Skip to main content
Log in

Divergence of the Host-Associated Microbiota with the Genetic Distance of Host Individuals Within a Parthenogenetic Daphnia Species

  • Research
  • Published:
Microbial Ecology Aims and scope Submit manuscript

Abstract

The taxonomic composition of the microbiota in the gut and epidermis of animals is known to vary among genetically and physiologically different host individuals within the same species. However, it is not clear whether the taxonomic composition diverges with increasing genetic distance of the host individuals. To unveil this uncertainty, we compared the host-associated microbiota among the genotypes within and between genetically distant lineages of parthenogenetic Daphnia cf. pulex across different physiological states, namely, well-fed, starved, and dead. Metagenomic analysis with 16S rRNA showed that, regardless of the host genotypes, diversity of the host-associated microbiota was high when the host individuals were fed food and gradually decreased when they were starved until they died. However, the difference in the host-associated microbiota, that is, β-diversity, was significant among the genotypes within and between the host lineages when they were fed. Although some bacteria in the microbiota, such as Limnohabitans, Rhodococcus, and Aeromicrobium, were found abundantly and commonly in all host genotypes; others, such as those of Holosoporacea, were found only in the genotypes of a specific lineage. Accordingly, the β-diversity tended to increase with increasing genetic distance of the host individuals. These results support an idea that the host-associated microbiota diverged with genetic divergence in the host species and that at least some bacteria are highly dependent on the genetically specific metabolites produced by the host individuals.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data Availability

All the raw read files are available from the DDBJ database (DRA accession number: DRA014777).

References

  1. Davidson SK, Stahl DA (2008) Selective recruitment of bacteria during embryogenesis of an earthworm. ISME J 2:510–518. https://doi.org/10.1038/ismej.2008.16

    Article  PubMed  Google Scholar 

  2. Nicholson JK, Holmes E, Kinross J et al (2012) Host-gut microbiota metabolic interactions. Science 336:1262–1267. https://doi.org/10.1126/science.1223813

    Article  CAS  PubMed  Google Scholar 

  3. Valzania L, Martinson VG, Harrison RE et al (2018) Both living bacteria and eukaryotes in the mosquito gut promote growth of larvae. PLOS Negl Trop Dis 12:e0006638. https://doi.org/10.1371/journal.pntd.0006638

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Wong AC-N, Wang Q-P, Morimoto J et al (2017) Gut microbiota modifies olfactory-guided microbial preferences and foraging decisions in Drosophila. Curr Biol 27:2397-2404.e4. https://doi.org/10.1016/j.cub.2017.07.022

    Article  CAS  PubMed  Google Scholar 

  5. Wong AC-N, Dobson AJ, Douglas AE (2014) Gut microbiota dictates the metabolic response of Drosophila to diet. J Exp Biol 217:1894–1901. https://doi.org/10.1242/jeb.101725

    Article  PubMed  PubMed Central  Google Scholar 

  6. Peerakietkhajorn S, Kato Y, Kasalický V et al (2016) Betaproteobacteria Limnohabitans strains increase fecundity in the crustacean Daphnia magna: symbiotic relationship between major bacterioplankton and zooplankton in freshwater ecosystem: Limnohabitans strains increase Daphnia fecundity. Environ Microbiol 18:2366–2374. https://doi.org/10.1111/1462-2920.12919

    Article  CAS  PubMed  Google Scholar 

  7. Peerakietkhajorn S, Tsukada K, Kato Y et al (2015) Symbiotic bacteria contribute to increasing the population size of a freshwater crustacean, Daphnia magna: the role of symbiotic bacteria in Daphnia. Environ Microbiol Rep 7:364–372. https://doi.org/10.1111/1758-2229.12260

    Article  CAS  PubMed  Google Scholar 

  8. Zhang X, Ohtsuki H, Makino W et al (2021) Variations in effects of ectosymbiotic microbes on the growth rates among different species and genotypes of Daphnia fed different algal diets. Ecol Res 36:303–312. https://doi.org/10.1111/1440-1703.12194

    Article  CAS  Google Scholar 

  9. Kovacs A, Ben-Jacob N, Tayem H et al (2011) Genotype is a stronger determinant than sex of the mouse gut microbiota. Microb Ecol 61:423–428. https://doi.org/10.1007/s00248-010-9787-2

    Article  PubMed  Google Scholar 

  10. Sullam KE, Pichon S, Schaer TMM, Ebert D (2017) The combined effect of temperature and host clonal line on the microbiota of a planktonic crustacean. Microb Ecol. https://doi.org/10.1007/s00248-017-1126-4

    Article  PubMed  Google Scholar 

  11. Frankel-Bricker J, Song MJ, Benner MJ, Schaack S (2019) Variation in the microbiota associated with Daphnia magna across genotypes, populations, and temperature. Microb Ecol. https://doi.org/10.1007/s00248-019-01412-9

    Article  PubMed  PubMed Central  Google Scholar 

  12. Frankel-Bricker J, Song MJ, Benner MJ, Schaack S (2020) Variation in the microbiota associated with Daphnia magna across genotypes, populations, and temperature. Microb Ecol 79:731–742. https://doi.org/10.1007/s00248-019-01412-9

    Article  CAS  PubMed  Google Scholar 

  13. Akbar S, Li X, Ding Z et al (2022) Disentangling diet- and medium-associated microbes in shaping Daphnia gut microbiome. Microb Ecol 84:911–921. https://doi.org/10.1007/s00248-021-01900-x

    Article  CAS  PubMed  Google Scholar 

  14. Colbourne JK, Crease TJ, Weider LJ et al (1998) Phylogenetics and evolution of a circumarctic species complex (Cladocera: Daphnia pulex). Biol J Lin Soc 65:347–365. https://doi.org/10.1111/j.1095-8312.1998.tb01146.x

    Article  Google Scholar 

  15. Ma X, Petrusek A, Wolinska J et al (2019) Lineage diversity and reproductive modes of the Daphnia pulex group in Chinese lakes and reservoirs. Mol Phylogenet Evol 130:424–433. https://doi.org/10.1016/j.ympev.2018.08.004

    Article  PubMed  Google Scholar 

  16. So M, Ohtsuki H, Makino W et al (2015) Invasion and molecular evolution of Daphnia pulex in Japan. Limnol Oceanogr 60:1129–1138. https://doi.org/10.1002/lno.10087

    Article  Google Scholar 

  17. Ohtsuki H, Norimatsu H, Makino T, Urabe J (2022) Invasions of an obligate asexual daphnid species support the nearly neutral theory. Sci Rep 12:7305. https://doi.org/10.1038/s41598-022-11218-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Kilham SS, Kreeger DA, Lynn SG et al (1998) COMBO: a defined freshwater culture medium for algae and zooplankton. Hydrobiologia 377:147–159. https://doi.org/10.1023/A:1003231628456

    Article  CAS  Google Scholar 

  19. Bolyen E, Rideout JR, Dillon MR et al (2019) Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37:852–857. https://doi.org/10.1038/s41587-019-0209-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Callahan BJ, McMurdie PJ, Rosen MJ et al (2016) DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13:581–583. https://doi.org/10.1038/nmeth.3869

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Katoh K, Standley DM (2013) MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol 30:772–780. https://doi.org/10.1093/molbev/mst010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Price MN, Dehal PS, Arkin AP (2010) FastTree 2 – Approximately maximum-likelihood trees for large alignments. PLOS One 5:e9490. https://doi.org/10.1371/journal.pone.0009490

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830

    Google Scholar 

  24. Quast C, Pruesse E, Yilmaz P et al (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41:D590–D596. https://doi.org/10.1093/nar/gks1219

    Article  CAS  PubMed  Google Scholar 

  25. Bisanz JE (2018) qiime2R: Importing QIIME2 artifacts and associated data into R sessions. https://github.com/jbisanz/qiime2R

  26. R Core Team (2020) R: a language and environment for statistical computing. https://www.r-project.org/

  27. McMurdie PJ, Holmes S (2013) phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8:e61217. https://doi.org/10.1371/journal.pone.0061217

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Nearing JT, Douglas GM, Hayes MG et al (2022) Microbiome differential abundance methods produce different results across 38 datasets. Nat Commun 13:342. https://doi.org/10.1038/s41467-022-28034-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Fernandes AD, Reid JN, Macklaim JM et al (2014) Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2:15. https://doi.org/10.1186/2049-2618-2-15

    Article  PubMed  PubMed Central  Google Scholar 

  30. Fernandes AD, Macklaim JM, Linn TG et al (2013) ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-Seq. PLOS One 8:e67019. https://doi.org/10.1371/journal.pone.0067019

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550. https://doi.org/10.1186/s13059-014-0550-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Hamady M, Lozupone C, Knight R (2010) Fast UniFrac: facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and PhyloChip data. ISME J 4:17–27. https://doi.org/10.1038/ismej.2009.97

    Article  CAS  PubMed  Google Scholar 

  33. Lozupone C, Knight R (2005) UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 71:8228–8235. https://doi.org/10.1128/AEM.71.12.8228-8235.2005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Lozupone CA, Hamady M, Kelley ST, Knight R (2007) Quantitative and qualitative β diversity measures lead to different insights into factors that structure microbial communities. Appl Environ Microbiol 73:1576–1585. https://doi.org/10.1128/AEM.01996-06

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Anderson MJ (2017) Permutational multivariate analysis of variance (PERMANOVA). In: Wiley StatsRef: Statistics reference online. Am Cancer Soc 1–15

  36. Oksanen J, Blanchet FG, Friendly M et al (2019) vegan: community ecology package

  37. Wickham H (2016) ggplot2: Elegant graphics for data analysis. Springer-Verlag, New York

    Book  Google Scholar 

  38. Yu G, Smith DK, Zhu H et al (2017) ggtree: an r package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol Evol 8:28–36. https://doi.org/10.1111/2041-210X.12628

    Article  Google Scholar 

  39. Ochman H, Worobey M, Kuo C-H et al (2010) Evolutionary relationships of wild hominids recapitulated by gut microbial communities. PLOS Biol 8:e1000546. https://doi.org/10.1371/journal.pbio.1000546

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Macke E, Tasiemski A, Massol F et al (2017) Life history and eco-evolutionary dynamics in light of the gut microbiota. Oikos 126:508–531. https://doi.org/10.1111/oik.03900

    Article  Google Scholar 

  41. Obrestad K, Einum S, Vadstein O (2022) Stochastic variation in gut bacterial community affects reproductive rates in the water flea Daphnia magna. FEMS Microbiology Ecology 98:fiac105. https://doi.org/10.1093/femsec/fiac105

  42. Preiswerk D, Walser J-C, Ebert D (2018) Temporal dynamics of microbiota before and after host death. ISME J. https://doi.org/10.1038/s41396-018-0157-2

    Article  PubMed  PubMed Central  Google Scholar 

  43. Gorokhova E, Rivetti C, Furuhagen S et al (2015) Bacteria-mediated effects of antibiotics on Daphnia nutrition. Environ Sci Technol 49:5779–5787. https://doi.org/10.1021/acs.est.5b00833

    Article  CAS  PubMed  Google Scholar 

  44. Kalmbach S, Manz W, Bendinger B, Szewzyk U (2000) In situ probing reveals Aquabacterium commune as a widespread and highly abundant bacterial species in drinking water biofilms. Water Res 34:575–581. https://doi.org/10.1016/S0043-1354(99)00179-7

    Article  CAS  Google Scholar 

  45. Cheng J, Kalliomäki M, Heilig HG et al (2013) Duodenal microbiota composition and mucosal homeostasis in pediatric celiac disease. BMC Gastroenterol 13:113. https://doi.org/10.1186/1471-230X-13-113

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Pittman GW, Brumbley SM, Allsopp PG, O’Neill SL (2008) Assessment of gut bacteria for a paratransgenic approach to control Dermolepida albohirtum larvae. Appl Environ Microbiol 74:4036–4043. https://doi.org/10.1128/AEM.02609-07

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Fokin SI, Serra V, Ferrantini F et al (2019) “Candidatus Hafkinia simulans” gen. nov., sp. nov., a novel Holospora-like bacterium from the macronucleus of the rare brackish water ciliate Frontonia salmastra (Oligohymenophorea, Ciliophora): multidisciplinary characterization of the new endosymbiont and its host. Microb Ecol 77:1092–1106. https://doi.org/10.1007/s00248-018-1311-0

    Article  CAS  PubMed  Google Scholar 

  48. Fujishima M, Kodama Y (2012) Endosymbionts in paramecium. Eur J Protistol 48:124–137. https://doi.org/10.1016/j.ejop.2011.10.002

    Article  PubMed  Google Scholar 

  49. Lanzoni O, Fokin SI, Lebedeva N et al (2016) Rare freshwater ciliate Paramecium chlorelligerum Kahl, 1935 and its macronuclear symbiotic bacterium “Candidatus Holospora parva”. PLOS One 11:e0167928. https://doi.org/10.1371/journal.pone.0167928

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Fujishima M, Kawai M, Yamamoto R (2005) Paramecium caudatum acquires heat-shock resistance in ciliary movement by infection with the endonuclear symbiotic bacterium Holospora obtusa. FEMS Microbiol Lett 243:101–105. https://doi.org/10.1016/j.femsle.2004.11.053

    Article  CAS  PubMed  Google Scholar 

  51. Hori M, Fujishima M (2003) The endosymbiotie bacterium Holospora obtusa enhances heat-shock gene expression of the host Paramecium caudatum. J Eukaryot Microbiol 50:293–298. https://doi.org/10.1111/j.1550-7408.2003.tb00137.x

    Article  CAS  PubMed  Google Scholar 

  52. Nunan LM, Pantoja CR, Gomez-Jimenez S, Lightner DV (2013) “Candidatus Hepatobacter penaei”, an intracellular pathogenic enteric bacterium in the hepatopancreas of the marine shrimp Penaeus vannamei (Crustacea: Decapoda). Appl Environ Microbiol 79:1407–1409. https://doi.org/10.1128/AEM.02425-12

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Ryazanova TV, Eliseikina MG, Kukhlevsky AD (2020) First record of new rickettsia-like organism in the blue king crab Paralithodes platypus from the Sea of Okhotsk: distribution, morphological evidence and genetic analysis. J Invertebr Pathol 170:107325. https://doi.org/10.1016/j.jip.2020.107325

    Article  CAS  PubMed  Google Scholar 

  54. Otake Y, Ohtsuki H, Urabe J et al (2021) Long-term changes in morphological traits of Daphnia pulex in Lake Fukami-ike, Japan. Limnology. https://doi.org/10.1007/s10201-021-00659-x

  55. Bernardet J-F, Bowman JP (2006) The genus Flavobacterium. In: Dworkin M, Falkow S, Rosenberg E et al (eds) The prokaryotes. Springer, New York, New York, NY, pp 481–531

    Chapter  Google Scholar 

  56. Moisander PH, Sexton AD, Daley MC (2015) Stable associations masked by temporal variability in the marine copepod microbiome. PLOS One 10:e0138967. https://doi.org/10.1371/journal.pone.0138967

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank H. Ohtsuki, W. Makino, and N. Maruoka for their help with the laboratory work.

Funding

This study was financially supported by the Japan Science and Technology Agency for the SPRING program (JPMJSP2114) and the Japan Society for the Promotion of Science Grant-in-Aid for Scientific Research (KAKENHI:20H03315 and 23H05482).

Author information

Authors and Affiliations

Authors

Contributions

I. R. and J. U. designed the study; I. R. performed the experiments; and I. R. and J. U. analyzed the data and wrote the manuscript.

Corresponding author

Correspondence to Ryotaro Ichige.

Ethics declarations

Conflict of Interest

The authors declare no competing interests.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 1092 KB)

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

Ichige, R., Urabe, J. Divergence of the Host-Associated Microbiota with the Genetic Distance of Host Individuals Within a Parthenogenetic Daphnia Species. Microb Ecol 86, 2097–2108 (2023). https://doi.org/10.1007/s00248-023-02219-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00248-023-02219-5

Keywords

Navigation