Novel innate immune systems in pristine Antarctic soils

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Van Goethem, Oliver K. I. Bezuidt, Rian Pierneef, Surendra Vikram, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4437132/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Antarctic environments are dominated by microorganisms, which are vulnerable to viral infection. Although several studies have investigated the phylogenetic repertoire of bacteria and viruses in these poly-extreme environments, the evolutionary mechanisms governing microbial immunity remain poorly understood. Results Using genome resolved metagenomics, we test the hypothesis that these poly extreme high-latitude microbiomes harbour diverse innate immune systems. Our analysis reveals the prevalence of prophages in bacterial genomes (Bacteroidota and Verrucomicrobiota), suggesting the significance of lysogenic infection strategies in Antarctic soils. Furthermore, we demonstrate the presence of diverse CRISPR-Cas arrays, including Class 1 arrays (Types I-B, I-C, and I-E), alongside systems exhibiting novel gene architecture among their effector cas genes. Notably, a Class 2 system featuring type V variants lacks CRISPR arrays, Cas1 and Cas2 adaptation module genes. Phylogenetic analysis of Cas12 effector proteins hints at divergent evolutionary histories compared to classified type V effectors. Conclusions Our findings suggest substantial sequence novelty in Antarctic cas sequences, likely driven by strong selective pressures. These results underscore the role of viral infection as a key evolutionary driver shaping polar microbiomes. Antarctica archaea antiphage innate immunity evolutionary drivers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Understanding the ecological role played by viruses in altering ecosystem processes through their influence on both the phylogenetic and functional diversity of their hosts remains a major ecological endeavour 1 – 5 . In soils, viruses affect the diversity and abundance of microorganisms 6 – 9 thereby influencing processes such as nutrient cycling and carbon sequestration 10 – 13 . Their role as mediators of ecosystem services is pronounced in the poly-extreme environments of the McMurdo Dry Valleys of Eastern Antarctica where prokaryotes govern nutrient cycling. While Antarctic soils harbour diverse microbes and viruses 14 – 17 , host-virus interactions remain relatively unexplored. This is despite the fact that viral infections may pose direct threats to microorganisms by influencing nutrient/biomass turnover, and on ecosystem functioning 18 . However, few studies have assessed these relationships, especially in poly-extreme environments, such as Antarctica, where microbial communities disproportionately influence ecosystem functions. Likewise, understanding the range of defence mechanisms used by microbes, to avoid viral infections, is crucial 19 – 22 . A notable prokaryotic defence mechanism, the CRISPR-Cas (clustered regularly interspaced short palindromic repeats - CRISPR associated) system 23 – 27 , allows microorganisms to specifically target and degrade viral DNA or RNA 28 – 33 . CRISPR-Cas systems are widespread accessory elements across bacterial and archaeal plasmids 34 , 35 , and have been the subject of extensive studies in the last decade owing to their efficacy in genome editing 36 – 38 . Consequently, the repertoire of known CRISPR-Cas systems has expanded significantly in terms of both quantity and diversity 35 , 39 – 42 . These systems recognize the foreign DNA of an invading phage and cleave sequences from its genome, which become integrated as spacers within the host’s CRISPR array 43 . The CRISPR-Cas system thus provides signatures of previous infection events by retaining the ‘genomic scars’ of historical infections 36 , 44 . We hypothesise that this prokaryotic immune system may be more pronounced in poly-extreme ecosystems, where evolution is remarkably constrained and under strong selective pressures due to several stressors. Because CRISPR-Cas systems represent an ancient adaptive immune strategy in prokaryotes 45 elucidating this antiviral defence pathway may reveal the immunological memories within bacterial genomes from pristine Antarctic soils. However, the extent to which these mechanisms may influence the diversity and function of Antarctic terrestrial communities remains poorly understood. The Mackay Glacier region in Antarctica is one of the most remote and challenging poly-extreme environments on Earth where sub-zero temperatures, very low nutrient status and an absence of precipitation render soils virtually inhospitable 46 – 48 . While the diversity and evolution of defence mechanisms in this ecosystem have not been studied previously, a recent study on rock-associated Antarctic communities in the Miers Dry Valley has shown that poly-extreme environments harbour diverse innate immune systems 49 . The results of this study suggest the presence of several innate immune systems including BREX (BacteRiophage EXclusion) and DISARM (Defence Island System Associated with Restriction-Modification), which were the predominant modes of antiphage immunity employed by bacteria in Antarctic desert hypoliths 49 . However, hypoliths represent a small fraction of the Dry Valley terrain and studies in expose surface soils are lacking. Here we aim to provide insights on adaptive immune systems through (i) characterizing the CRISPR-Cas systems within bacterial genomes, and (ii) by investigating their role in viral defence mechanisms. By studying the presence of CRISPR-Cas systems in this unique ecosystem, we hope to gain a better understanding of the mechanisms by which microorganisms protect themselves from viral infections in extreme environments. We hypothesize that the CRISPR-Cas systems, present in these Antarctic soils, play crucial roles in protecting microorganisms from viral infections and maintaining the stability of the ecosystem. Using genome-resolved metagenomic analysis, we provide the first insights of bacterial adaptive immunity, and bacterial-viral associations in Antarctic soils. Results and discussion Strong evidence of predation-prey associations in Antarctic soils Our study expands current insights regarding the genetic mechanisms explaining prey-predator co-evolutionary associations between bacteria and their viruses in poly-extreme Antarctic conditions ( Supplementary Table 1 ). Following metagenome sequencing, assembly, and genome binning ( Supplementary Tables S2 and S3 ), we recovered 18 medium- to high-quality metagenome-assembled genomes (MAGs) ( Fig. 1a ). These MAGs include three Acidobacteriota , one Cyanobacteria , twelve Bacteroidota and two Verrucomicrobiota , representing both dominant, and rare bacterial phyla in these soils ( Supplementary Note 1 and Supplementary Fig. 1 ). The genome sizes of these bacteria ranged from 2.7–5.9 Mb, when accounting for completeness. These genomes had moderately low G + C contents (mean = 42.8%), which is surprising given the expectation that extreme environments may select for organisms with high G + C content 50 , 51 . We estimated the genome replication rates for each MAG 52 and found that the highest genome replication rates were associated with Acidobacteriota (mean = 3.06) and the Verrucomicrobiota (mean = 2.90). These differed compared with Bacteroidota (mean = 2.48) and Cyanobacteria (2.04). Estimating the minimal doubling time with codon usage bias 53 suggests that the rates across all MAGs may be low with only members of the Flavisolibacter predicted to double in under five hours. Overall, these patterns suggest extremely slow growth rates which is expected given the poly-extreme conditions in this region (see additional information regarding the climatic conditions and these taxa in Supplementary Note 1 ). Given the comparatively low division rates, it is reasonable to predict that the slow evolutionary processes acting on microbial communities in this environment are likely to delay selection. There is some support for this assertion including the divergent ecological patterns of Antarctic microbiota and the substantial differences compared with those from outside the continent. Our study provides strong evidence that Antarctic bacterial communities may have ancient origins. Bayesian evolutionary analysis, used to produce time-measured phylogenies, suggests that the genomes retrieved from our studies ranged between 500 and 1,200 Mya ( Fig. 1b ). These findings are consistent with previous estimates of cryptoendolith divergence in Antarctica 54 . The results also support approximations for other genomes retrieved from Antarctic soils 46 . Altogether, the unique monophyletic clades of our Antarctic MAGs were distinct suggesting that these bacteria diverged from other known taxa during the Precambrian (541 Mya). Taxonomy analysis indicated that 12 of our MAGs potentially represent novel species. Considering this evidence, we predict that bacteriophages associated with these bacteria may have also been co-separated from other microorganisms for a similar length of time since host-virus specificity is mostly strain specific. We hypothesized that this distinct, and specific, co-evolution may be corroborated by the recovery of uncharacterized and potentially novel adaptive immune signatures in Antarctic host genomes. To test this hypothesis, we characterized prophages in our Antarctic genomes. We found one prophage, in the Verrucomicrobiota MS5-1_8 genome, as well as two prophages, in a Bacteroidota genome (TG5-1_3). Coincidently, these two taxa were predicted to be the slowest growing. This finding provides direct evidence linking prophages to both Verrucomicrobiota and Bacteroidota in this region ( Table 1 ). The prophage found in the Verrucomicrobiota genome was 4,483 bp in length and was similar to known Microviridae phages (BLASTn: 31% query coverage and 69.74% identity). The prophages identified in the Bacteroidota genome were 5,477 bp and 7,214 bp in length, although these had low sequence similarity to known phages (more information on the uncultivated viral genomes identified here in Supplementary Note 2 and Supplementary Fig. 2 ). While several studies have investigated the importance of prophages on the evolution of bacterial pathogens 55 , knowledge regarding their role in soil taxa remains limited and the lack of a comprehensive database of soil-dwelling viruses contributes to this knowledge gap 56 . Previous studies suggest that lysogenic conversion may confer new traits to bacteria 57 , which typically have novel metabolic functions through auxiliary metabolic genes (AMGs) 58 . For example, phages associated with Verrucomicrobiota encoded genes have previously been implicated in nitrogen fixation 59 . In oligotrophic soils, the benefits to the hosts may be vital for ecosystem services, facilitating access to alternative energy sources or stress avoidance mechanisms. Elucidating innate mechanisms may provide insights regarding host-virus interactions and reveal the extent of functional diversity in these soils. CRISPR systems provide evidence of bacterial virus attacks in Antarctica. The detection of prophages in MAGs and AMGs on phage contigs ( Supplementary Note 2 ) supports the prediction of unique host-virus histories, as most viral genomes are completely unrelated to known viruses ( Fig. 2 ) 60 – 62 . The results from this study suggest that the host adaptive immune system, associated with divergent microbiota in Antarctica soils, may be more prominent than initially envisaged. However, apart from previous studies on rock associated microbiota, there is a severe knowledge deficit regarding host adaptive immune systems associated with poly-extreme environments. To further explore host-virus histories associated with our MAGs, we searched for related diverse defence strategies against phage predation. As part of determining the adaptive immune system, we found putative CRISPR-Cas systems in 16 of the 18 MAGs ( ca . 89%). In terms of CRISPR arrays, the identified conserved repeats ranged from 23 to 30 bp across four of the retrieved genomes. The repeats were flanked by unique spacers, that were an average of 36 bp in length (range 34–38 bp). The largest set of CRISPR cassettes was found in the MS7-5_6 genome ( Acidobacteriota ), with 51 CRISPR spacers between housed 6 unique CRISPR repeats. These values are within the optimal number of spacers, previously suggested to range between 10–100 within bacterial genomes 63 . The CRISPR loci within bacterial genomes retain the memory of past viral infections 63 , 64 . Yet, the length of these loci appears to be directly related to the capacity to respond to an infection 65 . In other words, there appears to be a trade-off between maintaining a vast genetic memory of attacks (harbouring more spacers) and the functionality of the CRISPR mechanism 63 . The remaining genomes only had between three and 16 spacers, which is more similar to human gut microbes (average of 12 spacers) 66 than the average cassette size of between 20 and 40 spacers 67 . We speculate that the lower spacer count may be due limited encounters with a small set of phages. In this scenario, the spatial constraints of the soil microhabitat limit the number of potential interactions between phages and putative hosts. This suggests that phage diversity may be low in this region of Antarctica. Not only are cells immobilized by adsorption to soil particles of the Antarctic desert pavement, but rarely, if ever, subject to precipitation events which may allow for the mobilization of cells, thus reducing the spectrum of infection events considerably. In addition to the CRISPR-Cas cassettes, the 16 MAGs had relatively low abundances of cas genes, with between 6 and 42 loci per MAG. These cas genes constituted 122 unclassified sequences ( n = 221 total cas sequences), followed by several classified sequences including 48 type III, 31 type I, 20 type IV and 2 type V Cas systems. These Cas types are similar to those previously reported in Antarctic surface snow in which CRISPR-cas types I, II and III were most common 68 . The MS7-5_6 MAG ( Acidobacteriota ) had a contig with 10 genes associated with a hybrid CRISPR-Cas Class I system. This contig also had a GCN5-related N-acetyltransferase (GNAT) toxin domain 69 (see Fig. 3a ), which functions by acetylating charged tRNA molecules to prevent translation. Previous studies suggest that these GCN5-related N-acetyltransferase toxin domains may represent novel substrates for several enzymes linked to antibiotic modification 70 . We further investigated unbinned metagenomic contigs, which possessed eight or more co-localized cas genes, to determine if they represented novel CRISPR-Cas variants. Taxonomically, the CRISPR-Cas systems recovered from these contigs were affiliated members of the Acidobacteriota ( n = 6 contigs), Unclassified Bacteria ( n = 2), Chloroflexota ( n = 1) and Bacteroidota ( n = 1). However, the taxonomic relationships of these taxa suggest potentially shared histories with a variety of bacterial phyla ( Fig. 3b ). The architecture of effector complexes, within the CRISPR-Cas systems, suggests that most of these were class 1 with type I or type III systems. Genes for Cas1 and Cas2 proteins were ubiquitously distributed across all contigs ( Fig. 3a ). These genes were always structured as Cas1-Cas2 complexes. In four examples, the Cas1-Cas2 complex were flanked upstream by cas 4, which directly interacts with the Cas1-Cas2 complex, to process pre-spacers prior to integration as the Cas4-Cas1-Cas2 complex 71 . However, in two instances, we our analyses revealed that cas 4 was downstream of the Cas1-Cas2 complex, which is an unconventional arrangement of these genes based on data from previous studies 36 . In all 10 cases, the effector genes were located upstream of the Cas1-Cas2 operon. The remaining four CRISPR-Cas systems may represent novel variants, based on arrangements of their effector modules ( Fig. 3b ) 36 , 72 . These results imply ongoing horizontal gene transfer and recombination events of diverse CRISPR-Cas loci, led by continuous interactions with the same viruses. Notably, these uncategorized CRISPR-Cas system types were affiliated with members of the Acidobacteriota . They include FI-1_NODE_368 (cas2-cas1-cas4-cas3), which lacks an effector complex and seems to be closely related to Type IU. Contig FI-1_NODE_81 (cas4-cas2-cas1-cas6-cas3-cas5-cas7-cas8b1-cas7-cas8b1-cas7), which is a potential Type I-B variant based on multiple copies of cas7 and cas8 at the terminus of the array. Contig MtG-4_NODE_208 (cas2-cas1-cas1-RT-csm3gr7-csm3gr7-csm3gr7-cas10), which is potentially a Type IIIU array with three copies of csm3gr7. Finally, contig PT-2_NODE_41 (cas6-cas2-cas1-csm3gr7-csm3gr7-csm3gr7-cas10) may be a Type IIIA variant or Type IIIU variant since it lacks csm2, csm4 and csm5 genes. All 10 predicted CRISPR-Cas systems were associated with CRISPR arrays. These systems were composed of spacers that ranged from 2 to 122 bp in length, with an average length of 35 bp. The cas 2 sequences showed some divergence from those previously reported, and these results contrasted with our expectations. Instead, the cas 2 sequences clustered among unrelated phyla, in some cases grouping within the kingdom Archaea ( Fig. 3a ). Nevertheless, our results show several cases where Archaea cluster with Firmicutes and other unrelated phyla. These results are not surprising given the fact that these genes are known to be horizontally acquired. This may indicate that the cas 2 gene is not always taxonomically conserved. Instead, the result suggests mobilization via inter-phylum horizontal gene transfer (HGT) events or evidence of phylum-specific cas subtypes. A recent study showed that CRISPR-Cas systems may contribute to the propagation of transposable elements by facilitating transposition into specific sites 73 . Similarly, our results support previous reports since we found transposase elements on almost half of the 10 CRISPR-Cas-containing contigs analysed. Based on these data, we speculate that these Antarctic CRISPR-Cas systems were horizontally transferred as ancient mobilization events. This suggestion is supported by an evaluation of the G + C skew, among the 10 contigs containing cas genes, as a proxy for the timing of insertion events 74 . Here, we inferred HGT through the detection of strong deviations in G + C content for a genomic fragment compared to the remaining genomic signature. Specifically, on NODE_81 from the FI-1 metagenome, the G + C content over the Cas proteins varied minimally across each gene yet is markedly different from the G + C content of the CRISPR array upstream of the cas genes ( Fig. 4 ). By contrast, the contigs containing integrated prophages within the microbial genomes showed very high variations in G + C content (i.e. G + C skew) across the contig which possibly indicates a foreign origin 75 ( Supplementary Fig. 3 ). Our Bayesian diversity estimates also indicated ancient divergence events of our MAGs from known bacteria. It is thus likely that the phages of these bacteria have similarly ancient Precambrian histories, which offers a possible explanation for their unique gene compositions. Following this, we explored our data for the diversity of type V CRISPR-Cas systems. From the data, we identified a total of 216 contigs longer than 1 kb from 16 of the 18 metagenomes with predicted cas12 effectors proteins. Of these, 112 contigs with sizes ranging from 1,007 to 48,306 bp that possessed non-partial cas12 proteins were retained for downstream analyses. The lengths of effector proteins in these contigs varied from 89 to 630 amino acids, and this contrasted with previous reports that have indicated the average lengths for type V associated effector proteins to be ~ 400 amino acids and longer 76 , 77 . As effector proteins associated with type V CRISPR-Cas systems are mainly distinguished by the possession of a RuvC nuclease domain, we also found these to be characteristic of our 111 effectors, including the smallest (89 aa) putative cas12 protein. Only one of these lacked a RuvC domain but possessed a helix-turn-helix domain. Further inspection of contigs possessing these indicated that only 13 of our effectors were proximal to CRISPR arrays, and unlike typical CRISPR-Cas systems none of the 112 were co-localized with the cas1-cas2 complex. Phylogenetic analysis of these indicated that just nine of our effectors (Ant Cas U5-8) clustered with previously characterized cas12 effectors. We then observed that 18 of our other effectors indicated a close phylogenetic relationship with transposon encoded TnpB proteins, further suggesting that type V effectors may have evolved from TnpB associated nucleases 78 . We observed a further 83 additional effectors from our data that formed a distinct clade (indicated as Ant Cas U4), potentially representing a novel subgroup of cas12-like effectors ( Fig. 5 ). Altogether, we speculate that the unique diversity of the genes found in these Antarctic soils may be the result of a ‘slowed down’ evolution of genes selected during warmer periods of time. The Antarctic continent was a temperate rainforest during the mid-Cretaceous period ~ 140 Mya 79 and we speculate that the subsequent cooling of the continent may have constrained evolutionary forces from acting at their previous pace. Combined, these lines of evidence point to an ancient, acquired immunity of bacteria in Antarctica while contemporary infection events continue to occur through lysogenic phage infections. We used metagenomes from remote and pristine Antarctic soils to assess their viral and bacterial diversity. Multiple lines of evidence suggest extensive phage-host interactions, potentially novel viral diversity, and CRISPR-Cas variants. The phage signatures (vOTUs) were linked to the infection of dominant soil bacterial lineages in these surface soils, including members of the Bacteroidota and Acidobacteriota , while prophages embedded within Verrucomicrobiota and Bacteroidota MAGs offer further insight into contemporary infections. CRISPR-Cas systems, part of the bacterial adaptive immune system, were common to 4 of 18 MAGs analyzed, indicating acquired immunity in both Bacteroidota and Acidobacteriota . Additional Class I CRISPR-Cas arrays (types I-B, I-C and I-E) were detected in the assembled metagenomes, where four CRISPR-Cas arrays did not perfectly match existing architectures and thus may be novel variants. Our analysis of G + C content and GC skew across CRISPR-Cas contigs showed low variations in G + C skew in CRISRP-Cas arrays, but more variation in prophages, suggesting that these acquired immunity markers are ancient whereas proviral elements appear to be the result of recent foreign DNA transfer as further evidenced by the description of novel, Antarctic exclusive cas12-like effectors. Materials and Methods Sample collection and preparation Surface soils were collected from 18 remote sites in Eastern Antarctica, between the Mackay Glacier and the Drygalski Ice Tongue 16 . Methods of DNA isolations, soil physicochemical analyses, soil isotopic measurements and soil respiration experiments have been reported previously 80 . After eDNA extractions, metagenomes were sequenced on an Illumina HiSeq 2000 instrument producing 250 bp paired-end libraries. Sequencing depth was determined using Nonpareil v3.301 81 . Metagenome analysis and assembly All metagenomes were filtered to cull low-quality reads and those containing ambiguous bases (internal N ’s) using Prinseq-lite v0.20.4 82 . All Illumina PhiX sequences were identified and removed using BBDuk 83 to eliminate the potential of contaminating viral signals in our analysis 84 . We determined the microbial taxonomy of each sample using SingleM, which relies on analyses of universal single-copy ribosomal subunit proteins (rpl), rather than the 16S rRNA gene to infer taxonomy ( https://github.com/wwood/singlem ). Each filtered metagenome was individually assembled using metaSPAdes v3.12 85 . Reconstruction of microbial genomes From the assemblies we reconstructed microbial genome bins using MetaBAT 2 v2.12.1 86 . All contigs > 1,500 bp in length were retained and depth coverage information was obtained using BBMap 87 by mapping corresponding metagenomic reads back to those contigs. The bins were assessed for completeness using CheckM v0.7.0 88 and metagenome-assembled genomes (MAGs) that were > 50% complete and were < 10% contaminated were retained for further analyses. Next, we queried indicators of genome quality, such as the presence of 5S, 16S and 23S ribosomal subunit genes and the presence of at least 18 unique tRNAs according to MIMAG standards 89 . Taxonomy was then assigned using the Genome Taxonomy Database Toolkit (GTDB-Tk) v1.1.0 release 89 90 on KBase 91 . CARD RGI was used to determine the prevalence of antibiotic resistance genes (ARGs) as hallmarks of resistance to bacterial antibiotic production 92 . The 18 retained MAGs were inspected for CRISPR-Cas repeats using MinCED 93 and for prophages (integrated viral genomes) using VirSorter v1.0.6 94 . A maximum likelihood phylogenomic tree, based on 49 core bacterial genes from our 18 MAGs and 100 reference genomes (RefSeq database), was built using FastTree2 v2.1.10. Phylogenomic trees were visualized in iTOL v6.34 95 . We used iRep to estimate bacterial replication rates 52 and gRodon to calculate growth rate 53 . Viral taxonomic analysis We also explored each metagenomic assembly for bacteriophages using VirSorter v1.0.6 94 . Contigs were manually inspected for viral “hallmark” genes from categories 1 and 2 (complete viral contigs), and 4 and 5 (prophages). The quality of the predicted viral contigs was assessed using CheckV v1.0 96 . Contigs > 10 kb that were thought to be viral were then clustered using vConTACT2 v0.9.19 97 to establish a network of protein clusters among known phages from the Virome database. The edges of the network are significant gene-sharing similarities between contigs, which are represented as nodes. These uncultivated viral genomes (UViGs) were also inspected for possible auxiliary metabolic genes (AMGs) that could have been acquired from their host. We built phylogenetic trees using MAFFT v7.294b 98 of the AMGs identified in UViGs and their homologs in host MAGs to determine the possible origin of the gene. Finally we used the dbCAN2 web server 99 to identify glycosyl hydrolases (GH) and glycoside transferases (GT) in the MAGs. UViGs were clustered into viral OTUs (vOTUs) at 95% average nucleotide identity (ANI) and 85% alignment fraction (AF). CRISPR spacer analysis and protein structure analysis using AlphaFold2 Metagenomes and MAGs were explored for the presence of adaptive immunity systems such as CRISPR-cas gene types using hmmsearch with (E-value 1e -05 ) against Cas gene profiles obtained from a study by Makarova, et al. 36 . These were further assessed for the presence of innate immune response genes using RPS-BLAST (E-value 1e -05 ) against conserved domain databases (CDD) of clusters of orthologous groups (COGs) and protein families (Pfams) 100 . Results from these searches were manually filtered for the identification of CRISPR-Cas systems, toxin-antitoxins (TA), restriction-modification (RM), bacteriophage exclusion (BREX), abortive infection (Abi), defense island system associated with restriction-modification (DISARM), and other recently identified systems using a refined list of COG and Pfam identifiers reported to be associated with these defense mechanisms 36 , 101 . Cas reference sequences were extracted from UniProtKB/Swiss-Prot ( https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.fasta.gz ; accessed 2021/05/25); a high-quality, manually annotated and non-redundant protein sequence database 102 . For each Cas protein family the reference amino acid sequences and our amino acid sequences were included in multiple sequence alignment using Mafft v7.294b with the “linsi” parameter specified 98 . Trees were constructed based on the multiple sequence alignments using IQ-TREE v2.1.2 103 with the “MFP” parameter invoked which uses ModelFinder 104 to determine the most appropriate model. Tree refinement was performed in iTOL v6.3 95 . For the Cas G + C plots we used the R v4.0.2 statistical environment while the G + C skew was calculated using iREP v1.10 52 . Cas protein structures were determined using AlphaFold2 105 . Bayesian analysis Divergence time was estimated using Bayesian analyses. A multiple protein sequence alignment was constructed using GTDB-Tk v.1.7.0 90 and was based on the 120 GTDB core bacterial marker genes. The alignment included a set of 32 reference outgroups previously used to calibrate the crown bacteria 46 , 54 . BEAUti v.1.10.4 106 was used to specify parameters for Markov chain Monte Carlo (MCMC) tree analyses in BEAST v.1.10.4 107 . The multiple protein sequence alignment was imported and a Gamma Site Model with four categories was selected. The LG amino acid substitution model was used and a Relaxed Clock model with a Log Normal distribution. A Coalescent Constant Population tree prior was chosen and bacterial divergence (crown) was calibrated using a prior on the root of 3,453 million years ago (Ma) and a standard deviation of 60 Ma 46 , 54 , 108 with a Log Normal distribution specified. MCMC parameters were set at 10,000,000–40,000,000 Chain Lengths with a sampling frequency of 1,000. Tracer v.1.7.2 109 was used to assess convergence with burn in percentages between 10 and 80 to obtain the optimal effective sample size. A Maximum clade credibility tree and Mean heights were selected to produce a summarized target MCMC tree with TreeAnnotator v.1.10.4 107 . iTOL was used for final tree visualization and analysis 95 . Declarations Availability of data and materials The quality-filtered, unassembled metagenomic sequences are available on the MG-RAST server under the accession numbers 4667018.3 to 4667036. All contigs longer than 200 bp from the assembled metagenomes are deposited on the NCBI under the BioProject PRJNA376086. Code for statistical analyses is available at https://github.com/SAmicrobiomes/. Acknowledgements We wish to thank the Centre for High Performance Computing and the University of Pretoria’s Centre for Bioinformatics and Computational Biology for server access. TRN was supported by the Office of Science Early Career Research Program Office of Biological and Environmental Research, Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 to Lawrence Berkeley National Laboratory. Funding We gratefully acknowledge the National Research Foundation (NRF; Grant ID 118981), the South African National Antarctic Programme (SANAP 110717), and the University of Pretoria for funding. We acknowledge Antarctica NZ for logistics and support and thank Professor Ian Hogg (University of Waikato, NZ) as the leader of Event K024. Contributions T.P.M designed the research with input from M.W.V.G, R.P., and O.K.I.B. M.W.V.G., R.P., O.K.I.B., S.V. analyzed the data. D.A.C, I.D.H., D.W.H., T.A., G.H., S.W., T.R.N., W.K., D.D., Y.V.d.P., M.D.B. and T.P.M. coordinated field and laboratory operations. The manuscript was written by M.W.V.G. and T.P.M. with contributions from all co-authors. 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Makhalanyane","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApElEQVRIiWNgGAWjYHACxgekqedhYGA2IFkLmwRpWuylu9Mqf7bdYeBvP8D84QdRtsic3Xabt+0Zg8SZBAbDHqK0SORuu83YdpiB4QYDQwIPsVoKfwK1yAO1HPxDrBYGXqAWgxsMjM3E2XLn7GZpnnOHeQzPJDYzyxCjhX1278aPP8oOy8kdP3z44xtitDBAIwXoJMYGojTAtYyCUTAKRsEowA0AmPouj9t+YcUAAAAASUVORK5CYII=","orcid":"","institution":"Stellenbosch University","correspondingAuthor":true,"prefix":"","firstName":"Thulani","middleName":"P.","lastName":"Makhalanyane","suffix":""}],"badges":[],"createdAt":"2024-05-17 13:47:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4437132/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4437132/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57472849,"identity":"756b7b57-4067-4303-aa0d-f16f975dd05f","added_by":"auto","created_at":"2024-05-31 07:14:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":688329,"visible":true,"origin":"","legend":"\u003cp\u003eFigure legend not available with this version.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4437132/v1/1c49265295bb1feecf73c6a7.jpg"},{"id":57472852,"identity":"6e3c1c8a-ee92-4d7e-9249-fbb5fd523e2c","added_by":"auto","created_at":"2024-05-31 07:14:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":280610,"visible":true,"origin":"","legend":"\u003cp\u003eFigure legend not available with this version.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4437132/v1/493e061d0eebbfe884d1e4b2.jpg"},{"id":57472850,"identity":"89ca82f3-6aed-49ba-84c9-d9230a83a4e9","added_by":"auto","created_at":"2024-05-31 07:14:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":590361,"visible":true,"origin":"","legend":"\u003cp\u003eFigure legend not available with this version.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4437132/v1/238fb08184a7a70e5017d78e.jpg"},{"id":57473333,"identity":"aa718c05-06bd-4297-978d-e4e1f0d2dc40","added_by":"auto","created_at":"2024-05-31 07:22:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":226602,"visible":true,"origin":"","legend":"\u003cp\u003eFigure legend not available with this version.\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4437132/v1/4580a1bf6b6ae02f57d20f17.jpg"},{"id":57473334,"identity":"3737ea79-9934-47b9-8c1f-ccaeaa024fc7","added_by":"auto","created_at":"2024-05-31 07:22:55","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":363436,"visible":true,"origin":"","legend":"\u003cp\u003eFigure legend not available with this version.\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4437132/v1/ebf18c03dd8a5652e47ab5ea.jpg"},{"id":59246452,"identity":"45ed2f8a-efcb-441a-ac12-4d8a926d5ebc","added_by":"auto","created_at":"2024-06-28 06:39:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2836232,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4437132/v1/98f61f07-e9ee-400c-8803-307752acaa12.pdf"},{"id":57472855,"identity":"e06e79c6-d182-4a06-a6b3-04fd4329c4eb","added_by":"auto","created_at":"2024-05-31 07:14:55","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":4618553,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialsfor.docx","url":"https://assets-eu.researchsquare.com/files/rs-4437132/v1/c542e9ba3a08593d66c05934.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Novel innate immune systems in pristine Antarctic soils","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUnderstanding the ecological role played by viruses in altering ecosystem processes through their influence on both the phylogenetic and functional diversity of their hosts remains a major ecological endeavour \u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In soils, viruses affect the diversity and abundance of microorganisms \u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e thereby influencing processes such as nutrient cycling and carbon sequestration \u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Their role as mediators of ecosystem services is pronounced in the poly-extreme environments of the McMurdo Dry Valleys of Eastern Antarctica where prokaryotes govern nutrient cycling. While Antarctic soils harbour diverse microbes and viruses \u003csup\u003e\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, host-virus interactions remain relatively unexplored. This is despite the fact that viral infections may pose direct threats to microorganisms by influencing nutrient/biomass turnover, and on ecosystem functioning \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. However, few studies have assessed these relationships, especially in poly-extreme environments, such as Antarctica, where microbial communities disproportionately influence ecosystem functions. Likewise, understanding the range of defence mechanisms used by microbes, to avoid viral infections, is crucial \u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA notable prokaryotic defence mechanism, the CRISPR-Cas (clustered regularly interspaced short palindromic repeats - CRISPR associated) system \u003csup\u003e\u003cspan additionalcitationids=\"CR24 CR25 CR26\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, allows microorganisms to specifically target and degrade viral DNA or RNA \u003csup\u003e\u003cspan additionalcitationids=\"CR29 CR30 CR31 CR32\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. CRISPR-Cas systems are widespread accessory elements across bacterial and archaeal plasmids \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, and have been the subject of extensive studies in the last decade owing to their efficacy in genome editing \u003csup\u003e\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Consequently, the repertoire of known CRISPR-Cas systems has expanded significantly in terms of both quantity and diversity \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan additionalcitationids=\"CR40 CR41\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. These systems recognize the foreign DNA of an invading phage and cleave sequences from its genome, which become integrated as spacers within the host\u0026rsquo;s CRISPR array \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. The CRISPR-Cas system thus provides signatures of previous infection events by retaining the \u0026lsquo;genomic scars\u0026rsquo; of historical infections \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. We hypothesise that this prokaryotic immune system may be more pronounced in poly-extreme ecosystems, where evolution is remarkably constrained and under strong selective pressures due to several stressors. Because CRISPR-Cas systems represent an ancient adaptive immune strategy in prokaryotes \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e elucidating this antiviral defence pathway may reveal the immunological memories within bacterial genomes from pristine Antarctic soils. However, the extent to which these mechanisms may influence the diversity and function of Antarctic terrestrial communities remains poorly understood.\u003c/p\u003e \u003cp\u003eThe Mackay Glacier region in Antarctica is one of the most remote and challenging poly-extreme environments on Earth where sub-zero temperatures, very low nutrient status and an absence of precipitation render soils virtually inhospitable \u003csup\u003e\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. While the diversity and evolution of defence mechanisms in this ecosystem have not been studied previously, a recent study on rock-associated Antarctic communities in the Miers Dry Valley has shown that poly-extreme environments harbour diverse innate immune systems \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. The results of this study suggest the presence of several innate immune systems including BREX (BacteRiophage EXclusion) and DISARM (Defence Island System Associated with Restriction-Modification), which were the predominant modes of antiphage immunity employed by bacteria in Antarctic desert hypoliths \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. However, hypoliths represent a small fraction of the Dry Valley terrain and studies in expose surface soils are lacking. Here we aim to provide insights on adaptive immune systems through (i) characterizing the CRISPR-Cas systems within bacterial genomes, and (ii) by investigating their role in viral defence mechanisms. By studying the presence of CRISPR-Cas systems in this unique ecosystem, we hope to gain a better understanding of the mechanisms by which microorganisms protect themselves from viral infections in extreme environments. We hypothesize that the CRISPR-Cas systems, present in these Antarctic soils, play crucial roles in protecting microorganisms from viral infections and maintaining the stability of the ecosystem. Using genome-resolved metagenomic analysis, we provide the first insights of bacterial adaptive immunity, and bacterial-viral associations in Antarctic soils.\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStrong evidence of predation-prey associations in Antarctic soils\u003c/h2\u003e \u003cp\u003eOur study expands current insights regarding the genetic mechanisms explaining prey-predator co-evolutionary associations between bacteria and their viruses in poly-extreme Antarctic conditions (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). Following metagenome sequencing, assembly, and genome binning (\u003cb\u003eSupplementary Tables S2 and S3\u003c/b\u003e), we recovered 18 medium- to high-quality metagenome-assembled genomes (MAGs) (\u003cb\u003eFig.\u0026nbsp;1a\u003c/b\u003e). These MAGs include three \u003cem\u003eAcidobacteriota\u003c/em\u003e, one \u003cem\u003eCyanobacteria\u003c/em\u003e, twelve \u003cem\u003eBacteroidota\u003c/em\u003e and two \u003cem\u003eVerrucomicrobiota\u003c/em\u003e, representing both dominant, and rare bacterial phyla in these soils (\u003cb\u003eSupplementary Note 1\u003c/b\u003e and \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e). The genome sizes of these bacteria ranged from 2.7\u0026ndash;5.9 Mb, when accounting for completeness. These genomes had moderately low G\u0026thinsp;+\u0026thinsp;C contents (mean\u0026thinsp;=\u0026thinsp;42.8%), which is surprising given the expectation that extreme environments may select for organisms with high G\u0026thinsp;+\u0026thinsp;C content \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. We estimated the genome replication rates for each MAG \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e and found that the highest genome replication rates were associated with \u003cem\u003eAcidobacteriota\u003c/em\u003e (mean\u0026thinsp;=\u0026thinsp;3.06) and the \u003cem\u003eVerrucomicrobiota\u003c/em\u003e (mean\u0026thinsp;=\u0026thinsp;2.90). These differed compared with \u003cem\u003eBacteroidota\u003c/em\u003e (mean\u0026thinsp;=\u0026thinsp;2.48) and \u003cem\u003eCyanobacteria\u003c/em\u003e (2.04). Estimating the minimal doubling time with codon usage bias \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e suggests that the rates across all MAGs may be low with only members of the \u003cem\u003eFlavisolibacter\u003c/em\u003e predicted to double in under five hours. Overall, these patterns suggest extremely slow growth rates which is expected given the poly-extreme conditions in this region (see additional information regarding the climatic conditions and these taxa in \u003cb\u003eSupplementary Note 1\u003c/b\u003e). Given the comparatively low division rates, it is reasonable to predict that the slow evolutionary processes acting on microbial communities in this environment are likely to delay selection. There is some support for this assertion including the divergent ecological patterns of Antarctic microbiota and the substantial differences compared with those from outside the continent.\u003c/p\u003e \u003cp\u003eOur study provides strong evidence that Antarctic bacterial communities may have ancient origins. Bayesian evolutionary analysis, used to produce time-measured phylogenies, suggests that the genomes retrieved from our studies ranged between 500 and 1,200 Mya (\u003cb\u003eFig.\u0026nbsp;1b\u003c/b\u003e). These findings are consistent with previous estimates of cryptoendolith divergence in Antarctica \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. The results also support approximations for other genomes retrieved from Antarctic soils \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Altogether, the unique monophyletic clades of our Antarctic MAGs were distinct suggesting that these bacteria diverged from other known taxa during the Precambrian (541 Mya). Taxonomy analysis indicated that 12 of our MAGs potentially represent novel species. Considering this evidence, we predict that bacteriophages associated with these bacteria may have also been co-separated from other microorganisms for a similar length of time since host-virus specificity is mostly strain specific. We hypothesized that this distinct, and specific, co-evolution may be corroborated by the recovery of uncharacterized and potentially novel adaptive immune signatures in Antarctic host genomes.\u003c/p\u003e \u003cp\u003eTo test this hypothesis, we characterized prophages in our Antarctic genomes. We found one prophage, in the \u003cem\u003eVerrucomicrobiota\u003c/em\u003e MS5-1_8 genome, as well as two prophages, in a \u003cem\u003eBacteroidota\u003c/em\u003e genome (TG5-1_3). Coincidently, these two taxa were predicted to be the slowest growing. This finding provides direct evidence linking prophages to both \u003cem\u003eVerrucomicrobiota\u003c/em\u003e and \u003cem\u003eBacteroidota\u003c/em\u003e in this region (\u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e). The prophage found in the \u003cem\u003eVerrucomicrobiota\u003c/em\u003e genome was 4,483 bp in length and was similar to known \u003cem\u003eMicroviridae\u003c/em\u003e phages (BLASTn: 31% query coverage and 69.74% identity). The prophages identified in the \u003cem\u003eBacteroidota\u003c/em\u003e genome were 5,477 bp and 7,214 bp in length, although these had low sequence similarity to known phages (more information on the uncultivated viral genomes identified here in \u003cb\u003eSupplementary Note 2\u003c/b\u003e and \u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e). While several studies have investigated the importance of prophages on the evolution of bacterial pathogens \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, knowledge regarding their role in soil taxa remains limited and the lack of a comprehensive database of soil-dwelling viruses contributes to this knowledge gap \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Previous studies suggest that lysogenic conversion may confer new traits to bacteria \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, which typically have novel metabolic functions through auxiliary metabolic genes (AMGs) \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. For example, phages associated with \u003cem\u003eVerrucomicrobiota\u003c/em\u003e encoded genes have previously been implicated in nitrogen fixation \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. In oligotrophic soils, the benefits to the hosts may be vital for ecosystem services, facilitating access to alternative energy sources or stress avoidance mechanisms. Elucidating innate mechanisms may provide insights regarding host-virus interactions and reveal the extent of functional diversity in these soils.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCRISPR systems provide evidence of bacterial virus attacks in Antarctica.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe detection of prophages in MAGs and AMGs on phage contigs (\u003cb\u003eSupplementary Note 2\u003c/b\u003e) supports the prediction of unique host-virus histories, as most viral genomes are completely unrelated to known viruses (\u003cb\u003eFig.\u0026nbsp;2\u003c/b\u003e) \u003csup\u003e\u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. The results from this study suggest that the host adaptive immune system, associated with divergent microbiota in Antarctica soils, may be more prominent than initially envisaged. However, apart from previous studies on rock associated microbiota, there is a severe knowledge deficit regarding host adaptive immune systems associated with poly-extreme environments.\u003c/p\u003e \u003cp\u003eTo further explore host-virus histories associated with our MAGs, we searched for related diverse defence strategies against phage predation. As part of determining the adaptive immune system, we found putative CRISPR-Cas systems in 16 of the 18 MAGs (\u003cem\u003eca\u003c/em\u003e. 89%). In terms of CRISPR arrays, the identified conserved repeats ranged from 23 to 30 bp across four of the retrieved genomes. The repeats were flanked by unique spacers, that were an average of 36 bp in length (range 34\u0026ndash;38 bp). The largest set of CRISPR cassettes was found in the MS7-5_6 genome (\u003cem\u003eAcidobacteriota\u003c/em\u003e), with 51 CRISPR spacers between housed 6 unique CRISPR repeats. These values are within the optimal number of spacers, previously suggested to range between 10\u0026ndash;100 within bacterial genomes \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. The CRISPR loci within bacterial genomes retain the memory of past viral infections \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Yet, the length of these loci appears to be directly related to the capacity to respond to an infection \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. In other words, there appears to be a trade-off between maintaining a vast genetic memory of attacks (harbouring more spacers) and the functionality of the CRISPR mechanism \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. The remaining genomes only had between three and 16 spacers, which is more similar to human gut microbes (average of 12 spacers) \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e than the average cassette size of between 20 and 40 spacers \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. We speculate that the lower spacer count may be due limited encounters with a small set of phages. In this scenario, the spatial constraints of the soil microhabitat limit the number of potential interactions between phages and putative hosts. This suggests that phage diversity may be low in this region of Antarctica. Not only are cells immobilized by adsorption to soil particles of the Antarctic desert pavement, but rarely, if ever, subject to precipitation events which may allow for the mobilization of cells, thus reducing the spectrum of infection events considerably.\u003c/p\u003e \u003cp\u003eIn addition to the CRISPR-Cas cassettes, the 16 MAGs had relatively low abundances of \u003cem\u003ecas\u003c/em\u003e genes, with between 6 and 42 loci per MAG. These \u003cem\u003ecas\u003c/em\u003e genes constituted 122 unclassified sequences (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;221 total \u003cem\u003ecas\u003c/em\u003e sequences), followed by several classified sequences including 48 type III, 31 type I, 20 type IV and 2 type V Cas systems. These Cas types are similar to those previously reported in Antarctic surface snow in which CRISPR-cas types I, II and III were most common \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. The MS7-5_6 MAG (\u003cem\u003eAcidobacteriota\u003c/em\u003e) had a contig with 10 genes associated with a hybrid CRISPR-Cas Class I system. This contig also had a GCN5-related N-acetyltransferase (GNAT) toxin domain \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e (see \u003cb\u003eFig.\u0026nbsp;3a\u003c/b\u003e), which functions by acetylating charged tRNA molecules to prevent translation. Previous studies suggest that these GCN5-related N-acetyltransferase toxin domains may represent novel substrates for several enzymes linked to antibiotic modification \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe further investigated unbinned metagenomic contigs, which possessed eight or more co-localized \u003cem\u003ecas\u003c/em\u003e genes, to determine if they represented novel CRISPR-Cas variants. Taxonomically, the CRISPR-Cas systems recovered from these contigs were affiliated members of the \u003cem\u003eAcidobacteriota\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6 contigs), Unclassified Bacteria (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2), \u003cem\u003eChloroflexota\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1) and \u003cem\u003eBacteroidota\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1). However, the taxonomic relationships of these taxa suggest potentially shared histories with a variety of bacterial phyla (\u003cb\u003eFig.\u0026nbsp;3b\u003c/b\u003e). The architecture of effector complexes, within the CRISPR-Cas systems, suggests that most of these were class 1 with type I or type III systems. Genes for Cas1 and Cas2 proteins were ubiquitously distributed across all contigs (\u003cb\u003eFig.\u0026nbsp;3a\u003c/b\u003e). These genes were always structured as Cas1-Cas2 complexes. In four examples, the Cas1-Cas2 complex were flanked upstream by \u003cem\u003ecas\u003c/em\u003e4, which directly interacts with the Cas1-Cas2 complex, to process pre-spacers prior to integration as the Cas4-Cas1-Cas2 complex \u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. However, in two instances, we our analyses revealed that \u003cem\u003ecas\u003c/em\u003e4 was downstream of the Cas1-Cas2 complex, which is an unconventional arrangement of these genes based on data from previous studies \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. In all 10 cases, the effector genes were located upstream of the Cas1-Cas2 operon.\u003c/p\u003e \u003cp\u003eThe remaining four CRISPR-Cas systems may represent novel variants, based on arrangements of their effector modules (\u003cb\u003eFig.\u0026nbsp;3b\u003c/b\u003e) \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. These results imply ongoing horizontal gene transfer and recombination events of diverse CRISPR-Cas loci, led by continuous interactions with the same viruses. Notably, these uncategorized CRISPR-Cas system types were affiliated with members of the \u003cem\u003eAcidobacteriota\u003c/em\u003e. They include FI-1_NODE_368 (cas2-cas1-cas4-cas3), which lacks an effector complex and seems to be closely related to Type IU. Contig FI-1_NODE_81 (cas4-cas2-cas1-cas6-cas3-cas5-cas7-cas8b1-cas7-cas8b1-cas7), which is a potential Type I-B variant based on multiple copies of cas7 and cas8 at the terminus of the array. Contig MtG-4_NODE_208 (cas2-cas1-cas1-RT-csm3gr7-csm3gr7-csm3gr7-cas10), which is potentially a Type IIIU array with three copies of csm3gr7. Finally, contig PT-2_NODE_41 (cas6-cas2-cas1-csm3gr7-csm3gr7-csm3gr7-cas10) may be a Type IIIA variant or Type IIIU variant since it lacks csm2, csm4 and csm5 genes.\u003c/p\u003e \u003cp\u003eAll 10 predicted CRISPR-Cas systems were associated with CRISPR arrays. These systems were composed of spacers that ranged from 2 to 122 bp in length, with an average length of 35 bp. The \u003cem\u003ecas\u003c/em\u003e2 sequences showed some divergence from those previously reported, and these results contrasted with our expectations. Instead, the \u003cem\u003ecas\u003c/em\u003e2 sequences clustered among unrelated phyla, in some cases grouping within the kingdom \u003cem\u003eArchaea\u003c/em\u003e (\u003cb\u003eFig.\u0026nbsp;3a\u003c/b\u003e). Nevertheless, our results show several cases where \u003cem\u003eArchaea\u003c/em\u003e cluster with \u003cem\u003eFirmicutes\u003c/em\u003e and other unrelated phyla. These results are not surprising given the fact that these genes are known to be horizontally acquired. This may indicate that the \u003cem\u003ecas\u003c/em\u003e2 gene is not always taxonomically conserved. Instead, the result suggests mobilization via inter-phylum horizontal gene transfer (HGT) events or evidence of phylum-specific cas subtypes. A recent study showed that CRISPR-Cas systems may contribute to the propagation of transposable elements by facilitating transposition into specific sites \u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. Similarly, our results support previous reports since we found transposase elements on almost half of the 10 CRISPR-Cas-containing contigs analysed.\u003c/p\u003e \u003cp\u003eBased on these data, we speculate that these Antarctic CRISPR-Cas systems were horizontally transferred as ancient mobilization events. This suggestion is supported by an evaluation of the G\u0026thinsp;+\u0026thinsp;C skew, among the 10 contigs containing \u003cem\u003ecas\u003c/em\u003e genes, as a proxy for the timing of insertion events \u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Here, we inferred HGT through the detection of strong deviations in G\u0026thinsp;+\u0026thinsp;C content for a genomic fragment compared to the remaining genomic signature. Specifically, on NODE_81 from the FI-1 metagenome, the G\u0026thinsp;+\u0026thinsp;C content over the Cas proteins varied minimally across each gene yet is markedly different from the G\u0026thinsp;+\u0026thinsp;C content of the CRISPR array upstream of the \u003cem\u003ecas\u003c/em\u003e genes (\u003cb\u003eFig.\u0026nbsp;4\u003c/b\u003e). By contrast, the contigs containing integrated prophages within the microbial genomes showed very high variations in G\u0026thinsp;+\u0026thinsp;C content (i.e. G\u0026thinsp;+\u0026thinsp;C skew) across the contig which possibly indicates a foreign origin \u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e (\u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e). Our Bayesian diversity estimates also indicated ancient divergence events of our MAGs from known bacteria. It is thus likely that the phages of these bacteria have similarly ancient Precambrian histories, which offers a possible explanation for their unique gene compositions.\u003c/p\u003e \u003cp\u003eFollowing this, we explored our data for the diversity of type V CRISPR-Cas systems. From the data, we identified a total of 216 contigs longer than 1 kb from 16 of the 18 metagenomes with predicted cas12 effectors proteins. Of these, 112 contigs with sizes ranging from 1,007 to 48,306 bp that possessed non-partial cas12 proteins were retained for downstream analyses. The lengths of effector proteins in these contigs varied from 89 to 630 amino acids, and this contrasted with previous reports that have indicated the average lengths for type V associated effector proteins to be ~\u0026thinsp;400 amino acids and longer \u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e,\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. As effector proteins associated with type V CRISPR-Cas systems are mainly distinguished by the possession of a RuvC nuclease domain, we also found these to be characteristic of our 111 effectors, including the smallest (89 aa) putative cas12 protein. Only one of these lacked a RuvC domain but possessed a helix-turn-helix domain. Further inspection of contigs possessing these indicated that only 13 of our effectors were proximal to CRISPR arrays, and unlike typical CRISPR-Cas systems none of the 112 were co-localized with the cas1-cas2 complex. Phylogenetic analysis of these indicated that just nine of our effectors (Ant Cas U5-8) clustered with previously characterized cas12 effectors. We then observed that 18 of our other effectors indicated a close phylogenetic relationship with transposon encoded TnpB proteins, further suggesting that type V effectors may have evolved from TnpB associated nucleases \u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. We observed a further 83 additional effectors from our data that formed a distinct clade (indicated as Ant Cas U4), potentially representing a novel subgroup of cas12-like effectors (\u003cb\u003eFig.\u0026nbsp;5\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eAltogether, we speculate that the unique diversity of the genes found in these Antarctic soils may be the result of a \u0026lsquo;slowed down\u0026rsquo; evolution of genes selected during warmer periods of time. The Antarctic continent was a temperate rainforest during the mid-Cretaceous period\u0026thinsp;~\u0026thinsp;140 Mya \u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e and we speculate that the subsequent cooling of the continent may have constrained evolutionary forces from acting at their previous pace. Combined, these lines of evidence point to an ancient, acquired immunity of bacteria in Antarctica while contemporary infection events continue to occur through lysogenic phage infections.\u003c/p\u003e \u003cp\u003eWe used metagenomes from remote and pristine Antarctic soils to assess their viral and bacterial diversity. Multiple lines of evidence suggest extensive phage-host interactions, potentially novel viral diversity, and CRISPR-Cas variants. The phage signatures (vOTUs) were linked to the infection of dominant soil bacterial lineages in these surface soils, including members of the \u003cem\u003eBacteroidota\u003c/em\u003e and \u003cem\u003eAcidobacteriota\u003c/em\u003e, while prophages embedded within \u003cem\u003eVerrucomicrobiota\u003c/em\u003e and \u003cem\u003eBacteroidota\u003c/em\u003e MAGs offer further insight into contemporary infections. CRISPR-Cas systems, part of the bacterial adaptive immune system, were common to 4 of 18 MAGs analyzed, indicating acquired immunity in both \u003cem\u003eBacteroidota\u003c/em\u003e and \u003cem\u003eAcidobacteriota\u003c/em\u003e. Additional Class I CRISPR-Cas arrays (types I-B, I-C and I-E) were detected in the assembled metagenomes, where four CRISPR-Cas arrays did not perfectly match existing architectures and thus may be novel variants. Our analysis of G\u0026thinsp;+\u0026thinsp;C content and GC skew across CRISPR-Cas contigs showed low variations in G\u0026thinsp;+\u0026thinsp;C skew in CRISRP-Cas arrays, but more variation in prophages, suggesting that these acquired immunity markers are ancient whereas proviral elements appear to be the result of recent foreign DNA transfer as further evidenced by the description of novel, Antarctic exclusive cas12-like effectors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSample collection and preparation\u003c/h2\u003e \u003cp\u003eSurface soils were collected from 18 remote sites in Eastern Antarctica, between the Mackay Glacier and the Drygalski Ice Tongue \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Methods of DNA isolations, soil physicochemical analyses, soil isotopic measurements and soil respiration experiments have been reported previously \u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. After eDNA extractions, metagenomes were sequenced on an Illumina HiSeq 2000 instrument producing 250 bp paired-end libraries. Sequencing depth was determined using Nonpareil v3.301 \u003csup\u003e81\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMetagenome analysis and assembly\u003c/h2\u003e \u003cp\u003eAll metagenomes were filtered to cull low-quality reads and those containing ambiguous bases (internal \u003cem\u003eN\u003c/em\u003e\u0026rsquo;s) using Prinseq-lite v0.20.4 \u003csup\u003e82\u003c/sup\u003e. All Illumina PhiX sequences were identified and removed using BBDuk \u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e to eliminate the potential of contaminating viral signals in our analysis \u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. We determined the microbial taxonomy of each sample using SingleM, which relies on analyses of universal single-copy ribosomal subunit proteins (rpl), rather than the 16S rRNA gene to infer taxonomy (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/wwood/singlem\u003c/span\u003e\u003cspan address=\"https://github.com/wwood/singlem\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Each filtered metagenome was individually assembled using metaSPAdes v3.12 \u003csup\u003e85\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eReconstruction of microbial genomes\u003c/h2\u003e \u003cp\u003eFrom the assemblies we reconstructed microbial genome bins using MetaBAT 2 v2.12.1 \u003csup\u003e86\u003c/sup\u003e. All contigs\u0026thinsp;\u0026gt;\u0026thinsp;1,500 bp in length were retained and depth coverage information was obtained using BBMap \u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e by mapping corresponding metagenomic reads back to those contigs. The bins were assessed for completeness using CheckM v0.7.0 \u003csup\u003e88\u003c/sup\u003e and metagenome-assembled genomes (MAGs) that were \u0026gt;\u0026thinsp;50% complete and were \u0026lt;\u0026thinsp;10% contaminated were retained for further analyses. Next, we queried indicators of genome quality, such as the presence of 5S, 16S and 23S ribosomal subunit genes and the presence of at least 18 unique tRNAs according to MIMAG standards \u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e. Taxonomy was then assigned using the Genome Taxonomy Database Toolkit (GTDB-Tk) v1.1.0 release 89 \u003csup\u003e90\u003c/sup\u003e on KBase \u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e. CARD RGI was used to determine the prevalence of antibiotic resistance genes (ARGs) as hallmarks of resistance to bacterial antibiotic production \u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e. The 18 retained MAGs were inspected for CRISPR-Cas repeats using MinCED \u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e and for prophages (integrated viral genomes) using VirSorter v1.0.6 \u003csup\u003e94\u003c/sup\u003e. A maximum likelihood phylogenomic tree, based on 49 core bacterial genes from our 18 MAGs and 100 reference genomes (RefSeq database), was built using FastTree2 v2.1.10. Phylogenomic trees were visualized in iTOL v6.34 \u003csup\u003e95\u003c/sup\u003e. We used iRep to estimate bacterial replication rates \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e and gRodon to calculate growth rate \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eViral taxonomic analysis\u003c/h2\u003e \u003cp\u003eWe also explored each metagenomic assembly for bacteriophages using VirSorter v1.0.6 \u003csup\u003e94\u003c/sup\u003e. Contigs were manually inspected for viral \u0026ldquo;hallmark\u0026rdquo; genes from categories 1 and 2 (complete viral contigs), and 4 and 5 (prophages). The quality of the predicted viral contigs was assessed using CheckV v1.0 \u003csup\u003e96\u003c/sup\u003e. Contigs\u0026thinsp;\u0026gt;\u0026thinsp;10 kb that were thought to be viral were then clustered using vConTACT2 v0.9.19 \u003csup\u003e97\u003c/sup\u003e to establish a network of protein clusters among known phages from the Virome database. The edges of the network are significant gene-sharing similarities between contigs, which are represented as nodes. These uncultivated viral genomes (UViGs) were also inspected for possible auxiliary metabolic genes (AMGs) that could have been acquired from their host. We built phylogenetic trees using MAFFT v7.294b \u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e of the AMGs identified in UViGs and their homologs in host MAGs to determine the possible origin of the gene. Finally we used the dbCAN2 web server \u003csup\u003e\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e to identify glycosyl hydrolases (GH) and glycoside transferases (GT) in the MAGs. UViGs were clustered into viral OTUs (vOTUs) at 95% average nucleotide identity (ANI) and 85% alignment fraction (AF).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCRISPR spacer analysis and protein structure analysis using AlphaFold2\u003c/h2\u003e \u003cp\u003eMetagenomes and MAGs were explored for the presence of adaptive immunity systems such as CRISPR-cas gene types using hmmsearch with (E-value 1e\u003csup\u003e-05\u003c/sup\u003e) against Cas gene profiles obtained from a study by Makarova, et al. \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. These were further assessed for the presence of innate immune response genes using RPS-BLAST (E-value 1e\u003csup\u003e-05\u003c/sup\u003e) against conserved domain databases (CDD) of clusters of orthologous groups (COGs) and protein families (Pfams) \u003csup\u003e\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e. Results from these searches were manually filtered for the identification of CRISPR-Cas systems, toxin-antitoxins (TA), restriction-modification (RM), bacteriophage exclusion (BREX), abortive infection (Abi), defense island system associated with restriction-modification (DISARM), and other recently identified systems using a refined list of COG and Pfam identifiers reported to be associated with these defense mechanisms \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCas reference sequences were extracted from UniProtKB/Swiss-Prot (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.fasta.gz\u003c/span\u003e\u003cspan address=\"https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.fasta.gz\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; accessed 2021/05/25); a high-quality, manually annotated and non-redundant protein sequence database \u003csup\u003e\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e\u003c/sup\u003e. For each Cas protein family the reference amino acid sequences and our amino acid sequences were included in multiple sequence alignment using Mafft v7.294b with the \u0026ldquo;linsi\u0026rdquo; parameter specified \u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e. Trees were constructed based on the multiple sequence alignments using IQ-TREE v2.1.2 \u003csup\u003e103\u003c/sup\u003e with the \u0026ldquo;MFP\u0026rdquo; parameter invoked which uses ModelFinder \u003csup\u003e\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e to determine the most appropriate model. Tree refinement was performed in iTOL v6.3 \u003csup\u003e95\u003c/sup\u003e. For the Cas G\u0026thinsp;+\u0026thinsp;C plots we used the R v4.0.2 statistical environment while the G\u0026thinsp;+\u0026thinsp;C skew was calculated using iREP v1.10 \u003csup\u003e52\u003c/sup\u003e. Cas protein structures were determined using AlphaFold2 \u003csup\u003e105\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBayesian analysis\u003c/h2\u003e \u003cp\u003eDivergence time was estimated using Bayesian analyses. A multiple protein sequence alignment was constructed using GTDB-Tk v.1.7.0 \u003csup\u003e90\u003c/sup\u003e and was based on the 120 GTDB core bacterial marker genes. The alignment included a set of 32 reference outgroups previously used to calibrate the crown bacteria \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. BEAUti v.1.10.4 \u003csup\u003e106\u003c/sup\u003e was used to specify parameters for Markov chain Monte Carlo (MCMC) tree analyses in BEAST v.1.10.4 \u003csup\u003e107\u003c/sup\u003e. The multiple protein sequence alignment was imported and a Gamma Site Model with four categories was selected. The LG amino acid substitution model was used and a Relaxed Clock model with a Log Normal distribution. A Coalescent Constant Population tree prior was chosen and bacterial divergence (crown) was calibrated using a prior on the root of 3,453\u0026nbsp;million years ago (Ma) and a standard deviation of 60 Ma \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e\u003c/sup\u003e with a Log Normal distribution specified. MCMC parameters were set at 10,000,000\u0026ndash;40,000,000 Chain Lengths with a sampling frequency of 1,000. Tracer v.1.7.2 \u003csup\u003e109\u003c/sup\u003e was used to assess convergence with burn in percentages between 10 and 80 to obtain the optimal effective sample size. A Maximum clade credibility tree and Mean heights were selected to produce a summarized target MCMC tree with TreeAnnotator v.1.10.4 \u003csup\u003e107\u003c/sup\u003e. iTOL was used for final tree visualization and analysis \u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe quality-filtered, unassembled metagenomic sequences are available on the MG-RAST server under the accession numbers 4667018.3 to 4667036. All contigs longer than 200 bp from the assembled metagenomes are deposited on the NCBI under the BioProject PRJNA376086. Code for statistical analyses is available at https://github.com/SAmicrobiomes/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe wish to thank the Centre for High Performance Computing and the University of Pretoria\u0026rsquo;s Centre for Bioinformatics and Computational Biology for server access. TRN was supported by the Office of Science Early Career Research Program Office of Biological and Environmental Research, Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 to Lawrence Berkeley National Laboratory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the National Research Foundation (NRF; Grant ID 118981), the South African National Antarctic Programme (SANAP 110717), and the University of Pretoria for funding. We acknowledge Antarctica NZ for logistics and support and thank Professor Ian Hogg (University of Waikato, NZ) as the leader of Event K024.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.P.M designed the research with input from M.W.V.G, R.P., and O.K.I.B. M.W.V.G., R.P., O.K.I.B., S.V. analyzed the data. D.A.C, I.D.H., D.W.H., T.A., G.H., S.W., T.R.N., W.K., D.D., Y.V.d.P., M.D.B. and T.P.M. coordinated field and laboratory operations. The manuscript was written by M.W.V.G. and T.P.M. with contributions from all co-authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to TP Makhalanyane\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRodriguez-Valera, F. \u003cem\u003eet al.\u003c/em\u003e Explaining microbial population genomics through phage predation. 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Posterior summarization in Bayesian phylogenetics using Tracer 1.7. \u003cem\u003eSystematic biology\u003c/em\u003e 67, 901\u0026ndash;904 (2018).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Antarctica, archaea, antiphage, innate immunity, evolutionary drivers","lastPublishedDoi":"10.21203/rs.3.rs-4437132/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4437132/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAntarctic environments are dominated by microorganisms, which are vulnerable to viral infection. Although several studies have investigated the phylogenetic repertoire of bacteria and viruses in these poly-extreme environments, the evolutionary mechanisms governing microbial immunity remain poorly understood.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUsing genome resolved metagenomics, we test the hypothesis that these poly extreme high-latitude microbiomes harbour diverse innate immune systems. Our analysis reveals the prevalence of prophages in bacterial genomes (Bacteroidota and Verrucomicrobiota), suggesting the significance of lysogenic infection strategies in Antarctic soils. Furthermore, we demonstrate the presence of diverse CRISPR-Cas arrays, including Class 1 arrays (Types I-B, I-C, and I-E), alongside systems exhibiting novel gene architecture among their effector cas genes. Notably, a Class 2 system featuring type V variants lacks CRISPR arrays, Cas1 and Cas2 adaptation module genes. Phylogenetic analysis of Cas12 effector proteins hints at divergent evolutionary histories compared to classified type V effectors.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings suggest substantial sequence novelty in Antarctic cas sequences, likely driven by strong selective pressures. These results underscore the role of viral infection as a key evolutionary driver shaping polar microbiomes.\u003c/p\u003e","manuscriptTitle":"Novel innate immune systems in pristine Antarctic soils","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-31 07:14:50","doi":"10.21203/rs.3.rs-4437132/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"62ec23b1-48ab-44c1-a2d6-3819110a467b","owner":[],"postedDate":"May 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-28T06:31:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-31 07:14:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4437132","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4437132","identity":"rs-4437132","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}