Viruses under the Antarctic Ice Shelf are active and potentially involved in global nutrient cycles

Domain: earth_science
Published: 2023-12-14
Editorial outcome: major_revision

Abstract
Viruses play an important role in the marine ecosystem. However, our comprehension of viruses inhabiting the dark ocean, and in particular, under the Antarctic Ice Shelves, remains limited. Here, we mine single-cell genomic, transcriptomic, and metagenomic data to uncover the viral diversity, biogeography, activity, and their role as metabolic facilitators of microbes beneath the Ross Ice Shelf. This is the largest Antarctic ice shelf with a major impact on global carbon cycle. The viral community found in the cavity under the ice shelf mainly comprises endemic viruses adapted to polar and mesopelagic environments. The low abundance of genes related to lysogenic lifestyle (<3%) does not support a predominance of the Piggyback-the-Winner hypothesis, consistent with a low-productivity habitat. Our results indicate a viral community actively infecting key ammonium and sulfur-oxidizing chemolithoautotrophs (e.g. Nitrosopumilus spp, Thioglobus spp.), supporting a “kill-the-winner” dynamic. Based on genome analysis, these viruses carry specific auxiliary metabolic genes potentially involved in nitrogen, sulfur, and phosphorus acquisition. Altogether, the viruses under Antarctic ice shelves are putatively involved in programming the metabolism of ecologically relevant microbes that maintain primary production in these chemosynthetically-driven ecosystems, which have a major role in global nutrient cycles.

Introduction
The Antarctic Ice Shelf is an expansive mass of floating ice that extends from the Antarctic continent into the surrounding Southern Ocean and exerts a profound influence on global carbon cycles1,2. Ice shelves comprise a top freshwater layer of meteoric snow and a bottom layer of marine ice (i.e. frozen seawater) that can be as thick as several hundred meters1,3,4. In the past, ice shelves were considered inactive elements of the C cycle and mostly ignored in global models2. However, research conducted in the last decade has revolutionized this perspective, revealing the presence of uniquely adapted microbial communities that drive key biogeochemical cycles2,5, high rates of biogeochemical/physical weathering in ice sheets, and storage and cycling of organic carbon (>104 Pg C) and nutrients2. Thus, spanning vast areas, the ice shelf plays a pivotal role in nutrient cycling and the sequestration and release of carbon, contributing to regulating atmospheric CO2 levels and the overall climate system1,2.

The Ross Ice Shelf (RIS), in particular, represents a dominant feature within the polar region, both in terms of its vast size, ecological importance, and singular physico-chemical properties, such as the coldest seawater in the oceans limiting the life6,7. The RIS encompasses an area of ~487,000 km2, such as the size of France, and is the largest ice shelf in Antarctica floating atop a 54,000 km3 ocean cavity that act as a critical component of the Antarctic ice system6,7. Its unique characteristics, make it a significant contributor to the global climate system, influencing the marine ecosystem’s dynamics and acting as a key regulator of local and regional biogeochemical cycles. These polar ecosystems are vast reservoirs of life with a remarkable diversity of (micro)-organisms across various habitats that serve as sentinels in climate change8–10. However, the Antarctic Ice Shelves´s stability is increasingly threatened due to climate change1,3,11. Rising temperatures and changing ocean currents have resulted in accelerated ice melt, which has important implications for carbon cycling.

A recent study characterized the microbial community beneath the RIS, shedding light on their functional diversity and ecological role in one of the least-studied ecosystems in the world´s ocean5. This study uncovered the global biogeochemical processes taking place under the ice, in a vast area of Antarctic continent (> a million km2) that has major impact on global ecosystem processes. In that survey, seawater samples collected from three different depths of the ocean cavity were used to generate a microbial dataset to unveil the metabolic capabilities of microbes inhabiting these dark environments. Near the base of the RIS, high concentrations of ammonium drove high abundances and activities of ammonium oxidizing archaeon Nitrosopumilus spp. These archaea together with sulfur-oxidizing bacteria, such as Thioglobus spp., were the main primary producers and source of new organic matter in the ecosystem. In addition, a mixture of metabolically versatile and diverse heterotrophic bacteria were also abundant relying on complex organic matter compounds5.

Nowadays, the impact of viruses on ecosystem functioning is unquestionable12–16. The study of marine viruses has gained significant attention due to their ecological and biogeochemical impact on marine ecosystems14. Laboratory and regional scale observations have revealed that viruses play a crucial role in the biological carbon pump, specifically through viral “shunt” and “shuttle” mechanisms. For instance, some viruses predicted to infect ecologically important hosts, explain 67% of the variation in the organic carbon export in coastal and open ocean16. Of all marine viruses, those residing in the meso- and bathypelagic marine environments have garnered particular interest, as these constitute the largest ecosystems on Earth and are crucial components of the global carbon cycle13,17–19. Despite the crucial ecological significance of viruses inhabiting the dark ocean environments17,20–22, our comprehension of their diversity, abundance, and ecological roles remains significantly limited, more even for those thriving in the permanently dark ocean under the polar Antarctic Ice Shelves, due to titanic sampling efforts that involved drilling through hundreds of meters of ice in a remote location. Thus, the identification of major viruses both in terms of abundance, potential ecological role and influence on this globally relevant ecosystem remains enigmatic.

Given the extent and ecological significance of the Antarctic Ice Shelves, advancing our understanding of the virioplankton and its interaction with their hosts in these singular, ecologically important dark environments is essential for developing a comprehensive understanding of the ecology and biogeochemistry of these ecosystems. This is particularly relevant in light of climate change, which dramatically affects these ecosystems. To fill this gap, here we studied the viral diversity, biogeography, activity, and their role as metabolic facilitators of microbes inhabiting beneath the RIS. In this work, we show that the viral community found in the cavity under the ice shelf mainly comprises novel endemic viruses, actively infecting key abundant ammonium and sulfur-oxidizing chemolithoautotrophs that sustain primary production in these chemosynthetically-driven habitats.

Results
Virioplankton community structure beneath the RIS
In this study, we accessed and mined the genetic information of viruses collected beneath the RIS from previously reported cellular metagenomes, metagenome assembled genomes (MAGs), metatranscriptomes, and single-amplified genomes (SAGs) datasets5. The obtained data were used to unveil the ecogenomics of viruses that were likely been transcribed23, infecting single cells24–26, and/or present in the cell fraction as prophage or lytic virus27,28 (Fig. 1a). Ice drilling was performed in 2017 at site HWD-2, which is located 300 km from the RIS front. During the RIS Program, seawater samples were collected at three depths (30, 180, and 330 m) below the bottom of the shelf. It is important to clarify that specific sampling for assessing the free living fraction of the viral community was not originally conceived in the RIS Program and thus, such data were not available. Using a rather conservative method (combination of Virsorter 2.029, CheckV30, and PPR-meta programs31; see “Methods” section for details), we found a total of 607 bonafide viral genomes. Since taxonomic classification of viruses is complex when addressing uncultured viruses, we used two different approaches: classification by Virsorter 2.0 program that identifies viral hallmark genes for different type of viruses (e.g. RNA or ssDNA viruses), and also using Genomad with the most updated classification and database from the International Committee on Taxonomy of Viruses (ICTV) (Fig. 1b). According to ICTV classification, most of the recovered viral genome fragments (≈90% of assembled viral contigs) belonged to Caudoviricetes (Duplodnaviria; dsDNA viruses, Fig. 1b). Nearly all detected Caudoviricites displayed an uncertain classification indicating that they could correspond to novel families (Source data are provided in a Source data file). Other less abundant viral contigs recovered from our transcriptomic and metagenomic datasets belonged to ssDNA viruses (Monodnaviria, 3% of total detected viruses), RNA viruses (Riboviria), and Varidnaviria (including for instance nucleocytoplasmic large DNA viruses (NCLDV) and virophages) (Fig. 1c). Common hallmark genes of these viral groups were clearly detected such as single-stranded binding proteins for ssDNA32 viruses or RNA-directed RNA polymerase for RNA viruses33,34 (Fig. 1d and Source data file), such as in the case for RNA virus k121_168914, which, as discussed below, was one of the most transcribed viruses. As expected, the recovered size of assembled genome fragments (mean ≈4 kb) from ssDNA and RNA viruses were significantly lower than dsDNA viruses (mean contig size of 19,4 kb; Fig. 1c). Gene annotation of predicted ORFs (n=11,017) corroborated that the retained contigs were indeed viruses containing common viral hallmark genes, such as capsid and other virion structural proteins (Fig. 1d). Standard viral metagenomic techniques used in our study are well optimized for recovering dsDNA viruses35, and therefore we cannot rule out that some technical limitations and biases during sampling and processing have affected the recovery of RNA viruses36–38 that commonly are less stable. However, our employed experimental and bioinformatic methodologies to recover RNA viral genomes from transcriptomics have been successfully proven in environmental virology and are very useful to uncover abundant and active RNA viruses in soil and aquatic environments29,39–41.

Endemicity, global biogeography and lifestyle of the RIS viruses
Beneath the RIS, prokaryoplankton were of comparable diversity and abundance, though distinct composition, relative to those in the open, meso-, and bathypelagic ocean5. To put the viroplankton RIS community into global context, we compared all the predicted viral genes against the GOV 2.0 Tara virome datasets including polar stations and Malaspina expedition data from the deep ocean20,42–44, isolated viruses deposited in the Genbank database, and virome datasets obtained from the Southern Ocean45. Half of the predicted viral genes beneath the RIS show no homology with viral databases, while the other half showed mostly homology with ORFs from viruses mainly obtained from polar stations (Fig. 2). Overall, within GOV 2.0 database, most of the hits were against viruses obtained in mesopelagic (≈44%) followed by deep-chlorophyl maximum zone (27%) and surface (26%). Our results are thus consistent with the prokaryoplankton results5, indicating that a large fraction of the virioplankton under the RIS is locally adapted to polar environments and more similar to open ocean meso- and bathypelagic than to surface communities.

We also performed a further comparison using viral protein sharing network with more than 5000 representative viruses including reference isolates and uncultured viral representatives from some of the most abundant clusters from Tara expedition42 and other surveys, such as virus vSAG37-F6; supposed to be one of the most abundant virus in the surface ocean46. We found that 56% of the recovered viruses under the RIS represented singletons or outliers in the network without any connection with other known viruses and viral clusters (Fig. 2), consistent with the gene search similarity analysis discussed above. This suggests that a significant fraction of the virioplankton that resides under RIS is unique and might be represented by novel families never described before20,46. A deeper genomic analysis comparison of all viruses showed that our dataset was comprised of ≈600 different genera (according to thresholds demarcated by ICTV criteria), with only a few viral members belonging to the same species or genus, which agreed with viral network analysis data, since these ≈600 different genera mostly belonged to singleton viral clusters (Source data are provided in a Source data file). This suggests a high genomic diversity under the RIS. ICTV has implemented genome-based criteria47 and recently updated viral taxonomy (see ICTV webpage); although, demarcation of viral genera or families remain controversial and complicated for uncultured viruses46,47. Our network analyses on the RIS viroplankton also revealed a high density connection with viruses from the Southern Ocean (Fig. 2). Physical isolation and high productivity characterize the Southern Ocean (SO), which is responsible for up to one-fifth of the total carbon drawdown worldwide48. In the Southern Ocean, a positive selection of several viral protein clusters related to cold-shock-event responses and quorum-sensing mechanisms involved in the lysogenic-lytic cycle shift decision suggested marked temperature-driven genetic selection in the SO45. Piggyback-the-Winner (PtW) predicts that phages integrate into their hosts’ genomes as prophages when microbial abundances and growth rates are high49, such as in the gut50 or other productive environments51. Other strategies have been proposed, such as the Piggy-back-the-Persistent, in which viral community is dominated by temperate rather lytic lifestyles in ultra-oligotrophic polluted environments52. However, under the RIS, only 3% viruses contained integrases or were detected as prophages (Supplementary Material). Microbial cell abundance (9×104–1.2×105 cells mL−1) and prokaryotic heterotrophic production under the RIS, are rather low with turnover time of the microbial community between 339-461 days5. Thus, the lack of integrases in our data is consistent with the lifestyle of microbial community where peaks of high growth and production are not expected.

Local abundance and activity of virioplankton and host prediction
Despite the relatively low proportion of viruses assigned to their hosts (≈10%), albeit within the value obtained in other metagenomic surveys20, we were able to unveil host-virus pairs (n≈60) representing the most ecologically important prokaryotic members under the RIS (Fig. 3) involved in primary production, such as Thioglobus spp. and Nitrosopumilus spp. (sulfur and ammonia oxidation, respectively), and in carbon remineralization (Fig. 3, Source data file). A combination of multiple in silico approaches were implemented to assign virus to hosts, such as detection of CRISPR spacer-protospacer or tRNA match (see supplementary for more details) amongst others.

Metagenomic fragment recruitment has been commonly used to estimate the in silico abundances of viruses20,53. Here, as viromes are unavailable, we used metagenomes and metatranscriptomes, which provided information about viral abundances in the cell fraction (i.e. likely viruses infecting cells) and transcription activity for RNA and DNA viruses. Nearly all detected viruses (Fig. 3), except a few cases (<5%) exhibited no significant variation in fragment recruitment values among the different samples collected, indicating very similar abundances along the depth profile (Source data are provided in a Source data file). The top 3 most abundant viruses in the cell metagenomes (Fig. 3) belonged to Duplodnaviria, and one of them likely infected a marine Archaea. Remarkably, despite the abundance of RNA viruses recovered under the RIS was low, one of the most transcribed viruses were precisely one Riboviria (RNA virus “contig K121_168914” belonging to Chrymotiviricetes) infecting a putative unknown eukaryotic host. Other highly transcribed viruses were a dsDNA Caudoviricetes virus infecting a Nitrosphirales archaeon and a virus putatively infecting Pelagibacter spp (i.e. Pelagiphage, viral contig k121_349967). Under the RIS, nitrifying marine archaea are key abundant and active community members and contribute significantly to primary production5. Thus, to find archaeal viruses within the top 3 most abundant and active viruses was consistent with the “kill-the-winner” model54–56.

A diverse Pelagibacter spp. population with a moderate-low transcriptional activity was also discovered beneath the RIS5. Remarkably, our viral network analysis and genetic comparison indicated that the active transcribed Pelagiphage shared several orthologues genes, including the hallmark capsid viral gene (ORF 9), with virus vSAG37-F6 (confirmed by 3D-capsid protein structure prediction (Fig. S1). Virus 37-F6 infects Pelagibacter spp. and was discovered by single-virus genomics in temperate and tropical and subtropical surface oceanic regions as well as in some deep ocean samples25,46,57–60. The capsid protein of vSAG37-F6 (ORF9) was the most abundant protein in viral proteomes generated from Tara expedition. We also found that several viruses (n=19) from the RIS belonged to 37-F6 viral group sharing the orthologue capsid gene ORF9 (average identity 61.3% and query coverage 93.3%; Source data file). Despite the high abundance of Pelagiphages in marine viromes (i.e. metagenomes from free viral particles in the sea)20,46,57,61,62, this group overall showed significantly lower transcription rates than other marine viruses (e.g. viruses infecting the SAR116)63. In our study, the uncultured pelagiphages k121_349967 belonging to vSAG 37-F6 viral group was one of the most active members, especially in the sample collected at 180 m depth below the ice. It appears contradictory that some of the most actively transcribed viruses were viruses infecting a common slow-growth microbe, such as Pelagibacter spp. Nevertheless, considering the extreme conditions of the RIS, with the lowest possible temperature in the sea, it is plausible that slow-growing microbes and their viruses thrive and compete under these environmental conditions, where killing the winner dynamics co-exist alongside other viral strategies for infection. Indeed, high viral to prokaryotic ratios in low productive areas have previously been reported in bathypelagic layers of the Pacific and Atlantic Oceans64,65 suggesting that at low-temperature, viruses remain active for an extended period of time and infect slow-growing bacteria66, such as under the RIS.

Viruses as microbial helpers under the ice: niche-specific auxiliary metabolic genes
Our data showed that most of the detected viruses infect microbial hosts that play a crucial role in the biogeochemical cycles (Fig. 3). A large fraction of environmental viruses possess AMGs that participate in host metabolism facilitating host adaptation67–69. To investigate the niche specialization and ecological functions of these viruses, and how they affect marine ecosystem function beneath the Antarctic Ice Shelf, we further identified, based on genome analysis, the key potential AMGs they carry. In total, 52 different viruses from our virus genomic dataset potentially had niche-specific AMG (Fig. 4 and Source data file). Molecular chaperone and cold shock proteins were fairly abundant in different viruses infecting numerous microbial phyla in our data (Fig. 4). These findings suggest that under RIS extreme conditions, there is a strong selective pressure favoring adaptation to low temperatures5. Similar adaptations have been observed in other environments, such as the Southern Ocean, where viral proteins exhibit a lower hydrophobicity index pattern20. This adaptation favors protein flexibility and improves performance at lower temperatures20, which has been also observed here in the globally widespread open ocean uncultured vSAG37-F6 virus, where the hydrophobicity index of the capsid gene was −0.29 or lower (Fig. S1 and Source data file).

The most striking result was that several viruses infecting key chemolithoautotrohps, such as Thioglobus spp. (that relies on available reduced sulfur compounds), putatively carry iron-sulfur cluster proteins. It has been described that these proteins can bind sulfur and/or iron (i.e. Fe/S cluster protein) before being transferred to apoproteins. 3D-Prediction structure of selected viral AMGs (see for instance gene product ORF2 of virus “metag_c_000000144917” classified as high-confidence viral genome) showed nearly identical protein structure to that of potential hosts relying on sulfur oxidation (Figs. 4 and S3). Likewise, Fe/S cluster proteins were reported as abundant AMG in viruses infecting symbiont SUP05 sulfur-oxidizing bacteria70. In that model, it has been proposed that this viral AMG encodes a Fe-S cluster proteins that supplement the assembly of Fe-S clusters in SUP05, thereby increasing the efficiency of energy conservation from sulfur oxidation. Similarly, we propose that viruses beneath the RIS potentially boost sulfur metabolism of chemolithoautotrophs, as in the SUP05 bacterial group by providing an auxiliary gene encoding for proteins with high affinity to sulfur. In other sulfur oxidizing bacteria, Fe-S cluster proteins (i.e. encoded by gene IscA) resulted to be essential for sulfur oxidation71. Indeed, when an homologs Fe-S gene was experimentally deleted in Allochromatium vinosum, the sulfur oxidation rates were significantly reduced71. These proteins are essential in supporting bacterial life as they are involved in three vital processes such as, photosynthesis, nitrogen fixation and oxidation/respiration, and hydrogen/sulfur metabolism72,73. Considering that photosynthesis and nitrogen fixation were absent under RIS5, it is reasonable to speculate that this Fe-S cluster proteins boost sulfur metabolism as shown in other sulfur-oxidizing bacterial models71. This could be potentially equivalent to viral photosynthesis performed by cyanophages in sunlit waters74–76. Interestingly, ferredoxin, which is also involved in Fe/S transportation, was another highly abundant viral AMG found in several viruses (Figs. 4 and S4 and Suppl Dataset), likely involved in cell respiration and electron transport chain. Moreover, considering the ubiquity of Fe-S cluster proteins detected in viruses infecting sulfur-oxidizing bacteria, it is also possible that these proteins may also participate in other house-keeping processes beyond sulfur metabolism63.

Furthermore, Caudoviricetes viruses beneath the RIS showed the potential to facilitate phosphorous acquisition, since PhoH and acid phosphatase genes were detected in viruses putatively infecting Marinisomataceae (formerly named as SAR406) and Nitrosphirales (SAG_4 and SAG_57), respectively (see Fig. S4 and Source data file). In addition, a glutamine synthetase AMG involved in nitrogen metabolism and biosynthesis was found in viruses (e.g. virus “metag_c_000000198991”, ORF_7) putatively infecting SAR86 (SAG_12 and MAG_167). Interestingly, both genomes had homologs glutamine synthetase. This enzyme is fundamental to the nitrogen metabolism of bacteria by catalyzing the condensation of glutamate and ammonia to form glutamine. Ammonia, which is abundant in this habitat, can be used both as an electron donor by chemolithoautotrophs and/or as a nitrogen source for nitrogen assimilatory pathway by different microbial groups5. Likewise, although speculative, isocitrate lyase involved in anaplerotic fixation in planktonic marine bacteria77, was found in viruses infecting Pelagibacter spp. and Woesiales (Fig. S2). Finally, as expected multiple AMGs homologs to host genes involved nucleotide/nucleoside synthesis and recycling and DNA processing were detected (Figs. 4 and S2 and Source data file). Although the discovery of AMGs are solely based on genome analysis, it is reasonable to consider our hypotheses plausible. Our findings are supported by previous AMG reports from different reference marine viral models, such as viral photosynthesis78, which were later confirmed through experimental studies75.

In conclusion, we have characterized the viral diversity, biogeography, and activity (i.e. transcriptional activity) inhabiting beneath the Antarctic Ice Shelf. Our results unveiled a unique viroplankton diversity and community composition, mostly comprised of unknown, endemic, and novel taxa. Consistent with the RIS prokaryoplankton, the viroplankton community was more related to open ocean polar and mesopelagic communities. The virus-host dynamics revealed insights into the ecology of these viruses and did not support a Piggyback-the-Winner, consistent with a very low productive environment. The analysis of viral transcripts also revealed an active viral community that was more actively infecting key active prokaryotes driving elemental cycling (e.g. Nitrosopumilus spp, Thioglobus spp.), supporting a kill-the-winner dynamic. Moreover, genome analysis of these viruses showed the presence of specific AMGs putatively involved in nitrogen, sulfur, and phosphorus acquisition. Altogether, most of the viruses below the RIS are novel and have the potential to impact the cycling of nitrogen, phosphorus, and sulfur in this ecosystem, consequently influencing the global biogeochemical cycles. Further mechanistic studies will help to provide more biological insights into the role of viruses in one of the most underexplored ecosystems on Earth.

Methods
Sampling, assembly, and viral contig detection
All details regarding site selection, ice drilling and sampling are described in ref. 5. Briefly, hot drilling and seawater sampling was conducted from the sub-shelf water column in the central region of the RIS (Latitude −80.6577N, Longitude 174.4626W). The sampling site was located ≈300 km from the shelf front. A borehole (30 cm diameter) conducted by hot water drilling was used for direct sampling of seawater from three depths (400 m, 550 m, and 700 m from the top of the shelf, which correspond to 30 m, 180 m, and 330 m from the bottom of the ice shelf, respectively). Seawater samples were processed accordingly for single cell genomics, metagenomics, and transcriptomics as described5, and the resulting assembled and co-assembled contigs (min. length 1 kb) from single-amplified genomes, bins and transcriptomics were mined for detecting viral contigs. Since viral metagenomes were not available for these collected samples nor conceived at the moment of sampling, we applied here a rather conservative method for detecting and considering a bonafide viral genome fragment as follows. Initially, a first round of viral contig detection was performed with Virsorter 2.029 and CheckV30 for the presence of RNA viruses, Lavidaviridae, NCVLD, dsDNA and ssDNA viruses. A total of 37,674 putative viral contigs were detected (contig length ≥1 kb). Then, the dataset was filtered out by contig length criteria, considering only contigs ≥10 kb for those potentially classified as dsDNA viruses, NCVLD, and Lavidaviridae. For RNA and ssDNA viruses we applied a lower cut-off, 1.5 and 2.5 kb, respectively. For the latter, very small genomes from marine ssDNA and RNA viruses have been described79,80. Then, the resulting viral contig dataset was filtered out in a second round using several parameters obtained from the program CheckV as follows: only bonafide viral genome fragments were considered when at least 1 or more viral hallmark genes were detected in a contig-non classified as provirus or integrated-as long as the ratio of cell host genes to viral genes were <1. For those viral contigs classified as provirus integrated in the host genome, the criterium was more flexible, and a viral contig was considered as long as 1 viral hallmark was detected. Viral contigs with 0 host genes and 0 viral hallmark genes were not further considered. This resulted in a total of 607 bonafide viral contigs. Finally, in a third round, the curated viral contig dataset was again independently screened with the program PPR-meta31 which showed that more than 94% of these contigs were of viral origin and only in a few cases the source was unclear.

Genome classification, annotation, viral genome network, and host assignment
Putative viral contigs were initially classified into five groups according to Virsorter 2.0 five groups (dsDNA, ssDNA, and RNA viruses, Lavidaviridae, and NCLDV). Then, they were formally classified with the current most updated classification recently released by ICTV using the program geNomad with the following parameters: genomad end-to-end --min-score 0.7 –cleanup. Viral contigs were annotated with program DRAM-v with default parameters81. Genomic comparison of all viral contigs was performed with the VIRIDIC program82, with default parameters in order to estimate the genetic relatedness. Viral network interaction showing taxonomic assignment and relatedness of viruses from the RIS with other viruses in databases was performed with vConTACT v.2.0 as described46,83,84. For this analysis, a total of 5461 viruses were compared including reference viruses recovered from Tara and Malaspina expeditions20,42, isolated reference viruses from Genbank and ICTV, and marine viruses from different samples of the Southern Ocean45. Viral proteins were predicted and compared through all-verses-all BLASTP with an E-value threshold of 10−5 and 50 for bit score46. Viral protein clusters (PCs) were then defined using Markov Clustering Algorithm (MCL)85, using default parameters and 2 for an inflation value. vContact (https://bitbucket.org/MAVERICLab/vcontact) was then used to estimate a similar score between every pair of viral genomes based on the number of PCs shared between two sequences and all pairs using the hypergeometric similarity, as previously described20,86. MCL was applied to the similarity scores using a threshold of 1 and MCL inflation of 2 to generate viral clusters (VCs, ≥2 sequences). Sequences were analyzed to identify highly similar VCs from the dataset using the Jaccard similarity as described46, predicted taxonomy using reference sequences present within the VCs, and constructed a network87 using the similarity scores generated by vContact between each genome pair. The final dataset was exported to Cytoscape (v3.3.0)87 and images were post-processed.

Comparison of virus abundance and viral protein in GOV 2.0/Tara and RIS datasets
Abundances in cell metagenomes and transcriptomes of the recovered viral contigs beneath the RIS were estimated in silico by metagenomic fragment recruitment. In addition, abundances in marine Tara viromes from different samples were also estimated. This analysis was performed as previously described in20,46,61, employing the following two identity thresholds (query coverage ≥85% plus nucleotide identity cutoff ≥95% and query coverage ≥50% plus nucleotide identity cutoff ≥70%) in order to estimate the normalized abundance of the viral populations at the species and genus level, respectively46, and expressed as recruited kilobases per a genome kilobase and a metagenome (or Metatranscriptome) gigabase (KPKG). To complement these analyses, viral proteins predicted from the RIS viruses were compared against the whole GOV 2.0 protein database and proteome from the Southern Ocean viruses45 using blastp with the following cutoffs: e-value better than 0.00005 and query coverage and identity values ≥50%. In addition, we also searched specifically for the presence of hallmark genes and orthologues genes of virus vSAG37-F6 using the previously mentioned parameters in the detected viral contigs.

Detection of auxiliary metabolic genes in viral contigs and virus-host assignment
To search for the presence of auxiliary metabolic genes (AMG) in viral contigs, all the predicted viral proteins, regardless if the function was known or not (e.g. hypothetical proteins), were queried against all viral proteins obtained from binned MAGs and SAGs obtained from the same samples5. For that, BLASTp was used with the following criteria: e-value better than 0.00005 and query coverage and identity values ≥50%.

Detection of CRISPR arrays in MAGs and SAGs was performed with CRT Tool88 and a search of match between host spacer and viral protospacer was performed with BLASTn adjusting parameters to short sequences according to program´s manual. The obtained viral tRNA were queried against MAG and SAGs database using BLASTn according to the following thresholds previously used (alignment ≥ 60bp, identity ≥97%, mismatches <10 search89). Codon usage was calculated using the on-line Sequence Manipulation Suite90. WisH program was also used for assigning virus to host considering only those pairs that showed a Log Likelihood value between −1.30 and −1.20 according to reported data91. The presence of provirus was conducted with CheckV program30. It is important to remark that sometimes binner softwares could wrongly place a viral contig in a MAG based simply on similar % GC or sequencing coverage. In our analysis, we did not assume that the simple presence of a viral contig in a MAG or bin was enough to conclude such host assignment but other complementary and more unequivocal proofs were needed, such as a match of CRISPR spacer-protospacer or tRNA, and/or presence of a host homologs auxiliary metabolic gene in the viral genome. In some virus-host pairs, multiple coincident proofs were obtained.

The hydropathicity index of proteins was calculated using Protscale Expasy tool (https://web.expasy.org/protscale/) according to92, which represents the hydrophobic or hydrophilic properties of a protein. The larger the number is, the more hydrophobic the protein, while the lower the number is, the more hydrophilic the protein. Robetta on-line software (https://robetta.bakerlab.org/submit.php) was used for protein 3D-structure prediction.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study investigates the metabolic diversity and putative ecology of viruses below the RIS via analysis of a previously published set of metagenomes and metatranscriptomes. Although the novelty of the dataset increases its significance, the findings (which are entirely based on inference) are consistent with expectations based on the known taxonomy of the microbial community. The study as presented is largely a data report. As such, while the data might merit publication I don't think it rises to the level expected for Nature Communications.\nGrammatical and structural errors throughout, I recommend all authors given the manuscript a very close read and restructure the story so that the most significant findings are clear.", "reviewer_2": "The paper provides valuable insights into the viral diversity, abundance, and ecological roles in the dark environments beneath the Antarctic Ice Shelf. The study utilized a combination of MAGs, metatranscriptomes, and SAGs to characterise the viral community structure. The authors identify many novel viral taxa and investigated their potential ecological functions. The study contributes to our understanding of the Antarctic under-ice-shelf ecosystem and its response to climate change.\nThe abstract and introduction are too focused for a broad readership. For example, the deep sea is the largest habitat on Earth. The limited viral knowledge of this habitat is an excellent point that brings importance and novelty to the paper. Focusing on the Ross Ice Shelf brings further novelty and creates a manageable and defined volume of study.\nAs discussed below, finding new viruses is common. Similarly, the endemism claim is based on limited comparisons. It is good but could be stronger than it is.\nThe results presented in the abstract are novel, but it does not seem like a low-productivity habitat, given the viral abudance.\nThere are numerous grammatical errors. The word ‘reprogramming’ in the third to last line of the abstract is the wrong word. Similarly, hyperbole obscures the presentation of a clear picture. For example, ‘an expansive mass of floating ice’ is clearly and ‘quantitatively described by pointing out that the Ross Ice Shelf is the same area as France. Alternatively, just give the square kilometres. The square kilometres are finally given later, but it is too late and makes it repetitive. Do not put text in parentheses.\nFrom the overall point of view, there are strengths and weaknesses. The strengths of the manuscript begin with a comprehensive sampling approach. They used multiple data sources to generate a comprehensive dataset for viral analysis. This approach enhances the reliability of the results and provides a more holistic understanding of the viral community beneath the Antarctic Ice Shelf.\nThe paper identifies a substantial number of novel viral taxa, highlighting the importance of the Antarctic Ice Shelf as a unique and diverse ecosystem. It should be noted, however, that given the huge number of viruses and undoubtedly viral species, the discovery of novel taxa is true wherever one looks. The discovery of niche-specific auxiliary metabolic genes is more interesting and more worthwhile than the novel taxa and suggests that viruses play a role in facilitating host adaptation and influencing biogeochemical cycles in this environment.\nThe study expands our understanding of under-ice-shelf ecosystems. Comparison to polar stations and the Southern Ocean viromes provides insights into the global biogeography of the viral community.\nThe comprehensive sampling approach did have weaknesses. The post-hoc nature of the work means the design was suboptimal for viromes.\nClassification of uncultured viruses is challenging, and the authors used multiple approaches to partially classify the viral genomes to redress this challenge. Many of the Caudoviricetes were uncertain classifications.", "reviewer_3": "The current manuscript is on the investigation of the potential contribution of viruses on the biogeochemical cycling under the Ross Ice Shelf (RIS). The authors mined their own dataset as well as publicly available databases containing viral genomes in order to get insight into how viruses might be affecting the microbial community found in the marine ecosystem under the RIS. They traced some of the viruses back to their hosts and hypothesized on the pathways the viruses could be involved in via protein-protein comparisons. Whereas the topic at large is quite interesting, the manuscript is not very well written, in many cases hyperbolic language is used, and the drawn conclusions are at times far-fetched, especially considering that the work is solely based on genome analysis with no mechanistic studies of the proposed interaction pathways.\nTitle: there is no direct evidence to show that these viruses are active or contribute to global biogeochemical cycles, please rephrase.\nIntroduction:\nOverall, the importance of the work and its novelty is not directly coming across from the introduction.\nLine 51: In general viruses are less studied in the environment, but why is this more dramatic for those that reside under the Antarctic ice shelves? Isn’t this just a matter of sampling frequency? Please clarify\nLines 53-67: The importance of the sampling site is not clearly explained. A reader with little to no knowledge of the effect of the Antarctic ice shelves and bottom water formation will not be able to understand the importance of this sampling site. Further here there is a lot of hyperbole, highlighted by the excessive use of the word “profound”. The authors should rewrite this paragraph in such a way that it does not come across as a lot of numbers and superlatives, but explains the system and its function on our planet better.\nLines 68-82: This paragraph is similar to the former one as it is again full of hyperbole. I find that it is a bit over-the-top that the authors call their own work (many authors of citation 18 and the current manuscript is the same) “a pioneer study”, “of utmost importance”, “unprecedented microbial dataset”. Please tune this down, and as with my previous suggestion, rather than using hyperbole, explain the former work in context. In addition, I find it a bit strange that the authors use one paragraph to discuss a single paper in the introduction rather than putting their work in context to make it easier for the readership at large to understand.\nLines 81-82: The data in the current manuscript could have as well been added to the former paper (citation 18). This way the virus component would not have remained enigmatic.\nIn general, I strongly advise the authors to rewrite their introduction to clearly put their work in context and why they have studied the viruses in this particular area.\nResults and Discussion:\nIn general, some parts of the results and discussion are written in a convoluted manner and at times the authors jump from one topic to the next and back again. Please streamline this section and refrain from repetitions.\nLines 97-100: Do the authors mean the samples were not taken with studying the viruses in mind? Still am I correct to understand that they managed to retrieve many virus genomes? Please clarify and very briefly discuss what kind of bias this sampling “without having specific sampling for viromics” might have introduced.\nLines: 100-102 I do not understand what is meant by the sentence starting with “Thus”. What does it mean likely present? Were the viral genomes obtained from this environment or not? Were viral transcripts inferred from general metatranscriptomics data? Please clarify and explain precisely.\nLines 138-140: Doesn’t this cold adaptation also apply to the host? Would it be reasonable to argue that the hosts adaptation might be more important?\nLines 193-197 vs lines 217-221: these parts are largely contradicting each other. Whereas I agree that there are different viral strategies even in the same environment, the authors write it as if it was a priori decided that a certain viral strategy would work. I would rework this section so that it comes across clearly that there might be different solutions to the same problem (from the virus POV), which I find quite an interesting point to discuss.\nLines 236-239: This is a bit convoluted to state that the capsids were relatively hydrophilic.\nLines 242-263: This section is very confusing and at times completely unclear. Many topics are touched upon, but none are clearly described and all remain at an unsatisfactorily superficial level.\nLine 242: Alphafold predictive software also has a component, which estimates the robustness of the predicted structure with different confidence levels calculated for the different parts of the predicted structure. I have not seen this in any of the structure predictions used in this manuscript. Please use this option so at least the reader can have an overview of the confidence levels of the predicted structures. As such, it is also not clear to me what the predicted structures add next to what is already gained from sequence similarity analyses. The authors should justify the inclusion of these predicted structures.\nLine 242: “carry sulfur compound transporters (e.g. Fe/S cluster protein)”. Fe/S proteins are not sulfur compound transporters. What exactly do the authors mean here?\nLines 245-246: Which proteins are meant here as being essential for bacteria to thrive? Fe/S proteins, sulfur compound transporters or some catalytic proteins active on sulfur compounds? The authors do not clearly explain why a microorganism would leave such a critical protein to be brought in by a virus.\nLine 245: what the mentioned protein is hypothesized to do is not clear from the figure and the caption.\nLine 252: It is unclear what kind of a ferrodoxin is meant here. In any case, ferrodoxins can be involved in many processes requiring low potential electrons as an electron carrier.\nLine 253: It is unclear what is meant by Fe/S transportation. Do the authors mean the transport of Fe and S to the cells or a Fe/S containing protein into the cell or the transfer of Fe/S cluster into an apoprotein. Please clarify.\nLine 258: What exactly is the function of nifU? Please explain more that “full activation of nitrogenase”, which is absolutely unclear. As it is not a part of the minimum catalytic system, I do not think it is absolutely necessary to carry this gene. Please clarify whether it is present in all nitrogen fixers, and what its presence might add to a microorganism (see for example https://pubs.acs.org/doi/10.1021/acs.chemrev.9b00489 for a review). This could be an interesting point to discuss for the beneficial side of a viral infection.\nLines 259-263: Both ammonium/ammonia as an electron source is discussed here. These are fundamentally different topics. Please clarify what exactly is meant here, do the authors mean GS is transferred to microorganisms via a viral infection?\nFigures: Some of the figures need some more work to better complement the manuscript. In figure two, what are the clouds above the line “isolated viruses”? it is unclear. In figure 4, what is the point of a gigantic cell, a miniature version of figure 3 and the structures depicted? There is so much information there that everything becomes unclear. Figure 5, this is a very unclear figure. I do not see the point of gigantic viruses being attached to cells. What is supposed to be happening here? The viruses are bringing in genes to cells that do not have them? What are the out of focus yellow and brown circles? Why are parts of the figure such as deep cavity circulation in lower resolution? How can be the authors so sure which part of the water column these interactions occur? They do not present any data to back this up. Please rework this figure or remove it.\nOverall, I believe that this manuscript is on an interesting topic. The manuscript requires a thorough rewriting to put the work better in context, to highlight the interesting points, and the hyperbole used needs to be tuned down to a very large extent. Furthermore, the authors should clearly indicate that everything stated here is hypothetical and based on genome sequence analyses."}, "author_response": "REFEREE 1\nGrammatical and structural errors throughout, I recommend all authors given the\nmanuscript a very close read and restructure the story so that the most significant findings\nare clear\nREPONSE: We have carefully revised our manuscript to fix those grammar and typo errors.\nThe study as presented is largely a data report. As such, while the data might merit\npublication I don't think it rises to the level expected for Nature Communications\nRESPONSE: We truly appreciate this comment. We would like to share with this referee\nthat we have received positive comments from the rest of referees. In our humble opinion, we\ndo not think this is a data report but the first discovery of the identity of some of the most\nabundant and active viruses under one of the largest and unknown marine environments with\na major impact on global carbon and climate systems. Indeed, we have provided some\nbiological insights into the potential impact of viruses on the metabolic reprogramming of\nkey microbes inhabiting and maintaining primary production under the Ross Ice shelf.\nREFEREE 2\nThe abstract and introduction are too focused for a broad readership. For example, the\ndeep sea is the largest habitat on Earth. The limited viral knowledge of this habitat is an\nexcellent point that brings importance and novelty to the paper. Focusing on the Ross Ice\nShelf brings further novelty and creates a manageable and defined volume of study.\nRESPONSE: We have modified the first sentences of the abstract and now in the new\nversion of the manuscript, it reads: “Viruses significantly influence the functioning of the\nmarine ecosystem. Despite exerting a profound influence on the global carbon cycle, our\ncomprehension of viruses inhabiting the dark ocean, and in particular under the Antarctic Ice\nShelves, remains very limited. Here, we uncover the viral diversity, biogeography, activity\nand their role as metabolic facilitators of microbes beneath the Ross Ice Shelf; the largest\nAntarctic ice shelf.”\nAs discussed below, finding new viruses is common. Similarly, the endemism claim is\nbased on limited comparisons. It is good but could be stronger than it is.\nREPONSE: We agree with this referee that finding new viruses is common. However, the\npoint here is to provide some contextual data on the genetic relatedness of the discovered\nviruses in comparison with other existing marine datasets. So far, the GOV 2.0 dataset is the\nmost comprehensive collection of marine viral genomes that was published by Sullivan´s\nlaboratory. This dataset includes viruses from pole to pole, west to east, and from surface to\nthe deep ocean. In addition to that, we have included in our analysis and comparisons viral\ngenomes specifically from the Sothern Ocean near Antarctica (Alarcon et al) and also from\nthe Malaspina Expedition that targeted more specifically viruses from the deep. Here the\nreferee argues that “endemism claim is based on limited comparisons. It is good but could be\nstronger than it is”. However, according to our analysis, and number and extension of the\nviral datasets used, we think that the claim of endemism is well supported, and we are not\nsure how it could be stronger, because we have used all most relevant viral databases to date.\nThe results presented in the abstract are novel, but it does not seem like a low-productivity\nhabitat, given the viral abudance.\nRESPOSE: We should distinguish between total cell/virus abundance and productivity in an\necosystem. According to Martinez-Perez survey published last year in Nat Comm on the\nmicrobial community inhabiting beneath the Ross Ice shelf from this same expedition,\nmicrobial cell abundance ranged from 0.9 to 1.2 × 105 cells mL−, while prokaryotic\nheterotrophic production (PHP, a proxy for growth of heterotrophic organisms) ranged from\n0.3 to 0.6 µmol C m−3 d−1, which was one to two orders of magnitude lower than at the\nmargins of the Ross Ice Shelf (~40 µmol C m−3 d−1) and the average global PHP rates in the\nmesopelagic (24 µmol C m−3 d−1) and bathypelagic (4 µmol C m−3 d−1) open ocean. Based on\nthese PHP rates, the estimated turnover time of the microbial community in the Ross Ice shelf\nranged between 339 and 461 days, which is extremely low compared to other values reported\non microbial and viral communities (see for instance Needham et al 2013 ISME J; Lopng et\nal 2020 in ISME J; Weissman et al 2021 in PNAS). High viral to prokaryotic ratios in low\nproductive areas were also previously reported in bathypelagic layers of the Pacific and\nAtlantic Oceans (De Corte 2012 ISME J, Yang 2014 AME) suggesting that at low-\ntemperature viruses remain active for an extended period of time (low viral decay Parada,\nAEM 2006). It is important to clarify, that we do not provide any abundance value of viruses.\nWe would love to have such data, but sampling water from a borehole during a drilling\nprogramme, such as the one conducted here, is a titanic effort, and volume sample was very\nlimited. In any case, we hope that with the data provided above, already published, we clarify\nwhy claim that this is a low-productivity environment. This statement is well supported by\nother existing papers.\nThere are numerous grammatical errors. The word ‘reprogramming’ in the third to last\nline of the abstract is the wrong word. Similarly, hyperbole obscures the presentation of a\nclear picture. For example, ‘an expansive mass of floating ice’ is clearly and\n‘quantitatively described by pointing out that the Ross Ice Shelf is the same area as\nFrance. Alternatively, just give the square kilometres. The square kilometres are finally\ngiven later, but it is too late and makes it repetitive. Do not put text in parentheses.\nRESPONSE: We appreciate this comment. All errors have been revised and corrected. In\naddition, we have modified the sentence according to the referee´s suggestion. We hope now\nin the new version is clearer, more focus and less repetitive.\nThe strengths of the manuscript begin with a comprehensive sampling approach. They\nused multiple data sources to generate a comprehensive dataset for viral analysis. This\napproach enhances the reliability of the results and provides a more holistic understanding\nof the viral community beneath the Antarctic Ice Shelf. The paper identifies a substantial\nnumber of novel viral taxa, highlighting the importance of the Antarctic Ice Shelf as a\nunique and diverse ecosystem. […]. The discovery of niche-specific auxiliary metabolic\ngenes is more interesting and more worthwhile than the novel taxa and suggests that\nviruses play a role in facilitating host adaptation and influencing biogeochemical cycles in\nthis environment. The study expands our understanding of under-ice-shelf ecosystems.\nComparison to polar stations and the Southern Ocean viromes provides insights into the\nglobal biogeography of the viral community.\nRESPONSE: We appreciate this very positive comment.\nThe comprehensive sampling approach did have weaknesses. The post-hoc nature of the\nwork means the design was suboptimal for viromes.\nRESPONSE: We agree with this referee. However, as stated above, preparing and designing\na drilling campaign in Antarctica, such as the one conducted for the Ross Ice Shelf, is a huge\neffort in terms of manpower, infrastructures, timing and funds. Unfortunately, we did not\nhave more available water samples to conduct additional experiments. We would love to have\nthem!  To try to complement this existing dataset, in a mid-term, we are in collaboration\nwith Professor Dr. Keith Makinson from BAS and collaborators in Norway to collect water\nsamples from several ice shelves (Fimbul and Nansen) during their campaigns in 23/24 and\n24/25 in order to perform complementary experiments.\nREFEREE 3\nIn many cases hyperbolic language is used, and the drawn conclusions are at times far-\nfetched, especially considering that the work is solely based on genome analysis with no\nmechanistic studies of the proposed interaction.\nRESPONSE: Following this referee´s suggestion, we have “tuned down” most of the\nstatements throughout the whole manuscript, including the abstract, results and discussion,\nand conclusion. Now, in this second version of the manuscript, we have clearly specified in\nthe abstract and the rest of the sections that some of the claims involved in metabolism could\nbe “potential” or “putative”. Indeed, we have included in several sections of the manuscript,\nincluding the abstract, that conclusions on AMG are extracted from genome analysis as per\nsuggestion of this referee. For instance, now in the new version of the abstract it reads “Based\non genome analysis, these viruses carry specific auxiliary metabolic genes potentially\ninvolved in nitrogen, sulfur, and phosphorus acquisition”. Another example, is the\nconclusion section, which now includes the following sentence: “Based on genome analysis,\nthese viruses carry specific AMGs putatively involved in nitrogen, sulfur and phosphorus\nacquisition. Altogether, most of the viruses below the RIS are novel and potentially impact\nthe cycling of nitrogen, phosphorus, and sulfur in this ecosystem, thus influencing global\nbiogeochemical cycles. Further mechanistic studies will help to investigate these proposed\ninteractions in one of the most underexplored ecosystem on Earth”. In addition, at the end of\nAMG section in the Results and Discussion, we have included a section discussing that these\ndiscoveries were carried out solely based on genome analysis. Now it reads: “Although these\ndiscoveries on AMGs are solely based on genome analysis, it is reasonable to consider that\nour hypotheses are plausible, more even according to previous AMG reports from different\nreference marine viral models, such as viral photosynthesis [60], that were indeed later\ncorroborated through experimental studies [57].\nTitle: there is no direct evidence to show that these viruses are active or contribute to\nglobal biogeochemical cycles, please rephrase.\nRESPONSE: Authors consider that there are enough data to claim that viruses are active. It\nis important to remark that many of the viruses discovered in the present study that infect key\nchemolithoautotrophic microbes and also heterotrophic bacteria involved in the global carbon\ncycle and key nutrient cycles have been found in 1) sorted single cells, 2) cell metagenomes,\nand are detected in 3) transcriptomic data as well. Indeed, the latter is an equivocal piece of\nevidence of activity. In fact, considering the opposite hypothesis/claim that the discovered\nviruses are inactive based on the analyzed data and type of material would make less sense\nfrom a biological perspective.\nFurthermore, there is an extensive literature on marine viruses discovered by single\ncell genomics applied to several prokaryote and eukaryote models (SAR11, cyanobacteria,\nmarine archaea, diatoms, etc…) from several groups, such as Stepanauskas, Rodriguez-\nValera, Beja, Lindell, Sullivan´s groups (and ourself) that commonly used genomic data of\nviruses found in single cells and also from prokaryotic cell metagenomes (i.e. viral genomes\nmined from sequencing data obtained from total DNA extracted from prokaryotic cell\nfraction).\nSomehow, we could come to agree with this referee on such argument (“no direct evidence”)\nwhen the analysis is only conducted from viromes, which is sequencing and obtaining viral\ngenomes only from free viruses and that obviously could contain both inactive and active\nviral fractions. In that case, unless, other complementary data is provided, it is somehow\nimpossible to discriminate whether a particular virus is active or not.\nHowever, our case is completely different because our data and claims are well\nsupported and substantiated by findings based on single-cell genomics, transcriptomics and\nviruses in cell metagenomes.\nFinally, as there are multiple pieces of evidence from different experimental\napproaches (single-cell genomics, metagenomics, transcriptomics, MAGs) indicating that\nindeed the recovered viruses infect key microbes involved in global nutrient cycles with a\nmajor role in these ecosystems, we believe that there is strong evidence to claim that title.\nIndeed, the other two referees have not argued against that and indeed they are quite\nconvinced about that (see for example comments by referee 2 in this regard “The discovery\nof niche-specific auxiliary metabolic genes is more interesting […] and suggests that\nviruses play a role in facilitating host adaptation and influencing biogeochemical cycles in\nthis environment. In fact, the current title includes the adjective “potentially” (already in the\nfirst version) in particular to be cautious with our discoveries. Thus, we consider that there is\nno need to rephrase that sentence. In any case, if editor considers that we need to find an\nalternative title, we would propose the following: “Viruses under the Antarctic Ice Shelf\nare active and infect key microbes involved in global nutrient cycles”. However, this\nother title is indeed “weird” because obviously if a virus infects a microbe that is paramount\nin the global carbon cycle in an ecosystem (like the Ross Ice Shelf), the impact of that virus is\nunequivocal and therefore the virus itself impacts on the global carbon cycle controlling such\npopulations. Thus, this second title might be interpreted as reiterative and circular.\nOverall, the importance of the work and its novelty is not directly coming across from the\nintroduction. Lines 53-67: The importance of the sampling site is not clearly explained. A\nreader with little to no knowledge of the effect of the Antarctic ice shelves and bottom water\nformation will not be able to understand the importance of this sampling site. Further here\nthere is a lot of hyperbole, highlighted by the excessive use of the word “profound”. The\nauthors should rewrite this paragraph in such a way that it does not come across as a lot of\nnumbers and superlatives, but explains the system and its function on our planet better.\nLines 68-82: This paragraph is similar to the former one as it is again full of hyperbole. I\nfind that it is a bit over-the-top that the authors call their own work (many authors of\ncitation 18 and the current manuscript is the same) “a pioneer study”, “of utmost\nimportance”, “unprecedented microbial dataset”. Please tune this down, and as with my\nprevious suggestion, rather than using hyperbole, explain the former work in context. In\naddition, I find it a bit strange that the authors use one paragraph to discuss a single paper\nin the introduction rather than putting their work in context to make it easier for the\nreadership at large to understand. Lines 81-82: The data in the current manuscript could\nhave as well been added to the former paper (citation 18). This way the virus component\nwould not have remained enigmatic. In general, I strongly advise the authors to rewrite\ntheir introduction to clearly put their work in context and why they have studied the viruses\nin this particular area.\nRESPONSE: We have modified the introduction in this new version of the manuscript\naccording to several suggestions from this referee. We honestly appreciate this comment. We\nhope now, the new version is more suitable for publication. In addition, we have substantially\nmodified all sentences to avoid “ hyperbole” and now several sentences have been “tuned\ndown”. Nevertheless, I suggest this referee to watch this short video (link below) on the\nsampling and survey carried out at the Ross Ice Shelf because we truly believe that some of\nthat hyperbole (unprecedented, pioneering study and etc…) is well deserved according to the\nhuge effort and the enormous difficulties for conducting this research that took more than 5\nyears of planning and design with multiple groups and countries involved. In any case, we\nhave followed the referee´s suggestions in all cases.\nhttps://www.youtube.com/watch?v=fyjt5zpNAeg\nLine 51: In general viruses are less studied in the environment, but why is this more\ndramatic for those that reside under the Antarctic ice shelves? Isn’t this just a matter of\nsampling frequency? Please clarify\nRESPONSE: We have added extra information to this sentence according to this suggestion.\nResults and Discussion:\nIn general, some parts of the results and discussion are written in a convoluted manner\nand at times the authors jump from one topic to the next and back again. Please streamline\nthis section and refrain from repetitions. Lines 97-100: Do the authors mean the samples\nwere not taken with studying the viruses in mind? Still am I correct to understand that they\nmanaged to retrieve many virus genomes? Please clarify and very briefly discuss what kind\nof bias this sampling “without having specific sampling for viromics” might have\nintroduced.\nRESPONSE: we have modified the new version of the manuscript and added a sentence to\nclearly specify that recovering viral genomes from free viral particles (what the referee calls\n“viromics”) could not be addressed. For clarification, in environmental virology, there are\nseveral ways to retrieve viral genomes present in nature, and all of them comprise different\ntools or approaches within viromics. Thus, Viromics is not only studying viruses from free\nviral particles, but it comprises different strategies very useful and complementary, such as\nobtaining viral genomes from single-cells, from cellular metagenomes, from free viral\nparticles and from cellular metagenomes. In our case here, the only method that we could not\ncarry out because of sample limitation was that of recovering viruses from free viral\nparticles (i.e. free viral fraction in which cells have been previously removed commonly by\nfiltration) containing both inactive and active viruses.\nLines: 100-102 I do not understand what is meant by the sentence starting with “Thus”.\nWhat does it mean likely present? Were the viral genomes obtained from this environment\nor not? Were viral transcripts inferred from general metatranscriptomics data? Please\nclarify and explain precisely.\nRESPONSE: We have rephrased this sentence and added a few point for clarification\nLines 138-140: Doesn’t this cold adaptation also apply to the host? Would it be reasonable\nto argue that the hosts adaptation might be more important?\nRESPONSE: Yes, we agree, but in this study, we focus on viral features and not in\nprokaryotic features, which is not the main scope of this study\nLines 193-197 vs lines 217-221: these parts are largely contradicting each other. Whereas I\nagree that there are different viral strategies even in the same environment, the authors\nwrite it as if it was a priori decided that a certain viral strategy would work. I would rework\nthis section so that it comes across clearly that there might be different solutions to the\nsame problem (from the virus POV), which I find quite an interesting point to discuss.\nRESPONSE: We appreciate this comment. Our intention was not like that, because we truly\nthink different strategies co-occur. We have modified and added a sentence to address this\npoint in the new version of the manuscript as per suggestion of the referee.\nLines 236-239: This is a bit convoluted to state that the capsids were relatively hydrophilic.\nRESPONSE: We have modified the sentence and now we hope that this is less convoluted in\nthe new version of the manuscript.\nLines 242-263: This section is very confusing and at times completely unclear. Many topics\nare touched upon, but none are clearly described and all remain at an unsatisfactorily\nsuperficial level.\nRESPONSE: This manuscript is for a multidisciplinary journal, and when we “designed”\nand wrote that section we had in mind that it will be read by researchers from different\ndisciplines. Basically, it shows the main points from auxiliary metabolic genes highlighting\ngeneral features. In our humble opinion, going too deep in the description of each gene makes\nthe section typically boring and too narrow for a multidisciplinary audience.\nLine 242: Alphafold predictive software also has a component, which estimates the\nrobustness of the predicted structure with different confidence levels calculated for the\ndifferent parts of the predicted structure. I have not seen this in any of the structure\npredictions used in this manuscript. Please use this option so at least the reader can have\nan overview of the confidence levels of the predicted structures. As such, it is also not clear\nto me what the predicted structures add next to what is already gained from sequence\nsimilarity analyses. The authors should justify the inclusion of these predicted structures.\nRESPONSE: As per suggestion of this referee, we have included a new supplementary\nfigure (Fig. S3) showing the confident values of that 3D folding prediction. As the referee\ncan check in that figure, the prediction is highly confident (dark blue >90% confident value)\nfor most of the protein length.\nSimilarity values tend to be a good indicator to ascertain whether two proteins are\nhomologues or not, and therefore display the same function. At the same time, it is also true,\nthat we can only be certain on that, when these similarity values are very high and thus the\n3D structure is maintained. However, the lower are the similarity values, the more uncertain\nis our prediction on homology and shared function. Indeed, when those similarity values are\nlower, the folding of the protein could be quite different, or even in some cases it has been\ndescribed that despite the sequence similarity is not very high, they still conserve the same\nfolding and very similar 3D structure; with being therefore same function and homologues\nprotein. Thus, here, we consider that is very important to show that if we want to state that\nthese two proteins are homologues in function, it is relevant to check that the 3D structure is\nmaintained, as it is in our case.\nLine 242: “carry sulfur compound transporters (e.g. Fe/S cluster protein)”. Fe/S proteins\nare not sulfur compound transporters. What exactly do the authors mean here?. Lines 245-\n246: Which proteins are meant here as being essential for bacteria to thrive? Fe/S proteins,\nsulfur compound transporters or some catalytic proteins active on sulfur compounds? The\nauthors do not clearly explain why a microorganism would leave such a critical protein to\nbe brought in by a virus. Line 245: what the mentioned protein is hypothesized to do is not\nclear from the figure and the caption. Line 252: It is unclear what kind of a ferrodoxin is\nmeant here. In any case, ferrodoxins can be involved in many processes requiring low\npotential electrons as an electron carrier. Line 253: It is unclear what is meant by Fe/S\ntransportation. Do the authors mean the transport of Fe and S to the cells or a Fe/S\ncontaining protein into the cell or the transfer of Fe/S cluster into an apoprotein. Please\nclarify.\nRESPONSE: We have substantially modified that paragraph in the new version of the\nmanuscript adding more information in this regards. We hope now is clear in the new\nversion. We appreciate this comment to improve our manuscript. Maybe, in the first version,\nwe aimed to be succinct and we agree that it was too succinct.\nLine 258: What exactly is the function of nifU? Please explain more that “full activation of\nnitrogenase”, which is absolutely unclear. As it is not a part of the minimum catalytic\nsystem, I do not think it is absolutely necessary to carry this gene. Please clarify whether it\nis present in all nitrogen fixers, and what its presence might add to a microorganism (see\nfor example https://pubs.acs.org/doi/10.1021/acs.chemrev.9b00489 for a review). This\ncould be an interesting point to discuss for the beneficial side of a viral infection.\nRESPONSE: We appreciate this very interesting comment. In the second version of the\nmanuscript, we have decided not going further on this issue, because after a careful\ninspection according to the interesting comment made by this referee, more data would be\nneeded to make a robust statement on this matter. On one side, nitrogen fixation and nif genes\nwere not found in any microbial genome retrieved under the Ross Ice shelf (please bear in\nmind that there is plenty of ammonia in the system; Martinez-Perez et al 2022), and thus it is\nobvious that this “version of the nifU” should be involved in other unknown functions. Thus,\nin this second version, this sentence has been removed.\nLines 259-263: Both ammonium/ammonia as an electron source is discussed here. These\nare fundamentally different topics. Please clarify what exactly is meant here, do the\nauthors mean GS is transferred to microorganisms via a viral infection?\nRESPONSE: We thank this comment. This sentence has been modified in the new version\nof the manuscript clearly differentiating both things and clarifying that issue.\nFigures: Some of the figures need some more work to better complement the manuscript.\nIn figure two, what are the clouds above the line “isolated viruses”? it is unclear. In figure\n4, what is the point of a gigantic cell, a miniature version of figure 3 and the structures\ndepicted? There is so much information there that everything becomes unclear. Figure 5,\nthis is a very unclear figure. I do not see the point of gigantic viruses being attached to\ncells. What is supposed to be happening here? The viruses are bringing in genes to cells\nthat do not have them? What are the out of focus yellow and brown circles? Why are parts\nof the figure such as deep cavity circulation in lower resolution? How can be the authors\nso sure which part of the water column these interactions occur? They do not present any\ndata to back this up. Please rework this figure or remove it.\nRESPONSE: Figure 2 has been modified according to this referee´s suggestion. We agree\nthat the “clouds” above the legend could be confusing. We have re-located those singleton\nclusters containing viruses from the Ross or the Southern Ocean in the bottom part of the\nfigure with the other singleton clusters with no connection with other clusters. We appreciate\nthis comment to improve this figure. We have also modified the figure 4 according to this\nreferee´s suggestions. Regarding figure 4, the point of that miniaturized figure 3\n(phylogenetic tree) was to contextualized in which exact pair virus-host were these AMGs\nfound. Sometimes in articles, we do see examples of AMGs, but in some of them specific\ndata on which virus-host pair it belongs is lacking. With that “idea in mind” we thought it\nwas more accurate to show exactly the phylogenetic identity indicated in the tree. We have\nre-sized the gigantic cell and added a few more informative descriptive panels indicating\nwhat is depicting each part of the figure. Overall, the figure 4 shows examples of viral AMG\nand indicates in detail the structure of the viral AMG and the corresponding homologues\ngenes found in its host (in particular for those AMG involved in S/Fe transportation). The\npoint here is that viruses are carrying AMG as discussed in the main text. Commonly, in\nenvironmental virology we agree that a virus carries an auxiliary metabolic gene, which tends\nto be a version of an existing homologue gene.\nFigure 5 is a conceptual summary of some of the findings on AMG following the same\nscheme already published in our previous article in Nature Communication on the\nmicrobiology under the Ross Ice Shelf (correspond to Fig. 6 in that paper). In the present\nstudy we have found AMGs clearly homologues to PhoH transporter involved in\nphosphorous acquisition, as well as other relevant AMGs. The idea behind the figure is to\ndepict in a simple and basic figure those viral features, in a similar manner as we did for our\nprevious microbial counterpart paper. Circles depict different prokaryote groups as indicated\nin the figure legend. We have modified the circles so that they are not out of focus anymore.\nWe know in which layer these interactions are occurring because in the previous paper, we\nrevealed where nitrifiers, and sulphur oxidizer are more abundant and active thanks to the\nprofile of nutrients and metagenomic recruitment from different layers and sample. We have\nincluded a brief description of this, citing the other Nature Communication paper in the figure\nlegend, to put things in context.\nFurthermore, the authors should clearly indicate that everything stated here is hypothetical\nand based on genome sequence analyses.\nRESPONSE: It has now been added in several sections of the manuscript including abstract,\nresults and discussion and conclusions."}, {"round": 2, "reviewer_comments": {"reviewer_2": "The writing remains unclear, e.g. dsDNA viruses were claimed to be dominant, but there is bias against RNA viruses due to technique and instability. They data mined, so don’t know the quality of all of the extractions. There is also the unresolved contradictory statement that an RNA viruse, Riboviria, was the most transcribed. What they need to do is be specific. They are trying to say that the dominant viruses are dsDNA viruses, but that is not what they wrote. Again, the writing needs a lot more work just so they are saying what they mean and not contradicting themselves. This is to clarify concept and content, not to get the grammar correct, that would take a major rewrite.\nYes, the archaeal virus looks like Kill the Winner, However, the Piggyback or Kill the Winner is binary. Since 2019 Piggyback the Persistent is more suitable for oligotrophic environments and that is not explored.\nFigure 2 is incomplete. There is insufficient information in the figure caption and the methods. The authors need to closely examine Fig. 2 – 4 of the Jang et al. 2019 paper they cite. Jang et al. use the mini- and micro-networks under the main network in that paper. They are labelled, discussed, and make a point. In the current paper there is no room for discussion of all the networks shown. Include only the main network. Then make the nodes smaller, separate the nodes into groups, colour the edges by weight, just saying > 1 is meaningless, e.g. 1.01 = 5,000, and make the node size a function of a relevant parameter. Then through the caption explain the figure better, ala Jang et al., and in discussion tie this directly to the overarching hypothesis.\nThe overall problem that remains is that there is no evidence for ‘reprogramming’. There needs to be an earlier multi-time-point sample set of the same size to use the ‘re’ in reprogramming, i.e. the ecosystem was going one direction and action by the viruses changed its direction. They do not have this data. That leaves just ‘programming’, which would still be good. Programming is possible, but here it is an inference based on the viral sequence data. Manipulative experimental data would be the gold standard, but having bacterial populations with the relevant genes dominating would help.\nThe authors have constructed a plausible hypothesis that is based on a single line of inference from sequences and data mining. Multiple lines of inference, preferably with any or multiple lines of experimental data, are needed to reliably support and test this hypothesis. The hypothesis is weakened because it is post hoc, and the collection was not planned to support this hypothesis by collecting bacterial samples and water chemistry. My previous positive comments remain. In short, the thesis of this paper might be correct, but the support depends on a series of inferences and the presentation of data and explanations are unclear and incomplete.", "reviewer_3": "The revised manuscript by Lopez-Simon et al. has greatly improved compared to its previous version. I thank the authors for their effort. I still have some remaining comments:\nLines 195-210 – was there any normalization done for the prediction of abundances from metagenomes and metatranscriptomes?\nLines 224-232 – I would argue that in the low-productivity environment the authors are studying, it is likely and “normal” to detect viruses that infect slow growing microorganisms. Wouldn’t most microorganisms be slow growing in this cold and low-productivity, dark environment? Please address this in the manuscript.\nLines 252 – 280: the evidence that the authors present here that these viruses boost sulfur oxidizers is quite weak. FeS cluster proteins are found across the tree of life and necessary for the function of a wide variety of very abundant proteins such as complexes I, II and III. In this context the fact that such a protein is encoded in a virus genome might be beneficial to many microorganisms indicating that the host of such a virus could be a number of distinct microorganisms, not necessarily sulfur oxidizers.\nLines 256 – 258 – did the authors check fits with other potential putative FeS cluster proteins that are not encoded by sulfur oxidizers? How conserved in general are these proteins the authors are discussing?\nLine 263 – please replace with “energy conservation”\nLine 275 – Ferrodoxin should be spelled as ferredoxin. They themselves are also FeS cluster proteins.\nLine 303 – I do not think the authors present data that show the activity of viruses. At best the authors show which hosts the viruses might potentially infect.\nFigures – Figure 3 has still parts that are difficult to read. I still do not see the point of figure 5. It looks like it would waste a lot of space without adding much to the manuscript. I suggest moving it to the supplementary information."}, "author_response": "REVIEWER COMMENTS\nReviewer #2 (Remarks to the Author):\nANSWER: First, we appreciate the feedback received from this ref. to improve the\nmanuscript.\nThe writing remains unclear, e.g. dsDNA viruses were claimed to be dominant, but there is bias\nagainst RNA viruses due to technique and instability. They data mined, so don’t know the\nquality of all of the extractions. There is also the unresolved contradictory statement that an RNA\nviruses, Riboviria, was the most transcribed. What they need to do is be specific. They are trying\nto say that the dominant viruses are dsDNA viruses, but that is not what they wrote. Again, the\nwriting needs a lot more work just so they are saying what they mean and not contradicting\nthemselves. This is to clarify concept and content, not to get the grammar correct, that would\ntake a major rewrite.\nANSWER: Sorry if we were misunderstood. We have substantially edited this new version\nof the manuscript to clarify what we meant in all instances. For example, we have removed\nthe expression “dominance” or “dominated by ” from the whole manuscript to avoid a\npossible confusion, and explained clearer what was meant. The fact that we have recovered\nhigher number of DNA viruses from SAGs, MAGs and transcriptomic datasets does not\nnecessarily contradict the finding that one of the RNA viruses is highly transcribed and\nwithin the top-3 most transcribed viruses at the moment of sampling. Please, remember\nthat we have not analyzed in this study the free viral fraction as discussed in the text. We\nagree that RNA viruses are less stable and that there are biases and limitations against\nRNA viruses, which we have now included in the text. We hope now the new version is\nclearer. Now the new version reads as follows:\n“According to ICTV classification, most of the recovered viral genome fragments (90% of\nassembled viral contigs) belonged to Caudoviricetes (Duplodnaviria; dsDNA viruses, Fig. 1B).\nNearly all detected Caudoviricites displayed an uncertain classification indicating that they\ncould correspond to novel families (Supplementary dataset). Other less abundant viral contigs\nrecovered in our transcriptomic and metagenomic datasets belonged to ssDNA viruses\n(Monodnaviria, 3% of total detected viruses), RNA viruses (Riboviria), and Varidnaviria\n(including for instance nucleocytoplasmic large DNA viruses (NCLDV) and virophages) (Fig.\n1C). Common hallmark genes of these viral groups were clearly detected such as single-stranded\nbinding proteins for ssDNA [35] viruses or RNA-directed RNA polymerase for RNA viruses [36,\n37] (Fig. 1D and Supplementary dataset), such as in the case for RNA virus k121_168914,\nwhich, as discussed below, was one of the most transcribed viruses. As expected, the recovered\nsize of assembled genome fragments (mean 4 kb) from ssDNA and RNA viruses were\nsignificantly lower than dsDNA viruses (mean contig size of 19,4 kb; Fig. 1C). Gene annotation\nof predicted ORFs (n=11,017) corroborated that the retained contigs were indeed viruses\ncontaining common viral hallmark genes, such as capsid and other virion structural proteins\n(Fig. 1D). Standard viral metagenomic techniques used in our study are well optimized for\nrecovering dsDNA viruses [38], and therefore we cannot rule out that some technical limitations\nand biases during sampling and processing have affected the recovery of RNA viruses that\ncommonly are less stable [39–41]. However, our employed experimental and bioinformatic\nmethodologies to recover RNA viral genomes from transcriptomics have been successfully\nproved in environmental virology and useful to uncover abundant and active RNA viruses in soil\nand aquatic environments [32, 42–44]”.\nYes, the archaeal virus looks like Kill the Winner, However, the Piggyback or Kill the Winner is\nbinary. Since 2019 Piggyback the Persistent is more suitable for oligotrophic environments and\nthat is not explored.\nANSWER: Thanks. We appreciate this comment and we have added a sentence discussing\nthis idea. The different scenarios were now explored. In Piggyback the Persistent, the\nauthors proposed that most of the phages are temperate and therefore integrated as\nprophage in the genome. In the original manuscript by Peterson et al. (2019), it reads:\n“Piggyback-the-Persistent (PtP) mechanism occurs when viruses become more dominated by\nthose exhibiting temperate rather than lytic lifestyles”. In this model, most of the phages were\nproposed to be prophages and the detection of viruses as free particles was almost near to\nthe detection limit. In our case, we have found no evidences that lysogenic lifestyle (i.e.\nprophages) is abundant since only a minor fraction (<3%) of the recovered viral\ncommunity contained integrases or endonucleases or were detected to be integrated in\nSAGs or MAGs. Unfortunately, in that original manuscript on Piggyback the persistent,\nauthors do not sequence the viral fraction in order to confirm that most of the detected\nphages are prophages integrated into the prokaryotic genomes. In addition, they\ndemonstrated that the possible explanation and environmental factor determining the\nobserved mechanism of very low ratio of viruses to cell was the contaminant\ntrichloroethene that exert a cell stress and as discussed by authors “Persistence creates the\npotential for TCE to alter microbial dynamics over long times and great distances. Therefore,\nalthough very interesting, in our case, data does not seem to support the Piggyback the\nPersistent.\nFigure 2 is incomplete. There is insufficient information in the figure caption and the methods.\nThe authors need to closely examine Fig. 2 – 4 of the Jang et al. 2019 paper they cite. Jang et al.\nuse the mini- and micro-networks under the main network in that paper. They are labelled,\ndiscussed, and make a point. In the current paper there is no room for discussion of all the\nnetworks shown. Include only the main network. Then make the nodes smaller, separate the\nnodes into groups, colour the edges by weight, just saying > 1 is meaningless, e.g. 1.01 = 5,000,\nand make the node size a function of a relevant parameter. Then through the caption explain the\nfigure better, ala Jang et al., and in discussion tie this directly to the overarching hypothesis.\nANSWER: We have modified Figure 2 and its associated information. Specifically, we\nadded a new paragraph in Method section explaining with more details our procedure. In\naddition, in the figure legend, we have indicated and referred to Method section for more\ndetails. We have also expanded the discussion with new text in results and discussion\nsection; it reads as follows: “Viral network interaction showing taxonomic assignment and\nand relatedness of viruses from the RIS with other viruses in databases was performed with\nvConTACT v.2.0 as described [50, 89, 90]. For this analysis, a total of 5,461 viruses were\ncompared including reference viruses recovered from Tara and Malaspina expeditions [45, 47],\nisolated reference viruses from Genbank and ICTV, and marine viruses from different samples of\nthe Southern Ocean [49]. Viral proteins were predicted and compared through all-verses-all\nBLASTP with an E-value threshold of 10-5 and 50 for bit score [50]. Viral protein clusters (PCs)\nwere then defined using Markov Clustering Algorithm (MCL) [91], using default parameters and\n2 for an inflation value. vContact (https://bitbucket.org/MAVERICLab/vcontact) was then used to\nestimate a similar score between every pair of viral genomes based on the number of PCs shared\nbetween two sequences and all pairs using the hypergeometric similarity, as previously described\n[23, 92]. MCL was applied to the similarity scores using a threshold of 1 and MCL inflation of 2\nto generate viral clusters (VCs, ≥2 sequences). Sequences were analyzed to identify highly\nsimilar VCs from the dataset using the Jaccard similarity as described [50], predicted taxonomy\nusing reference sequences present within the VCs, and constructed a network [93] using the\nsimilarity scores generated by vContact between each genome pair. The final dataset was\nexported to Cytoscape (v3.3.0)[93] and images post-processed”.\nWe have also expanded the result and discussion section on the viral-network analysis,\nand it reads now as follows: “We also performed a further comparison using viral protein sharing\nnetwork with more than 5,000 representative viruses including reference isolates and uncultured\nviral representatives from some of the most abundant clusters from Tara expedition [45] and\nother surveys, such as virus vSAG 37-F6; supposed to be one of the most abundant virus in the\nsurface ocean [50]. We found that 56% of the recovered viruses under the RIS represented\nsingletons or outliers in the network without any connection with other known viruses and viral\nclusters (Fig. 2), consistent with the gene search similarity analysis discussed above. This\nsuggests that a significant fraction of the virioplankton that resides under RIS is unique and\nmight be represented by novel families never described before [47, 50]. A deeper genomic\nanalysis comparison of all viruses showed that our dataset was comprised of 600 different\ngenera (according to thresholds demarcated by ICTV criteria), with only a few viral members\nbelonging to the same species or genus, which agreed with viral network analysis data, since\nthese 600 different genera mostly belonged to singleton viral clusters (Supplementary Dataset).\nThis suggests a high genomic diversity under the RIS. ICTV has implemented genome-based\ncriteria [51] and recently updated viral taxonomy (see ICTV webpage); although, demarcation\nof viral genera or families remain controversial and complicated for uncultured viruses [50, 51].\nOur network analyses on the RIS viroplankton also revealed a high density connection with\nviruses from the Southern Ocean (Fig. 2). Thus, now we have dedicated an entire long\nparagraph discussing the main points from viral-network analysis (Fig. 2). In addition, in\nSupplementary Dataset (excel sheet named “Vcontact table”), the reviewer/readers can\nfind all details, scoring, viral clustering, topology confidence score grouping, names of viral\nclusters, classification, etc…” obtained for the obtained viral network. We have also\ncolored and organized the viral-network to highlight the relatedness according to\nenvironment, which we think is more informative to discuss about endemicity and origin of\nthe discovered viruses. Obviously, there are several ways to present the same viral-network\nanalysis and Jang et al opted for some particular options. Our methodology has been\nrobustly published by independent groups (Sullivan´s group, Roux´s group, and etc…), and\nwe have cited several of these papers in method results and discussion sections. It is worth\nmentioning that our protocol used here has been included as a standardized protocol for\nanalysing and visualizing a viral-network analysis by Vcontact V2.0 and Cytoscape, which\nhas been published in Protocols.io (Please see https://www.protocols.io/view/Applying-\nvContact-to-Viral-Sequences-and-Visualizi-kqdg3pnql25z/v1). Furthermore, it is important\nto remark that the focus of Jang et al paper in Nat Biotech was discussing the potential use\nof viral network analysis by Vcontact 2.0 to proper classify the viruses according to current\nInternational Committee of Taxonomy of viruses (ICTV) as a general tool and rule, and\nthat is why they needed to show all these details in their networks, which is not the focus\nand point of our study. In our analysis, there are not different “groups” (as the referee\nmentions), but we feed the analysis with all viruses at the same time, with the only\nparticularity that they come from different environments; and this is exactly what we opted\nfor coloring in order to highlight how they cluster. As suggested by this referee, we would\nrather not colour the edges by weight and make the node size a function of a relevant\nparameter, such as depicted in Jang et al, because it is not the point of our manuscript to\nshow how well the matrix and network correlates with ICTV, such as in Jang et al paper\n(all of this information is already attached in the Supplementary Material). In Jang et al,\nfigure captions contain more information about thresholds and etc... because as mentioned\nabove the focus of the paper was the methodological application of viral network to ICTV\nclassification, while in our study, the main focus of Fig. 2 is to extract biological\ninformation on endemicity from clustering. In our case, we have opted to include that\nmethodological information (thresholds used and etc..) in the Method section in the new\nversion of the manuscript. Still, we are open for modification if the referee persists in\nhis/her opinion once we have better explained what message we wanted to convey with this\nfigure. Finally, another important point to consider is that the viral-network analysis and\nthe BLAST all versus all results, from more than 11,000 predicted proteins provided\nsimilar results of clustering; and this is depicted in Fig 2, as well, meaning that a similar\n a similar\nclustering was obtained.\nThe overall problem that remains is that there is no evidence for ‘reprogramming’. There needs\nto be an earlier multi-time-point sample set of the same size to use the ‘re’ in reprogramming, i.e.\nthe ecosystem was going one direction and action by the viruses changed its direction. They do\nnot have this data. That leaves just ‘programming’, which would still be good. Programming is\npossible, but here it is an inference based on the viral sequence data. Manipulative experimental\ndata would be the gold standard, but having bacterial populations with the relevant genes\ndominating would help. The authors have constructed a plausible hypothesis that is based on a\nsingle line of inference from sequences and data mining. Multiple lines of inference, preferably\nwith any or multiple lines of experimental data, are needed to reliably support and test this\nhypothesis. The hypothesis is weakened because it is post hoc, and the collection was not\nplanned to support this hypothesis by collecting bacterial samples and water chemistry.\nANSWER: We appreciate this comment and we agree with this referee that it would be\nmore suitable to use the expression “programming” instead, because there is no evidence\nfor “reprogramming” since this word somehow implies “that the ecosystems was going one\ndirection and action of viruses changed its direction. We have replaced the word\nreprogramming accordingly. Unfortunately, performing a long time series experiment\nfrom the same ice borehole through hundreds of meters of ice under the Ross Ice Shelf, is\nextremely complicated, i.e., the previous successful expedition prior to our was over 40\nyears ago and takes several years of preparation, several countries and high resources. Also\nit is worth mentioning that the sampling we performed in this expedition included a\nthorough study and characterization of the prokaryotic phylogenetic and functional\ndiversity within the physicochemical context of the study site, which we have recently\npublished in two paper (Martinez-Perez et al., 2022 Nature Communications; Baltar et al.,\nReviewer #3 (Remarks to the Author):\nThe revised manuscript by Lopez-Simon et al. has greatly improved compared to its previous\nversion. I thank the authors for their effort. I still have some remaining comments:\nANSWER: We appreciate the support of the reviewer\nLines 195-210 – was there any normalization done for the prediction of abundances from\nmetagenomes and metatranscriptomes?\nANSWER: Yes, it was normalized according to metagenomic standards considering\nmetagenome size or metatranscriptome size and viral genome size. A detailed explanation\ncan be found in the method section. “Abundances in cell metagenomes and transcriptomes of\nthe recovered viral contigs beneath the RIS were estimated in silico by metagenomic fragment\nrecruitment. In addition, abundances in marine Tara viromes from different samples were also\nestimated. This analysis was performed as previously described in [43, 58, 80], employing the\nfollowing two identity thresholds (query coverage ≥85% plus nucleotide identity cutoff ≥95% and\nquery coverage ≥50% plus nucleotide identity cutoff ≥70%) in order to estimate the normalized\nabundance of the viral populations at the species and genus level, respectively [43], and\nexpressed as recruited kilobases per a genome kilobase and a metagenome (or\nMetatranscriptome) gigabase (KPKG).\nLines 224-232 – I would argue that in the low-productivity environment the authors are studying,\nit is likely and “normal” to detect viruses that infect slow growing microorganisms. Wouldn’t\nmost microorganisms be slow growing in this cold and low-productivity, dark environment?\nPlease address this in the manuscript.\nANSWER: Yes, most of the microbes in this very cold and low-productivity environment\nare slow growing. As discussed in our previous rebuttal letter, the values of productivity\nand growth rate are very low in comparison with other marine environments. Thus, as\nstated by the referee, it is likely normal that we detect viruses infecting slow growing\nmicroorganism. We have added the following sentence in the discussion section as per\nsuggestion of this referee. “High viral to prokaryotic ratios in low productive areas were also\npreviously reported in bathypelagic layers of the Pacific and Atlantic Oceans (De Corte et al\n2012; Yang et al 2014) suggesting that at low-temperature viruses remain active for an\nextended period of time (Parada et al, AEM 2006), such as under the RIS”\nLines 252 – 280: the evidence that the authors present here that these viruses boost sulfur\noxidizers is quite weak. FeS cluster proteins are found across the tree of life and necessary for\nthe function of a wide variety of very abundant proteins such as complexes I, II and III. In this\ncontext the fact that such a protein is encoded in a virus genome might be beneficial to many\nmicroorganisms indicating that the host of such a virus could be a number of distinct\nmicroorganisms, not necessarily sulfur oxidizers. did the authors check fits with other potential\nputative FeS cluster proteins that are not encoded by sulfur oxidizers? How conserved in general\nare these proteins the authors are discussing?\nANSWER: Sorry if we did not explain this more clearly before. The FeS cluster protein\nobtained from several of the viruses in our study showed nearly identical protein structure\nto that of FeS cluster protein of the host that rely on sulphur oxidation performing\ntherefore the same function (see Fig. 4 and Fig. S3). In addition, Fe/S cluster proteins were\nreported as abundant AMG in viruses infecting symbiont SUP05 sulfur-oxidizing bacteria.\nFurthermore, in other sulfur oxidizing bacteria, Fe-S cluster proteins resulted to be\nessential for sulfur oxidation [ref 69]. Also, it has been shown that when a gene encoding\nFe/S clusters protein was experimentally deleted in Allochromatium vinosum, the sulfur\noxidation rates were reduced [69]. Thus, the fact of finding Fe/S cluster protein in viruses\ninfecting sulphur-oxidizing bacteria, such as in other proposed models discussed above,\nsuggest that similar hypothesis might take place under the RIS. We checked that the\ndetected Fe/S cluster proteins were highly similar to those present in sulfur oxidizing\nbacteria and not to non-sulphur oxidizing bacteria. These protein are not highly conserved;\nonly somehow the structure of the “pocket” hosting or linking metals, while there is high\nvariation in the structure for the rest of the protein. However, here, the entire protein is\nnearly identical to that of sulphur-oxidizing bacteria, as depicted in Figure 4, in which 3D\nstructure of both host and virus is shown. Finally, it is important to remember that we are\nthe first being very cautious in our statements, since in all cases, we use expressions like “it\nis reasonable to speculate”, “we propose”, or “it is also possible” […], “have the potential to\nimpact the cycling of […] sulfur in this ecosystem”, and in any case we affirm categorically\nour discoveries, but we propose ideas and hypothesis to our discoveries. Nevertheless, we\nend the section saying that “it is also possible that these proteins may also participate in\nother house-keeping processes beyond sulfur metabolism”.\n-Line 263 – please replace with “energy conservation”\nANSWER: It has been replaced in the new version of the manuscript\n-Line 275 – Ferrodoxin should be spelled as ferredoxin. They themselves are also FeS cluster\nproteins.\nANSWER: It has been corrected in the new version of the manuscript\n-Line 303 – I do not think the authors present data that show the activity of viruses. At best the\nauthors show which hosts the viruses might potentially infect.\nANSWER: We demonstrated that many viruses are transcribed and some of them highly\ntranscribed by transcriptomics. Thus, this is proof that viruses are actively infecting the\ncell. We have clarified in that sentence what do we mean with “activity”. Now the sentence\nreads as follows: “Collectively, we have characterized the viral diversity, biogeography, and\nactivity (i.e. transcriptional activity) inhabiting beneath the Antarctic Ice Shelf.”\nFigures – Figure 3 has still parts that are difficult to read.\nANSWER: we have increased the font size (to no. 10) of Figure 3 in the new version of the\nmanuscript according to referee´s suggestions and make all the items larger.\n-I still do not see the point of figure 5. It looks like it would waste a lot of space without adding\nmuch to the manuscript. I suggest moving it to the supplementary information.\nANSWER: We have moved Figure 5 to supplementary figure as per suggestion of this\nreferee.\nWe appreciate the feedback received from the reviewers."}, {"round": 3, "reviewer_comments": {"reviewer_2": "The authors have met all of my concerns very well.", "reviewer_3": "I have no further comments."}, "author_response": ""}]

Decision letter