Hologenomic insights demonstrate co-evolution between an intestinal Mycoplasma and its salmonid host

Hologenomic insights demonstrate co-evolution between an intestinal Mycoplasma and its salmonid host | 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 Hologenomic insights demonstrate co-evolution between an intestinal Mycoplasma and its salmonid host Jacob Rasmussen, Pia Kiilerich, Rune Waagbø, Erik-Jan Lock, Madsen Lise, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1814912/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 The study of co-evolution between host and microbes has the potential to transform how we understand evolutionary adaptations, yet genuine co-evolutionary relationships are challenging to show. The host's intestinal environment shapes the gut microbiota through the co-existence of bacteria and host. This co-existence influences the fitness of both bacteria and host. How this affects the co-evolution occurring in animals is largely unexplored, mainly due to the complexity of the environment, low host selection, and microbial communities. We present the first metagenomic derived genome of gut microbiota from wild Atlantic salmon (Salmo salar), a new wild model organism with the microbiota of low complexity and known population structures amenable for investigating co-evolution. Our data reveal a strong host selection of a core gut microbiota dominated by a single Mycoplasma species. Interestingly, we found a concordance between the population structure of the Atlantic salmon host and nucleotide variability of the intestinal Mycoplasma populations conforming to expectations from co-evolution between host and microbe. Our results show that the stable microbiota of Atlantic salmon has evolved with its salmonid host populations while potentially providing adaptive traits to the salmon host populations, including defence mechanisms and protein synthesis. We highlight Atlantic salmon as a novel model for studying co-evolution between vertebrate hosts and their microbiota. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Co-evolutionary interactions between animals and their microbiota have become a key topic in evolutionary biology because of the adaptive relevance to understanding basic eco-evolutionary processes 1 . However, vertebrates and their associated microbial communities have not yet been systematically studied. One major drawback for understanding evolutionary host-microbiota relationships in many vertebrates, such as mammals, is that they are confounded by the high complexity of their microbial communities, challenging the detection of specific host-microbe interactions 2 . Dynamics in these complex systems are also often shaped by environmental factors limiting the ability to answer basic questions about broader evolutionary trends between host and microbe 3 . One way to overcome these limitations is to investigate microbiota of low diversity related to a host with a well-known population structure 3 . Teleosts offer an attractive option as the more than 33,000 described species represent nearly half of all vertebrates. Many teleosts have well-characterised population structures due to their commercial, cultural, and recreational importance 4 . Despite representing the largest vertebrate group, very few host-bacteria systems have been investigated in teleosts compared to terrestrial species, such as hibernating brown bears 5 , plant-eating rodents 6 , and production animals such as pigs 7 and ruminants 8,9 . The enigmatic Atlantic salmon ( Salmo salar ) provides a well-studied model system thanks to its significant commercial, cultural, and recreational importance. It has led to a well-described genome, evolution, and population structure throughout its North Atlantic distribution 10,11 . These assets make Atlantic salmon an ideal model for studying host-microbe co-evolution in vertebrates. Adult salmon are piscivorous, expecting the associated microbiota to show physiological adaptations for a strictly carnivorous diet 12 . Recent surveys of gut microbial communities in salmonid species revealed a general dominance of a unique Mycoplasma species suggesting a close relationship with the salmon host 13–15 . Salmonid-related Mycoplasmas are hypothesised to be vertically transmitted between generations since they have not yet been discovered in the environment 16 , are prominent in both wild and farmed salmon 14–17 , and do not follow neutral processes in both wild and farmed individuals 15 . Furthermore, these previous studies have shown that salmonid intestinal microbiotas are less diverse than the microbiota of more often studied warm-blooded animals 16–20 . This consistent trend of intestinal microbial communities characterised by an extremely low diversity makes Atlantic salmon a practical model for studying co-evolutionary relationships between vertebrate hosts and their core bacteria 21,22 . Genome-resolved metagenomics offers a detailed resolution of microbial communities. However, the often-reported low biomass of the intestinal microbiota in salmon has limited the broader application of shotgun metagenomics sequencing in this species 13,14 . Therefore, no previous studies have used detailed genome-resolved metagenomics to uncover the functional and genomic legacy of the gut microbiome in wild populations of Atlantic salmon. Here, we use state-of-the-art genome resolved metagenomics and comparative genomics to investigate the metagenomic dynamics and hologenome of more than 70 adult foraging wild Atlantic salmon. We further compare the intestinal microbiotas from Atlantic salmon with gut bacteria of other sympatric teleost species to elucidate environmental and host-related dynamics. Our study presents the first genome-resolved metagenome from wild Atlantic salmon, extending decades of PCR-based gene surveys, and highlights the value of applying a hologenomics approach 3 to study host-microbiota co-evolution in light of understanding adaptive evolutionary processes. Results Metagenomes were recovered from 75 wild Atlantic salmon individuals from five different regions in northern Norway, including Alta, Andaøya, Bugøynes, Månes/Tosken, and Nordkapp, ranging from more than 700 kilometres across the north Atlantic Ocean. Whole metagenomic sequencing of all individuals resulted in 5,806 million reads. Re-mapping of the host genome revealed that 53.82 % (SD ± 27.72 %) of the reads were the host, which was lower than previous studies 14,23 . Reads were quality controlled, and non-microbial data was removed, resulting in a recovery of 40.74 % (SD ± 27.85 %) of the reads ( Suppl. Table S1, Suppl. Fig. S1 ). The reads were used as input for metagenomic co-assemblies to recover metagenome-assembled genomes (MAGs) and investigate bacterial strain variation per host individual. We identified 2,241,791 non-redundant genes in scaffolds longer than 1,000 nucleotides. Investigation of sequencing depth and gene calls' rarefaction indicated sufficient metagenomic data saturation for a representative metagenomic analysis ( Fig. 1a ). Automatic binning and manual curation of co-assembly resulted in 19 non-redundant MAGs containing 50,153,577 bp. The MAG catalogue represented 99.98 % of the size-normalised bacterial data. Counting 71 bacterial single-copy core genes (SCGs) across the metagenome reveals that 23 bacterial genomes were potentially present in the metagenome, indicating that we recovered the majority of the MAGs (82.6 %) and that the metagenome of wild Atlantic salmon is characterised by an incredibly trivial diversity ( Fig. 1b-c ). The reconstruction of these MAGs complements decades of biomonitoring efforts using the 16S rRNA marker gene by providing genomic information for lineages missing a functional context and allowing us to search for strain variation within a large pool of microbial populations associated with salmonid hosts. Genome resolved metagenomics reveals a low diverse gut microbiota of the Atlantic salmon following strong host selection Diversity analysis of MAGs revealed a gut microbiome of low diversity during the marine foraging life stage. We analysed the taxonomy of all MAGs using phylogenomics with 3207 bacterial reference genomes ( Suppl. Fig. S1 ). Our analysis revealed no significant differences in microbiota composition between sampling locations ( Fig. 2 a-b, Suppl. Fig. S3 ). Mycoplasma was highly dominant (90.41% of the MAG profiled reads), consistent with previous findings 14,16,17 . We found an intermittent high abundance of Photobacterium phosphoreum , a species of Brachyspira (formerly classified as Brevinema andersonii ), and Shewanella MAGs across eight samples ( Fig. 2 a ). Subsequently, we recovered low amounts of Synechococcus, Vibrio, Aliivibrio salmonicida, and Methanocaldococcus across the 75 Atlantic salmon. A large proportion of the data represented Mycoplasma , which was overall dominating and represented over 90.67 % of all host filtered reads. Of the 19 MAGs recovered, five MAGs were within the family of Mycoplasmataceae ( Fig. 2b ). Subsequently, one MAG of Mycoplasma, Candidatus Mycoplasma salmoninae salar (MSS), alone accounted for 83.41% of the host filtered reads. Our analysis revealed remarkable stability of MSS across any noticed environmental factor, clearly suggesting host selection favouring MSS ( Suppl. Fig. S4-S5) . One interesting trend was that the richness of Mycoplasma MAGs followed a longitudinal pattern showing a mixture between Mycoplasma MAGs (NWS_MAG_00006 and NWS_MAG_00013), where NWS_MAG_00013 became more abundant in northern regions, indicating that more Mycoplasma species could originate from northern Norway or Russia. We analysed data from TARA Oceans to investigate the origin of MSS. Still, no Mycoplasma MAGs were found across the Arctic Ocean, suggesting that MSS was not obtained from the ocean in the adult life stages of Atlantic salmon ( Suppl. Fig. S6) . Our results indicate that investigations of salmonid Mycoplasma are more complex than previously anticipated and that 16S rRNA gene investigations at the genus level will often simplify the actual strain-level variation in the microbiota. Reference-based mapping and taxonomy annotation of metagenomics sequence data revealed eukaryotic gut content. Before data analysis, we hypothesised that the gut content might consist of diet or gut microfauna (e.g., tapeworms, nematodes, and Myxozoa). Vertebrate diet content in the gut included Atlantic herring ( Clupea harengus ) ( Fig. 2c ). Subsequently, invertebrate content had Arthropoda, Cnidaria, Nematoda, and Platyhelminthes, where arthropods were thought to originate from the diet like krill. Cnidaria, Nematoda, and Platyhelminthes were considered parasitic microfauna, including tapeworms ( Eubothrium ), anisakid nematodes ( Anisakis simplex ), and salmon-related Myxozoan ( Fig. 2c ). The composition of invertebrates differed between locations, where cnidarian content dominated the gut content in the south and Platyhelminthes was the dominating phylum in the gut content of northern individuals ( Fig. 2c ). Despite the compositional differentiation of microfauna and diet, no effect on the microbiota was detected, confirming our hypothesis of a high selection pressure within the intestinal tract of Atlantic salmon. Meta-pangenomics reveals high host specificity of Mycoplasma and hosts complementing gene clusters Our analysis revealed that the highly abundant Mycoplasma MAG was closely related to previous, recovered salmonid-related Mycoplasma MAGs from salmonids raised in aquaculture ( Fig. 3a ). Our phylogenomic and comparative analysis revealed that NWS_MAG_00006 and Candidatus Mycoplasma salmoninae salar (MSS) were more closely related to each other than the Mycoplasma, Candidatus Mycoplasma salmoninae mykiss (MSM), related to farmed rainbow trout ( Oncorhynchus mykiss ), which underpins the specificity of these salmonid related Mycoplasma MAGs to their salmonid host regardless of a farmed or wild origin ( Fig. 3 a-b ). The four other Mycoplasma-related MAGs were all found to have low abundance across all individuals and clustered with various salmonid-related clades like NWS_MAG_00013 as an outgroup to the clade of Atlantic salmon, rainbow trout, and NWS_MAG_00006. We found two new MAGs related to Candidatus Mycoplasma lavaretus (ML) and Mycoplasma mobile 163K. Further, we found one MAG (NWS_MAG_00007) associated with Ureaplasma . Analysis of the gene clusters among all Mycoplasma MAGs further confirmed the differentiation observed between MAGs from rainbow trout and Atlantic salmon compared to the other MAGs ( Fig. 3a-b ). These findings reveal a clade of abundant Mycoplasma highly prevalent in salmonids and living in coexistence with other low abundant Mycoplasma species; this further supports a high host selection pressure, especially when previous investigations could not detect Mycoplasma in the surrounding water 16 . Interestingly, we found that all highly abundant MAGs in the salmonid clade were not only phylogenetically separated from the other Mycoplasma MAG but also by metabolism ( Fig. 3c ). We applied KOfam metabolism estimates across the fish-related Mycoplasma to decipher the potential functional differences between high and low abundant Mycoplasma , confirming our previous phylogenetic differentiation. Interestingly, we found KOfams in the “high abundant” clade, which could be potentially beneficial for its host, including genes encoding enzymes involved in thiamine (vitamin B1) metabolism, nicotinamide/niacin (vitamin B3) metabolism, pantothenate (vitamin B5) metabolism, and synthesis of two essential amino acids for Atlantic salmon (lysine and threonine), suggesting a beneficial role of MSS. Furthermore, we found that the highly abundant clade could function by an acetogenic lifestyle, a function lacking for the other fish-related Mycoplasma MAGs, clearly suggesting this to be an essential feature of acetate fermentation in the predominantly anaerobic gut ecosystem of salmonid hosts ( Fig. 3c ). Mirrored patterns of population structures suggest co-evolution between Atlantic salmon and Candidatus Mycoplasma salmoninae salar To estimate the putative lineage of foraging wild Atlantic salmon across our sampled locations, we compared our data with Atlantic salmon genotypes of already known origins across the north- and southern Norway from publicly available genomes. Principal component analysis (PCA) of host genotype likelihoods revealed three main clusters explained by PC1 and PC2 ( Fig. 4a ). These clusters were mainly defined by latitude consistent with previous work splitting Norwegian salmon populations into three main lineages: Cluster 1 (northern Norway), Cluster 2 (southern Norway), and a Baltic population 10,11 . Of the investigated individuals, we detected 24 (32 %) individuals from Norwegian lineages clustered by latitude, with lineages from the Baltic Sea and the White Sea as outgroups. Our analysis revealed that far from all individuals were closely related to the Norwegian genotypes, indicating that several foraging individuals were from genotypes originally reported outside of Norway. These Norwegian lineages were used to infer co-evolution between MSS and Atlantic salmon based on their genotypes. Of the 24 Norwegian individuals, we successfully recovered single nucleotide variants (SNVs) from MSS from 19 individuals ( Fig. 4b ). Analysing SNVs in MSS revealed a total of 136 SNVs present among 90 % of the 19 individual metagenomes. Analysis of the Norwegian salmon genotype and MSS showed co-clustering between host populations of Atlantic salmon and the variability of single nucleotide variants (SNVs) in MSS, further supporting a co-evolutionary pattern between MSS and Atlantic salmon. Overall, the co-clustering analysis between MSS and Atlantic salmon and the constant MAG composition across any measured environmental factor suggests a high selection pressure on the microbiota. A selection pressure evolved as a response to co-adaptation between Atlantic salmon and at least one Mycoplasma species ( Fig. 4b ). We looked for evidence that the salmonid MSS population had been subjected to high selection pressure by calculating the average polymorphism rates of nonsynonymous to synonymous mutations (pN/pS) for each gene within MSS. The genes with pN/pS values higher than one suggested that MSS individuals had been subjected to positive selection pressure ( Fig. 4c ). Most genes with a high pNpS ratio were unknown but included defence mechanisms and translational ribosomal structure biogenesis. We inferred single amino acid variants (SAAVs) to characterise non-synonymous mutations between MSS variants and thereby investigate the putative phenotypic variation in MSS prevalent to its host genotype. Before exploring SAAVs, we predicted protein structures from 141 genes containing SNVs resulting in 51 (36.2 %) predicted protein structures, resulting in 1,813 SAAVs across all individuals. Of the 141 genes containing SNVs, 51 recovered predicted proteins from the MSS genome. Several of the predicted proteins included SAAVs that were highly prevalent in the related genotype cluster of host salmon ( Fig. 5 ). Three predicted proteins were outstanding in prevalence and relation to Atlantic salmon and intestinal environment. These proteins included (I) ornithine carbamoyltransferase, which is highly related to the urea cycle and arginine biosynthesis, II) glutathione peroxidase, an often peroxide-related defence mechanism in intestinal environments 24 ), and (II) thiamine biosynthesis protein, related cofactor metabolism, clearly showing host genotypic related variation to protein structure within the MSS genome. These results enable us to predict the genotype of Atlantic salmon by the predicted protein structure variation within MSS conforming with co-evolution between Atlantic salmon and MSS. Discussion Previous investigations of associations between vertebrate hosts and their microbiota in wild-living species, including teleosts, have mainly been covered by amplicon-based approaches with a low taxonomic resolution and limited functional inference 25–27 . Moving beyond gene amplicon surveys, we provide the first metagenomic description related to the gastrointestinal environment of multiple wild Atlantic salmon ranging in size, stomach content, diet, microfauna, genotype, and location. We acknowledge that MAG technology has limitations, resulting in potential false positives. To minimise these drawbacks, we applied state-of-the-art binning methods, combining automatic binning and manual curation with anvi’o and applied good practice for the reliable generation of MAGs 28 . Moving beyond gene amplicon surveys, we provide the first metagenomic description related to the gastrointestinal environment of multiple wild Atlantic salmon ranging in size, stomach content, diet, microfauna, genotype, and location. The microbiome's functional potential has emerged as an essential piece for understanding how their host organisms have evolved depending on the phenomic plasticity of both entities 1 . Hence, co-evolution has historically been defined as the mutual alterations experienced by (at least) two organisms as the result of a selection process that they enforce on each other, promoting continuous co-adaptation 29 . While numerous such cases have been documented in mammals 30–33 , other warm-blooded organisms such as vultures 34 , and invertebrates 35,36 , very little is known about the adaptive potential of associated microbiota in teleosts representing more than half of all vertebrate species. Our data also confirm Mycoplasma -dominated gut microbiota in Atlantic salmon 14,16,17 . Furthermore, the microbial variation was low across all 75 investigated individuals despite being exposed to different conditions for multiple environmental factors, indicating high host selection pressure favouring MSS. Therefore we hypothesise that this Mycoplasma-defined microbiota is pristine in Atlantic salmon, making our data set a valuable reference for future salmonid microbiota studies, as in aquaculture, where the gut microbiota investigations have been a research field of increasing interest 18,19,37–41 . Groussin et al. recently formulated a guideline model to separate co-phylogeny from co-evolution by rejecting the geographic isolation of two host populations 2 . While demonstrations of co-evolution in mammalian and bacterial genomes appear to be demanding 2 , our study shows that salmon can serve as a model for studying co-evolution in vertebrate host species. Indeed, according to Groussin et al., three independent prerequisites are needed for co-evolution to occur with high stability, including (I) strong fitness dependencies, (II) stable transmission across generations and (III) strong host selectivity 2 . First, for mammals, Groussin et al. argue that it is non-trivial to convincingly show a direct fitness of symbiotic microbe on its host as the same function can easily be provided by other related species; however, we argue that this is unlikely in salmonids due to the low diversity of the microbiota and the low biomass observed. Second, the phylogenomic observation that Mycoplasma strains cluster according to both host species, despite being farmed or wild, and population clusters strongly suggest vertical transmission across generations, given that this pattern remains for sympatric hosts caught at the exact location. Though evidence for vertical transmission has not yet been established, other observations besides our study indicate such transmissions, such as the lack of Mycoplasma in the surrounding rivers 16 or the Arctic oceans and that Mycoplasma species are intracellular living and highly host-dependent. Thirdly, our results are in line with previous observations that independently showed positive correlations between Mycoplasma abundance and fitness of the salmonid host following strong host selectivity on Mycoplasma abundance 18,42–44 . Overall, our results follow established expectations for co-evolution, presenting salmon as a new model for understanding holobiont evolution 2,3 . Despite the detailed environmental and life-history metadata for each sampled individual, no concluding variation of MAGs was explained by this information. Interestingly, we found that the SNVs variation of the highly abundant MSS was better explained by the genetic background of the host individual rather than sampling location, indicating a unique and intriguing host-bacteria co-evolution. Indeed, such a co-evolutionary relationship is further supported by findings showing that Mycoplasma does not follow a neutral model in Atlantic salmon 15 . We found several SAAVs with a high prevalence to host genotype, indicating an apparent fixation of mutations within the salmonid-related Mycoplasma populations. Our data provide additional insights into the dominance of Mycoplasma and the overall gut microbiota of Atlantic salmon by revealing that several novels low abundant and dynamic MAGs of Mycoplasma co-exist with highly abundant Mycoplasma . Furthermore, our data show a clear phylogenomic and functional differentiation between Mycoplasma clades of high and low abundance, which will be difficult to decipher using an amplicon-based approach. Specifically, we found a higher completion of biosynthesis of essential amino acids (lysine and threonine) 45 and biosynthesis of B-vitamins, like pantothenic acid (B5), niacin (B3), and thiamine (B1) in the highly abundant Mycoplasma . B-vitamins are often deficient in Atlantic salmon, suggesting that MSS could be crucial for Atlantic salmon in the wild 46 . Interestingly, using untargeted metabolomics, we previously found a higher amount of B5-vitamins in rainbow trout associated with Candidatus Mycoplasma salmoninae mykiss, further supporting some fitness dependency of Mycoplasma to its salmonid host 23 , which fulfils one of the co-evolutionary prerequisites stated by Groussin et al. 2 . Understanding the functional interdependence between hosts and their microbiota by studying holobionts stands to be one of the most revolutionising theories and disciplines within the field of evolutionary biology 3,47,48 . Our study should inspire similar investigations in systems previously described via amplicon-based markers to reveal the intriguing functional host-microbiota interactions. The case of Atlantic salmon studied here has not only enhanced our evolutionary understanding of this species, but the findings also hold potential for further discoveries towards feed or health optimisation resulting in more sustainable solutions. Methods Ethical approval Atlantic salmon included in this study were sacrificed immediately upon the catch, resulting in instant death before tissue and gut samples were taken. While no separate licence is required for such sampling, we stress that all fish handling was supervised by experienced and trained staff following standard and legal procedures in Norway. Sample collection Sample collections for Atlantic salmon were taken from five locations across the coast of northern Norway, including Månes/Torsken, Nordkapp, Alta, Bugøynes, Andøya. We caught all Atlantic salmon with a gillnet in the open sea. We froze individuals whole immediately after the catch. Sample collections of gut content and gut scrapings were carried out for midgut and hindgut at the National Institute of Nutrition and Seafood Research (NIFES). Atlantic salmon were thawed overnight. Gut sections were divided and, subsequently, content squeezed out of the gut using a sterile scalpel. All samples were snap-frozen with dry ice immediately after sample collection. While sample collection was carried out, stomach content, fish weight, length, gonad score, sex, nematodes, and tapeworm were noticed for subsequent analysis. DNA extraction DNA was extracted using ZymoBiomics DNA miniPrep (Zymo Research), following the manufacturer's protocol. Initial quality control of samples was performed using a Qubit 3.0 fluorometer, following the manufacturer's protocol. DNA sequencing The DNA was shipped to Novogene (Cambridge, UK) post-extraction for DNA fragmentation by sonication. Library preparation was carried out using Novogene NGS DNA Library Prep Set (Cat No.PT004). Further quality control included qPCR for quantification, and size distribution was detected using Bioanalyzer 2100 (Agilent). Quantified libraries were pooled and sequenced on the Illumina NovaSeq 6000 with a 150 bp paired-end strategy. Genome-resolved metagenomics Raw sequence reads were quality controlled, using FastQC/v0.11.8 to assess filtering and quality steps. Adapters and low-quality reads were removed with AdapterRemoval/v2.2.4, with a quality base of 30 and a minimum length of 50bp. We removed duplicates, and reads were re-paired to remove singletons, using bbmap/v.38.35. We filtered data for the host genome using bwa mem 49 to increase assembly efficiency by reducing eukaryotic contaminants. Subsequently, we applied a reference-based mapping and taxonomy annotation to i) remove unknown eukaryotic contaminants and ii) carry out an analysis of eukaryotic gut content (diet and microfauna) using MGmapper 50 . Filtered data were co-assembled using MEGAHIT with metagenomic sensitive presets 51 . Assembled contigs were quality assessed with Quast/v.5.02. Filtering of a minimal length of 1000 bp per scaffold was applied. To increase effective binning, we used the anvi’o pipeline 52 (available from http://merenlab.org/software/anvio ) following the workflow outlined at http://merenlab.org/2016/06/22/anvio-tutorial-v2/ . Briefly, i) anvi’o was used to profile the scaffolds using Prodigal/v2.6.3 53 with default parameters to identify genes and HMMER/v.3.3 54 to identify genes matching to archaeal 55 , protists (based on http://merenlab.org/delmont-euk-scgs ), and bacterial 55 single-copy core gene collections with hidden Markov models (HMMs). Also, ribosomal RNA-based HMMs were identified (based on https://github.com/tseemann/barrnap ). The HMMs were used to determine the completeness of metagenome-assembled genomes (MAGs); ii) we mapped short reads from the metagenomic set to the scaffolds using BWA/v0.7.1596 (minimum identity of 95%) and stored the recruited reads as BAM files using samtools 56 . We binned contigs automatically using CONCOCT 57 and subsequently refined using the anvi’o platform. Identification, refinement, taxonomic and functional inference of MAGs Each CONCOCT bin was manually curated using the anvi'o interactive interface to ensure high completion and low redundancy. The interface considers each scaffold's sequence composition, differential coverage, GC content, and taxonomic signal 52,58 . We defined all bins with >50% completeness or > 1 Mbp length as MAGs. We used anvi’o to infer the taxonomy of MAGs based on the proximity of single-copy gene markers. Subsequently, we applied Kaiju 59 with NCBI’s non-redundant protein database ‘nr’ to infer the taxonomy of genes (as described in http://merenlab.org/2016/06/18/importing-taxonomy/ ). For functional inference, we used clusters of orthologous (COGs) 60 , Kyoto encyclopedia of genes and genomes (KEGG) 61 , and protein families (Pfam) 62 , which were annotated through the anvi’o platform. A summary of the MAGs generated for this study is available at 10.6084/m9.figshare.20043452. Phylogenomic and comparative analysis of MAGs Mycoplasma genomes from different species were selected based on relatedness to MAGs from this study. Selected genomes are referred to as external genomes. These external genomes were compared with wild salmonid MAGs, using anvi’o/v7.1 as in the previous studies 14,58 . Similarities of each amino acid sequence in every genome were calculated against every other amino acid sequence across all genomes, using blastp. We implemented Minbit heuristics of 0.5 to eliminate weak matches between two amino acid sequences 63 and an MCL inflation of 2. We used the MCL algorithms to identify gene clusters in amino acid sequence similarity 64 . We calculated ANI using PyANI 65 . Euclidean distance and ward linkage were used to organise gene clusters and genomes. A summary of the pan-genome generated for this study is available at 10.6084/m9.figshare.20043419 . Presence of Candidatus Mycoplasma salmoninae salar in the Arctic ocean We analysed data from TARA Ocean to investigate the environmental presence of Mycoplasma and especially Candidatus Mycoplasma salmoninae salar. We analysed the presence of 1,888 MAGs in the Arctic ocean using supplementary information (Table S2) from Delmont et al. 2022 66 ; see link: Delmont et al., 2022 Table S2 . Population genetics of Candidatus Mycoplasma salmoninae salar We use the term “population” to describe an assemblage of co-existing microbial genomes in an environment that are similar enough to map to the context of the same reference genome 67 . Single nucleotide variants (SNVs), single codon variants (SCVs), and single amino acid variants (SAAVs) Usage of SNVs was related to interest in single nucleotide positions or non-coding regions, whereas SCVs were applied to resolve codon variants, synonymity, and calling of pNpS sites. Lastly, we analysed the structural differences in the encoded protein using SAAV inferring. Analysis of SNVs, SCVs, and SAAVs was conducted through anvi’o/7.1, following the tutorial: https://merenlab.org/2015/07/20/analyzing-variability/#matrix-output-those-unique-to-saavs . Inferring of Atlantic salmon population structure Raw sequence reads were quality controlled, using FastQC/v0.11.8 to assess filtering and quality steps. Removal of adapters and low-quality reads were done with AdapterRemoval/v2.2.4, with a quality base of 30 and a minimum length of 50bp. Duplicates were removed, and reads were re-paired to remove singletons, using bbmap/v.38.35. Subsequently, data were mapped back to the Ssal_v3.1 Atlantic salmon genome (Genbank accession GCA_905237065.2), using BWA mem. To estimate the origin of our samples, we spiked in data from Atlantic salmon originating from southern and northern Norway, which are detailed in supplementary table SX . To estimate the population structure of wild Atlantic salmon across Norway, we applied a genotype likelihood approach to handle low and medium coverage data, using ANGSD 68 . For estimation of genotype likelihoods, we discarded reads with mapping quality below 20 and bases with base quality below 30. For the PCA analysis, we used PCAngsd, based on genotype likelihoods from variable sites 69 . We filtered genotypic sites for all population structure measurements to minimise false positives related to low coverage and variant depth across genomes. Filters included filtering for polymorphic sites with an SNP p-value of 1e-6 and a minimum major/minor allele frequency of 0.05. Furthermore, genotypic sites should be present in 50% of the investigated individuals. World maps We used the ggplot2 70 packages for R to visualise the metagenomic sets, eukaryotic gut compositions, and relative distribution of MAGs in the world map. Statistical analyses Rarefaction curves were estimated using r-package vegan 71 to infer suitable sequencing depth. The abundance of MAGs was based on mean coverage across each MAG. Data were normalised based on MAG length and were sum-normalised prior to analysis of ecological dynamics based on TMP normalisation, using r-package ADImpute 72 . Microbial composition analysis was carried out using r-package phyloseq 73 . Correlated response models were used to infer the variation of each MAG related to environmental factors using r-package BORAL 74 as previously carried out 42 . Statistical assumptions for parametric and non-parametric analysis were tested, using Levene’s test for homogeneity and the Shapiro-Wilk test for normality of data. Declarations Data availability The raw dataset generated during the current study is available in the European Nucleotide Archive (ENA) repository with project accession number PRJEB54005. Furthermore, data for estimating the origin of Atlantic salmon populations are available in the ENA repository with project accession number PRJEB38061. Data for comparing cod metagenomes are available in the ENA repository with project accession number PRJEB29346. Code availability Any code used for the study to generate results reported in the paper and central to its main claims is available at https://github.com/JacobAgerbo/Norwegian_wild_Salmon_2022 . Acknowledgements The research was funded by The Independent Research Fund Denmark (“HappyFish”, grant No. 8022-00005B) and by The Danish National Research Foundation grant no. DNRF143 to M.T.P.G. Conflict of interest The authors declare no conflicts of interest. Author Contributions JAR and MTL conceived the study with input from MTPG, KK. PK organised trial and sampling. JAR carried out laboratory work and performed the computational analysis. The authors read and approved the final manuscript. References 1. 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Genomics and taxonomy in diagnostics for food security: soft-rotting enterobacterial plant pathogens. Anal. Methods 8 , 12–24 (2015). 66. Delmont, T. O. et al. Heterotrophic bacterial diazotrophs are more abundant than their cyanobacterial counterparts in metagenomes covering most of the sunlit ocean. ISME J. 16 , 927–936 (2022). 67. Delmont, T. O. et al. Single-amino acid variants reveal evolutionary processes that shape the biogeography of a global SAR11 subclade. Elife 8 , (2019). 68. Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: Analysis of Next Generation Sequencing Data. BMC Bioinformatics 15 , 356 (2014). 69. Fumagalli, M. et al. Quantifying population genetic differentiation from next-generation sequencing data. Genetics 195 , 979–992 (2013). 70. Ginestet, C. ggplot2: elegant graphics for data analysis. JOURNAL-ROYAL STATISTICAL SOCIETY SERIES A 174 , 245–245 (2011). 71. Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14 , 927–930 (2003). 72. Xu, L., Xu, Y., Xue, T., Zhang, X. & Li, J. AdImpute: An Imputation Method for Single-Cell RNA-Seq Data Based on Semi-Supervised Autoencoders. Front. Genet. 12 , 739677 (2021). 73. McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8 , e61217 (2013). 74. Hui, F. K. C. boral - Bayesian Ordination and Regression Analysis of Multivariate Abundance Data in r. Methods Ecol. Evol. 7 , 744–750 (2016). 75. Riiser, E. S. et al. Switching on the light: using metagenomic shotgun sequencing to characterise the intestinal microbiome of Atlantic cod. Environ. Microbiol. 21 , 2576–2594 (2019). Additional Declarations There is NO Competing Interest. Supplementary Files NWSSupplementaryKKMTLJAR.pdf Supplementary Information GraphicalAbstract.jpg Graphical Abstract Cite Share Download PDF Status: Posted 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1814912","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":117831333,"identity":"aa6575b7-bde2-4a8c-8492-5455799287f4","order_by":0,"name":"Jacob Rasmussen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYBACxgYGBmZkATkDZogo4wxitRgT1AICKFoSNzAQ0MLcfvjh4wKGe/Ly084Yf/xRcSd9OzvzAcaZbQyyMxtwOKwnzdh4BkOx4YbbOWbSPGee5e5sZktg3NjGYDwbp19y2KR5GBIYN0jnmDEzth3O3XCYx4DxYRtD4jxcWvrfgLXYz5+dY/zx57/D6QYEtcyA2JLYcDvHQIK34XACWAvQYYk4HTbjGdAvBgnJG26nlUnzHDtsCPLLwRnnJIxxed+wPxkYYhUJtvNnJ2/++KPmsLw5/+GDD3vKbGRnHMChBWyUAZooULEEDmcxMMjjlBkFo2AUjIJRAAMA/y5aM5AXEbQAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-7710-8912","institution":"University of Copenhagen","correspondingAuthor":true,"prefix":"","firstName":"Jacob","middleName":"","lastName":"Rasmussen","suffix":""},{"id":117831334,"identity":"78968a0e-0daa-4786-bc2b-ea87a5daade3","order_by":1,"name":"Pia Kiilerich","email":"","orcid":"","institution":"Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut","correspondingAuthor":false,"prefix":"","firstName":"Pia","middleName":"","lastName":"Kiilerich","suffix":""},{"id":117831335,"identity":"9443f264-1c47-4c58-9524-45b4f7f855ab","order_by":2,"name":"Rune Waagbø","email":"","orcid":"","institution":"Institute of Marine Research","correspondingAuthor":false,"prefix":"","firstName":"Rune","middleName":"","lastName":"Waagbø","suffix":""},{"id":117831336,"identity":"46bacbca-2e5d-4895-aa7b-687319e48e99","order_by":3,"name":"Erik-Jan Lock","email":"","orcid":"","institution":"Institute of Marine Research","correspondingAuthor":false,"prefix":"","firstName":"Erik-Jan","middleName":"","lastName":"Lock","suffix":""},{"id":117831337,"identity":"92c51369-51cf-4d94-9b50-7b84555e45b7","order_by":4,"name":"Madsen Lise","email":"","orcid":"","institution":"Institute of Marine Research","correspondingAuthor":false,"prefix":"","firstName":"Madsen","middleName":"","lastName":"Lise","suffix":""},{"id":117831338,"identity":"b9d6f927-03f4-42c8-93df-603fd6dfc5d2","order_by":5,"name":"M Thomas P Gilbert","email":"","orcid":"https://orcid.org/0000-0002-5805-7195","institution":"University of Copenhagen","correspondingAuthor":false,"prefix":"","firstName":"M","middleName":"Thomas P","lastName":"Gilbert","suffix":""},{"id":117831339,"identity":"d3f02de6-f0ae-48c3-8d4a-791ad1e982ec","order_by":6,"name":"Karsten Kristiansen","email":"","orcid":"https://orcid.org/0000-0002-6024-0917","institution":"University of Copenhagen","correspondingAuthor":false,"prefix":"","firstName":"Karsten","middleName":"","lastName":"Kristiansen","suffix":""},{"id":117831340,"identity":"e35a588a-50fb-4a35-8d83-522631f1e119","order_by":7,"name":"Morten Limborg","email":"","orcid":"https://orcid.org/0000-0002-7718-6531","institution":"Center for Evolutionary Hologenomics","correspondingAuthor":false,"prefix":"","firstName":"Morten","middleName":"","lastName":"Limborg","suffix":""}],"badges":[],"createdAt":"2022-07-01 09:31:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1814912/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1814912/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":23349292,"identity":"a3775d7b-ba66-4ba0-9cbc-97afe0b2e6e8","added_by":"auto","created_at":"2022-07-01 20:50:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":195408,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of the Atlantic salmon metagenome. \u003c/strong\u003eA) Rarefaction curves of gene calls across all samples from the co-assembled metagenome. B) Single-copy core gene estimates of present protists, bacteria, and archaea. C) Coverage heatmap of recovered MAGs from the gut microbiota of Atlantic salmon. The top bar plot reports the number of single nucleotide variants (SNVs) reported per sample, and the bar plot below reports read depth per sample. From the left, grey bars indicate the length of each MAG and unbinned contigs, green bars indicate GC-content (%) per MAG, and the heatmap of the MAGs is based on mean coverage per sample. White indicates low coverage, whereas black indicates high coverage. The intensity of each MAG is normalised according to the highest coverage of each MAG across all samples. Blue bars indicate completion (%) of each MAG, whereas burgundy indicates redundancy (%).\u0026nbsp;\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-1814912/v1/91bc5494a36311597e8320fb.png"},{"id":23349531,"identity":"e3b57539-f335-4200-b08d-1462d204bd66","added_by":"auto","created_at":"2022-07-01 20:55:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":230264,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of gut microbiota and macrofauna composition of wild Atlantic salmon.\u003c/strong\u003e Atlantic salmon were sampled across five locations in northern Norway, including Andøye, Torsken, Alta, Nordkapp, and Bugøynes, as indicated by black dots. A) illustrates the relative MAG abundance of all MAGs, including all genera, and B) focuses on the relative MAG abundance in the \u003cem\u003eMycoplasma \u003c/em\u003egenus. C) Pie charts indicate microfauna and diet composition. The right pie chart indicates vertebrate composition, and the left indicates invertebrate composition.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-1814912/v1/88152174595ba242c876171a.png"},{"id":23349296,"identity":"8c18879b-f7d5-48a7-ae0b-46b9e54667f3","added_by":"auto","created_at":"2022-07-01 20:50:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":271873,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogenomics and comparative genomics of co-assembled MAGs from wild Atlantic salmon and closely related \u003cem\u003eMycoplasma\u003c/em\u003e species and candidate genomes.\u003c/strong\u003e A) unrooted phylogenomic maximum likelihood tree, constructed based on 934 HMM hits from 71 single-copy core genes across 17 genomes and MAGs. B) Heatmap of Average nucleotide identity (ANI) similarity between salmonid-related \u003cem\u003eMycoplasma\u003c/em\u003e MAGs from this study and farmed salmonids \u003ca href=\"https://paperpile.com/c/YjsgAe/rY0k\" rel=\"noopener noreferrer\" target=\"_blank\"\u003e\u003csup\u003e14\u003c/sup\u003e\u003c/a\u003e, and \u003cem\u003eMycoplasma\u003c/em\u003e MAGs related to Atlantic cod from the same environment \u003ca href=\"https://paperpile.com/c/YjsgAe/8b1z\" rel=\"noopener noreferrer\" target=\"_blank\"\u003e\u003csup\u003e75\u003c/sup\u003e\u003c/a\u003e. C) Heatmap of the metabolic reconstruction and comparison of salmonid-related \u003cem\u003eMycoplasma\u003c/em\u003e versus other fish-related \u003cem\u003eMycoplasma.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-1814912/v1/aff278a6d7814328d7e7daa3.png"},{"id":23349532,"identity":"f55d3655-967b-45e8-889f-864425602095","added_by":"auto","created_at":"2022-07-01 20:55:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":258299,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of host genotypes in Norwegian Atlantic salmon (Host origin) and SNV variability from high abundant \u003cem\u003eMycoplasma\u003c/em\u003e (\u003cem\u003eCandidatus \u003c/em\u003eMycoplasma salmoninae salar).\u003c/strong\u003e A) Genotype clusters of 206 Norwegian Atlantic salmon, including publicly available genomes and individuals from this study (labelled with black sample names). B) Variability of Single nucleotide variants (SNVs in the high-abundant\u003cem\u003e Mycoplasma\u003c/em\u003e) ordered along the x-axis. Only SNVs with a minimum of 20X coverage in at least 90 % of all individual metagenomes were considered. Each layer represents a sample, which has been ordered based on the composition of SNV variability (departure from consensus) in MSS across all samples. For each layer, bars for each SNV illustrate variability from zero to one between competing nucleotides (NTs). The co-occurrence of SNVs across samples depicts the order of SNVs. The right bar plots represent the host genotype clusters, location, and coverage of MSS. The environmental information is coloured as indicated by the legend. C) Scatterplot of pNpS ratio for investigating selection pressure on microbial genes within the MSS MAG. The dashed line indicates a ratio between pN and pS of one. Dot colours indicate functions for genes with a pNpS ratio higher than one, indicating selection, as noted in the legend.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-1814912/v1/8fa2ead81db89aff2dcda0df.png"},{"id":23349294,"identity":"5bc70b23-03f8-41e1-b295-40a93e1250b7","added_by":"auto","created_at":"2022-07-01 20:50:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":538774,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExamples of predicted protein structure variants in \u003cem\u003eCandidatus Mycoplasma salmoninae salar\u003c/em\u003e related to the host genotype.\u003c/strong\u003e Single amino acid variants (SAAVs) on the predicted protein structures of five example genes from MSS across 19 metagenomes from the two Atlantic salmon genotypic clusters. The sphere in each predicted protein structure illustrates a SAAV, where colour indicates prevalence concerning the specific host genotype and size of the sphere indicates BLOSUM62 index, as indicated by the legend.\u0026nbsp;\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-1814912/v1/07b5b5955c2d48abf4dbded5.png"},{"id":23598696,"identity":"da257e3d-c767-42c9-b88b-385b2a7fc575","added_by":"auto","created_at":"2022-07-07 17:11:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2081275,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1814912/v1/0a7b318a-9564-4244-8bd0-193ab024b8f7.pdf"},{"id":23349298,"identity":"e045d803-e1eb-4e12-a38a-bc1ecf02f2d5","added_by":"auto","created_at":"2022-07-01 20:50:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":795600,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"NWSSupplementaryKKMTLJAR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1814912/v1/d6dd7cf7e959e1ab27337732.pdf"},{"id":23349293,"identity":"730af251-fae3-4e77-899d-59663364222a","added_by":"auto","created_at":"2022-07-01 20:50:25","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":482515,"visible":true,"origin":"","legend":"Graphical Abstract","description":"","filename":"GraphicalAbstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1814912/v1/714b2b00d26fb70a33b02e3b.jpg"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"\u003cp\u003eHologenomic insights demonstrate co-evolution between an intestinal \u003cem\u003eMycoplasma\u003c/em\u003e and its salmonid host\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCo-evolutionary interactions between animals and their microbiota have become a key topic in evolutionary biology because of the adaptive relevance to understanding basic eco-evolutionary processes\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/FaLV\"\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/a\u003e. However, vertebrates and their associated microbial communities have not yet been systematically studied. One major drawback for understanding evolutionary host-microbiota relationships in many vertebrates, such as mammals, is that they are confounded by the high complexity of their microbial communities, challenging the detection of specific host-microbe interactions\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/qM3I\"\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/a\u003e. Dynamics in these complex systems are also often shaped by environmental factors limiting the ability to answer basic questions about broader evolutionary trends between host and microbe\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/yki5\"\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/a\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne way to overcome these limitations is to investigate microbiota of low diversity related to a host with a well-known population structure\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/yki5\"\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/a\u003e. Teleosts offer an attractive option as the more than 33,000 described species represent nearly half of all vertebrates. Many teleosts have well-characterised population structures due to their commercial, cultural, and recreational importance\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/j4l2\"\u003e\u003csup\u003e4\u003c/sup\u003e\u003c/a\u003e. Despite representing the largest vertebrate group, very few host-bacteria systems have been investigated in teleosts compared to terrestrial species, such as hibernating brown bears\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/efDy\"\u003e\u003csup\u003e5\u003c/sup\u003e\u003c/a\u003e, plant-eating rodents\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/2LlB\"\u003e\u003csup\u003e6\u003c/sup\u003e\u003c/a\u003e, and production animals such as pigs\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/Z03P\"\u003e\u003csup\u003e7\u003c/sup\u003e\u003c/a\u003e and ruminants\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/wLAJ+V7QZ\"\u003e\u003csup\u003e8,9\u003c/sup\u003e\u003c/a\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe enigmatic Atlantic salmon (\u003cem\u003eSalmo salar\u003c/em\u003e) provides a well-studied model system thanks to its significant commercial, cultural, and recreational importance. It has led to a well-described genome, evolution, and population structure throughout its North Atlantic distribution\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/kTy7+uZjy\"\u003e\u003csup\u003e10,11\u003c/sup\u003e\u003c/a\u003e. These assets make Atlantic salmon an ideal model for studying host-microbe co-evolution in vertebrates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdult salmon are piscivorous, expecting the associated microbiota to show physiological adaptations for a strictly carnivorous diet\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/DW1Q\"\u003e\u003csup\u003e12\u003c/sup\u003e\u003c/a\u003e. Recent surveys of gut microbial communities in salmonid species revealed a general dominance of a unique \u003cem\u003eMycoplasma\u0026nbsp;\u003c/em\u003especies suggesting a close relationship with the salmon host\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/WBuC+rY0k+ed0V\"\u003e\u003csup\u003e13\u0026ndash;15\u003c/sup\u003e\u003c/a\u003e. Salmonid-related \u003cem\u003eMycoplasmas\u0026nbsp;\u003c/em\u003eare hypothesised to be vertically transmitted between generations since they have not yet been discovered in the environment\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/PDQ3\"\u003e\u003csup\u003e16\u003c/sup\u003e\u003c/a\u003e, are prominent in both wild and farmed salmon\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/bE4d+ed0V+PDQ3+rY0k\"\u003e\u003csup\u003e14\u0026ndash;17\u003c/sup\u003e\u003c/a\u003e, and do not follow neutral processes in both wild and farmed individuals\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/ed0V\"\u003e\u003csup\u003e15\u003c/sup\u003e\u003c/a\u003e. Furthermore, these previous studies have shown that salmonid intestinal microbiotas are less diverse than the microbiota of more often studied warm-blooded animals\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/bE4d+PDQ3+4dBJ+e391+Y6bi\"\u003e\u003csup\u003e16\u0026ndash;20\u003c/sup\u003e\u003c/a\u003e. This consistent trend of intestinal microbial communities characterised by an extremely low diversity makes Atlantic salmon a practical model for studying co-evolutionary relationships between vertebrate hosts and their core bacteria\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/UZV1+v3YB\"\u003e\u003csup\u003e21,22\u003c/sup\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGenome-resolved metagenomics offers a detailed resolution of microbial communities. However, the often-reported low biomass of the intestinal microbiota in salmon has limited the broader application of shotgun metagenomics sequencing in this species\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/WBuC+rY0k\"\u003e\u003csup\u003e13,14\u003c/sup\u003e\u003c/a\u003e. Therefore, no previous studies have used detailed genome-resolved metagenomics to uncover the functional and genomic legacy of the gut microbiome in wild populations of Atlantic salmon. Here, we use state-of-the-art genome resolved metagenomics and comparative genomics to investigate the metagenomic dynamics and hologenome of more than 70 adult foraging wild Atlantic salmon. We further compare the intestinal microbiotas from Atlantic salmon with gut bacteria of other sympatric teleost species to elucidate environmental and host-related dynamics. Our study presents the first genome-resolved metagenome from wild Atlantic salmon, extending decades of PCR-based gene surveys, and highlights the value of applying a hologenomics approach\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/yki5\"\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/a\u003e to study host-microbiota co-evolution in light of understanding adaptive evolutionary processes.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eMetagenomes were recovered from 75 wild Atlantic salmon individuals from five different regions in northern Norway, including Alta, Anda\u0026oslash;ya, Bug\u0026oslash;ynes, M\u0026aring;nes/Tosken, and Nordkapp, ranging from more than 700 kilometres across the north Atlantic Ocean. Whole metagenomic sequencing of all individuals resulted in 5,806 million reads. Re-mapping of the host genome revealed that 53.82 % (SD \u0026plusmn; 27.72 %) of the reads were the host, which was lower than previous studies\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/rY0k+KNRT\"\u003e\u003csup\u003e14,23\u003c/sup\u003e\u003c/a\u003e. Reads were quality controlled, and non-microbial data was removed, resulting in a recovery of 40.74 % (SD \u0026plusmn; 27.85 %) of the reads (\u003cstrong\u003eSuppl. Table S1, Suppl. Fig. S1\u003c/strong\u003e). The reads were used as input for metagenomic co-assemblies to recover metagenome-assembled genomes (MAGs) and investigate bacterial strain variation per host individual. We identified 2,241,791 non-redundant genes in scaffolds longer than 1,000 nucleotides. Investigation of sequencing depth and gene calls\u0026apos; rarefaction indicated sufficient metagenomic data saturation for a representative metagenomic analysis (\u003cstrong\u003eFig. 1a\u003c/strong\u003e). Automatic binning and manual curation of co-assembly resulted in 19 non-redundant MAGs containing 50,153,577 bp. The MAG catalogue represented 99.98 % of the size-normalised bacterial data. Counting 71 bacterial single-copy core genes (SCGs) across the metagenome reveals that 23 bacterial genomes were potentially present in the metagenome, indicating that we recovered the majority of the MAGs (82.6 %) and that the metagenome of wild Atlantic salmon is characterised by an incredibly trivial diversity (\u003cstrong\u003eFig. 1b-c\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe reconstruction of these MAGs complements decades of biomonitoring efforts using the 16S rRNA marker gene by providing genomic information for lineages missing a functional context and allowing us to search for strain variation within a large pool of microbial populations associated with salmonid hosts.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eGenome resolved metagenomics reveals a low diverse gut microbiota of the Atlantic salmon following strong host selection\u003c/h3\u003e\n\u003cp\u003eDiversity analysis of MAGs revealed a gut microbiome of low diversity during the marine foraging life stage. We analysed the taxonomy of all MAGs using phylogenomics with 3207 bacterial reference genomes (\u003cstrong\u003eSuppl. Fig. S1\u003c/strong\u003e). Our analysis revealed no significant differences in microbiota composition between sampling locations (\u003cstrong\u003eFig. 2 a-b, Suppl. Fig. S3\u003c/strong\u003e). \u003cem\u003eMycoplasma\u003c/em\u003e was highly dominant (90.41% of the MAG profiled reads), consistent with previous findings\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/bE4d+PDQ3+rY0k\"\u003e\u003csup\u003e14,16,17\u003c/sup\u003e\u003c/a\u003e. We found an intermittent high abundance of \u003cem\u003ePhotobacterium phosphoreum\u003c/em\u003e, a species of Brachyspira (formerly classified as \u003cem\u003eBrevinema andersonii\u003c/em\u003e), and \u003cem\u003eShewanella\u003c/em\u003e MAGs across eight samples (\u003cstrong\u003eFig. 2 a\u003c/strong\u003e). Subsequently, we recovered low amounts of \u003cem\u003eSynechococcus, Vibrio, Aliivibrio salmonicida,\u0026nbsp;\u003c/em\u003eand \u003cem\u003eMethanocaldococcus\u0026nbsp;\u003c/em\u003eacross the 75 Atlantic salmon.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA large proportion of the data represented \u003cem\u003eMycoplasma\u003c/em\u003e, which was overall dominating and represented over 90.67 % of all host filtered reads. Of the 19 MAGs recovered, five MAGs were within the family of Mycoplasmataceae (\u003cstrong\u003eFig. 2b\u003c/strong\u003e). Subsequently, one MAG of \u003cem\u003eMycoplasma, Candidatus\u0026nbsp;\u003c/em\u003eMycoplasma salmoninae salar (MSS), alone accounted for 83.41% of the host filtered reads. Our analysis revealed remarkable stability of MSS across any noticed environmental factor, clearly suggesting host selection favouring MSS (\u003cstrong\u003eSuppl. Fig. S4-S5)\u003c/strong\u003e. One interesting trend was that the richness of \u003cem\u003eMycoplasma\u003c/em\u003e MAGs followed a longitudinal pattern showing a mixture between \u003cem\u003eMycoplasma\u0026nbsp;\u003c/em\u003eMAGs (NWS_MAG_00006 and NWS_MAG_00013), where NWS_MAG_00013 became more abundant in northern regions, indicating that more \u003cem\u003eMycoplasma\u0026nbsp;\u003c/em\u003especies could originate from northern Norway or Russia. We analysed data from \u003cem\u003eTARA\u003c/em\u003e Oceans to investigate the origin of MSS. Still, no \u003cem\u003eMycoplasma\u0026nbsp;\u003c/em\u003eMAGs were found across the Arctic Ocean, suggesting that MSS was not obtained from the ocean in the adult life stages of Atlantic salmon (\u003cstrong\u003eSuppl. Fig. S6)\u003c/strong\u003e. Our results indicate that investigations of salmonid \u003cem\u003eMycoplasma\u003c/em\u003e are more complex than previously anticipated and that 16S rRNA gene investigations at the genus level will often simplify the actual strain-level variation in the microbiota.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eReference-based mapping and taxonomy annotation of metagenomics sequence data revealed eukaryotic gut content. Before data analysis, we hypothesised that the gut content might consist of diet or gut microfauna (e.g., tapeworms, nematodes, and Myxozoa). Vertebrate diet content in the gut included Atlantic herring (\u003cem\u003eClupea harengus\u003c/em\u003e) (\u003cstrong\u003eFig. 2c\u003c/strong\u003e). Subsequently, invertebrate content had Arthropoda, Cnidaria, Nematoda, and Platyhelminthes, where arthropods were thought to originate from the diet like krill. Cnidaria, Nematoda, and Platyhelminthes were considered parasitic microfauna, including tapeworms (\u003cem\u003eEubothrium\u003c/em\u003e), anisakid nematodes (\u003cem\u003eAnisakis simplex\u003c/em\u003e), and salmon-related Myxozoan (\u003cstrong\u003eFig. 2c\u003c/strong\u003e). The composition of invertebrates differed between locations, where cnidarian content dominated the gut content in the south and Platyhelminthes was the dominating phylum in the gut content of northern individuals (\u003cstrong\u003eFig. 2c\u003c/strong\u003e). Despite the compositional differentiation of microfauna and diet, no effect on the microbiota was detected, confirming our hypothesis of a high selection pressure within the intestinal tract of Atlantic salmon.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eMeta-pangenomics reveals high host specificity of \u003cem\u003eMycoplasma\u003c/em\u003e and hosts complementing gene clusters\u003c/h3\u003e\n\u003cp\u003eOur analysis revealed that the highly abundant \u003cem\u003eMycoplasma\u003c/em\u003e MAG was closely related to previous, recovered salmonid-related \u003cem\u003eMycoplasma\u0026nbsp;\u003c/em\u003eMAGs from salmonids raised in aquaculture (\u003cstrong\u003eFig. 3a\u003c/strong\u003e). Our phylogenomic and comparative analysis revealed that NWS_MAG_00006 and \u003cem\u003eCandidatus\u0026nbsp;\u003c/em\u003eMycoplasma salmoninae salar (MSS) were more closely related to each other than the \u003cem\u003eMycoplasma, Candidatus\u0026nbsp;\u003c/em\u003eMycoplasma salmoninae mykiss (MSM), related to farmed rainbow trout (\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e), which underpins the specificity of these salmonid related \u003cem\u003eMycoplasma\u0026nbsp;\u003c/em\u003eMAGs to their salmonid host regardless of a farmed or wild origin (\u003cstrong\u003eFig. 3 a-b\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe four other \u003cem\u003eMycoplasma-related\u003c/em\u003e MAGs were all found to have low abundance across all individuals and clustered with various salmonid-related clades like NWS_MAG_00013 as an outgroup to the clade of Atlantic salmon, rainbow trout, and NWS_MAG_00006.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe found two new MAGs related to \u003cem\u003eCandidatus\u0026nbsp;\u003c/em\u003eMycoplasma lavaretus (ML) and \u003cem\u003eMycoplasma mobile\u0026nbsp;\u003c/em\u003e163K. Further, we found one MAG (NWS_MAG_00007) associated with \u003cem\u003eUreaplasma\u003c/em\u003e. Analysis of the gene clusters among all \u003cem\u003eMycoplasma\u0026nbsp;\u003c/em\u003eMAGs further confirmed the differentiation observed between MAGs from rainbow trout and Atlantic salmon compared to the other MAGs (\u003cstrong\u003eFig. 3a-b\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese findings reveal a clade of abundant \u003cem\u003eMycoplasma\u003c/em\u003e highly prevalent in salmonids and living in coexistence with other low abundant \u003cem\u003eMycoplasma\u0026nbsp;\u003c/em\u003especies; this further supports a high host selection pressure, especially when previous investigations could not detect Mycoplasma in the surrounding water\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/PDQ3\"\u003e\u003csup\u003e16\u003c/sup\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInterestingly, we found that all highly abundant MAGs in the salmonid clade were not only phylogenetically separated from the other \u003cem\u003eMycoplasma\u0026nbsp;\u003c/em\u003eMAG but also by metabolism (\u003cstrong\u003eFig. 3c\u003c/strong\u003e). We applied KOfam metabolism estimates across the fish-related \u003cem\u003eMycoplasma\u0026nbsp;\u003c/em\u003eto decipher the potential functional differences between high and low abundant \u003cem\u003eMycoplasma\u003c/em\u003e, confirming our previous phylogenetic differentiation. Interestingly, we found KOfams in the \u0026ldquo;high abundant\u0026rdquo; clade, which could be potentially beneficial for its host, including genes encoding enzymes involved in thiamine (vitamin B1) metabolism, nicotinamide/niacin (vitamin B3) metabolism, pantothenate (vitamin B5) metabolism, and synthesis of two essential amino acids for Atlantic salmon (lysine and threonine), suggesting a beneficial role of MSS. Furthermore, we found that the highly abundant clade could function by an acetogenic lifestyle, a function lacking for the other fish-related \u003cem\u003eMycoplasma\u003c/em\u003e MAGs, clearly suggesting this to be an essential feature of acetate fermentation in the predominantly anaerobic gut ecosystem of salmonid hosts (\u003cstrong\u003eFig. 3c\u003c/strong\u003e).\u003c/p\u003e\n\u003ch3\u003eMirrored patterns of population structures suggest co-evolution between Atlantic salmon and \u003cem\u003eCandidatus\u0026nbsp;\u003c/em\u003eMycoplasma salmoninae salar\u003c/h3\u003e\n\u003cp\u003eTo estimate the putative lineage of foraging wild Atlantic salmon across our sampled locations, we compared our data with Atlantic salmon genotypes of already known origins across the north- and southern Norway from publicly available genomes. Principal component analysis (PCA) of host genotype likelihoods revealed three main clusters explained by PC1 and PC2 (\u003cstrong\u003eFig. 4a\u003c/strong\u003e). These clusters were mainly defined by latitude consistent with previous work splitting Norwegian salmon populations into three main lineages: Cluster 1 (northern Norway), Cluster 2 (southern Norway), and a Baltic population\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/kTy7+uZjy\"\u003e\u003csup\u003e10,11\u003c/sup\u003e\u003c/a\u003e. Of the investigated individuals, we detected 24 (32 %) individuals from Norwegian lineages clustered by latitude, with lineages from the Baltic Sea and the White Sea as outgroups. Our analysis revealed that far from all individuals were closely related to the Norwegian genotypes, indicating that several foraging individuals were from genotypes originally reported outside of Norway.\u003c/p\u003e\n\u003cp\u003eThese Norwegian lineages were used to infer co-evolution between MSS and Atlantic salmon based on their genotypes. Of the 24 Norwegian individuals, we successfully recovered single nucleotide variants (SNVs) from MSS from 19 individuals (\u003cstrong\u003eFig. 4b\u003c/strong\u003e). Analysing SNVs in MSS revealed a total of 136 SNVs present among 90 % of the 19 individual metagenomes. Analysis of the Norwegian salmon genotype and MSS showed co-clustering between host populations of Atlantic salmon and the variability of single nucleotide variants (SNVs) in MSS, further supporting a co-evolutionary pattern between MSS and Atlantic salmon. Overall, the co-clustering analysis between MSS and Atlantic salmon and the constant MAG composition across any measured environmental factor suggests a high selection pressure on the microbiota. A selection pressure evolved as a response to co-adaptation between Atlantic salmon and at least one \u003cem\u003eMycoplasma\u003c/em\u003e species (\u003cstrong\u003eFig. 4b\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe looked for evidence that the salmonid MSS population had been subjected to high selection pressure by calculating the average polymorphism rates of nonsynonymous to synonymous mutations (pN/pS) for each gene within MSS. The genes with pN/pS values higher than one suggested that MSS individuals had been subjected to positive selection pressure (\u003cstrong\u003eFig. 4c\u003c/strong\u003e). Most genes with a high pNpS ratio were unknown but included defence mechanisms and translational ribosomal structure biogenesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe inferred single amino acid variants (SAAVs) to characterise non-synonymous mutations between MSS variants and thereby investigate the putative phenotypic variation in MSS prevalent to its host genotype. Before exploring SAAVs, we predicted protein structures from 141 genes containing SNVs resulting in 51 (36.2 %) predicted protein structures, resulting in 1,813 SAAVs across all individuals. Of the 141 genes containing SNVs, 51 recovered predicted proteins from the MSS genome. Several of the predicted proteins included SAAVs that were highly prevalent in the related genotype cluster of host salmon (\u003cstrong\u003eFig. 5\u003c/strong\u003e). Three predicted proteins were outstanding in prevalence and relation to Atlantic salmon and intestinal environment. These proteins included (I) ornithine carbamoyltransferase, which is highly related to the urea cycle and arginine biosynthesis, II) glutathione peroxidase, an often peroxide-related\u003csub\u003e\u0026nbsp;\u003c/sub\u003edefence mechanism in intestinal environments\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/FghO\"\u003e\u003csup\u003e24\u003c/sup\u003e\u003c/a\u003e), and (II) thiamine biosynthesis protein, related cofactor metabolism, clearly showing host genotypic related variation to protein structure within the MSS genome. These results enable us to predict the genotype of Atlantic salmon by the predicted protein structure variation within MSS conforming with co-evolution between Atlantic salmon and MSS.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrevious investigations of associations between vertebrate hosts and their microbiota in wild-living species, including teleosts, have mainly been covered by amplicon-based approaches with a low taxonomic resolution and limited functional inference\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/XHy0+dExa+FP2I\"\u003e\u003csup\u003e25\u0026ndash;27\u003c/sup\u003e\u003c/a\u003e. Moving beyond gene amplicon surveys, we provide the first metagenomic description related to the gastrointestinal environment of multiple wild Atlantic salmon ranging in size, stomach content, diet, microfauna, genotype, and location. We acknowledge that MAG technology has limitations, resulting in potential false positives. To minimise these drawbacks, we applied state-of-the-art binning methods, combining automatic binning and manual curation with anvi\u0026rsquo;o and applied good practice for the reliable generation of MAGs\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/0M2k\"\u003e\u003csup\u003e28\u003c/sup\u003e\u003c/a\u003e. Moving beyond gene amplicon surveys, we provide the first metagenomic description related to the gastrointestinal environment of multiple wild Atlantic salmon ranging in size, stomach content, diet, microfauna, genotype, and location.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe microbiome\u0026apos;s functional potential has emerged as an essential piece for understanding how their host organisms have evolved depending on the phenomic plasticity of both entities\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/FaLV\"\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/a\u003e. Hence, co-evolution has historically been defined as the mutual alterations experienced by (at least) two organisms as the result of a selection process that they enforce on each other, promoting continuous co-adaptation\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/lfxU\"\u003e\u003csup\u003e29\u003c/sup\u003e\u003c/a\u003e. While numerous such cases have been documented in mammals\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/4sfq+HAMF+rju4+6gt3\"\u003e\u003csup\u003e30\u0026ndash;33\u003c/sup\u003e\u003c/a\u003e, other warm-blooded organisms such as vultures\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/vxDI\"\u003e\u003csup\u003e34\u003c/sup\u003e\u003c/a\u003e, and invertebrates\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/7Idp+F3pR\"\u003e\u003csup\u003e35,36\u003c/sup\u003e\u003c/a\u003e, very little is known about the adaptive potential of associated microbiota in teleosts representing more than half of all vertebrate species.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur data also confirm \u003cem\u003eMycoplasma\u003c/em\u003e-dominated gut microbiota in Atlantic salmon\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/bE4d+PDQ3+rY0k\"\u003e\u003csup\u003e14,16,17\u003c/sup\u003e\u003c/a\u003e. Furthermore, the microbial variation was low across all 75 investigated individuals despite being exposed to different conditions for multiple environmental factors, indicating high host selection pressure favouring MSS. Therefore we hypothesise that this \u003cem\u003eMycoplasma-defined\u003c/em\u003e microbiota is pristine in Atlantic salmon, making our data set a valuable reference for future salmonid microbiota studies, as in aquaculture, where the gut microbiota investigations have been a research field of increasing interest\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/4dBJ+e391+c4Sx+uOnG+OCAf+02LZ+H2DP\"\u003e\u003csup\u003e18,19,37\u0026ndash;41\u003c/sup\u003e\u003c/a\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGroussin et al. recently formulated a guideline model to separate co-phylogeny from co-evolution by rejecting the geographic isolation of two host populations\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/qM3I\"\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/a\u003e\u003cem\u003e.\u003c/em\u003e While demonstrations of co-evolution in mammalian and bacterial genomes appear to be demanding\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/qM3I\"\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/a\u003e, our study shows that salmon can serve as a model for studying co-evolution in vertebrate host species. Indeed, according to Groussin et al., three independent prerequisites are needed for co-evolution to occur with high stability, including (I) strong fitness dependencies, (II) stable transmission across generations and (III) strong host selectivity\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/qM3I\"\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/a\u003e. First, for mammals, Groussin et al. argue that it is non-trivial to convincingly show a direct fitness of symbiotic microbe on its host as the same function can easily be provided by other related species; however, we argue that this is unlikely in salmonids due to the low diversity of the microbiota and the low biomass observed. Second, the phylogenomic observation that \u003cem\u003eMycoplasma\u003c/em\u003e strains cluster according to both host species, despite being farmed or wild, and population clusters strongly suggest vertical transmission across generations, given that this pattern remains for sympatric hosts caught at the exact location. Though evidence for vertical transmission has not yet been established, other observations besides our study indicate such transmissions, such as the lack of \u003cem\u003eMycoplasma\u003c/em\u003e in the surrounding rivers\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/PDQ3\"\u003e\u003csup\u003e16\u003c/sup\u003e\u003c/a\u003e or the Arctic oceans and that \u003cem\u003eMycoplasma\u003c/em\u003e species are intracellular living and highly host-dependent. Thirdly, our results are in line with previous observations that independently showed positive correlations between \u003cem\u003eMycoplasma\u003c/em\u003e abundance and fitness of the salmonid host following strong host selectivity on \u003cem\u003eMycoplasma\u003c/em\u003e abundance\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/4dBJ+6Fkn+TrIX+5pFY\"\u003e\u003csup\u003e18,42\u0026ndash;44\u003c/sup\u003e\u003c/a\u003e. Overall, our results follow established expectations for co-evolution, presenting salmon as a new model for understanding holobiont evolution\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/yki5+qM3I\"\u003e\u003csup\u003e2,3\u003c/sup\u003e\u003c/a\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite the detailed environmental and life-history metadata for each sampled individual, no concluding variation of MAGs was explained by this information. Interestingly, we found that the SNVs variation of the highly abundant MSS\u003cem\u003e\u0026nbsp;\u003c/em\u003ewas better explained by the genetic background of the host individual rather than sampling location, indicating a unique and intriguing host-bacteria co-evolution. Indeed, such a co-evolutionary relationship is further supported by findings showing that \u003cem\u003eMycoplasma\u003c/em\u003e does not follow a neutral model in Atlantic salmon\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/ed0V\"\u003e\u003csup\u003e15\u003c/sup\u003e\u003c/a\u003e. We found several SAAVs with a high prevalence to host genotype, indicating an apparent fixation of mutations within the salmonid-related \u003cem\u003eMycoplasma\u003c/em\u003e populations.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003eOur data provide additional insights into the dominance of \u003cem\u003eMycoplasma\u003c/em\u003e and the overall gut microbiota of Atlantic salmon by revealing that several novels low abundant and dynamic MAGs of \u003cem\u003eMycoplasma\u003c/em\u003e co-exist with highly abundant \u003cem\u003eMycoplasma\u003c/em\u003e. Furthermore, our data show a clear phylogenomic and functional differentiation between \u003cem\u003eMycoplasma\u003c/em\u003e clades of high and low abundance, which will be difficult to decipher using an amplicon-based approach. Specifically, we found a higher completion of biosynthesis of essential amino acids (lysine and threonine)\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/xQYD\"\u003e\u003csup\u003e45\u003c/sup\u003e\u003c/a\u003e and biosynthesis of B-vitamins, like pantothenic acid (B5), niacin (B3), and thiamine (B1) in the highly abundant \u003cem\u003eMycoplasma\u003c/em\u003e. B-vitamins are often deficient in Atlantic salmon, suggesting that MSS could be crucial for Atlantic salmon in the wild\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/JOjM\"\u003e\u003csup\u003e46\u003c/sup\u003e\u003c/a\u003e. Interestingly, using untargeted metabolomics, we previously found a higher amount of B5-vitamins in rainbow trout associated with \u003cem\u003eCandidatus\u0026nbsp;\u003c/em\u003eMycoplasma salmoninae mykiss, further supporting some fitness dependency of \u003cem\u003eMycoplasma\u003c/em\u003e to its salmonid host\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/KNRT\"\u003e\u003csup\u003e23\u003c/sup\u003e\u003c/a\u003e, which fulfils one of the co-evolutionary prerequisites stated by Groussin et al.\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/qM3I\"\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/a\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnderstanding the functional interdependence between hosts and their microbiota by studying holobionts stands to be one of the most revolutionising theories and disciplines within the field of evolutionary biology\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/SHR3+8z4a+yki5\"\u003e\u003csup\u003e3,47,48\u003c/sup\u003e\u003c/a\u003e. Our study should inspire similar investigations in systems previously described via amplicon-based markers to reveal the intriguing functional host-microbiota interactions. The case of Atlantic salmon studied here has not only enhanced our evolutionary understanding of this species, but the findings also hold potential for further discoveries towards feed or health optimisation resulting in more sustainable solutions.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003eEthical approval\u003c/h3\u003e\n\u003cp\u003eAtlantic salmon included in this study were sacrificed immediately upon the catch, resulting in instant death before tissue and gut samples were taken. While no separate licence is required for such sampling, we stress that all fish handling was supervised by experienced and trained staff following standard and legal procedures in Norway.\u003c/p\u003e\n\u003ch3\u003eSample collection\u003c/h3\u003e\n\u003cp\u003eSample collections for Atlantic salmon were taken from five locations across the coast of northern Norway, including M\u0026aring;nes/Torsken, Nordkapp, Alta, Bug\u0026oslash;ynes, And\u0026oslash;ya. We caught all Atlantic salmon with a gillnet in the open sea. We froze individuals whole immediately after the catch. Sample collections of gut content and gut scrapings were carried out for midgut and hindgut at the National Institute of Nutrition and Seafood Research (NIFES). Atlantic salmon were thawed overnight. Gut sections were divided and, subsequently, content squeezed out of the gut using a sterile scalpel. All samples were snap-frozen with dry ice immediately after sample collection. While sample collection was carried out, stomach content, fish weight, length, gonad score, sex, nematodes, and tapeworm were noticed for subsequent analysis.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eDNA extraction\u003c/h3\u003e\n\u003cp\u003eDNA was extracted using ZymoBiomics DNA miniPrep (Zymo Research), following the manufacturer\u0026apos;s protocol. Initial quality control of samples was performed using a Qubit 3.0 fluorometer, following the manufacturer\u0026apos;s protocol.\u003c/p\u003e\n\u003ch3\u003eDNA sequencing\u003c/h3\u003e\n\u003cp\u003eThe DNA was shipped to Novogene (Cambridge, UK) post-extraction for DNA fragmentation by sonication. Library preparation was carried out using Novogene NGS DNA Library Prep Set (Cat No.PT004). Further quality control included qPCR for quantification, and size distribution was detected using Bioanalyzer 2100 (Agilent). Quantified libraries were pooled and sequenced on the Illumina NovaSeq 6000 with a 150 bp paired-end strategy.\u003c/p\u003e\n\u003ch3\u003eGenome-resolved metagenomics\u003c/h3\u003e\n\u003cp\u003eRaw sequence reads were quality controlled, using FastQC/v0.11.8 to assess filtering and quality steps. Adapters and low-quality reads were removed with AdapterRemoval/v2.2.4, with a quality base of 30 and a minimum length of 50bp. We removed duplicates, and reads were re-paired to remove singletons, using bbmap/v.38.35. We filtered data for the host genome using bwa mem\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/hZaT\"\u003e\u003csup\u003e49\u003c/sup\u003e\u003c/a\u003e to increase assembly efficiency by reducing eukaryotic contaminants. Subsequently, we applied a reference-based mapping and taxonomy annotation to i) remove unknown eukaryotic contaminants and ii) carry out an analysis of eukaryotic gut content (diet and microfauna) using MGmapper\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/a1Vc\"\u003e\u003csup\u003e50\u003c/sup\u003e\u003c/a\u003e. Filtered data were co-assembled using MEGAHIT with metagenomic sensitive presets\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/bWc8\"\u003e\u003csup\u003e51\u003c/sup\u003e\u003c/a\u003e. Assembled contigs were quality assessed with Quast/v.5.02. Filtering of a minimal length of 1000 bp per scaffold was applied. To increase effective binning, we used the anvi\u0026rsquo;o pipeline\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/S9cmV\"\u003e\u003csup\u003e52\u003c/sup\u003e\u003c/a\u003e (available from\u0026nbsp;\u003ca href=\"http://merenlab.org/software/anvio\"\u003ehttp://merenlab.org/software/anvio\u003c/a\u003e) following the workflow outlined at\u0026nbsp;\u003ca href=\"http://merenlab.org/2016/06/22/anvio-tutorial-v2/\"\u003ehttp://merenlab.org/2016/06/22/anvio-tutorial-v2/\u003c/a\u003e. Briefly, i) anvi\u0026rsquo;o was used to profile the scaffolds using Prodigal/v2.6.3\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/MiPNM\"\u003e\u003csup\u003e53\u003c/sup\u003e\u003c/a\u003e with default parameters to identify genes and HMMER/v.3.3\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/IuaPI\"\u003e\u003csup\u003e54\u003c/sup\u003e\u003c/a\u003e to identify genes matching to archaeal\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/U8R3g\"\u003e\u003csup\u003e55\u003c/sup\u003e\u003c/a\u003e, protists (based on\u0026nbsp;\u003ca href=\"http://merenlab.org/delmont-euk-scgs\"\u003ehttp://merenlab.org/delmont-euk-scgs\u003c/a\u003e), and bacterial\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/U8R3g\"\u003e\u003csup\u003e55\u003c/sup\u003e\u003c/a\u003e single-copy core gene collections with hidden Markov models (HMMs). Also, ribosomal RNA-based HMMs were identified (based on\u0026nbsp;\u003ca href=\"https://github.com/tseemann/barrnap\"\u003ehttps://github.com/tseemann/barrnap\u003c/a\u003e). The HMMs were used to determine the completeness of metagenome-assembled genomes (MAGs); ii) we mapped short reads from the metagenomic set to the scaffolds using BWA/v0.7.1596 (minimum identity of 95%) and stored the recruited reads as BAM files using samtools\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/oukuj\"\u003e\u003csup\u003e56\u003c/sup\u003e\u003c/a\u003e. We binned contigs automatically using CONCOCT\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/CARB0\"\u003e\u003csup\u003e57\u003c/sup\u003e\u003c/a\u003e and subsequently refined using the anvi\u0026rsquo;o platform.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eIdentification, refinement, taxonomic and functional inference of MAGs\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eEach CONCOCT bin was manually curated using the anvi\u0026apos;o interactive interface to ensure high completion and low redundancy. The interface considers each scaffold\u0026apos;s sequence composition, differential coverage, GC content, and taxonomic signal\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/S9cmV+PGFbH\"\u003e\u003csup\u003e52,58\u003c/sup\u003e\u003c/a\u003e. We defined all bins with \u0026gt;50% completeness or \u0026gt; 1\u0026thinsp;Mbp length as MAGs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe used anvi\u0026rsquo;o to infer the taxonomy of MAGs based on the proximity of single-copy gene markers. Subsequently, we applied Kaiju\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/SGKMZ\"\u003e\u003csup\u003e59\u003c/sup\u003e\u003c/a\u003e with NCBI\u0026rsquo;s non-redundant protein database \u0026lsquo;nr\u0026rsquo; to infer the taxonomy of genes (as described in\u0026nbsp;\u003ca href=\"http://merenlab.org/2016/06/18/importing-taxonomy/\"\u003ehttp://merenlab.org/2016/06/18/importing-taxonomy/\u003c/a\u003e). For functional inference, we used clusters of orthologous (COGs)\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/0HLp\"\u003e\u003csup\u003e60\u003c/sup\u003e\u003c/a\u003e, Kyoto encyclopedia of genes and genomes (KEGG)\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/XYla\"\u003e\u003csup\u003e61\u003c/sup\u003e\u003c/a\u003e, and protein families (Pfam)\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/t3jJ\"\u003e\u003csup\u003e62\u003c/sup\u003e\u003c/a\u003e, which were annotated through the anvi\u0026rsquo;o platform. A summary of the MAGs generated for this study is available at \u003cstrong\u003e10.6084/m9.figshare.20043452.\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003ePhylogenomic and comparative analysis of MAGs\u003c/h3\u003e\n\u003cp\u003eMycoplasma genomes from different species were selected based on relatedness to MAGs from this study. Selected genomes are referred to as external genomes. These external genomes were compared with wild salmonid MAGs, using anvi\u0026rsquo;o/v7.1 as in the previous studies\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/PGFbH+rY0k\"\u003e\u003csup\u003e14,58\u003c/sup\u003e\u003c/a\u003e. Similarities of each amino acid sequence in every genome were calculated against every other amino acid sequence across all genomes, using blastp. We implemented Minbit heuristics of 0.5 to eliminate weak matches between two amino acid sequences\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/7Y9l\"\u003e\u003csup\u003e63\u003c/sup\u003e\u003c/a\u003e and an MCL inflation of 2. We used the MCL algorithms to identify gene clusters in amino acid sequence similarity\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/VmR4\"\u003e\u003csup\u003e64\u003c/sup\u003e\u003c/a\u003e. We calculated ANI using PyANI\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/yY9H\"\u003e\u003csup\u003e65\u003c/sup\u003e\u003c/a\u003e. Euclidean distance and ward linkage were used to organise gene clusters and genomes. A summary of the pan-genome generated for this study is available at \u003cstrong\u003e10.6084/m9.figshare.20043419\u003c/strong\u003e.\u003c/p\u003e\n\u003ch3\u003ePresence of \u003cem\u003eCandidatus\u0026nbsp;\u003c/em\u003eMycoplasma salmoninae salar in the Arctic ocean\u003c/h3\u003e\n\u003cp\u003eWe analysed data from TARA Ocean to investigate the environmental presence of Mycoplasma and especially \u003cem\u003eCandidatus\u0026nbsp;\u003c/em\u003eMycoplasma salmoninae salar. We analysed the presence of 1,888 MAGs in the Arctic ocean using supplementary information (Table S2) from Delmont et al. 2022\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/8lKd\"\u003e\u003csup\u003e66\u003c/sup\u003e\u003c/a\u003e; see link:\u0026nbsp;\u003ca href=\"https://static-content.springer.com/esm/art%3A10.1038%2Fs41396-021-01135-1/MediaObjects/41396_2021_1135_MOESM3_ESM.xlsx\"\u003eDelmont et al., 2022 Table S2\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003ePopulation genetics of \u003cem\u003eCandidatus\u0026nbsp;\u003c/em\u003eMycoplasma salmoninae salar\u003c/h3\u003e\n\u003cp\u003eWe use the term \u0026ldquo;population\u0026rdquo; to describe an assemblage of co-existing microbial genomes in an environment that are similar enough to map to the context of the same reference genome\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/Wn0c\"\u003e\u003csup\u003e67\u003c/sup\u003e\u003c/a\u003e. Single nucleotide variants (SNVs), single codon variants (SCVs), and single amino acid variants (SAAVs)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsage of SNVs was related to interest in single nucleotide positions or non-coding regions, whereas SCVs were applied to resolve codon variants, synonymity, and calling of pNpS sites. Lastly, we analysed the structural differences in the encoded protein using SAAV inferring. Analysis of SNVs, SCVs, and SAAVs was conducted through anvi\u0026rsquo;o/7.1, following the tutorial:\u0026nbsp;\u003ca href=\"https://merenlab.org/2015/07/20/analyzing-variability/#matrix-output-those-unique-to-saavs\"\u003ehttps://merenlab.org/2015/07/20/analyzing-variability/#matrix-output-those-unique-to-saavs\u003c/a\u003e.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eInferring of Atlantic salmon population structure\u003c/h3\u003e\n\u003cp\u003eRaw sequence reads were quality controlled, using FastQC/v0.11.8 to assess filtering and quality steps. Removal of adapters and low-quality reads were done with AdapterRemoval/v2.2.4, with a quality base of 30 and a minimum length of 50bp. Duplicates were removed, and reads were re-paired to remove singletons, using bbmap/v.38.35. Subsequently, data were mapped back to the Ssal_v3.1 Atlantic salmon genome (Genbank accession GCA_905237065.2), using BWA mem. To estimate the origin of our samples, we spiked in data from Atlantic salmon originating from southern and northern Norway, which are detailed in supplementary table \u003cstrong\u003eSX\u003c/strong\u003e. To estimate the population structure of wild Atlantic salmon across Norway, we applied a genotype likelihood approach to handle low and medium coverage data, using ANGSD\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/kOKI\"\u003e\u003csup\u003e68\u003c/sup\u003e\u003c/a\u003e. For estimation of genotype likelihoods, we discarded reads with mapping quality below 20 and bases with base quality below 30.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the PCA analysis, we used PCAngsd, based on genotype likelihoods from variable sites\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/INAQ\"\u003e\u003csup\u003e69\u003c/sup\u003e\u003c/a\u003e. We filtered genotypic sites for all population structure measurements to minimise false positives related to low coverage and variant depth across genomes. Filters included filtering for polymorphic sites with an SNP p-value of 1e-6 and a minimum major/minor allele frequency of 0.05. Furthermore, genotypic sites should be present in 50% of the investigated individuals.\u003c/p\u003e\n\u003ch3\u003eWorld maps\u003c/h3\u003e\n\u003cp\u003eWe used the ggplot2\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/Qgrw\"\u003e\u003csup\u003e70\u003c/sup\u003e\u003c/a\u003e packages for R to visualise the metagenomic sets, eukaryotic gut compositions, and relative distribution of MAGs in the world map.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eRarefaction curves were estimated using \u003cem\u003er-package vegan\u003c/em\u003e \u003ca href=\"https://paperpile.com/c/YjsgAe/7FEf\"\u003e\u003csup\u003e71\u003c/sup\u003e\u003c/a\u003e to infer suitable sequencing depth. The abundance of MAGs was based on mean coverage across each MAG. Data were normalised based on MAG length and were sum-normalised prior to analysis of ecological dynamics based on TMP normalisation, using \u003cem\u003er-package ADImpute\u0026nbsp;\u003c/em\u003e\u003ca href=\"https://paperpile.com/c/YjsgAe/MUo0\"\u003e\u003cem\u003e\u003csup\u003e72\u003c/sup\u003e\u003c/em\u003e\u003c/a\u003e. Microbial composition analysis was carried out using \u003cem\u003er-package phyloseq\u0026nbsp;\u003c/em\u003e\u003ca href=\"https://paperpile.com/c/YjsgAe/pJcJ\"\u003e\u003cem\u003e\u003csup\u003e73\u003c/sup\u003e\u003c/em\u003e\u003c/a\u003e\u003cem\u003e.\u003c/em\u003e Correlated response models were used to infer the variation of each MAG related to environmental factors using \u003cem\u003er-package BORAL\u0026nbsp;\u003c/em\u003e\u003ca href=\"https://paperpile.com/c/YjsgAe/mWnL\"\u003e\u003cem\u003e\u003csup\u003e74\u003c/sup\u003e\u003c/em\u003e\u003c/a\u003e as previously carried out\u0026nbsp;\u003ca href=\"https://paperpile.com/c/YjsgAe/6Fkn\"\u003e\u003csup\u003e42\u003c/sup\u003e\u003c/a\u003e. Statistical assumptions for parametric and non-parametric analysis were tested, using Levene\u0026rsquo;s test for homogeneity and the Shapiro-Wilk test for normality of data.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe raw dataset generated during the current study is available in the European Nucleotide Archive (ENA) repository with project accession number PRJEB54005. Furthermore, data for estimating the origin of Atlantic salmon populations are available in the ENA repository with project accession number PRJEB38061. Data for comparing cod metagenomes are available in the ENA repository with project accession number PRJEB29346.\u003c/p\u003e\n\u003cp\u003eCode availability\u003c/p\u003e\n\u003cp\u003eAny code used for the study to generate results reported in the paper and central to its main claims is available at\u0026nbsp;\u003ca href=\"https://github.com/JacobAgerbo/Norwegian_wild_Salmon_2022\"\u003ehttps://github.com/JacobAgerbo/Norwegian_wild_Salmon_2022\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe research was funded by The Independent Research Fund Denmark (\u0026ldquo;HappyFish\u0026rdquo;, grant No. 8022-00005B) and by The Danish National Research Foundation grant no. DNRF143 to M.T.P.G.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConflict of interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eJAR and MTL conceived the study with input from MTPG, KK. PK organised trial and sampling. JAR carried out laboratory work and performed the computational analysis. The authors read and approved the final manuscript.\u003cbr\u003e \u003c/p\u003e"},{"header":"References","content":"\u003cp\u003e1.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/FaLV\"\u003eAlberdi, A., Aizpurua, O., Bohmann, K., Zepeda-Mendoza, M. L. \u0026amp; Gilbert, M. T. P. Do Vertebrate Gut Metagenomes Confer Rapid Ecological Adaptation?\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/FaLV\"\u003e\u003cem\u003eTrends Ecol. 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Ecol.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/DW1Q\"\u003e\u0026nbsp;(2020) doi:\u003c/a\u003e\u003ca href=\"http://dx.doi.org/10.1111/mec.15699\"\u003e10.1111/mec.15699\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/DW1Q\"\u003e.\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e13.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/WBuC\"\u003eCheaib, B.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/WBuC\"\u003e\u003cem\u003eet al.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/WBuC\"\u003e\u0026nbsp;Genome erosion and evidence for an intracellular niche \u0026ndash; exploring the biology of mycoplasmas in Atlantic salmon.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/WBuC\"\u003e\u003cem\u003eAquaculture\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/WBuC\"\u003e\u0026nbsp;736772 (2021).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e14.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/rY0k\"\u003eRasmussen, J. 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Ecol.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/bE4d\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/bE4d\"\u003e\u003cstrong\u003e44\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/bE4d\"\u003e, 175\u0026ndash;185 (2002).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e18.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/4dBJ\"\u003eBozzi, D.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/4dBJ\"\u003e\u003cem\u003eet al.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/4dBJ\"\u003e\u0026nbsp;Salmon gut microbiota correlates with disease infection status: potential for monitoring health in farmed animals.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/4dBJ\"\u003e\u003cem\u003eAnimal Microbiome\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/4dBJ\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/4dBJ\"\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/4dBJ\"\u003e, 1\u0026ndash;17 (2021).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e19.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/e391\"\u003eWang, J.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/e391\"\u003e\u003cem\u003eet al.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/e391\"\u003e\u0026nbsp;Microbiota in intestinal digesta of Atlantic salmon (Salmo salar), observed from late freshwater stage until one year in seawater, and effects of functional ingredients: a case study from a commercial sized research site in the Arctic region.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/e391\"\u003e\u003cem\u003eAnim Microbiome\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/e391\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/e391\"\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/e391\"\u003e, 14 (2021).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e20.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/Y6bi\"\u003eRasmussen, J.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/Y6bi\"\u003e\u003cem\u003eet al.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/Y6bi\"\u003e\u0026nbsp;A multi omics approach unravels metagenomic and metabolic alterations of a probiotic in rainbow trout (Oncorhynchus mykiss).\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/Y6bi\"\u003e\u003cem\u003eResearch Square\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/Y6bi\"\u003e\u0026nbsp;(2021) doi:\u003c/a\u003e\u003ca href=\"http://dx.doi.org/10.21203/rs.3.rs-459562/v1\"\u003e10.21203/rs.3.rs-459562/v1\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/Y6bi\"\u003e.\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e21.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/UZV1\"\u003eLimborg, M. 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Pathogen control at the intestinal mucosa - H2O2 to the rescue.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/FghO\"\u003e\u003cem\u003eGut Microbes\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/FghO\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/FghO\"\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/FghO\"\u003e, 67\u0026ndash;74 (2017).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e25.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/XHy0\"\u003eFietz, K.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/XHy0\"\u003e\u003cem\u003eet al.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/XHy0\"\u003e\u0026nbsp;Mind the gut: genomic insights to population divergence and gut microbial composition of two marine keystone species.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/XHy0\"\u003e\u003cem\u003eMicrobiome\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/XHy0\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/XHy0\"\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/XHy0\"\u003e, 82 (2018).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e26.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/dExa\"\u003eSevellec, M., Derome, N. \u0026amp; Bernatchez, L. Holobionts and ecological speciation: the intestinal microbiota of lake whitefish species pairs.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/dExa\"\u003e\u003cem\u003eMicrobiome\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/dExa\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/dExa\"\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/dExa\"\u003e, 47 (2018).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e27.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/FP2I\"\u003eSmith, C. C. R., Snowberg, L. K., Gregory Caporaso, J., Knight, R. \u0026amp; Bolnick, D. I. Dietary input of microbes and host genetic variation shape among-population differences in stickleback gut microbiota.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/FP2I\"\u003e\u003cem\u003eISME J.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/FP2I\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/FP2I\"\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/FP2I\"\u003e, 2515\u0026ndash;2526 (2015).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e28.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/0M2k\"\u003eMeziti, A.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/0M2k\"\u003e\u003cem\u003eet al.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/0M2k\"\u003e\u0026nbsp;The Reliability of Metagenome-Assembled Genomes (MAGs) in Representing Natural Populations: Insights from Comparing MAGs against Isolate Genomes Derived from the Same Fecal Sample.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/0M2k\"\u003e\u003cem\u003eAppl. 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The Coevolution of Bacterial Endosymbionts and Phloem-Feeding Insects.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/7Idp\"\u003e\u003cem\u003eAnn. Mo. Bot. Gard.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/7Idp\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/7Idp\"\u003e\u003cstrong\u003e88\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/7Idp\"\u003e, 35\u0026ndash;44 (2001).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e36.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/F3pR\"\u003eBrucker, R. M. \u0026amp; Bordenstein, S. R. The roles of host evolutionary relationships (genus: Nasonia) and development in structuring microbial communities.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/F3pR\"\u003e\u003cem\u003eEvolution\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/F3pR\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/F3pR\"\u003e\u003cstrong\u003e66\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/F3pR\"\u003e, 349\u0026ndash;362 (2012).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e37.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/c4Sx\"\u003eCiric, M., Waite, D., Draper, J. \u0026amp; Jones, J., Brian. Characterization of mid-intestinal microbiota of farmed Chinook salmon using 16S rRNA gene metabarcoding.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/c4Sx\"\u003e\u003cem\u003eArch. Biol. Sci.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/c4Sx\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/c4Sx\"\u003e\u003cstrong\u003e71\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/c4Sx\"\u003e, 577\u0026ndash;587 (2019).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e38.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/uOnG\"\u003eZhao, R.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/uOnG\"\u003e\u003cem\u003eet al.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/uOnG\"\u003e\u0026nbsp;Salinity and fish age affect the gut microbiota of farmed Chinook salmon (Oncorhynchus tshawytscha).\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/uOnG\"\u003e\u003cem\u003eAquaculture\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/uOnG\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/uOnG\"\u003e\u003cstrong\u003e528\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/uOnG\"\u003e, 735539 (2020).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e39.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/OCAf\"\u003eP\u0026eacute;rez-Pascual, D.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/OCAf\"\u003e\u003cem\u003eet al.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/OCAf\"\u003e\u0026nbsp;Gnotobiotic rainbow trout (Oncorhynchus mykiss) model reveals endogenous bacteria that protect against Flavobacterium columnare infection.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/OCAf\"\u003e\u003cem\u003ePLoS Pathog.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/OCAf\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/OCAf\"\u003e\u003cstrong\u003e17\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/OCAf\"\u003e, e1009302 (2021).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e40.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/02LZ\"\u003eZhao, R.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/02LZ\"\u003e\u003cem\u003eet al.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/02LZ\"\u003e\u0026nbsp;Effects of feed ration and temperature on Chinook salmon (Oncorhynchus tshawytscha) microbiota in freshwater recirculating aquaculture systems.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/02LZ\"\u003e\u003cem\u003eAquaculture\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/02LZ\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/02LZ\"\u003e\u003cstrong\u003e543\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/02LZ\"\u003e, 736965 (2021).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e41.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/H2DP\"\u003eRimoldi, S., Antonini, M., Gasco, L., Moroni, F. \u0026amp; Terova, G. Intestinal microbial communities of rainbow trout (Oncorhynchus mykiss) may be improved by feeding a Hermetia illucens meal/low-fishmeal diet.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/H2DP\"\u003e\u003cem\u003eFish Physiol. Biochem.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/H2DP\"\u003e\u0026nbsp;(2021) doi:\u003c/a\u003e\u003ca href=\"http://dx.doi.org/10.1007/s10695-020-00918-1\"\u003e10.1007/s10695-020-00918-1\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/H2DP\"\u003e.\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e42.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/6Fkn\"\u003eRasmussen, J. 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Microbiol.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/6Fkn\"\u003e\u0026nbsp;(2022) doi:\u003c/a\u003e\u003ca href=\"http://dx.doi.org/10.1111/jam.15433\"\u003e10.1111/jam.15433\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/6Fkn\"\u003e.\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e43.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/TrIX\"\u003eKarlsen, C.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/TrIX\"\u003e\u003cem\u003eet al.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/TrIX\"\u003e\u0026nbsp;The environmental and host-associated bacterial microbiota of Arctic seawater-farmed Atlantic salmon with ulcerative disorders.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/TrIX\"\u003e\u003cem\u003eJ. 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Methods\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/CARB0\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/CARB0\"\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/CARB0\"\u003e, 1144\u0026ndash;1146 (2014).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e58.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/PGFbH\"\u003eDelmont, T. O. \u0026amp; Eren, A. M. Linking pangenomes and metagenomes: the Prochlorococcus metapangenome.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/PGFbH\"\u003e\u003cem\u003ePeerJ\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/PGFbH\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/PGFbH\"\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/PGFbH\"\u003e, e4320 (2018).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e59.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/SGKMZ\"\u003eMenzel, P., Ng, K. L. \u0026amp; Krogh, A. Fast and sensitive taxonomic classification for metagenomics with Kaiju.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/SGKMZ\"\u003e\u003cem\u003eNat. 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KEGG: kyoto encyclopedia of genes and genomes.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/XYla\"\u003e\u003cem\u003eNucleic Acids Res.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/XYla\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/XYla\"\u003e\u003cstrong\u003e28\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/XYla\"\u003e, 27\u0026ndash;30 (2000).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e62.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/t3jJ\"\u003eEl-Gebali, S.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/t3jJ\"\u003e\u003cem\u003eet al.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/t3jJ\"\u003e\u0026nbsp;The Pfam protein families database in 2019.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/t3jJ\"\u003e\u003cem\u003eNucleic Acids Res.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/t3jJ\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/t3jJ\"\u003e\u003cstrong\u003e47\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/t3jJ\"\u003e, D427\u0026ndash;D432 (2019).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e63.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/7Y9l\"\u003eBenedict, M. N., Henriksen, J. R., Metcalf, W. W., Whitaker, R. J. \u0026amp; Price, N. D. ITEP: an integrated toolkit for exploration of microbial pan-genomes.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/7Y9l\"\u003e\u003cem\u003eBMC Genomics\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/7Y9l\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/7Y9l\"\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/7Y9l\"\u003e, 8 (2014).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e64.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/VmR4\"\u003evan Dongen, S. \u0026amp; Abreu-Goodger, C. Using MCL to extract clusters from networks.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/VmR4\"\u003e\u003cem\u003eMethods Mol. Biol.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/VmR4\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/VmR4\"\u003e\u003cstrong\u003e804\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/VmR4\"\u003e, 281\u0026ndash;295 (2012).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e65.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/yY9H\"\u003ePritchard, L., Glover, R. H., Humphris, S., Elphinstone, J. G. \u0026amp; Toth, I. K. Genomics and taxonomy in diagnostics for food security: soft-rotting enterobacterial plant pathogens.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/yY9H\"\u003e\u003cem\u003eAnal. Methods\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/yY9H\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/yY9H\"\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/yY9H\"\u003e, 12\u0026ndash;24 (2015).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e66.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/8lKd\"\u003eDelmont, T. 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ANGSD: Analysis of Next Generation Sequencing Data.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/kOKI\"\u003e\u003cem\u003eBMC Bioinformatics\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/kOKI\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/kOKI\"\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/kOKI\"\u003e, 356 (2014).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e69.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/INAQ\"\u003eFumagalli, M.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/INAQ\"\u003e\u003cem\u003eet al.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/INAQ\"\u003e\u0026nbsp;Quantifying population genetic differentiation from next-generation sequencing data.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/INAQ\"\u003e\u003cem\u003eGenetics\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/INAQ\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/INAQ\"\u003e\u003cstrong\u003e195\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/INAQ\"\u003e, 979\u0026ndash;992 (2013).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e70.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/Qgrw\"\u003eGinestet, C. ggplot2: elegant graphics for data analysis.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/Qgrw\"\u003e\u003cem\u003eJOURNAL-ROYAL STATISTICAL SOCIETY SERIES A\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/Qgrw\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/Qgrw\"\u003e\u003cstrong\u003e174\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/Qgrw\"\u003e, 245\u0026ndash;245 (2011).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e71.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/7FEf\"\u003eDixon, P. VEGAN, a package of R functions for community ecology.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/7FEf\"\u003e\u003cem\u003eJ. Veg. Sci.\u003c/em\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/7FEf\"\u003e\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/7FEf\"\u003e\u003cstrong\u003e14\u003c/strong\u003e\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/7FEf\"\u003e, 927\u0026ndash;930 (2003).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e72.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003ca href=\"http://paperpile.com/b/YjsgAe/MUo0\"\u003eXu, L., Xu, Y., Xue, T., Zhang, X. \u0026amp; Li, J. AdImpute: An Imputation Method for Single-Cell RNA-Seq Data Based on Semi-Supervised Autoencoders.\u0026nbsp;\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/YjsgAe/MUo0\"\u003e\u003cem\u003eFront. 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The host's intestinal environment shapes the gut microbiota through the co-existence of bacteria and host. This co-existence influences the fitness of both bacteria and host. How this affects the co-evolution occurring in animals is largely unexplored, mainly due to the complexity of the environment, low host selection, and microbial communities. \r\nWe present the first metagenomic derived genome of gut microbiota from wild Atlantic salmon (Salmo salar), a new wild model organism with the microbiota of low complexity and known population structures amenable for investigating co-evolution. Our data reveal a strong host selection of a core gut microbiota dominated by a single Mycoplasma species. Interestingly, we found a concordance between the population structure of the Atlantic salmon host and nucleotide variability of the intestinal Mycoplasma populations conforming to expectations from co-evolution between host and microbe. \r\nOur results show that the stable microbiota of Atlantic salmon has evolved with its salmonid host populations while potentially providing adaptive traits to the salmon host populations, including defence mechanisms and protein synthesis. We highlight Atlantic salmon as a novel model for studying co-evolution between vertebrate hosts and their microbiota.","manuscriptTitle":"Hologenomic insights demonstrate co-evolution between an intestinal Mycoplasma and its salmonid host","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-07-01 20:50:23","doi":"10.21203/rs.3.rs-1814912/v1","editorialEvents":[],"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":"a9906015-1a60-468f-8106-efa605eb6f67","owner":[],"postedDate":"July 1st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-07-07T20:35:43+00:00","versionOfRecord":[],"versionCreatedAt":"2022-07-01 20:50:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1814912","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1814912","identity":"rs-1814912","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}