Functional diversity enables multiple symbiont strains to coexist in deep-sea mussels


Genetic diversity of closely related free-living microorganisms is widespread and underpins ecosystem functioning, but most evolutionary theories predict that it destabilizes intimate mutualisms. Accordingly, strain diversity is assumed to be highly restricted in intracellular bacteria associated with animals. Here, we sequenced metagenomes and metatranscriptomes of 18 Bathymodiolus mussel individuals from four species, covering their known distribution range at deep-sea hydrothermal vents in the Atlantic. We show that as many as 16 strains of intracellular, sulfur-oxidizing symbionts coexist in individual Bathymodiolus mussels. Co-occurring symbiont strains differed extensively in key functions, such as the use of energy and nutrient sources, electron acceptors and viral defence mechanisms. Most strain-specific genes were expressed, highlighting their potential to affect fitness. We show that fine-scale diversity is pervasive in Bathymodiolus sulfur-oxidizing symbionts, and hypothesize that it may be widespread in low-cost symbioses where the environment, rather than the host, feeds the symbionts.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Overview of the workflow developed for this study.
Fig. 2: The population genetic measures π and FST show that mussels from the same site host similar symbiont populations.
Fig. 3: Gene version reconstruction reveals up to 16 co-occurring SOX symbiont strains in individual Bathymodiolus mussels.
Fig. 4: Strain number estimates from PacBio sequencing confirm the strain estimation approach from gene version reconstruction of Illumina sequences.
Fig. 5: Strain-specific genes encode potential key functions in SOX symbionts, including energy production and interactions with hosts and phages.
Fig. 6: Simultaneous FISH of key genes of the hydrogenase operon and 16S rRNA of the SOX symbiont in gill tissue of B. azoricus from Lucky Strike.

Data availability

Sequence data were deposited in the European Nucleotide Archive92 using the data brokerage service of the German Federation for Biological Data93, in compliance with the Minimal Information about any (x) Sequence standard94. All metagenomic sequencing reads and symbiont bins used in this study can be found at the European Nucleotide Archive under project accession number PRJEB32787, and all metatranscriptomic reads are under project accession number PRJEB32788. Any further data that support the findings of this study are available from the corresponding author upon request.

Code availability

Custom code and detailed information on the computing steps are available from the GitHub repository ( The code used to calculate the nucleotide diversity, π, and the fixation index, FST, is available from the GitHub repository at


  1. 1.

    Kashtan, N. et al. Single-cell genomics reveals hundreds of coexisting subpopulations in wild Prochlorococcus. Science 344, 416–420 (2014).

    CAS  PubMed  Google Scholar 

  2. 2.

    Ackermann, M. Microbial individuality in the natural environment. ISME J. 7, 465–467 (2013).

    CAS  PubMed  Google Scholar 

  3. 3.

    Pankey, M. S. et al. Host-selected mutations converging on a global regulator drive an adaptive leap towards symbiosis in bacteria. eLife 6, e24414 (2017).

    Google Scholar 

  4. 4.

    Viana, D. et al. A single natural nucleotide mutation alters bacterial pathogen host-tropism. Nat. Genet. 47, 361–366 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Greenblum, S., Carr, R. & Borenstein, E. Extensive strain-level copy-number variation across human gut microbiome species. Cell 160, 583–594 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).

    Google Scholar 

  7. 7.

    Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).

    Google Scholar 

  8. 8.

    Frank, S. A. Host–symbiont conflict over the mixing of symbiotic lineages. Proc. R. Soc. Lond. B 263, 339–344 (1996).

    CAS  Google Scholar 

  9. 9.

    Sachs, J. L. et al. Host control over infection and proliferation of a cheater symbiont. J. Evol. Biol. 23, 1919–1927 (2010).

    CAS  PubMed  Google Scholar 

  10. 10.

    Bulgheresi, S. et al. A new C-type lectin similar to the human immunoreceptor DC-SIGN mediates symbiont acquisition by a marine nematode. Appl. Environ. Microbiol. 72, 2950–2956 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Nyholm, S. V. & McFall-Ngai, M. The winnowing: establishing the squid–Vibrio symbiosis. Nat. Rev. Microbiol. 2, 632–642 (2004).

    CAS  PubMed  Google Scholar 

  12. 12.

    Palmer, T. M. et al. Synergy of multiple partners, including freeloaders, increases host fitness in a multispecies mutualism. Proc. Natl Acad. Sci. USA 107, 17234–17239 (2010).

    CAS  PubMed  Google Scholar 

  13. 13.

    Baumann, P. Biology of bacteriocyte-associated endosymbionts of plant sap-sucking insects. Annu. Rev. Microbiol. 59, 155–189 (2005).

    CAS  PubMed  Google Scholar 

  14. 14.

    Ellegaard, K. M. & Engel, P. Genomic diversity landscape of the honey bee gut microbiota. Nat. Commun. 10, 446 (2019).

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Perez, M. & Juniper, S. K. Is the trophosome of Ridgeia piscesae monoclonal? Symbiosis 74, 55–65 (2017).

    Google Scholar 

  16. 16.

    Russell, S. L., Corbett-Detig, R. B. & Cavanaugh, C. M. Mixed transmission modes and dynamic genome evolution in an obligate animal–bacterial symbiosis. ISME J. 11, 1359–1371 (2017).

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Guyomar, C. et al. Multi-scale characterization of symbiont diversity in the pea aphid complex through metagenomic approaches. Microbiome 6, 181 (2018).

    PubMed  PubMed Central  Google Scholar 

  18. 18.

    Foster, K. R., Schluter, J., Coyte, K. Z. & Rakoff-Nahoum, S. The evolution of the host microbiome as an ecosystem on a leash. Nature 548, 43–51 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Bongrand, C. et al. A genomic comparison of 13 symbiotic Vibrio fischeri isolates from the perspective of their host source and colonization behavior. ISME J. 10, 2907–2917 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Quince, C. et al. DESMAN: a new tool for de novo extraction of strains from metagenomes. Genome Biol. 18, 181 (2017).

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Cleary, B. et al. Detection of low-abundance bacterial strains in metagenomic datasets by eigengenome partitioning. Nat. Biotechnol. 33, 1053–1060 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Nielsen, H. B. et al. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat. Biotechnol. 32, 822–828 (2014).

    CAS  PubMed  Google Scholar 

  23. 23.

    Petersen, J. M. & Dubilier, N. Methanotrophic symbioses in marine invertebrates. Environ. Microbiol. Rep. 1, 319–335 (2009).

    CAS  PubMed  Google Scholar 

  24. 24.

    Dubilier, N., Bergin, C. & Lott, C. Symbiotic diversity in marine animals: the art of harnessing chemosynthesis. Nat. Rev. Microbiol. 6, 725–740 (2008).

    CAS  PubMed  Google Scholar 

  25. 25.

    Duperron, S. et al. A dual symbiosis shared by two mussel species, Bathymodiolus azoricus and Bathymodiolus puteoserpentis (Bivalvia: Mytilidae), from hydrothermal vents along the northern Mid-Atlantic Ridge. Environ. Microbiol. 8, 1441–1447 (2006).

    CAS  PubMed  Google Scholar 

  26. 26.

    Laming, S. R., Duperron, S., Cunha, M. R. & Gaudron, S. M. Settled, symbiotic, then sexually mature: adaptive developmental anatomy in the deep-sea, chemosymbiotic mussel Idas modiolaeformis. Mar. Biol. 161, 1319–1333 (2014).

    Google Scholar 

  27. 27.

    Wentrup, C., Wendeberg, A., Schimak, M., Borowski, C. & Dubilier, N. Forever competent: deep-sea bivalves are colonized by their chemosynthetic symbionts throughout their lifetime. Environ. Microbiol. 16, 3699–3713 (2014).

    PubMed  Google Scholar 

  28. 28.

    Duperron, S. in The Vent and Seep Biota: Aspects from Microbes to Ecosystems (ed. Kiel, S.) 137–167 (Springer, 2010).

  29. 29.

    Won, Y.-J., Jones, W. J. & Vrijenhoek, R. C. Absence of cospeciation between deep-sea mytilids and their thiotrophic endosymbionts. J. Shellfish Res. 27, 129–138 (2008).

    Google Scholar 

  30. 30.

    DeChaine, E. G. & Cavanaugh, C. M. in Molecular Basis of Symbiosis (ed. Overmann, P. D. J.) 227–249 (Springer, 2005).

  31. 31.

    Won, Y.-J. et al. Environmental acquisition of thiotrophic endosymbionts by deep-sea mussels of the genus Bathymodiolus. Appl. Environ. Microbiol. 69, 6785–6792 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Petersen, J. M., Wentrup, C., Verna, C., Knittel, K. & Dubilier, N. Origins and evolutionary flexibility of chemosynthetic symbionts from deep-sea animals. Biol. Bull. 223, 123–137 (2012).

    CAS  PubMed  Google Scholar 

  33. 33.

    Ikuta, T. et al. Heterogeneous composition of key metabolic gene clusters in a vent mussel symbiont population. ISME J. 10, 990–1001 (2016).

    PubMed  Google Scholar 

  34. 34.

    Heath, K. D. & Stinchcombe, J. R. Explaining mutualism variation: a new evolutionary paradox? Evolution 68, 309–317 (2014).

    PubMed  Google Scholar 

  35. 35.

    Polzin, J., Arevalo, P., Nussbaumer, T., Polz, M. F. & Bright, M. Polyclonal symbiont populations in hydrothermal vent tubeworms and the environment. Proc. R. Soc. B Biol. Sci. 286, 20181281 (2019).

    CAS  Google Scholar 

  36. 36.

    Russell, S. L. & Cavanaugh, C. M. Intrahost genetic diversity of bacterial symbionts exhibits evidence of mixed infections and recombinant haplotypes. Mol. Biol. Evol. 34, 2747–2761 (2017).

    CAS  PubMed  Google Scholar 

  37. 37.

    Schloissnig, S. et al. Genomic variation landscape of the human gut microbiome. Nature 493, 45–50 (2013).

    PubMed  Google Scholar 

  38. 38.

    Douglas, A. E. The ecology of symbiotic micro-organisms. Adv. Ecol. Res. 26, 69–103 (1995).

    Google Scholar 

  39. 39.

    Wright, S. Evolution and the Genetics of Populations: The Theory of Gene Frequencies Vol. 2 (Univ. Chicago Press, 1969).

  40. 40.

    Wright, S. Isolation by distance. Genetics 28, 114–138 (1943).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Lan, Y., Rosen, G. & Hershberg, R. Marker genes that are less conserved in their sequences are useful for predicting genome-wide similarity levels between closely related prokaryotic strains. Microbiome 4, 18 (2016).

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Hsieh, Y.-J. & Wanner, B. L. Global regulation by the seven-component Pi signaling system. Curr. Opin. Microbiol. 13, 198–203 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Romano, S., Schulz-Vogt, H. N., González, J. M. & Bondarev, V. Phosphate limitation induces drastic physiological changes, virulence-related gene expression, and secondary metabolite production in Pseudovibrio sp. strain FO-BEG1. Appl. Environ. Microbiol. 81, 3518–3528 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Santos-Beneit, F. The Pho regulon: a huge regulatory network in bacteria. Front. Microbiol. 6, 402 (2015).

    PubMed  PubMed Central  Google Scholar 

  45. 45.

    Lamarche, M. G., Wanner, B. L., Crépin, S. & Harel, J. The phosphate regulon and bacterial virulence: a regulatory network connecting phosphate homeostasis and pathogenesis. FEMS Microbiol. Rev. 32, 461–473 (2008).

    CAS  PubMed  Google Scholar 

  46. 46.

    Martiny, A. C., Huang, Y. & Li, W. Occurrence of phosphate acquisition genes in Prochlorococcus cells from different ocean regions. Environ. Microbiol. 11, 1340–1347 (2009).

    CAS  PubMed  Google Scholar 

  47. 47.

    Martiny, A. C., Coleman, M. L. & Chisholm, S. W. Phosphate acquisition genes in Prochlorococcus ecotypes: evidence for genome-wide adaptation. Proc. Natl Acad. Sci. USA 103, 12552–12557 (2006).

    CAS  PubMed  Google Scholar 

  48. 48.

    Zielinski, F. U., Gennerich, H.-H., Borowski, C., Wenzhöfer, F. & Dubilier, N. In situ measurements of hydrogen sulfide, oxygen, and temperature in diffuse fluids of an ultramafic-hosted hydrothermal vent field (Logatchev, 14°45′N, Mid-Atlantic Ridge): implications for chemosymbiotic bathymodiolin mussels. Geochem. Geophys. Geosyst. 12, Q0AE04 (2011).

    Google Scholar 

  49. 49.

    Kuwahara, H. et al. Reduced genome of the thioautotrophic intracellular symbiont in a deep-sea clam, Calyptogena okutanii. Curr. Biol. 17, 881–886 (2007).

    CAS  PubMed  Google Scholar 

  50. 50.

    Hentschel, U., Hand, S. & Felbeck, H. The contribution of nitrate respiration to the energy budget of the symbiont-containing clam Lucinoma aequizonata: a calorimetric study. J. Exp. Biol. 199, 427–433 (1996).

    CAS  PubMed  Google Scholar 

  51. 51.

    Hentschel, U., Cary, S. C. & Felbeck, H. Nitrate respiration in chemoautotrophic symbionts of the bivalve Lucinoma aequizonata. Mar. Ecol. Prog. Ser. 94, 35–41 (1993).

    CAS  Google Scholar 

  52. 52.

    Kraft, B., Strous, M. & Tegetmeyer, H. E. Microbial nitrate respiration—genes, enzymes and environmental distribution. J. Biotechnol. 155, 104–117 (2011).

    CAS  PubMed  Google Scholar 

  53. 53.

    Shah, V., Chang, B. X. & Morris, R. M. Cultivation of a chemoautotroph from the SUP05 clade of marine bacteria that produces nitrite and consumes ammonium. ISME J. 11, 263–271 (2017).

    CAS  PubMed  Google Scholar 

  54. 54.

    Kleiner, M., Petersen, J. M. & Dubilier, N. Convergent and divergent evolution of metabolism in sulfur-oxidizing symbionts and the role of horizontal gene transfer. Curr. Opin. Microbiol. 15, 621–631 (2012).

    CAS  PubMed  Google Scholar 

  55. 55.

    Savage, V. M., Webb, C. T. & Norberg, J. A general multi-trait-based framework for studying the effects of biodiversity on ecosystem functioning. J. Theor. Biol. 247, 213–229 (2007).

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Lindemann, S. R. et al. Engineering microbial consortia for controllable outputs. ISME J. 10, 2077–2084 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).

    CAS  Google Scholar 

  58. 58.

    Ghoul, M. & Mitri, S. The ecology and evolution of microbial competition. Trends Microbiol. 24, 833–845 (2016).

    CAS  Google Scholar 

  59. 59.

    Hardin, G. The competitive exclusion principle. Science 131, 1292–1297 (1960).

    CAS  PubMed  Google Scholar 

  60. 60.

    Udvardi, M. & Poole, P. S. Transport and metabolism in legume–Rhizobia symbioses. Annu. Rev. Plant Biol. 64, 781–805 (2013).

    CAS  PubMed  Google Scholar 

  61. 61.

    Ponnudurai, R. et al. Metabolic and physiological interdependencies in the Bathymodiolus azoricus symbiosis. ISME J. 11, 463–477 (2017).

    CAS  PubMed  Google Scholar 

  62. 62.

    Douglas, A. E. Conflict, cheats and the persistence of symbioses. New Phytol. 177, 849–858 (2008).

    PubMed  Google Scholar 

  63. 63.

    Sayavedra, L. et al. Horizontal acquisition followed by expansion and diversification of toxin-related genes in deep-sea bivalve symbiontrs. Preprint at (2019).

  64. 64.

    Sayavedra, L. et al. Abundant toxin-related genes in the genomes of beneficial symbionts from deep-sea hydrothermal vent mussels. eLife 4, e07966 (2015).

    PubMed  PubMed Central  Google Scholar 

  65. 65.

    Batstone, R. T., Carscadden, K. A., Afkhami, M. E. & Frederickson, M. E. Using niche breadth theory to explain generalization in mutualisms. Ecology 99, 1039–1050 (2018).

    PubMed  Google Scholar 

  66. 66.

    McLaren, M. R. & Callahan, B. J. In nature, there is only diversity. mBio 9, e02149-17 (2018).

    PubMed  PubMed Central  Google Scholar 

  67. 67.

    Wooldridge Scott, A. Is the coral–algae symbiosis really ‘mutually beneficial’ for the partners? BioEssays 32, 615–625 (2010).

    CAS  PubMed  Google Scholar 

  68. 68.

    Oppen, M. J. H., van Palstra, F. P., Piquet, A. M.-T. & Miller, D. J. Patterns of coral–dinoflagellate associations in Acropora: significance of local availability and physiology of Symbiodinium strains and host–symbiont selectivity. Proc. R. Soc. Lond. B Biol. Sci. 268, 1759–1767 (2001).

    Google Scholar 

  69. 69.

    Rowan, R. & Knowlton, N. Intraspecific diversity and ecological zonation in coral–algal symbiosis. Proc. Natl Acad. Sci. USA 92, 2850–2853 (1995).

    CAS  PubMed  Google Scholar 

  70. 70.

    Sayavedra, L. Host–Symbiont Interactions and Metabolism of Chemosynthetic Symbiosis in Deep-Sea Bathymodiolus Mussels (Univ. Bremen, 2016).

  71. 71.

    Zhou, J., Bruns, M. A. & Tiedje, J. M. DNA recovery from soils of diverse composition. Appl. Environ. Microbiol. 62, 316–322 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Peng, Y., Leung, H. C. M., Yiu, S. M. & Chin, F. Y. L. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28, 1420–1428 (2012).

    CAS  PubMed  Google Scholar 

  73. 73.

    Seah, B. K. B. & Gruber-Vodicka, H. R. gbtools: interactive visualization of metagenome bins in R. Front. Microbiol. 6, 1451 (2015).

    PubMed  PubMed Central  Google Scholar 

  74. 74.

    Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Brettin, T. et al. RASTtk: a modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes. Sci. Rep. 5, 8365 (2015).

    PubMed  PubMed Central  Google Scholar 

  77. 77.

    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).

    CAS  Google Scholar 

  78. 78.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  Google Scholar 

  79. 79.

    Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-Seq data. Genome Biol. 11, R25 (2010).

    PubMed  PubMed Central  Google Scholar 

  80. 80.

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed  PubMed Central  Google Scholar 

  81. 81.

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Hudson, R. R., Slatkin, M. & Maddison, W. P. Estimation of levels of gene flow from DNA sequence data. Genetics 132, 583–589 (1992).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. 83.

    R Core Development Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2016).

  84. 84.

    Contreras-Moreira, B. & Vinuesa, P. GET_HOMOLOGUES, a versatile software package for scalable and robust microbial pangenome analysis. Appl. Environ. Microbiol. 79, 7696–7701 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. 86.

    Brown, C. T., Olm, M. R., Thomas, B. C. & Banfield, J. F. Measurement of bacterial replication rates in microbial communities. Nat. Biotechnol. 34, 1256–1263 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. 87.

    Wang, Z. & Wu, M. A phylum-level bacterial phylogenetic marker database. Mol. Biol. Evol. 30, 1258–1262 (2013).

    CAS  PubMed  Google Scholar 

  88. 88.

    Jayasundara, D. et al. ViQuaS: an improved reconstruction pipeline for viral quasispecies spectra generated by next-generation sequencing. Bioinformatics 31, 886–896 (2015).

    CAS  PubMed  Google Scholar 

  89. 89.

    Huang, W., Li, L., Myers, J. R. & Marth, G. T. ART: a next-generation sequencing read simulator. Bioinformatics 28, 593–594 (2012).

    PubMed  Google Scholar 

  90. 90.

    Barrero‐Canosa, J., Moraru, C., Zeugner, L., Fuchs, B. M. & Amann, R. Direct-geneFISH: a simplified protocol for the simultaneous detection and quantification of genes and rRNA in microorganisms. Environ. Microbiol. 19, 70–82 (2017).

    PubMed  Google Scholar 

  91. 91.

    Moraru, C., Moraru, G., Fuchs, B. M. & Amann, R. Concepts and software for a rational design of polynucleotide probes. Environ. Microbiol. Rep. 3, 69–78 (2011).

    CAS  PubMed  Google Scholar 

  92. 92.

    Harrison, P. W. et al. The European Nucleotide Archive in 2018. Nucleic Acids Res. 47, D84–D88 (2019).

    CAS  PubMed  Google Scholar 

  93. 93.

    Diepenbroek, M. et al. Towards an Integrated Biodiversity and Ecological Research Data Management and Archiving Platform: the German Federation for the Curation of Biological Data (GFBio) (Gesellschaft für Informatik, 2014).

  94. 94.

    Yilmaz, P. et al. Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications. Nat. Biotechnol. 29, 415–420 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Wilmes, P., Simmons, S. L., Denef, V. J. & Banfield, J. F. The dynamic genetic repertoire of microbial communities. FEMS Microbiol. Rev. 33, 109–132 (2009).

    CAS  PubMed  Google Scholar 

  96. 96.

    Kunin, V. et al. A bacterial metapopulation adapts locally to phage predation despite global dispersal. Genome Res. 18, 293–297 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. 97.

    Lo, I. et al. Strain-resolved community proteomics reveals recombining genomes of acidophilic bacteria. Nature 446, 537–541 (2007).

    CAS  PubMed  Google Scholar 

  98. 98.

    Strous, M. et al. Deciphering the evolution and metabolism of an anammox bacterium from a community genome. Nature 440, 790–794 (2006).

    PubMed  Google Scholar 

  99. 99.

    Simmons, S. L. Population genomic analysis of strain variation in Leptospirillum group II bacteria involved in acid mine drainage formation. PLoS Biol. 6, e177 (2008).

    PubMed  PubMed Central  Google Scholar 

  100. 100.

    Van Ham, R. C. H. J. et al. Reductive genome evolution in Buchnera aphidicola. Proc. Natl Acad. Sci. USA 100, 581–586 (2003).

    CAS  PubMed  Google Scholar 

  101. 101.

    Robidart, J. C. et al. Metabolic versatility of the Riftia pachyptila endosymbiont revealed through metagenomics. Environ. Microbiol. 10, 727–737 (2008).

    CAS  PubMed  Google Scholar 

  102. 102.

    Tyson, G. W. et al. Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428, 37–43 (2004).

    CAS  PubMed  Google Scholar 

  103. 103.

    Allen, E. E. et al. Genome dynamics in a natural archaeal population. Proc. Natl Acad. Sci. USA 104, 1883–1888 (2007).

    CAS  PubMed  Google Scholar 

  104. 104.

    Schoenfeld, T. et al. Assembly of viral metagenomes from Yellowstone hot springs. Appl. Environ. Microbiol. 74, 4164–4174 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  105. 105.

    Andersson, A. F. & Banfield, J. F. Virus population dynamics and acquired virus resistance in natural microbial communities. Science 320, 1047–1050 (2008).

    CAS  PubMed  Google Scholar 

Download references


We thank the captains, crews and remotely operated underwater vehicle teams on the cruises BioBaz (2013), ODEMAR (2014), M78-2 (2009) and Atalante Cruise Leg 2 (2008) on board the research vessels Pourquoi Pas?, FS Meteor and L’Atalante, as well as the chief scientists F. Lallier, J. Excartin and M. Andreani, and R. Seifert and C. Devey. We are grateful to M. Meyer for de novo production of the geneFISH probes, to A. Assié, C. Borowski, C. Breusing and K. van der Heijden for sample collection and fixation on board, and to M. Tietjen for the extraction of RNA from the samples of the vent fields Semenov, Clueless and Lilliput. We also thank C. Quast and H. Teeling for technical support, as well as T. Dagan for discussions and input during the project and on the written manuscript. This study was funded by the Max Planck Society, an ERC Advanced Grant (BathyBiome, 340535), a Gordon and Betty Moore Foundation Marine Microbial Initiative Investigator Award to N.D. (grant GBMF3811), the MARUM DFG Research Center/Excellence Cluster ‘The Ocean in the Earth System’ at the University of Bremen, the DFG CRC 1182 ‘Origin and Function of Metaorganisms’ and the German Research Foundation (RV Meteor M78-2 cruise).

Author information




R.A., J.P., L.S. and N.D. conceived the study. R.A. and J.P. wrote the manuscript, with support from N.D. and contributions and revisions from all other co-authors. R.A. developed the metagenomic workflow for polymorphism detection, strain reconstruction and the identification of strain-specific genes, and analysed the data with the exceptions described hereafter. S.R. conducted the core-genome calculation, read simulation analyses, provided support for the statistical analyses and drafted respective manuscript sections. L.S. extracted nucleic acids for samples from Lucky Strike, Semenov and Wideawake, and conducted and evaluated the PacBio assembly. M.A.G.P. designed the geneFISH probes, produced geneFISH images on gill tissue sections, and drafted the respective sections in the manuscript. A.K. developed and provided an R script for the calculation of π and FST. H.E.T. sequenced the metagenomes from the vent fields Clueless and Lilliput.

Corresponding authors

Correspondence to Nicole Dubilier or Jillian Petersen.

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.

Extended data

Extended Data Fig. 1 Number of SNPs in the symbiont 16S rRNA gene within individual hosts at the four vent fields.

The position on the 16S rRNA gene, SNP frequencies and nucleotide changes are indicated in light grey boxes. The following host species correspond to listed vent fields: Lucky Strike – B. azoricus, Semenov – B. puteoserpentis, Clueless – B. sp., Lilliput – B. sp.

Extended Data Fig. 2 Single nucleotide polymorphisms (SNPs) of within-host symbiont populations.

SNPs/kbp are shown in (a) core genes and (b) whole genomes including non-coding regions. Vent fields (host species) are LS: Lucky Strike (B. azoricus), Se: Semenov (B. puteoserpentis), Cl: Clueless (B. sp.), Li: Lilliput (B. sp.).

Extended Data Fig. 3 Counts and percentages of low-coverage genes in within-host symbiont populations.

Subsets represent counts that excluded all genes annotated as “hypothetical protein” and are further defined as strain- specific genes (one-copy genes with lower coverage in most or all symbiont populations from that site) and low-coverage genes with further copies in the genome. The following host species correspond to listed vent fields: Lucky Strike – B.azoricus, Semenov – B. puteoserpentis, Clueless – B. sp., Lilliput – B. sp.

Extended Data Fig. 4 Theoretical model predicting the influence of symbiont transmission on population genomic signatures (π, FST).

On the left a scenario is depicted in which the symbionts are acquired only once by juvenile mussels during a restricted time window, followed exclusively by repeated self-infection. On the right a scenario is depicted in which the symbionts are continuously released and taken up by host individuals throughout their lifetime.

Extended Data Fig. 5 π-values within symbiont populations of single host individuals and in pairwise calculation between each two host individuals from all four vent fields.

(a) Each within-host π value (white) per individual and between-host π value (colored) per pair of individuals is shown separately. NLS = 3320, NSe = 2,435, NCl = 3,023, NLi = 3,352 genes for which a π value was calculated. (b) Within-host (white) and between-host (colored) π values are grouped together, respectively. LS: Lucky Strike, Se: Semenov, Cl: Clueless, Li: Lilliput vent fields. In the box-plots, line represents mean, upper and lower hinges represent the first (25th percentile) and third (75th percentile) quartiles, whiskers represent 1.5x interquartile range, individual points are outliers. NLS = 16,600 (white) / 33,200 (colored), NSe = 7,305 (both, white and colored), NCl = 15,115 (white) / 30,230 (colored), NLi = 16,760 (white) / 33,520 (colored) genes for which a π value was calculated. Source data

Extended Data Fig. 6 Spearman correlation between the difference (a) or sum (b) in shell lengths of two compared hosts with the pairwise FST; and correlation of shell length with intra-host SNP density (c).

= spearmans correlation coefficient rho, p = p-value (two- sided); for vent field Lucky Strike white symbols = host pairs from different sites Eiffel Tower and Montsegur, red symbols = host pairs from the same site; LS: Lucky Strike, Se: Semenov, Cl: Clueless, Li: Lilliput. For a and b NLS = 10, NSe = 3, NCl = 10, NLi = 10 pairwise FST between each two per-mussel symbiont populations; for c NLS = 5, NSe = 3, NCl = 5, NLi = 5 per-mussel symbiont populations. Semenov did not have enough data to infer values for r and p. Source data

Extended Data Fig. 7 Cumulative gene counts of distinct numbers of gene versions.

Cumulative gene version count is shown for gammaproteobacterial marker genes from PhylaAmphora and the extended set of genes that had a read coverage within the coverage range of gammaproteobacterial marker genes, indicating that each strain in the population encoded these. Strain numbers were estimated for the marker gene set with 100x read coverage (a) and full read coverage (b) and for the entire gene set with full read coverage per host individual. Full read coverage was 100–120x for LS, 280–370x for Se, 150–215x for Cl and 190–218x for Li mussels. LS: Lucky Strike, Se: Semenov, Cl: Clueless, Li: Lilliput, cov = coverage. Source data

Extended Data Fig. 8 Comparison of reported SNP densities from published studies to our present dataset.

Data from Bathymodiolus SOX symbionts of this study are depicted in bold. Many of the reported values were previously summed up in Wilmes et al.95. NA = information was missing or could not be retrieved96,97,98,99,100,101,102,103,104,105.

Extended Data Fig. 9 Representation of denitrification genes among strains of the SOX symbiont.

A gene is absent (white mussel symbols), strain specific (blue mussel symbols), or present in all strains (red mussel symbols) in a single host individual. When some host individuals from the same vent site had symbiont populations where a gene is strain specific and others where the entire population encoded that gene, mussel symbol is split into red and blue color. LS: Lucky Strike (B. azoricus), Se: Semenov (B. puteoserpentis), Cl: Clueless (B. sp.), Li: Lilliput (B. sp.), NAR: respiratory nitrate reductase, NIR: nitrite reductase, NOR: nitric oxide reductase, NOS: nitrous oxide reductase, NarK: nitrate transporter, NAS A: assimilatory nitrate reductase.

Extended Data Fig. 10 Representation of CRISPR-Cas gene clusters in the SOX symbiont strains showing strain- specific genes (blue), genes present in all strains (red), and CRISPR- arrays (striped boxes).

LS: Lucky Strike, Se: Semenov, Cl: Clueless, Li: Lilliput.

Supplementary information

Supplementary Information

Supplementary Discussion, Supplementary Tables 1–5 and 7–11, and Supplementary Figs. 1–5.

Reporting Summary

Supplementary Table 6

List of strain-specific genes, excluding all hypothetical proteins.

Source data

Source Data Fig. 2

Pi values per gene used in Permanova analysis and for Fig. 2a, as well as FST values used in Fig. 2b.

Source Data Fig. 3

Source data for cumulative density plots and maximum strain numbers of 100× coverage (Fig. 3a) and source data for correlation of strain count with coverage (Fig. 3b).

Source Data Fig. 4

Cumulative density data of strain counts for PacBio and Illumina Data for vent field Wideawake.

Source Data Extended Data Fig. 5

Pi values per gene plotted in Extended Data Fig. 5.

Source Data Extended Data Fig. 6

Source Data for correlation analysis of FST and SNP counts with mussel size, size sum and size differences.

Source Data Extended Data Fig. 7

Source data for cumulative density plots and maximum strain numbers of full coverage, and phylaAmphora marker genes in full and 100× coverage.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ansorge, R., Romano, S., Sayavedra, L. et al. Functional diversity enables multiple symbiont strains to coexist in deep-sea mussels. Nat Microbiol 4, 2487–2497 (2019).

Download citation

Further reading