>Protein-based taxonomic assignments for domain and phylum were extracted from the best hit to the
STRING database (E-value threshold 10<sup>−10</sup>). In addition, SSU rRNAs were extracted
by hmm rRNA. The Bayesian classifier (<xref rid="b58" ref-type="bibr">Wang <italic>et al.</italic>,
2007</xref>) (using bootstrap cut-off > 60) was used to annotate 16S rRNA genes
against a custom curated freshwater-specific database (<xref rid="b42" ref-type="bibr">Newton
<italic>et al.</italic>, 2011</xref>) and the SILVA database using taxonomy after SILVA (<xref rid="b46" ref-type="bibr">Quast <italic>et al.</italic>, 2013</xref>). The number of reads annotated
to the different bacterial phyla and bacterial ‘genera’ were extracted and ordinated
using R (<xref rid="b44" ref-type="bibr">Oksanen <italic>et al.</italic>, 2008</xref>). A procrustes
test was used to compare the functional annotations with the taxonomic annotations at the genus
level.</p></sec><sec><title>Phylogenetic analyses</title><p>Reference (master) sequences for mmoA, nirK, pstA/B, RuBisCo and the nifH/bchL/chlL family were
obtained from Swiss-Prot. After six-frame-shift translation of the sequences (using a minimum length
of 50 aa), homologous ORFs in the 29 metagenomes were identified based on blastp searches
using an E-value threshold of 1e-10 and per cent identity of 40%. Alignments of master
sequences were obtained for each of the five genes using Multiple sequence comparison by
log-expectation (MUSCLE) (default settings; <xref rid="b15" ref-type="bibr">Edgar, 2004</xref>).
Preliminary multiple sequence alignment were obtained for the metagenomic ORFs by MUSCLE using
settings -maxiters 1 and -diags to increase speed. These ‘slave’ alignments were then
aligned against the master alignment with muscle using function ‘-profile’.
Bootstrapped Random Axelerated Maximum Likelihood (RAxML) trees (<xref rid="b9005" ref-type="bibr">Stamatakis <italic>et al.</italic>, 2008</xref>) were computed based on trimmed master alignments
using standard model JTTF and default convergence criteria. Trees and alignments were imported into
ARB (<xref rid="b39" ref-type="bibr">Ludwig <italic>et al.</italic>, 2004</xref>), and the quick
parsimony option was used to add the aligned metagenomic ORFs to the RAxML master trees. For 16S
rRNA genes, the procedure outlined in <xref rid="b45" ref-type="bibr">Peura and colleagues
(2012)</xref> was used to insert metagenomic 16S rRNA homologues into the SILVA106 reference tree.
Phylogenetic trees were visualized using iTOL (<xref rid="b34" ref-type="bibr">Letunic and Bork,
2011</xref>) and analysed using PHYLOCOM (<xref rid="b60" ref-type="bibr">Webb <italic>et
al.</italic>, 2011</xref>). The phylocom function ‘comdistnt’ was used to infer if
freshwater and marine sequences were phylogenetically distinct by estimating the βNTI. Here,
we used both the marine and freshwater biomes as separate groups. Mean nearest taxon distance (MNTD)
was estimated for within each biome and between biomes. To weigh phylogenetic distances by taxa
abundances, the average distance among random individuals drawn from each of the two biomes was
calculated. The NTI was quantified by the number of standard deviations that the observed MNTD is
from the mean of the null distribution (999 randomizations; MNTDnull). MNTDnull is found by
randomizing OTUs across the phylogeny and recalculating MNTD 999 times.</p><p>NTI = −1*(MNTDobs-mean MNTDnull/sdMNTDnull) (<xref rid="b59" ref-type="bibr">Webb <italic>et al.</italic>, 2002</xref>).</p><p>For a single community, NTI greater than +2 indicates that coexisting taxa are more
closely related than expected by chance (phylogenetic clustering). NTI less than −2 indicates
coexisting taxa are more distantly related than expected by chance (phylogenetic overdispersion).
βNTI is the between-group analogue of NTI (<xref rid="b20" ref-type="bibr">Fine and Kembel,
2011</xref>; <xref rid="b60" ref-type="bibr">Webb <italic>et al.</italic>, 2011</xref>).</p></sec></sec></body><back><ack><p>We want to thank the Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX)
for access to data storage and computing resources under project b2011105. This work was supported
by the Swedish Foundation for Strategic Research (Grant Number ICA10-0015 to AE), the Swedish
Research Council (Grant Numbers 349-2007-831, 621-2008-3259 and 621-2011-4669 to SGEA; 2009-3784,
2008-1923 and 2012-3892 to SB), the National Science Foundation [Awards CBET-0644949
(CAREER), MCB-0702653 (Microbial Observatories Program) to KD and DEB-841933 to RS],
DEB-0822700 (Long Term Ecological Research, NTL LTER to KDM), the European Union (grant to SGEA),
the Göran Gustafsson Foundation (grant to SGEA), the Knut and Alice Wallenberg Foundation
(Grant Numbers KAW-2011.0148 and KAW-2012.0075 to SGEA), and the Swedish Wennergren Foundation (to
KDM and SB). Pyrosequencing was partially supported by an instrument grant from the K&A
Wallenberg foundation. Friederike Heinrich and Lorena Grubisic assisted with sampling and provided
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publisher's web-site:</p><supplementary-material content-type="local-data" id="SD1"><label>Fig S1</label><caption><p> Boxplots depicting the GC % of reads from each metagenome.</p></caption><media mimetype="eps" mime-subtype="eps" xlink:href="emi0016-2682-SD1.eps" xlink:type="simple" id="d35e5792" position="anchor"></media></supplementary-material><supplementary-material content-type="local-data" id="SD2"><label>Fig S2</label><caption><p> MEGAN classification into Bacteria, Archaea, Eukaryota and viruses.</p></caption><media mimetype="eps" mime-subtype="eps" xlink:href="emi0016-2682-SD2.eps" xlink:type="simple" id="d35e5799" position="anchor"></media></supplementary-material><supplementary-material content-type="local-data" id="SD3"><label>Fig S3</label><caption><p> Non-metric multidimensional scaling plot comparing marine and freshwater metagenomes
(stress-value = 0.10). This plot is based on Horn–Morisita distances
from COGs abundance lists of 25 marine and freshwater metagenomes.</p></caption><media mimetype="tif" mime-subtype="tif" xlink:href="emi0016-2682-SD3.tif" xlink:type="simple" id="d35e5806" position="anchor"></media></supplementary-material><supplementary-material content-type="local-data" id="SD4"><label>Fig S4</label><caption><p> Examples for phylogenetic trees of metagenomic sequences representing homologues of the
nirK (A), pstA/B (B) and the nifH/bchL/chlL family (C). Trees were constructed by using the quick
parsimony option (in ARB) to add aligned metagenomic sequences to RAxML master trees. For the 16S
rRNA gene, the SILVA106 reference tree was used as the master tree. Blue indicates sequences
obtained from marine, whereas red indicates samples from freshwater systems.</p></caption><media mimetype="eps" mime-subtype="eps" xlink:href="emi0016-2682-SD4.eps" xlink:type="simple" id="d35e5813" position="anchor"></media></supplementary-material><supplementary-material content-type="local-data" id="SD5"><label>Table S1</label><caption><p> Characteristics of marine metagenomes used for comparative analyses.</p></caption><media mimetype="xls" mime-subtype="xls" xlink:href="emi0016-2682-SD5.xls" xlink:type="simple" id="d35e5820" position="anchor"></media></supplementary-material><supplementary-material content-type="local-data" id="SD6"><label>Table S2</label><caption><p> Information about the number of raw reads and the removal of reads during the
preprocessing steps. The last columns represent the number of reads and basepairs used for
subsequent analyses.</p></caption><media mimetype="xls" mime-subtype="xls" xlink:href="emi0016-2682-SD6.xls" xlink:type="simple" id="d35e5827" position="anchor"></media></supplementary-material><supplementary-material content-type="local-data" id="SD7"><label>Table S3</label><caption><p> List of single copy core COGs) used for normalization (<xref rid="b9" ref-type="bibr">Ciccarelli <italic>et al</italic>., 2006</xref>; <xref rid="b47" ref-type="bibr">Raes
<italic>et al</italic>., 2007</xref>).</p></caption><media mimetype="xls" mime-subtype="xls" xlink:href="emi0016-2682-SD7.xls" xlink:type="simple" id="d35e5846" position="anchor"></media></supplementary-material><supplementary-material content-type="local-data" id="SD8"><label>Table S4</label><caption><p> COGs that were significantly over- or under-represented in the freshwater metagenomes
when compared with the marine metagenomes. Results from Wilcoxon test.</p></caption><media mimetype="xls" mime-subtype="xls" xlink:href="emi0016-2682-SD8.xls" xlink:type="simple" id="d35e5853" position="anchor"></media></supplementary-material><supplementary-material content-type="local-data" id="SD9"><label>Table S5</label><caption><p> COGs that were significantly related with total phosphorus concentrations in the
freshwater metagenomes. Results from MINE (Reshef <italic>et al</italic>., 2011).</p></caption><media mimetype="xls" mime-subtype="xls" xlink:href="emi0016-2682-SD9.xls" xlink:type="simple" id="d35e5863" position="anchor"></media></supplementary-material></sec></back></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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