Endemic dark ocean microbiomes drive carbon cycling in the Southern Ocean

Endemic dark ocean microbiomes drive carbon cycling in the Southern Ocean | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Endemic dark ocean microbiomes drive carbon cycling in the Southern Ocean Thulani Makhalanyane, Oliver K. Bezuidt, Diego Castillo, Tiffany du Plessis, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7078079/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract The Southern Ocean hosts a high degree of endemic plants and animals, yet the genetic diversity, function and evolutionary relationships of microbial communities remains unexplored, particularly in the aphotic “dark ocean,” where microbes play critical roles in local and global food webs. Here, we performed a metagenomic analysis of 44 aphotic seawater samples collected from multiple depths across the Southern Ocean to characterize the functional gene repertoire of these microbial communities. Of the 11,896,546 species-level unigenes 1 identified, ~ 87% appear specific to the Southern Ocean and are distinct from other major ocean datasets. We reconstructed 502 bacterial and 108 archaeal metagenome-assembled genomes (MAGs), revealing widespread capacities for inorganic carbon fixation via the Calvin cycle, the hydroxypropionate-hydroxybutyrate cycle, and the 3-hydroxypropionate bi-cycle. Metapangenomic analyses indicated that several genes involved in the oxidation of reduced nutrients including ammonia, nitrite, and thiosulfate, are shared across the aphotic water column through horizontal gene transfer. MAGs belonging to Acidimicrobia, Gammaproteobacteria, and SAR324 were abundant throughout the dark Southern Ocean and showed potential for both chemolithoautotrophy and carbohydrate degradation, suggesting mixotrophy as a key metabolic strategy. Together, these findings reveal the unique functional and genomic diversity of deep Southern Ocean microbiomes and provide insights into their roles in carbon cycling within one of Earth’s most important marine carbon sinks. Biological sciences/Microbiology/Environmental microbiology Biological sciences/Ecology/Microbial ecology chemolithoautotrophy dark ocean Southern Ocean metagenome assembled genomes MAGs mesopelagic bathypelagic Figures Figure 1 Figure 2 Figure 3 Introduction Aphotic zones of the ocean (typically below 200m) constitute the largest ecosystem on the planet, but remain underexplored 2 . In these oceanic regions, bacteria and archaea play essential roles in cycling carbon and other important nutrients and ultimately sustain local and global food webs 3 – 7 . There is some evidence showing that the Southern Ocean (SO) is the most efficient marine carbon sink globally due to its profound role in sequestering large amounts of natural and anthropogenic atmospheric CO 2 8–11 . Phytoplankton-derived organic matter is exported to the dark ocean, removing approximately 3 Pg of carbon annually from the sea-surface south of 30° 12 . Previous studies have assessed microbial communities in the SO euphotic and mesopelagic zones, revealing substantial levels of phylogenetic and functional diversity in this region 13 , 14 . These include the Tara Oceans expedition, which undertook a global microbiome survey from the ocean’s surface, deep chlorophyll maximum, and some mesopelagic waters, with depth profiles ranging from 5 to 1000 m 15 . However, our knowledge of the diversity and metabolic capabilities of prokaryotes in aphotic zones globally and in the SO remains rudimentary. Previous oceanic studies have largely focused on marine prokaryotes as players in heterotrophic processes, and consumers of phytoplankton-derived nutrients in the form of particulate organic matter transported from the euphotic zone 16 – 18 . However, it has recently been proposed that chemolithoautotrophs, which use inorganic carbon sources and reduced inorganic chemicals, are an important alternative source of organic carbon and other nutrients in the ocean 17 , 19 – 23 . This could resolve the disparity between high heterotrophic activity in the dark ocean and the low rates of organic material import from euphotic zones 16 , 24 , 25 . The low rates of organic matter exported into aphotic layers may be of particular importance in SO, characterised by large regions where planktonic photosynthesis is limited by a lack of bioavailable trace elements, such as iron and manganese 26 – 33 . Therefore, the distribution and diversity of chemolithoautotrophic microorganisms in the dark SO may be pivotal for sustaining aphotic food webs in this habitat. The Malaspina circumnavigation expedition recently surveyed microorganisms in tropical and subtropical bathypelagic oceanic zones with an average depth of ~ 4000 m. These efforts revealed that microorganisms in bathypelagic zones of the ocean harbour a remarkable array of metabolic traits, including diverse chemolithotrophy pathways 34 . The importance of chemolithoautotrophy in the dark ocean has also been demonstrated in the North Pacific, South Atlantic, and Arctic oceans 22 , 35 , 36 . Drawing on these observations, several other studies with a focus on evolutionary analysis inferred that genes that relate to these metabolic properties undergo horizontal gene transfer (HGT) 37 , 38 . However, the extent of chemolithoautotrophic potential has not been determined in the SO. Here, we investigate the diversity and metabolic potential of bacteria and archaea along the aphotic water column. We obtained 44 bulk water samples - aboard the RV SA Agulhas II (Fig. 1 A) - from nine frequently sampled locations, along a roughly 2,800 km transect, spanning all major SO zones including the Antarctic-, Polar-, Sub-Antarctic-, and Sub-Tropical zones. Using a combination of metagenomics and metapangenomics, we provide evidence that chemolithoautotrophy is widespread in the dark SO, and that bacterial and archaeal genomes recovered from all samples harbour remarkable metabolic genes that are subject to HGT. Results Southern Ocean specific genes derived from the Southern Ocean seasonal Experiment (SCALE) gene catalogue A total of 22,469,074 predicted protein-coding genes, larger than 100bp, were identified in metagenomes from 44 samples obtained at four aphotic depths – 200 m, 500 m, 1500 m below the surface, and 10 m above the sea floor (ranging from 2,600 to 4,666 m deep) – along the major fronts and zones of the Atlantic sector of the SO (Fig. 1 B). To construct the SCALE gene catalogue - named after the African led S outhern o C ean se A sonaL E xperiment ( SCALE ) expedition - the protein coding genes were reduced to a non-redundant species-level set of 11,896,546 gene clusters using an ANI threshold of ≥ 95% nt identity 1 . To derive the genes exclusive to SCALE, we compared our gene clusters to a set of 46,775,154 non-redundant genes from the Tara Oceans Microbial Reference Gene Catalog version 2 (OM-RGC.v2). Only 1,553,911 of the 11,896,546 SCALE genes clustered at the species level (> 95% nucleotide identity) with the OM-RGC.v2 genes. Most genes from the Tara dataset, which shared sequence similarity with those from the SCALE dataset, were linked to samples acquired from the Arctic (386,940), South (257,356) and North (172,609) Atlantic, and the Southern Ocean waters (156,717), respectively. The remaining 10,342,635 clusters were further compared to 647,817 Malaspina-exclusive genes 34 , which only accounted for 26,127 of our clusters. The resultant 10,316,508 SCALE-specific genes were subsequently annotated using EggNOG 39 , and KofamScan 40 . Of these, only 3,336,565 and 494,274 gene sets were functionally annotated by both EggNOG and Kofamscan, respectively. Both annotation tools resulted in a set of 3,337,036 non-overlapping functionally annotated genes. The majority of the EggNOG annotated genes (2,766,170) had clusters of orthologous gene (COG) functional assignments enriched in categories related to amino acid transport and metabolism (COG E), inorganic ion transport and metabolism (COG P), and carbohydrate transport and metabolism (COG G) 41 . Functional annotations from Kofamscan were also overrepresented in genes annotated as transporters including ABC transporters, followed by DNA repair and recombination proteins, peptidases, and inhibitors, and two component systems 41 , 42 . The SCALE-specific dataset was further screened for the presence of key marker genes that mediate important metabolic processes in the deep ocean using a set of 83 KOs 43 . Of these, 46 KOs were detected in the SCALE-specific gene catalogue (Supplementary Table 1). Among these were Sox complex marker genes for sulfur metabolism, and autotrophic carbon fixation markers (phosphoribulokinase, and RuBisCO – large and small subunits) The SCALE metagenome-assembled genome compendium We reconstructed 821 metagenome-assembled genomes (MAGs) from the 44 metagenomic datasets. A total of 610 medium- to high-quality MAGs were retained and herein referred to as the SCALE MAGs compendium 44 (Supplementary Table 2). A total of 105 MAGs (17.2% of the SCALE MAGs compendium) were novel at the species-level based on the commonly used threshold of 95% ANI (Supplementary Table 3) and 10 of these were assigned novel species names (Supplementary Table 4, and SeqCode Register List seqco.de/r:osstzyo2). The average completeness, mean contig length, and N50 were 78.3%, 9.1 kbp, and 14.4 kbp, respectively. Median values for genome statistics were 82.16%, 7.0 kbp, and 9.6 kbp, respectively. Genome size, GC content and coding density was consistent within each taxonomic group (Fig. 1 C). Taxonomic analysis of the SCALE MAGs compendium revealed a diverse assemblage of prokaryotic groups in the meso-, bathy- and abyssopelagic zones of the Southern Ocean (Fig. 2 and Supplementary Table 1). Each class formed a defined clade in the constructed phylogenomic tree (Fig. 2 ). We obtained 108 archaeal MAGs, of which 99 were classified by GTDB-Tk as Poseidoniia (Thermoplasmatota phylum) and 9 as Nitrososphaeria (Thermoproteota phylum). Bacterial MAGs affiliated to 14 different phyla, 18 classes, 35 orders, 74 genera, and 78 species. The most represented classes included Gammaproteobacteria ( n = 158 MAGs), Acidimicrobiia ( n = 73), SAR324 ( n = 55), Dehalococcoidia ( n = 50), Verrucomicrobiae ( n = 48), Marinisomatia ( n = 42) and Gemmatimonadota ( n = 27). Metabolic profiling of the SCALE MAGs compendium A wide range of metabolic pathways were identified in the SCALE MAGs compendium. Four types of CAZymes were common across at least 90 MAGs, suggesting heterotrophic potential. These comprised genes for degradation of chitin (present in 206 MAGs), polyphenolics ( n = 176), arabinose cleavage ( n = 168) and pectin ( n = 89). Certain taxonomic groups presented higher frequencies of specific CAZymes, for instance, ~ 40% of archaeal MAGs belonging to the family Thalassoarchaeaceae harboured genes for xylan and lactate degradation (Supplementary Table 5). Only one MAG, reconstructed from a sample obtained at 200 m below the sea level, was classified as Synechococcus , and harboured photosynthetic genes. Approximately 35% of the SCALE MAGs compendium (n = 215 MAGs) encoded at least one of the key genes for three carbon fixation pathways, namely the Calvin cycle, the hydroxypropionate-hydroxybutyrate cycle, and the 3-hydroxypropionate bi-cycle. The most prevalent carbon fixation pathway in the dataset was the 3-hydroxypropionate bi-cycle, with 91, 67 and 45 MAGs harbouring at least one copy of genes encoding 2-methylfumaryl-CoA isomerase ( mct ), malyl-CoA/(S)-citramalyl-CoA lyase ( mcl ), and 3-methylfumaryl-CoA hydratase ( meh ), respectively. Regarding the hydroxypropionate-hydroxybutyrate cycle, 58 MAGs had at least one copy of enoyl-CoA hydratase / 3-hydroxyacyl-CoA dehydrogenase and 35 had genes encoding 4-hydroxybutyryl-CoA dehydratase/vinylacetyl-CoA-delta-isomerase ( abf D). Finally, 41 MAGs encoded the large chain ribulose-bisphosphate carboxylase of the Calvin cycle ( rbc L), 37 MAGs the small chain ribulose-bisphosphate carboxylase (rbcS), and 30 MAGs had at least one copy of the phosphoribulokinase gene ( prk B) (Supplementary Table 6). Following the definition of chemolithoautotrophy, MAGs were classified as putative chemolithoautotrophs if they had at least one key gene linked to a carbon oxidation pathway and one key gene for the oxidation of a reduced compound (Supplementary Table 6). Notably, 30% of the SCALE MAGs compendium ( n = 187 MAGs) have chemolithoautotrophic potential, harbouring key genes for carbon monoxide oxidation ( n = 149 MAGs), thiosulfate oxidation (n = 74), nitrite oxidation (n = 21), and ammonia oxidation (Fig. 2 A and Table 6). Chemolithoautotrophic MAGs belong to seven bacterial and two archaeal classes, namely Acidimicrobiia (n = 68 MAGs), Gammaproteobacteria (n = 61), SAR324 (n = 41), Gemmatimonadetes (n = 6), Alphaproteobacteria (n = 1), Dehalococcoidia (n = 1), DG-26_A (n = 1), Nitrososphaeria (n = 5), and Poseidoniia (n = 1). Furthermore, mixotrophy appears to be an important metabolic strategy in the aphotic Southern Ocean since ~ 75% of the putative chemolithoautotrophic MAGs encode at least one CAZYme that could be used in heterotrophic metabolism. Indeed, 20% of putative chemolithoautotrophic MAGs encode at least three different CAZymes, of which the majority belong to SAR324 and Gammaproteobacteria . The most common CAZYmes encoded by putative chemolithoautotrophic MAGs were linked to the metabolism of arabinose (n = 90 MAGs), chitin (n = 63), polyphenolics (n = 57) and xyloglucan (n = 29) (Supplementary Table 7). Chemolithoautotrophic gene clusters are shared amongst diverse MAGs An all-against-all protein alignment of the 187 putative chemolithoautotrophic MAGs was conducted to estimate the extent to which HGT may have played a role in the observed widespread chemolithoautotrophic potential in the deep Southern Ocean microbiome. The 187 MAGs, representing nine diverse classes with potential for chemolithoautotrophy, resulted in 325 clusters of orthologous proteins. Among these, we observed clusters comprised of proteins shared between distantly related MAGs. These include orthologous protein clusters for ribulose-biphosphate carboxylase (rbcS/L), nitrification / methane oxidation (PmoA/B), and sulfur metabolism (SoxY/Z), suggesting that HGT may facilitate the spread of these metabolic processes in the SO. For instance, proteins representing the carbon monoxide oxidation (CO) small and medium subunits CoxS (K03518), and M (K03519) were observed to be shared between SAR324 (22) and Gammaproteobacteria (4) MAGs. To confirm that these genes are subject to HGT, we incorporated the large CoxL subunit into the analysis and concatenated all three protein sequences into a CoxSML operon (Supplementary Fig. 1). Phylogenetic analysis using these and other CO subunits from a subset of representative chemolithoautotrophic SAR324 and Gammaproteobacteria MAGs resulted in a discordant evolutionary tree (Fig. 2 B), indicating likely gene transfer events between these distinct classes. Putative chemolithoautotrophic populations were found throughout the aphotic SO water column Read mapping of the 44 metagenomes was used to estimate relative abundances of MAGs in each sample. We identified putative chemolithoautotrophs across all samples and found a widespread distribution of these populations across all the stations, encompassing all the major SO fronts and zones at all sampling depths (from 200 m to 10 m above the sea floor) (Fig. 3 ). Representative MAGs affiliated to the classes Gammaproteobacteria , Nitrososphaeria , Acidimicrobiia and SAR324 were the most abundant chemolithoautotrophs along the sampled transect. Sulphur oxidising Gammaproteobacteria and ammonia oxidising Nitrososphaeria MAGs were highly abundant at 200 and 500 m but were also present in bathypelagic zones (> 1000m below the sea surface). Putative carbon monoxide oxidisers belonging to Acidimicrobiia were more abundant in the deep ocean, as they were found in higher abundances at the 1500 m and deep samples. Chemoautolithotrophic SAR324 MAGs were present in lower abundances when compared to Nitrososphaeria and Gammaproteobacteria MAGs but were consistently identified along the water column and across the different stations spanning all SO zones (Fig. 3 ). Discussion Here we present the first microbial gene and genome catalogue for the Southern Ocean. Using a species-level gene concept 45 , we assessed the functional and biogeographical novelty of this underexplored biome. Consistent with previous results, 11,896,546 species-level genes were mostly unique to the Southern Ocean (87%) and absent from both Tara Oceans and Malaspina gene sets. Less than half of the gene families, from the SCALE gene catalogue 32.3% (3,337,044), could be functionally annotated. Most of these genes 68% (6,979,464), could not be functionally annotated and did not share sequence similarity, representing a unique and novel functional repertoire specific to the Southern Ocean. We reconstructed 610 medium to high quality MAGs linked to a diverse assemblage of prokaryotes in the aphotic SO. Many MAGs were affiliated with well-studied and ubiquitous classes, such as members of the Gammaproteobacteria 22 , 34 , 46 , 47 . However, some MAGs were affiliated with less well-known taxa, many of which lack cultured representatives including Acidimicrobiia , SAR324, Dehalococcoidia , Verrucomicrobiae , Marinisomatia , and Gemmatimonadetes 34 , 48 – 52 . A few Thermoplasmatota MAGs, belonging to Marine Group (MG) III, and known to occur in deep-sea environments were also observed 53 , 54 . Most of these taxa were affiliated with members of the order Poseidoniales (formerly MGII). Although most of the described Poseidoniales genomes have been identified in epipelagic zones and are thought to rely on a photoheterotrophic metabolism 55 – 58 , there is some evidence that certain obligate heterotrophs may have been sampled from deep-sea 59 , 60 . Our findings appear to corroborate these reports and provide the first evidence of this acquisition in aphotic zones of the Southern Ocean. Interestingly, we identified several diverse bacterial and archaeal MAGs with predicted heterotrophic and chemolithoautotrophic capabilities. Several heterotrophic MAGs, including Gammaproteobacteria, Verrucomicrobiae , Marinisomatia , and Poseidoniia , harboured a suite of genes encoding enzymes linked to the degradation and cleavage of carbohydrates. Previous studies have shown that, below the euphotic zone, particulate organic matter produced by phytoplankton sinks to aphotic waters, sustaining meso-, bathy- and abyssopelagic food webs through the biological pump 61 – 65 . While the extent of heterotrophy in the dark SO remains largely unknown, our analyses provide evidence that the degradation of organic matter throughout the aphotic water column may be mediated by microbial heterotrophs. However, in the Southern Ocean, primary production of organic carbon in the surface water is limited by lack of trace elements necessary for photosynthesis 66 . Here we posit that widespread chemolithoautotrophic potential identified in the aphotic zones provides fixed organic carbon to support this ecosystem. We obtained 186 MAGs, comprising nine distinct classes of bacteria, with putative chemolithoautotrophic metabolisms. Most of these microorganisms rely on the Calvin and Arnon-Buchanan cycles to obtain inorganic carbon, and a diverse array of reduced inorganic nutrients as energy sources. The Southern Ocean exports inorganic carbon to deeper waters through a vigorous solubility pump, responsible for the dissolution of atmospheric CO 2 into the surface. This export is enhanced by cold surface waters and the formation of intermediate water masses, as observed in high latitude sites 8 , 10 , 11 , 67 . The enhanced inorganic carbon export, in addition to constrained heterotrophy due to low primary production and low concentrations of organic matter 68 , may favour chemolithoautotrophic growth in the dark Southern Ocean. Previous studies appear to support this contention as the ubiquitous nature of chemolithoautotrophic organisms was previously predicted in Antarctic Peninsula coastal surface waters 69 . Our data suggest that chemolithoautotrophy may be a widespread and under-appreciated microbial strategy in aphotic oceanic ecosystems. A reciprocal protein BLAST analysis of the 186 MAGs provided insights regarding evolutionary mechanisms that may influence the distribution of chemolithoautotrophic metabolisms. Clusters of variable orthologous proteins from phylogenetically distinct MAGs were identified, including those comprised of genes known to encode for sulphur, ammonia, and carbon oxidation. Irrespective of sampling depth, MAGs belonging to diverse assemblages of Nitrososphaeria, Gammaproteobacteria, Marinisomatia , and SAR324 shared clusters harbouring amo AB genes (ammonia oxidation), SOX gene complexes (sulphur oxidation), and Cox subunits (anaerobic oxidation of CO). These data suggest that horizontal acquisition may provide certain selective advantages by contributing to microbial adaptation to new environments or to the acquisition of new metabolic functions 70 – 72 . Based on the results of reciprocal protein BLAST and phylogenetic analyses on Cox subunits, we predict that the capacity to oxidise inorganic nutrients, required for chemolithoautotrophy, may have been laterally transferred across distinct classes. This transfer may include intra-domain HGT, between bacteria and archaea, as shown previously in marine environments 56 , 59 , 73 . There is some support from metagenomic studies showing that HGT may be common including studies of deep seawater collected from the Mediterranean Ocean. The samples revealed that interdomain HGT may be a common occurrence 59 . These datasets show that 23.9% of marine Thaumarchaeota genes may have been acquired horizontally from bacteria 59 . In our study, the decreased availability of organic matter in the aphotic SO, combined with the increased solubility of CO 2 may be important factors promoting the frequency and extent of HGT events. Importantly, our protein BLAST analyses did not reveal a depth-dependent distribution pattern of inorganic nutrient oxidation for chemolithotrophy. This result is incongruous with previous studies on SAR324 from the North Pacific, which demonstrated depth specific niche structure 49 . We predict that seasonal mixing of Southern Ocean waters, and continuous upwelling of deep Antarctic waters, enhanced by the Antarctic Circumpolar Current 74 , 75 , may be key contributors to prevalent HGT of these genes throughout the aphotic water column in the SO. MAGs belonging to Nitrososphaeria , Gammaproteobacteria , Acidimicrobiia , and SAR324 were the most abundant chemolithoautotrophs across all sampling stations. Nitrosophaeria MAGs were particularly abundant in samples collected from 200 to 1500 m below sea level. Ammonia oxidizing archaea are ubiquitous along the water column, from epipelagic to abyssopelagic zones 22 , 34 , 76 – 78 . A recent study, by Acinas et al . (2021), identified MAGs affiliated with members of the class Nitrosophaeria in multiple bathypelagic ocean basins including the Pacific, Atlantic and Indian oceans. Nitrification is assumed to be the main metabolic pathway for chemoautotrophic production in Antarctic waters during winter 69 , 79 , 80 . However, this is the first report demonstrating the widespread abundance of ammonia oxidizing archaea across different depths in aphotic zones of the SO. The precise implications of widespread ammonia oxidation on the coupled nitrogen and carbon cycles are likely important, as ammonium consumption by prokaryotes in the SO cannot be assumed to be predominantly heterotrophic, as previously speculated 81 . In addition to ammonia oxidizing archaea, another common marine chemolithoautotrophic group is the ubiquitous SAR324 49,82 . Metabolically versatile members of this clade have been retrieved from polar marine environments 34 , 50 , 83 . Consistent with previous findings 22 , 49 , 84 , 85 , we show evidence of SAR324 throughout the aphotic water column. We also show that these SAR324 likely oxidise sulphur, via the thiosulfate oxidation pathway. Likewise, Gammaproteobacterial MAGs, which were present at high relative abundances, also have the potential for sulphur oxidation. Together with SAR324, these Gammaproteobacteria encode various CAZymes, a potential indicator of mixotrophic lifestyles. Mixotrophy is known to play a significant role in the Southern Ocean 86 – 90 , however, previous studies generally neglect the potential contributions of mixotrophic prokaryotic organisms, and their scope is typically restricted to euphotic waters. Conclusions Although the polar Arctic Ocean has recently been the subject of extensive studies, we lack comparative insights regarding the Southern Ocean. These insights are critical for assessing the impacts of climate change. The Southern Ocean is important for slowing the extent of climate change, and, by absorbing the most heat in the planet’s atmosphere, provides a significant buffer. The SCALE gene and genome catalogue provides a roadmap of the diverse phylogenomic and metabolic potential of bacteria and archaea in the largest marine carbon sink on the planet. In addition to expanding the repertoire of known genes and genomes, the data highlight the widespread distribution of diverse chemolithoautotrophic microorganisms from mesopelagic to bathypelagic. The extensive sampling, along an approximately 2800 km transect, includes all major SO fronts and zones. We suggest that the endemicity reported for macro-fauna may extend to microorganisms in the Southern Ocean based on the extent of ecosystem-specific genes. We provide a sound basis for future experiments aimed at characterizing the ecological significance of mixotrophic microorganisms. This work provides fundamental data required for understanding food webs in the dark Southern Ocean and suggests that chemolithoautotrophs may be fundamental. The gene and genome catalogue also provides a valuable resource required for bioactive compound bioprospecting. Methodology Sampling and DNA extraction Samples were collected aboard the RV SA Agulhas II (Figure 1A), using a CTD rosette multi-sampler (24 x 20 litre) Niskin bottle system, as part of two expeditions under the auspices of the (www.scale.org.za). Samples were collected at 200 m, 500 m, 1500 m, and at ~10 m above the sea floor (referred as “deep” in figures). Five litres of water from each sampling depth were filtered on board through Polyethersulfone (PES) Membrane Filter discs of 0.22 µm pore size, and 47 mm diameter (Millipore Sigma, MA, USA), using a Merck Millipore (MA, USA) filtration system. Membrane filters were stored onboard at -20ºC and transported frozen to the laboratory (Centre for Microbial Ecology and Genomics, University of Pretoria, South Africa) for further analyses. DNA was extracted from half of each filter using a protocol optimized for low biomass marine samples 91 . Briefly, filters were cut to small species (~5mm 2 ) and transferred to 2-ml Eppendorf tubes. Cells were lysed by adding 400µl of lysis buffer (400mM Tris-HCL (pH 8.0), 60 mM EDTA, 150 mM NaCl and 1% (w/v) SDS) and incubating for 10 min. at 60ºC. A volume of 120 ul of 3M potassium acetate (pH 4.8) was added and the samples were incubated on ice for 5 min, followed by centrifugation at 21000 g . The supernatant treated with C4 solution of the DNeasy PowerSoil Kit (QIAGEN, Hilden, Germany). The kits protocol was followed from this step forward as per manufacturer’s instructions and the concentration of the extracted DNA was analysed with a Qubit 3.0 (Invitrogen, MA, USA), using the kit for high-sensitivity assays. Library preparation, sequencing, and pre-processing of reads Libraries for next generation sequencing were prepared using the Nextera XT DNA library kit (Illumina, CA, USA), as per manufacturer’s instructions. Shotgun whole genome sequencing of 150 bp, paired end reads, was performed using an Illumina (CA, USA) HiSeq X sequencer, by Admera Health, LLC (NJ, USA). The sequencing depth was of 70 million reads (37.5 in each direction). Sequencing reads were pre-processed with Trimmomatic v0.36 92 , for the removal of low-quality reads and Nextera PE-PE adapters. Assembly and generating SO exclusive gene catalogue Paired-end high quality forward and reverse reads were assembled using SPAdes v3.13.0 93,94 with the flag --meta for the assembly of metagenomes. Quality of assemblies was assessed with QUAST v5.0.2 95 . To construct a Southern Ocean specific gene catalogue, contigs from all 44 metagenomes were merged and predicted for coding sequences using prodigal with -p meta and -c parameters. To generate a non-redundant gene set, coding sequences with lengths >100bp were clustered at 95% sequence similarity across 90% of the shortest sequence using cd-hit-est with -c 0.95 -G 0 -aS 0.9 -g 1 -r 1 parameters. The non-redundant gene set was further clustered with coding sequences from both the Ocean Microbial-Reference Gene Catalogue ( Tara OM-RGC.v2) and Malaspina Gene Database (M-GeneDB) using cd-hit-est-2d with -c 0.95 -G 0 -aS 0.9 -g 1 -r 1 parameters. Functional annotation of the resultant set of genes specific for the Southern Ocean was further conducted using eggNOG (DIAMOND mode) version 2.1.6 and KofamScan version 1.3.0. Binning of MAGs To reconstruct Metagenome Assembled Genomes (MAG) we performed mapping of the reads to the assemblies to improve binning of metagenomes using BBMap 96 . Binning was performed using three different tools: MetaBAT 2 v2.12.1 97 , MaxBin 2 v2.2.6 98 and CONCOCT v1.1.0 99 ; followed by dereplication of bins with DAS Tool v1.1.2 100 . We then assessed quality of bins with CheckM2 v1.0.1 101 . Bins were then classified into low-, medium- and high-quality MAGs according to the minimum information about metagenome-assembled genome standards 44 . Medium- and high-quality genomes were retained for further analyses. Analysis of compendium of 610 MAGs Taxonomic classification of MAGs was performed with GTDB-Tk v2.3.2 102 . This tool was also used to establish whether our MAGs were novel by performing ANI against all the GTDB-Tk FastANI reference genomes. MAGs with <95% similarity or no hits were determined as novel. Functional annotation and metabolic profiling was achieved with DRAM distillation and annotation v1.2.0 103 . Statistics of each MAG obtained from CheckM2 v1.0.1 were used to build frequency plots as well as box and whisker plots using ggplot2 104 in RStudio (R version 4.0.3). Phylogenomic tree was constructed using GToTree v1.6.11 105-110 with the Bacteria_and_Archaea single copy gene HMM set. Genomes that had below 25% of the target hit genes were filtered out, and tree construction was performed with IQ-TREE 111 . The phylogenomic tree was visualized and annotated using iTOL v6 112 . Mapping of metagenomic reads to MAGs to obtain relative abundance estimates was performed using CoverM v.0.6.1 (https://github.com/wwood/CoverM). Dereplication of MAGs to plot relative abundance of mapped metagenomics read and general metabolic potential of all genomes was performed with dRep v3.0.0 113 . All tools mentioned in this section were used with default parameters, unless noted otherwise. Orthologous protein analysis of chemolithoautotrophic MAGs An all-against-all sequence comparison of protein sequences from 187 MAGs with the potential for chemolithoautotrophy was conducted using proteinortho 114 with blast 115 option. Proteins sequences that shared 70% minimum percent identity and 50% minimum alignment coverage of the shortest sequences were filtered for downstream analysis. Phylogenetic reconstruction of the coxSML genes between SAR324 and Gammaproteobacteria Genes for the coxSML operon observed to have undergone horizontal transfer between SAR324 and Gammaproteobacteria. To determine potential gene exchanges of these operons we acquired a subset of MAGs that possessed two or three of these genes within the same contigs. Cox genes from these were individually aligned using mafft 116 with the linsi parameter, trimmed using Trimal 110 with the no-allgaps parameter, and concatenated using FASconCAT-G 117 . A maximum likelihood phylogenetic tree of the concatenated alignments was then reconstructed using IQ-TREE 111 with a 1000 bootstraps. Metagenomic fragment recruitment by the 610 MAGs To estimate the distribution of the taxa associated with our 610 medium to high quality MAGs we performed a read recruitment analysis. To conduct this, paired-end high quality forward and reverse reads from the 44 metagenomes were mapped against a genomic database comprising of all the 610 MAGs using Burrows-Wheeler Aligner (BWA) 118 with the default parameters. The resultant alignment files from each metagenome were further filtered to identify reads aligning at >= 95% percent identity with alignment coverage of >= 90% Declarations Acknowledgements We thank the captain and crew of the RV SA Agulhas II for assisting with sample acquisition. The authors also acknowledge members of the SCALE consortium. We gratefully acknowledge the National Research Foundation of South Africa (NRF) (UID 129225, 136491, 148867, 150276). We acknowledge funding received from the European Union’s Horizon 2020 research and innovation programme as part of the AtlantECO project under grant agreement number 862923. We also thank the Centre for High Performance Computing (Cape Town, South Africa) and the Centre for Bioinformatics and Computational Biology, University of Pretoria, for providing computational resources. We thank the Technology Innovation Agency (TIA) for supporting this project. Author contributions T.P.M designed the study, coordinated sampling logistics and provided project oversight. D.J.C, O.K.I.B, and T.d.P. analysed the shotgun metagenomics data. M.P., and S.N.V., conducted nomenclature and taxonomic work. D.J.C wrote the paper with contributions from O.K.I.B, T.d.P., P.H. and T.P.M. Competing interests The authors declare no competing interests. Online content Data availability The metagenomic data have been deposited at NCBI and available with the following accession PRJNA1138941. All 610 high and medium quality MAGs can be directly downloaded from https://10.6084/m9.figshare.21673346. A fasta file with coding sequences that represent a catalogue of SCALE-specific genes can be downloaded from https://10.6084/m9.figshare.21673886. Both EggNOG and Kofamscan functional annotation output files for these SCALE-specific genes can also be directly downloaded from https://10.6084/m9.figshare.21673139. References Coelho, L. P. et al. Towards the biogeography of prokaryotic genes. Nature 601 , 252-256 (2022). Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proceedings of the National Academy of Sciences 115 , 6506-6511 (2018). Azam, F. & Malfatti, F. Microbial structuring of marine ecosystems. Nature Reviews Microbiology 5 , 782-791 (2007). Cho, B. C. & Azam, F. Major role of bacteria in biogeochemical fluxes in the ocean's interior. Nature 332 , 441-443 (1988). Fuhrman, J. A., McCallum, K. & Davis, A. A. Novel major archaebacterial group from marine plankton. Nature 356 , 148-149 (1992). Offre, P., Spang, A. & Schleper, C. Archaea in biogeochemical cycles. Annual review of microbiology 67 , 437-457 (2013). Whitman, W. B., Coleman, D. C. & Wiebe, W. J. Prokaryotes: the unseen majority. Proceedings of the National Academy of Sciences 95 , 6578-6583 (1998). Landschützer, P. et al. The reinvigoration of the Southern Ocean carbon sink. Science 349 , 1221-1224 (2015). Frölicher, T. L. et al. Dominance of the Southern Ocean in anthropogenic carbon and heat uptake in CMIP5 models. Journal of Climate 28 , 862-886 (2015). Sabine, C. L. et al. The oceanic sink for anthropogenic CO2. science 305 , 367-371 (2004). Gruber, N., Landschützer, P. & Lovenduski, N. S. The variable Southern Ocean carbon sink. Annual review of marine science 11 , 159-186 (2019). Takahashi, T. et al. Global sea–air CO2 flux based on climatological surface ocean pCO2, and seasonal biological and temperature effects. Deep Sea Research Part II: Topical Studies in Oceanography 49 , 1601-1622 (2002). Cavicchioli, R. Microbial ecology of Antarctic aquatic systems. Nature Reviews Microbiology 13 , 691-706 (2015). Williams, T. J. et al. The role of planktonic F lavobacteria in processing algal organic matter in coastal E ast A ntarctica revealed using metagenomics and metaproteomics. Environmental microbiology 15 , 1302-1317 (2013). Salazar, G. et al. Gene Expression Changes and Community Turnover Differentially Shape the Global Ocean Metatranscriptome. Cell 179 , 1068-1083 e1021 (2019). https://doi.org:10.1016/j.cell.2019.10.014 Arístegui, J., Gasol, J. M., Duarte, C. M. & Herndld, G. J. Microbial oceanography of the dark ocean's pelagic realm. Limnology and Oceanography 54 , 1501-1529 (2009). Baltar, F. et al. Significance of non‐sinking particulate organic carbon and dark CO2 fixation to heterotrophic carbon demand in the mesopelagic northeast Atlantic. Geophysical Research Letters 37 (2010). Fouilland, E. & Mostajir, B. Revisited phytoplanktonic carbon dependency of heterotrophic bacteria in freshwaters, transitional, coastal and oceanic waters. FEMS microbiology ecology 73 , 419-429 (2010). Zhao, Y. et al. Dark carbon fixation and chemolithotrophic microbial community in surface sediments of the cascade reservoirs, Southwest China. Science of The Total Environment 698 , 134316 (2020). Leprich, D. J. et al. Sulfur bacteria promote dissolution of authigenic carbonates at marine methane seeps. The ISME Journal , 1-14 (2021). Mueller, A. J. et al. Genomic and kinetic analysis of novel Nitrospinae enriched by cell sorting. The ISME journal 15 , 732-745 (2021). Swan, B. K. et al. Potential for chemolithoautotrophy among ubiquitous bacteria lineages in the dark ocean. Science 333 , 1296-1300 (2011). Reinthaler, T., van Aken, H. M. & Herndl, G. J. Major contribution of autotrophy to microbial carbon cycling in the deep North Atlantic’s interior. Deep Sea Research Part II: Topical Studies in Oceanography 57 , 1572-1580 (2010). Arístegui, J. et al. (American Association for the Advancement of Science). Del Giorgio, P. A. & Duarte, C. M. Respiration in the open ocean. Nature 420 , 379-384 (2002). Mitchell, B. G., Brody, E. A., Holm‐Hansen, O., McClain, C. & Bishop, J. Light limitation of phytoplankton biomass and macronutrient utilization in the Southern Ocean. Limnology and Oceanography 36 , 1662-1677 (1991). Boyd, P. W. Environmental factors controlling phytoplankton processes in the Southern Ocean1. Journal of Phycology 38 , 844-861 (2002). Pitchford, J. W. & Brindley, J. Iron limitation, grazing pressure and oceanic high nutrient-low chlorophyll (HNLC) regions. Journal of Plankton Research 21 (1999). Venables, H. & Moore, C. M. Phytoplankton and light limitation in the Southern Ocean: Learning from high‐nutrient, high‐chlorophyll areas. Journal of Geophysical Research: Oceans 115 (2010). Boyd, P. W., Arrigo, K. R., Strzepek, R. & Van Dijken, G. Mapping phytoplankton iron utilization: Insights into Southern Ocean supply mechanisms. Journal of Geophysical Research: Oceans 117 (2012). Brzezinski, M. A., Jones, J. L. & Demarest, M. S. Control of silica production by iron and silicic acid during the Southern Ocean Iron Experiment (SOFeX). Limnology and Oceanography 50 , 810-824 (2005). Martin, J. H. & Fitzwater, S. E. Iron deficiency limits phytoplankton growth in the north-east Pacific subarctic. Nature 331 , 341-343 (1988). Moore, C. et al. Processes and patterns of oceanic nutrient limitation. Nature geoscience 6 , 701-710 (2013). Acinas, S. G. et al. Deep ocean metagenomes provide insight into the metabolic architecture of bathypelagic microbial communities. Communications biology 4 , 1-15 (2021). Royo-Llonch, M. et al. Ecogenomics of key prokaryotes in the arctic ocean. (2020). Pachiadaki, M. G. et al. Major role of nitrite-oxidizing bacteria in dark ocean carbon fixation. Science 358 , 1046-1051 (2017). Scott, K. M. et al. The genome of deep-sea vent chemolithoautotroph Thiomicrospira crunogena XCL-2. PLoS Biol 4 , e383 (2006). https://doi.org:10.1371/journal.pbio.0040383 Jaffe, A. L., Castelle, C. J., Dupont, C. L. & Banfield, J. F. Lateral Gene Transfer Shapes the Distribution of RuBisCO among Candidate Phyla Radiation Bacteria and DPANN Archaea. Mol Biol Evol 36 , 435-446 (2019). https://doi.org:10.1093/molbev/msy234 Cantalapiedra, C. P., Hernandez-Plaza, A., Letunic, I., Bork, P. & Huerta-Cepas, J. eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale. Mol Biol Evol 38 , 5825-5829 (2021). https://doi.org:10.1093/molbev/msab293 Aramaki, T. et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 36 , 2251-2252 (2020). https://doi.org:10.1093/bioinformatics/btz859 Bergauer, K. et al. Organic matter processing by microbial communities throughout the Atlantic water column as revealed by metaproteomics. Proc Natl Acad Sci U S A 115 , E400-E408 (2018). https://doi.org:10.1073/pnas.1708779115 Zhao, Z., Baltar, F. & Herndl, G. J. Linking extracellular enzymes to phylogeny indicates a predominantly particle-associated lifestyle of deep-sea prokaryotes. Sci Adv 6 , eaaz4354 (2020). https://doi.org:10.1126/sciadv.aaz4354 Acinas, S. G. et al. Deep ocean metagenomes provide insight into the metabolic architecture of bathypelagic microbial communities. Commun Biol 4 , 604 (2021). https://doi.org:10.1038/s42003-021-02112-2 Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nature biotechnology 35 , 725-731 (2017). A framework for human microbiome research. Nature 486 , 215-221 (2012). https://doi.org:10.1038/nature11209 Dyksma, S. et al. Ubiquitous Gammaproteobacteria dominate dark carbon fixation in coastal sediments. The ISME journal 10 , 1939-1953 (2016). Orcutt, B. N., Sylvan, J. B., Knab, N. J. & Edwards, K. J. Microbial ecology of the dark ocean above, at, and below the seafloor. Microbiology and molecular biology reviews 75 , 361-422 (2011). Freitas, S. et al. Global distribution and diversity of marine Verrucomicrobia. The ISME journal 6 , 1499-1505 (2012). Boeuf, D. et al. Metapangenomics reveals depth-dependent shifts in metabolic potential for the ubiquitous marine bacterial SAR324 lineage. Microbiome 9 , 172 (2021). https://doi.org:10.1186/s40168-021-01119-5 Coutinho, F. et al. Ecogenomics and metabolic potential of the South Atlantic Ocean microbiome. Science of The Total Environment 765 , 142758 (2021). Tarn, J., Peoples, L. M., Hardy, K., Cameron, J. & Bartlett, D. H. Identification of free-living and particle-associated microbial communities present in hadal regions of the Mariana Trench. Frontiers in Microbiology 7 , 665 (2016). Eloe, E. A. et al. Compositional differences in particle‐associated and free‐living microbial assemblages from an extreme deep‐ocean environment. Environmental microbiology reports 3 , 449-458 (2011). Fuhrman, J. A. & Davis, A. A. Widespread Archaea and novel Bacteria from the deep sea as shown by 16S rRNA gene sequences. Marine Ecology Progress Series 150 , 275-285 (1997). Li, M. et al. Genomic and transcriptomic evidence for scavenging of diverse organic compounds by widespread deep-sea archaea. Nature Communications 6 , 1-6 (2015). Zhang, C. L., Xie, W., Martin-Cuadrado, A.-B. & Rodriguez-Valera, F. Marine Group II Archaea, potentially important players in the global ocean carbon cycle. Frontiers in Microbiology 6 , 1108 (2015). Frigaard, N.-U., Martinez, A., Mincer, T. J. & DeLong, E. F. Proteorhodopsin lateral gene transfer between marine planktonic Bacteria and Archaea. Nature 439 , 847-850 (2006). Ottesen, E. A. et al. Pattern and synchrony of gene expression among sympatric marine microbial populations. Proceedings of the National Academy of Sciences 110 , E488-E497 (2013). Martin-Cuadrado, A.-B. et al. A new class of marine Euryarchaeota group II from the mediterranean deep chlorophyll maximum. The ISME journal 9 , 1619-1634 (2015). Deschamps, P., Zivanovic, Y., Moreira, D., Rodriguez-Valera, F. & López-García, P. Pangenome evidence for extensive interdomain horizontal transfer affecting lineage core and shell genes in uncultured planktonic thaumarchaeota and euryarchaeota. Genome biology and evolution 6 , 1549-1563 (2014). Rinke, C. et al. A phylogenomic and ecological analysis of the globally abundant Marine Group II archaea (Ca. Poseidoniales ord. nov.). The ISME journal 13 , 663-675 (2019). Simon, M., Rosenstock, B. & Zwisler, W. Coupling of epipelagic and mesopelagic heterotrophic picoplankton production to phytoplankton biomass in the Antarctic polar frontal region. Limnology and oceanography 49 , 1035-1043 (2004). Herndl, G. J. & Reinthaler, T. Microbial control of the dark end of the biological pump. Nature geoscience 6 , 718-724 (2013). Worden, A. Z. et al. Rethinking the marine carbon cycle: factoring in the multifarious lifestyles of microbes. Science 347 (2015). Milici, M. et al. Diversity and community composition of particle‐associated and free‐living bacteria in mesopelagic and bathypelagic Southern Ocean water masses: Evidence of dispersal limitation in the Bransfield Strait. Limnol Oceanogr 62 , 1080-1095 (2017). Longhurst, A. R. & Harrison, W. G. The biological pump: profiles of plankton production and consumption in the upper ocean. Progress in Oceanography 22 , 47-123 (1989). O'Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Research 44 , D733-D745 (2016). https://doi.org:10.1093/nar/gkv1189 van Heuven, S. M., Hoppema, M., Jones, E. M. & de Baar, H. J. Rapid invasion of anthropogenic CO2 into the deep circulation of the Weddell Gyre. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 372 , 20130056 (2014). Church, M. J., Hutchins, D. A. & Ducklow, H. W. Limitation of bacterial growth by dissolved organic matter and iron in the Southern Ocean. Applied and Environmental Microbiology 66 , 455-466 (2000). Grzymski, J. J. et al. A metagenomic assessment of winter and summer bacterioplankton from Antarctica Peninsula coastal surface waters. The ISME journal 6 , 1901-1915 (2012). Brockhurst, M. A. et al. The ecology and evolution of pangenomes. Current Biology 29 , R1094-R1103 (2019). Gogarten, J. P. & Townsend, J. P. Horizontal gene transfer, genome innovation and evolution. Nature Reviews Microbiology 3 , 679-687 (2005). Koonin, E. V., Makarova, K. S. & Aravind, L. Horizontal gene transfer in prokaryotes: quantification and classification. Annual Reviews in Microbiology 55 , 709-742 (2001). Brochier-Armanet, C. et al. Complete-fosmid and fosmid-end sequences reveal frequent horizontal gene transfers in marine uncultured planktonic archaea. The ISME journal 5 , 1291-1302 (2011). Chapman, C. C., Lea, M.-A., Meyer, A., Sallée, J.-B. & Hindell, M. Defining Southern Ocean fronts and their influence on biological and physical processes in a changing climate. Nature Climate Change 10 , 209-219 (2020). Tamsitt, V. et al. Spiraling pathways of global deep waters to the surface of the Southern Ocean. Nature communications 8 , 1-10 (2017). Francis, C. A., Roberts, K. J., Beman, J. M., Santoro, A. E. & Oakley, B. B. Ubiquity and diversity of ammonia-oxidizing archaea in water columns and sediments of the ocean. Proceedings of the National Academy of Sciences 102 , 14683-14688 (2005). Kalanetra, K. M., Bano, N. & Hollibaugh, J. T. Ammonia‐oxidizing Archaea in the Arctic Ocean and Antarctic coastal waters. Environmental Microbiology 11 , 2434-2445 (2009). Qin, W. et al. Alternative strategies of nutrient acquisition and energy conservation map to the biogeography of marine ammonia-oxidizing archaea. The ISME journal 14 , 2595-2609 (2020). Manganelli, M. et al. Major role of microbes in carbon fluxes during austral winter in the Southern Drake Passage. PloS one 4 , e6941 (2009). Williams, T. J. et al. A metaproteomic assessment of winter and summer bacterioplankton from Antarctic Peninsula coastal surface waters. The ISME journal 6 , 1883-1900 (2012). Mdutyana, M., Thomalla, S. J., Philibert, R., Ward, B. B. & Fawcett, S. E. The seasonal cycle of nitrogen uptake and nitrification in the Atlantic sector of the Southern Ocean. Global Biogeochemical Cycles 34 , e2019GB006363 (2020). Wright, T. D., Vergin, K. L., Boyd, P. W. & Giovannoni, S. J. A novel delta-subdivision proteobacterial lineage from the lower ocean surface layer. Applied and Environmental Microbiology 63 , 1441-1448 (1997). Cao, S. et al. Structure and function of the Arctic and Antarctic marine microbiota as revealed by metagenomics. Microbiome 8 , 1-12 (2020). Cao, H. et al. Delta-proteobacterial SAR324 group in hydrothermal plumes on the South Mid-Atlantic Ridge. Scientific reports 6 , 1-9 (2016). Sheik, C. S., Jain, S. & Dick, G. J. Metabolic flexibility of enigmatic SAR 324 revealed through metagenomics and metatranscriptomics. Environmental microbiology 16 , 304-317 (2014). Christaki, U. et al. Microbial food web structure in a naturally iron-fertilized area in the Southern Ocean (Kerguelen Plateau). Deep Sea Research Part II: Topical Studies in Oceanography 55 , 706-719 (2008). Moorthi, S., Caron, D. A., Gast, R. J. & Sanders, R. W. Mixotrophy: a widespread and important ecological strategy for planktonic and sea-ice nanoflagellates in the Ross Sea, Antarctica. Aquatic Microbial Ecology 54 , 269-277 (2009). Norbury, J., Moroz, I. M. & Cropp, R. The role of mixotrophy in Southern Ocean ecosystems. Environmental Modeling & Assessment 24 , 421-435 (2019). Stoecker, D. K., Hansen, P. J., Caron, D. A. & Mitra, A. Mixotrophy in the marine plankton. Annual Review of Marine Science 9 , 311-335 (2017). Yelton, A. P. et al. Global genetic capacity for mixotrophy in marine picocyanobacteria. The ISME journal 10 , 2946-2957 (2016). Hirai, M. et al. Library construction from subnanogram DNA for pelagic sea water and deep-sea sediments. Microbes and environments , ME17132 (2017). Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30 , 2114-2120 (2014). Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. Journal of computational biology 19 , 455-477 (2012). Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome research 27 , 824-834 (2017). Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 29 , 1072-1075 (2013). Bushnell, B. BBMap: a fast, accurate, splice-aware aligner. (Lawrence Berkeley National Lab.(LBNL), Berkeley, CA (United States), 2014). Kang, D. D., Froula, J., Egan, R. & Wang, Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3 , e1165 (2015). Wu, Y.-W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32 , 605-607 (2016). Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nature methods 11 , 1144-1146 (2014). Sieber, C. M. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nature microbiology 3 , 836-843 (2018). Chklovski, A., Parks, D. H., Woodcroft, B. J. & Tyson, G. W. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Nat Methods 20 , 1203-1212 (2023). Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. (Oxford University Press, 2020). Shaffer, M. et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic acids research 48 , 8883-8900 (2020). Wickham, H. ggplot2. Wiley Interdisciplinary Reviews: Computational Statistics 3 , 180-185 (2011). Lee, M. D. GToTree: a user-friendly workflow for phylogenomics. Bioinformatics 35 , 4162-4164 (2019). https://doi.org:10.1093/bioinformatics/btz188 Cock, P. J. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25 , 1422-1423 (2009). Eddy, S. R. Accelerated profile HMM searches. PLoS computational biology 7 , e1002195 (2011). Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC bioinformatics 11 , 1-11 (2010). Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic acids research 32 , 1792-1797 (2004). Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25 , 1972-1973 (2009). Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Molecular biology and evolution 32 , 268-274 (2015). https://doi.org:10.1093/molbev/msu300 Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics 23 , 127-128 (2007). Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. The ISME journal 11 , 2864-2868 (2017). Lechner, M. et al. Proteinortho: detection of (co-)orthologs in large-scale analysis. BMC Bioinformatics 12 , 124 (2011). https://doi.org:10.1186/1471-2105-12-124 Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J Mol Biol 215 , 403-410 (1990). https://doi.org:10.1016/s0022-2836(05)80360-2 Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Molecular biology and evolution 30 , 772-780 (2013). https://doi.org:10.1093/molbev/mst010 Kuck, P. & Longo, G. C. FASconCAT-G: extensive functions for multiple sequence alignment preparations concerning phylogenetic studies. Front Zool 11 , 81 (2014). https://doi.org:10.1186/s12983-014-0081-x Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25 , 1754-1760 (2009). https://doi.org:10.1093/bioinformatics/btp324 Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryTables.xlsx Supplementary information SupplementaryFigure1.png Supplementary Figure 1. A comparison cox SML operons which suggests horizontal origin in SAR324 and Gammaproteobacteria metagenome-assembled genomes. Homologous cox SML genes were assigned similar colours, between operons, and the thick black lines connecting the genes denote high similarity shared. Accesstoseqdataandcode.txt Access to seq and raw data Bezuidtetalnrreportingsummary.pdf Reporting Summary Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Bezuidt","email":"","orcid":"","institution":"University of Pretoria","correspondingAuthor":false,"prefix":"","firstName":"Oliver","middleName":"K.","lastName":"Bezuidt","suffix":""},{"id":492118958,"identity":"b49659ac-88ac-490b-a545-b576989b6339","order_by":2,"name":"Diego Castillo","email":"","orcid":"","institution":"University of Pretoria","correspondingAuthor":false,"prefix":"","firstName":"Diego","middleName":"","lastName":"Castillo","suffix":""},{"id":492118959,"identity":"f4be52c6-14e2-41ec-a272-4d3459bbddca","order_by":3,"name":"Tiffany du Plessis","email":"","orcid":"","institution":"University of Pretoria","correspondingAuthor":false,"prefix":"","firstName":"Tiffany","middleName":"","lastName":"du Plessis","suffix":""},{"id":492118960,"identity":"588b5d3e-7616-4881-ac9c-c022c373f129","order_by":4,"name":"Marike Palmer","email":"","orcid":"","institution":"University of Manitoba,","correspondingAuthor":false,"prefix":"","firstName":"Marike","middleName":"","lastName":"Palmer","suffix":""},{"id":492118961,"identity":"d7dc23f5-e7c6-47eb-b2ca-5906372d1a21","order_by":5,"name":"Philip Hugenholtz","email":"","orcid":"https://orcid.org/0000-0001-5386-7925","institution":"The University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Philip","middleName":"","lastName":"Hugenholtz","suffix":""}],"badges":[],"createdAt":"2025-07-08 21:25:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7078079/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7078079/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87822327,"identity":"d7fc6506-a054-4310-8169-ab4a819939ce","added_by":"auto","created_at":"2025-07-29 11:11:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":9375227,"visible":true,"origin":"","legend":"\u003cp\u003eThe Research Vessel, sampling sites and and genome statistics. (\u003cstrong\u003eA\u003c/strong\u003e) Water samples were collected aboard the R.V. SA. Agulhas II along an ~2800 km sampling transect. (\u003cstrong\u003eB\u003c/strong\u003e) The locations include sampling points across the Subtropical Zone (stations GT10, GT9), Sub-Antarctic Zone (GT7, GT6, GT5), Polar Front Zone (GT3), and Antarctic Zone (GT1, MIZ1, MIZ2). (\u003cstrong\u003eC\u003c/strong\u003e) Genome statistics of the 628 metagenome-assembled genomes (MAGs) are shown by frequency plots representing genome completeness (%), number of contigs, mean contig length (kbp), longest contig (kbp), N50 (kbp) and number of tRNA genes; as well as genome size (Mbp), GC content (%) and coding density (%) of MAGs per taxonomic group, only including taxa with more than one representative. Taxonomic groups are presented and coloured in ascending order for each genome feature.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7078079/v1/bd40d6e42db4127fe8af81da.png"},{"id":87822591,"identity":"b5a6d6bb-3bc7-4a07-bf8e-9841a1035561","added_by":"auto","created_at":"2025-07-29 11:19:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":414128,"visible":true,"origin":"","legend":"\u003cp\u003eThe phylogeny of bacterial and archaeal genomes in the Southern Ocean and an overview of horizontally acquired genes in dominant taxa. (\u003cstrong\u003eA\u003c/strong\u003e) A mid-point rooted circular tree of archaeal and bacterial metagenome-assembled genomes (MAGs) reconstructed. The genomes were reconstructed from aphotic Southern Ocean samples. The tree shows high phylogenetic diversity with genomes from several taxonomic groups including Gammaproteobacteria, Poseidonii and the ubiquitous SAR324. The genome statistics, sampling depths, and the predicted chemolithoautotrophic groups are indicated. Triangles indicate MAGs that which were investigated in detail for HGT events. (\u003cstrong\u003eB\u003c/strong\u003e) Horizontal gene transfer (of CoxSML genes) between SAR324 and Gammaproteobacteria. A maximum likelihood tree illustrating a potential gene exchange of \u003cem\u003ecox\u003c/em\u003eSML operons between putative chemoautolithotrophic MAGs belonging to SAR324 and Gammaproteobacteria classes. Evidence of horizontal acquisition is indicated by the arrow.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7078079/v1/9e96c57458be0450a177b5b3.png"},{"id":87822590,"identity":"69e3a1b1-8c49-41c8-b770-412bea7cdf56","added_by":"auto","created_at":"2025-07-29 11:19:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96740,"visible":true,"origin":"","legend":"\u003cp\u003eThe abundances of metagenome-assembled genomes (MAGs) based on read mapping recruitment. The analysis suggests widespread abundance of MAGs with chemolithoautotrophic potential. The MAGs were reconstructed from metagenomes obtained along the aphotic water column and across sampling stations spanning all major zones of the Southern Ocean. The figure also shows the sampling locations relative to Antarctica and the Southern Ocean.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7078079/v1/9d025b76cff552cdec610412.png"},{"id":87823621,"identity":"d1642b5c-661a-443a-9b03-f36183f131fe","added_by":"auto","created_at":"2025-07-29 11:27:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15421225,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7078079/v1/6f85e8e5-f607-410d-a3d7-995bc6b442da.pdf"},{"id":87822324,"identity":"71b5249d-d58b-421f-9ae1-50783b7beccf","added_by":"auto","created_at":"2025-07-29 11:11:32","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":312068,"visible":true,"origin":"","legend":"Supplementary information","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7078079/v1/c81378b87b93c9c1dd1d773b.xlsx"},{"id":87822593,"identity":"8bb688fc-9a95-4a70-ab9d-a0442c403d8e","added_by":"auto","created_at":"2025-07-29 11:19:32","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":329997,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1.\u003c/strong\u003e A comparison \u003cem\u003ecox\u003c/em\u003eSML operons which suggests horizontal origin in SAR324 and Gammaproteobacteria metagenome-assembled genomes. Homologous \u003cem\u003ecox\u003c/em\u003eSML genes were assigned similar colours, between operons, and the thick black lines connecting the genes denote high similarity shared.\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7078079/v1/1475a81aa2d7131b72e9e300.png"},{"id":87823596,"identity":"e8641c18-68f8-47b1-babf-f92c8a1b7795","added_by":"auto","created_at":"2025-07-29 11:27:32","extension":"txt","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":602,"visible":true,"origin":"","legend":"Access to seq and raw data","description":"","filename":"Accesstoseqdataandcode.txt","url":"https://assets-eu.researchsquare.com/files/rs-7078079/v1/1b796ece818fa5a998e3409e.txt"},{"id":87822594,"identity":"85b1b40e-a230-4cf6-ae39-3f835a63f885","added_by":"auto","created_at":"2025-07-29 11:19:32","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1665948,"visible":true,"origin":"","legend":"Reporting Summary","description":"","filename":"Bezuidtetalnrreportingsummary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7078079/v1/66eb3ddfeaf6f6326160aebd.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Endemic dark ocean microbiomes drive carbon cycling in the Southern Ocean","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAphotic zones of the ocean (typically below 200m) constitute the largest ecosystem on the planet, but remain underexplored \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In these oceanic regions, bacteria and archaea play essential roles in cycling carbon and other important nutrients and ultimately sustain local and global food webs \u003csup\u003e\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. There is some evidence showing that the Southern Ocean (SO) is the most efficient marine carbon sink globally due to its profound role in sequestering large amounts of natural and anthropogenic atmospheric CO\u003csub\u003e2\u003c/sub\u003e \u003csup\u003e8\u0026ndash;11\u003c/sup\u003e. Phytoplankton-derived organic matter is exported to the dark ocean, removing approximately 3 Pg of carbon annually from the sea-surface south of 30\u0026deg; \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Previous studies have assessed microbial communities in the SO euphotic and mesopelagic zones, revealing substantial levels of phylogenetic and functional diversity in this region \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. These include the \u003cem\u003eTara\u003c/em\u003e Oceans expedition, which undertook a global microbiome survey from the ocean\u0026rsquo;s surface, deep chlorophyll maximum, and some mesopelagic waters, with depth profiles ranging from 5 to 1000 m \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. However, our knowledge of the diversity and metabolic capabilities of prokaryotes in aphotic zones globally and in the SO remains rudimentary.\u003c/p\u003e\u003cp\u003ePrevious oceanic studies have largely focused on marine prokaryotes as players in heterotrophic processes, and consumers of phytoplankton-derived nutrients in the form of particulate organic matter transported from the euphotic zone \u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. However, it has recently been proposed that chemolithoautotrophs, which use inorganic carbon sources and reduced inorganic chemicals, are an important alternative source of organic carbon and other nutrients in the ocean\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan additionalcitationids=\"CR20 CR21 CR22\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. This could resolve the disparity between high heterotrophic activity in the dark ocean and the low rates of organic material import from euphotic zones \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The low rates of organic matter exported into aphotic layers may be of particular importance in SO, characterised by large regions where planktonic photosynthesis is limited by a lack of bioavailable trace elements, such as iron and manganese \u003csup\u003e\u003cspan additionalcitationids=\"CR27 CR28 CR29 CR30 CR31 CR32\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Therefore, the distribution and diversity of chemolithoautotrophic microorganisms in the dark SO may be pivotal for sustaining aphotic food webs in this habitat.\u003c/p\u003e\u003cp\u003eThe Malaspina circumnavigation expedition recently surveyed microorganisms in tropical and subtropical bathypelagic oceanic zones with an average depth of ~\u0026thinsp;4000 m. These efforts revealed that microorganisms in bathypelagic zones of the ocean harbour a remarkable array of metabolic traits, including diverse chemolithotrophy pathways \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The importance of chemolithoautotrophy in the dark ocean has also been demonstrated in the North Pacific, South Atlantic, and Arctic oceans \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Drawing on these observations, several other studies with a focus on evolutionary analysis inferred that genes that relate to these metabolic properties undergo horizontal gene transfer (HGT) \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. However, the extent of chemolithoautotrophic potential has not been determined in the SO. Here, we investigate the diversity and metabolic potential of bacteria and archaea along the aphotic water column. We obtained 44 bulk water samples - aboard the RV SA Agulhas II (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) - from nine frequently sampled locations, along a roughly 2,800 km transect, spanning all major SO zones including the Antarctic-, Polar-, Sub-Antarctic-, and Sub-Tropical zones. Using a combination of metagenomics and metapangenomics, we provide evidence that chemolithoautotrophy is widespread in the dark SO, and that bacterial and archaeal genomes recovered from all samples harbour remarkable metabolic genes that are subject to HGT.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eSouthern Ocean specific genes derived from the\u003c/em\u003e Southern Ocean seasonal Experiment (SCALE) \u003cem\u003egene catalogue\u003c/em\u003e\u003c/p\u003e\u003cp\u003eA total of 22,469,074 predicted protein-coding genes, larger than 100bp, were identified in metagenomes from 44 samples obtained at four aphotic depths \u0026ndash; 200 m, 500 m, 1500 m below the surface, and 10 m above the sea floor (ranging from 2,600 to 4,666 m deep) \u0026ndash; along the major fronts and zones of the Atlantic sector of the SO (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). To construct the SCALE gene catalogue - named after the African led \u003cb\u003eS\u003c/b\u003eouthern o\u003cb\u003eC\u003c/b\u003eean se\u003cb\u003eA\u003c/b\u003esonaL \u003cb\u003eE\u003c/b\u003experiment (\u003cb\u003eSCALE\u003c/b\u003e) expedition - the protein coding genes were reduced to a non-redundant species-level set of 11,896,546 gene clusters using an ANI threshold of \u0026ge;\u0026thinsp;95% nt identity \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. To derive the genes exclusive to SCALE, we compared our gene clusters to a set of 46,775,154 non-redundant genes from the \u003cem\u003eTara\u003c/em\u003e Oceans Microbial Reference Gene Catalog version 2 (OM-RGC.v2). Only 1,553,911 of the 11,896,546 SCALE genes clustered at the species level (\u0026gt;\u0026thinsp;95% nucleotide identity) with the OM-RGC.v2 genes. Most genes from the \u003cem\u003eTara\u003c/em\u003e dataset, which shared sequence similarity with those from the SCALE dataset, were linked to samples acquired from the Arctic (386,940), South (257,356) and North (172,609) Atlantic, and the Southern Ocean waters (156,717), respectively. The remaining 10,342,635 clusters were further compared to 647,817 Malaspina-exclusive genes \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, which only accounted for 26,127 of our clusters. The resultant 10,316,508 SCALE-specific genes were subsequently annotated using EggNOG\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, and KofamScan\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Of these, only 3,336,565 and 494,274 gene sets were functionally annotated by both EggNOG and Kofamscan, respectively. Both annotation tools resulted in a set of 3,337,036 non-overlapping functionally annotated genes. The majority of the EggNOG annotated genes (2,766,170) had clusters of orthologous gene (COG) functional assignments enriched in categories related to amino acid transport and metabolism (COG E), inorganic ion transport and metabolism (COG P), and carbohydrate transport and metabolism (COG G)\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Functional annotations from Kofamscan were also overrepresented in genes annotated as transporters including ABC transporters, followed by DNA repair and recombination proteins, peptidases, and inhibitors, and two component systems\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The SCALE-specific dataset was further screened for the presence of key marker genes that mediate important metabolic processes in the deep ocean using a set of 83 KOs \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Of these, 46 KOs were detected in the SCALE-specific gene catalogue (Supplementary Table\u0026nbsp;1). Among these were \u003cem\u003eSox\u003c/em\u003e complex marker genes for sulfur metabolism, and autotrophic carbon fixation markers (phosphoribulokinase, and RuBisCO \u0026ndash; large and small subunits)\u003c/p\u003e\u003cp\u003e\u003cem\u003eThe SCALE metagenome-assembled genome compendium\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWe reconstructed 821 metagenome-assembled genomes (MAGs) from the 44 metagenomic datasets. A total of 610 medium- to high-quality MAGs were retained and herein referred to as the SCALE MAGs compendium \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e (Supplementary Table\u0026nbsp;2). A total of 105 MAGs (17.2% of the SCALE MAGs compendium) were novel at the species-level based on the commonly used threshold of 95% ANI (Supplementary Table\u0026nbsp;3) and 10 of these were assigned novel species names (Supplementary Table\u0026nbsp;4, and SeqCode Register List seqco.de/r:osstzyo2). The average completeness, mean contig length, and N50 were 78.3%, 9.1 kbp, and 14.4 kbp, respectively. Median values for genome statistics were 82.16%, 7.0 kbp, and 9.6 kbp, respectively. Genome size, GC content and coding density was consistent within each taxonomic group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eTaxonomic analysis of the SCALE MAGs compendium revealed a diverse assemblage of prokaryotic groups in the meso-, bathy- and abyssopelagic zones of the Southern Ocean (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Table\u0026nbsp;1). Each class formed a defined clade in the constructed phylogenomic tree (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We obtained 108 archaeal MAGs, of which 99 were classified by GTDB-Tk as \u003cem\u003ePoseidoniia\u003c/em\u003e (Thermoplasmatota phylum) and 9 as \u003cem\u003eNitrososphaeria\u003c/em\u003e (Thermoproteota phylum). Bacterial MAGs affiliated to 14 different phyla, 18 classes, 35 orders, 74 genera, and 78 species. The most represented classes included \u003cem\u003eGammaproteobacteria\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;158 MAGs), \u003cem\u003eAcidimicrobiia\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;73), SAR324 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;55), \u003cem\u003eDehalococcoidia\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;50), \u003cem\u003eVerrucomicrobiae\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;48), \u003cem\u003eMarinisomatia\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;42) and \u003cem\u003eGemmatimonadota\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;27).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eMetabolic profiling of the SCALE MAGs compendium\u003c/em\u003e\u003c/p\u003e\u003cp\u003eA wide range of metabolic pathways were identified in the SCALE MAGs compendium. Four types of CAZymes were common across at least 90 MAGs, suggesting heterotrophic potential. These comprised genes for degradation of chitin (present in 206 MAGs), polyphenolics (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;176), arabinose cleavage (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;168) and pectin (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;89). Certain taxonomic groups presented higher frequencies of specific CAZymes, for instance, ~\u0026thinsp;40% of archaeal MAGs belonging to the family \u003cem\u003eThalassoarchaeaceae\u003c/em\u003e harboured genes for xylan and lactate degradation (Supplementary Table\u0026nbsp;5).\u003c/p\u003e\u003cp\u003eOnly one MAG, reconstructed from a sample obtained at 200 m below the sea level, was classified as \u003cem\u003eSynechococcus\u003c/em\u003e, and harboured photosynthetic genes. Approximately 35% of the SCALE MAGs compendium (n\u0026thinsp;=\u0026thinsp;215 MAGs) encoded at least one of the key genes for three carbon fixation pathways, namely the Calvin cycle, the hydroxypropionate-hydroxybutyrate cycle, and the 3-hydroxypropionate bi-cycle. The most prevalent carbon fixation pathway in the dataset was the 3-hydroxypropionate bi-cycle, with 91, 67 and 45 MAGs harbouring at least one copy of genes encoding 2-methylfumaryl-CoA isomerase (\u003cem\u003emct\u003c/em\u003e), malyl-CoA/(S)-citramalyl-CoA lyase (\u003cem\u003emcl\u003c/em\u003e), and 3-methylfumaryl-CoA hydratase (\u003cem\u003emeh\u003c/em\u003e), respectively. Regarding the hydroxypropionate-hydroxybutyrate cycle, 58 MAGs had at least one copy of enoyl-CoA hydratase / 3-hydroxyacyl-CoA dehydrogenase and 35 had genes encoding 4-hydroxybutyryl-CoA dehydratase/vinylacetyl-CoA-delta-isomerase (\u003cem\u003eabf\u003c/em\u003eD). Finally, 41 MAGs encoded the large chain ribulose-bisphosphate carboxylase of the Calvin cycle (\u003cem\u003erbc\u003c/em\u003eL), 37 MAGs the small chain ribulose-bisphosphate carboxylase (rbcS), and 30 MAGs had at least one copy of the phosphoribulokinase gene (\u003cem\u003eprk\u003c/em\u003eB) (Supplementary Table\u0026nbsp;6).\u003c/p\u003e\u003cp\u003eFollowing the definition of chemolithoautotrophy, MAGs were classified as putative chemolithoautotrophs if they had at least one key gene linked to a carbon oxidation pathway and one key gene for the oxidation of a reduced compound (Supplementary Table\u0026nbsp;6). Notably, 30% of the SCALE MAGs compendium (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;187 MAGs) have chemolithoautotrophic potential, harbouring key genes for carbon monoxide oxidation (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;149 MAGs), thiosulfate oxidation (n\u0026thinsp;=\u0026thinsp;74), nitrite oxidation (n\u0026thinsp;=\u0026thinsp;21), and ammonia oxidation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and Table\u0026nbsp;6). Chemolithoautotrophic MAGs belong to seven bacterial and two archaeal classes, namely \u003cem\u003eAcidimicrobiia\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;68 MAGs), \u003cem\u003eGammaproteobacteria\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;61), SAR324 (n\u0026thinsp;=\u0026thinsp;41), \u003cem\u003eGemmatimonadetes\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;6), \u003cem\u003eAlphaproteobacteria\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;1), \u003cem\u003eDehalococcoidia\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;1), DG-26_A (n\u0026thinsp;=\u0026thinsp;1), \u003cem\u003eNitrososphaeria\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;5), and \u003cem\u003ePoseidoniia\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e\u003cp\u003eFurthermore, mixotrophy appears to be an important metabolic strategy in the aphotic Southern Ocean since ~\u0026thinsp;75% of the putative chemolithoautotrophic MAGs encode at least one CAZYme that could be used in heterotrophic metabolism. Indeed, 20% of putative chemolithoautotrophic MAGs encode at least three different CAZymes, of which the majority belong to SAR324 and \u003cem\u003eGammaproteobacteria\u003c/em\u003e. The most common CAZYmes encoded by putative chemolithoautotrophic MAGs were linked to the metabolism of arabinose (n\u0026thinsp;=\u0026thinsp;90 MAGs), chitin (n\u0026thinsp;=\u0026thinsp;63), polyphenolics (n\u0026thinsp;=\u0026thinsp;57) and xyloglucan (n\u0026thinsp;=\u0026thinsp;29) (Supplementary Table\u0026nbsp;7).\u003c/p\u003e\u003cp\u003e\u003cem\u003eChemolithoautotrophic gene clusters are shared amongst diverse MAGs\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAn all-against-all protein alignment of the 187 putative chemolithoautotrophic MAGs was conducted to estimate the extent to which HGT may have played a role in the observed widespread chemolithoautotrophic potential in the deep Southern Ocean microbiome. The 187 MAGs, representing nine diverse classes with potential for chemolithoautotrophy, resulted in 325 clusters of orthologous proteins. Among these, we observed clusters comprised of proteins shared between distantly related MAGs. These include orthologous protein clusters for ribulose-biphosphate carboxylase (rbcS/L), nitrification / methane oxidation (PmoA/B), and sulfur metabolism (SoxY/Z), suggesting that HGT may facilitate the spread of these metabolic processes in the SO. For instance, proteins representing the carbon monoxide oxidation (CO) small and medium subunits CoxS (K03518), and M (K03519) were observed to be shared between SAR324 (22) and Gammaproteobacteria (4) MAGs. To confirm that these genes are subject to HGT, we incorporated the large CoxL subunit into the analysis and concatenated all three protein sequences into a CoxSML operon (Supplementary Fig.\u0026nbsp;1). Phylogenetic analysis using these and other CO subunits from a subset of representative chemolithoautotrophic SAR324 and Gammaproteobacteria MAGs resulted in a discordant evolutionary tree (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), indicating likely gene transfer events between these distinct classes.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePutative chemolithoautotrophic populations were found throughout the aphotic SO water column\u003c/em\u003e\u003c/p\u003e\u003cp\u003eRead mapping of the 44 metagenomes was used to estimate relative abundances of MAGs in each sample. We identified putative chemolithoautotrophs across all samples and found a widespread distribution of these populations across all the stations, encompassing all the major SO fronts and zones at all sampling depths (from 200 m to 10 m above the sea floor) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Representative MAGs affiliated to the classes \u003cem\u003eGammaproteobacteria\u003c/em\u003e, \u003cem\u003eNitrososphaeria\u003c/em\u003e, \u003cem\u003eAcidimicrobiia\u003c/em\u003e and SAR324 were the most abundant chemolithoautotrophs along the sampled transect. Sulphur oxidising \u003cem\u003eGammaproteobacteria\u003c/em\u003e and ammonia oxidising \u003cem\u003eNitrososphaeria\u003c/em\u003e MAGs were highly abundant at 200 and 500 m but were also present in bathypelagic zones (\u0026gt;\u0026thinsp;1000m below the sea surface). Putative carbon monoxide oxidisers belonging to \u003cem\u003eAcidimicrobiia\u003c/em\u003e were more abundant in the deep ocean, as they were found in higher abundances at the 1500 m and deep samples. Chemoautolithotrophic SAR324 MAGs were present in lower abundances when compared to \u003cem\u003eNitrososphaeria\u003c/em\u003e and \u003cem\u003eGammaproteobacteria\u003c/em\u003e MAGs but were consistently identified along the water column and across the different stations spanning all SO zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHere we present the first microbial gene and genome catalogue for the Southern Ocean. Using a species-level gene concept \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, we assessed the functional and biogeographical novelty of this underexplored biome. Consistent with previous results, 11,896,546 species-level genes were mostly unique to the Southern Ocean (87%) and absent from both \u003cem\u003eTara\u003c/em\u003e Oceans and Malaspina gene sets. Less than half of the gene families, from the SCALE gene catalogue 32.3% (3,337,044), could be functionally annotated. Most of these genes 68% (6,979,464), could not be functionally annotated and did not share sequence similarity, representing a unique and novel functional repertoire specific to the Southern Ocean.\u003c/p\u003e\u003cp\u003eWe reconstructed 610 medium to high quality MAGs linked to a diverse assemblage of prokaryotes in the aphotic SO. Many MAGs were affiliated with well-studied and ubiquitous classes, such as members of the \u003cem\u003eGammaproteobacteria\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. However, some MAGs were affiliated with less well-known taxa, many of which lack cultured representatives including \u003cem\u003eAcidimicrobiia\u003c/em\u003e, SAR324, \u003cem\u003eDehalococcoidia\u003c/em\u003e, \u003cem\u003eVerrucomicrobiae\u003c/em\u003e, \u003cem\u003eMarinisomatia\u003c/em\u003e, and \u003cem\u003eGemmatimonadetes\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan additionalcitationids=\"CR49 CR50 CR51\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. A few Thermoplasmatota MAGs, belonging to Marine Group (MG) III, and known to occur in deep-sea environments were also observed \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Most of these taxa were affiliated with members of the order \u003cem\u003ePoseidoniales\u003c/em\u003e (formerly MGII). Although most of the described \u003cem\u003ePoseidoniales\u003c/em\u003e genomes have been identified in epipelagic zones and are thought to rely on a photoheterotrophic metabolism \u003csup\u003e\u003cspan additionalcitationids=\"CR56 CR57\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, there is some evidence that certain obligate heterotrophs may have been sampled from deep-sea \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Our findings appear to corroborate these reports and provide the first evidence of this acquisition in aphotic zones of the Southern Ocean.\u003c/p\u003e\u003cp\u003eInterestingly, we identified several diverse bacterial and archaeal MAGs with predicted heterotrophic and chemolithoautotrophic capabilities. Several heterotrophic MAGs, including \u003cem\u003eGammaproteobacteria, Verrucomicrobiae\u003c/em\u003e, \u003cem\u003eMarinisomatia\u003c/em\u003e, and \u003cem\u003ePoseidoniia\u003c/em\u003e, harboured a suite of genes encoding enzymes linked to the degradation and cleavage of carbohydrates. Previous studies have shown that, below the euphotic zone, particulate organic matter produced by phytoplankton sinks to aphotic waters, sustaining meso-, bathy- and abyssopelagic food webs through the biological pump \u003csup\u003e\u003cspan additionalcitationids=\"CR62 CR63 CR64\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. While the extent of heterotrophy in the dark SO remains largely unknown, our analyses provide evidence that the degradation of organic matter throughout the aphotic water column may be mediated by microbial heterotrophs. However, in the Southern Ocean, primary production of organic carbon in the surface water is limited by lack of trace elements necessary for photosynthesis \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Here we posit that widespread chemolithoautotrophic potential identified in the aphotic zones provides fixed organic carbon to support this ecosystem. We obtained 186 MAGs, comprising nine distinct classes of bacteria, with putative chemolithoautotrophic metabolisms. Most of these microorganisms rely on the Calvin and Arnon-Buchanan cycles to obtain inorganic carbon, and a diverse array of reduced inorganic nutrients as energy sources. The Southern Ocean exports inorganic carbon to deeper waters through a vigorous solubility pump, responsible for the dissolution of atmospheric CO\u003csub\u003e2\u003c/sub\u003e into the surface. This export is enhanced by cold surface waters and the formation of intermediate water masses, as observed in high latitude sites \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. The enhanced inorganic carbon export, in addition to constrained heterotrophy due to low primary production and low concentrations of organic matter \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e, may favour chemolithoautotrophic growth in the dark Southern Ocean. Previous studies appear to support this contention as the ubiquitous nature of chemolithoautotrophic organisms was previously predicted in Antarctic Peninsula coastal surface waters \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. Our data suggest that chemolithoautotrophy may be a widespread and under-appreciated microbial strategy in aphotic oceanic ecosystems.\u003c/p\u003e\u003cp\u003eA reciprocal protein BLAST analysis of the 186 MAGs provided insights regarding evolutionary mechanisms that may influence the distribution of chemolithoautotrophic metabolisms. Clusters of variable orthologous proteins from phylogenetically distinct MAGs were identified, including those comprised of genes known to encode for sulphur, ammonia, and carbon oxidation. Irrespective of sampling depth, MAGs belonging to diverse assemblages of \u003cem\u003eNitrososphaeria, Gammaproteobacteria, Marinisomatia\u003c/em\u003e, and SAR324 shared clusters harbouring \u003cem\u003eamo\u003c/em\u003eAB genes (ammonia oxidation), SOX gene complexes (sulphur oxidation), and Cox subunits (anaerobic oxidation of CO). These data suggest that horizontal acquisition may provide certain selective advantages by contributing to microbial adaptation to new environments or to the acquisition of new metabolic functions \u003csup\u003e\u003cspan additionalcitationids=\"CR71\" citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Based on the results of reciprocal protein BLAST and phylogenetic analyses on Cox subunits, we predict that the capacity to oxidise inorganic nutrients, required for chemolithoautotrophy, may have been laterally transferred across distinct classes. This transfer may include intra-domain HGT, between bacteria and archaea, as shown previously in marine environments \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. There is some support from metagenomic studies showing that HGT may be common including studies of deep seawater collected from the Mediterranean Ocean. The samples revealed that interdomain HGT may be a common occurrence \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. These datasets show that 23.9% of marine Thaumarchaeota genes may have been acquired horizontally from bacteria \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. In our study, the decreased availability of organic matter in the aphotic SO, combined with the increased solubility of CO\u003csub\u003e2\u003c/sub\u003e may be important factors promoting the frequency and extent of HGT events. Importantly, our protein BLAST analyses did not reveal a depth-dependent distribution pattern of inorganic nutrient oxidation for chemolithotrophy. This result is incongruous with previous studies on SAR324 from the North Pacific, which demonstrated depth specific niche structure \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. We predict that seasonal mixing of Southern Ocean waters, and continuous upwelling of deep Antarctic waters, enhanced by the Antarctic Circumpolar Current \u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e,\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e, may be key contributors to prevalent HGT of these genes throughout the aphotic water column in the SO.\u003c/p\u003e\u003cp\u003eMAGs belonging to \u003cem\u003eNitrososphaeria\u003c/em\u003e, \u003cem\u003eGammaproteobacteria\u003c/em\u003e, \u003cem\u003eAcidimicrobiia\u003c/em\u003e, and SAR324 were the most abundant chemolithoautotrophs across all sampling stations. \u003cem\u003eNitrosophaeria\u003c/em\u003e MAGs were particularly abundant in samples collected from 200 to 1500 m below sea level. Ammonia oxidizing archaea are ubiquitous along the water column, from epipelagic to abyssopelagic zones \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan additionalcitationids=\"CR77\" citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. A recent study, by Acinas \u003cem\u003eet al\u003c/em\u003e. (2021), identified MAGs affiliated with members of the class \u003cem\u003eNitrosophaeria\u003c/em\u003e in multiple bathypelagic ocean basins including the Pacific, Atlantic and Indian oceans. Nitrification is assumed to be the main metabolic pathway for chemoautotrophic production in Antarctic waters during winter \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e,\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. However, this is the first report demonstrating the widespread abundance of ammonia oxidizing archaea across different depths in aphotic zones of the SO. The precise implications of widespread ammonia oxidation on the coupled nitrogen and carbon cycles are likely important, as ammonium consumption by prokaryotes in the SO cannot be assumed to be predominantly heterotrophic, as previously speculated \u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. In addition to ammonia oxidizing archaea, another common marine chemolithoautotrophic group is the ubiquitous SAR324 \u003csup\u003e49,82\u003c/sup\u003e. Metabolically versatile members of this clade have been retrieved from polar marine environments \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. Consistent with previous findings \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e,\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e, we show evidence of SAR324 throughout the aphotic water column. We also show that these SAR324 likely oxidise sulphur, via the thiosulfate oxidation pathway. Likewise, Gammaproteobacterial MAGs, which were present at high relative abundances, also have the potential for sulphur oxidation. Together with SAR324, these Gammaproteobacteria encode various CAZymes, a potential indicator of mixotrophic lifestyles. Mixotrophy is known to play a significant role in the Southern Ocean \u003csup\u003e\u003cspan additionalcitationids=\"CR87 CR88 CR89\" citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e, however, previous studies generally neglect the potential contributions of mixotrophic prokaryotic organisms, and their scope is typically restricted to euphotic waters.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAlthough the polar Arctic Ocean has recently been the subject of extensive studies, we lack comparative insights regarding the Southern Ocean. These insights are critical for assessing the impacts of climate change. The Southern Ocean is important for slowing the extent of climate change, and, by absorbing the most heat in the planet\u0026rsquo;s atmosphere, provides a significant buffer.\u003c/p\u003e\u003cp\u003eThe SCALE gene and genome catalogue provides a roadmap of the diverse phylogenomic and metabolic potential of bacteria and archaea in the largest marine carbon sink on the planet. In addition to expanding the repertoire of known genes and genomes, the data highlight the widespread distribution of diverse chemolithoautotrophic microorganisms from mesopelagic to bathypelagic. The extensive sampling, along an approximately 2800 km transect, includes all major SO fronts and zones. We suggest that the endemicity reported for macro-fauna may extend to microorganisms in the Southern Ocean based on the extent of ecosystem-specific genes. We provide a sound basis for future experiments aimed at characterizing the ecological significance of mixotrophic microorganisms. This work provides fundamental data required for understanding food webs in the dark Southern Ocean and suggests that chemolithoautotrophs may be fundamental. The gene and genome catalogue also provides a valuable resource required for bioactive compound bioprospecting.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cem\u003eSampling and DNA extraction\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSamples were collected aboard the \u003cem\u003eRV SA Agulhas II\u003c/em\u003e (Figure 1A), using a CTD rosette multi-sampler (24 x 20 litre) Niskin bottle system, as part of two expeditions under the auspices of the (www.scale.org.za). Samples were collected at 200 m, 500 m, 1500 m, and at ~10 m above the sea floor (referred as \u0026ldquo;deep\u0026rdquo; in figures). Five litres of water from each sampling depth were filtered on board through Polyethersulfone (PES) Membrane Filter discs of 0.22 \u0026micro;m pore size, and 47 mm diameter (Millipore Sigma, MA, USA), using a Merck Millipore (MA, USA) filtration system. Membrane filters were stored onboard at -20\u0026ordm;C and transported frozen to the laboratory (Centre for Microbial Ecology and Genomics, University of Pretoria, South Africa) for further analyses. DNA was extracted from half of each filter using a protocol optimized for low biomass marine samples \u003csup\u003e91\u003c/sup\u003e. Briefly, filters were cut to small species (~5mm\u003csup\u003e2\u003c/sup\u003e) and transferred to 2-ml Eppendorf tubes. Cells were lysed by adding 400\u0026micro;l of lysis buffer (400mM Tris-HCL (pH 8.0), 60 mM EDTA, 150 mM NaCl and 1% (w/v) SDS) and incubating for 10 min. at 60\u0026ordm;C. A volume of 120 ul of 3M potassium acetate (pH 4.8) was added and the samples were incubated on ice for 5 min, followed by centrifugation at 21000 \u003cem\u003eg\u003c/em\u003e. The supernatant treated with C4 solution of the DNeasy PowerSoil Kit (QIAGEN, Hilden, Germany). The kits protocol was followed from this step forward as per manufacturer\u0026rsquo;s instructions and the concentration of the extracted DNA was analysed with a Qubit 3.0 (Invitrogen, MA, USA), using the kit for high-sensitivity assays.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLibrary preparation, sequencing, and pre-processing of reads\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLibraries for next generation sequencing were prepared using the Nextera XT DNA library kit (Illumina, CA, USA), as per manufacturer\u0026rsquo;s instructions. Shotgun whole genome sequencing of 150 bp, paired end reads, was performed using an Illumina (CA, USA) HiSeq X sequencer, by Admera Health, LLC (NJ, USA). The sequencing depth was of 70 million reads (37.5 in each direction). Sequencing reads were pre-processed with Trimmomatic v0.36 \u003csup\u003e92\u003c/sup\u003e, for the removal of low-quality reads and Nextera PE-PE adapters. \u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAssembly and generating SO exclusive gene catalogue\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePaired-end high quality forward and reverse reads were assembled using SPAdes v3.13.0 \u003csup\u003e93,94\u003c/sup\u003e with the flag --meta for the assembly of metagenomes. Quality of assemblies was assessed with QUAST v5.0.2 \u003csup\u003e95\u003c/sup\u003e. To construct a Southern Ocean specific gene catalogue, contigs from all 44 metagenomes were merged and predicted for coding sequences using prodigal with -p meta and -c parameters. To generate a non-redundant gene set, coding sequences with lengths \u0026gt;100bp were clustered at 95% sequence similarity across 90% of the shortest sequence using cd-hit-est with -c 0.95 -G 0 -aS 0.9 -g 1 -r 1 parameters. The non-redundant gene set was further clustered with coding sequences from both the Ocean Microbial-Reference Gene Catalogue (\u003cem\u003eTara\u0026nbsp;\u003c/em\u003eOM-RGC.v2) and Malaspina Gene Database (M-GeneDB) using cd-hit-est-2d with -c 0.95 -G 0 -aS 0.9 -g 1 -r 1 parameters. Functional annotation of the resultant set of genes specific for the Southern Ocean was further conducted using eggNOG (DIAMOND mode) version 2.1.6 and KofamScan version 1.3.0. \u0026nbsp; \u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBinning of MAGs\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo reconstruct Metagenome Assembled Genomes (MAG) we performed mapping of the reads to the assemblies to improve binning of metagenomes using BBMap \u003csup\u003e96\u003c/sup\u003e. Binning was performed using three different tools: MetaBAT 2 v2.12.1 \u003csup\u003e97\u003c/sup\u003e, MaxBin 2 v2.2.6 \u003csup\u003e98\u003c/sup\u003e and CONCOCT v1.1.0 \u003csup\u003e99\u003c/sup\u003e; followed by dereplication of bins with DAS Tool v1.1.2 \u003csup\u003e100\u003c/sup\u003e. We then assessed quality of bins with CheckM2 v1.0.1\u003csup\u003e101\u003c/sup\u003e. Bins were then classified into low-, medium- and high-quality MAGs according to the minimum information about \u0026nbsp;metagenome-assembled genome standards \u003csup\u003e44\u003c/sup\u003e. Medium- and high-quality genomes were retained for further analyses.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAnalysis of compendium of 610 MAGs\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTaxonomic classification of MAGs was performed with GTDB-Tk v2.3.2 \u003csup\u003e102\u003c/sup\u003e. This tool was also used to establish whether our MAGs were novel by performing ANI against all the GTDB-Tk FastANI reference genomes. MAGs with \u0026lt;95% similarity or no hits were determined as novel. \u0026nbsp;Functional annotation and metabolic profiling was achieved with DRAM distillation and annotation v1.2.0 \u003csup\u003e103\u003c/sup\u003e. Statistics of each MAG obtained from CheckM2 v1.0.1 were used to build frequency plots as well as box and whisker plots using ggplot2 \u003csup\u003e104\u003c/sup\u003e in RStudio (R version \u0026nbsp; 4.0.3). Phylogenomic tree was constructed using GToTree v1.6.11 \u003csup\u003e105-110\u003c/sup\u003e with the Bacteria_and_Archaea single copy gene HMM set. Genomes that had below 25% of the target hit genes were filtered out, and tree construction was performed with IQ-TREE \u003csup\u003e111\u003c/sup\u003e. The phylogenomic tree was visualized and annotated using iTOL v6 \u003csup\u003e112\u003c/sup\u003e. Mapping of metagenomic reads to MAGs to obtain relative abundance estimates was performed using CoverM v.0.6.1 (https://github.com/wwood/CoverM). Dereplication of MAGs to plot relative abundance of mapped metagenomics read and general metabolic potential of all genomes was performed with dRep v3.0.0 \u003csup\u003e113\u003c/sup\u003e. All tools mentioned in this section were used with default parameters, unless noted otherwise.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOrthologous protein analysis of chemolithoautotrophic MAGs\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAn all-against-all sequence comparison of protein sequences from 187 MAGs with the potential for chemolithoautotrophy was conducted using proteinortho\u003csup\u003e114\u003c/sup\u003e with blast\u003csup\u003e115\u003c/sup\u003e option. Proteins sequences that shared 70% minimum percent identity and 50% minimum alignment coverage of the shortest sequences were filtered for downstream analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePhylogenetic reconstruction of the coxSML genes between SAR324 and Gammaproteobacteria\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGenes for the coxSML operon observed to have undergone horizontal transfer between SAR324 and Gammaproteobacteria. To determine potential gene exchanges of these operons we acquired a subset of MAGs that possessed two or three of these genes within the same contigs. Cox genes from these were individually aligned using mafft\u003csup\u003e116\u003c/sup\u003e with the linsi parameter, trimmed using Trimal\u003csup\u003e110\u003c/sup\u003e with the no-allgaps parameter, and concatenated using FASconCAT-G\u003csup\u003e117\u003c/sup\u003e. A maximum likelihood phylogenetic tree of the concatenated alignments was then reconstructed using IQ-TREE\u003csup\u003e111\u003c/sup\u003e with a 1000 bootstraps. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMetagenomic fragment recruitment by the 610 MAGs\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo estimate the distribution of the taxa associated with our 610 medium to high quality MAGs we performed a read recruitment analysis. To conduct this, paired-end high quality forward and reverse reads from the 44 metagenomes were mapped against a genomic database comprising of all the 610 MAGs using Burrows-Wheeler Aligner (BWA)\u003csup\u003e118\u003c/sup\u003e with the default parameters. The resultant alignment files from each metagenome were further filtered to identify reads aligning at \u0026gt;= 95% percent identity with alignment coverage of \u0026gt;= 90%\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the captain and crew of the RV SA Agulhas II for assisting with sample acquisition. The authors also acknowledge members of the SCALE consortium. We gratefully acknowledge the National Research Foundation of South Africa (NRF) (UID 129225, 136491, 148867, 150276). We acknowledge funding received from the European Union\u0026rsquo;s Horizon 2020 research and innovation programme as part of the AtlantECO project under grant agreement number 862923. We also thank the Centre for High Performance Computing (Cape Town, South Africa) and the Centre for Bioinformatics and Computational Biology, University of Pretoria, for providing computational resources. We thank the Technology Innovation Agency (TIA) for supporting this project.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.P.M designed the study, coordinated sampling logistics and provided project oversight. D.J.C, O.K.I.B, and T.d.P. analysed the shotgun metagenomics data. M.P., and S.N.V., conducted nomenclature and taxonomic work. D.J.C wrote the paper with contributions from O.K.I.B, T.d.P., P.H. and T.P.M. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests. \u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOnline content\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData availability\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe metagenomic data have been deposited at NCBI and available with the following accession PRJNA1138941. All 610 high and medium quality MAGs can be directly downloaded from https://10.6084/m9.figshare.21673346. A fasta file with coding sequences that represent a catalogue of SCALE-specific genes can be downloaded from https://10.6084/m9.figshare.21673886. Both EggNOG and Kofamscan functional annotation output files for these SCALE-specific genes can also be directly downloaded from https://10.6084/m9.figshare.21673139.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCoelho, L. P.\u003cem\u003e et al.\u003c/em\u003e Towards the biogeography of prokaryotic genes. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e601\u003c/strong\u003e, 252-256 (2022). \u003c/li\u003e\n\u003cli\u003eBar-On, Y. M., Phillips, R. \u0026amp; Milo, R. The biomass distribution on Earth. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e115\u003c/strong\u003e, 6506-6511 (2018). \u003c/li\u003e\n\u003cli\u003eAzam, F. \u0026amp; Malfatti, F. Microbial structuring of marine ecosystems. \u003cem\u003eNature Reviews Microbiology\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 782-791 (2007). \u003c/li\u003e\n\u003cli\u003eCho, B. C. \u0026amp; Azam, F. Major role of bacteria in biogeochemical fluxes in the ocean\u0026apos;s interior. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e332\u003c/strong\u003e, 441-443 (1988). \u003c/li\u003e\n\u003cli\u003eFuhrman, J. A., McCallum, K. \u0026amp; Davis, A. A. Novel major archaebacterial group from marine plankton. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e356\u003c/strong\u003e, 148-149 (1992). \u003c/li\u003e\n\u003cli\u003eOffre, P., Spang, A. \u0026amp; Schleper, C. Archaea in biogeochemical cycles. \u003cem\u003eAnnual review of microbiology\u003c/em\u003e \u003cstrong\u003e67\u003c/strong\u003e, 437-457 (2013). \u003c/li\u003e\n\u003cli\u003eWhitman, W. B., Coleman, D. C. \u0026amp; Wiebe, W. J. Prokaryotes: the unseen majority. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e95\u003c/strong\u003e, 6578-6583 (1998). \u003c/li\u003e\n\u003cli\u003eLandsch\u0026uuml;tzer, P.\u003cem\u003e et al.\u003c/em\u003e The reinvigoration of the Southern Ocean carbon sink. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e349\u003c/strong\u003e, 1221-1224 (2015). \u003c/li\u003e\n\u003cli\u003eFr\u0026ouml;licher, T. L.\u003cem\u003e et al.\u003c/em\u003e Dominance of the Southern Ocean in anthropogenic carbon and heat uptake in CMIP5 models. \u003cem\u003eJournal of Climate\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 862-886 (2015). \u003c/li\u003e\n\u003cli\u003eSabine, C. L.\u003cem\u003e et al.\u003c/em\u003e The oceanic sink for anthropogenic CO2. \u003cem\u003escience\u003c/em\u003e \u003cstrong\u003e305\u003c/strong\u003e, 367-371 (2004). \u003c/li\u003e\n\u003cli\u003eGruber, N., Landsch\u0026uuml;tzer, P. \u0026amp; Lovenduski, N. S. The variable Southern Ocean carbon sink. \u003cem\u003eAnnual review of marine science\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 159-186 (2019). \u003c/li\u003e\n\u003cli\u003eTakahashi, T.\u003cem\u003e et al.\u003c/em\u003e Global sea\u0026ndash;air CO2 flux based on climatological surface ocean pCO2, and seasonal biological and temperature effects. \u003cem\u003eDeep Sea Research Part II: Topical Studies in Oceanography\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, 1601-1622 (2002). \u003c/li\u003e\n\u003cli\u003eCavicchioli, R. Microbial ecology of Antarctic aquatic systems. \u003cem\u003eNature Reviews Microbiology\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 691-706 (2015). \u003c/li\u003e\n\u003cli\u003eWilliams, T. J.\u003cem\u003e et al.\u003c/em\u003e The role of planktonic F lavobacteria in processing algal organic matter in coastal E ast A ntarctica revealed using metagenomics and metaproteomics. \u003cem\u003eEnvironmental microbiology\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 1302-1317 (2013). \u003c/li\u003e\n\u003cli\u003eSalazar, G.\u003cem\u003e et al.\u003c/em\u003e Gene Expression Changes and Community Turnover Differentially Shape the Global Ocean Metatranscriptome. \u003cem\u003eCell\u003c/em\u003e \u003cstrong\u003e179\u003c/strong\u003e, 1068-1083 e1021 (2019). https://doi.org:10.1016/j.cell.2019.10.014\u003c/li\u003e\n\u003cli\u003eAr\u0026iacute;stegui, J., Gasol, J. M., Duarte, C. M. \u0026amp; Herndld, G. J. Microbial oceanography of the dark ocean\u0026apos;s pelagic realm. \u003cem\u003eLimnology and Oceanography\u003c/em\u003e \u003cstrong\u003e54\u003c/strong\u003e, 1501-1529 (2009). \u003c/li\u003e\n\u003cli\u003eBaltar, F.\u003cem\u003e et al.\u003c/em\u003e Significance of non‐sinking particulate organic carbon and dark CO2 fixation to heterotrophic carbon demand in the mesopelagic northeast Atlantic. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e (2010). \u003c/li\u003e\n\u003cli\u003eFouilland, E. \u0026amp; Mostajir, B. Revisited phytoplanktonic carbon dependency of heterotrophic bacteria in freshwaters, transitional, coastal and oceanic waters. \u003cem\u003eFEMS microbiology ecology\u003c/em\u003e \u003cstrong\u003e73\u003c/strong\u003e, 419-429 (2010). \u003c/li\u003e\n\u003cli\u003eZhao, Y.\u003cem\u003e et al.\u003c/em\u003e Dark carbon fixation and chemolithotrophic microbial community in surface sediments of the cascade reservoirs, Southwest China. \u003cem\u003eScience of The Total Environment\u003c/em\u003e \u003cstrong\u003e698\u003c/strong\u003e, 134316 (2020). \u003c/li\u003e\n\u003cli\u003eLeprich, D. J.\u003cem\u003e et al.\u003c/em\u003e Sulfur bacteria promote dissolution of authigenic carbonates at marine methane seeps. \u003cem\u003eThe ISME Journal\u003c/em\u003e, 1-14 (2021). \u003c/li\u003e\n\u003cli\u003eMueller, A. J.\u003cem\u003e et al.\u003c/em\u003e Genomic and kinetic analysis of novel Nitrospinae enriched by cell sorting. \u003cem\u003eThe ISME journal\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 732-745 (2021). \u003c/li\u003e\n\u003cli\u003eSwan, B. K.\u003cem\u003e et al.\u003c/em\u003e Potential for chemolithoautotrophy among ubiquitous bacteria lineages in the dark ocean. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e333\u003c/strong\u003e, 1296-1300 (2011). \u003c/li\u003e\n\u003cli\u003eReinthaler, T., van Aken, H. M. \u0026amp; Herndl, G. J. Major contribution of autotrophy to microbial carbon cycling in the deep North Atlantic\u0026rsquo;s interior. \u003cem\u003eDeep Sea Research Part II: Topical Studies in Oceanography\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, 1572-1580 (2010). \u003c/li\u003e\n\u003cli\u003eAr\u0026iacute;stegui, J.\u003cem\u003e et al.\u003c/em\u003e (American Association for the Advancement of Science).\u003c/li\u003e\n\u003cli\u003eDel Giorgio, P. A. \u0026amp; Duarte, C. M. Respiration in the open ocean. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e420\u003c/strong\u003e, 379-384 (2002). \u003c/li\u003e\n\u003cli\u003eMitchell, B. G., Brody, E. A., Holm‐Hansen, O., McClain, C. \u0026amp; Bishop, J. Light limitation of phytoplankton biomass and macronutrient utilization in the Southern Ocean. \u003cem\u003eLimnology and Oceanography\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 1662-1677 (1991). \u003c/li\u003e\n\u003cli\u003eBoyd, P. W. Environmental factors controlling phytoplankton processes in the Southern Ocean1. \u003cem\u003eJournal of Phycology\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 844-861 (2002). \u003c/li\u003e\n\u003cli\u003ePitchford, J. W. \u0026amp; Brindley, J. Iron limitation, grazing pressure and oceanic high nutrient-low chlorophyll (HNLC) regions. \u003cem\u003eJournal of Plankton Research\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e (1999). \u003c/li\u003e\n\u003cli\u003eVenables, H. \u0026amp; Moore, C. M. Phytoplankton and light limitation in the Southern Ocean: Learning from high‐nutrient, high‐chlorophyll areas. \u003cem\u003eJournal of Geophysical Research: Oceans\u003c/em\u003e \u003cstrong\u003e115\u003c/strong\u003e (2010). \u003c/li\u003e\n\u003cli\u003eBoyd, P. W., Arrigo, K. R., Strzepek, R. \u0026amp; Van Dijken, G. Mapping phytoplankton iron utilization: Insights into Southern Ocean supply mechanisms. \u003cem\u003eJournal of Geophysical Research: Oceans\u003c/em\u003e \u003cstrong\u003e117\u003c/strong\u003e (2012). \u003c/li\u003e\n\u003cli\u003eBrzezinski, M. A., Jones, J. L. \u0026amp; Demarest, M. S. Control of silica production by iron and silicic acid during the Southern Ocean Iron Experiment (SOFeX). \u003cem\u003eLimnology and Oceanography\u003c/em\u003e \u003cstrong\u003e50\u003c/strong\u003e, 810-824 (2005). \u003c/li\u003e\n\u003cli\u003eMartin, J. H. \u0026amp; Fitzwater, S. E. Iron deficiency limits phytoplankton growth in the north-east Pacific subarctic. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e331\u003c/strong\u003e, 341-343 (1988). \u003c/li\u003e\n\u003cli\u003eMoore, C.\u003cem\u003e et al.\u003c/em\u003e Processes and patterns of oceanic nutrient limitation. \u003cem\u003eNature geoscience\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 701-710 (2013). \u003c/li\u003e\n\u003cli\u003eAcinas, S. G.\u003cem\u003e et al.\u003c/em\u003e Deep ocean metagenomes provide insight into the metabolic architecture of bathypelagic microbial communities. \u003cem\u003eCommunications biology\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 1-15 (2021). \u003c/li\u003e\n\u003cli\u003eRoyo-Llonch, M.\u003cem\u003e et al.\u003c/em\u003e Ecogenomics of key prokaryotes in the arctic ocean. (2020). \u003c/li\u003e\n\u003cli\u003ePachiadaki, M. G.\u003cem\u003e et al.\u003c/em\u003e Major role of nitrite-oxidizing bacteria in dark ocean carbon fixation. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e358\u003c/strong\u003e, 1046-1051 (2017). \u003c/li\u003e\n\u003cli\u003eScott, K. M.\u003cem\u003e et al.\u003c/em\u003e The genome of deep-sea vent chemolithoautotroph Thiomicrospira crunogena XCL-2. \u003cem\u003ePLoS Biol\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, e383 (2006). https://doi.org:10.1371/journal.pbio.0040383\u003c/li\u003e\n\u003cli\u003eJaffe, A. L., Castelle, C. J., Dupont, C. L. \u0026amp; Banfield, J. F. Lateral Gene Transfer Shapes the Distribution of RuBisCO among Candidate Phyla Radiation Bacteria and DPANN Archaea. \u003cem\u003eMol Biol Evol\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 435-446 (2019). https://doi.org:10.1093/molbev/msy234\u003c/li\u003e\n\u003cli\u003eCantalapiedra, C. P., Hernandez-Plaza, A., Letunic, I., Bork, P. \u0026amp; Huerta-Cepas, J. eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale. \u003cem\u003eMol Biol Evol\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 5825-5829 (2021). https://doi.org:10.1093/molbev/msab293\u003c/li\u003e\n\u003cli\u003eAramaki, T.\u003cem\u003e et al.\u003c/em\u003e KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 2251-2252 (2020). https://doi.org:10.1093/bioinformatics/btz859\u003c/li\u003e\n\u003cli\u003eBergauer, K.\u003cem\u003e et al.\u003c/em\u003e Organic matter processing by microbial communities throughout the Atlantic water column as revealed by metaproteomics. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e \u003cstrong\u003e115\u003c/strong\u003e, E400-E408 (2018). https://doi.org:10.1073/pnas.1708779115\u003c/li\u003e\n\u003cli\u003eZhao, Z., Baltar, F. \u0026amp; Herndl, G. J. Linking extracellular enzymes to phylogeny indicates a predominantly particle-associated lifestyle of deep-sea prokaryotes. \u003cem\u003eSci Adv\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, eaaz4354 (2020). https://doi.org:10.1126/sciadv.aaz4354\u003c/li\u003e\n\u003cli\u003eAcinas, S. G.\u003cem\u003e et al.\u003c/em\u003e Deep ocean metagenomes provide insight into the metabolic architecture of bathypelagic microbial communities. \u003cem\u003eCommun Biol\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 604 (2021). https://doi.org:10.1038/s42003-021-02112-2\u003c/li\u003e\n\u003cli\u003eBowers, R. M.\u003cem\u003e et al.\u003c/em\u003e Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. \u003cem\u003eNature biotechnology\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 725-731 (2017). \u003c/li\u003e\n\u003cli\u003eA framework for human microbiome research. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e486\u003c/strong\u003e, 215-221 (2012). https://doi.org:10.1038/nature11209\u003c/li\u003e\n\u003cli\u003eDyksma, S.\u003cem\u003e et al.\u003c/em\u003e Ubiquitous Gammaproteobacteria dominate dark carbon fixation in coastal sediments. \u003cem\u003eThe ISME journal\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 1939-1953 (2016). \u003c/li\u003e\n\u003cli\u003eOrcutt, B. N., Sylvan, J. B., Knab, N. J. \u0026amp; Edwards, K. J. Microbial ecology of the dark ocean above, at, and below the seafloor. \u003cem\u003eMicrobiology and molecular biology reviews\u003c/em\u003e \u003cstrong\u003e75\u003c/strong\u003e, 361-422 (2011). \u003c/li\u003e\n\u003cli\u003eFreitas, S.\u003cem\u003e et al.\u003c/em\u003e Global distribution and diversity of marine Verrucomicrobia. \u003cem\u003eThe ISME journal\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 1499-1505 (2012). \u003c/li\u003e\n\u003cli\u003eBoeuf, D.\u003cem\u003e et al.\u003c/em\u003e Metapangenomics reveals depth-dependent shifts in metabolic potential for the ubiquitous marine bacterial SAR324 lineage. \u003cem\u003eMicrobiome\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 172 (2021). https://doi.org:10.1186/s40168-021-01119-5\u003c/li\u003e\n\u003cli\u003eCoutinho, F.\u003cem\u003e et al.\u003c/em\u003e Ecogenomics and metabolic potential of the South Atlantic Ocean microbiome. \u003cem\u003eScience of The Total Environment\u003c/em\u003e \u003cstrong\u003e765\u003c/strong\u003e, 142758 (2021). \u003c/li\u003e\n\u003cli\u003eTarn, J., Peoples, L. M., Hardy, K., Cameron, J. \u0026amp; Bartlett, D. H. Identification of free-living and particle-associated microbial communities present in hadal regions of the Mariana Trench. \u003cem\u003eFrontiers in Microbiology\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 665 (2016). \u003c/li\u003e\n\u003cli\u003eEloe, E. A.\u003cem\u003e et al.\u003c/em\u003e Compositional differences in particle‐associated and free‐living microbial assemblages from an extreme deep‐ocean environment. \u003cem\u003eEnvironmental microbiology reports\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 449-458 (2011). \u003c/li\u003e\n\u003cli\u003eFuhrman, J. A. \u0026amp; Davis, A. A. Widespread Archaea and novel Bacteria from the deep sea as shown by 16S rRNA gene sequences. \u003cem\u003eMarine Ecology Progress Series\u003c/em\u003e \u003cstrong\u003e150\u003c/strong\u003e, 275-285 (1997). \u003c/li\u003e\n\u003cli\u003eLi, M.\u003cem\u003e et al.\u003c/em\u003e Genomic and transcriptomic evidence for scavenging of diverse organic compounds by widespread deep-sea archaea. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 1-6 (2015). \u003c/li\u003e\n\u003cli\u003eZhang, C. L., Xie, W., Martin-Cuadrado, A.-B. \u0026amp; Rodriguez-Valera, F. Marine Group II Archaea, potentially important players in the global ocean carbon cycle. \u003cem\u003eFrontiers in Microbiology\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 1108 (2015). \u003c/li\u003e\n\u003cli\u003eFrigaard, N.-U., Martinez, A., Mincer, T. J. \u0026amp; DeLong, E. F. Proteorhodopsin lateral gene transfer between marine planktonic Bacteria and Archaea. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e439\u003c/strong\u003e, 847-850 (2006). \u003c/li\u003e\n\u003cli\u003eOttesen, E. A.\u003cem\u003e et al.\u003c/em\u003e Pattern and synchrony of gene expression among sympatric marine microbial populations. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e110\u003c/strong\u003e, E488-E497 (2013). \u003c/li\u003e\n\u003cli\u003eMartin-Cuadrado, A.-B.\u003cem\u003e et al.\u003c/em\u003e A new class of marine Euryarchaeota group II from the mediterranean deep chlorophyll maximum. \u003cem\u003eThe ISME journal\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 1619-1634 (2015). \u003c/li\u003e\n\u003cli\u003eDeschamps, P., Zivanovic, Y., Moreira, D., Rodriguez-Valera, F. \u0026amp; L\u0026oacute;pez-Garc\u0026iacute;a, P. Pangenome evidence for extensive interdomain horizontal transfer affecting lineage core and shell genes in uncultured planktonic thaumarchaeota and euryarchaeota. \u003cem\u003eGenome biology and evolution\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 1549-1563 (2014). \u003c/li\u003e\n\u003cli\u003eRinke, C.\u003cem\u003e et al.\u003c/em\u003e A phylogenomic and ecological analysis of the globally abundant Marine Group II archaea (Ca. Poseidoniales ord. nov.). \u003cem\u003eThe ISME journal\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 663-675 (2019). \u003c/li\u003e\n\u003cli\u003eSimon, M., Rosenstock, B. \u0026amp; Zwisler, W. Coupling of epipelagic and mesopelagic heterotrophic picoplankton production to phytoplankton biomass in the Antarctic polar frontal region. \u003cem\u003eLimnology and oceanography\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, 1035-1043 (2004). \u003c/li\u003e\n\u003cli\u003eHerndl, G. J. \u0026amp; Reinthaler, T. Microbial control of the dark end of the biological pump. \u003cem\u003eNature geoscience\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 718-724 (2013). \u003c/li\u003e\n\u003cli\u003eWorden, A. Z.\u003cem\u003e et al.\u003c/em\u003e Rethinking the marine carbon cycle: factoring in the multifarious lifestyles of microbes. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e347\u003c/strong\u003e (2015). \u003c/li\u003e\n\u003cli\u003eMilici, M.\u003cem\u003e et al.\u003c/em\u003e Diversity and community composition of particle‐associated and free‐living bacteria in mesopelagic and bathypelagic Southern Ocean water masses: Evidence of dispersal limitation in the Bransfield Strait. \u003cem\u003eLimnol Oceanogr\u003c/em\u003e \u003cstrong\u003e62\u003c/strong\u003e, 1080-1095 (2017). \u003c/li\u003e\n\u003cli\u003eLonghurst, A. R. \u0026amp; Harrison, W. G. The biological pump: profiles of plankton production and consumption in the upper ocean. \u003cem\u003eProgress in Oceanography\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 47-123 (1989). \u003c/li\u003e\n\u003cli\u003eO\u0026apos;Leary, N. A.\u003cem\u003e et al.\u003c/em\u003e Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. \u003cem\u003eNucleic Acids Research\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, D733-D745 (2016). https://doi.org:10.1093/nar/gkv1189\u003c/li\u003e\n\u003cli\u003evan Heuven, S. M., Hoppema, M., Jones, E. M. \u0026amp; de Baar, H. J. Rapid invasion of anthropogenic CO2 into the deep circulation of the Weddell Gyre. \u003cem\u003ePhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences\u003c/em\u003e \u003cstrong\u003e372\u003c/strong\u003e, 20130056 (2014). \u003c/li\u003e\n\u003cli\u003eChurch, M. J., Hutchins, D. A. \u0026amp; Ducklow, H. W. Limitation of bacterial growth by dissolved organic matter and iron in the Southern Ocean. \u003cem\u003eApplied and Environmental Microbiology\u003c/em\u003e \u003cstrong\u003e66\u003c/strong\u003e, 455-466 (2000). \u003c/li\u003e\n\u003cli\u003eGrzymski, J. J.\u003cem\u003e et al.\u003c/em\u003e A metagenomic assessment of winter and summer bacterioplankton from Antarctica Peninsula coastal surface waters. \u003cem\u003eThe ISME journal\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 1901-1915 (2012). \u003c/li\u003e\n\u003cli\u003eBrockhurst, M. A.\u003cem\u003e et al.\u003c/em\u003e The ecology and evolution of pangenomes. \u003cem\u003eCurrent Biology\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, R1094-R1103 (2019). \u003c/li\u003e\n\u003cli\u003eGogarten, J. P. \u0026amp; Townsend, J. P. Horizontal gene transfer, genome innovation and evolution. \u003cem\u003eNature Reviews Microbiology\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 679-687 (2005). \u003c/li\u003e\n\u003cli\u003eKoonin, E. V., Makarova, K. S. \u0026amp; Aravind, L. Horizontal gene transfer in prokaryotes: quantification and classification. \u003cem\u003eAnnual Reviews in Microbiology\u003c/em\u003e \u003cstrong\u003e55\u003c/strong\u003e, 709-742 (2001). \u003c/li\u003e\n\u003cli\u003eBrochier-Armanet, C.\u003cem\u003e et al.\u003c/em\u003e Complete-fosmid and fosmid-end sequences reveal frequent horizontal gene transfers in marine uncultured planktonic archaea. \u003cem\u003eThe ISME journal\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 1291-1302 (2011). \u003c/li\u003e\n\u003cli\u003eChapman, C. C., Lea, M.-A., Meyer, A., Sall\u0026eacute;e, J.-B. \u0026amp; Hindell, M. Defining Southern Ocean fronts and their influence on biological and physical processes in a changing climate. \u003cem\u003eNature Climate Change\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 209-219 (2020). \u003c/li\u003e\n\u003cli\u003eTamsitt, V.\u003cem\u003e et al.\u003c/em\u003e Spiraling pathways of global deep waters to the surface of the Southern Ocean. \u003cem\u003eNature communications\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 1-10 (2017). \u003c/li\u003e\n\u003cli\u003eFrancis, C. A., Roberts, K. J., Beman, J. M., Santoro, A. E. \u0026amp; Oakley, B. B. Ubiquity and diversity of ammonia-oxidizing archaea in water columns and sediments of the ocean. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e102\u003c/strong\u003e, 14683-14688 (2005). \u003c/li\u003e\n\u003cli\u003eKalanetra, K. M., Bano, N. \u0026amp; Hollibaugh, J. T. Ammonia‐oxidizing Archaea in the Arctic Ocean and Antarctic coastal waters. \u003cem\u003eEnvironmental Microbiology\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 2434-2445 (2009). \u003c/li\u003e\n\u003cli\u003eQin, W.\u003cem\u003e et al.\u003c/em\u003e Alternative strategies of nutrient acquisition and energy conservation map to the biogeography of marine ammonia-oxidizing archaea. \u003cem\u003eThe ISME journal\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 2595-2609 (2020). \u003c/li\u003e\n\u003cli\u003eManganelli, M.\u003cem\u003e et al.\u003c/em\u003e Major role of microbes in carbon fluxes during austral winter in the Southern Drake Passage. \u003cem\u003ePloS one\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, e6941 (2009). \u003c/li\u003e\n\u003cli\u003eWilliams, T. J.\u003cem\u003e et al.\u003c/em\u003e A metaproteomic assessment of winter and summer bacterioplankton from Antarctic Peninsula coastal surface waters. \u003cem\u003eThe ISME journal\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 1883-1900 (2012). \u003c/li\u003e\n\u003cli\u003eMdutyana, M., Thomalla, S. J., Philibert, R., Ward, B. B. \u0026amp; Fawcett, S. E. The seasonal cycle of nitrogen uptake and nitrification in the Atlantic sector of the Southern Ocean. \u003cem\u003eGlobal Biogeochemical Cycles\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, e2019GB006363 (2020). \u003c/li\u003e\n\u003cli\u003eWright, T. D., Vergin, K. L., Boyd, P. W. \u0026amp; Giovannoni, S. J. A novel delta-subdivision proteobacterial lineage from the lower ocean surface layer. \u003cem\u003eApplied and Environmental Microbiology\u003c/em\u003e \u003cstrong\u003e63\u003c/strong\u003e, 1441-1448 (1997). \u003c/li\u003e\n\u003cli\u003eCao, S.\u003cem\u003e et al.\u003c/em\u003e Structure and function of the Arctic and Antarctic marine microbiota as revealed by metagenomics. \u003cem\u003eMicrobiome\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 1-12 (2020). \u003c/li\u003e\n\u003cli\u003eCao, H.\u003cem\u003e et al.\u003c/em\u003e Delta-proteobacterial SAR324 group in hydrothermal plumes on the South Mid-Atlantic Ridge. \u003cem\u003eScientific reports\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 1-9 (2016). \u003c/li\u003e\n\u003cli\u003eSheik, C. S., Jain, S. \u0026amp; Dick, G. J. Metabolic flexibility of enigmatic SAR 324 revealed through metagenomics and metatranscriptomics. \u003cem\u003eEnvironmental microbiology\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 304-317 (2014). \u003c/li\u003e\n\u003cli\u003eChristaki, U.\u003cem\u003e et al.\u003c/em\u003e Microbial food web structure in a naturally iron-fertilized area in the Southern Ocean (Kerguelen Plateau). \u003cem\u003eDeep Sea Research Part II: Topical Studies in Oceanography\u003c/em\u003e \u003cstrong\u003e55\u003c/strong\u003e, 706-719 (2008). \u003c/li\u003e\n\u003cli\u003eMoorthi, S., Caron, D. A., Gast, R. J. \u0026amp; Sanders, R. W. Mixotrophy: a widespread and important ecological strategy for planktonic and sea-ice nanoflagellates in the Ross Sea, Antarctica. \u003cem\u003eAquatic Microbial Ecology\u003c/em\u003e \u003cstrong\u003e54\u003c/strong\u003e, 269-277 (2009). \u003c/li\u003e\n\u003cli\u003eNorbury, J., Moroz, I. M. \u0026amp; Cropp, R. The role of mixotrophy in Southern Ocean ecosystems. \u003cem\u003eEnvironmental Modeling \u0026amp; Assessment\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 421-435 (2019). \u003c/li\u003e\n\u003cli\u003eStoecker, D. K., Hansen, P. J., Caron, D. A. \u0026amp; Mitra, A. Mixotrophy in the marine plankton. \u003cem\u003eAnnual Review of Marine Science\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 311-335 (2017). \u003c/li\u003e\n\u003cli\u003eYelton, A. P.\u003cem\u003e et al.\u003c/em\u003e Global genetic capacity for mixotrophy in marine picocyanobacteria. \u003cem\u003eThe ISME journal\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 2946-2957 (2016). \u003c/li\u003e\n\u003cli\u003eHirai, M.\u003cem\u003e et al.\u003c/em\u003e Library construction from subnanogram DNA for pelagic sea water and deep-sea sediments. \u003cem\u003eMicrobes and environments\u003c/em\u003e, ME17132 (2017). \u003c/li\u003e\n\u003cli\u003eBolger, A. M., Lohse, M. \u0026amp; Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 2114-2120 (2014). \u003c/li\u003e\n\u003cli\u003eBankevich, A.\u003cem\u003e et al.\u003c/em\u003e SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. \u003cem\u003eJournal of computational biology\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 455-477 (2012). \u003c/li\u003e\n\u003cli\u003eNurk, S., Meleshko, D., Korobeynikov, A. \u0026amp; Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. \u003cem\u003eGenome research\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 824-834 (2017). \u003c/li\u003e\n\u003cli\u003eGurevich, A., Saveliev, V., Vyahhi, N. \u0026amp; Tesler, G. QUAST: quality assessment tool for genome assemblies. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 1072-1075 (2013). \u003c/li\u003e\n\u003cli\u003eBushnell, B. BBMap: a fast, accurate, splice-aware aligner. (Lawrence Berkeley National Lab.(LBNL), Berkeley, CA (United States), 2014).\u003c/li\u003e\n\u003cli\u003eKang, D. D., Froula, J., Egan, R. \u0026amp; Wang, Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. \u003cem\u003ePeerJ\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, e1165 (2015). \u003c/li\u003e\n\u003cli\u003eWu, Y.-W., Simmons, B. A. \u0026amp; Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 605-607 (2016). \u003c/li\u003e\n\u003cli\u003eAlneberg, J.\u003cem\u003e et al.\u003c/em\u003e Binning metagenomic contigs by coverage and composition. \u003cem\u003eNature methods\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1144-1146 (2014). \u003c/li\u003e\n\u003cli\u003eSieber, C. M.\u003cem\u003e et al.\u003c/em\u003e Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. \u003cem\u003eNature microbiology\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 836-843 (2018). \u003c/li\u003e\n\u003cli\u003eChklovski, A., Parks, D. H., Woodcroft, B. J. \u0026amp; Tyson, G. W. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. \u003cem\u003eNat Methods\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 1203-1212 (2023). \u003c/li\u003e\n\u003cli\u003eChaumeil, P.-A., Mussig, A. J., Hugenholtz, P. \u0026amp; Parks, D. H. (Oxford University Press, 2020).\u003c/li\u003e\n\u003cli\u003eShaffer, M.\u003cem\u003e et al.\u003c/em\u003e DRAM for distilling microbial metabolism to automate the curation of microbiome function. \u003cem\u003eNucleic acids research\u003c/em\u003e \u003cstrong\u003e48\u003c/strong\u003e, 8883-8900 (2020). \u003c/li\u003e\n\u003cli\u003eWickham, H. ggplot2. \u003cem\u003eWiley Interdisciplinary Reviews: Computational Statistics\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 180-185 (2011). \u003c/li\u003e\n\u003cli\u003eLee, M. D. GToTree: a user-friendly workflow for phylogenomics. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 4162-4164 (2019). https://doi.org:10.1093/bioinformatics/btz188\u003c/li\u003e\n\u003cli\u003eCock, P. J.\u003cem\u003e et al.\u003c/em\u003e Biopython: freely available Python tools for computational molecular biology and bioinformatics. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 1422-1423 (2009). \u003c/li\u003e\n\u003cli\u003eEddy, S. R. Accelerated profile HMM searches. \u003cem\u003ePLoS computational biology\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, e1002195 (2011). \u003c/li\u003e\n\u003cli\u003eHyatt, D.\u003cem\u003e et al.\u003c/em\u003e Prodigal: prokaryotic gene recognition and translation initiation site identification. \u003cem\u003eBMC bioinformatics\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1-11 (2010). \u003c/li\u003e\n\u003cli\u003eEdgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. \u003cem\u003eNucleic acids research\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 1792-1797 (2004). \u003c/li\u003e\n\u003cli\u003eCapella-Guti\u0026eacute;rrez, S., Silla-Mart\u0026iacute;nez, J. M. \u0026amp; Gabald\u0026oacute;n, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 1972-1973 (2009). \u003c/li\u003e\n\u003cli\u003eNguyen, L.-T., Schmidt, H. A., von Haeseler, A. \u0026amp; Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. \u003cem\u003eMolecular biology and evolution\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 268-274 (2015). https://doi.org:10.1093/molbev/msu300\u003c/li\u003e\n\u003cli\u003eLetunic, I. \u0026amp; Bork, P. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 127-128 (2007). \u003c/li\u003e\n\u003cli\u003eOlm, M. R., Brown, C. T., Brooks, B. \u0026amp; Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. \u003cem\u003eThe ISME journal\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 2864-2868 (2017). \u003c/li\u003e\n\u003cli\u003eLechner, M.\u003cem\u003e et al.\u003c/em\u003e Proteinortho: detection of (co-)orthologs in large-scale analysis. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 124 (2011). https://doi.org:10.1186/1471-2105-12-124\u003c/li\u003e\n\u003cli\u003eAltschul, S. F., Gish, W., Miller, W., Myers, E. W. \u0026amp; Lipman, D. J. Basic local alignment search tool. \u003cem\u003eJ Mol Biol\u003c/em\u003e \u003cstrong\u003e215\u003c/strong\u003e, 403-410 (1990). https://doi.org:10.1016/s0022-2836(05)80360-2\u003c/li\u003e\n\u003cli\u003eKatoh, K. \u0026amp; Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. \u003cem\u003eMolecular biology and evolution\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 772-780 (2013). https://doi.org:10.1093/molbev/mst010\u003c/li\u003e\n\u003cli\u003eKuck, P. \u0026amp; Longo, G. C. FASconCAT-G: extensive functions for multiple sequence alignment preparations concerning phylogenetic studies. \u003cem\u003eFront Zool\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 81 (2014). https://doi.org:10.1186/s12983-014-0081-x\u003c/li\u003e\n\u003cli\u003eLi, H. \u0026amp; Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 1754-1760 (2009). https://doi.org:10.1093/bioinformatics/btp324\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"chemolithoautotrophy, dark ocean, Southern Ocean, metagenome assembled genomes, MAGs, mesopelagic, bathypelagic","lastPublishedDoi":"10.21203/rs.3.rs-7078079/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7078079/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Southern Ocean hosts a high degree of endemic plants and animals, yet the genetic diversity, function and evolutionary relationships of microbial communities remains unexplored, particularly in the aphotic \u0026ldquo;dark ocean,\u0026rdquo; where microbes play critical roles in local and global food webs. Here, we performed a metagenomic analysis of 44 aphotic seawater samples collected from multiple depths across the Southern Ocean to characterize the functional gene repertoire of these microbial communities. Of the 11,896,546 species-level unigenes\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e identified, ~\u0026thinsp;87% appear specific to the Southern Ocean and are distinct from other major ocean datasets. We reconstructed 502 bacterial and 108 archaeal metagenome-assembled genomes (MAGs), revealing widespread capacities for inorganic carbon fixation via the Calvin cycle, the hydroxypropionate-hydroxybutyrate cycle, and the 3-hydroxypropionate bi-cycle. Metapangenomic analyses indicated that several genes involved in the oxidation of reduced nutrients including ammonia, nitrite, and thiosulfate, are shared across the aphotic water column through horizontal gene transfer. MAGs belonging to Acidimicrobia, Gammaproteobacteria, and SAR324 were abundant throughout the dark Southern Ocean and showed potential for both chemolithoautotrophy and carbohydrate degradation, suggesting mixotrophy as a key metabolic strategy. Together, these findings reveal the unique functional and genomic diversity of deep Southern Ocean microbiomes and provide insights into their roles in carbon cycling within one of Earth\u0026rsquo;s most important marine carbon sinks.\u003c/p\u003e","manuscriptTitle":"Endemic dark ocean microbiomes drive carbon cycling in the Southern Ocean","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-29 11:11:27","doi":"10.21203/rs.3.rs-7078079/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"62ec23b1-48ab-44c1-a2d6-3819110a467b","owner":[],"postedDate":"July 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":52249929,"name":"Biological sciences/Microbiology/Environmental microbiology"},{"id":52249930,"name":"Biological sciences/Ecology/Microbial ecology"}],"tags":[],"updatedAt":"2026-05-13T13:36:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-29 11:11:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7078079","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7078079","identity":"rs-7078079","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}