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Microbial communities in oxygen minimum zones (OMZs) are known to have significant impacts on global biogeochemical cycles, but viral influence on microbial processes in these regions are much less studied. Here we provide baseline ecological patterns using microscopy and viral metagenomics from the Eastern Tropical North Pacific (ETNP) OMZ region that enhance our understanding of viruses in these climate-critical systems. While extracellular viral abundance decreased below the oxycline, viral diversity and lytic infection frequency remained high within the OMZ, demonstrating that viral influences on microbial communities were still substantial without the detectable presence of oxygen. Viral community composition was strongly related to oxygen concentration, with viral populations in low-oxygen portions of the water column being distinct from their surface layer counterparts. However, this divergence was not accompanied by the expected differences in viral-encoded auxiliary metabolic genes (AMGs) relating to nitrogen and sulfur metabolisms that are known to be performed by microbial communities in these low-oxygen and anoxic regions. Instead, several abundant AMGs were identified in the oxycline and OMZ that may modulate host responses to low-oxygen stress. We hypothesize that this is due to selection for viral-encoded genes that influence host survivability rather than modulating host metabolic reactions within the ETNP OMZ. Together, this study shows that viruses are not only diverse throughout the water column in the ETNP, including the OMZ, but their infection of microorganisms has the potential to alter host physiological state within these biogeochemically important regions of the ocean.

This article is contribution number 131 of Tara Oceans.-- 11 pages, 5 figures, 1 table, 1 box, Microbial communities have essential roles in ocean ecology and planetary health. Microbes participate in nutrient cycles, remove huge quantities of carbon dioxide from the air and support ocean food webs. The taxonomic and functional diversity of the global ocean microbiome has been revealed by technological advances in sampling, DNA sequencing and bioinformatics. A better understanding of the ocean microbiome could underpin strategies to address environmental and societal challenges, including achievement of multiple Sustainable Development Goals way beyond SDG 14 ‘life below water’. We propose a set of priorities for understanding and protecting the ocean microbiome, which include delineating interactions between microbiota, sustainably applying resources from oceanic microorganisms and creating policy- and funder-friendly ocean education resources, and discuss how to achieve these ambitious goals, We thank R. Zaayman-Gallant, T. Rauscher and F. Ibarbalz for preparation of the figures, and the European Union’s Horizon 2020 research and innovation project AtlantECO, under grant agreement no. 862923, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)

Additional file 1: Figure S1. Heatmap showing % average nucleotide identities (ANI) similarities among the different strains and populations in the 30 mock communities. Figure S2. Validating MetaPop���s macrodiversity and codon bias analyses. (A) Tornado plot showing the relative abundances of Staphylococcus aureus, Staphylococcus epidermidis, and Bacillus subtilis across the 30 mock communities in the actual synthesized community and as determined by MetaPop. Bar charts contained within the gray bar to the left of the tornado plot reveal the number strains per each bacterial species, with three being the highest number of strains per species. (B) Boxplots showing median and quartiles of different ��-diversity indices (richness, Shannon���s H, and Peilou���s J) compared between the actual and MetaPop derived abundances. The Wilcoxon test p-values above are the result from comparing actual and MetaPop derived ��-diversity indices. (C) Heatmaps of ��-diversity Bray-Curtis dissimilarity distances calculated using the actual and MetaPop derived abundances. Figure S3. Genome map of genes with outlier codon usage in ST5 Staphylococcus aureus ECT-R2. Figure S4. Validating MetaPops���s microdiversity analyses using the Global Oceans Virome 2 dataset. (A-E, right) Line plots sorted by the original average nucleotide diversity (��) values from [20] and (A-E, left) scatter plots comparing the average �� for the Tara Oceans stations in the GOV2 dataset derived from the (A) original GOV2 values versus MetaPops���s PHRED���30 local SNP calls, (B) original GOV2 values versus MetaPops���s PHRED���30 global SNP calls, (C) original GOV2 values versus MetaPops���s PHRED���20 global SNP calls, (D) MetaPops���s PHRED���20 global SNP calls versus MetaPops���s PHRED���30 global SNP calls, and (E) MetaPops���s PHRED���30 global SNP calls versus MetaPops���s PHRED���30 local SNP calls. The dashed line in the scatter plot represents the linear regression. (F, left to right) Bar plots showing the biological microdiveristy trends across the ecological zones defined in [20] derived from the original GOV2 values, MetaPops���s PHRED���20 global SNP calls, MetaPops���s PHRED���30 global SNP calls, and PHRED���30 local SNP calls. Figure S5. Scatterplots with Loess smoothing displaying runtime per sample for the rate-limiting part of MetaPop (i.e. pre-processing and the SNP calling section of microdiversity) as a factor of file size in megabytes on (left and right) the biological datasets and (center) the synthetic dataset.

Historically, virus taxonomy has been limited to describing viruses that were readily cultivated in the laboratory or emerging in natural biomes. Metagenomic analyses, single-particle sequencing, and database mining efforts have yielded new sequence data on an astounding number of previously unknown viruses. As metagenomes are relatively free of biases, these data provide an unprecedented insight into the vastness of the virosphere, but to properly value the extent of this diversity it is critical that the viruses are taxonomically classified. Inclusion of uncultivated viruses has already improved the process as well as the understanding of the taxa, viruses, and their evolutionary relationships. The continuous development and testing of computational tools will be required to maintain a dynamic virus taxonomy that can accommodate the new discoveries. ispartof: CURRENT OPINION IN VIROLOGY vol:51 pages:1-9 ispartof: location:Netherlands status: published

This article is contribution number 120 of Tara Oceans.-- 15 pages, 4 figures, supplementary materials https://www.science.org/doi/suppl/10.1126/sciadv.abg1921/suppl_file/sciadv.abg1921_SM.pdf.-- Data and materials availability: Data described here are available at the EBI under the project identifiers PRJEB402 and PRJEB7988 and at PANGAEA (96). All data (raw abundance matrices and interactome graphML files) needed to evaluate the conclusions of the paper are available in the Supplementary Materials. A web server for exploring and searching the global ocean interactome is available at https://saas.ls2n.fr/Tara-Oceans-interactome/, Marine plankton form complex communities of interacting organisms at the base of the food web, which sustain oceanic biogeochemical cycles and help regulate climate. Although global surveys are starting to reveal ecological drivers underlying planktonic community structure and predicted climate change responses, it is unclear how community-scale species interactions will be affected by climate change. Here, we leveraged Tara Oceans sampling to infer a global ocean cross-domain plankton co-occurrence network—the community interactome—and used niche modeling to assess its vulnerabilities to environmental change. Globally, this revealed a plankton interactome self-organized latitudinally into marine biomes (Trades, Westerlies, Polar) and more connected poleward. Integrated niche modeling revealed biome-specific community interactome responses to environmental change and forecasted the most affected lineages for each community. These results provide baseline approaches to assess community structure and organismal interactions under climate scenarios while identifying plausible plankton bioindicators for ocean monitoring of climate change, We further thank the commitment of the following sponsors: CNRS (in particular Groupement de Recherche GDR3280 and the Research Federation for the study of Global Ocean Systems Ecology and Evolution, FR2022/Tara Oceans-GOSEE), European Molecular Biology Laboratory (EMBL), Genoscope/CEA, the French Ministry of Research, the French Government “Investissements d’Avenir” programmes OCEANOMICS (ANR-11-BTBR-0008), FRANCE GENOMIQUE (ANR-10-INBS-09-08), MEMO LIFE (ANR-10-LABX-54), PSL* Research University (ANR-11-IDEX-0001-02), ETH and the Helmut Horten Foundation, MEXT/JSPS/KAKENHI (projects 16H06429, 16K21723, 16H06437, and 18H02279), the Spanish Ministry of Economy and Competitiveness (project MAGGY-CTM2017-87736-R), ERC Advanced Award Diatomic (grant agreement 835067 to CB), the CNRS MITI through the interdisciplinary program Modélisation du Vivant (GOBITMAP grant to SC), and the H2020 European Commission project AtlantECO (award number 862923). […]. E.D. is supported by the RFI ATLANSTIC2020 grant (PROBIOSTIC grant to DE). M.Bu. received financial support from the French Facility for Global Environment (FFEM) as part of the “Ocean Plankton, Climate and Development” project. P.C.J. was supported by Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP (PhD grant 2017/26786-1). H.S. is supported by a Brazilian Research Council (CNPq) productivity grant (process 309514/2017-7) and FAPESP (grant 2014/14139-3). […] Additional funding from the Natural Sciences and Engineering Council (NSERC) Canada Discovery program is gratefully acknowledged., With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)

Ocean plankton comprise organisms from viruses to fish larvae that are fundamental to ecosystem functioning and the provision of marine services such as fisheries and CO2 sequestration. The latter services are partly governed by variations in plankton community composition and the expression of traits such as body size at community-level. While community assembly has been thoroughly studied for the smaller end of the plankton size spectrum, the larger end comprises ectotherms that are often studied at the species, or group-level, rather than as communities. The body size of marine ectotherms decreases with temperature, but controls on community-level traits remain elusive, hindering the predictability of marine services provision. Here, we leverage Tara Oceans datasets to determine how zooplankton community composition and size structure varies with latitude, temperature and productivity-related covariates in the global surface ocean. Zooplankton abundance and median size decreased towards warmer and less productive environments, as a result of changes in copepod composition. However, some clades displayed the opposite relationships, which may be ascribed to alternative feeding strategies. Given that climate models predict increasingly warmed and stratified oceans, our findings suggest that zooplankton communities will shift towards smaller organisms which might weaken their contribution to the biological carbon pump, This article is contribution number 121 of Tara Oceans.-- 19 pages, 6 figures, 2 tables, supplementary information https://doi.org/10.1038/s41598-021-94615-5.-- Data availability: Median ESD and abundance values by zooplankton groups are available at https://doi.org/10.17632/nwvjwccgvh.1. Zooplankton imaging datasets from the Tara Oceans expeditions are available through the collaborative web Ecotaxa application and repository under the addresses: https://ecotaxa.obs-vlfr.fr/prj/377, https://ecotaxa.obs-vlfr.fr/prj/2245, https://ecotaxa.obs-vlfr.fr/prj/378 for the WP2 net; https://ecotaxa.obs-vlfr.fr/prj/397, https://ecotaxa.obs-vlfr.fr/prj/398, https://ecotaxa.obs-vlfr.fr/prj/395 for the Bongo net; https://ecotaxa.obs-vlfr.fr/prj/415, https://ecotaxa.obs-vlfr.fr/prj/409, https://ecotaxa.obs-vlfr.fr/prj/408, https://ecotaxa.obs-vlfr.fr/prj/411, https://ecotaxa.obs-vlfr.fr/prj/412 for the Régent net. Contextual data from the Tara Oceans expedition, including those that are newly released from the Arctic Ocean, are available at https://doi.org/10.1594/PANGAEA.875582, Tara Oceans (which includes both the Tara Oceans and Tara Oceans Polar Circle expeditions) would not exist without the leadership of the Tara Expeditions Foundation and the continuous support of 23 institutes (http://oceans.taraexpeditions.org). We further thank the commitment of the following sponsors: CNRS (in particular Groupement de Recherche GDR3280 and the Research Federation for the study of Global Ocean Systems Ecology and Evolution, FR2022/Tara Oceans-GOSEE), European Molecular Biology Laboratory (EMBL), Genoscope/CEA, The French Ministry of Research, and the French Government ‘Investissements d’Avenir’ programmes OCEANOMICS (ANR-11-BTBR-0008), FRANCE GENOMIQUE (ANR-10-INBS-09-08), MEMO LIFE (ANR-10-LABX-54), and PSL Research University (ANR-11-IDEX-0001-02). M.C.B. acknowledges postdoc fellowships from the Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES) (99999.000487/2016-03) and the Fonds Français pour l'Environnement Mondial (FFEM). F.B. received support from ETH Zürich and from the European Union’s Horizon 2020 research and innovation program under grant agreement n°SEP-210591007, With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)

This article is contribution number 121 of Tara Oceans.-- 19 pages, 6 figures, 2 tables, supplementary information https://doi.org/10.1038/s41598-021-94615-5.-- Data availability: Median ESD and abundance values by zooplankton groups are available at https://doi.org/10.17632/nwvjwccgvh.1. Zooplankton imaging datasets from the Tara Oceans expeditions are available through the collaborative web Ecotaxa application and repository under the addresses: https://ecotaxa.obs-vlfr.fr/prj/377, https://ecotaxa.obs-vlfr.fr/prj/2245, https://ecotaxa.obs-vlfr.fr/prj/378 for the WP2 net; https://ecotaxa.obs-vlfr.fr/prj/397, https://ecotaxa.obs-vlfr.fr/prj/398, https://ecotaxa.obs-vlfr.fr/prj/395 for the Bongo net; https://ecotaxa.obs-vlfr.fr/prj/415, https://ecotaxa.obs-vlfr.fr/prj/409, https://ecotaxa.obs-vlfr.fr/prj/408, https://ecotaxa.obs-vlfr.fr/prj/411, https://ecotaxa.obs-vlfr.fr/prj/412 for the Régent net. Contextual data from the Tara Oceans expedition, including those that are newly released from the Arctic Ocean, are available at https://doi.org/10.1594/PANGAEA.875582, Ocean plankton comprise organisms from viruses to fish larvae that are fundamental to ecosystem functioning and the provision of marine services such as fisheries and CO2 sequestration. The latter services are partly governed by variations in plankton community composition and the expression of traits such as body size at community-level. While community assembly has been thoroughly studied for the smaller end of the plankton size spectrum, the larger end comprises ectotherms that are often studied at the species, or group-level, rather than as communities. The body size of marine ectotherms decreases with temperature, but controls on community-level traits remain elusive, hindering the predictability of marine services provision. Here, we leverage Tara Oceans datasets to determine how zooplankton community composition and size structure varies with latitude, temperature and productivity-related covariates in the global surface ocean. Zooplankton abundance and median size decreased towards warmer and less productive environments, as a result of changes in copepod composition. However, some clades displayed the opposite relationships, which may be ascribed to alternative feeding strategies. Given that climate models predict increasingly warmed and stratified oceans, our findings suggest that zooplankton communities will shift towards smaller organisms which might weaken their contribution to the biological carbon pump, Tara Oceans (which includes both the Tara Oceans and Tara Oceans Polar Circle expeditions) would not exist without the leadership of the Tara Expeditions Foundation and the continuous support of 23 institutes (http://oceans.taraexpeditions.org). We further thank the commitment of the following sponsors: CNRS (in particular Groupement de Recherche GDR3280 and the Research Federation for the study of Global Ocean Systems Ecology and Evolution, FR2022/Tara Oceans-GOSEE), European Molecular Biology Laboratory (EMBL), Genoscope/CEA, The French Ministry of Research, and the French Government ‘Investissements d’Avenir’ programmes OCEANOMICS (ANR-11-BTBR-0008), FRANCE GENOMIQUE (ANR-10-INBS-09-08), MEMO LIFE (ANR-10-LABX-54), and PSL Research University (ANR-11-IDEX-0001-02). M.C.B. acknowledges postdoc fellowships from the Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES) (99999.000487/2016-03) and the Fonds Français pour l'Environnement Mondial (FFEM). F.B. received support from ETH Zürich and from the European Union’s Horizon 2020 research and innovation program under grant agreement n°SEP-210591007, With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)

MotivationViruses infect, reprogram and kill microbes, leading to profound ecosystem consequences, from elemental cycling in oceans and soils to microbiome-modulated diseases in plants and animals. Although metagenomic datasets are increasingly available, identifying viruses in them is challenging due to poor representation and annotation of viral sequences in databases.ResultsHere, we establish efam, an expanded collection of Hidden Markov Model (HMM) profiles that represent viral protein families conservatively identified from the Global Ocean Virome 2.0 dataset. This resulted in 240 311 HMM profiles, each with at least 2 protein sequences, making efam >7-fold larger than the next largest, pan-ecosystem viral HMM profile database. Adjusting the criteria for viral contig confidence from 'conservative' to 'eXtremely Conservative' resulted in 37 841 HMM profiles in our efam-XC database. To assess the value of this resource, we integrated efam-XC into VirSorter viral discovery software to discover viruses from less-studied, ecologically distinct oxygen minimum zone (OMZ) marine habitats. This expanded database led to an increase in viruses recovered from every tested OMZ virome by ∼24% on average (up to ∼42%) and especially improved the recovery of often-missed shorter contigs (

Viruses play an important role in the ecology and biogeochemistry of marine ecosystems. Beyond mortality and gene transfer, viruses can reprogram microbial metabolism during infection by expressing auxiliary metabolic genes (AMGs) involved in photosynthesis, central carbon metabolism, and nutrient cycling. While previous studies have focused on AMG diversity in the sunlit and dark ocean, less is known about the role of viruses in shaping metabolic networks along redox gradients associated with marine oxygen minimum zones (OMZs). Here, we analyzed relatively quantitative viral metagenomic datasets that profiled the oxygen gradient across Eastern Tropical South Pacific (ETSP) OMZ waters, assessing whether OMZ viruses might impact nitrogen (N) cycling via AMGs. Identified viral genomes encoded six N-cycle AMGs associated with denitrification, nitrification, assimilatory nitrate reduction, and nitrite transport. The majority of these AMGs (80%) were identified in T4-like Myoviridae phages, predicted to infect Cyanobacteria and Proteobacteria, or in unclassified archaeal viruses predicted to infect Thaumarchaeota. Four AMGs were exclusive to anoxic waters and had distributions that paralleled homologous microbial genes. Together, these findings suggest viruses modulate N-cycling processes within the ETSP OMZ and may contribute to nitrogen loss throughout the global oceans thus providing a baseline for their inclusion in the ecosystem and geochemical models.

Additional file 1: Figure S1. Recall comparisons of tools on dsDNA phages from different data sources. VirSorter2 consistently has comparable or better performance than existing tools in identifying dsDNA phages. Genome fragments of different lengths (x-axis) are generated from genomes in the order Caudovirales in NCBI Viral RefSeq (A), proviruses extracted from microbial genomes in NCBI RefSeq (B) and other sources (C) (described in the “Training classifier” part of the Method section). An equal number (50) of viral and non-viral (archaea and bacteria, fungi and protozoa, and plasmids) genome fragments were combined as an input for the tested tools. Error bars show 95% confidence intervals over five replicates (100 sequences each as described above). Recall is used as the metric (y-axis) to compare tools. The dotted line is y = 0.8. Figure S2. Precision comparisons of tools on dsDNA phages from different data sources. Genome fragments of different lengths (x-axis) are generated from genomes in the order Caudovirales in NCBI Viral RefSeq (A), proviruses extracted from microbial genomes in NCBI RefSeq (B) and other sources (C) (described in the “Training classifier” part of the Method section). An equal number (50) of viral and non-viral (archaea and bacteria, fungi and protozoa, and plasmids) genome fragments were combined as an input for the tested tools. Error bars show 95% confidence intervals over five replicates (100 sequences each as described above). Precision is used as the metric (y-axis) to compare tools. The dotted line is y = 0.8. Figure S3. Tool performances with viral sequences having < 25% of the genes annotated as viral. Genome fragments of different lengths (x-axis) were generated from Caudovirales genomes from both NCBI RefSeq genomes and other sources. Only data sources with > 10 viral sequences that have < 25% genes annotated as viral were kept. Then equal numbers (50) of viral and non-viral (archaea and bacteria, fungi and protozoa, and plasmids) genome fragments were combined as an input for the tested tools. F1 score is used as the metric (y-axis) to compare tools. vs2 = VirSorter2; vs1 = VirSorter; vf = VirFinder; dvf = DeepVirFinder; mv = MARVEL; vb = VIBRANT. Figure S4. Importance of different features for viral sequence identification across viral groups. The y-axis shows the relative contribution of individual features in separating the training viral and nonviral (bacterial and archaea, fungi and protozoa, and plasmid) data (total is 1), provided by the Random Forest classifier after processing training data, and based on the F1 score. Top four features from each viral group (10 in total) are shown. In the features (color), “vir” (% of viral genes) is calculated as the percent of genes annotated as viral (best hit to viral HMMs) of all genes; “bac” (% of bacterial genes) is calculated as the percent of genes annotated as bacterial (best hit to bacterial HMMs) of all genes; “hallmark” (hallmark gene count) is the count of hallmark genes in a viral sequence; “mix” (% of mixed genes) is calculated as the percent of genes with best hit to HMMs not specific to virus or non-virus; “Strand_switch_perc” (Strand switching frequency) is the percent of genes located on a different strand from the previous gene (scanning from 5′ to 3′ in the + strand); “density” (Gene density) is the average number of genes in every 1000 bp sequence (total number of genes divided by contig length and then multiply by 1000); “gc_mean” (Mean GC content) is the mean of GC content of all genes in a contig; “atg_perc” (% of ATG as start codon) is the percent of genes with ATG as a starting codon; “rbs_None” is the percent of ribosomal binding sites (RBS) with no motif detected; “rbs_TATATA_3-6” is the percent of RBS with “TATATA_3-6” motif. Figure S5. Recall comparisons of tools on different viral groups (other than dsDNA phage) from different data sources. Genome fragments of different lengths (x-axis) are generated from NCBI RefSeq (“refseq”) genomes in each viral group and other sources (“non-refseq”). Then equal numbers (50) of viral and non-viral (archaea and bacteria, fungi and protozoa, and plasmids) genome fragments were combined as input for tools. Recall was used as the metric (y-axis) to compare tools. The dotted horizontal line is y = 0.8. vs2 = VirSorter2; vs1 = VirSorter; vf = VirFinder; dvf = DeepVirFinder; mv = MARVEL; vb = VIBRANT. Figure S6. Precision comparisons of tools on different viral groups (other than dsDNA phage) from different data sources. Genome fragments of different lengths (x-axis) are generated from NCBI RefSeq (“refseq”) genomes in each viral group and other sources (“non-refseq”). Then equal numbers (50) of viral and non-viral (archaea and bacteria, fungi and protozoa, and plasmids) genome fragments were combined as input for tools. Precision was used as the metric (y-axis) to compare tools. The dotted horizontal line is y = 0.8. vs2 = VirSorter2; vs1 = VirSorter; vf = VirFinder; dvf = DeepVirFinder; mv = MARVEL; vb = VIBRANT. Figure S7. False positives by VirSorter2 on eukaryotes and plasmids. Genome fragments (50) of different lengths (x-axis) are generated from eukaryotic genomes (fungi and protozoa) in NCBI RefSeq, and plasmids. Percent of genome fragments classified as viral was used as the metric (y-axis) to evaluate tools. Plot A and C show contribution of each classifier (color) to total false positives in VirSorter2 (as shown in Fig. 4) for eukaryotes and plasmid respectively. Plot B and D show the total false positive in VirSorter2 after excluding NCLDV, RNA, and Lavidaviridae classifiers. vs2 = VirSorter2. Figure S8. CPU time and peak memory comparison among tools across data sizes. Tools were run on different input sizes of 10, 100, 1000 sequences with 10 kb in length. Plot (A) shows all tools scale nearly linearly with data size, and (B) shows peak memory usage of all tools are

Additional file2: Figure S1 Ice core sampling and preparation in the laboratory. (a) The cold work room (−5°C) with band saw, BioGard laminar flow hood and wash systems. (b) the outer layer of the ice section being removed by the band saw. (c) The ice section being washed with 95% ethanol and (d) with water. (e) The “clean” inner ice is preserved in the autoclaved beakers or bottles. Figure S2 Microbial communities at genus level (a) and overlapped OTUs (b) of removed and inner ice samples collected during decontamination procedures. The most abundant genera (n = 30) and OTUs (n = 33) are illustrated. Cut, Wash and Inner represent ice samples collected from band saw scrapping, water washing and the inner ice, respectively. Figure S3 Rarefaction curves of two glacier-ice viromes by vOTU numbers. Rarefaction curves were constructed by the change of vOTUs (≥10 kb) number along sequencing depth (i.e., read number) obtained by subsampling quality-controlled reads. Figure S4 The unrooted neighbor-joining phylogenetic tree of Mu N genes from eight Methylobacterium viruses. The tree was constructed using the predicted amino acid sequences of the N genes from two glacier ice viruses (i.e., D25_14_65719 and D49_170_39214; in bold font) and six prophages identified from bacterial genomes. Each viral contig contains two copies of N genes. Viruses belonged to the same VC (i.e., VC0_0 or VC8_0) are indicated in the same color. Bootstrap values (expressed as percentages of 1,000 replications) are shown at the branch points. The scale bar indicates a distance of 0.2. Figure S5 Characterization of virus-encoded auxiliary metabolic genes (AMGs). (a) Genome map of glacier-ice virus D25_22_20338 encoding AMGs (motility genes motA and motB). CheckV was used to assess host-virus boundaries and remove potential host fractions on the viral contig (See Materials and Methods). Genes were marked by four colors to illustrate AMGs (red), phage genes (orange), potential cellular genes (green), and unaffiliated genes (grey). AMGs were detected by DRAM-v and following manual inspection; The latter three groups of genes were classified by comparing their predicted protein sequences to those of a large database of 15,958 profile hidden Markov models by CheckV and of viral genes in the extended RefSeqABVir database by VirSorter v1 in virome decontamination mode. Genes were marked as “phage genes” if they were matched to the genes of viruses in RefSeqABVir database or CheckV databases. Genes were marked as “potential cellular genes” if they were matched to the genes of bacteria or archaea by CheckV. Genes were considered “unaffiliated” if they had no hit to a sequence in RefSeqABVir or CheckV databases. (b-c) Predicted three-dimensional (3D) structures of AMG products and templates. The 3D structure of template protein for each AMG is at the right (i.e., c6ykmB and v3ckhnB). Both AMG products are linked to their closest template protein with 100% confidence score by phyre2. (d-e) Multiple alignments of protein sequences for two AMGs and 10 closest related bacteria-originated genes. The AMG and 10 closest related bacteria-related genes are numbered as 1 and 2-11, respectively. Conserved motif of the MotB was indicated by black boxes and notes (i.e., conserved peptidoglycan-binding motif). MotA does not have a conserved motif. ‘h’ indicates hydrophobic amino acid and ‘x’ indicates any amino acid. The protein sequences were aligned using MAFFT (v.7.017) with the E-INS-I strategy for 1000 iteration. The position numbers of aligned sequences are indicated at the top. Figure S6 Phylogenetic analysis of two novel AMG products MotA (A) and MotB (B). Phylogenetic trees are inferred using maximum likelihood method with amino acid sequences (see Materials and Methods). The genes from glacier-ice virus (i.e., AMGs) and the NCBI RefSeq database (release v99) are colored in red and black, respectively. The scale bars indicate a distance of 0.1. Bootstrap values (expressed as percentages of 1000 replications) ≥50 are shown at the branch points. Figure S7 Heatmap showing the viral community compositions of two glacier-ice and one river-water viromes. Glacier ice samples: D25 and D49; River water sample: RiverV. The coverages of 140 vOTUs (>10 kb; 33 and 107 vOTUs from glacier ice and river water, respectively) were normalized to per gigabase of metagenome.

14 pages, 6 figures, additional information https://doi.org/10.1038/s41564-021-00979-9.-- Data availability: Accession numbers for the data used and generated in this study can be found in Supplementary Table 12, which includes the Arctic MAGs Catalogue and their functional annotation (European Bioinformatics Institute BioStudies ID: S-BSST451) and the co-assembly of metagenomic samples used to generate the metagenomic bins (European Nucleotide Archive PRJEB41575). Accession numbers for the metagenomic and metatranscriptomic samples used in the fragment recruitment analyses can be found in Supplementary Table 13. Publicly available datasets used in this study include the following: CheckM v.1.0.11 (https://github.com/Ecogenomics/CheckM/releases/tag/v1.1.0), GTDB release 89 (https://data.gtdb.ecogenomic.org/releases/release89/), SILVA 132 (https://www.arb-silva.de/documentation/release-132/), KEGG release 89.1 (https://www.genome.jp/kegg/docs/relnote.html) and Pfam database release 31.0 (http://ftp.ebi.ac.uk/pub/databases/Pfam/releases/Pfam31.0/). Source data are provided with this paper, The role of the Arctic Ocean ecosystem in climate regulation may depend on the responses of marine microorganisms to environmental change. We applied genome-resolved metagenomics to 41 Arctic seawater samples, collected at various depths in different seasons during the Tara Oceans Polar Circle expedition, to evaluate the ecology, metabolic potential and activity of resident bacteria and archaea. We assembled 530 metagenome-assembled genomes (MAGs) to form the Arctic MAGs catalogue comprising 526 species. A total of 441 MAGs belonged to species that have not previously been reported and 299 genomes showed an exclusively polar distribution. Most Arctic MAGs have large genomes and the potential for fast generation times, both of which may enable adaptation to a copiotrophic lifestyle in nutrient-rich waters. We identified 38 habitat generalists and 111 specialists in the Arctic Ocean. We also found a general prevalence of 14 mixotrophs, while chemolithoautotrophs were mostly present in the mesopelagic layer during spring and autumn. We revealed 62 MAGs classified as key Arctic species, found only in the Arctic Ocean, showing the highest gene expression values and predicted to have habitat-specific traits. The Artic MAGs catalogue will inform our understanding of polar microorganisms that drive global biogeochemical cycles, This work acknowledges the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S). We thank the commitment of the following sponsors and research funding agencies: the Spanish Ministry of Economy and Competitiveness (project MAGGY, grant no. CTM2017-87736-R and Polar EcoGen PID2020-116489RB-I00), Horizon 2020-Research and Innovation Framework Programme (Atlantic ECOsystems assessment, forecasting & sustainability, grant no. H2020-BG-2019-2), Centre National de la Recherche Scientifique (in particular Groupement de Recherche GDR3280 and the Research Federation for the study of Global Ocean Systems Ecology and Evolution, FR2022/Tara Oceans-GOSEE), European Molecular Biology Laboratory, Genoscope/Commissariat à l’Énergie Atomique et aux Énergies Alternatives, the French Ministry of Research and the French Government’s ‘Investissements d’Avenir’ programmes OCEANOMICS (project no. ANR-11-BTBR-0008), FRANCE GENOMIQUE (project no. ANR-10-INBS-09-08), MEMO LIFE (project no. ANR-10-LABX-54), Paris Sciences et Lettres University (project no. ANR-11-IDEX-0001-02), Eidgenössische Technische Hochschule Zürich and Helmut Horten Foundation, the Swiss National Foundation (project no. 205321_184955), MEXT/JSPS/KAKENHI (project nos. 16H06429, 16K21723, 16H06437 and 18H02279)

Additional file 2. Case-study: identifying viral contig from a Tara Oceans virome dataset