Diversity and potential host-interactions of viruses inhabiting deep-sea seamount sediments

Domain: earth_science
Published: 2024-04-15
Editorial outcome: major_revision

Abstract
Seamounts are globally distributed across the oceans and form one of the major oceanic biomes. Here, we utilized combined analyses of bulk metagenome and virome to study viral communities in seamount sediments in the western Pacific Ocean. Phylogenetic analyses and the protein-sharing network demonstrate extensive diversity and previously unknown viral clades. Inference of virus-host linkages uncovers extensive interactions between viruses and dominant prokaryote lineages, and suggests that viruses play significant roles in carbon, sulfur, and nitrogen cycling by compensating or augmenting host metabolisms. Moreover, temperate viruses are predicted to be prevalent in seamount sediments, which tend to carry auxiliary metabolic genes for host survivability. Intriguingly, the geographical features of seamounts likely compromise the connectivity of viral communities and thus contribute to the high divergence of viral genetic spaces and populations across seamounts. Altogether, these findings provides knowledge essential for understanding the biogeography and ecological roles of viruses in globally widespread seamounts.

Introduction
Seamounts can be both isolated and clustered and are ubiquitous and prominent features of the world’s underwater topography, thus forming one of the major biomes of the ocean1. The geographic features of seamounts exert complex effects on oceanic circulation and mixing at a scale ranging from regional to more local effects2. Interactions between seamounts and steady and variable flows have been described, providing a better perspective for understanding the mechanisms underlying processes that influence biology3. As unique ecosystems in the deep ocean, seamounts are generally considered oases of biomass abundance and hotspots of species richness1,4. Previous studies on seamount fauna showed that seamounts have a diverse trophic architecture and tend to support aggregations of higher consumers, such as fish4.

So far, most of our knowledge on seamount biodiversity is derived from studies on seamount fauna, while the diversity and ecology of microbial communities are much less understood in general. Over the past decades, with the application of metagenomics, significant efforts have been made to explore the diversity, function, and ecology of the prokaryotes inhabiting seamount environments5–9. For example, Jacobson Meyers et al.7 explored the extracellular enzyme activity and microbial diversity on seafloor exposed basalts from Lō’ihi Seamount; they suggested that prokaryotes on basaltic rock play a substantial and quantifiable role in benthic biogeochemical processes through transforming organic matter6. Huo et al. utilized fosmid sequencing to explore the ecological functions of microbes in a sediment sample collected from the cobalt-rich ferromanganese crust of a seamount region in the central Pacific9. They suggested that microbes are involved in the nitrogen cycle, and a high frequency of horizontal gene transfer events, as well as genomic divergence, contributed to the adaption of microbes to their deep-sea environment. Collectively, these studies have suggested that the prokaryotes in seamount ecosystems have extensive diversity and play important roles. However, little is known about viral communities and their roles in seamount ecosystems.

Viruses are the most abundant and ubiquitous biological entities on the planet. The vast majority of environmental viruses are phages that infect bacteria10,11. Because of their enormous abundance and genetic diversity, viruses are major players in marine ecosystems: (1) Viruses control host abundance and affect the host community structure by killing hosts12. (2) Viruses influence host diversity, evolution, and environmental adaptation through horizontal gene transfer, resistance selection, and host metabolism programming13–15. (3) Viruses drive biogeochemical cycling by releasing intracellular organic matter from hosts and promoting the transformation of particulate organic matter to dissolved organic matter16,17. (4) Viruses assist microbial-mediated biogeochemical cycling processes by expressing auxiliary metabolic genes (AMGs)18,19.

Currently, there are significant gaps in our knowledge regarding the community structure, genetic diversity, and ecological roles of viruses in seamount ecosystems. To date, only one publication used epifluorescence microscopy to count virus-like particles (VLPs) in deep-sea sediments around two seamounts in the Tyrrhenian Sea. The results showed that benthic viral production was much higher in sediments around seamounts than in non-seamount sediments20. Moreover, only one culturable virus has been isolated from seamount environments to date21. In view of the central roles viruses play in shaping host communities and mediating biogeochemical cycles, exploring viral communities in seamount ecosystems is essential. In addition, the question of whether seamounts are isolated habitats with highly endemic faunas and related questions of connectivity have been the subject of many studies over the last 30 years1,3,4. Overall, scientists have concluded that seamounts do not generally support high levels of endemism3. However, this conclusion has been challenged by studies on certain fauna taxa whose life history is characterized by poor dispersal and thus showing low connectivity between seamounts with high endemism at a local level22. In this context, research on the effects of geographic features of seamounts on the resulting viral community may also offer novel insights into this controversial topic as well as deepen our understanding of underlying mechanisms regarding the generation and maintenance of local viral diversity.

Here, we utilized high-depth sequencing and viral-sequence specific bioinformatic tools to explore the diversities, biogeography, and potential ecological roles of viruses in seamount ecosystems. The results show that seamount sediments are reservoirs of extremely diverse and previously unknown viruses. Extensive interactions between viruses and dominant prokaryote lineages, as well as the presence of abundant AMGs in virus genomes, highlight the central roles viruses play in shaping the structure and function of seamount microbiomes and in influencing the biogeochemical processes mediated by seamount microorganisms. Furthermore, the geographical features of seamounts likely compromise the connectivity of viral communities, highlighting the important role of the topography of the deep-sea landscape in shaping local viral communities.

Results
To explore the diversity, host interaction, and ecological function of viruses inhabiting deep-sea seamount sediments, 16S rRNA genes, metagenomes, and viromes were sequenced from seven sediment samples collected from the deep-sea seamount region in the Northwest Pacific Ocean. To fully consider the effect of the geographic features of seamounts on microbial and viral communities, sediment samples were collected across the seamount region. This region encompasses the C1 basin and three surrounding seamounts (i.e., NA, NLG, and MP4) with varying sampling locations in the bottom, hillside, and summit areas (Fig. 1 and Supplementary Data 1).

Overview of prokaryotic communities
To assess the overall prokaryotic compositions in the sampled seamount sediments, the v3–v4 region of 16S rRNA genes was sequenced and analyzed. As shown in Supplementary Fig. 1, most prokaryotes in these sediments were bacteria, dominated by Chloroflexi (38.0% on average), Proteobacteria (35.7%), Planctomycetota (5.3%), SAR324 clade (Marine group B, 3.6%), Actinobacteriota (2.7%), Acidobacteriota (2.7%), Nitrospinota (1.2%), and Firmicutes (0.8%). Chloroflexi and Proteobacteria are the most abundant in all sediment samples, together accounting for more than 50% of the total relative abundance. Consistently, dominance of Proteobacteria in prokaryotic communities is also observed in a variety of deep-sea sediments, including cold seep, hydrothermal vents, and trenches23–26. Mantel’s correlation analysis was performed to explore the effects of environmental physicochemical factors on prokaryotic communities, and the results showed that depth but organic carbon and nitrogen had a significant impact on prokaryotic communities (Supplementary Fig. 2).

Denovo assembly and binning of metagenomes resulted in 136 high- or medium-quality microbial metagenome-assembled genomes (MAGs) with completeness ≥50% and contamination ≤10%27. These MAGs were then clustered at 95% average nucleotide identity (ANI) to generate 59 bacterial and three archaeal MAGs, representing species-level groups spanning 18 phyla (Fig. 2 and Supplementary Data 2). Most of the bacterial MAGs belong to dominant lineages, such as Proteobacteria (n=26), Actinobacteriota (n=5), Acidobacteriota (n=3), Planctomycetota (n=3), Nitrospirota (n=2), Chloroflexota (n=1), Nitrospinota (n=1), and SAR324 (n=1), while all archaeal MAGs belong to Thermoproteota (n=3). These results were in line with the results of previous metagenome studies on microbial mat samples collected from Lō’ihi Seamount28, and abyssal crust collected from Takuyo-Daigo Seamount29; in these samples, bacterial MAGs were dominated by Proteobacteria and archaeal MAGs accounted for less than 5% of the total MAGs. Based on the read coverage of MAGs among samples, while most of the MAGs were present in all six sediment samples, some of the MAGs were specific to certain samples; for example, bin.80 (Methylomirabilota), bin.52 (Proteobacteria), and bin.117 (Bacteroidota) were only present at sites NLG_S05, NA_S06, and MP4_S01, respectively (Fig. 2). In addition to 151 medium- and high-quality MAGs assembled from the metagenome of Axial Seamount and Lō’ihi Seamount samples30–32, as well as the publicly available microbial genomes, these MAGs provide a good basis to infer linkages between viruses and prokaryotes.

Viral community of seamount sediments
Currently, two metagenome approaches are available to study environmental viral communities, i.e., viromes of separated ambient viruses and bulk metagenomes containing sequences of diverse origins. Both approaches have powerfully expanded our knowledge of environmental viruses33–35. However, they differ greatly in their recovering efficiency of different viral populations, since viromes are greatly enriched in ambient viruses whereas bulk metagenomes are depleted from certain free viruses and enriched in actively infecting and temperate viruses (cellular fraction)36,37. Therefore, in this paper, we utilize both bulk metagenome and virome to offer complementary perspectives of viral communities in seamount sediments. Three pipelines were used to identify viral sequences from bulk metagenome and virome datasets (Supplementary Fig. 3a), resulting in 2099 putative viral sequences (Contigs ≥5kb or ≥2kb and circular). Small circular Contigs (≥2kb) were retained because they may represent small circular rep encoding single-stranded virus genomes of 2–25 kb size, such as phages belonging to Microviridae and eukaryotic viruses belonging to Circoviridae and Geminiviridae38. To obtain large viral assemblies, viral sequences were further binned using vRhyme v1.1.039; the resulting viral Contigs and MAGs were then clustered at 95% identity and 85% coverage to generate 1600 viral operational taxonomic units (vOTUs) that represent approximately species-level taxonomy40 (Supplementary Data 3). The GC content and size of these vOTUs ranged from 30.21 to 71.08% and from 2001 to 3,472,510 bp, respectively (Supplementary Fig. 3b). Fifty-nine vOTUs have a length of more than 200 Kb and possibly corresponded to giant viruses. Interestingly, three vOTUs are larger than 2.5 Mb, which is the approximately largest genome size of known isolated Pandoravirus viruses, a group of nucleocytoplasmic large DNA virus (NCLDV) related viruses that infect Ameba41,42. Further inspection of these three vOTUs showed that they all contain NCLDV marker genes, and a large fraction of their ORFs is homologous to NCLDV genomes (Supplementary Fig. 4). In addition, no known host contamination was found in these vOTUs, suggesting that they are likely bona fide NCLDV genomes. The high occurrence of extremely large viral genomes in our study may be due to the fact that we used Metaviralspades v3.15.543 for large contig assembly and vRhyme v1.1.039 for binning. Assessment of the quality of these vOTUs by CheckV v0.9.044 showed that 326 vOTUs (20.3%) were of medium quality and above, including complete (1.3%), high-quality (9.3%), and medium-quality (9.7%) (Supplementary Data 3 and Supplementary Fig. 3c). Mantel’s correlation analysis showed that none of the three measured physicochemical factors (depth, organic carbon, and nitrogen) had a significant impact on viral communities (Supplementary Fig. 2).

Taxonomic affiliations of 1600 vOTUs were determined by comparing predicted ORFs against the NCBI viral_Refseq database (v94) based on the Last Common Ancestor algorithm. As shown in Fig. 3a and Supplementary Data 3, 88% of vOTUs (n=1413) could be taxonomically affiliated at the phylum level, with primary assignment to Uroviricota (n=1298, dsDNA tailed prokaryotic virus), Nucleocytoviricota (n=60, dsDNA NCLDV), and Artverviricota (n=31, reverse transcriptase-encoding ssRNA or ssDNA virus). Among Uroviricota, a total of 21 families were identified for vOTUs, including Peduoviridae (n=36), Kyanoviridae (n=21), Autographiviridae (n=14), and Mesyanzhinovviridae (n=12) (Fig. 3b). In all Nucleocytoviricota-affiliated vOTUs, matches associated with the families Phycodnaviridae (n=26) and Mimiviridae (n=7) were the most common (Fig. 3d), whereas viruses belonging to Artverviricota are most affiliated with the family Metaviridae (n=26) (Fig. 3c).

To examine the viral community structures in seamount sediments, clean reads of virome and bulk metagenome were separately mapped to vOTUs to calculate reads per kilobase per million mapped reads (RPKM) values for each vOTU. As shown in Fig. 3e, although both virome and bulk metagenome identify abundant dsDNA viruses, ssDNA viruses and RNA viruses, they differ in the relative abundance of virus sub-populations. For example, except for Duneviridae and Tectivirida, nearly all detected dsDNA viral families showed higher relative abundance in the metagenome than in the virome. We suggest that this bias is likely caused by the filtration step used to remove hosts in the preparation of the virome, which may also remove certain large dsDNA viruses, in particular giant viruses belonging to NCLDV. In addition, a portion of these dsDNA viruses may be temperate and thus difficult to be detected by viromes. Moreover, virome and metagenome have varying effectiveness levels for detecting ssDNA viruses depending on the target viral groups. For example, Inoviridae and Circoviridae were only detected in the virome and metagenome, respectively. The divergence of virome and bulk metagenome in the detection of different virus sub-populations was also observed previously in a comparative study of human gut virome and bulk metagenome. In that study, Gregory et al. found no significant difference between the bulk metagenome and virome in terms of the number of viral Contigs recovered, but the sub-population of viruses captured by the two approaches clearly differed, and the metagenome outperformed the virome in terms of the viral detection rate36.

Seamount viruses are diverse and novel
In seven seamount sediment samples, a significant portion of vOTUs were classified as members of the Caudoviricetes class, constituting ~81% of the total identified vOTUs. Caudoviricetes form a class of tailed dsDNA phages and are usually the most retrieved dsDNA viruses in the environment33–35. It is worth noting that the viral sequenced databases still have very limited diversity represented, and the predicted dominance of Caudoviricetes in viromes is likely due to the high proportion of Caudoviricetes references in current databases. To infer DNA packaging mechanisms of these Caudoviricetes, a phylogenetic tree was generated based on a conserved gene coding for the terminase large subunit (TerL), which is necessary for DNA packaging during the maturation of tailed phages45. A total of 414 complete open reading frames (ORFs) encoding TerL were identified from seamount vOTUs and used for phylogenetic analysis along with the reference sequence (Supplementary Data 4). As shown in Supplementary Fig. 5a, seamount Caudoviricetes adopt diverse DNA packaging mechanisms, highlighting a remarkable diversity of Caudoviricetes within seamount sediment ecosystems.

ssDNA viruses are among the smallest and simplest viruses with genome sizes ranging from 2 to 25 kb. With the application of metagenomics and viromics, ssDNA viral sequences have been found to be abundant and diverse across various habitats46, including marine sediments47–49. However, in our study, only a small fraction of vOTUs (n=16) were identified as ssDNA viruses, including Microviridae (n=11), Circoviridae (n=2), Inoviridae (n=1), Spiraviridae (n=1), and Smacoviridae (n=1). For Microviridae, eight complete or near-complete genomes (4361–5527 bp) were recovered from the seamount dataset. Blastn results showed that, according to NCBI’s NT database, all of the seamount microviruses shared <93% identities relative to their best matches, highlighting the novelty of this viral family in seamount sediments. To obtain further insights into the evolution of seamount microviruses, their genome structures and gene sequence conservation levels were compared with known Microviridae genomes (Supplementary Fig. 5b). Like other known Microviridae genomes50, the characteristic genes encoding well-conserved major capsid protein (VP1), DNA pilot protein (VP2), and replication protein (VP4) were identified in the genomes of all seamount microviruses. Six seamount microviruses that are homologous to Gokushovirinae possess an additional gene coding for internal scaffolding protein (VP3) and, in most cases, also a DNA binding protein (VP5). Nevertheless, certain seamount microviruses are quite different from known Microviridae genomes in terms of their gene organization and the content of unconserved genes. For example, the viral genome homologous to group D viruses (genome V_C1_S01_k141_1198016) displays distinct gene organizations from other microviruses and contains a gene downstream of vp4 that exhibits no similarity with other group D viruses.

To further examine the diversity of viral communities in seamount sediments and their relationships with viral sequences identified from other marine habitats, a gene-sharing network was constructed using vConTACT251. Such a weighted network can assign viral sequences into viral clusters (VCs) that correspond to genus-level groups51. The vOTUs identified from GOV2.052, cold seep34, trench53, and seamount were clustered into 17,518 VCs (Fig. 4a), while taxonomically known viruses from NCBI RefSeq only formed 345 VCs; this vast difference highlights the enormous and as yet undescribed diversity of marine viruses (Fig. 4a). As the largest marine virus database to date, GOV2.0 datasets contributed the largest number of VCs (n=14,159), while trench, cold seep, and seamount databases contributed 2171, 801, and 186 VCs, respectively (Fig. 4a and Supplementary Data 5 and 6). In line with previous research34,35, only five VCs were shared among all marine ecosystems, reflecting a high degree of variations in viral communities across various marine habitats (Fig. 4b). Among the 1600 vOTUs from seamount sediments, only 353 vOTUs were clustered into 186 VCs, the majority of which (77.94%) have no homologs in other marine databases and NCBI RefSeq databases; this suggests that most seamount sediment viruses are unique to the seamount habitat (Supplementary Data 6). Among the 186 seamount sediment VCs, only 18 VCs were shared with the GOV2.0 database, 73 VCs were shared with the trench database, 4 VCs were shared with the cold seep database, and 8 VCs were shared with the NCBI RefSeq database. The remaining 79 VCs (~42%) of viruses were exclusive to seamount viruses, which may represent candidate novel genera (Fig. 4b). These seamount-specific VCs contained 193 vOTUs, with the majority (n=161, ~83%) affiliated as Caudoviricetes, followed by Nucleocytoviricota (n=16), Artverviricota (n=5), and ssDNA viruses (n=2). The remaining nine vOTUs could not be taxonomically assigned at the family or even higher level at this time.

Virus-host linkages and viral lifestyles
Viruses affect various microbe-mediated processes through interactions with their hosts13,54,55. Considering that a large fraction of microbes is infected by viruses at any given time56, the interactions between viruses and their hosts must play important roles in the dynamics, evolution, and ecology of microbial communities. To explore virus-host interactions in seamount sediments, potential hosts were predicted for 1600 vOTUs using a combination of four bioinformatic approaches, including CRISPR-spacers matching, tRNA matching, nucleotide sequence homology, and k-mer frequencies57. To predict virus-host connections, we used a combination of host databases to infer virus-host linkages, including the Genome Taxonomy database (GTDB-tk), MAGs binned from seamount samples in this study, and seamount samples from previous studies30–32 (Supplementary Data 7). As a result, 3923 virus-host linkages were predicted, most of which were predicted by tRNA-matches (n=3316), followed by nucleotide sequence homology (n=587), CRISPR-spacers matches (n=54), and k-mer frequencies (n=53) (Fig. 5a and Supplementary Data 8). Among them, 75 virus-host connections were supported by two or more prediction approaches. Consistent with previous studies34,35, putative hosts were predicted for only a small fraction (n=253, ~16%) of the 1600 seamount vOTUs. Most of these vOTUs were predicted to infect specific hosts within the same phyla, and only 51 vOTUs were linked to a broader range of hosts across different phyla. These results agree with previous observations showing that most viruses only infect a narrow range of hosts11,34,35. A total of 2007 prokaryotes were predicted to be potential hosts for seamount vOTUs, most of which (n=1953, ~97%) were predicted from the GTDB-tk database. Interestingly, all remaining potential hosts were predicted from the MAGs assembled from our study, and no hosts were predicted from MAGs assembled from other seamount metagenomes. This result implies the potential high divergence of viral communities across different seamount habitats.

Phylogenetic analysis showed that the predicted prokaryotic hosts of seamount viruses spanned two archaeal and 23 bacterial phyla (Fig. 5b). A total of 251 vOTUs were associated with bacteria. Of these, Proteobacteria was the most frequently predicted host phylum (137 associated vOTUs), followed by Actinobacteriota (43 vOTUs), Bacteroidota (26 vOTUs), Planctomycetota (15 vOTUs), and Gemmatimonadota (12 vOTUs). Most of these predicted hosts were among the most abundant bacterial lineages in the sampled seamount sediments, as indicated by the results of 16S rRNA gene profiling (Supplementary Fig. 1). For example, Proteobacteria—the dominant taxa in seamounts—contributed most (n=2763, 70.4%) of the virus-host linkages. These results are in line with the widely recognized kill-the-winner hypothesis, which suggests that abundant microbes are more likely to be infected and lysed by viruses because a high population density increases the host-virus encounter rate58. Further taxonomic analysis showed that the most frequently predicted hosts within Proteobacteria were the subgroup Gammaproteobacteria (69 associated vOTUs) and Alphaproteobacteria (33 vOTUs); both subgroups are widely distributed across marine ecosystems, typically showing high abundance in seamount sediments and other deep-sea sediments59–61. Twenty-one Proteobacteria MAGs were predicted as hosts for 99 vOTUs (Fig. 2 and Supplementary Data 8). Remarkably, according to the host metabolic capability predicted based on the presence of metabolic genes within vOTU-associated MAGs, putative virus-infecting Alphaproteobacteria and Gammaproteobacteria may play important roles in carbon, nitrogen, and sulfur cycles in seamount sediments (Supplementary Fig. 6 and Supplementary Data 9). For example, hexosaminidase-encoding gene, a gene involved in chitin degradation, is widespread in vOTU-associated Alphaproteobacteria and Gammaproteobacteria MAGs, implying potential roles of these MAGs in complex carbon degradation. In addition, several virus-infecting MAGs contain napA and nirBD genes, which are involved in the reductions of nitrate to nitrite and nitrite to ammonia, respectively. This finding is in accordance with previous studies showing that Proteobacteria play an important role in connecting nitrifying and heterotrophic microorganisms, as they can reduce nitrate to ammonia and thereby provide a nitrogen source for other microorganisms59,62. Finally, previous studies suggest that Gammaproteobacteria, an important sulfur-oxidizing and sulfuric-acid-reducing taxon, provide an important contribution to sulfur biogeochemical cycling by being involved in and even driving sulfur transformations in sediments60,61,63. Indeed, abundant dsrAB/sdo genes were identified in vOTU-associated Proteobacteria MAGs; these genes are involved in the oxidation of hydrosulphides and are important for both the detoxification and neutralization of hydrogen sulfide in sediments (Supplementary Fig. 6). Collectively, given that Proteobacteria are abundant, highly active, and frequently associated with viruses, their infections and lyses by viruses likely substantially impact the microbial community and biogeochemical cycling in seamount sediments.

As another abundant and ubiquitous group inhabiting marine sediments64, Actinobacteriota is the second most frequently predicted host phylum in seamount sediment samples and formed 946 virus-host linkages with 43 vOTUs. Four Actinobacteriota MAGs were predicted as hosts for 40 vOTUs (Fig. 2). Functional annotation of these four MAGs showed that they contain abundant genes involved in complex carbohydrate degradation, such as genes encoding for hexosaminidase, beta-glucuronidase, and isoamylase, which participate in the degradation of chitin, hemicellulose, and amylum, respectively (Supplementary Fig. 6 and Supplementary Data 9). Such complex carbohydrates are major components of crustacean shells, plant cell walls, and intercellular spaces, and they are also very difficult to degrade65; however, their biolysis is essential for biomass recycling in deep-sea sediments and critical in local and global carbon cycles. Therefore, infections and lyses of Actinobacteriota by viruses might play important roles in complex carbohydrate biolysis and carbon cycling.

Only four vOTUs were linked to archaea, including members of Thermoproteota (three vOTUs) and Crenarchaeota (one vOTU). The predicted archaeal hosts of Thermoproteota include three seamount MAGs assembled in this study (Fig. 2), which form three novel virus–host linkages (not found in the IMG/VR V3 database) with two vOTUs, including a vOTU from Caudovirales and another vOTU that could not be taxonomically assigned at this time. Based on their taxonomical annotation, these three MAGs were further affiliated with the genus DRGT01 of the phylum Thermoproteota. In the GTDB-tk database, only seven MAGs are affiliated with archaea of DRGT01, all of which are derived from sediment samples, indicating that this group maybe endemic to sediments and its connection with viruses has not been disclosed so far. As a ubiquitous group of archaea inhabiting various sediments, Thermoproteota is thought to be relevant to primary production in sediment through chemoenergetic autotrophic interactions and the ammonia oxidative metabolism66–68. Indeed, all of these three vOTU-linked seamount MAGs encode genes involved in the 3-hydroxypropionic acid/4-hydroxybutyric acid cycle that drive energy-efficient carbon fixation (Supplementary Fig. 6 and Supplementary Data 9)69.

The lifestyles for 1600 vOTUs were predicted based on lysogeny-specific features, i.e., the presence of lysogeny-specific genes (e.g., genes encoding for integrase, recombinase, and excisionase) and/or location within their host genomes. As a result, approximately one-third (n=548) of vOTUs were predicted to be lysogenic, 203 of which were predicted by both features. Based on abundances determined by read mapping, the relative abundance of temperate viruses in each sample was calculated for both virome and bulk metagenome datasets. As shown in Fig. 5c, both virome and bulk metagenome showed high occurrences of temperate viruses, accounting for averages of 34% and 27% of relative abundance in viral communities, respectively. As expected, the bulk metagenome exhibited a higher average ratio of temperate viruses than the virome, as it is conventionally enriched for genomes of temperate viruses that integrate into host genomes36,37. Given that a large fraction of viral genes is still poorly annotated at this time, certain bona fide lysogeny-specific genes may be neglected. Moreover, the incomplete assembly of viral genomes also makes it difficult to detect temperate viruses by identifying either lysogeny-specific genes or flanking host sequences, which further leads to an underestimation of lysogeny signals. Thus, temperate viruses may be even more prevalent in seamount sediments than identified in this study. This is further supported by the fact that 84% of all MAGs assembled here can be linked to vOTUs by nucleotide sequence homology matches; consequently, these may represent sequences that are acquired by the host through phage genome integrations57. The prevalence of temperate viruses has also been observed in a variety of deep-sea environments, such as deep water from the South China Sea and the western Pacific Ocean70 as well as deep-sea diffuse-flow hydrothermal vents71. Previous studies have suggested that the high occurrence of temperate viruses in the deep sea possibly promotes virus-mediated gene transfer and exchange, which may be important for the survival and stability of hosts in challenging environments70–72.

Potential impacts of viral AMGs on host metabolisms and biogeochemical cycles
Viruses can reshape the metabolism of their hosts through the expression of virus-encoded AMGs73,74. To better understand the impacts of viral AMGs on host metabolisms and relevant biogeochemical cycling, AMGs were identified from vOTUs by VIBRANT and DRAMv pipelines; they were further functionally annotated with Pfam, KEGG, and CAZy databases. As a result, after manual curation, a total of 331 genes were identified as putative AMGs (Supplementary Data 10). Because viruses generally obtain and maintain AMGs from their hosts, we further performed sequence homology searches of AMGs against the NCBI NR database to predict their putative hosts. As shown in Supplementary Fig. 7a, a large proportion (n=112, 33.8%) of these AMGs were probably derived from Proteobacteria, which is consistent with Proteobacteria being the most frequently predicted hosts for vOTUs in seamount sediments (Fig. 5a).

Based on KEGG annotation, the putative AMGs of seamount viruses were involved in diverse metabolic pathways, with a large portion participating in carbohydrate metabolism, cofactors and vitamins metabolism, as well as amino acid metabolism (Supplementary Fig. 7b). Notably, several AMGs were involved in carbon, nitrogen, and sulfur cycling. For example, six AMGs were affiliated with glycoside hydrolases predicted to catalyze the hydrolysis of complex polysaccharides, including trehalase and amylase. In marine sediments, these genes are essential for the recycling of detrital organic matter supplied from the overlying water column and thus may be critical in local and global carbon cycles75. Several AMGs were associated with sulfur cycling, including genes encoding for phosphoadenosine phosphosulphate reductase (CysH), cysteine synthase (CysK), sulfate adenylyltransferase (Sat), and methanethiol oxidase (SELENBP1). CysH and CysK participate in assimilatory sulfur reduction, whereas the Sat is involved in dissimilatory sulfur reduction/oxidation; both reactions are important for sulfur cycling76. SELENBP1 catalyzes the oxidation of methanethiol, which is a significant step in the sulfur cycle as methanethiol is an intermediate of the metabolism of globally important organosulfur compounds, including dimethylsulphoniopropionate77. The most common AMG related to nitrogen cycling is nitronate monooxygenase (ncd2), which catalyzes the oxidation of nitroalkane to nitrite78. Seamount microorganisms are involved in carbon degradation and sulfur, nitrogen, and metal cycling9,79. Therefore, the presence of these AMGs indicates that seamount viruses may extensively participate in local and global biogeochemical cycles by assisting microbes in driving biogeochemical cycles with AMGs.

To explore the relative abundance of AMGs, clean reads of both the virome and bulk metagenome were separately mapped to AMG-carrying vOTUs. As shown in Fig. 6a, virome and bulk metagenome showed different AMG profiles. In general, these results indicated a significantly elevated relative abundance of AMGs associated with processes such as protein folding, sorting, and degradation glycan biosynthesis and metabolism, xenobiotics biodegradation and metabolism, as well as lipid metabolism within the virome in comparison to bulk metagenome. Conversely, AMGs related to energy metabolism exhibited diminished abundance within the virome when compared with the metagenome. In addition, the bulk metagenome exhibited greater AMG diversity in pathways related to carbohydrate metabolism, amino acid metabolism, and metabolism of cofactors and vitamins. This clear dissimilarity in AMG compositions and the abundance between the virome and the bulk metagenome is likely caused by their bias in enrichment for viruses of different lifestyles (Fig. 5c). Luo et al. found that the viral lifestyle was more important than habitat and prokaryotic host in driving viral AMG profiles74. They suggested that lytic viruses tended to encode AMGs that could boost progeny reproduction, whereas temperate viruses tended to encode AMGs for host survivability. Consistently, similar trends were also observed in our study. For example, the virome that was more enriched for lytic viruses exhibited higher relative abundances of fabG and queC-E; fabG is involved in fatty acid synthesis, while queC-E plays a role in redirecting host protein synthesis, thus improving host translation efficiency and viral progeny reproduction80. In contrast, glycoside hydrolases were more diverse and frequently encoded in the bulk metagenome, which was more enriched for temperate viruses. Such AMGs potentially facilitate the decomposition and utilization of complex carbohydrates in hosts in deep-sea sediments, thereby enhancing host adaptation to their environments.

To gain deeper insights into the functions of seamount AMGs, the specific impacts of AMGs on the host metabolism were further examined based on predicted host-virus linkages. As shown in Fig. 6b, 16 AMG-carrying vOTUs were predicted to infect 13 seamount MAGs (Supplementary Data 11 and 12), forming 16 host-virus linkages. All of these vOTUs are tailed phages belonging to Caudoviricetes, while the hosts spanned seven phyla, including Bacteroidota, Proteobacteria, and Nitrospirota. Interestingly, all these vOTUs are predicted to be temperate viruses, which tend to encode AMGs that benefit both hosts and viruses74. Indeed, among 10 host-virus linkages, viral AMGs exert potential compensatory effects on the host metabolism because their homologs were not found in host MAGs. These AMGs were involved in a variety of host metabolisms, including energy metabolism (cysH and SELENBP1), metabolism of cofactors and vitamins (ubiG, nadM, and hspA), amino acid metabolism (tyr, dnmt1, and phnZ), glycan biosynthesis and metabolism (kdsA, nadM, and hspA), and carbohydrate metabolism (aceB). For instance, in the sulfate reduction step of the sulfur cycle pathway, the MAG of bin.117 encodes two genes (cysC and cysN) that catalyze the conversion of sulfate to phosphoadenylyl sulfate (PAPS) but lack the cysH gene that catalyzes the reduction of PAPS to sulfite (Fig. 6c). However, its associated vOTU (V_MP4_S05_vRhyme_bin_185) contains AMGs encoding for CysH. Therefore, lysogenic viral infection likely compensates for host metabolic capabilities in sulfate reduction, further highlighting the potentially important roles of viral AMGs in sulfur cycling. In the other six virus-host linkages, host MAGs contain the homologs of AMGs that are carried in associated vOTUs. The impacts of these viral AMGs on host metabolisms are unknown to date, but we suspect that they may augment host metabolic flux by overexpressing host metabolic genes81.

The impacts of seamount geographical features on local viral communities
Several studies on fauna have suggested that isolation of seamount habitats would promote localized speciation, giving rise to high levels of endemism on seamounts1,82. This so-called “seamount endemism hypothesis” was challenged by accumulating morphological and genetic evidence on the fauna, suggesting that seamounts do not generally support high levels of endemism83,84. To examine whether geographic features of seamounts cause high divergence in viral communities, we used two levels of information: (1) viral genetic space represented by protein clusters (PCs)85 and (2) species-level viral populations represented by vOTUs. Totals of 790,756 PCs and 1600 vOTUs were identified across the seven samples. The accumulation curves based on pan PCs (Fig. 7a above) and vOTUs (Fig. 7a below) showed that the 5th samples and beyond still added PCs and vOTUs, but also depicted a trend to approach a plateau. These results suggest that, although it is impossible to obtain a complete sample, viral genetics and populations (in particular dsDNA viruses) from seamount sediments are relatively well sampled. Comparative analysis showed that 66% (n=522,201) of PCs were only present within a single sample, and only 3.3% (n=25,932) of PCs were shared by all seven samples, suggesting a high divergence of viral genetic space in these samples (Fig. 7b above). Similarly, comparative analysis of vOTUs on the population level showed that most vOTUs (n=1526, 95.4%) were unique to a single sample, further supporting the remarkably high level of divergence among viruses across seamount sediments (Fig. 7b below).

To further explore whether the geographical features of seamounts contribute to the high divergence of seamount viruses, the connectivity of viral populations between neighboring seamount sampling sites was calculated. As shown in Fig. 7c, neighboring sites not separated by seamounts generally showed strong connectivity among viral populations, whereas neighboring sites across the same seamount (i.e., with the summit in the middle) showed decreased connectivity. For example, NLG_S03 and NLG_S05 sites, which are located on two different seamounts but have no large geographic barrier between them, showed high correlation in their viral communities. However, the MP4_S01 site showed considerably weaker connection with the MP4_S05 site, even though both are located on the same seamount but on opposite slopes. Moreover, regardless of the substantial difference in the depth between MP4_S03 and NLG_S05 sites, both displayed much stronger connectivity than most pairs of neighboring sites, even though some pairs have comparable depths (such as C1_S01 and NLG_S01). This observation suggests that depth is not one of the primary factors causing divergence in viral communities across seamount sites, which was also supported by the result of Pearson correlation analysis (p>0.05). Collectively, our results suggest that the physical barrier of the seamount rather than the isolation of these seamounts or site depth may explain the degree to which viruses are highly divergent across seamounts. Because viruses require host organisms to replicate, we further attempted to examine the impact of seamount geographical features on the connectivity of prokaryotes. Although the connectivity of the prokaryotic community was also generally impaired by seamounts, the prokaryotic community did not follow the same connectivity pattern that was observed for the viral community (Fig. 7c). The different geographic distribution patterns between viral and prokaryotic communities may be explained by the fact that the factors influencing prokaryotic communities are complex and diverse86,87. While the virus is indeed a significant factor in shaping host communities, other factors such as grazing and environmental conditions (e.g., depth, salinity, temperature, and nutrients) also play important roles in shaping prokaryotic communities86,87. In addition, although our results showed that virus and host communities in deep-sea seamount sediments are interacting through close associations, a substantial proportion of seamount viruses was revealed to be temperate (Fig. 5c), which tend to coexist with hosts, thus normally don’t directly impact host communities by causing host mortality. Previous studies have suggested that a significant fraction of sediment viruses are derived from sinking within the overlying seawater, either on host cells or on particulate matter88,89; some of these viruses may even persist in marine sediments for more than thousands of years88. The viral population could be passively transported on oceanic currents90 and seamounts exert complex effects on deep ocean circulation and mixing (e.g., deflection of major currents)3; therefore, we suggest that the physical barrier of the seamount suppresses the passive dispersal of ambient viruses by comprising the mixing effects of deep currents, thereby leading to a high level of divergence in viral communities. This study provides the first evidence on the effects of seamount geographic features on the assembly of local viral diversity; however, closer integration of molecular, oceanographic, geological, and ecological research with more well-characterized sediment samples is needed in the future to verify our hypothesis.

Methods
Sample collection and physicochemical measurements
Deep-sea seamount sediment samples were collected from the seamount region in the Northwest Pacific (Fig. 1 and Supplementary Data 1) during the COMRA cruise DY45 in July and August 2017. To fully consider the effect of the geographic features of seamounts on microbial and viral communities, sediment samples were collected across seamounts with varying locations in the bottom, hillside, and summit areas (Fig. 1 and Supplementary Data 1). NA sample was collected from the base of the NA Seamount, and C1_S01 sample was collected from the sediment of the C1 basin between the NA and NLG Seamounts. NLG_S01, NLG_S03, and NLG_S05 samples were collected from the base, summit, and hillside of the NLG Seamount, respectively. MP4_S01 and MP4_S05 samples were collected from the base of the MP4 Seamount but on opposite sides. The sediment samples were collected using a multi-tube sampling approach, and only sediment layers at 2–6 cm below the seafloor were selected for further analysis. The detailed geological information on collected sediments is listed in Supplementary Data 1. The collected sediment samples were frozen at −80°C on board until further analysis. Nutrient concentrations, including total nitrogen and total organic carbon, were determined at the Qingdao Science Standard Testing platform (Qingdao, China) using standard methods.

Taxonomic profiling of microbial communities by 16S rRNA gene analysis
The total DNA was extracted from sediment samples using the Powersoil DNA Isolation Kit (MoBio, USA) according to the manufacturer’s instructions. The V3–V4 region of the 16S rRNA gene was amplified from total DNA using the forward primer 341F (5′ CCTACGGGNGGCWGCAG 3′) and the reverse primer 805R (5′ GACTACHVGGGTATCTAATCC 3′). The library was prepared using the TruSeqTM DNA Sample Prep Kit (Illumina, USA) and sequenced on the Illumina MiSeq platform (Illumina Inc., San Diego, CA, USA) by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China). The raw data was first imported into the QIIME2 v2022.2.0 pipeline91 via the “tools import” command to produce a .qza format file suitable for downstream analysis. Sequence quality was assessed via the demux plugin, followed by quality control, denoising, and generation of OTU tables using the DADA2 plugin. Taxonomic annotation was performed using the SILVA database (132_99_16S) via the feature-classifier plugin.

Bulk metagenomic sequencing and assembly
For metagenomics sequencing, a paired-end library was generated from total DNA using NEXTFLEX Rapid DNA-Seq Kit (Bioo Scientific, USA) and sequenced on an Illumina NovaSeq 6000 platform (Illumina Inc., San Diego, CA, USA) by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China). The raw reads were trimmed and quality filtered using fastp v0.23.292 to generate clean reads with high quality. The Contigs were then assembled from clean reads using MEGAHIT v1.2.993 software (--k-list 21, 29, 39, 59, 79, 99, 119, and 141), and subsequently quality assessed using QUAST v5.2.094.

Generation and analysis of prokaryotic metagenome-assembled genomes
The Contigs assembled from the metagenome were binned by the MetaWRAP v1.3.095 binning module based on maxbin2, metabat1, and metabat2 methods. The original bins were refined using the MetaWRAP v1.3.095 bin_refinement module (with parameters -c 50 -m 10), which were then quality checked by CheckM v1.0.1296. The high- and medium-quality bins (completeness ≥50% and contamination ≤10%) were then aggregated and dereplicated at 95% ANI using dRrep v3.3.097, resulting in a total of 63 species-level MAGs. MAGs were taxonomically assigned using GTDB-tk v2.1.098 based on classify_wf workflows. Maximum-likelihood phylogeny of MAGs was inferred using IQ-TREE v2.2.0.399 from a concatenation of 120 bacterial or 122 archaeal marker genes produced by GTDB-tk v2.1.098; the generated tree was visualized using iTOL v4 (https://itol.embl.de/). To determine the relative abundance of MAGs in each sample, clean reads were mapped to MAGs using CoverM v0.6.1100 (with parameters -contig-m rpkm --trim-min 5 --trim-max 95) to calculate RPKM values. Functional annotation of MAGs was performed using METABOLIC v4.0101 (-m-cutoff 0.75 -kofam-db full) (Supplementary Data 9).

Virus purification
Ambient viruses were purified from seamount sediment samples according to methods described previously33. Briefly, 20 g of seamount sediment sample was suspended in 30 ml of SM solution (100 mM NaCl, 8 mM MgSO4·7H2O, and 50 mM Tris/HCl; pH 7.5), shaken for 30 min at 4°C and centrifuged at 3000 × g for 15 min at 4°C. The precipitated sediment particles were then repeatedly extracted with SM solution, and the resulting supernatants from both extractions were combined. After filtering through a 0.45 μm mesh, the viral particles in the supernatant were enriched using 100 kDa centrifugal ultrafiltration tubes by centrifugation at 4000 × g until the final sample volume measured less than 1 ml. We used 0.45 µm filters to enrich ambient viruses instead of 0.22 µm filters, because 0.45-μm filtration offers advantages such as avoidance of missing large viruses crucial for assessing diversity, more comprehensive virus detection, and reduction of processing losses102.

Virome sequencing and assembly
Prior to viral DNA extraction, virus concentrates were treated with DNase I at 37°C for 1 h to remove exogenous DNA. Encapsidated viral DNA was then extracted as described by Thurber et al.103. The library was prepared using the TruSeq DNA Sample Prep Kit (Illumina, USA) and sequenced on the Illumina HiSeq 2000 platform (Illumina Inc., San Diego, CA, USA) by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China). The raw reads were trimmed and quality filtered using fastp v0.23.292 to generate clean reads with high quality. Contigs were then assembled from clean reads using MEGAHIT v1.2.993 software (--k-list 21, 29, 39, 59, 79, 99, 119, 141).

Identification of viral sequences
To obtain as many bona fide viral sequences as possible, viral sequences were identified by the following three pipelines: (i) Contigs ≥2kb from metagenome assemblies, as well as virome assemblies, were used to recover viral sequences by VirSorter2 v2.2.3104 and VIBRANT v1.2.0105 using default settings. CheckV 0.9.044 was then used to evaluate the quality of viral sequences and remove host contaminations. Only viral sequences containing at least one of the viral hallmark genes (such as virion morphogenesis gene and terminase gene) were retained. (ii) What the Phage (Wtp) v1.1.0106 was used to identify viral sequences from virome assemblies and only complete, high-quality, or circular viral sequences recognized by CheckV v0.9.044 were retained. (iii) Metaviral SPAdes v3.15.543 was used to assemble and identify viral sequences from the clean reads of the virome, and only complete, high-quality, or circular viral sequences recognized by CheckV v0.9.044 were retained. Viral sequences identified by pipelines (i) and (ii) were combined and binned using vRhyme v1.1.039 to generate vMAGs. The remaining un-binned sequences were combined with viral sequences identified by pipeline (iii) and were regarded as vContigs. The resulting vContigs/vMAGs were de-redundant by vRhyme v1.1.039 (with parameters --derep_only --method longest --derep_id 0.95 --frac 0.80) to generate vOTUs. An overview of the bioinformatic workflow used for the identification of viral sequences is shown in Supplementary Fig. 3a.

Taxonomic assignments and abundance profiles of viruses
ORFs were predicted from vOTU sequences by Prodigal v2.6.3107 (-p meta -g 11 -m -c). Predicted ORFs were then mapped against the NCBI viral_Refseq database (2023-04-26) using CAT v5.0.3108 to determine the taxonomic affiliation of vOTUs based on the Last Common Ancestor algorithm. To determine the relative abundance of vOTUs in each sample, clean reads were mapped to vOTUs using the contig mode and genome mode of CoverM v0.6.1100 (with parameters -m rpkm --trim-min 5 --trim-max 95), to calculate RPKM values.

Mantel’s correlation analysis
Pairwise comparisons of environmental variables (organic carbon, depth, and nitrogen) were calculated by Pearson correlation analysis. The correlations between Bray–Curtis dissimilarity of prokaryotic and viral OTU profiles and Euclidean distances of environmental variables were assessed using the Mantel test with the “ggcor” package in R109,110. Each environmental variable was related to prokaryotic and viral OTU profiles by the Mantel test (function mantel; permutations = 9999 and method = “pearson”). Finally, Pearson correlations and the Mantel test were visualized using R software via the “ggcor” package109,110.

Prediction of virus-host linkages
Virus-host linkages were predicted based on four different in silico strategies35: (1) CRISPR-spacers match. A CRISPR spacer database was constructed for a set of microbial genomes using MinCED v0.4.2111. CRISPR spacers were then queried for exact sequence matches against viral Contigs using BLAST+ v2.9.0112 (with parameters -task blastn-short -word_size 7 -perc_identity 95 -qcov_hsp_perc 95). Only matches of at least 95% identity over 95% spacer length and only ≤1 mismatch were regarded as highly confident virus-host linkages. (2) tRNA match. tRNAs were identified from microbial genome dataset and vOTUs using ARAGORN v1.2.41113. Recovered tRNAs were matched using BLASTn v2.9.0112 (blastn -perc_identity 100 -qcov_hsp_perc 100), and only those with exact match (100% identity over 100% coverage) were regarded as highly confident virus-host linkages. (3) Nucleotide sequence homology. Sequences of vOTUs were compared with the dataset of microbial genomes by BLASTn v2.9.0112 (blastn -perc_identity 70 -qcov_hsp_perc 75 -e -3). The match requirements were 70% minimum nucleotide identity, 75% minimum coverage of the viral contig length, 50 minimum bit score, and 0.001 maximum e-value. (4) k-mer frequencies. WIsH v1.0114 was run using default parameters to infer a connection between viruses and hosts based on k-mer frequencies, and p ≤ 0.005 was considered as a match (Supplementary Data 8). Whenever multiple hosts were predicted for a specific vOTU, the one supported by multiple approaches was chosen. The microbial genome dataset used above was composed of (1) all reference genomes from GTDB-tk (release 207_v2, n=24,778), (2) All MAGs (≥50% completeness and ≤10% contamination, n=62) recovered from seamount metagenomes in this study, and (3) other seamount MAGs obtained from IMG/MR (n=151, Supplementary Data 7).

Life strategy prediction
Life strategies of vOTUs were predicted by the following two pipelines. (1) VIBRANT v1.2.0105 and CheckV v0.9.044 were used to infer temperate life strategies by identifying vOTUs that contain provirus integration sites or integrase genes. (2) ORFs from all vOTUs were functionally annotated using eggNOG-mapper v2.1.6115, and sequences containing lysogeny-specific genes (i.e., genes encoding for integrases, recombinases, transposases, excisionases, CI/Cro repressor, and parAB) were selected and manually inspected. The vOTUs identified by the above pipelines were considered temperate, while others were considered unknown.

Construction of phylogenetic trees on major virus groups
Phylogenetic trees of Caudovirales and Microviridae were generated based on TerL and VP1, respectively. The Contigs assembled from the seamount sediment metagenome and the virome were searched by HMMER v3.3.2116 (hmmsearch model) using the Hidden Markov Model (PF03237.hmm, PF04466.hmm, and PF05876.hmm for TerL, and PF02305.hmm fro VP1); sequences with an e-value ≤ 0.05 were retained. The obtained sequences and reference sequences (Supplementary Data 4 and 13) were aligned by MUSCLE v5.1117. The alignments were then trimmed using TrimAL v1.4.rev15118 (-automated1). The maximum-likelihood phylogenetic tree was then constructed using IQ-TREE v2.2.0.399 (-bb 1000 -nt AUTO -m MFP), and the support for nodes in the trees was evaluated with 1000 bootstrap replicates. The resulting phylogenetic trees were visualized by iTOL (https://itol.embl.de/).

AMG identification and annotation
Viral AMGs were identified and annotated using both VIBRANT v1.2.0105 and DRAMv v1.3.5119 pipelines as follows: (1) VIBRANT pipeline. The AMGs in vOTU sequences were identified by VIBRANT v1.2.0105 using default parameters. (2) DRAMv pipeline. The VirSorter2 v2.2.3104 (--prep-for-dramv) was run on the vOTU sequences, and the AMGs (auxiliary scores <4) were predicted from the resulting sequences by DRAMv v1.3.5119. The AMGs predicted by the above pipelines were combined, and manual curation was carried out to remove illegal AMGs (such as nucleotide metabolism, DNA-related reactions, modification of viral components, modification of viral components, ribosomal proteins, transcriptional/translational regulators, and viral invasion). Putative AMGs were further annotated using the dbCAN2 server (https://bcb.unl.edu/dbCAN2/) and NCBI CD-search tool (https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi) with the threshold value of e-value < 10−5. To infer the host source of AMGs, they were queried against the NR database (2021-01-07) by BLASTp. For the prediction of the AMG 3D structure, Colabfold v1.5.2120 was used (Supplementary Data 10 and 11). The relative abundance of AMGs is determined by calculating the relative abundance of vOTUs carrying those AMGs in each sample.

Comparisons to viral sequences from other marine environments and RefSeq database
To compare the viral sequences from seamount sediments with those from other marine environments and the RefSeq database, protein-sharing network analysis of seamount sediment vOTUs, Global Oceans Viromes 2 (GOV2.0) database52 (>10kb, n=195,728), and viral Contigs from cold seep (n=2885)34 and trench (n=12,700)53 were performed. For each viral Contig, ORFs were predicted using Prodigal v2.6.3107 (-p meta -g 11 -m -c), and predicted protein sequences were then subjected to all-to-all BLASTp using Diamond v2.0.15121; the result file was used as input for vConTACT2 v0.11.351. Viral RefSeq (v94) was used as referenced database to generate the protein-sharing network (Supplementary Data 5 and 6), and Cytoscape v3.9.1122 was used to visualize the network.

The effects of geographical features of seamounts on viruses
To generate viral core and pan PCs in these amount virus dataset, ORFs were called from virome-assembled Contigs using Prodigal v2.6.3107 and were then aligned to the GOV2.052 and IMG/VR (release version 2022-12-19_7.1)123 database using Diamond v2.0.15121 with a threshold of e-value ≤ 1e−5, identity ≥ 30%, and coverage ≥ 50%53. The resulting ORFs were translated into proteins and clustered at 60% identity and 80 coverage using CD-HIT v4.6124 to generate non-redundant viral PCs. Pan PCs were obtained by merging PCs from different samples, and core PCs were acquired by identifying the PCs that were shared by all samples.

The connectivity of viral and prokaryotic populations between neighboring seamount sampling sites was calculated based on their similarity in the pOTU and vOTU profiles, respectively. Briefly, pOTU and vOTU matrix tables were used as import files for the vegan package of the R software110,125; the Bray–Curtis distance matrix between each sample was calculated and further converted to similarity values using the following formula: similarity value = 1 / (1 + distance matrix)126.

Plotting
Boxplots, heatmaps, bar stacking plots, and gene maps were drawn using the R packages ggplot2 v4.3.2127, pheatmap v1.0.12128, ggpubr v0.6.0129, and gggenes v0.5.0 (https://cran.r-project.org/web/packages/gggenes), respectively. Venn and upset plots were plotted by TBtools v1.120130.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript “Globally novel and locally endemic viruses inhabiting deep-sea seamountsediments” presents a thorough and systematic study evaluating bacteria, archaea, and viruses in several seamount sediments. The authors use genetic information recovered from bacterial, archaeal, and viral communities to identify differences in the composition of these three communities from different, but relatively close, locations. The primary observations highlighted in the abstract suggest that there is evidence of extensive and novel diversity, extensive virus-host interactions, significant roles for viruses in carbon, sulfur and nitrogen cycling, prevalence of temperate phages with the potential to modify host metabolisms, and geographic limitations to virus connectivity. These are among the many characteristics of viral communities in nature and highlights their known roles in ecology and evolution. The manuscript is generally well written and this work provides a significant amount of new sequence information with corresponding analyses.\nBroader comments\nMy key concern is that the manuscript is an excellent descriptive account but one that lacks a significant novel finding. For example, one of several key findings listed in the title, abstract, and conclusion is that viruses in seamount sediments are locally endemic. The introduction uses this concept as a key justification for the study (lines 132-144) and it is discussed in some detail in the last paragraph and conclusion, but the data suggesting that there is evidence of local endemism is not presented until the end of the paper (Figure 8). It seems unusual to have a novel or key finding discussed at the end of a manuscript. Much of what comes before is a very detailed and systematic description of microbial and viral diversity at different sites. To make this a more compelling argument, the manuscript needs some oceanographic or geological data that describe the types of sediment samples that were collected.\nOne of the other issues I have with the locally endemic finding is that data highlighting host-virus interactions and the explanation for virus endemism are somewhat contradictory. In the section that covers this topic, the authors note that viruses and prokaryotes do not exhibit the same patterns and suggest that endemism for viruses is likely due to geographic features that limit viral dispersion through mixing (lines 573-588). The authors bring this up again in the conclusion, but they also note that there was extensive interactions between viruses and dominant prokaryotic lineages, as well as the occurrence of AMGs with the potential to shape community structure and functon(lines 596-600). If virus and host communities in sediments are interacting through close associations, how does dispersal though physical processes such as mixing impact one community and not the other? Doesn’t the interaction data suggest that viruses and their hosts are connected in sediments? Wouldn’t this imply that even if they had different sources, they have changed together over time and should therefore have similar trends regarding geographic distributions?\nAlong those lines, there is also no physical mixing data and very little chemical data. Nutrient measurements for carbon and nitrogen are mentioned in the methods and appear in one supplemental table, but with no discussion. The authors rule out depth of the water column as a driving factor, but there is no information about the method used to collect or characterize the sediments. How were the sediments selected, collected, characterized? Can the differences in sample types, chemistry, age, depth, etc. tell us anything about the types of sediments that were collected?\nLastly, the accumulation curves in Figure 8 (panel a) suggests that protein clusters and vOTUs captured most of the diversity in the samples. However, there is no description of how protein clusters were determined, only a reference. And viral identifications were limited using strict criteria to identify virus genes that were similar to viral genes in other known viruses. Both methods are prone to systematic errors, in that identifications of new protein clusters and viruses are limited to what is in the published search database.\nMore specific comments:\nLines 344-347: The authors state that 97% of all virus hosts (1,953) were predicted from MAGs in GTDB and that all remaining hosts were from seamount sediment MAGs generated in their study, with no MAGs identified as hosts from other sediment samples. Does this mean that only 60 sediment MAGs were potential hosts? This seems like a relatively small number to make the broad statement that this implies endemism across different seamount habitats. Aren’t seamount habitats under sampled? The author’s show data which suggest that very different virus communities are identified depending on the method used to sequence them, such as a metagenome versus a metavirome. Can samples be compared across different seamounts? If so, how many studies have been conducted? Do they have similar depth of coverage?\nMuch is missing from the methods:\n1. Sediment selection, collection, and characterization details are missing\n2. DNA extraction methods are missing.\n3. Details about viral purification. Many bacteria will pass through a 0.45 micron mesh. Was bacterial DNA identified in the metaviromes? If so, how much of it was bacterial and how was it removed?", "reviewer_2": "This study uses metagenomic and viromic sequencing and bioinformatic efforts to explore how microbial and viral community diversity, viraus-host relationships, and viral ecological roles differ between various seamounts. The authors find extensive viral diversity and identify several viral-host relationships. AMGs encoded by viruses may ultimately play a role in influencing host metabolism and affect carbon, nitrogen, and sulfur cycling in these sediments while also enabling host survival in these environments. Authors also reveal that no hosts were predicted form MAGs assembles from other seamount sediments suggesting endemism of viral communities within seamounts.\nI thoroughly enjoyed reading this paper. I feel that this paper went above and beyond by analyzing prokaryotic and viral communities in seamount sediments, identifying viral diverisity, virus-host linkages, and viral lifestyles, impacts of viruses on host metabolism, and searching for impacts of seamount features on the presence of local viral communities. In some cases, I think the analyses went beyond the current capabilities/knowledge base of the field and potentially made some bold statements that I feel can’t be justified (i.e., classifying viral taxonomy based on a somewhat conserved viral gene). Overall, the work does support the conclusion and the claims. The methods are sound and thorough. I do believe this is the first paper assessing viral diversity and ecological roles in deep-sea seamount sediments, which is extremely exciting.\nI have made some specific comments on areas where I would improve the paper, but ultimately these notes are more focused on removing or rephrasing information as opposed to adding additional analyses.\nSpecific comments:\nThe introduction holds a lot of great information, but the first two paragraphs could be made more concise.\nIt would be good to include citations for the statement made in Line 133.\nLine 190-194: This seems a bit obvious. I would remove “as several studies have suggested.” I think it should be reworded because these are two very different methods and viral recovery is not coincidentally better if you are enriching for viruses. Typically, metagenomes are acquired by filtering onto 0.2um filters and therefore viruses slip through and are not accounted for during sequencing (with the exception of temperate phage and viruses that are actively infecting the cellular fraction). I think this is made clear towards the end of the sentence, so I would remove “as several studies have suggested” because this really is common practice and knowledge.\nLine 208: “to huge viruses”\nI believe they are more commonly referred to as “giant viruses”?\nLine 239:\nCouldn’t this also be because these viruses may be temperate or actively infecting the hosts (i.e., are not ambient and wouldn’t otherwise be detected in the free viral population)?\nFigure 3e: This is a great figure.\nLine 254: Might be worth noting the bias that the viral database itself plays in recognizing Caudovirales viruses (since these makes up a majority of the viral database). It therefore makes sense that the viral database continues to largely identify Caudovirales.\nLines 261-267: I don’t think that placing viral sequences in a phylogenetic tree and making inferences on lineages is important in the goal of this paper, and I don’t think it is correct practice. (If it is, several papers need to be cited to support this method and claim.) It is complicated to assess viral phylogeny because they do not have conserved genes, and even if a gene is conserved, I’m not sure you can make claims regarding viral diversity based on this one gen. You also come to this conclusion/ show this in Lines 290-295.\nFigure 4: It is very hard to tell which are the reference genes as the grey circles are too small in 4a. I would find a way to make this more obvious. I also feel that more justification is needed in order to try to classify different viruses based on a shared gene. Figure 4a doesn’t seem to mean very much as it is now. Are you trying to define taxonomy based on a gene? If so this should be its own paper in itself. I would either remove Figure 4 or move it to the supplemental. I would also be more clear on what your goal is in trying to map viral phylogeny. If it is for viral diversity, then I would be careful not to make claims about viruses forming different groups based on a gene that really can’t be used as an identifier since they are not conserved. Instead you can focus on Figure 4b and talk about how genome arrangements are different, but again this is expected, so consider moving this to the supplements.\nFigure 5a: The text could explain what we are looking at a bit better. The caption may want to include the organization of nodes from greater to fewer.\nFigure 5b: The two numbered scales to the left need labels to be more clear. I love this figure.\nLine 330 & 343: The full name is GTDB-tk and should be used as such.\nLine 346: Very interesting!\nLine 607: I believe VLPs refer to viral like particles, not necessarily ambient or free viruses. I would describe these as ambient viruses instead of VLPs.\nOverall, I would absolutely recommend this paper for publishing."}, "author_response": "Responses to the reviewers’ comments (point-to-point)\nReviewer 1:\nComment#1:The manuscript “Globally novel and locally endemic viruses\ninhabiting deep-sea seamount sediments” presents a thorough and systematic study\nevaluating bacteria, archaea, and viruses in several seamount sediments. The authors\nuse genetic information recovered from bacterial, archaeal, and viral communities to\nidentify differences in the composition of these three communities from different, but\nrelatively close, locations. The primary observations highlighted in the abstract suggest\nthat there is evidence of extensive and novel diversity, extensive virus-host interactions,\nsignificant roles for viruses in carbon, sulfur and nitrogen cycling, prevalence of\ntemperate phages with the potential to modify host metabolisms, and geographic\nlimitations to virus connectivity. These are among the many characteristics of viral\ncommunities in nature and highlights their known roles in ecology and evolution. The\nmanuscript is generally well written and this work provides a significant amount of new\nsequence information with corresponding analyses.\nResponse: Thanks very much for your positive feedbacks. Your comments have all\nbeen most valuable and helpful for improving the manuscript. The manuscript has been\nextensively revised according to your comments as listed below. All the changes in this\nrevision are marked in Red color for easy recognition.\nComment#2: My key concern is that the manuscript is an excellent descriptive\naccount but one that lacks a significant novel finding. For example, one of several key\nfindings listed in the title, abstract, and conclusion is that viruses in seamount sediments\nare locally endemic. The introduction uses this concept as a key justification for the\nstudy (lines 132-144) and it is discussed in some detail in the last paragraph and\nconclusion, but the data suggesting that there is evidence of local endemism is not\npresented until the end of the paper (Figure 8). It seems unusual to have a novel or key\nfinding discussed at the end of a manuscript. Much of what comes before is a very\ndetailed and systematic description of microbial and viral diversity at different sites. To\nmake this a more compelling argument, the manuscript needs some oceanographic or\ngeological data that describe the types of sediment samples that were collected.\nResponse: To the best of our knowledge, this is the first paper assessing viral\ncommunities in deep-sea seamount sediments in-depth, and many findings reported\nhere are novel to viruses inhabiting seamount ecosystems. We provide detailed and\nsystematic descriptions of viral diversity and virus-host interactions in the earlier parts\nof the manuscript, because these data are very important for understanding the diversity\nand ecological roles of seamount viruses, and are thus also one of the key findings. In\naddition, the investigation of local viral endemism is a more in-depth and\nenvironmentally specific analysis integrating seamount geographical features, which is\nbuilt on our previous data on viral diversity and virus-host interaction in the earlier parts\nof the manuscript. Thus, we put the descriptions of local viral endemism at the end of\nthe manuscript.\nAs for the response to the comment “the manuscript needs some oceanographic or\ngeological data that describe the types of sediment samples that were collected”, please\nrefer to the response to Comment#4 below.\nComment#3: One of the other issues I have with the locally endemic finding is that\ndata highlighting host-virus interactions and the explanation for virus endemism are\nsomewhat contradictory. In the section that covers this topic, the authors note that\nviruses and prokaryotes do not exhibit the same patterns and suggest that endemism for\nviruses is likely due to geographic features that limit viral dispersion through mixing\n(lines 573-588). The authors bring this up again in the conclusion, but they also note\nthat there was extensive interactions between viruses and dominant prokaryotic\nlineages, as well as the occurrence of AMGs with the potential to shape community\nstructure and functon(lines 596-600). If virus and host communities in sediments are\ninteracting through close associations, how does dispersal though physical processes\nsuch as mixing impact one community and not the other? Doesn’t the interaction data\nsuggest that viruses and their hosts are connected in sediments? Wouldn’t this imply\nthat even if they had different sources, they have changed together over time and should\ntherefore have similar trends regarding geographic distributions?\nResponse: It is a very good question. The reasons for the seemingly “contradictory”\nare as follows:\n(i) In fact, the factors influencing prokaryotic communities are complex and diverse\n(Ambati and Kumar, 2022; Mojica and Brussaard, 2014). While the virus is indeed a\nsignificant factor in shaping host communities, other factors such as grazing and\nenvironmental conditions (e.g., depth, salinity, temperature, and nutrients) also play\nimportant roles in shaping prokaryotic communities (Ambati and Kumar, 2022; Mojica\nand Brussaard, 2014).\n(ii) Although our results showed that virus and host communities in deep-sea\nseamount sediments are interacting through close associations, a substantial proportion\nof seamount viruses was revealed to be temperate (Fig. 5C), which tend to coexist with\nthe host, thus don’t shape host communities directly by causing host mortality.\nTo make the manuscript more clear, we have provided the above explanations for the\ndifferent geographic distribution patterns between viral and prokaryotic communities\nin Lines 578-587.\nReferences\nAmbati M, Kumar M S. Microbial Diversity in the Indian Ocean Sediments: An Insight\ninto the Distribution and Associated Factors [J]. Current Microbiology, 2022, 79(4):\n115.\nMojica K D A, Brussaard C P D. Factors affecting virus dynamics and microbial host–\nvirus interactions in marine environments[J]. FEMS Microbiology Ecology, 2014,\n89(3): 495-515.\nComment#4: Along those lines, there is also no physical mixing data and very little\nchemical data. Nutrient measurements for carbon and nitrogen are mentioned in the\nmethods and appear in one supplemental table, but with no discussion. The authors rule\nout depth of the water column as a driving factor, but there is no information about the\nmethod used to collect or characterize the sediments. How were the sediments selected,\ncollected, characterized? Can the differences in sample types, chemistry, age, depth,\netc. tell us anything about the types of sediments that were collected?\nResponse: We have linked the nutrient measurements with viral and prokaryotic\ncommunities using RDA analysis. The relevant data and descriptions are provided in\nSupplementary Fig. 2 and Lines 155-158 and 214-215. The detailed information\nregarding the sediment selection, collection, and characterization has been provided in\nLines 625-639. Besides, the detailed geological information on the types of sediments\nhas been provided in Supplementary Table 1.\nComment#5: Lastly, the accumulation curves in Figure 8 (panel a) suggests that\nprotein clusters and vOTUs captured most of the diversity in the samples. However,\nthere is no description of how protein clusters were determined, only a reference. And\nviral identifications were limited using strict criteria to identify virus genes that were\nsimilar to viral genes in other known viruses. Both methods are prone to systematic\nerrors, in that identifications of new protein clusters and viruses are limited to what is\nin the published search database.\nResponse: We have provided the detailed method for the generation of viral protein\nclusters in Lines 806-813. (i) Our previous method used to generate viral protein\nclusters was based on the sequence similarity with known viral genomes (NR viral\nRefSeq database). Indeed, this method is limited to identifying viral protein clusters\nthat are similar to known viral genomes in the NR viral RefSeq database. In order to\nidentify bona fide viral protein clusters as much as possible, we re-performed the\nidentification of viral protein clusters in seamount sediment viromes using GOV2.0 and\nIMG/VR database as described by Jian et al. (Lines 806-813). The new method\nidentified much more viral protein clusters than the previous method (790,756 vs\n19,761 PCs), and the results of the new analysis also support our conclusion that viruses\nin seamount sediments have a high degree of endemism (Lines 536-544). (ii) For the\nidentification of viruses, we used a combination of three pipelines (Lines 701-720 and\nSupplementary Fig. 3a), and these pipelines have been widely used for the identification\nof viral sequences (Cheng R et al, 2022; Li Z et al, 2021; Luo X Q et al, 2022). In fact,\nthese pipelines identify not only viral sequences similar to known viral genomes, but\nalso identify novel viral sequences based on several virus-specific features (e.g., the\npresence of viral hallmark genes) (Nayfach S et al, 2021; Guo J et al, 2021; Kieft K et\nal, 2020).\nReference\nJian H, Yi Y, Wang J, et al. Diversity and distribution of viruses inhabiting the deepest\nocean on Earth[J]. The ISME journal, 2021, 15(10): 3094-3110.\nCheng R, Li X, Jiang L, et al. Virus diversity and interactions with hosts in deep-sea\nhydrothermal vents[J]. Microbiome, 2022, 10(1): 1-17.\nLi Z, Pan D, Wei G, et al. Deep sea sediments associated with cold seeps are a\nsubsurface reservoir of viral diversity[J]. The ISME Journal, 2021, 15(8): 2366-\n2378.\nLuo X Q, Wang P, Li J L, et al. Viral community-wide auxiliary metabolic genes differ\nby lifestyles, habitats, and hosts[J]. Microbiome, 2022, 10(1): 1-18.\nNayfach S, Camargo A P, Schulz F, et al. CheckV assesses the quality and\ncompleteness of metagenome-assembled viral genomes[J]. Nature biotechnology,\n2021, 39(5): 578-585.\nGuo J, Bolduc B, Zayed A A, et al. VirSorter2: a multi-classifier, expert-guided\napproach to detect diverse DNA and RNA viruses[J]. Microbiome, 2021, 9: 1-13.\nKieft K, Zhou Z, Anantharaman K. VIBRANT: automated recovery, annotation and\ncuration of microbial viruses, and evaluation of viral community function from\ngenomic sequences[J]. Microbiome, 2020, 8(1): 1-23.\nComment#6: Lines 344-347: The authors state that 97% of all virus hosts (1,953)\nwere predicted from MAGs in GTDB and that all remaining hosts were from seamount\nsediment MAGs generated in their study, with no MAGs identified as hosts from other\nsediment samples. Does this mean that only 60 sediment MAGs were potential hosts?\nThis seems like a relatively small number to make the broad statement that this implies\nendemism across different seamount habitats. Aren’t seamount habitats under\nsampled?\nResponse: It is a good question. Yes, seamount habitats are relatively under-\nsampled, because we can obtain only 151 seamount MAGs from the IMG/M database,\nalthough they already represent the largest seamount MAG dataset currently available.\nThese 151 seamount MAGs are assembled from several different seamount\nenvironments by several studies (Supplementary Table 12). Despite the limited number\nof seamount MAGs used for host prediction, there are notable differences in the\nprediction frequency between MAGs generated in this study and MAGs from other\nseamount sediment samples. Fifty-nine of 63 MAGs assembled in this study were\npredicted hosts for viruses, whereas none of 151 MAGs from other seamount sediment\nsamples were predicted hosts. In our opinion, this notable divergence should support\nour claim that “This result implies the potential endemism of viruses across different\nseamount habitats”.\nComment7#: The author’s show data which suggest that very different virus\ncommunities are identified depending on the method used to sequence them, such as a\nmetagenome versus a metavirome. Can samples be compared across different\nseamounts? If so, how many studies have been conducted? Do they have similar depth\nof coverage?\nResponse: To the best of our knowledge, this is the first paper assessing viral\ncommunities in deep-sea seamount sediments using either metagenome or virome.\nThus, there is no relevant study on seamount viruses that could be compared with our\nstudy. In fact, we only utilized MAGs assembled from other seamount samples for the\nprediction of host-virus linkages. Thus, the issue of similar depth of coverage is not\nrelevant to this analysis.\nComment#8: Much is missing from the methods:\n1. Sediment selection, collection, and characterization details are missing\n2. DNA extraction methods are missing.\n3. Details about viral purification. Many bacteria will pass through a 0.45 micron\nmesh. Was bacterial DNA identified in the metaviromes? If so, how much of it was\nbacterial and how was it removed?\nResponse: The detailed information for sediment sample selection, collection, and\ncharacterization has been provided in Lines 625-639. The information for DNA\nextraction methods has been provided in Lines 643-644. Besides, the detailed\ninformation for viral purification has been provided in Lines 681-690.\nYes, bacterial DNA was frequently identified in the viromes. However, bacterial\nDNA was removed for further analysis, since our pipelines used strict criteria to identify\nviral sequences from both virome and bulk metagenome (Lines 701-720 and\nSupplementary Fig. 3a), and no host contamination should be included for the viral\nanalysis.\nReviewer 2:\nComment#1: This study uses metagenomic and viromic sequencing and\nbioinformatic efforts to explore how microbial and viral community diversity, virus-\nhost relationships, and viral ecological roles differ between various seamounts. The\nauthors find extensive viral diversity and identify several viral-host relationships.\nAMGs encoded by viruses may ultimately play a role in influencing host metabolism\nand affect carbon, nitrogen, and sulfur cycling in these sediments while also enabling\nhost survival in these environments. Authors also reveal that no hosts were predicted\nform MAGs assembles from other seamount sediments suggesting endemism of viral\ncommunities within seamounts. I thoroughly enjoyed reading this paper. I feel that this\npaper went above and beyond by analyzing prokaryotic and viral communities in\nseamount sediments, identifying viral diversity, virus-host linkages, and viral lifestyles,\nimpacts of viruses on host metabolism, and searching for impacts of seamount features\non the presence of local viral communities. In some cases, I think the analyses\nwent beyond the current capabilities/knowledge base of the field and potentially made some\nbold statements that I feel can’t be justified (i.e., classifying viral taxonomy based on a\nsomewhat conserved viral gene). Overall, the work does support the conclusion and the\nclaims. The methods are sound and thorough. I do believe this is the first paper\nassessing viral diversity and ecological roles in deep-sea seamount sediments, which is\nextremely exciting. I have made some specific comments on areas where I would\nimprove the paper, but ultimately these notes are more focused on removing or\nrephrasing information as opposed to adding additional analyses.\nResponse: Thanks very much for your positive feedbacks. Your comments have all\nbeen most valuable and helpful for improving the manuscript. The manuscript has been\nextensively revised according to your comments as listed below. All the changes in this\nrevision are marked in Red color for easy recognition.\nComment#2: The introduction holds a lot of great information, but the first two\nparagraphs could be made more concise.\nResponse: We have revised the first two paragraphs to make them more concise\nas suggested (Lines 75-93).\nComment#3: It would be good to include citations for the statement made in Line\n133.\nResponse: We have added references for this statement in Line 124\nComment#4: Line 190-194: This seems a bit obvious. I would remove “as several\nstudies have suggested.” I think it should be reworded because these are two very\ndifferent methods and viral recovery is not coincidentally better if you are enriching for\nviruses. Typically, metagenomes are acquired by filtering onto 0.2um filters and\ntherefore viruses slip through and are not accounted for during sequencing (with the\nexception of temperate phage and viruses that are actively infecting the cellular\nfraction). I think this is made clear towards the end of the sentence, so I would remove\n“as several studies have suggested” because this really is common practice and\nknowledge.\nResponse: We have revised the sentence as suggested in Lines 184-187.\nComment#5: T Line 208: “to huge viruses” I believe they are more commonly\nreferred to as “giant viruses”?\nResponse: We have revised the term “huge viruses” to “giant viruses” as suggested\nin Line 201.\nComment#6: Line 239:\nCouldn’t this also be because these viruses may be temperate or actively infecting\nthe hosts (i.e., are not ambient and wouldn’t otherwise be detected in the free viral\npopulation)?\nResponse:This possibility has also been discussed in the text in L238-240.\nComment#7: Figure 3e: This is a great figure.\nResponse:Thanks very much for your positive feedback.\nComment#8: Line 254: Might be worth noting the bias that the viral database itself\nplays in recognizing Caudovirales viruses (since these makes up a majority of the viral\ndatabase). It therefore makes sense that the viral database continues to largely identify\nCaudovirales.\nResponse: This note has been added as suggested in Lines 254-257.\nComment#9: Lines 261-267: I don’t think that placing viral sequences in a\nphylogenetic tree and making inferences on lineages is important in the goal of this\npaper, and I don’t think it is correct practice. (If it is, several papers need to be cited to\nsupport this method and claim.) It is complicated to assess viral phylogeny because they\ndo not have conserved genes, and even if a gene is conserved, I’m not sure you can\nmake claims regarding viral diversity based on this one gene. You also come to this\nconclusion/ show this in Lines 290-295.\nResponse: This is a very good question. We agree with you that making\nphylogenetic inferences based on one conserved viral gene may be problematic, since\nsignificant genetic divergence exists amongst viral subgroups. In addition, we also\nagree with you that placing viral sequences in a phylogenetic tree and making\ninferences on lineages is not our primary goal for this paper. Therefore, we have placed\nFig. 4 into supplements (Supplementary Fig. 5), and deleted relevant claims on viruses\nforming different groups based on the phylogeny of a single gene (Lines 257-265 and\n276-289).\nComment#10: Figure 4: It is very hard to tell which are the reference genes as the\ngrey circles are too small in 4a. I would find a way to make this more obvious. I also\nfeel that more justification is needed in order to try to classify different viruses based\non a shared gene. Figure 4a doesn’t seem to mean very much as it is now. Are you\ntrying to define taxonomy based on a gene? If so this should be its own paper in itself.\nI would either remove Figure 4 or move it to the supplemental. I would also be more\nclear on what your goal is in trying to map viral phylogeny. If it is for viral diversity,\nthen I would be careful not to make claims about viruses forming different groups based\non a gene that really can’t be used as an identifier since they are not conserved.\nInstead you can focus on Figure 4b and talk about how genome arrangements are different,\nbut again this is expected, so consider moving this to the supplements.\nResponse: This is a very good question. We agree with you that making\nphylogenetic inferences based on one conserved viral gene may be problematic, since\nsignificant genetic divergence exists amongst viral subgroups. In addition, we also\nagree with you that placing viral sequences in a phylogenetic tree and making\ninferences on lineages is not our primary goal for this paper. Therefore, we have placed\nFig. 4 into supplements (Supplementary Fig. 5), and deleted relevant claims on viruses\nforming different groups based on the phylogeny of a single gene (Lines 257-265 and\n276-289). Moreover, we have revised Fig. 4a (now Supplementary Fig. 5) to mark\nreference genes with black dots, which will make them more obvious.\nComment#11: Figure 5a: The text could explain what we are looking at a bit better.\nThe caption may want to include the organization of nodes from greater to fewer.\nResponse: For easy understanding of Fig. 5a (now Fig. 4a), the positions of\nseamount viruses are marked with black circles. In addition, the caption has been\norganized according to the node number from greater to fewer as suggested.\nComment#12: Figure 5b: The two numbered scales to the left need labels to be\nmore clear. I love this figure.\nResponse: We have added labels to the two scales on the left in Fig. 5b (now Fig.\n4b) as suggested.\nComment#13: Line 330 & 343: The full name is GTDB-tk and should be used as\nsuch.\nResponse: We have revised the phrase as suggested throughout the manuscript.\nComment#14: Line 346: Very interesting!\nResponse: Thanks very much for your positive feedback.\nComment#15: Line 607: I believe VLPs refer to viral like particles, not necessarily\nambient or free viruses. I would describe these as ambient viruses instead of VLPs.\nResponse: We have replaced the phrase VLPs with ambient viruses throughout the\nmanuscript as suggested.\nComment#16: Overall, I would absolutely recommend this paper for publishing.\nResponse: Thanks very much for your positive feedback."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have have not adequately addressed my key concerns regarding their conclusion that endemism among viruses in sediments is a key finding from this research. They have added a supplemental table that provides some additional information about the sediment samples that were collected and they have analyzed OTUs in a new way that is presented in a supplemental figure that compares shifts in community structure of both prokaryotes and viruses with measured environmental variables such as carbon, nitrogen, and depth. I appreciate the new information but feel that the evidence for endemism is insufficient. This is primarily because 1, there are not enough well characterized sediment types and not enough geological or oceanographic data to support this conclusion and 2, the data in the new supplemental figure (2) and main figure (7) do not agree.\n1. Differences in sediment sample types are not statistically robust. I would expect to see replicates from different types of well characterized sediments and or geological or oceanographic features to draw the endemism conclusion.\n2. The RDA analyses suggest that communities are similar in very distant samples, and that they are similar for both prokaryotes and viruses. For example, NP4_S05 and NA_S06 group together relative to all the other samples, for prokaryotes along axis RDA2 and for viruses along axis RDA1. Similarly, the vectors for carbon, nitrogen, and depth indicate positive correlations in the direction of NP4_S05 and NA_S06 for both prokaryotes and viruses. Similary, NLG_S05 groups separately for prokaryotes along axis RDA1 and for viruses along axis RDA2. Axes in these types of plots can be rotated. If the prokaryote plot is rotated counterclockwise so that the variably on axes switches, it is very similar to the plot for viruses, along both axes and with the measured environmental variables. To me, this suggests that prokaryote and virus communities are similar in samples driven by nutrient concentrations and rather than geography.\nMore data characterizing more sediment types, geology, or physical oceanography is really needed to draw these types of conclusions, especially the idea of endemism. However, a robust characterization of prokaryotes and viruses in diverse seamount sediment types is warranted.\nThe methods are still lacking in detail. Very little information is provided for the RDA analyses, for example. Were the data normalized, how many species (OTUs) were used in each analysis, were outlier analyses conducted, what distance measure was used, what was the final stress or P value . . .", "reviewer_2": "Authors have addressed all questions and comments that were previously made. The addition of the RDA analysis and details on DNA extraction and viral purification methods improved the manuscript. I do worry about DNA extraction and amplification bias towards prokaryotes if you are simply removing metagenomes from the sequenced “virome” that passed through 0.45um. This is very much not common practice (the virome would typically be acquired from 0.2um filtrate or collected onto 0.02um filters) and it should be addressed how this method could impact virome recovery and results.\nMinor comments on text:\nLine 90: Year for Myrna et al. not provided.\nLine 93: Year for Huo et al. not provided.\nLine 257: Typo: “the” should be “then”"}, "author_response": "Responses to reviewers’ comments (point-to-point)\nReviewer 1:\nComment#1: The authors have not adequately addressed my key concerns\nregarding their conclusion that endemism among viruses in sediments is a key finding\nfrom this research. They have added a supplemental table that provides some\nadditional information about the sediment samples that were collected and they have\nanalyzed OTUs in a new way that is presented in a supplemental figure that compares\nshifts in community structure of both prokaryotes and viruses with measured\nenvironmental variables such as carbon, nitrogen, and depth. I appreciate the new\ninformation but feel that the evidence for endemism is insufficient. This is primarily\nbecause 1, there are not enough well-characterized sediment types and not enough\ngeological or oceanographic data to support this conclusion and 2, the data in the new\nsupplemental figure (2) and main figure (7) do not agree.\nResponse: Thanks very much for your comments, which have all been most\nvaluable and helpful for improving the manuscript. The manuscript has been\nextensively revised according to your comments, which are listed below. All the\nchanges in this revision are marked in Red color for easy recognition.\nIt is a very good question. After careful consideration and evaluation, we agree\nthat our study could not adequately support our previous bold conclusion on viral\nendemism among seamount sediments. Indeed, more data characterizing more\nsediment types, geology, or physical oceanography is needed to draw the bold\nconclusion of viral endemism. Therefore, we have removed the claims of viral\nendemism throughout the manuscript; instead, we have rephrased relevant sentences\nto tone down the claims, i.e., high divergence of viral communities across seamounts.\nThe revisions are in the Title, Abstract section (Lines 40-42), Results and Discussion\nsection (Lines 343-344, 534-536, 543-552, 566-568, 588-591), and Conclusion\nsection (Lines 606-609). In addition, we have provided more discussions to point out\nthe need for more well-characterized sediments and geological/oceanographic data in\nthe future to verify our hypothesis (Lines 591-595). As for concerns raised about the\nRDA analysis, please refer to the response to Comment#3 below.\nComment#2: 1. Differences in sediment sample types are not statistically robust. I\nwould expect to see replicates from different types of well characterized sediments\nand or geological or oceanographic features to draw the endemism conclusion.\nResponse: Indeed, more data characterizing more sediment types, geology, or\nphysical oceanography is needed to draw the bold conclusion of viral endemism.\nTherefore, we have removed the claims of viral endemism throughout the manuscript;\ninstead, we have rephrased relevant sentences to tone down the claims, i.e., high\ndivergence of viral communities across seamounts. The revisions are in the Title,\nAbstract section (Lines 40-42), Results and Discussion section (Lines 343-344, 534-\n536, 543-552, 566-568, 588-591), and Conclusion section (Lines 606-609). In\naddition, we have provided more discussions to point out the need for more well-\ncharacterized sediments and geological/oceanographic data in the future to verify our\nhypothesis (Lines 591-595). As for concerns raised about the RDA analysis, please\nrefer to the response to Comment#3 below.\nComment#3: The RDA analyses suggest that communities are similar in very\ndistant samples, and that they are similar for both prokaryotes and viruses. For\nexample, NP4_S05 and NA_S06 group together relative to all the other samples, for\nprokaryotes along axis RDA2 and for viruses along axis RDA1. Similarly, the vectors\nfor carbon, nitrogen, and depth indicate positive correlations in the direction of\nNP4_S05 and NA_S06 for both prokaryotes and viruses. Similary, NLG_S05 groups\nseparately for prokaryotes along axis RDA1 and for viruses along axis RDA2. Axes in\nthese types of plots can be rotated. If the prokaryote plot is rotated counterclockwise\nso that the variably on axes switches, it is very similar to the plot for viruses, along\nboth axes and with the measured environmental variables. To me, this suggests that\nprokaryote and virus communities are similar in samples driven by nutrient\nconcentrations and rather than geography.\nResponse: Thanks very much for pointing out the problems with the Redundancy\nAnalysis (RDA) analysis. To evaluate the robustness of the RDA model, we\nperformed a permutation test. The detailed method for RDA analysis and permutation\ntest is provided at the end of this Response. As shown in Table R1 below, the p values\nfor the RDA axes and the entire RDA model for viruses are very large (>0.5), and the\np values for the RDA axes 2 and 3 for prokaryotes are >0.3. This result indicates that\nthese RDA models are not reliable and, thus, should be removed. Sorry for our\nmistakes. We have replaced the RDA analysis with Mantel’s correlation analysis for a\nrobust evaluation of the correlation of environmental variables with prokaryote and\nvirus communities. The detailed method for Mantel’s correlation analysis has been\nprovided in the Methods and Materials section in Lines 729-737. The relevant data\nand descriptions for Mantel’s correlation analysis are provided in Supplementary Fig.\n2 and Lines 155-159 and 215-217. The detailed values for the Mantel’s correlation\nanalysis are listed in Table R2 below.\nTable R1 Permutation test to evaluate the robustness of the RDA model\nProkaryotes Viruses\np Sig. p Sig.\nRDA axis 1 0.03333 Y 0.7986 N\nRDA axis 2 0.38333 N 0.9417 N\nRDA axis 3 0.5125 N 0.6819 N\nModel 0.03056 Y 0.9 N\nTable R2 Correlation analysis between environmental variables and prokaryotic and\nviral OTU profiles by the Mantel test\nOTU Environmental variable r p rd\nProkaryotes Depth 0.678164 0.041667 >0.4\nProkaryotes Organic carbon 0.201183 0.159722 0.2 - 0.4\nProkaryotes Nitrogen 0.730804 0.0625 > 0.4\nViruses Depth -0.59131 0.9375 < 0.2\nViruses Organic carbon -0.04508 0.522222 < 0.2\nViruses Nitrogen -0.63308 0.902778 < 0.2\nDetailed method for RDA analysis and permutation test\nProkaryotic and viral OTU abundance data were Hellinger transformed using the\nR package vegan v2.6-4 (function decosdtand) (Dixon P, 2003) in order to reduce the\nweight of abundant OTUs while preserving Euclidean distances between samples in\nthe multidimensional space (Legendre P & Gallagher E D, 2001). A preliminary\ndetrended correspondence analysis performed on the transformed OTU data revealed\na gradient length of 3.22 (for prokaryotes) and 1.74 (for viruses). We performed RDA\nanalysis using the function rda and tested the significance using the function anova.\nSelection was performed using the function ordiR2step with permutation test (999\npermutations) in the R package vegan v2.6-4 (Dixon P, 2003). The selected variables\nwere then used as explanatory variables for RDA, and ANOVA was run with 999\npermutations to assess the significance of constraints using the function anova in the\nR package vegan v2.6-4 (Dixon P, 2003).\nReference:\nDixon P. VEGAN, a package of R functions for community ecology[J]. Journal of\nVegetation Science, 2003, 14(6): 927-930.\nLegendre P, Gallagher E D. Ecologically meaningful transformations for ordination of\nspecies data[J]. Oecologia, 2001, 129: 271-280.\nComment#4: More data characterizing more sediment types, geology, or physical\noceanography is really needed to draw these types of conclusions, especially the idea\nof endemism. However, a robust characterization of prokaryotes and viruses in\ndiverse seamount sediment types is warranted.\nResponse: Indeed, more data characterizing more sediment types, geology, or\nphysical oceanography is needed to draw the bold conclusion of viral endemism.\nTherefore, we have removed the claims of viral endemism throughout the manuscript;\ninstead, we have rephrased relevant sentences to tone down the claims, i.e., high\ndivergence of viral communities across seamounts. The revisions are in the Title,\nAbstract section (Lines 40-42), Results and Discussion section (Lines 343-344, 534-\n536, 543-552, 566-568, 588-591), and Conclusion section (Lines 606-609). In\naddition, we have provided more discussions to point out the need for more well-\ncharacterized sediments and geological/oceanographic data in the future to verify our\nhypothesis (Lines 591-595). As for concerns raised about the RDA analysis, please\nrefer to the response to Comment#3 below.\nComment#5: The methods are still lacking in detail. Very little information is\nprovided for the RDA analyses, for example. Were the data normalized, how many\nspecies (OTUs) were used in each analysis, were outlier analyses conducted, what\ndistance measure was used, what was the final stress or P value . . .\nResponse: Since the RDA analysis is replaced with the Mantel’s correlation\nanalysis, the RDA method has been removed from the manuscript. Nevertheless, the\ndetailed RDA method is provided below for your reference. Besides, the detailed\nmethod for Mantel’s correlation analysis has been provided in the Methods and\nMaterials section in Lines 729-737.\nDetailed method for RDA analysis and permutation test\nProkaryotic and viral OTU abundance data were Hellinger transformed using the\nR package vegan v2.6-4 (function decosdtand) (Dixon P, 2003) in order to reduce the\nweight of abundant OTUs while preserving Euclidean distances between samples in\nthe multidimensional space (Legendre P & Gallagher E D, 2001). A preliminary\ndetrended correspondence analysis performed on the transformed OTU data revealed\na gradient length of 3.22 (for prokaryotes) and 1.74 (for viruses). We performed RDA\nanalysis using the function rda and tested the significance using the function anova.\nSelection was performed using the function ordiR2step with permutation test (999\npermutations) in the R package vegan v2.6-4 (Dixon P, 2003). The selected variables\nwere then used as explanatory variables for RDA, and ANOVA was run with 999\npermutations to assess the significance of constraints using the function anova in the\nR package vegan v2.6-4 (Dixon P, 2003).\nReference:\nDixon P. VEGAN, a package of R functions for community ecology[J]. Journal of\nVegetation Science, 2003, 14(6): 927-930.\nLegendre P, Gallagher E D. Ecologically meaningful transformations for ordination of\nspecies data[J]. Oecologia, 2001, 129: 271-280.\nReviewer 2:\nComment#1: Authors have addressed all questions and comments that were\npreviously made. The addition of the RDA analysis and details on DNA extraction\nand viral purification methods improved the manuscript. I do worry about DNA\nextraction and amplification bias towards prokaryotes if you are simply removing\nmetagenomes from the sequenced “virome” that passed through 0.45um. This is very\nmuch not common practice (the virome would typically be acquired from 0.2um\nfiltrate or collected onto 0.02um filters) and it should be addressed how this method\ncould impact virome recovery and results.\nResponse: Thanks very much for your comments, which have all been most\nvaluable and helpful for improving the manuscript. The manuscript has been\nextensively revised according to your comments, which are listed below. All the\nchanges in this revision are marked in Red color for easy recognition.\nIt is a very good question. Indeed, viromes were initially typically acquired from\n0.2 µm filtrate (Breitbart M et al., 2002; 2004). However, the discoveries of viruses\nwith increasingly larger capsid sizes necessitate the re-evaluation of common\nfiltration methods when attempting to capture entire virus communities (Claverie J M\net al., 2018). It is found that the 0.2-μm-size fraction excludes many diverse large\nviruses (Göller P C et al., 2020; Conceição-Neto N et al., 2015). For example, in one\nstudy of soil virome, the use of 0.45-μm-pore-size filters approximately doubled the\nnumber of observable virus-like particles compared to that with the 0.2-μm-pore-size\nfilters (Göller P C et al., 2020). In particular, many nucleocytoplasmic large DNA\nviruses (NCLDV) can be excluded from the virome obtained by 0.22-μm filtration,\nand the virome obtained by 0.45-μm filtration has the potential for higher giant\nviruses recovery (Göller P C et al.,2020; Conceição-Neto N et al., 2015). In this\ncontext, more and more studies have opted for the use of 0.45-μm-pore-size filters in\nvirome research in recent years. (Vibin J et al, 2018; Deng L et al, 2019; Bekliz M et\nal, 2019; Lachnit et al, 2019; Adriaenssens et al, 2021). In addition, several studies\nindicate the pervasive presence of non-viral sequence contamination in viromes, no\nmatter whether a 0.2 μm or 0.45 μm filter is used (Perlejewski K et al, 2015; Moustafa\nA et al, 2017; Asplund M et al, 2019; Jurasz H et al, 2021). The non-viral sequence\ncontamination in viromes seems challenging to avoid (Zolfo M et al., 2019).\nNevertheless, our powerful pipelines used strict criteria to identify viral sequences\nfrom virome (Lines 701-720 and Supplementary Fig. 3a), and no host contamination\nshould be included in the viral analysis. To make the manuscript clearer, we have\nadded some brief discussions on the impact of 0.45-μm filtration on virome recovery\nand results (Lines 687-690).\nReference\nAdriaenssens E M, McDonald J E, Jones D L, et al. Tracing the fate of wastewater\nviruses reveals catchment-scale virome diversity and connectivity[J]. Water\nResearch, 2021, 203: 117568.\nAsplund M, Kjartansdóttir K R, Mollerup S, et al. Contaminating viral sequences in\nhigh-throughput sequencing viromics: a linkage study of 700 sequencing\nlibraries[J]. Clinical Microbiology and Infection, 2019, 25(10): 1277-1285.\nBekliz M, Brandani J, Bourquin M, et al. Benchmarking protocols for the\nmetagenomic analysis of stream biofilm viromes[J]. PeerJ, 2019, 7: e8187.\nBreitbart M, Felts B, Kelley S, et al. Diversity and population structure of a near–\nshore marine–sediment viral community[J]. Proceedings of the Royal Society of\nLondon. Series B: Biological Sciences, 2004, 271(1539): 565-574.\nBreitbart M, Salamon P, Andresen B, et al. Genomic analysis of uncultured marine\nviral communities[J]. Proceedings of the National Academy of Sciences USA,\n2002, 99(22): 14250-14255.\nClaverie J M, Abergel C. Mimiviridae: an expanding family of highly diverse large\ndsDNA viruses infecting a wide phylogenetic range of aquatic eukaryotes[J].\nViruses, 2018, 10(9): 506.\nConceição-Neto N, Zeller M, Lefrère H, et al. Modular approach to customise sample\npreparation procedures for viral metagenomics: a reproducible protocol for virome\nanalysis[J]. Scientific reports, 2015, 5(1): 16532.\nDeng L, Silins R, Castro-Mejía J L, et al. A protocol for extraction of infective\nviromes suitable for metagenomics sequencing from low volume fecal samples[J].\nViruses, 2019, 11(7): 667.\nGöller P C, Haro-Moreno J M, Rodriguez-Valera F, et al. Uncovering a hidden\ndiversity: optimized protocols for the extraction of dsDNA bacteriophages from\nsoil[J]. Microbiome, 2020, 8: 1-16.\nJurasz H, Pawłowski T, Perlejewski K. Contamination issue in viral metagenomics:\nproblems, solutions, and clinical perspectives[J]. Frontiers in Microbiology, 2021,\n12: 745076.\nLachnit T, Dafforn K A, Johnston E L, et al. Contrasting distributions of\nbacteriophages and eukaryotic viruses from contaminated coastal sediments[J].\nEnvironmental Microbiology, 2019, 21(6): 1929-1941.\nMoustafa A, Xie C, Kirkness E, et al. The blood DNA virome in 8,000 humans[J].\nPLoS pathogens, 2017, 13(3): e1006292.\nPerlejewski K, Popiel M, Laskus T, et al. Next-generation sequencing (NGS) in the\nidentification of encephalitis-causing viruses: unexpected detection of human\nherpesvirus 1 while searching for RNA pathogens[J]. Journal of Virological\nMethods, 2015, 226: 1-6.\nVibin J, Chamings A, Collier F, et al. Metagenomics detection and characterisation of\nviruses in faecal samples from Australian wild birds[J]. Scientific Reports, 2018,\n8(1): 8686.\nZolfo M, Pinto F, Asnicar F, et al. Detecting contamination in viromes using\nViromeQC[J]. Nature Biotechnology, 2019, 37(12): 1408-1412.\nComment#2: Line 90: Year for Myrna et al. not provided.\nResponse: We have provided the year for Myrna et al. in Line 90.\nComment#3: Line 93: Year for Huo et al. not provided.\nResponse: We have provided the year for Huo et al. in Line 93.\nComment#4: Line 257: Typo: “the” should be “then”\nResponse: We have corrected the sentence in Line 259."}, {"round": 3, "reviewer_comments": {"reviewer_1": "I am satisfied with the major changes the authors made to this manuscript.", "reviewer_2": "All previous comments have been adequately addressed."}, "author_response": ""}]

Decision letter