Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Candidatus Nemesobacterales is a sponge-specific clade of the candidate phylum Desulfobacterota adapted to a symbiotic lifestyle

The ISME Journal volume 17, pages 1808–1818 (2023)Cite this article

Subjects

Abstract

Members of the candidate phylum Dadabacteria, recently reassigned to the phylum Candidatus Desulfobacterota, are cosmopolitan in the marine environment found both free-living and associated with hosts that are mainly marine sponges. Yet, these microorganisms are poorly characterized, with no cultured representatives and an ambiguous phylogenetic position in the tree of life. Here, we performed genome-centric metagenomics to elucidate their phylogenomic placement and predict the metabolism of the sponge-associated members of this lineage. Rank-based phylogenomics revealed several new species and a novel family (Candidatus Spongomicrobiaceae) within a sponge-specific order, named here Candidatus Nemesobacterales. Metabolic reconstruction suggests that Ca. Nemesobacterales are aerobic heterotrophs, capable of synthesizing most amino acids, vitamins and cofactors and degrading complex carbohydrates. We also report functional divergence between sponge- and seawater-associated metagenome-assembled genomes. Niche-specific adaptations to the sponge holobiont were evident from significantly enriched genes involved in defense mechanisms against foreign DNA and environmental stressors, host-symbiont interactions and secondary metabolite production. Fluorescence in situ hybridization gave a first glimpse of the morphology and lifestyle of a member of Ca. Desulfobacterota. Candidatus Nemesobacterales spp. were found both inside sponge cells centred around sponge nuclei and in the mesohyl of the sponge Geodia barretti. This study sheds light on the enigmatic group Ca. Nemesobacterales and their functional characteristics that reflect a symbiotic lifestyle.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Phylogenomic tree of Ca. Dadabacteria (GTDB-Desulfobacterota__D) based on the concatenated alignment of 120 bacterial single-copy marker protein sequences (5 037 positions).
Fig. 2: Schematic map representing the global distribution of Ca. Nemesobacterales.
Fig. 3: Schematic overview of the inferred metabolism of Ca. Nemesobacterales.
Fig. 4: Functional comparison of Ca. Dadabacteria (GTDB-Desulfobacterota__D) MAGs associated with marine sponges and seawater.
Fig. 5: Imaging of Ca. Nemesobacterales spp. in G. barretti.

Similar content being viewed by others

Data availability

Raw reads, metagenome assemblies and MAGs generated for this study can be found under the European Nucleotide Archive (ENA) project accession numbers PRJEB54590, PRJEB51534 and PRJEB51535. All accession numbers for the data included in this study are included in the supplementary information of this article.

References

  1. Bond PL, Hugenholtz P, Keller J, Blackall LL. Bacterial community structures of phosphate-removing and non-phosphate-removing activated sludges from sequencing batch reactors. Appl Environ Microbiol. 1995;61:1910–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Wang Z, Guo F, Liu L, Zhang T. Evidence of carbon fixation pathway in a bacterium from candidate phylum SBR1093 revealed with genomic analysis. PLoS ONE. 2014;9:e109571.

    PubMed  PubMed Central  Google Scholar 

  3. Hug LA, Thomas BC, Sharon I, Brown CT, Sharma R, Hettich RL, et al. Critical biogeochemical functions in the subsurface are associated with bacteria from new phyla and little studied lineages. Environ Microbiol. 2016;18:159–73.

    CAS  PubMed  Google Scholar 

  4. Waite DW, Chuvochina M, Pelikan C, Parks DH, Yilmaz P, Wagner M, et al. Proposal to reclassify the proteobacterial classes Deltaproteobacteria and Oligoflexia, and the phylum Thermodesulfobacteria into four phyla reflecting major functional capabilities. Int J Syst Evol Microbiol. 2020;70:5972–6016.

    CAS  PubMed  Google Scholar 

  5. Konstantinidis KT, Rossello-Mora R, Amann R. Uncultivated microbes in need of their own taxonomy. ISME J. 2017;11:2399–406.

    PubMed  PubMed Central  Google Scholar 

  6. Hugenholtz P, Chuvochina M, Oren A, Parks DH, Soo RM. Prokaryotic taxonomy and nomenclature in the age of big sequence data. ISME J. 2021;15:1879–92.

    PubMed  PubMed Central  Google Scholar 

  7. Parks DH, Chuvochina M, Chaumeil PA, Rinke C, Mussig AJ, Hugenholtz P. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat Biotechnol. 2020;38:1079–86.

    CAS  PubMed  Google Scholar 

  8. Graham ED, Tully BJ. Marine Dadabacteria exhibit genome streamlining and phototrophy-driven niche partitioning. ISME J. 2021;15:1248–56.

    CAS  PubMed  Google Scholar 

  9. Thomas T, Moitinho-Silva L, Lurgi M, Bjork JR, Easson C, Astudillo-Garcia C, et al. Diversity, structure and convergent evolution of the global sponge microbiome. Nat Commun. 2016;7:11870.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Pita L, Rix L, Slaby BM, Franke A, Hentschel U. The sponge holobiont in a changing ocean: from microbes to ecosystems. Microbiome. 2018;6:46.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Webster NS, Taylor MW. Marine sponges and their microbial symbionts: love and other relationships. Environ Microbiol. 2012;14:335–46.

    CAS  PubMed  Google Scholar 

  12. Podell S, Blanton JM, Neu A, Agarwal V, Biggs JS, Moore BS, et al. Pangenomic comparison of globally distributed Poribacteria associated with sponge hosts and marine particles. ISME J. 2019;13:468–81.

    CAS  PubMed  Google Scholar 

  13. Lackner G, Peters EE, Helfrich EJ, Piel J. Insights into the lifestyle of uncultured bacterial natural product factories associated with marine sponges. Proc Natl Acad Sci USA. 2017;114:E347–56.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Slaby BM, Hackl T, Horn H, Bayer K, Hentschel U. Metagenomic binning of a marine sponge microbiome reveals unity in defense but metabolic specialization. ISME J. 2017;11:2465–78.

    PubMed  PubMed Central  Google Scholar 

  15. Diez-Vives C, Moitinho-Silva L, Nielsen S, Reynolds D, Thomas T. Expression of eukaryotic-like protein in the microbiome of sponges. Mol Ecol. 2017;26:1432–51.

    CAS  PubMed  Google Scholar 

  16. Astudillo-Garcia C, Slaby BM, Waite DW, Bayer K, Hentschel U, Taylor MW. Phylogeny and genomics of SAUL, an enigmatic bacterial lineage frequently associated with marine sponges. Environ Microbiol. 2018;20:561–76.

    CAS  PubMed  Google Scholar 

  17. Sizikov S, Burgsdorf I, Handley KM, Lahyani M, Haber M, Steindler L. Characterization of sponge-associated Verrucomicrobia: microcompartment-based sugar utilization and enhanced toxin-antitoxin modules as features of host-associated Opitutales. Environ Microbiol. 2020;22:4669–88.

    CAS  PubMed  Google Scholar 

  18. Engelberts JP, Robbins SJ, de Goeij JM, Aranda M, Bell SC, Webster NS. Characterization of a sponge microbiome using an integrative genome-centric approach. ISME J. 2020;14:1100–10.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Robbins SJ, Song W, Engelberts JP, Glasl B, Slaby BM, Boyd J, et al. A genomic view of the microbiome of coral reef demosponges. ISME J. 2021;15:1641–54.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Taylor JA, Palladino G, Wemheuer B, Steinert G, Sipkema D, Williams TJ, et al. Phylogeny resolved, metabolism revealed: functional radiation within a widespread and divergent clade of sponge symbionts. ISME J. 2021;15:503–19.

    CAS  PubMed  Google Scholar 

  21. Chaib De Mares M, Jimenez DJ, Palladino G, Gutleben J, Lebrun LA, Muller EEL, et al. Expressed protein profile of a Tectomicrobium and other microbial symbionts in the marine sponge Aplysina aerophoba as evidenced by metaproteomics. Sci Rep. 2018;8:11795.

    PubMed  PubMed Central  Google Scholar 

  22. Steffen K, Indraningrat AAG, Erngren I, Haglöf J, Becking LE, Smidt H, et al. Oceanographic setting influences the prokaryotic community and metabolome in deep-sea sponges. Sci Rep. 2022;12:3356.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Loureiro C, Galani A, Gavriilidou A, Chaib de Mares M, van der Oost J, Medema MH, et al. Comparative metagenomic analysis of biosynthetic diversity across sponge microbiomes highlights metabolic novelty, conservation, and diversification. mSystems. 2022;7:e0035722.

    PubMed  PubMed Central  Google Scholar 

  24. Peters EE, Cahn JKB, Lotti A, Gavriilidou A, Steffens UAE., Loureiro C, et al. Distribution and diversity of ‘Tectomicrobia’, a deep-branching uncultivated bacterial lineage harboring rich producers of bioactive metabolites. ISME Commun. 2023;3:50.

  25. Roume H, Heintz-Buschart A, Muller EE, Wilmes P. Sequential isolation of metabolites, RNA, DNA, and proteins from the same unique sample. Methods Enzymol. 2013;531:219–36.

    CAS  PubMed  Google Scholar 

  26. Andrews S FASTQC: a quality control tool for high throughput sequence data. 2010. http://www.bioinformatics.babraham.ac.uk/projects/fastqc.

  27. Bushnell B, Rood J, Singer E. BBMerge - Accurate paired shotgun read merging via overlap. PLoS ONE. 2017;12:e0185056.

    PubMed  PubMed Central  Google Scholar 

  28. Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome. 2018;6:1–13.

    Google Scholar 

  30. Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D, Reddy TBK, et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotechnol. 2017;35:725–31.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359.

    PubMed  PubMed Central  Google Scholar 

  32. Wu YW, Tang YH, Tringe SG, Simmons BA, Singer SW. MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm. Microbiome. 2014;2:26.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.

    CAS  PubMed  Google Scholar 

  34. Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2019;36:1925–7.

    PubMed  PubMed Central  Google Scholar 

  36. Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar, et al. ARB: a software environment for sequence data. Nucleic Acids Res. 2004;32:1363–71.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Capella-Gutierrez S, Silla-Martinez JM, Gabaldon T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics. 2009;25:1972–3.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD, von Haeseler A, et al. IQ-TREE 2: New models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol. 2020;37:1530–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14:587–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Letunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49:W293–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Rinke C, Chuvochina M, Mussig AJ, Chaumeil PA, Davin AA, Waite DW, et al. A standardized archaeal taxonomy for the Genome Taxonomy Database. Nat Microbiol. 2021;6:946–59.

    CAS  PubMed  Google Scholar 

  43. Price MN, Dehal PS, Arkin AP. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:e9490.

    PubMed  PubMed Central  Google Scholar 

  44. Chuvochina M, Rinke C, Parks DH, Rappe MS, Tyson GW, Yilmaz P, et al. The importance of designating type material for uncultured taxa. Syst Appl Microbiol. 2019;42:15–21.

    PubMed  Google Scholar 

  45. Zhou Z, Tran PQ, Kieft K, Anantharaman K. Genome diversification in globally distributed novel marine Proteobacteria is linked to environmental adaptation. ISME J. 2020;14:2060–77.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Chen IA, Chu K, Palaniappan K, Pillay M, Ratner A, Huang J, et al. IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res. 2019;47:D666–77.

    CAS  PubMed  Google Scholar 

  47. Yarza P, Yilmaz P, Pruesse E, Glockner FO, Ludwig W, Schleifer KH, et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat Rev Microbiol. 2014;12:635–45.

    CAS  PubMed  Google Scholar 

  48. R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2020.

  49. RStudio Team. RStudio: integrated development of R. Boston, MA: RStudio, PBC; 2020.

  50. Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. Vegan: community ecology package. R package version 2.5-2. 2018. https://CRAN.R-project.org/package=vegan.

  51. Wickham H. ggplot2: Elegant graphics for data analysis. Verlag New York: Springer; 2016.

  52. Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Eddy SR. HMMER User’s guide: biological sequence analysis using profile hidden Markov models. 3.2.1 ed2018.

  54. Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2018;46:W95–101.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Blin K, Shaw S, Kloosterman AM, Charlop-Powers Z, van Wezel GP, Medema MH, et al. antiSMASH 6.0: improving cluster detection and comparison capabilities. Nucleic Acids Res. 2021;49:W29–35.

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Pruesse E, Peplies J, Glockner FO. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics. 2012;28:1823–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.

    CAS  PubMed  Google Scholar 

  58. Stamatakis A. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics. 2006;22:2688–90.

    CAS  PubMed  Google Scholar 

  59. Yilmaz LS, Parnerkar S, Noguera DR. mathFISH, a web tool that uses thermodynamics-based mathematical models for in silico evaluation of oligonucleotide probes for fluorescence in situ hybridization. Appl Environ Microbiol. 2011;77:1118–22.

    CAS  PubMed  Google Scholar 

  60. Radax R, Rattei T, Lanzen A, Bayer C, Rapp HT, Urich T, et al. Metatranscriptomics of the marine sponge Geodia barretti: tackling phylogeny and function of its microbial community. Environ Microbiol. 2012;14:1308–24.

    CAS  PubMed  Google Scholar 

  61. Schimak MP, Kleiner M, Wetzel S, Liebeke M, Dubilier N, Fuchs BM. MiL-FISH: multilabeled oligonucleotides for fluorescence in situ hybridization improve visualization of bacterial cells. Appl Environ Microbiol. 2016;82:62–70.

    CAS  PubMed  Google Scholar 

  62. Yilmaz P, Parfrey LW, Yarza P, Gerken J, Pruesse E, Quast C, et al. The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Res. 2014;42:D643–8.

    CAS  PubMed  Google Scholar 

  63. Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil PA, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.

    CAS  PubMed  Google Scholar 

  64. Whitman WB. Genome sequences as the type material for taxonomic descriptions of prokaryotes. Syst Appl Microbiol. 2015;38:217–22.

    CAS  PubMed  Google Scholar 

  65. Koedooder C, Guéneuguès A, Van Geersdaële R, Vergé V, Bouget F-Y, Labreuche Y, et al. The role of the glyoxylate shunt in the acclimation to iron limitation in marine heterotrophic bacteria. Front Mar Sci. 2018;5:435.

  66. Bayer K, Jahn MT, Slaby BM, Moitinho-Silva L, Hentschel U. Marine sponges as Chloroflexi hot spots: Genomic insights and high-resolution visualization of an abundant and diverse symbiotic clade. mSystems. 2018;3:e00150–18.

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Taylor MW, Radax R, Steger D, Wagner M. Sponge-associated microorganisms: evolution, ecology, and biotechnological potential. Microbiol Mol Biol Rev. 2007;71:295–347.

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Taylor MW, Hill RT, Piel J, Thacker RW, Hentschel U. Soaking it up: the complex lives of marine sponges and their microbial associates. ISME J. 2007;1:187–90.

    PubMed  Google Scholar 

  69. Thomas T, Rusch D, DeMaere MZ, Yung PY, Lewis M, Halpern A, et al. Functional genomic signatures of sponge bacteria reveal unique and shared features of symbiosis. ISME J. 2010;4:1557–67.

    CAS  PubMed  Google Scholar 

  70. Fan L, Reynolds D, Liu M, Stark M, Kjelleberg S, Webster NS, et al. Functional equivalence and evolutionary convergence in complex communities of microbial sponge symbionts. Proc Natl Acad Sci USA. 2012;109:E1878–87.

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Webster NS, Thomas T. The sponge hologenome. MBio. 2016;7:e00135–16.

    PubMed  PubMed Central  Google Scholar 

  72. Kamke J, Sczyrba A, Ivanova N, Schwientek P, Rinke C, Mavromatis K, et al. Single-cell genomics reveals complex carbohydrate degradation patterns in poribacterial symbionts of marine sponges. ISME J 2013;7:2287–300.

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Bennke CM, Kruger K, Kappelmann L, Huang S, Gobet A, Schuler M, et al. Polysaccharide utilisation loci of Bacteroidetes from two contrasting open ocean sites in the North Atlantic. Environ Microbiol. 2016;18:4456–70.

    CAS  PubMed  Google Scholar 

  74. Gavriilidou A, Gutleben J, Versluis D, Forgiarini F, van Passel MWJ, Ingham CJ, et al. Comparative genomic analysis of Flavobacteriaceae: insights into carbohydrate metabolism, gliding motility and secondary metabolite biosynthesis. BMC Genomics. 2020;21:569.

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Haber M, Burgsdorf I, Handley KM, Rubin-Blum M, Steindler L. Genomic insights into the lifestyles of Thaumarchaeota inside sponges. Front Microbiol. 2020;11:622824.

    PubMed  Google Scholar 

  76. Munroe S, Sandoval K, Martens DE, Sipkema D, Pomponi SA. Genetic algorithm as an optimization tool for the development of sponge cell culture media. Vitr Cell Dev Biol Anim. 2019;55:149–58.

    Google Scholar 

  77. Fan L, Reynolds D, Liu M, Stark M, Kjelleberg S, Websterf NS, et al. Functional equivalence and evolutionary convergence in complex communities of microbial sponge symbionts. Proc Natl Acad Sci USA. 2012;109:1879–87.

    Google Scholar 

  78. Horn H, Slaby BM, Jahn MT, Bayer K, Moitinho-Silva L, Forster F, et al. An enrichment of CRISPR and other defense-related features in marine aponge-associated microbial metagenomes. Front Microbiol. 2016;7:1751.

    PubMed  PubMed Central  Google Scholar 

  79. Reynolds D, Thomas T. Evolution and function of eukaryotic-like proteins from sponge symbionts. Mol Ecol. 2016;25:5242–53.

    CAS  PubMed  Google Scholar 

  80. Moreno-Pino M, Cristi A, Gillooly JF, Trefault N. Characterizing the microbiomes of Antarctic sponges: a functional metagenomic approach. Sci Rep. 2020;10:645.

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Makarova KS, Wolf YI, Koonin EV. Comparative genomics of defense systems in archaea and bacteria. Nucleic Acids Res. 2013;41:4360–77.

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Harms A, Brodersen DE, Mitarai N, Gerdes K. Toxins, targets, and triggers: an overview of toxin-antitoxin biology. Mol Cell. 2018;70:768–84.

    CAS  PubMed  Google Scholar 

  83. Nguyen MT, Liu M, Thomas T. Ankyrin-repeat proteins from sponge symbionts modulate amoebal phagocytosis. Mol Ecol. 2014;23:1635–45.

    CAS  PubMed  Google Scholar 

  84. Fiore CL, Labrie M, Jarett JK, Lesser MP. Transcriptional activity of the giant barrel sponge, Xestospongia muta Holobiont: molecular evidence for metabolic interchange. Front Microbiol. 2015;6:364.

    PubMed  PubMed Central  Google Scholar 

  85. Cerveny L, Straskova A, Dankova V, Hartlova A, Ceckova M, Staud F, et al. Tetratricopeptide repeat motifs in the world of bacterial pathogens: role in virulence mechanisms. Infect Immun. 2013;81:629–35.

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Piel J. Metabolites from symbiotic bacteria. Nat Prod Rep. 2009;26:338–62.

    CAS  PubMed  Google Scholar 

  87. Indraningrat AA, Smidt H, Sipkema D. Bioprospecting sponge-associated microbes for antimicrobial compounds. Mar Drugs. 2016;14:87.

    PubMed  PubMed Central  Google Scholar 

  88. Brinkmann CM, Marker A, Ipek Kurtböke D. An overview on marine sponge-symbiotic bacteria as unexhausted sources for natural product discovery. Diversity. 2017;9:40.

  89. Karimi E, Ramos M, Goncalves JMS, Xavier JR, Reis MP, Costa R. Comparative metagenomics reveals the distinctive adaptive features of the Spongia officinalis endosymbiotic consortium. Front Microbiol 2017;8:2499.

    PubMed  PubMed Central  Google Scholar 

  90. Konstantinou D, Mavrogonatou E, Zervou SK, Giannogonas P, Gkelis S. Bioprospecting sponge-associated marine Cyanobacteria to produce bioactive compounds. Toxins. 2020;12:73.

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Gavriilidou A, Mackenzie TA, Sanchez P, Tormo JR, Ingham C, Smidt H, et al. Bioactivity screening and gene-trait matching across marine sponge-associated bacteria. Mar Drugs. 2021;19:75.

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Piel J, Hui D, Wen G, Butzke D, Platzer M, Fusetani N, et al. Antitumor polyketide biosynthesis by an uncultivated bacterial symbiont of the marine sponge Theonella swinhoei. Proc Natl Acad Sci USA. 2004;101:16222–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Robinson SL, Christenson JK, Wackett LP. Biosynthesis and chemical diversity of β-lactone natural products. Nat Prod Rep. 2019;20:458–75.

    Google Scholar 

  94. Kirchberger PC, Schmidt ML, Ochman H. The ingenuity of bacterial genomes. Ann Rev Microbiol 2020;74:815–34.

    CAS  Google Scholar 

  95. Maldonado M. Intergenerational transmission of symbiotic bacteria in oviparous and viviparous demosponges, with emphasis on intracytoplasmically-compartmented bacterial types. J Mar Biol Assoc. 2007;87:1701–13.

    Google Scholar 

  96. Vacelet J, Donadey C. Electron microscope study of the association between some sponges and bacteria. J Exp Mar Biol Ecol. 1977;30:301–14.

    Google Scholar 

  97. Moran NA, Wernegreen JJ. Lifestyle evolution in symbiotic bacteria: insights from genomics. Trends Ecol Evol. 2000;15:321–6.

    CAS  PubMed  Google Scholar 

  98. Usher KM, Kuo J, Fromont J, Sutton DC. Vertical transmission of cyanobacterial symbionts in the marine sponge Chondrilla australiensis (Demospongiae). Hydrobiologia. 2001;461:9–13.

    Google Scholar 

  99. Diez-Vives C, Koutsouveli V, Conejero M, Riesgo A. Global patterns in symbiont selection and transmission strategies in sponges. Front Ecol Evol. 2022;10:1015592.

  100. Carrier TJ, Maldonado M, Schmittmann L, Pita L, Bosch TCG, Hentschel U. Symbiont transmission in marine sponges: reproduction, development, and metamorphosis. BMC Biol. 2022;20:100.

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the late Hans Tore Rapp for his invaluable help in collecting G. barretti samples from Norway, Ellen Kenchington for the Canadian G. barretti samples, Vasilis Gerovasileiou for performing the sampling of P. ficiformis and Andriaan Schrier for supporting the sponge collection in Dominica. Henk Schipper is acknowledged for helping with the sponge tissue processing. The authors would like to thank Catarina Loureiro for performing the preprocessing of the reads, the metagenome assembly and binning of the A. aerophoba samples and for her feedback regarding the manuscript. We thank Paco Cárdenas and Karin Steffen for the identification of G. atlantica. We also thank Torsten Thomas for providing us with additional data for our analysis. Maria Chuvochina, Donovan Parks and Philip Hugenholtz are acknowledged for their advice on taxonomy and rank assignment. This research was financially supported by the European Commission through the SponGES project (Grant agreement ID: 679849) to DS and AsG, a Marie Skłodowska-Curie Individual Fellowship COSMos (Grant agreement ID: 897121) to MAS, and by grants from European Research Council (ERC consolidator grant 817834), the Dutch Research Council (NWO-VICI grant VI.C.192.016), and the Moore–Simons Project on the Origin of the Eukaryotic Cell (Simons Foundation 735925LPI) to TJGE.

Author information

Authors and Affiliations

  1. Laboratory of Microbiology, Wageningen University & Research, Stippeneng 4, 6708WE, Wageningen, The Netherlands

    Asimenia Gavriilidou, Burak Avcı, Anastasia Galani, Michelle A. Schorn, Thijs J. G. Ettema, Hauke Smidt & Detmer Sipkema

  2. Hoekmine BV, Verenigingstraat 36, 3515GJ, Utrecht, The Netherlands

    Colin J. Ingham

Authors
  1. Asimenia Gavriilidou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Burak Avcı
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anastasia Galani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michelle A. Schorn
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Colin J. Ingham
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Thijs J. G. Ettema
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hauke Smidt
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Detmer Sipkema
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AsG, CJI, HS and DS conceived the study. AsG, AnG and DS collected the sponge samples. AsG, AnG and MAS processed the samples for sequencing. AnG and MAS did the quality filtering of raw reads, metagenome assemblies, binning and taxonomic classification (Aply, DOM and PF samples). AsG performed the binning and taxonomic classification of the GA and GB samples. AsG performed the MAG collection, phylogenetic analysis, metabolic reconstruction, comparative genomic analysis, statistical analysis and visualization. BA performed the oligonucleotide probe design, FISH and microscopy. All authors contributed to the interpretation of the results. AsG wrote the first draft and all authors edited and approved the manuscript.

Corresponding authors

Correspondence to Asimenia Gavriilidou or Detmer Sipkema.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gavriilidou, A., Avcı, B., Galani, A. et al. Candidatus Nemesobacterales is a sponge-specific clade of the candidate phylum Desulfobacterota adapted to a symbiotic lifestyle. ISME J 17, 1808–1818 (2023). https://doi.org/10.1038/s41396-023-01484-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41396-023-01484-z

This article is cited by

Search

Advanced search

Quick links