Endemism shapes viral ecology and evolution in globally distributed hydrothermal vent ecosystems

Domain: earth_science
Published: 2025-05-01
Editorial outcome: minor_revision

Abstract
Viruses are ubiquitous in deep-sea hydrothermal vents, where they influence microbial communities and biogeochemistry. Yet, viral ecology and evolution remain understudied in these environments. Here, we identify 49,962 viruses from 52 globally distributed hydrothermal vent samples (10 plume, 40 deposit, and 2 diffuse flow metagenomes), and reconstruct 5708 viral metagenome-assembled genomes, the majority of which were bacteriophages. Hydrothermal viruses were largely endemic, however, some viruses were shared between geographically separated vents, predominantly between the Lau Basin and Brothers Volcano in the Pacific Ocean. Geographically distant viruses shared proteins related to core functions such as structural proteins, and rarely, proteins of auxiliary functions involved in processes such as fermentation and cobalamin biosynthesis. Common microbial hosts of viruses included members of Campylobacterota, Alpha-, and Gammaproteobacteria in deposits, and Gammaproteobacteria in plumes. Campylobacterota- and Gammaproteobacteria-infecting viruses reflected variations in hydrothermal chemistry and functional redundancy in their predicted microbial hosts, suggesting that hydrothermal geology is a driver of viral ecology and coevolution of viruses and hosts. Our results indicate that viral ecology and evolution in globally distributed hydrothermal vents is shaped by endemism and thus may have increased susceptibility to the negative impacts of deep-sea mining and anthropogenic change in ocean ecosystems.

Introduction
An estimated 10^30 viruses in the world’s oceans are predicted to lyse and kill 20% of microbial biomass daily1. In marine systems, global sampling efforts have resulted in the recovery of viruses at a broad scale, enabling investigations of their ecology, evolution, and biogeography2–4. These studies revealed that viruses in the epipelagic ocean are passively transported via ocean currents5, distinct viral groups exist across five oceanic ecological zones4, and broad differences in the protein content of viruses occur across depth6. Though these studies provide valuable insights on viruses globally, they have mainly focused on comparative analyses of photic and aphotic viruses. Investigations of viruses in the deep ocean are lacking.

In deep-sea hydrothermal vents, viruses are an important source of predation on chemolithoautotrophic microorganisms at the base of the food web7. As a result of these infections, viruses have the potential to change microbial community composition and population sizes. Hydrothermal vent viruses are also capable of “reprogramming” host metabolism using auxiliary metabolic genes (AMGs). For example, vent viruses were the first viruses discovered to encode reverse dissimilatory sulfite reductase (rdsr), which can manipulate sulfur-based chemolithotrophy in the dark ocean8. To date, few studies have conducted comparative analyses of hydrothermal vent viruses globally, and those completed provide evidence that viruses are endemic to vent sites and habitat types at the genus level and that viruses infect ecologically important, abundant taxa such as Gammaproteobacteria and Campylobacterota9. Other studies have suggested vent viruses are predominantly lysogenic10, have limited dispersal, and narrow host ranges11.

Despite these advances, the factors controlling hydrothermal vent virus biogeography, ecology, and evolution remain poorly constrained. The biogeography of viruses is thought to be determined by a complex interplay between abiotic factors, virus traits (e.g., lifecycle, virion size, burst size), and host traits (e.g., abundance, size, distribution)12. Abiotic factors such as vent chemistry have been shown to dictate microbial community composition, where differences in the chemical profiles of geographically close vent sites can result in distinct microbial communities13,14. Given their dependence on microbial hosts, viral biogeography is intimately linked to theories on microbial biogeography, however, these remain in their infancy15,16. Increasingly available hydrothermal vent metagenomic data presents opportunities to examine vent viral biogeography, ecology, and evolution at a global scale. This, coupled with advances in software for rapid, accurate comparison of microbial metagenome-assembled genomes (MAGs)17 and the generation of viral MAGs (vMAGs)18, promise to enable more accurate representations of environmental viruses and allow finer resolution comparisons of their community structure and ecology.

In this study, we catalog and describe viruses, largely from two types of hydrothermal vent environments, on a global scale: high-temperature hydrothermal deposits that host biofilms of thermophilic Bacteria and Archaea, and hydrothermal plumes hosting psychrophilic and mesophilic Bacteria and Archaea. These samples have previously been investigated for microbial diversity13,14,19, but their viral communities are largely unexplored. Using 10 hydrothermal plumes, 40 vent deposits, and 2 diffuse flow samples collected from seven distinct hydrothermal systems in the Pacific and Atlantic Oceans (Guaymas Basin, Mid-Cayman Rise, Mid-Atlantic Ridge, Axial Seamount, Brothers Volcano, East Pacific Rise, and the Lau Basin), we leverage metagenomics and statistical analyses to reconstruct viral genomes and study viral communities through inter- and intra-vent comparative analyses. Our findings demonstrate that endemism is a key driver shaping the ecology and evolution of hydrothermal vent viruses through distinct viral and microbial traits.

Results
We identified 63,826 viral scaffolds from 10 hydrothermal plumes, 40 hydrothermal vent deposits, and 2 diffuse flow metagenomes (Supplementary Data 1). Following viral identification, we conducted viral genome binning to reconstruct 5708 vMAGs. The vMAGs comprise 19,572 viral scaffolds (30.7% of scaffolds), leaving 44,254 unbinned viruses (69.3% of scaffolds). Thus, after viral binning, we recovered a total of 49,962 viruses from globally distributed hydrothermal vents (Fig. 1A). Of these, 1833 were characterized as medium-, high-quality, or complete (Supplementary Fig. 1), and 20,305 viruses encoded one or more viral hallmark genes (Supplementary Data 2). Most of the hydrothermal vent viruses were classified as lytic rather than lysogenic (47,571 lytic versus 2391 lysogenic), and this remains true when only examining viruses of medium-quality or better (1505 lytic versus 328 lysogenic), as well as only complete viruses (109 lytic versus 4 lysogenic). In addition, 32,442 vent viruses had a genome size range of 1–5 kb, while the remaining had genome sizes of 6–561 kb (Supplementary Fig. 2). Taxonomic predictions at the class level showed that most viruses are double-stranded DNA viruses within the realm Duplodnaviria (39,056), class Caudoviricetes (Supplementary Fig. 3). Caudoviricetes viruses were also the most abundant class of viruses in the dataset based on relative abundance (Fig. 1B and Supplementary Data 3). Most viruses were reconstructed from deposit samples from Brother’s Volcano and the Lau Basin (35,094 viruses). These sites produced some of the largest assemblies of the datasets analyzed here (up to 1.2 Gb) and were the most intensively sampled compared to other sites (Supplementary Data 4).

Viruses are rarely shared between geographically distant hydrothermal vents or the global ocean
To understand how hydrothermal vent viruses are related, we conducted similarity analyses of viruses across and within hydrothermal vents. We used clustering analysis based on average nucleotide identity (ANI), as well as read mapping of genomes. Clustering of ≥3 kb viral genomes identified 866 non-singleton clusters containing 1950 viruses, and most clusters (687/866) were composed of two viral genomes (Supplementary Data 5). Thus, most vent viruses were not included in ANI-based clusters, suggesting they have low relatedness at the nucleotide level. In addition, no clusters contained viruses from both hydrothermal plumes and hydrothermal deposits, indicating these habitats support distinct viral communities. Some viruses fell within the same nucleotide clusters, yet were reconstructed from geographically distant sites (Fig. 2A). Specifically, 65 clusters contained 152 viruses from geographically distinct vent deposits, with an ANI ranging from 70–99%. When examining the clusters, we found that the predicted viral genome sizes, completeness, hosts, and lifestyles were largely aligned. Most of the clusters (51/65) contained viruses from the Lau Basin and Brothers Volcano, which are both located in the South Pacific Ocean. Viral genomes in these clusters shared significant overlap even across geographically separated vents in different ocean basins, such as between the Mid-Atlantic Ridge in the Atlantic Ocean and Brothers Volcano in the Pacific Ocean, or between Axial Seamount and Guaymas Basin in the Pacific Ocean (Supplementary Data 5). Although viruses in the 65 geographically distinct clusters are predominantly predicted to be low-quality, we determined that the regions of overlap in these viruses are significant in length and/or annotation, and thus we find support for shared viral genomic regions between geographically separated vents (See Supplementary Note 1 and Supplementary Data 6 and 7).

Some shared viruses may be missed when examining viruses identified from metagenomes, because viral sequences may only be present in the reads and not the assemblies. To address this issue, we also used read mapping of all ≥3 kb viruses to identify those that were present in multiple samples. While nucleotide clustering indicated that no viruses were shared between hydrothermal vent plumes and deposits, read mapping-based detection identified 36 such viruses (Fig. 2B and Supplementary Data 8). Most of the read mapping analyses identified viruses shared between Lau Basin (26/36) deposits or plumes and the same or different sites in Lau Basin. These results suggest that some viruses may be shared between vent deposits and plumes.

To understand hydrothermal vent virus biogeography more broadly, we used nucleotide clustering to compare the viruses recovered here to the Global Ocean Virome 4 (GOV2.0, see methods). This analysis revealed 36 clusters where hydrothermal vent viruses exhibited ≥70% ANI with those from the GOV2.0 dataset (Supplementary Data 9). In some cases, these viruses were sampled in relatively close geographic proximity, such as a Guaymas Basin virus with 79% ANI to a virus from Tara Oceans Station 137, ~1575 km away. However, we also observed some instances where distant locations had similar viruses. For example, a complete genome of a Lau Basin (South Pacific Ocean) virus had 81% ANI to a virus from Tara Oceans Station 68 in the South Atlantic Ocean. These viruses were also collected at distinct depths, and in some clusters, we observed viruses that were separated by over 2000 m depth. Thus, although we largely observed endemism in vent viruses, we observed rare cases where vent viruses were distantly related to viruses from different ocean basins and/or depths.

Viruses are more similar within a hydrothermal field
In addition to understanding how viruses are related between geographically distant vents, we examined intra-vent viral relatedness, where 417 clusters contained 992 viruses from distinct sites within a vent field (Fig. 2A and Supplementary Data 5). Just over half of the clusters (272/417) contained viruses that were related between hydrothermal plumes in Lau Basin, including Lau Basin ABE, Kilo Moana, Mariner, Tahi Moana, and Tui Malila. These plumes are at most separated by 1–2° latitude (Supplementary Data 1). In the Lau Basin, 163/272 plume viral clusters had an average intra-cluster ANI ≥85%, and 90/272 had an average ANI ≥95%. This indicated that many of these Lau Basin plume viruses were related at the genus and species level, or for those with 100% identity and 100% completeness, were identical. Almost all the viruses in Lau Basin plume clusters were predicted to be lytic and were represented by Microviridae, Caudoviricetes, Inoviridae, Schitoviridae, Cressdnaviricota, and Demerecviridae.

Lau Basin vent deposit samples had the next highest number of viral clusters shared between different vent fields (94/417 clusters, Supplementary Data 5). Of the 94 clusters, 16 contained medium-quality or better viruses, and nearly all of these were Caudoviricetes. Guaymas Basin and Brothers Volcano vent deposits, respectively, also contained many viral clusters shared between different vent fields. In Guaymas Basin, most clusters were shared between sites 4561 (380 and 384) and 4571-419 (38/47), which are geographically close but separated by a depth of 22 m. Similarly, in Brothers Volcano, many clusters were shared between Northwest Caldera Wall A (NWC-A) and Northwest Caldera Wall B and Upper Caldera Wall (NWC-B + UCW) (13/30), which are geographically close to each other (~1570 m distance), though they have distinct microbial communities13. Further, the Mid-Cayman Rise, Axial Seamount, and the East Pacific Rise had some intra-vent related viruses. To complement our clustering analyses, we also conducted read mapping-based detection, which indicated that viruses were related between intra-vent sites. Viruses from Lau Basin plumes were highly related (especially Kilo Moana and Abe, Tahi Moana and Kilo Moana). We also identified many viruses that were shared between Lau Basin deposits (Tui Malila, ABE, and Mariner), Brothers Volcano deposits (NWC-A and NWC-B), Guaymas Basin deposits (4571-419 and 4561), Mid-Atlantic Ridge deposits (Lucky and Rainbow), and Mid-Cayman Rise plumes (Von Damm and Piccard). Overall, read mapping-based detection reaffirmed viral relatedness between sites identified with ANI clustering but also identified numerous connections that were not observed through the ANI clustering analysis. This was especially true for overlap between viruses in hydrothermal plumes and deposits, viruses from the Mid-Atlantic Ridge, and viruses between more geographically distant vent fields like Kilo Moana and Cayman Shallow plumes since these patterns were uniquely observed with read mapping.

Hydrothermal vents share viral protein families dominated by proteins of unknown function
To understand how hydrothermal vent viruses are related at the protein level, we clustered all 595,416 vent virus proteins. This produced 74,940 clusters of two or more virus proteins. Of these, 152 clusters contained proteins shared between vent deposits and plumes (773 proteins), and 23,351 clusters have proteins shared between geographically separated vents (84,223 proteins or 14.2% of the protein dataset; Fig. 3A and Supplementary Data 10). Of the 84,259 total proteins shared across distant sites or sample types, 40,645 were annotated (48.2%), largely as hypothetical or uncharacterized functions, viral terminases, DNA/RNA polymerases, capsid, baseplate, tail, and portal proteins (Fig. 3B). These results were supported using a virus-specific protein database (PHROGs v4.0, see methods), where 49.8% of the annotations were characterized as unknown in function (Supplementary Data 11). Of the 152 clusters shared between vent plumes and deposits, 89 contained proteins with annotations, and the top annotation categories included helix-turn-helix domains, domains of unknown function, phage tail tube protein, hypothetical proteins, nucleotide kinase, and essential recombination function protein. The largest cluster of proteins from geographically separated vents had 88 proteins from deposit samples, including Brothers Volcano, the Lau Basin, and the Mid-Atlantic Ridge. This cluster contained a protein of unknown function that is known to often be encoded in phage genomes (PF09343 or IPR011740). Several of the next largest protein clusters contained proteins from the same sites (Brothers Volcano, Lau Basin, and Mid-Atlantic Ridge) that are of unknown function or AAA domain (PF13479). Many clusters with proteins from distant sites were also functionally related to pyrimidine metabolism (e.g., dCTP deaminase, dTMP kinase, and dUTP diphosphatase), purine metabolism (phosphoribosylformylglycinamidine cyclo-ligase and phosphoribosylamine-glycine ligase (PurD), and nucleotide metabolism (ribonucleotide reductase). Several clusters contained phosphate starvation inducible proteins (PhoH), which have previously been identified as widespread in marine phages and proposed as a marker of marine phage diversity20. Some protein clusters that were highly similar between viruses from geographically distant vents contained proteins that do not have known core functions, including pyruvate formate-lyase activating enzyme (PflA) and cobaltochelatase subunits CobS and CobT (Supplementary Data 10). PflA is a key enzyme in fermentation and has been identified in a mimivirus, TetV-121. CobS and CobT are involved in the synthesis of cobaltochelatase for cobalamin biosynthesis and have been identified previously in viruses22. Viruses have been hypothesized to use CobST to hijack host CobN, allowing for increased cobalamin production and synthesis of viral progeny.

Viral biogeography is closely tied to the geographic distribution and abundance of their hosts
To explore microbial drivers of viral biogeography, we predicted the microbial hosts and calculated the relative abundance of all viruses and their hosts (Supplementary Data 12, 13, and 14). Of the 49,962 viruses, 14% had a predicted host (7001 viruses, Supplementary Fig. 4). For these viruses, the infection range was largely narrow where, 6387 viruses were predicted to infect one host, and the remaining 614 viruses were predicted to infect >1 host. Most host predictions were for deposit viruses (84.7%), which are predicted to infect a greater diversity and larger number of microbial phyla compared to viruses in plumes (Supplementary Note 2). Among plume viruses, most predicted hosts were members of the phyla Pseudomonadota (formerly Proteobacteria, 44.6%) and Bacteroidota (17.2%), while in deposits, most were members of Campylobacterota (25%) and Pseudomonadota (21.2%, primarily Gamma- and Alphaproteobacteria). This aligned with relative abundance data, where the most abundant plume virus (Axial Plume, 1.3% relative abundance) infected a Proteobacteria in the class Gammaproteobacteria, genus Thioglobus, or SUP05. This sulfur-oxidizing bacterium is abundant in hydrothermal plumes globally23,24. In deposits and diffuse samples, the most abundant virus with a predicted host (Brothers Volcano site Diffuse, 1% relative abundance) was predicted to infect a member of Campylobacterota in the family Sulfurimonadaceae (genus CAITKP01). Bacteria in this family and genus are the most abundant among all the deposit samples, and isolates in this family from hydrothermal vents are known to be chemolithoautotrophic sulfur oxidizers14,25.

Hydrothermal geology and chemistry drives coevolution of viruses and hosts
In both deposits and plumes, microbial hosts from the phylum Pseudomonadota were largely associated with the class Gammaproteobacteria. Previously, using the same metagenomes in this study, functional redundancy was observed between members of Gammaproteobacteria and Campylobacterota, where these taxa shifted as dominant community members depending on hydrothermal geology and chemistry. These microbial lineages had similar metabolic potential, and thus, their dominance at one hydrothermal vent site or another was attributed to ecophysiological and growth differences, or distinct metabolic machinery for the same metabolic pathway14. Given this observation and our findings of the ubiquity of viral hosts from Gammaproteobacteria and Campylobacterota, we investigated patterns of relative abundance in the viruses that infect them. We found that Gammaproteobacteria- and Campylobacterota-infecting viruses reflected abundance patterns of their host (Fig. 4). For example, Campylobacterota-infecting viruses were abundant in vent deposits at Brothers Volcano site NWC-A, however, there was a shift to more abundant Gammaproteobacteria-infecting viruses at the geographically close site Brothers Volcano NWC-B. This was reflected in the abundance of the microbial host taxa (Fig. 4). Consistent with these qualitative patterns, compositional correlational analysis exhibited statistical congruence between Campylobacterota and the viruses that infect them, as well as Gammaproteobacteria and their viruses (see methods, Supplementary Data 15). Campylobacterota and Campylobacterota-infecting viruses exhibited relatively high proportionality values (ρ = 0.90)26, and similarly, Gammaproteobacteria and Gammaproteobacteria-infecting viruses were correlated but exhibited a weaker relationship (ρ = 0.67). From the clustering results, we also identified 55 viruses from geographically separated vents that had a predicted host. Of these, 25 were predicted to infect Campylobacterota or Gammaproteobacteria (Supplementary Data 5), further underscoring the importance of these microorganisms in driving viral coevolution in hydrothermal vents. In contrast to these taxa, phyla such as Aenigmatarchaeota, Micrarchaeota, an unknown bacterial phylum (EX4484-52), and Iainarchaeota were low abundance microbial community members (less than 1% relative abundance at all sites, Supplementary Data 14). In line with this, the viruses predicted to infect these microorganisms were few in number (29 viruses total), were not present in the nucleotide clusters (inter or intra vent), and were low in relative abundance.

Hydrothermal viruses encode auxiliary metabolic genes associated with redox processes and detoxification
The presence of auxiliary metabolic genes, or AMGs, in viruses, may increase a virus’ potential geographic range12. Viruses that encode AMGs have an increased ability to boost energy levels for viral progeny production or reduce the viral latent period and, thus, should be able to disperse more widely than viruses without AMGs. To investigate these dynamics, we searched for AMGs in all viruses using DRAM-v (distill mode, Supplementary Data 16). This allowed us to verify that AMGs were flanked by genes of viral or viral-like origin and were not present on the ends of genomic scaffolds. AMGs identified in the hydrothermal vents examined here were involved in sulfur metabolism, arsenic metabolism, nitrogen metabolism, and central carbon metabolism.

AMGs were rare in our dataset. According to DRAM-v, 2615 viruses encoded one or more AMGs, or ~5% of vent viruses. We identified a lytic Caudoviricetes virus reconstructed from Brothers Volcano that encoded adenylylsulfate reductase (AprB, K00395), which catalyzes the reduction of adenylylsulfate to sulfite in the dissimilatory sulfate reduction pathway, or the reverse reaction in dissimilatory sulfur oxidation. A lysogenic Caudoviricetes virus from Lau Basin Mariner encoded an arsenate reductase (ArsC, K00537), which functions in arsenate detoxification by reducing As(V) to an excretable form, arsenite or As(III)27. ArsC has been identified in soil viruses, where there is evidence that arsC-encoding viruses may contribute to metal resistance in their microbial host28,29. In addition to arsenate metabolism, we identified a virus encoding cytochrome bd ubiquinol oxidase subunit I and II (CydAB, K00425, and K00426), which acts as a terminal electron acceptor in the electron transport chain of microorganisms during respiration30. Although cydAB has not been described in other viruses, phage integration has been found to reprogram the regulation of anaerobic respiration in Escherichia coli, and thus CydAB may be another mechanism by which viruses manipulate host respiration31. Finally, a Caudoviricetes virus from the Lau Basin deposit was predicted to encode a nitric oxide reductase subunit B (NorB, K04561). NorB is the large subunit of nitric oxide reductase, which catalyzes the reduction of nitric oxide to nitrous oxide, the penultimate step of the denitrification pathway. NorB has previously been identified in viruses from an oxygen minimum zone in the Eastern Tropical South Pacific Ocean32.

Methods
Sample collection
This research complies with all relevant ethical regulations. All necessary permits and approvals for sample collection were obtained from the appropriate authorities, and all procedures were conducted in accordance with established ethical guidelines. Hydrothermal plume samples were collected from the corresponding cruises (Supplementary Data 1): R/V New Horizon in Guaymas Basin, Gulf of California (July 2004)47–49, R/V Atlantis in Mid-Cayman Rise, Caribbean Sea (Jan 2012)48, R/V Thomas G. Thompson in the Eastern Lau Spreading Center (ELSC) western Pacific Ocean (May-July 2009)8,48, and R/V Thomas G. Thompson in Axial Seamount, Juan de Fuca Ridge, northeastern Pacific Ocean (Aug 2015)50.

Guaymas Basin plume samples were collected by “tow-yo” casts using a CTD rosette in 10 L Niskin bottles49. This water was then filtered onto 142 mm 0.2 µm polycarbonate filters by N2 gas pressure filtration and preserved in RNAlater51. Mid-Cayman plume samples were collected using a Suspended Particle Rosette Sampler (SUPR) by filtering 10–60 L plume water onto 142 mm 0.2 µm SUPOR membranes48. These samples were then preserved in RNAlater in situ. In Lau Basin, SUPR-collected samples were filtered onto 0.2 and 0.8 μm pore size SUPOR polyethersulfone membranes in situ and preserved in RNAlater-flooded vials34. In Axial Seamount, plume samples were collected by a Seabird SBE 911 CTD and 10 L Niskin bottles50. Samples of 3 L were then transferred into cubitainers and filtered through 0.22 μm Sterivex filters.

Hydrothermal deposit samples were collected from the corresponding cruises: R/V Thomas G. Thompson in Brothers Volcano, western Pacific Ocean (March 2018)13, R/V Roger Revelle (April and May 2015)13 and R/V Melville (April 2005)14 in the ELSC, western Pacific Ocean, R/V Atlantis in Guaymas Basin, Gulf of California (Nov and Dec 2009)14,52, R/V Atlantis in the Mid-Atlantic Ridge, Atlantic Ocean (July 2008)14, and R/V Roger Revelle in the East Pacific Rise, Pacific Ocean (March 2004 and December 2006)14. Once on the ship, deposit samples were subsampled with the outer few millimeters (up to approximately 5 mm) kept separate from the bulk sample. These exterior samples were homogenized and stored at −80 °C for subsequent DNA extraction33.

DNA extraction and sequencing
For Guaymas Basin, Mid-Cayman Rise, and Lau Basin plume samples, DNA was extracted from ¼ filters using chemical and physical lysis methods as described in Dick and Tebo (2010)47 and Li and Dick (2015)48, and sequenced with Illumina HiSeq 2000 at the University of Michigan DNA Sequencing Core. Axial Seamount plume samples were extracted using a phenol-chloroform extraction, and metagenomic libraries were constructed using the Ovation Ultralow Library DR multiplex system50. Sequencing was completed using a NextSeq 500 at the W.M. Keck sequencing facility, Marine Biological Laboratory, in Woods Hole, MA.

For Brothers Volcano and ELSC (2015) deposit samples, DNA was extracted from homogenized deposits using the DNeasy PowerSoil kit (Qiagen), and metagenomic libraries were constructed using Nextera DNA Library Prep kits (Illumina), as described in Reysenbach et al., (2020)13. Sequencing was completed at the Oregon State University Center for Genome Research and Computing on an Illumina HiSeq 3000. For ELSC (2005), MAR, EPR, and Guaymas Basin, DNA was extracted using the Ultra Clean Soil DNA Isolation Kit (MoBio Laboratories, Carlsbad, CA, USA)36. Metagenomic libraries were prepared and sequenced at the Department of Energy, Joint Genome Institute (JGI)14.

Metagenomic assembly and microbial binning
Hydrothermal plume assemblies and microbial MAGs were generated as described in Zhou et al., 202319. Briefly, metagenomic assemblies were constructed from QC-processed reads with MEGAHIT v1.1.253 using the following parameters: --k-min 45 --k-max 95 --k-step 10. Plume assemblies from Mid-Cayman Rise, Lau Basin Abe, Mariner, and Tahi Moana represent combined plume and background seawater, meaning plume reads were co-assembled with background seawater reads. Microbial MAGs were generated using MetaBAT v0.32.454 using 12 combinations of parameters, followed by DAS Tool v1.055 to generate consensus MAGs. Following MAG refinement and contaminant removal, only MAGs with >50% completeness and <10% contamination were retained, as determined by CheckM v1.0.756.

Hydrothermal deposit assemblies and microbial MAGs were generated as described in Zhou et al., 202214. Briefly, reads from Brothers Volcano and ELSC (2015) were quality-filtered using FastQC v0.11.857 and de novo assembled using metaSPAdes v3.12.058 with the parameters: -k 21,33,55,77,99,127 -m 400 –meta. Reads from ELSC (2005), MAR, EPR, and Guaymas Basin were assembled by the Department of Energy, Joint Genome Institute (JGI) using metaSPAdes v3.11.158 with the following parameters: -k 33,55,77,99,127 –only-assembler –meta. MetaWRAP v1.2.259 was used to generate microbial MAGs with parameters –metabat2 –metabat1 –maxbin2. DAS Tool v1.055 was then used to generate consensus MAGs.

Virus identification and binning
VIBRANT v1.2.160 was run with default parameters to identify viruses from the genomic assemblies of the 52 hydrothermal vent samples, resulting in 63,826 viral scaffolds. Viral scaffolds were binned using vRhyme v1.1.0 on each of the 52 hydrothermal vent samples with default parameters and bam files18. The sorted bam files used in binning were generated by mapping the fastq reads for a particular sample to the genomic assembly reconstructed for the same sample. Specifically, a custom Python script was used to run BWA-MEM v0.7.1761 to map reads to assemblies and samtools v1.762 to convert sam files to bam format and then obtain sorted bam files. In total, we reconstructed 49,962 viral genomes. vMAGs were screened for high protein redundancy and binning of lytic and lysogenic viruses, where those with ≥2 redundant proteins and/or ≥2 lysogenic scaffolds were broken back into individual scaffolds and retained in the dataset as unbinned viruses. Finally, vMAGs >10 scaffolds were retained in the dataset as unbinned viruses.

For finalized vMAGs, the vRhyme script link_bin_sequences.py was used with default parameters to generate one scaffold vMAGs, where each scaffold is linked by 1,500 Ns. This is required by some downstream tools that expect viral genomes to be on one scaffold (e.g., CheckV, iPHoP) as described below. Viral genome size was determined using SeqKit v2.6.1 on unbinned viral scaffolds and binned viruses without N-links63. To visualize the number of viruses reconstructed from each site, Fig. 1 was generated in R v4.4.064 using a custom R script (see SitesMap.R at https://github.com/mlangwig/HydrothermalVent_Viruses)65. Viruses were designated as lytic or lysogenic based on VIBRANT, which uses the presence/absence of integrase or the excision of a viral region from metagenomics scaffolds to determine whether a virus is lytic or lysogenic. For vMAGs that had lytic and lysogenic scaffolds binned into one genome, the genome was designated as lysogenic (VentVirus_Analysis2.R).

Virus taxonomy, marker genes, and quality
Virus taxonomy was determined using geNomad v1.5.166, which utilizes taxonomically informed marker genes to determine the most specific taxon supported by most of the viral genes in the genome. Taxa are defined according to the International Committee on the Taxonomy of Viruses (ICTV)67. The end_to_end pipeline was run with default parameters. This also allowed us to determine the number of viral hallmarks encoded by each viral genome. Virus genome quality was determined using the CheckV v0.8.1 end_to_end pipeline with default parameters to obtain predictions for low-, medium-, and high-quality as well as complete genomes68. For both geNomad and CheckV, vMAGs were input with scaffolds concatenated by 1500 Ns.

Virus nucleotide clustering
The average nucleotide identity (ANI) of ≥3 kb viruses was calculated with skani v0.2.017 and clustered using the Markov Clustering Algorithm (mcl, release 14-137)69. Vskani v0.0.1 (available at https://github.com/cody-mar10/skani-vMAG) was used to run skani and mcl, treating vMAGs as genomes and unbinned viruses as single scaffolds, with the following parameters: vskani skani -c unbinned_PlumeVent_viruses.fna -d fna_vMAGs -x .fasta -m 200 -cm 30 -s 70 -f 50 -ma .7. Option -m signifies the number of marker k-mers used per base and was lowered to 200 from the default 1000 due to the smaller genome size of viruses compared to microorganisms. The compression factor (parameter -cm, equivalent to -cor --slow in skani) was lowered from the default of 125 to 30 to provide more accurate estimates of aligned fractions (AF) for distantly related viral genomes. The screen parameter (-s) removed pairs with less than 70% identity, while the minimum aligned fraction parameter (-f) kept ANI values where one genome had an aligned fraction greater than or equal to 50%. Finally, -ma signifies minimum ANI, which was lowered to 70% from the default 95% to capture a broader range of viral relatedness (the family and genus level). Mcl clustering was completed using default parameters.

The resulting table of skani-produced ANI and AF values was modified using a bash script, where ANI was normalized by the lowest AF (ANI*AF/100^2), filtered for ≥70% ANI (now corrected for AF), and formatted as input for the mcxload function of mcl. Mcl clustering was then run with default parameters. The resulting cluster file was input into R to calculate the average ANI per cluster, map metadata to the clusters, and determine which clusters contained viruses from geographically distinct sites (ANI_clust.R). The same nucleotide clustering methodology and R script were used to compare the viruses recovered in this study to the Global Ocean Virome 2.0 dataset4, downloaded from iVirus on CyVerse (https://datacommons.cyverse.org/browse/iplant/home/shared/iVirus/GOV2.0).

To identify the regions of overlap between low-quality viruses, BLASTN v2.14.170 was run per cluster on all viruses within a cluster. First, a blast database was made for each cluster with the following command: makeblastdb -in -out -dbtype nucl. Then, blastn was run on each cluster with the following command: blastn -query -db -out -outfmt “6 qseqid sseqid evalue bitscore length pident qstart qend sstart send” -max_target_seqs 2 -max_hsps 1. The aligned coordinates were obtained from these output files. Finally, bedtools v2.31.071 was used to extract these coordinates from the amino acid-format virus genome files with the following command: bedtools intersect -a bed_GeoDistinct_VirusClusts.tsv -b bed_PlumeVent_viruses.tsv -wa -wb > result.txt. The -a bed file contains the coordinates from the blastn results, and the -b bed file contains the coordinates of all the ORFs in all the viral genomes. This allowed us to obtain the gene annotations of the regions of the viral genomes that had identical nucleotides.

Virus protein clustering
Mmseqs2 (v15.6f452)72 was used to cluster viral proteins. First, the createdb option was used to create an mmseqs database of the 595,416 hydrothermal vent virus proteins. Next, the cluster option was used with the following parameters: -cov-mode 0 --min-seq-id 0.75. These options signify that the alignment covers at least 80% of the query and of the target and that the minimum sequence identity is 75%. The default clustering algorithm, the greedy set cover algorithm, was used. This algorithm iteratively selects the node with the most connections and all its connected nodes to form a cluster and repeats this process until all nodes are within a cluster. Next, the tsv option was used to generate a tsv file of the cluster output. This file was parsed and analyzed in R (Protein_clustering2.R). The clusters were visualized in R with an UpSet plot using the ComplexUpset package (v1.3.3)73.

Protein annotations
Viral proteins were annotated using VIBRANT60, which employs hmmsearch to annotate viral proteins with KEGG, VOG, and Pfam HMM databases. To determine the best-supported VIBRANT hits from the three databases, the annotation with the highest bit score was chosen, followed by the lowest e-value, and finally, the highest viral score. We also used the PHROGs database (v4.0)74 to annotate viral proteins using default parameters. The output was filtered for hits with ≥80% coverage and ≥75% sequence identity, and these results were parsed for the best-hit protein using the smallest e-value and the largest bit score. Potential viral AMGs were identified using DRAM-v v1.4.675. To run DRAM-v, VIBRANT-identified viruses were input into Virsorter2 v2.2.476 to obtain the input file needed for the DRAM-v software. Because Virsorter2 was used for downstream analyses and not viral discovery, we used the following parameters: virsorter run --keep-original-seq --prep-for-dramv --include-groups dsDNAphage,NCLDV,RNA,ssDNA,lavidaviridae --provirus-off --viral-gene-enrich-off --min-score 0.0. DRAM-v annotate was run with the default parameters. DRAM-v distill was run with default parameters to obtain annotations that are supported as AMGs. Briefly, this mode produces an AMG summary file where genes are shown that have an auxiliary score of 1-3. This signifies that the genes with putative AMGs are flanked by two viral hallmark genes (score of 1), flanked by one viral hallmark gene and one viral-like gene (score of 2), or flanked by two viral-like genes (score of 3).

Protein annotations that were within clusters from geographically distant vents or sample types were visualized in a heatmap (Fig. 3B) using the ComplexHeatmap package77 (v2.20.0) in R (Protein_clustering2.R). Annotations were summed by site, and these counts were normalized by dividing the number of annotations by the total number of proteins annotated at a site. Functional categories were assigned to the annotations from three separate databases using a Python script available at: https://github.com/cody-mar10/protein_set_transformer/blob/main/manuscript/protein_embeddings/annotations/relabel_VOG.ipynb.

Read mapping
All ≥3 kb viral genomes were mapped to all 163 paired-end fastq reads using CoverM v0.6.178 (https://github.com/wwood/CoverM) with the options --methods count relative_abundance --min-covered-fraction 0. For read mapping used to determine connectivity between vents, the output was filtered in R to only retain viruses where reads mapped to ≥70% of the viral genome (Coverm_circos.R). To obtain normalized relative abundance, the number of reads mapped to a virus from each sample was divided by the number of reads in that respective sample. This methodology was repeated with the microbial MAGs to obtain their normalized relative abundances.

Host prediction
iPHoP v1.3.3 was used to identify virus-host links between hydrothermal vent viruses and a custom database of 3872 MAGs reconstructed from the same sites14,19,79. To predict potential bacterial and archaeal hosts, the iPHoP tool leverages a combination of approaches, such as blastn matches to host genomes and CRISPR spacer databases, as well as k-mer composition similarity. Before building the custom database, BLASTN70 was used to search all viruses against all microbial MAGs with the following command: nohup blastn -query -db -out -outfmt “6 qseqid length qlen slen pident bitscore stitle”. Hits with 100% identity and 100% coverage were considered viral contamination (generated by misbinning) and were removed from microbial MAGs to prevent false positives based on blastn searches to the host genomes. Following this step, the custom MAG database was created using the add_to_db option, and iPHoP was run using the following parameters: iphop predict --db_dir --no_qc. The input file included unbinned viral scaffolds and vMAGs concatenated into one scaffold using 1500 Ns to enable one prediction per vMAG.

Following host prediction, viruses that infect Campylobacterota and Gammaproteobacteria were subset from the CoverM read mapping data and used to visualize their relative abundance patterns (shown in Fig. 4). To determine whether the observed qualitative relationship between their relative abundances was quantitatively supported, we calculated proportionality using the propr package in R (v5.1.4, AbunCorr.R)26. The input data consisted of the sum of the number of reads mapped to each group (Campylobacterota, Gammaproteobacteria, Campylobacterota-infecting viruses, and Gammaproteobacteria-infecting viruses). This input was used to calculate the association matrix using the proportionality metric “rho” (ρ) and the centered log-ratio (clr) transformation.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Comment by Claire Geslin:\nIn this study, the authors explore the endemism that shapes viral ecology and evolution in globally distributed\nhydrothermal vent ecosystems. This is a very interesting topic, as the diversity and function of viruses and\nproviruses in deep sea hydrothermal ecosystems remain largely unexplored. This study complements and supports the few studies on the subject,\nincluding, for example, two recently works published and referenced in this manuscript (Thomas E. et al., 2021 and Cheng\nR. et al., 2022; references 11 and 9 respectively).\nThe large-scale viral metagenomics approach employed in this study is well-suited for investigating viral diversity in\nhydrothermal vent ecosystems. The data, methodology, and analytical techniques align with established standards in the\nfield.\nRegarding methodological considerations, analyses of viral metagenome-assembled genomes (vMAGs) revealed a\npredominance of bacteriophages.\nWhile similar findings have been reported in previous studies, it is important to note that the overrepresentation of\nbacteriophages in vMAG analyses could be influenced by methodological biases. For instance, the VIBRANT tool used for\nviral identification from genomic assemblies is primarily designed for prokaryotic phages.\nFurthermore, the reliance on DNA extraction and sequencing limits the scope of this study, as it excludes RNA and maybe\neukaryotic viruses. That's understandable, but it needs to be specified.\nOther technical points:\nHave the authors cross-referenced several analyses, enabling them to support the conclusion that the identified viruses are\nmainly virulent, i.e. undergo a lytic cycle?\nThe absence of CRISPR analysis in this study raises the question of whether this approach could have offered insights into\nhost-virus interactions.\nRegarding Figure 2, the authors should provide additional context and explanation for the substantial discrepancy observed\nbetween the two analytical methods. “Nucleotide clustering indicated that no viruses were shared between hydrothermal\nvent plumes and deposits, whereas read mapping-based detection identified 36 such viruses ».\nThe discussion section should incorporate a more comprehensive context to differentiate between biologically meaningful\nfindings and potential methodological limitations, such as sampling biases and inadequate databases for archaeal and\neukaryotic viruses in these extreme marine environments.\nThe conclusion regarding the potential impacts of mining and anthropogenic activities on deep-sea ecosystems and the link\nwith viral ecology should be explained in a little more detail.\nComments by Julien Lossouarn : Early Career Researcher participating in our co-review\nThe langwig et al. manuscript reports an interesting metagenomic study, in which hydrothermal vent prokaryotic and viral\ncommunities are investigated at a global scale. By comparing for the first time viral compositions in both vent chimney\ndeposits and plumes, this work contributes to giving us a better overall view of viral ecology in deep-sea hydrothermal vents,\nwhich remains largely understudied. Whether the study was generally well done and the manuscript well written, it still\nneeds to be improved by adressing the following comments.\nOn a semantic level :\n• I would prefer you to distinguish between virulent viruses (only capable of performing a lytic cycle) and temperate viruses\n(capable of performing a lysogenic or a lytic cycle). Keeping in mind that this distinction virulent/temperate exclude other\ntypes of host relationships, both virulent and temperate viruses are also able to entertain pseudolysogeny while other\nviruses are responsible for chronic infections.\n• I think a bit inappropriate to talk about viruses in the text, since most of the viral sequences are not complete and\ncorrespond to viral scaffolds or vMAGs.\nConcerning the results and mat and met parts :\n• You write in the results part (l.81-85) that you were able to identify 63,826 viral scaffolds from your 52 samples, from which\nyou obtained 49,962 vMAGs after the binning step. These numbers are apparently not the same in the mat and met section,\nwhere the total number of viral scaffolds seems to be 64,220 (l.488-489).\n• In my opinion, there is an ambiguity between what you call hydrothermal plume assemblies and microbial MAGs (l.468-\n469). By hydrothermal plume assemblies, do you mean assemblies obtained from all the 0.2 µm filtered plume viral fractions\n(l.432-440) ? If it is the case, what do you precisely call microbial MAGs ? Do they correspond to what you have assembled\nfrom the corresponding prokaryotic fractions ? In the same way, what you call hydrothermal deposit assemblies and\nmicrobial MAGs remains ambiguous. In this case, do you mean viruses and prokaryotes were sequenced simultaneously ?\nThis would need to be reformulated. A little more information would be useful in the mat and met part about MAGs and how\nthey were analysed.\n• To evaluate the quality of the assembly step, it is a good idea whenever possible to calculate percentage of reads mapping\nonto the overall set of scaffolds, and then vMAGs.\n• By the way, I wonder if it would be not interesting to challenge a bit the way in building up your viral dataset. It is indeed\nrelatively frequent to definitively remove at least scaffolds < 2 kb, which correspond to the minimal size reported for DNA\nviruses (if I do not a mistake you only keep scaffolds > 3kb for many analyses). Then a clustering could be directly performed\nfor the remaining scaffolds at the 95% ANI, corresponding to species boundaries. I would then advise the authors to use\nother viral prediction tools, in addition to Vibrant, such as geNomad and VirSorter2 to try to get a more exhaustive detection\nof what we could call viral operational taxonomic units (vOTUs).\n• It would be interesting to use an additional viral database like Phrogs to annotate the viral proteins\n• I advise you to be cautious about your virulent/temperate classification (l.88-92). The analysis performed on the overall\ndataset is not relevant given most of your viral scaffolds are 1-5 kbs. The smaller (incomplete) the scaffold, the lower the\nchances of detecting integrases/excisionnases/repressors. I would recommend to perform this analysis to high quality and\nbetter scaffolds only, and then including medium quality scaffolds but > 20 kb. Just to evaluate if this impacts the trend that\nyou observe when you consider medium and better quality scaffolds. Keeping also in mind that « plasmidic » temperate\nviruses that do not encode integrases exist.\n• I would also advise more caution about narrow vs broad host range phage conclusions given this is based solely on 14% of the\nviral dataset.\n• l.266-270 : In my opininion, it is not all the time an easy task to clearly distinguish between AMGs and morons (by definition\nsolely encoded by temperate viruses and which the expression provides an advantage to the lysogenized host), particularly\nin this case.\n• Figure 1B : (l.113) yellow circles ? Virus class names are shown on the top x axis and the horizontal names above them\nshow virus realms but Revtraviricetes is a class, not a realm. Ribozyviria are missing ?\n(l. 161) family names has to be written in italic", "reviewer_2": "Title: Endemism shapes viral ecology and evolution in globally distributed hydrothermal vent ecosystem\nSummary\nIn this study, Langwig et al. studied viral ecology and evolution from reconstructed viral communities in deep-sea\nhydrothermal environments globally in the ocean using metagenomic sequences. The authors explored the biogeographical\ndistribution patterns of these viruses and concluded most were endemic, though a few were shared between vents. The\nauthors evaluated inter- and intra-vent viral relatedness based on sequence similarity and read abundance. Furthermore,\nthey explored function roles of these viruses based on protein and auxiliary metabolic genes (AMGs), as well as inferring\ntheir probable hosts.\nWe commend the authors for providing global context to deep-sea hydrothermal vent viruses and using state-of-the-art\nanalytics to explore these viruses' ecological and functional patterns. Overall, the study is well-written and presents deep-\nsea hydrothermal-associated viruses globally. However, there are several concerns that the authors need to address, which\nare outlined:\nComments\n● The authors claimed endemism of hydrothermal vent viruses by comparing viruses within hydrothermal environments.\nHowever, it may be confusing to state endemism without comparing the viruses with those identified in the global oceans\nand deep ocean environments from published work.\nLine 18: What taxonomic rank are the 49,962 vOTU ~species?\nLine 24: What are the metabolic functional roles of the AMGs? Briefly state this.\nLine 86-87: The authors describe three states of the vMAGs, high, medium, and low confidences, what parameters/criteria\nwas used to assign them to each of these categories. I could not find this in the MM\n● How do the 49,962 viruses identified in this study compare with the Global Ocean Viromes dataset and viruses reported\nfrom deep ocean environments?\nLine 88-89: The authors provide evidence of highly lytic compared to lysogenic states based on the presence of the\nintegrase gene. Given that these findings contradict the commonly held paradigm, as the authors define in the introduction\n(Line 55), the authors need to provide biological justification for why they observe different findings.\n->There needs to be a limitation paragraph, given this only based on evidence of a single gene.\nFigure 1: Make A and B as a panel rather than standalone figures.\nLine 100: The authors should include the depth information in Figure 1A.\nLine 103: Could you switch the order of the realm Varidnaviria and Unknown on Figure 1B, making the unknown the last\ncolumn?\n-> Also unknown could be multiple viruses, so those should be in abundance figure.\nLine 117-129: While the biogeographical distribution patterns are primarily based on clustering, it seems read mapping was\nonly done on a subset of those found in multiple areas. I suggest the authors do a read mapping using their ~49k viruses and\ncompare how this matches the geographical patterns of clustering.\n● This is mainly because the genome length will influence the clustering, thus likely missing out on some viruses\nLine 201: Why use a 3kb fraction rather than 5 kb or the standard 10kb for viral ecology work? How many viral hallmark\ngenes are present within a 3kb?\n● Given that viral ecology is determined at 10kb, I would recommend if the authors want to keep the read mapping at 3kb to\nprovide benchmarking showing that larger fragment (i.e., the standard 10kb) would not have affected their conclusions.\n● In addition, given that these are total metagenomes rather than enriched viral metagenomes, I would prefer larger viral\ncontigs to reduce the risk of cellular contamination.\nFigure 3: Complement this with panel B of the heatmap and clustering pattern showing protein function and\nbiogeographical distribution patterns.\nLine 252: I recommend moving this below to allow completion of the biogeography patterns/and story then discuss the\nAMGs story.\nLine 259: Please add what DRAM-v score cut-off applied for AMG identification. Were there efforts to curate this manually?\nLine: 289: A possible clear way would be to add the biogeographic information and abundance patterns within Figure 1 and\nthen tell the story of the viruses identified and their biogeographic patterns there. Currently, where it is within the manuscript\nfeels disjointed.\nLine 303: Need some statistical correlation analysis to support this conclusion, and the results from Figure 4\nLine 337: The authors need to add a limitation paragraph, given the limitations of some of the approaches they decided to\nuse. This would provide the reader with a full context of the work and factors to keep in mind as they read the manuscript.\nFor example, examining viral lifestyle remains an area of active research; the current approach of using a single gene, i.e.,\nthe integrase, is highly dependent on the genome fragments, especially in the case of metagenomes. Secondly, the authors\nuse smaller fragments > 3kb; the field standard seems to be around 10kb for viral ecology (read mapping to establish the\nabundance and distribution patterns).\nLine 336-338: As mentioned above, a statistical analysis is needed to establish a correlation between viral and\nmicrobial abundance. Currently, what is present in Fig 4 is a qualitative view. It can remain, but adding a panel with correlations is\nrecommended to give a robust conclusion.\nLine 401-404: The authors need to provide some hypothesis or explanation for why AMGs that ideally provide niche\nspecialization are absent within the dataset. What is the plausible explanation or mechanism that virus-host pairs are\nutilizing within this extreme ecosystem?\nFor ease of reading the supplementary tables, could the authors combine these supplementary tables into one workbook\nand provide the first tab as an outline of what each tab contains? In its current form, it is extremely difficult to track as one\nreads the manuscript.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": "POINT BY POINT RESPONSES TO REVIEWER COMMENTS\nDear Reviewers,\nThank you for your insightful comments on our work. We are glad the reviewers found our manuscript well-\nwritten, and our results clearly presented. We have made minor and major revisions to address the reviewers’\nconcerns. All line numbers mentioned here refer to the manuscript with tracked changes. Author responses to\nreviewer comments are in blue type.\nOn behalf of all authors,\nSincerely,\nMarguerite Langwig, first author\nKarthik Anantharaman, corresponding author\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nComment by Claire Geslin:\nIn this study, the authors explore the endemism that shapes viral ecology and evolution in globally distributed\nhydrothermal vent ecosystems. This is a very interesting topic, as the diversity and function of viruses and\nproviruses in deep sea hydrothermal ecosystems remain largely unexplored. This study complements and\nsupports the few studies on the subject, including, for example, two recently works published and referenced in\nthis manuscript (Thomas E. et al., 2021 and Cheng R. et al., 2022; references 11 and 9 respectively).\nThe large-scale viral metagenomics approach employed in this study is well-suited for investigating viral diversity\nin hydrothermal vent ecosystems. The data, methodology, and analytical techniques align with established\nstandards in the field.\nRegarding methodological considerations, analyses of viral metagenome-assembled genomes (vMAGs)\nrevealed a predominance of bacteriophages.\nWhile similar findings have been reported in previous studies, it is important to note that the overrepresentation\nof bacteriophages in vMAG analyses could be influenced by methodological biases. For instance, the VIBRANT\ntool used for viral identification from genomic assemblies is primarily designed for prokaryotic phages.\nFurthermore, the reliance on DNA extraction and sequencing limits the scope of this study, as it excludes RNA\nand maybe eukaryotic viruses. That's understandable, but it needs to be specified.\nWe have added a section about methodological biases in the discussion, including a limitations paragraph as\nsuggested by the reviewer.\nAddressed at lines 508-534.\nOther technical points:\nHave the authors cross-referenced several analyses, enabling them to support the conclusion that the identified\nviruses are mainly virulent, i.e. undergo a lytic cycle?\nYes, we used multiple tools to validate and support our conclusions of the abundance of lytic viruses. In addition\nto VIBRANT, we ran CheckV and geNomad, which provide predictions of lytic and lysogenic states. CheckV,\ngeNomad, and VIBRANT predicted 919, 181, and 2391 lysogenic viruses respectively. While these numbers\nseem to indicate a large range, they all suggest an overwhelming dominance of lytic viruses. We chose to use\nthe VIBRANT results, which estimated the highest number of lysogenic viruses among these tools (2,391\nlysogenic viruses when considering the entire dataset). VIBRANT is highly accurate in its prediction of lysogenic\nviruses using its “V-score metric” based approach.\nThe absence of CRISPR analysis in this study raises the question of whether this approach could have offered\ninsights into host-virus interactions.\nTo clarify, CRISPR spacer matches were indeed used as part of our host prediction approach. We used the\niPHoP tool that leverages CRISPR analysis (in addition to other approaches such as k-mer and tRNA matches)\nas part of the host prediction strategy. iPHoP is the most robust and comprehensive tool currently available for\nassessing virus-host linkages. We agree that CRISPR matches between viruses and hosts offer the highest\nconfidence and this is factored into a weighted strategy by iPHoP’s algorithm. To make this clear in the\nmanuscript, we have added an additional sentence explaining this in the methods under “Host Prediction.” Thank\nyou for this comment.\nAddressed at lines 747-749.\nRegarding Figure 2, the authors should provide additional context and explanation for the substantial\ndiscrepancy observed between the two analytical methods. “Nucleotide clustering indicated that no viruses were\nshared between hydrothermal vent plumes and deposits, whereas read mapping-based detection identified 36\nsuch viruses ».\nThe reasoning behind the discrepancy between these methods is stated in the second paragraph of the results\nsection ‘Viruses are rarely shared between geographically distant hydrothermal vents or the global ocean’ as,\n“Some shared viruses may be missed when examining viruses identified from metagenomes, because viral\nsequences may only be present in the reads and not the assemblies.” Nucleotide clustering compares viruses\nat the genome level and hence can only be used for assembled viruses – i.e., for comparisons of viruses that\nare present in the samples above a certain threshold abundance. The viruses that were only identified via read\nmapping may be present in the reads of another sample but were never assembled. This can happen for a\nvariety of reasons, including uneven coverage, errors in the reads, or repetitive DNA, which all make it difficult\nfor assemblers to resolve paths through the assembly graph.\nAddressed at lines 146-148.\nThe discussion section should incorporate a more comprehensive context to differentiate between biologically\nmeaningful findings and potential methodological limitations, such as sampling biases and inadequate databases\nfor archaeal and eukaryotic viruses in these extreme marine environments.\nThank you for pointing this out, we have now added a paragraph in the discussion addressing concerns related\nto focusing mainly on prokaryote-infecting viruses, viral lifestyle predictions, and AMG identification.\nAddressed at lines 508-534.\nThe conclusion regarding the potential impacts of mining and anthropogenic activities on deep-sea ecosystems\nand the link with viral ecology should be explained in a little more detail.\nTo clarify, this was added at the end of the discussion: “Specifically, mining activities will disrupt natural fluid flow\nfrom hydrothermal systems, potentially reducing the availability of chemical energy for primary producing\nmicrobial chemolithoautotrophs, heterotrophs, and the larger food web. This will affect the viruses that infect\nthese organisms.”\nAddressed at lines 540-543.\nComments by Julien Lossouarn : Early Career Researcher participating in our co-review\nThe langwig et al. manuscript reports an interesting metagenomic study, in which hydrothermal vent prokaryotic\nand viral communities are investigated at a global scale. By comparing for the first time viral compositions in both\nvent chimney deposits and plumes, this work contributes to giving us a better overall view of viral ecology in\ndeep-sea hydrothermal vents, which remains largely understudied. Whether the study was generally well done\nand the manuscript well written, it still needs to be improved by adressing the following comments.\nOn a semantic level :\n• I would prefer you to distinguish between virulent viruses (only capable of performing a lytic cycle) and\ntemperate viruses (capable of performing a lysogenic or a lytic cycle). Keeping in mind that this distinction\nvirulent/temperate exclude other types of host relationships, both virulent and temperate viruses are also able to\nentertain pseudolysogeny while other viruses are responsible for chronic infections.\nThe number of lytic and lysogenic viruses is stated in the results at line 91, “Most of the hydrothermal vent viruses\nwere classified as lytic rather than lysogenic (47,571 lytic versus 2,391 lysogenic)” and throughout the rest of the\nresults, when relevant. We have also added an explanation in the discussion of why this distinction can be difficult\nwhen examining incomplete viral genomes (lines 508-532).\nAddressed at lines 91 and 508-532.\n• I think a bit inappropriate to talk about viruses in the text, since most of the viral sequences are not complete\nand correspond to viral scaffolds or vMAGs.\nWe disagree with the reviewer here. Although many of the viruses are incomplete and identified from\nmetagenomic data, this is standard in the field to refer to the putative viruses as “virus” (see Tian et al., 2024\nand Roux et al., 2021 for examples). Numerous studies and computational tools have been benchmarked\ndemonstrating high confidence in viral scaffolds originating from viruses.\nTian, F., Wainaina, J. M., Howard-Varona, C., Domínguez-Huerta, G., Bolduc, B., Gazitúa, M. C., ... & Sullivan,\nM. B. (2024). Prokaryotic-virus-encoded auxiliary metabolic genes throughout the global oceans. Microbiome,\n12(1), 159.\nRoux, S., Matthijnssens, J., & Dutilh, B. E. (2021). Metagenomics in virology. Encyclopedia of Virology, 133.\nConcerning the results and mat and met parts :\n• You write in the results part (l.81-85) that you were able to identify 63,826 viral scaffolds from your 52 samples,\nfrom which you obtained 49,962 vMAGs after the binning step. These numbers are apparently not the same in\nthe mat and met section, where the total number of viral scaffolds seems to be 64,220 (l.488-489).\nThank you for catching this, this was a mistake and has been corrected with the correct number of scaffolds.\nAddressed at line 621.\n• In my opinion, there is an ambiguity between what you call hydrothermal plume assemblies and microbial MAGs\n(l.468-469). By hydrothermal plume assemblies, do you mean assemblies obtained from all the 0.2 µm filtered\nplume viral fractions (l.432-440) ? If it is the case, what do you precisely call microbial MAGs ? Do they\ncorrespond to what you have assembled from the corresponding prokaryotic fractions ? In the same way, what\nyou call hydrothermal deposit assemblies and microbial MAGs remains ambiguous. In this case, do you mean\nviruses and prokaryotes were sequenced simultaneously ? This would need to be reformulated. A little more\ninformation would be useful in the mat and met part about MAGs and how they were analysed.\nThe plume assemblies are largely obtained from 0.2 µm filters, as well as 0.8 µm filters. The DNA was extracted\nfrom these filters and sequenced to produce metagenomes. Both microbial MAGs and viruses are then identified\nand reconstructed from these metagenomes. For hydrothermal deposits, no filter is necessary as these samples\nare taken from the chimney deposits directly. DNA is extracted from deposit material and sequenced to produce\nmetagenomes. Both microbial MAGs and viruses (including vMAGs) are identified and reconstructed from these\nmetagenomes.\nAssemblies are the assembled contigs of metagenomic reads. MAGs are the metagenome-assembled genomes\nthat are reconstructed from the assembly after read mapping and binning. MAGs can be microbial or viral\n(vMAGs). Microbial MAGs and vMAGs can both be obtained from the same assembly.\n• To evaluate the quality of the assembly step, it is a good idea whenever possible to calculate the percentage\nof reads mapping onto the overall set of scaffolds, and then vMAGs.\nThe assemblies are all previously published and have been assessed for quality in those publications (Zhou et\nal., 2022 and Zhou et al., 2023). Here, because we are reusing these data to examine viruses, we completed\nthe read mapping to viral genomes and provide the data in the supplementary tables.\nZhou, Z., St John, E., Anantharaman, K. & Reysenbach, A.-L. Global patterns of diversity and metabolism of\nmicrobial communities in deep-sea hydrothermal vent deposits. Microbiome 10, 241 (2022).\nZhou, Z. et al. Sulfur cycling connects microbiomes and biogeochemistry in deep-sea hydrothermal plumes.\nISME J. (2023) doi:10.1038/s41396-023-01421-0.\n• By the way, I wonder if it would be not interesting to challenge a bit the way in building up your viral dataset. It\nis indeed relatively frequent to definitively remove at least scaffolds < 2 kb, which correspond to the minimal size\nreported for DNA viruses (if I do not a mistake you only keep scaffolds > 3kb for many analyses). Then a\nclustering could be directly performed for the remaining scaffolds at the 95% ANI, corresponding to species\nboundaries. I would then advise the authors to use other viral prediction tools, in addition to Vibrant, such as\ngeNomad and VirSorter2 to try to get a more exhaustive detection of what we could call viral operational\ntaxonomic units (vOTUs).\nWe agree with the reviewer that this is an exciting area of viromics research and will be interesting to incorporate\nin future studies. However, incorporating this is currently beyond the scope our study. While using multiple tools\nwould likely yield a few more viruses that would be added to our dataset, most of the viruses identified with\nmultiple tools are redundant. The central conclusion of the paper, that viruses are largely endemic in\nhydrothermal vents, is unlikely to change with the addition of some viruses, given the data and statistics are\napparent from thousands of these genomes. It is relatively common to still use one viral prediction tool (as seen\nin these recent examples VirSorter2: Simon et al., 2023 and Tian et al., 2024) and the benefits of using multiple\nvirus prediction tools and how they should be combined is still an active area of research.\nLopez-Simon, J., Vila-Nistal, M., Rosenova, A., De Corte, D., Baltar, F., & Martinez-Garcia, M. (2023). Viruses\nunder the Antarctic Ice Shelf are active and potentially involved in global nutrient cycles. Nature Communications,\n14(1), 8295.\nTian, F., Wainaina, J. M., Howard-Varona, C., Domínguez-Huerta, G., Bolduc, B., Gazitúa, M. C., ... & Sullivan,\nM. B. (2024). Prokaryotic-virus-encoded auxiliary metabolic genes throughout the global oceans. Microbiome,\n12(1), 159.\n• It would be interesting to use an additional viral database like Phrogs to annotate the viral proteins\nWe have now added this into Supplementary Table 11 as requested. These annotations are incorporated into\nour results and support our conclusion that most viral proteins are predicted to be hypothetical or of unknown\nfunction.\nAddressed at lines 239-241.\n• I advise you to be cautious about your virulent/temperate classification (l.88-92). The analysis performed on\nthe overall dataset is not relevant given most of your viral scaffolds are 1-5 kbs. The smaller (incomplete) the\nscaffold, the lower the chances of detecting integrases/excisionnases/repressors. I would recommend to perform\nthis analysis to high quality and better scaffolds only, and then including medium quality scaffolds but > 20 kb.\nJust to evaluate if this impacts the trend that you observe when you consider medium and better quality scaffolds.\nKeeping also in mind that « plasmidic » temperate viruses that do not encode integrases exist.\nWe agree and have added information in the discussion describing this limitation. We find the pattern holds when\nexamining medium-quality and above viruses, and when examining only complete viruses. This information has\nbeen added to the first paragraph of the results, where the entire sentence now reads, “Most of the hydrothermal\nvent viruses were classified as lytic rather than lysogenic (47,571 lytic versus 2,391 lysogenic) and this remains\ntrue when only examining viruses of medium-quality or better (1,505 lytic versus 328 lysogenic), as well as only\ncomplete viruses (109 lytic versus 4 lysogenic).”\nAddressed at lines 92-93.\n• I would also advise more caution about narrow vs broad host range phage conclusions given this is based\nsolely on 14% of the viral dataset.\nWe added “and for these viruses,” at the beginning of the results section ‘Viral biogeography is closely tied to\nthe geographic distribution and abundance of their hosts’ to reinforce that this is just for the 14% with a predicted\nhost.\nAddressed at line 288.\n• l.266-270 : In my opininion, it is not all the time an easy task to clearly distinguish between AMGs and morons\n(by definition solely encoded by temperate viruses and which the expression provides an advantage to the\nlysogenized host), particularly in this case.\nOur goal was solely to highlight genes coopted by viruses from their hosts and we only refer to AMGs in this\nmanuscript. We do not differentiate between morons and AMGs. For all practical purposes, morons are a subset\nof AMGs and current standards in viromics do not differentiate between these two designations. We agree with\nthe reviewer that differentiating between AMGs and morons as defined by the reviewer is difficult and may need\nexperimental validation, which is currently beyond the scope of our study.\n• Figure 1B : (l.113) yellow circles ? Virus class names are shown on the top x axis and the horizontal names\nabove them show virus realms but Revtraviricetes is a class, not a realm. Ribozyviria are missing ?\nThank you, good catch, this was from a previous color scheme for that figure and has been corrected to teal (line\n118).\nAnd thank you for the second catch, something happened with the name here and it has been corrected - the\nRevtraviricetes class is within the Riboviria realm, and this name is now with the other realm names (line 103,\nFigure 1B).\nAddressed at line 103 and 118.\n(l. 161) family names has to be written in italic\nThank you for catching this, we have italicized and checked the rest of the manuscript for this formatting (line\n179-180).\nAddressed at lines 179-180.\nReviewer #2 (Remarks to the Author):\nTitle: Endemism shapes viral ecology and evolution in globally distributed hydrothermal vent ecosystem\nSummary\nIn this study, Langwig et al. studied viral ecology and evolution from reconstructed viral communities in deep-\nsea hydrothermal environments globally in the ocean using metagenomic sequences. The authors explored the\nbiogeographical distribution patterns of these viruses and concluded most were endemic, though a few were\nshared between vents. The authors evaluated inter- and intra-vent viral relatedness based on sequence similarity\nand read abundance. Furthermore, they explored function roles of these viruses based on protein and auxiliary\nmetabolic genes (AMGs), as well as inferring their probable hosts.\nWe commend the authors for providing global context to deep-sea hydrothermal vent viruses and using state-\nof-the-art analytics to explore these viruses' ecological and functional patterns. Overall, the study is well-written\nand presents deep-sea hydrothermal-associated viruses globally. However, there are several concerns that the\nauthors need to address, which are outlined:\nComments\n● The authors claimed endemism of hydrothermal vent viruses by comparing viruses within hydrothermal\nenvironments. However, it may be confusing to state endemism without comparing the viruses with those\nidentified in the global oceans and deep ocean environments from published work.\nWe ran the same nucleotide clustering analysis on the vent viruses with the Global Ocean Virome 2.0 dataset\nand identified only 36 clusters to be shared between our hydrothermal vent samples and GOV. We have now\nadded a description of these results in this section now entitled ‘Viruses are rarely shared between geographically\ndistant hydrothermal vents or the global ocean’ (lines 155-166). This information is also provided in\nSupplementary Table 9.\nAddressed at lines 155-166.\nLine 18: What taxonomic rank are the 49,962 vOTU ~species?\nThe taxonomic predictions for the viral genomes were generated using geNomad and are available in\nSupplementary Table 2.\nLine 24: What are the metabolic functional roles of the AMGs? Briefly state this.\nThis has been added in the abstract at line 25. To clearly connect this statement to the results, we also added a\nbrief explanation of these genes in the results, at the end of the section ‘Hydrothermal vents share viral protein\nfamilies dominated by proteins of unknown function’ (lines 258-262).\nAddressed at lines 25 and 258-262.\nLine 86-87: The authors describe three states of the vMAGs, high, medium, and low confidences, what\nparameters/criteria was used to assign them to each of these categories. I could not find this in the MM\nThis was predicted using CheckV and is stated in the methods section ‘Virus taxonomy, marker genes, and\nquality’. We have added clarification to this sentence in the methods explicitly stating that the low-, medium-, and\nhigh-quality as well as complete predictions come from this tool (lines 651-652).\nAddressed at lines 651-652.\n● How do the 49,962 viruses identified in this study compare with the Global Ocean Viromes dataset and viruses\nreported from deep ocean environments?\nAs stated above, we ran the same nucleotide clustering analysis on the vent viruses with the Global Ocean\nVirome 2.0 dataset and identified only 36 clusters in total where vent viruses have ≥70% nucleotide identity to\nviruses from GOV (lines 155-166).\nAddressed at lines 155-166.\nLine 88-89: The authors provide evidence of highly lytic compared to lysogenic states based on the presence of\nthe integrase gene. Given that these findings contradict the commonly held paradigm, as the authors define in\nthe introduction (Line 55), the authors need to provide biological justification for why they observe different\nfindings.\n->There needs to be a limitation paragraph, given this only based on evidence of a single gene.\nWe agree and have added a limitations paragraph (lines 508-534) in the discussion stating the issues in\npredicting lifestyle introduced by incomplete viral genomes. We also note in the results that this pattern was\nobserved using medium-quality and above, as well as complete viral genomes (though the number of complete\nviral genomes in our study is low).\nAddressed at lines 508-534.\nFigure 1: Make A and B as a panel rather than standalone figures.\nThis is now adjusted in Figure 1 (line 103).\nAddressed at line 103.\nLine 100: The authors should include the depth information in Figure 1A.\nThank you for this suggestion, depth information is now included in the map (line 103).\nAddressed at line 103.\nLine 103: Could you switch the order of the realm Varidnaviria and Unknown on Figure 1B, making the unknown\nthe last column?\nYes, thank you, this has been changed and the figure replaced (line 103).\nAddressed at line 103.\n-> Also unknown could be multiple viruses, so those should be in abundance figure.\nThe abundance of all unknown viruses is in the last column as suggested (line 103).\nAddressed at line 103.\nLine 117-129: While the biogeographical distribution patterns are primarily based on clustering, it seems read\nmapping was only done on a subset of those found in multiple areas. I suggest the authors do a read mapping\nusing their ~49k viruses and compare how this matches the geographical patterns of clustering.\n● This is mainly because the genome length will influence the clustering, thus likely missing out on some viruses\nTo be clear, for both ANI-based clustering and read mapping-based clustering, the analyses were completed on\n≥3kb viruses, and thus both were completed on the same set of viruses.\nWe did not complete these analyses on the full set because viruses shorter than this length drastically reduced\nnoise and when manually examined, showed meaningful overlap in their annotations. To clarify this, we have\nadded “≥3kb” in the results section (line 127), ‘Viruses are rarely shared between geographically distant\nhydrothermal vents or the global ocean.’ This is also stated in the figure caption of Figure 2 (line 213) and was\ncorrected in the methods, line 732.\nAddressed at lines 127, 213, and 732.\nLine 201: Why use a 3kb fraction rather than 5 kb or the standard 10kb for viral ecology work? How many viral\nhallmark genes are present within a 3kb?\nIn our opinion, 10kb is an extremely strict cut off - while this ensures you have less to worry about in terms of\nmanually checking viruses it will certainly exclude real viruses and viral fragments, considering the smallest\nbacteriophages are 3kb (Rumnieks and Tars, 2012). In support of this, we observed several 3kb\nCressdnaviricota and Microviridae in our dataset that are predicted to be complete viral genomes. Since we\nplanned to use a genome clustering comparison method to describe viral biogeography, we wanted to be sure\nto capture as much data as possible - if we had only included 10kb viruses we would have missed additional\nrelated viruses that are fragmented or have a smaller genome size. Considering that we include these viruses\nand still identify so few shared viruses across geographic distances is telling.\nIn this dataset, 3kb viruses have 0-8 viral hallmark genes, according to geNomad. This was why we were very\ncareful in reporting an abundance of metadata with the viruses. The supplementary data includes all the\ninformation we have about the viruses that were found to overlap (gene annotations, estimates of completeness,\ngenome size, quality, etc.), and the annotations of the regions of overlap. For example, in cluster 12 that includes\n3-4kb viruses, the regions of overlap include base plate wedge proteins. While we agree that the inferences we\ncan make about these viruses outside of identifying these regions of overlap is limited, we think it is important to\ninclude them to ensure we are capturing as many viruses as possible, and their exclusion would only reinforce\nthe central theme of the paper, that vent viruses are largely endemic.\nRumnieks, J., & Tars, K. (2012). Diversity of pili-specific bacteriophages: genome sequence of IncM plasmid-\ndependent RNA phage M. BMC microbiology, 12, 1-8.\n● Given that viral ecology is determined at 10kb, I would recommend if the authors want to keep the read mapping\nat 3kb to provide benchmarking showing that larger fragment (i.e., the standard 10kb) would not have affected\ntheir conclusions.\n● In addition, given that these are total metagenomes rather than enriched viral metagenomes, I would prefer\nlarger viral contigs to reduce the risk of cellular contamination.\nThis information can be seen in the supplementary data, because 10 kb and above viruses were included in all\nanalyses - if you were to place the cut off at 10kb, there would only be 15 ANI-based clusters with viruses from\ngeographically distinct vents, as opposed to 65. Thus, the current conclusions are further reinforced, that\nendemism dominates.\nConcerns about viral contig size are addressed in your comment above.\nFigure 3: Complement this with panel B of the heatmap and clustering pattern showing protein function and\nbiogeographical distribution patterns.\nThank you for this suggestion, we have now added panel B showing the annotations of the proteins from part A\n(line 267). There are thousands of proteins within the clusters shown in panel A, thus the functional categories\nshown in B are broad because it would be impossible to display all the individual protein names. We also\nnormalized the protein counts (number of times a particular protein occurs at a site) by the total number of\nannotated proteins at a site to better show the patterns of protein distributions between sites.\nAddressed at line 267.\nLine 252: I recommend moving this below to allow completion of the biogeography patterns/and story then\ndiscuss the AMGs story.\nThank you for this suggestion, to improve flow, the AMGs are now last in the results and the patterns with host\nabundance and hydrothermal geology are now before it (line 282).\nAddressed at line 282.\nLine 259: Please add what DRAM-v score cut-off applied for AMG identification. Were there efforts to curate this\nmanually?\nTo address this, we have clarified language in the main text (lines 353-355) and added more detailed information\nin the methods (lines 719-730).\nAddressed at lines 353-355 and 719-730.\nLine: 289: A possible clear way would be to add the biogeographic information and abundance patterns within\nFigure 1 and then tell the story of the viruses identified and their biogeographic patterns there. Currently, where\nit is within the manuscript feels disjointed.\nPer your above suggestion, the AMG section is now last in the results, improving the flow of this section (line\n347).\nAddressed at line 347.\nLine 303: Need some statistical correlation analysis to support this conclusion, and the results from Figure 4.\nThank you for your feedback, this information was added to the results (lines 321-327) and supplementary data\n(Supplementary Table 15). Proportionality was calculated using the propr package in R and the methods\ndescribing this are detailed in the methods section ‘Host prediction’ (lines 757-763).\nQuinn, T. P., Richardson, M. F., Lovell, D., & Crowley, T. M. (2017). propr: an R-package for identifying\nproportionally abundant features using compositional data analysis. Scientific reports, 7(1), 1-9.\nAddressed at lines 321-327 and 757-763.\nLine 337: The authors need to add a limitation paragraph, given the limitations of some of the approaches they\ndecided to use. This would provide the reader with a full context of the work and factors to keep in mind as they\nread the manuscript.\nFor example, examining viral lifestyle remains an area of active research; the current approach of using a single\ngene, i.e., the integrase, is highly dependent on the genome fragments, especially in the case of metagenomes.\nSecondly, the authors use smaller fragments > 3kb; the field standard seems to be around 10kb for viral ecology\n(read mapping to establish the abundance and distribution patterns).\nWe agree, and this was mentioned by another reviewer, thank you for bringing this up. We have added this\nparagraph in the discussion, where we highlight concerns related to focusing on mainly prokaryote-infecting\nviruses, viral lifestyle predictions, and AMG identification (lines 507-531).\nAddressed at lines 507-531.\nLine 336-338: As mentioned above, a statistical analysis is needed to establish a correlation between viral and\nmicrobial abundance. Currently, what is present in Fig 4 is a qualitative view. It can remain, but adding a panel\nwith correlations is recommended to give a robust conclusion.\nAs stated above, proportionality was calculated to establish that this correlation is supported.\nAddressed at lines 321-327 and 757-763.\nLine 401-404: The authors need to provide some hypothesis or explanation for why AMGs that ideally provide\nniche specialization are absent within the dataset. What is the plausible explanation or mechanism that virus-\nhost pairs are utilizing within this extreme ecosystem?\nWe have now added a more comprehensive explanation of the limitations of AMG predictions in the discussion\nthat addresses this comment.\nAddressed at lines 507-531.\nFor ease of reading the supplementary tables, could the authors combine these supplementary tables into one\nworkbook and provide the first tab as an outline of what each tab contains? In its current form, it is extremely\ndifficult to track as one reads the manuscript.\nThank you for this suggestion, this helps improve readability a lot. All the tables are now in one excel file under\nmultiple sheets with a table of contents at the beginning. We have updated the supplementary table numbers\nthroughout the main text.\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early\nCareer Researchers who co-review manuscripts.\nWe thank this reviewer for their support of early career researchers.\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early\nCareer Researchers who co-review manuscripts.\nWe thank this reviewer for their support of early career researchers."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Dear Editor and Authors,\nThe authors have demonstrated careful consideration of our comments.\nEach point of discussion has been addressed, and the manuscript has been revised accordingly where appropriate.\nIn light of the incorporated new information and the implemented modifications, we believe the manuscript now meets the\nNature Communications's publication criteria.\nSigned : Reviewer 1 and early career researcher participating in our co-review.", "reviewer_2": "I thank the authors for thoroughly addressing my concerns as well as the additional analysis that they undertook. I have no\nfurther comments or issues and I am happy to see this paper published.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": ""}]

Decision letter