The need for high-resolution gut microbiome characterization to design efficient strategies for sustainable aquaculture production.


Journal

Communications biology
ISSN: 2399-3642
Titre abrégé: Commun Biol
Pays: England
ID NLM: 101719179

Informations de publication

Date de publication:
25 Oct 2024
Historique:
received: 05 03 2024
accepted: 15 10 2024
medline: 26 10 2024
pubmed: 26 10 2024
entrez: 25 10 2024
Statut: epublish

Résumé

Microbiome-directed dietary interventions such as microbiota-directed fibers (MDFs) have a proven track record in eliciting responses in beneficial gut microbes and are increasingly being promoted as an effective strategy to improve animal production systems. Here we used initial metataxonomic data on fish gut microbiomes as well as a wealth of a priori mammalian microbiome knowledge on α-mannooligosaccharides (MOS) and β-mannan-derived MDFs to study effects of such feed supplements in Atlantic salmon (Salmo salar) and their impact on its gut microbiome composition and functionalities. Our multi-omic analysis revealed that the investigated MDFs (two α-mannans and an acetylated β-galactoglucomannan), at a dose of 0.2% in the diet, had negligible effects on both host gene expression, and gut microbiome structure and function under the studied conditions. While a subsequent trial using a higher (4%) dietary inclusion of β-mannan significantly shifted the gut microbiome composition, there were still no biologically relevant effects on salmon metabolism and physiology. Only a single Burkholderia-Caballeronia-Paraburkholderia (BCP) population demonstrated consistent and significant abundance shifts across both feeding trials, although with no evidence of β-mannan utilization capabilities or changes in gene transcripts for producing metabolites beneficial to the host. In light of these findings, we revisited our omics data to predict and outline previously unreported and potentially beneficial endogenous lactic acid bacteria that should be targeted with future, conceivably more suitable, MDF strategies for salmon.

Identifiants

pubmed: 39455736
doi: 10.1038/s42003-024-07087-4
pii: 10.1038/s42003-024-07087-4
doi:

Substances chimiques

Mannans 0
Dietary Fiber 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1391

Informations de copyright

© 2024. The Author(s).

Références

Colombo, S. M. et al. Towards achieving circularity and sustainability in feeds for farmed blue foods. Rev. Aquac. 15, 1115–1141 (2023).
doi: 10.1111/raq.12766
Nagappan, S. et al. Potential of microalgae as a sustainable feed ingredient for aquaculture. J. Biotechnol. 341, 1–20 (2021).
pubmed: 34534593 doi: 10.1016/j.jbiotec.2021.09.003
La Rosa, S. L. et al. Glycan processing in gut microbiomes. Curr. Opin. Microbiol 67, 102143 (2022).
pubmed: 35338908 doi: 10.1016/j.mib.2022.102143
Wang, J. et al. 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. Anim. Microbiome 3, 14 (2021).
pubmed: 33509296 pmcid: 7841887 doi: 10.1186/s42523-021-00075-7
Torrecillas, S., Montero, D. & Izquierdo, M. Improved health and growth of fish fed mannan oligosaccharides: potential mode of action. Fish. Shellfish Immunol. 36, 525–544 (2014).
pubmed: 24412165 doi: 10.1016/j.fsi.2013.12.029
Grisdale-Helland, B., Helland, S. J. & Gatlin, D. M. The effects of dietary supplementation with mannanoligosaccharide, fructooligosaccharide or galactooligosaccharide on the growth and feed utilization of Atlantic salmon (Salmo salar). Aquaculture 283, 163–167 (2008).
doi: 10.1016/j.aquaculture.2008.07.012
Kazlauskaite, R. et al. SalmoSim: the development of a three-compartment in vitro simulator of the Atlantic salmon GI tract and associated microbial communities. Microbiome 9, 179 (2021).
pubmed: 34465363 pmcid: 8408954 doi: 10.1186/s40168-021-01134-6
Kazlauskaite, R. et al. Deploying an In Vitro Gut Model to Assay the Impact of the Mannan-Oligosaccharide Prebiotic Bio-Mos on the Atlantic Salmon (Salmo salar) Gut Microbiome. Microbiol. Spectr. 10, e01953–01921 (2022).
pubmed: 35532227 pmcid: 9241627 doi: 10.1128/spectrum.01953-21
La Rosa, S. L. et al. The human gut Firmicute Roseburia intestinalis is a primary degrader of dietary beta-mannans. Nat. Commun. 10, 905 (2019).
pubmed: 30796211 pmcid: 6385246 doi: 10.1038/s41467-019-08812-y
Lindstad, L. J. et al. Human Gut Faecalibacterium prausnitzii Deploys a Highly Efficient Conserved System To Cross-Feed on beta-Mannan-Derived Oligosaccharides. mBio 12, e0362820 (2021).
pubmed: 34061597 doi: 10.1128/mBio.03628-20
Michalak, L. et al. Microbiota-directed fibre activates both targeted and secondary metabolic shifts in the distal gut. Nat. Commun. 11, 5773 (2020).
pubmed: 33188211 pmcid: 7666174 doi: 10.1038/s41467-020-19585-0
Panwar, D., Shubhashini, A. & Kapoor, M. Complex alpha and beta mannan foraging by the human gut bacteria. Biotechnol. Adv. 66, 108166 (2023).
pubmed: 37121556 doi: 10.1016/j.biotechadv.2023.108166
Strand, M. A., Jin, Y., Sandve, S. R., Pope, P. B. & Hvidsten, T. R. Transkingdom network analysis provides insight into host-microbiome interactions in Atlantic salmon. Comput Struct. Biotechnol. J. 19, 1028–1034 (2021).
pubmed: 33613868 pmcid: 7876536 doi: 10.1016/j.csbj.2021.01.038
Legrand, T. P. R. A., Wynne, J. W., Weyrich, L. S. & Oxley, A. P. A. A microbial sea of possibilities: current knowledge and prospects for an improved understanding of the fish microbiome. Rev. Aquac. 12, 1101–1134 (2020).
doi: 10.1111/raq.12375
Cathers, H. S. et al. In silico, in vitro and in vivo characterization of host-associated Latilactobacillus curvatus strains for potential probiotic applications in farmed Atlantic salmon (Salmo salar). Sci. Rep. 12, 18417 (2022).
pubmed: 36319729 pmcid: 9626465 doi: 10.1038/s41598-022-23009-y
Rasmussen, J. A. et al. Co-diversification of an intestinal Mycoplasma and its salmonid host. ISME J. https://doi.org/10.1038/s41396-023-01379-z (2023).
doi: 10.1038/s41396-023-01379-z pubmed: 36807409 pmcid: 10119124
Rasmussen, J. A. et al. Genome-resolved metagenomics suggests a mutualistic relationship between Mycoplasma and salmonid hosts. Commun. Biol. 4, 579 (2021).
pubmed: 33990699 pmcid: 8121932 doi: 10.1038/s42003-021-02105-1
Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 35, 725–731 (2017).
pubmed: 28787424 pmcid: 6436528 doi: 10.1038/nbt.3893
Vera-Ponce de León, A. et al. Genomic and functional characterization of the Atlantic salmon gut microbiome in relation to nutrition and health. Nat Microbiol. https://doi.org/10.1038/s41564-024-01830-7 (2024).
Bozzi, D. et al. Salmon gut microbiota correlates with disease infection status: potential for monitoring health in farmed animals. Anim. Microbiome 3, 30 (2021).
pubmed: 33879261 pmcid: 8056536 doi: 10.1186/s42523-021-00096-2
Weththasinghe, P. et al. Modulation of Atlantic salmon (Salmo salar) gut microbiota composition and predicted metabolic capacity by feeding diets with processed black soldier fly (Hermetia illucens) larvae meals and fractions. Anim. Microbiome 4, 9 (2022).
pubmed: 35033208 pmcid: 8760679 doi: 10.1186/s42523-021-00161-w
Fogarty, C. et al. Diversity and composition of the gut microbiota of Atlantic salmon (Salmo salar) farmed in Irish waters. J. Appl. Microbiol. 127, 648–657 (2019).
pubmed: 31021487 doi: 10.1111/jam.14291
Wang, C., Sun, G., Li, S., Li, X., Liu, Y. Intestinal microbiota of healthy and unhealthy Atlantic salmon Salmo salar L. in a recirculating aquaculture system. J. Oceanol. Limnol. 36, 414–426 (2018).
Karlsen, C. et al. Feed microbiome: confounding factor affecting fish gut microbiome studies. ISME Commun. 2, 14 (2022).
pubmed: 37938665 pmcid: 9723547 doi: 10.1038/s43705-022-00096-6
Li, Y., Gajardo, K., Jaramillo-Torres, A., Kortner, T. M. & Krogdahl, Å. Consistent changes in the intestinal microbiota of Atlantic salmon fed insect meal diets. Anim. Microbiome 4, 8 (2022).
pubmed: 35012688 pmcid: 8750867 doi: 10.1186/s42523-021-00159-4
Cheaib, B. et al. Genome erosion and evidence for an intracellular niche - exploring the biology of mycoplasmas in Atlantic salmon. Aquaculture 541, 736772 (2021).
pubmed: 34471330 pmcid: 8192413 doi: 10.1016/j.aquaculture.2021.736772
Nguyen, C. D. H., Amoroso, G., Ventura, T. & Elizur, A. Assessing the Pyloric Caeca and Distal Gut Microbiota Correlation with Flesh Color in Atlantic Salmon (Salmo salar L., 1758). Microorganisms 8, https://doi.org/10.3390/microorganisms8081244 (2020).
Kortner, T. M. et al. A comprehensive transcriptional body map of Atlantic salmon unveils the vital role of the intestine in the immune system and highlights functional specialization within its compartments. Fish. Shellfish Immunol. 146, 109422 (2024).
pubmed: 38307300 doi: 10.1016/j.fsi.2024.109422
Michalak, L. et al. A pair of esterases from a commensal gut bacterium remove acetylations from all positions on complex beta-mannans. Proc. Natl Acad. Sci. USA 117, 7122–7130 (2020).
pubmed: 32170022 pmcid: 7132267 doi: 10.1073/pnas.1915376117
Sivaprakasam, S., Bhutia, Y. D., Yang, S. & Ganapathy, V. Short-Chain Fatty Acid Transporters: Role in Colonic Homeostasis. Compr. Physiol. 8, 299–314 (2017).
pubmed: 29357130 pmcid: 6019286 doi: 10.1002/cphy.c170014
Fremder, M. et al. A transepithelial pathway delivers succinate to macrophages, thus perpetuating their pro-inflammatory metabolic state. Cell Rep. 36, 109521 (2021).
pubmed: 34380041 doi: 10.1016/j.celrep.2021.109521
Gupta, S. et al. Lactobacillus Dominate in the Intestine of Atlantic Salmon Fed Dietary Probiotics. Front. Microbiol. 9, 3247 (2018).
pubmed: 30700981 doi: 10.3389/fmicb.2018.03247
Dysvik, A. et al. Secondary Lactic Acid Bacteria Fermentation with Wood-Derived Xylooligosaccharides as a Tool To Expedite Sour Beer Production. J. Agric Food Chem. 68, 301–314 (2020).
pubmed: 31820631 doi: 10.1021/acs.jafc.9b05459
Wall, T. & Bjerkås, E. A simplified method of scoring cataracts in fish. Bull. Eur. Assoc. Fish. Pathologists 19, 162–165 (1999).
Noble, C. G. K., Iversen, M. H., Kolarevic, J., Nilsson, J., Stien, L. H. & Turnbull, J. F. Welfare Indicators for farmed Atlantic salmon: tools for assessing fish welfare, http://hdl.handle.net/11250/2575780 (2018).
Morkore, T. et al. Dietary inclusion of Antarctic krill meal during the finishing feed period improves health and fillet quality of Atlantic salmon (Salmo salar L.). Br. J. Nutr. 124, 418–431 (2020).
pubmed: 32252833 pmcid: 7369378 doi: 10.1017/S0007114520001282
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. journal 17, 3 (2011).
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
pubmed: 31341288 pmcid: 7015180 doi: 10.1038/s41587-019-0209-9
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).
pubmed: 23193283 doi: 10.1093/nar/gks1219
Team, R. C. R: A language and environment for statistical computing. MSOR Connections 1, https://www.r-project.org/ (2014).
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013).
pubmed: 23630581 pmcid: 3632530 doi: 10.1371/journal.pone.0061217
Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 226 (2018).
pubmed: 30558668 pmcid: 6298009 doi: 10.1186/s40168-018-0605-2
Gupta, S. et al. Amplicon sequencing provides more accurate microbiome information in healthy children compared to culturing. Commun. Biol. 2, 291 (2019).
pubmed: 31396571 pmcid: 6683184 doi: 10.1038/s42003-019-0540-1
Ginestet, C. ggplot2: Elegant Graphics for Data Analysis. J. R. Stat. Soc. Ser. A 174, 245–245 (2011).
doi: 10.1111/j.1467-985X.2010.00676_9.x
Schubert, M., Lindgreen, S. & Orlando, L. AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC Res. Notes 9, 88 (2016).
pubmed: 26868221 pmcid: 4751634 doi: 10.1186/s13104-016-1900-2
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
pubmed: 23104886 doi: 10.1093/bioinformatics/bts635
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
pubmed: 24227677 doi: 10.1093/bioinformatics/btt656
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
pubmed: 25516281 pmcid: 4302049 doi: 10.1186/s13059-014-0550-8
Kopylova, E., Noe, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).
pubmed: 23071270 doi: 10.1093/bioinformatics/bts611
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
pubmed: 19505943 pmcid: 2723002 doi: 10.1093/bioinformatics/btp352
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
pubmed: 27043002 doi: 10.1038/nbt.3519
Li, D., Liu, C. M., Luo, R., Sadakane, K. & Lam, T. W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).
pubmed: 25609793 doi: 10.1093/bioinformatics/btv033
Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019).
pubmed: 31779668 pmcid: 6883579 doi: 10.1186/s13059-019-1891-0
Shaffer, M. et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 48, 8883–8900 (2020).
pubmed: 32766782 pmcid: 7498326 doi: 10.1093/nar/gkaa621
Drula, E. et al. The carbohydrate-active enzyme database: functions and literature. Nucleic Acids Res. 50, D571–D577 (2022).
pubmed: 34850161 doi: 10.1093/nar/gkab1045
Johnsen, L. G., Skou, P. B., Khakimov, B. & Bro, R. Gas chromatography - mass spectrometry data processing made easy. J. Chromatogr. A 1503, 57–64 (2017).
pubmed: 28499599 doi: 10.1016/j.chroma.2017.04.052
Pang, Z. et al. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 49, W388–W396 (2021).
pubmed: 34019663 pmcid: 8265181 doi: 10.1093/nar/gkab382

Auteurs

Shashank Gupta (S)

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

Arturo Vera-Ponce de León (A)

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

Miyako Kodama (M)

Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Copenhagen, Denmark.

Matthias Hoetzinger (M)

Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway.
Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, Uppsala, Sweden.

Cecilie G Clausen (CG)

Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Copenhagen, Denmark.

Louisa Pless (L)

Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Copenhagen, Denmark.

Ana R A Verissimo (ARA)

Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Copenhagen, Denmark.

Bruno Stengel (B)

Cargill Food Solutions - R&D - SST, Sandnes, Norway.

Virginia Calabuig (V)

Cargill Food Solutions - R&D - SST, Sandnes, Norway.

Renate Kvingedal (R)

Cargill Aqua Nutrition, Cargill, Sandnes, Norway.

Stanko Skugor (S)

Cargill Aqua Nutrition, Cargill, Sandnes, Norway.

Bjørge Westereng (B)

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

Thomas Nelson Harvey (TN)

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

Anna Nordborg (A)

Department of Biotechnology and Nanomedicine, SINTEF, Trondheim, Norway.

Stefan Bertilsson (S)

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

Morten T Limborg (MT)

Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Copenhagen, Denmark.

Turid Mørkøre (T)

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

Simen R Sandve (SR)

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

Phillip B Pope (PB)

Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway.
Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway.
Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology (QUT), Translational Research Institute, Woolloongabba, QLD, Australia.

Torgeir R Hvidsten (TR)

Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway. torgeir.r.hvidsten@nmbu.no.

Sabina Leanti La Rosa (SL)

Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway. sabina.leantilarosa@nmbu.no.
Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway. sabina.leantilarosa@nmbu.no.

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