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Genomic and functional characterization of the Atlantic salmon gut microbiome in relation to nutrition and health

Nature Microbiology volume 9, pages 3059–3074 (2024)Cite this article

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Abstract

To ensure sustainable aquaculture, it is essential to understand the path ‘from feed to fish’, whereby the gut microbiome plays an important role in digestion and metabolism, ultimately influencing host health and growth. Previous work has reported the taxonomic composition of the Atlantic salmon (Salmo salar) gut microbiome; however, functional insights are lacking. Here we present the Salmon Microbial Genome Atlas consisting of 211 high-quality bacterial genomes, recovered by cultivation (n = 131) and gut metagenomics (n = 80) from wild and farmed fish both in freshwater and seawater. Bacterial genomes were taxonomically assigned to 14 different orders, including 35 distinctive genera and 29 previously undescribed species. Using metatranscriptomics, we functionally characterized key bacterial populations, across five phyla, in the salmon gut. This included the ability to degrade diet-derived fibres and release vitamins and other exometabolites with known beneficial effects, which was supported by genome-scale metabolic modelling and in vitro cultivation of selected bacterial species coupled with untargeted metabolomic studies. Together, the Salmon Microbial Genome Atlas provides a genomic and functional resource to enable future studies on salmon nutrition and health.

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Fig. 1: Conceptual overview of the SMGA construction and its relevance in connecting the functional potential of the salmon gut microbiota to confirmed metabolic activities.
Fig. 2: Phylogenetic tree showing the diversity and source of the recovered bacterial genomes and MAGs.
Fig. 3: Metabolic functions and pathogenicity factors encoded by the 211 genomes in the SMGA.
Fig. 4: Schematic overview of selected active metabolic pathways detected in representative species of the SMGA based on metatranscriptomics, in vitro growth experiments and untargeted metabolomics of spent supernatants.

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Data availability

The Norwegian Atlantic Salmon Gut Bacteria Culture Collection is maintained and stored at the Norwegian University of Life Sciences. Bacterial strains are available from the lead contacts upon request by email to S.L.L.R. (sabina.leantilarosa@nmbu.no) or P.B.P. (phil.pope@nmbu.no). A five-working-day response time should be expected. Isolates will be sent as freeze stock cultures and shipped in dry ice via courier. Shipping costs will be charged to the person or institution requesting them. Isolates are for research purposes only; a declaration that the isolates will not be used for commercial purposes is required before sample shipment. Oxford Nanopore sequencing reads have been deposited in the SRA with project numbers PRJEB45024 and PRJEB61648. Publicly available 16S amplicon datasets of Atlantic salmon gut samples were downloaded from NCBI (bioprojects: PRJEB39298, PRJNA498084, PRJNA555355, PRJNA590084, PRJNA594310, PRJNA650141, PRJNA730696, PRJNA733893, PRJNA824235, PRJNA824256, PRJNA866155) together with data derived from two in-house trials with salmons feeding on a commercial standard diet (ImpTrial2: PRJEB60544 and ImpTrial1: PRJEB60545). Shotgun metagenomic reads have been deposited at SRA bioProjects PRJEB60591 and PRJNA947914. RNA sequencing reads can be found in SRA under accession number PRJEB60552. Mass spectrometry data for this study can be found on the Mass Spectrometry Interactive Virtual Environment (MassIVE) repository (massive.ucsd.edu) with accession number MSV000089895. The SMGA is publicly available via Figshare (Genomes_fasta, https://figshare.com/s/049af70f3d15d05e690d; Genes_nuc_fna, https://figshare.com/s/e56cdce3cd422e43a1e6; Genes_prot_faa, https://figshare.com/s/517b6e611fd896314dd0) and by the National e-Infrastructure for Research Data (NIRD) of Norway (https://ns9864k.web.sigma2.no/TheMEMOLab/projects/SMGA_v1.0/). Metabolic models can be inspected via the html files supplied in Supplementary Dataset 1. The complete list of detected pathogenicity factors is available at https://ns9864k.web.sigma2.no/TheMEMOLab/projects/SMGA_v1.0/annotation/SMGA_PathoFact_Annotation.csv.

Code availability

Scripts used to generate the genome-scale metabolic models, phylogenetic trees, the GIFTs and MCI can be found at https://github.com/TheMEMOLab/SalmonMicrobialGenomeAtlas_SMGA.

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Acknowledgements

This work was supported by the Research Council of Norway (project number 300846), the Swedish Research Council Formas (grant number 2019-02336), the European Union’s Horizon 2020 research and innovation programme under the ERA-Net Cofund project BlueBio (grant agreement number 311913) and the Danish National Research Foundation grant CEH-DNRF143. I.T. received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement number 757922) and from the Science Foundation Ireland under grant number 12/RC/2273-P2. Sequencing was performed by the SNP&SEQ Technology Platform in Uppsala, part of the National Genomics Infrastructure (NGI) Sweden and SciLifeLab. Cell sorting and whole-genome amplification was performed at the Microbial Single Cell Genomics Facility (MSCG) at SciLifeLab. Computations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) under projects SNIC 2021/5-51 and SNIC 2021/22-602. The Orion High Performance Computing Cluster at the Norwegian University of Life Sciences and Saga HPC Sigma2—the National Infrastructure for High Performance Computing and Data Storage in Norway are acknowledged for providing computational resources that have contributed to meta-omics analyses described in this study. We acknowledge Claudia Bergin at the SciLifeLab Microbial Single Cell Genomics Facility for support with cell sorting, genome amplification and library preparation.

Author information

Author notes

  1. These authors contributed equally: Phillip B. Pope, Sabina Leanti La Rosa.

Authors and Affiliations

  1. Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway

    Arturo Vera-Ponce de León, Matthias Hoetzinger, Shashank Gupta, Sander M. Johnsen, Knut Rudi, Lars Snipen, Torgeir R. Hvidsten, Phillip B. Pope & Sabina Leanti La Rosa

  2. Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway

    Arturo Vera-Ponce de León, Simen R. Sandve, Phillip B. Pope & Sabina Leanti La Rosa

  3. School of Medicine, University of Galway, Galway, Ireland

    Tim Hensen, Bronson Weston & Ines Thiele

  4. Digital Metabolic Twin Centre, University of Galway, Galway, Ireland

    Tim Hensen, Bronson Weston & Ines Thiele

  5. Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, Uppsala, Sweden

    Matthias Hoetzinger & Stefan Bertilsson

  6. Center for Evolutionary Hologenomics, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark

    Jacob A. Rasmussen, Cecilie Grønlund Clausen, Louisa Pless, Ana Raquel Andrade Veríssimo & Morten T. Limborg

  7. Department of Fish Health, Nofima, Tromsø, Norway

    Christian René Karlsen

  8. Discipline of Microbiology, University of Galway, Galway, Ireland

    Ines Thiele

  9. Ryan Institute, University of Galway, Galway, Ireland

    Ines Thiele

  10. APC Microbiome Ireland, Cork, Ireland

    Ines Thiele

  11. Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology (QUT), Translational Research Institute, Woolloongabba, Queensland, Australia

    Phillip B. Pope

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Contributions

P.B.P., S.L.L.R., A.V.-P.L., S.R.S., S.B. and T.R.H. designed the study. Isolation experiments and genomics analysis of the cultured microorganisms were carried out by A.V.-P.L., S.L.L.R. and S.M.J. Metagenomic analyses were performed by A.V.-P.L. and M.H. Culture experiments and untargeted metabolomic analyses were carried out by S.L.L.R. Constraint-based metabolic models were generated by T.H., B.W. and I.T. M.H. and S.G. conducted amplicon sequencing analyses. C.R.K., K.R., L.S., J.A.R., M.T.L. and S.B. obtained isolates and generated shotgun sequencing data. The draft of this paper was written by S.L.L.R., P.B.P., M.H., A.V.-P.L. and T.H. All authors contributed to the editing of the text and content and approved the final version.

Corresponding authors

Correspondence to Phillip B. Pope or Sabina Leanti La Rosa.

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The authors declare no competing interests.

Peer review

Peer review information

Nature Microbiology thanks Hilary Browne, Matthew Olm and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Strategy for the generation of the SMGA.

Digesta samples were collected from 107 farmed and 70 wild fish either at the freshwater or seawater stage. Genomic and metagenomic datasets were combined to generate a collection of 211 salmon gut microbial genomes. Green boxes indicate the number of genomes from bacterial isolates or bacterial MAGs obtained using two different approaches in this study. Turquoise boxes indicate the number of genomes for cultured isolates or bacterial MAGs from publicly available studies. For a description of the different assembly strategies, see the Methods section.

Extended Data Fig. 2 16S rRNA gene-based phylogenetic tree illustrating diversity and origin of SMGA’s bacterial genomes as well as identified close relatives in the Silva database.

The cladogram depicts the taxonomical position of all the sequences coloured by genus (outer ring). The sequence ID of each organism (in the SMGA and Silva database) used in the phylogeny are printed in the top of the outer ring. Grey dots in the cladogram indicates a Bootstrap support higher than 70 %. The genome of Prochlorococcus marinus subsp. marinus CCMP1375 (Silva ID: AE017126) was used as an outgroup. Scale bar indicates 5% estimated sequence divergence.

Extended Data Fig. 3 Coverage of the SMGA in shotgun metagenomic datasets generated from salmon digesta samples and mOTUs.

a) Percentage of metagenomic reads mapped against all reconstructed genomes and MAGs in the SMGA examined across 562 datasets (represented by a data point) from four publicly available Bioprojects. Mean values for each Bioproject are indicated on top of each boxplot. The boxes represent the interquartile range, with the central bars being the medians, and the whiskers denoting the lowest and highest values. Coverage % for each dataset can be found in Supplementary Table 1. The SMGA recruited 60.52% of metagenomic reads from a seawater dataset, 61.03% metagenomic reads from a flow-through system dataset, as well as 2.83% and 7.22% reads from a recirculated aquaculture system and a seawater land cages dataset, respectively. b) Pan- genome sizes of species-like mOTUs. The 211 SMGA genomes were clustered into 62 mOTUs (x-axis) based on 95% ANI. Bar heights indicate the number of protein clusters within the core and accessory genome of each mOTU.

Extended Data Fig. 4 Functional ordination of bacterial genomes from the SMGA and genome-inferred functional traits (GIFTs) abundance.

a) Ordination using a t-Distributed Stochastic Neighbor Embedding (t-SNE) analysis for clustering GIFTs values of the SMGA’s bacterial genomes. GIFTs values indicate functional capabilities of bacteria based on their genome content. These metrics are computed for individual functions within a genome, representing the proportion of biochemical reactions mediated by the genes to fulfill specific metabolic tasks. Genomes are colored by taxonomic Order. b) Identical ordination but genomes colored by their Metabolic Capacity Index (MIC). Color gradient indicates the increase of MIC; increasing MIC can be observed from bottom to top. Low-MCI: MCI < 0.15; mid-MCI: 0.15 < MCI < 0.3; high-MCI: MCI > 0.3. A PERMANOVA (Permutational Multivariate Analysis of Variance) was conducted to analyse differences in the MCI across genomes of bacteria collected from fish in fresh and seawater. PERMANOVA results are reported under the MCI legend, ** indicates a significance of p < 0.01. The PERMANOVA was two-sided, and no adjustments were made for multiple comparisons. c) Heatmap showing the abundance of GIFTs associated with a particular metabolic function (columns) in each bacterial genome from the SMGA (rows). Bacterial genomes were hierarchically clustered according to their GIFTs abundance (left dendrogram).

Extended Data Fig. 5 Detecting genomes from the SMGA in publicly available datasets.

The detection of isolate genomes and MAGs from the SMGA (y-axis) in selected publicly available 16S rRNA gene amplicon datasets (x-axis) based on alignment of 16S rRNA gene sequences. 16S rRNA gene detection is coloured based on the % identity of the gene alignment. At a 97% identity level to amplicon sequence variants (ASVs), 144 out of 146 SMGA bacteria were detected in publicly available 16S rRNA gene datasets from either in vivo trials or in vitro models with salmon gut microbial communities. This included prevalent genera such as Carnobacterium, Lactobacillus, Flavobacteria, Photobacterium, Shewanella, Vibrio and Aliivibrio that are routinely observed in salmon microbiome research1,2,3,4.

Extended Data Fig. 6 pFBA model predictions on a complex medium.

a) Heatmap of the normalised mean subsystem fluxes in a complex medium across all 211 strain models. The distributions of the mean subsystem fluxes have been averaged for each microbial genus. In addition, the metabolic subsystems have been ordered from most active (top) to least active (bottom). The heatmap cell colours represent the relative subsystem-level metabolic activity, which is defined as the mean of absolute flux values scaled by the sum of predicted flux values in the models. The values in each column sum up to 100%. Subsystems without flux in any of the models were further removed for this visualisation. This panel shows that the most active subsystems were related to energy metabolism and nucleotide synthesis and that substantial heterogeneity exists between the genera. Note that high fluxes in a subsystem do not mean that a subsystem is more important for cellular growth as all growth-associated reactions fluxes are needed to Panels b) and c) show heatmaps of the pFBA predicted exchange fluxes for the 25 metabolites with the highest predicted consumption rates (b) and 25 metabolites with the highest predicted secretion rates (c) across all 211 models. The shown fluxes represent the mean exchange flux for each microbial genus. Exchange fluxes below zero indicate a net metabolic uptake, while fluxes above zero indicate a predicted net secretion of a metabolite. Metabolites that were consumed the most on average include water, nitrate, L-malate, and D-glucose. Hydrogen, acetate, and formate were among the most secreted metabolites on average. Together, these simulations show that the metabolic models reflected expected metabolic properties, for example, higher metabolic fluxes in energy metabolism and nucleotide synthesis, of growing microbes. Note that these predictions are not necessarily representative of metabolic features of these microbes when directly in the salmon gut environment.

Extended Data Fig. 7 Heatmaps on agreements between measured consumed (a) and secreted (b) metabolites and predicted model capabilities in an unlimited aerobe medium.

The yellow cells show metabolites that were not consumed or not secreted in vitro. Turquoise cells indicate metabolites that were consumed or secreted in vitro but could not be consumed or secreted by the metabolic models, that is, the predictions were false negatives. Finally, the blue cells show which metabolites were also consumed or secreted in vitro and could be consumed or secreted by the metabolic models, that is, the true positives. The Photobacterium phosphoreum S39_bc34 metabolic model captured 78% of the 55 measured in vitro metabolic exchanges. Pseudomonas E_sp_S3_bc03 captured 75% of the 65 in vitro metabolic exchanges, while Serratia liquefaciens S38_bc38 captured 79% of the 68 in vitro exchanged metabolic exchanges. The false negative predictions were likely due to incomplete pathways in the respective strain-specific metabolic models and require further refinement.

Extended Data Fig. 8 CAZyme profiles of the 211 salmon microbial genomes and MAGs in the SMGA.

Heatmap showing the presence of CAZy families (listed on the righthand y-axis) arranged into the different glycan substrate categories (listed on lefthand y-axis) found in each genome that are arranged in taxonomic orders (x-axis). The presence of CAZy genes is denoted by grey-black boxes that are weighted for copy number. CAZy families that are not detected are represented by a white box. GH: glycoside hydrolase, PL: polysaccharide lyase, AA: auxiliary activity. While the dominant CAZy family within the SMGA was GH13, prevalent enzymes in Enterobacterales, Lactobacillales and Pseudomonadales were GH18 and GH19 (followed by GH20), which enable microorganisms to depolymerize chitin through the hydrolytic utilization pathway5. Intriguingly, AA10 LPMOs, that have been shown to be involved in an alternative (oxidative) chitin utilization pathway6, were detected in the genomes of Enterobacterales, Lactobacillales and a few Pseudomonadales. CAZymes involved in utilization of terrestrial and marine plant-derived carbohydrates (for example beta-mannan, beta-glucans, xylans, cello-oligosaccharides, manno-oligosaccharides and algal polysaccharides) included GH1, GH2, GH3, GH5, GH8, GH9, GH10, GH16, GH26, GH36, GH43 and GH94 among others5. In addition, CAZymes belonging to the families GH28, GH35, GH78, GH105, GH147, PL2, PL9 and PL22 for deconstruction of the plant pectic polysaccharide rhamnogalacturonan-I were detected in some Enterobacterales and Lactobacillales genomes5. A few Pseudomonadota and Bacteroidota genomes harboured genes encoding CAZymes for depolymerization of host mucin-derived oligosaccharides, including GH29, GH33, GH109, GH112 and GH1295.

Extended Data Fig. 9 Heatmap illustrating the application of the SMGA as a database to map metatranscriptomes from gut samples.

a) Variation in gene expression of all bacterial genes in the SMGA database (x-axis) and b) a subset of three Enterobacterales (Photobacterium phosphoreum S39_bc34, Pseudomonas_E sp. S3_bc03 and Serratia liquefaciens S38_bc38) in metatranscriptomes generated from gut samples obtained from 33 growing fish fed a standard commercial diet and collected at different life stages (y-axis). T0: 30 g fish (parr), freshwater; T1, 90 g fish (pre-smolt), freshwater; T2, 130 g fish (smolt), freshwater; T3, 300 g fish (adult), seawater.

Extended Data Fig. 10 Examples of the gating strategy used for flow cytometric data.

Examples of the gating strategy used for flow cytometric cell sorting. Cells were stained with SYBR Green I, gated based on forward scatter (x-axes) and fluorescence intensity at 488/530 nm excitation/emission (y-axes) and sorted into 384-well plates by collecting 1 - 200 events per well. Samples were further processed to obtain host-depleted mini-metagenomes, as described in the Method section.

Supplementary information

Supplementary Information

Extended Data Figures 1–10.

Reporting Summary

Peer Review File

Supplementary Table 1

Overview of the SMGA content and its presence in other metagenomic data.

Supplementary Table 2

Metabolomic predictions and models of the SMGA.

Supplementary Table 3

Overview of the in vitro metabolomic reactions of the SMGA.

Supplementary Table 4

CAZy genes encoded in the SMGA.

Supplementary Table 5

Number of expressed genes in terms of TPM in different genomes across different phyla in the SMGA.

Supplementary Table 6

Particularly expressed genes, in terms of TPM, from members of the Lactobacillales, Photobacterium, Serratia and Pseudomonas genomes of the SMGA.

Supplementary Table 7

Untargeted metabolomic values of the SMGA.

Supplementary Table 8

Details about the sequencing depth and mapping rates to non-host and host genome (Salmo salar) for each salmon gut metagenome samples.

Supplementary Dataset 1

Full reports on the benchmarking results on the draft and refined reconstructions from all 211 genome assemblies in the SMGA.

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Vera-Ponce de León, A., Hensen, T., Hoetzinger, M. et al. Genomic and functional characterization of the Atlantic salmon gut microbiome in relation to nutrition and health. Nat Microbiol 9, 3059–3074 (2024). https://doi.org/10.1038/s41564-024-01830-7

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