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Genome-guided design of a defined mouse microbiota that confers colonization resistance against Salmonella enterica serovar Typhimurium

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Abstract

Protection against enteric infections, also termed colonization resistance, results from mutualistic interactions of the host and its indigenous microbes. The gut microbiota of humans and mice is highly diverse and it is therefore challenging to assign specific properties to its individual members. Here, we have used a collection of murine bacterial strains and a modular design approach to create a minimal bacterial community that, once established in germ-free mice, provided colonization resistance against the human enteric pathogen Salmonella enterica serovar Typhimurium (S. Tm). Initially, a community of 12 strains, termed Oligo-Mouse-Microbiota (Oligo-MM12), representing members of the major bacterial phyla in the murine gut, was selected. This community was stable over consecutive mouse generations and provided colonization resistance against S. Tm infection, albeit not to the degree of a conventional complex microbiota. Comparative (meta)genome analyses identified functions represented in a conventional microbiome but absent from the Oligo-MM12. By genome-informed design, we created an improved version of the Oligo-MM community harbouring three facultative anaerobic bacteria from the mouse intestinal bacterial collection (miBC) that provided conventional-like colonization resistance. In conclusion, we have established a highly versatile experimental system that showed efficacy in an enteric infection model. Thus, in combination with exhaustive bacterial strain collections and systems-based approaches, genome-guided design can be used to generate insights into microbe–microbe and microbe–host interactions for the investigation of ecological and disease-relevant mechanisms in the intestine.

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Figure 1: The Oligo-MM12 consortium stably colonizes mice and is vertically transmitted across filial generations.
Figure 2: Transplantation of the Oligo-MM12 consortium leads to increased CR against oral S. Tm infection.
Figure 3: Clustering analysis of KEGG modules represented in draft genomes of ASF and Oligo-MM strains.
Figure 4: Clustering analysis of KEGG modules represented in artificial metagenomes of ASF8 and Oligo-MM12 and conventional metagenomes (CON).
Figure 5: Transplantation of three facultative anaerobic bacteria restores CR of Oligo-MM12.
Figure 6: S. Tm infection in gnotobiotic mice colonized with different defined bacterial consortia.

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  • 14 July 2017

    In the PDF version of this article previously published, the year of publication provided in the footer of each page and in the 'How to cite' section was erroneously given as 2017, it should have been 2016. This error has now been corrected. The HTML version of the article was not affected.

References

  1. 1

    Stecher, B., Berry, D. & Loy, A. Colonization resistance and microbial ecophysiology: using gnotobiotic mouse models and single-cell technology to explore the intestinal jungle. FEMS Microbiol. Rev. 37, 793–829 (2013).

    CAS  Article  Google Scholar 

  2. 2

    Ubeda, C. & Pamer, E. G. Antibiotics, microbiota, and immune defense. Trends Immunol. 33, 459–466 (2012).

    CAS  Article  Google Scholar 

  3. 3

    Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).

    Article  Google Scholar 

  4. 4

    Clavel, T., Lagkouvardos, I., Blaut, M. & Stecher, B. The mouse gut microbiome revisited: from complex diversity to model ecosystems. Int. J. Med. Microbiol. 306, 316–327 (2016).

    CAS  Article  Google Scholar 

  5. 5

    Smith, M. I. et al. Gut microbiomes of Malawian twin pairs discordant for kwashiorkor. Science 339, 548–554 (2013).

    CAS  Article  Google Scholar 

  6. 6

    Linnenbrink, M. et al. The role of biogeography in shaping diversity of the intestinal microbiota in house mice. Mol. Ecol. 22, 1904–1916 (2013).

    Article  Google Scholar 

  7. 7

    Xiao, L. et al. A catalog of the mouse gut metagenome. Nat. Biotechnol. 33, 1103–1108 (2015).

    CAS  Article  Google Scholar 

  8. 8

    Chung, H. et al. Gut immune maturation depends on colonization with a host-specific microbiota. Cell 149, 1578–1593 (2012).

    CAS  Article  Google Scholar 

  9. 9

    Fodor, A. A. et al. The ‘most wanted’ taxa from the human microbiome for whole genome sequencing. PLoS ONE 7, e41294 (2012).

    CAS  Article  Google Scholar 

  10. 10

    Dewhirst, F. E. et al. Phylogeny of the defined murine microbiota: altered Schaedler flora. Appl. Environ. Microbiol. 65, 3287–3292 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11

    Stecher, B. et al. Like will to like: abundances of closely related species can predict susceptibility to intestinal colonization by pathogenic and commensal bacteria. PLoS Pathog. 6, e1000711 (2010).

    Article  Google Scholar 

  12. 12

    Kim, O. S. et al. Introducing EzTaxon-e: a prokaryotic 16S rRNA gene sequence database with phylotypes that represent uncultured species. Int. J. Syst. Evol. Microbiol. 62, 716–721 (2012).

    CAS  Article  Google Scholar 

  13. 13

    Lagkouvardos, I. et al. The mouse intestinal bacterial collection (miBC) provides host-specific insight into cultivable diversity and functional potential of the mouse gut microbiota. Nat. Microbiol. 1, 16131 (2016).

  14. 14

    Kaiser, P., Diard, M., Stecher, B. & Hardt, W.-D. The streptomycin mouse model for Salmonella diarrhea: functional analysis of the microbiota, the pathogen's virulence factors, and the host's mucosal immune response. Immunol Rev. 245, 56–83 (2012).

    CAS  Article  Google Scholar 

  15. 15

    Stecher, B. et al. Salmonella enterica serovar typhimurium exploits inflammation to compete with the intestinal microbiota. PLoS Biol. 5, e244 (2007).

    Article  Google Scholar 

  16. 16

    Bleich, A. & Hansen, A. K. Time to include the gut microbiota in the hygienic standardisation of laboratory rodents. Comp. Immunol. Microbiol. Infect. Dis. 35, 81–92 (2012).

    Article  Google Scholar 

  17. 17

    Wannemuehler, M. J., Overstreet, A.-M., Ward, D. V. & Phillips, G. J. Draft genome sequences of the altered schaedler flora, a defined bacterial community from gnotobiotic mice. Genome Announc. 2, e00287 (2014).

    Article  Google Scholar 

  18. 18

    Wymore Brand, M. et al. The altered Schaedler flora continued applications of a defined murine microbial community. ILAR J. 56, 169–178 (2015).

    Article  Google Scholar 

  19. 19

    Kanehisa, M. et al. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 42, D199–D205 (2014).

    CAS  Article  Google Scholar 

  20. 20

    Syed, S. A., Abrams, G. D. & Freter, R. Efficiency of various intestinal bacteria in assuming normal functions of enteric flora after association with germ-free mice. Infect. Immun. 2, 376–386 (1970).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21

    Ng, K. M. et al. Microbiota-liberated host sugars facilitate post-antibiotic expansion of enteric pathogens. Nature 502, 96–99 (2013).

    CAS  Article  Google Scholar 

  22. 22

    Freter, R., Brickner, H., Botney, M., Cleven, D. & Aranki, A. Mechanisms that control bacterial populations in continuous-flow culture models of mouse large intestinal flora. Infect. Immun. 39, 676–685 (1983).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    Maier, L. et al. Microbiota-derived hydrogen fuels Salmonella typhimurium invasion of the gut ecosystem. Cell Host Microbe 14, 641–651 (2013).

    CAS  Article  Google Scholar 

  24. 24

    Deriu, E. et al. Probiotic bacteria reduce Salmonella typhimurium intestinal colonization by competing for iron. Cell Host Microbe 14, 26–37 (2013).

    CAS  Article  Google Scholar 

  25. 25

    Spees, A. M. et al. Streptomycin-induced inflammation enhances Escherichia coli gut colonization through nitrate respiration. mBio 4, e00430-13 (2013).

    Article  Google Scholar 

  26. 26

    Nuccio, S. P. & Baumler, A. J. Comparative analysis of Salmonella genomes identifies a metabolic network for escalating growth in the inflamed gut. mBio 5, e00929 (2014).

    Article  Google Scholar 

  27. 27

    Rivera-Chávez, F. et al. Depletion of butyrate-producing Clostridia from the gut microbiota drives an aerobic luminal expansion of salmonella. Cell Host Microbe 19, 443–454 (2016).

    Article  Google Scholar 

  28. 28

    Wells, C. L., Maddaus, M. A., Jechorek, R. P. & Simmons, R. L. Role of intestinal anaerobic bacteria in colonization resistance. Eur. J. Clin. Microbiol. Infect. Dis. 7, 107–113 (1988).

    CAS  Article  Google Scholar 

  29. 29

    Stecher, B. et al. Flagella and chemotaxis are required for efficient induction of Salmonella enterica serovar typhimurium colitis in streptomycin-pretreated mice. Infect. Immun. 72, 4138–4150 (2004).

    CAS  Article  Google Scholar 

  30. 30

    MacIntyre, D. L., Miyata, S. T., Kitaoka, M. & Pukatzki, S. The Vibrio cholerae type VI secretion system displays antimicrobial properties. Proc. Natl Acad. Sci. USA 107, 19520–19524 (2010).

    CAS  Article  Google Scholar 

  31. 31

    Arank, A., Syed, S. A., Kenney, E. B. & Freter, R. Isolation of anaerobic bacteria from human gingiva and mouse cecum by means of a simplified glove box procedure. Appl. Microbiol. 17, 568–576 (1969).

    CAS  PubMed  Google Scholar 

  32. 32

    Berer, K. et al. Commensal microbiota and myelin autoantigen cooperate to trigger autoimmune demyelination. Nature 479, 538–541 (2011).

    CAS  Article  Google Scholar 

  33. 33

    Clavel, T., Charrier, C. & Haller, D. Streptococcus danieliae sp. nov., a novel bacterium isolated from the caecum of a mouse. Arch. Microbiol. 195, 43–49 (2013).

    CAS  Article  Google Scholar 

  34. 34

    Derrien, M., Vaughan, E. E., Plugge, C. M. & de Vos, W. M. Akkermansia muciniphila gen. nov., sp. nov., a human intestinal mucin-degrading bacterium. Int. J. Syst. Evol. Microbiol. 54, 1469–1476 (2004).

    CAS  Article  Google Scholar 

  35. 35

    Hoiseth, S. K. & Stocker, B. A. D. Aromatic-dependent Salmonella typhimurium are non-virulent and effective as live vaccines. Nature 291, 238–239 (1981).

    CAS  Article  Google Scholar 

  36. 36

    Hapfelmeier, S. et al. The Salmonella pathogenicity island (SPI)-1 and SPI-2 type III secretion systems allow Salmonella serovar typhimurium to trigger colitis via MyD88-dependent and MyD88-independent mechanisms. J. Immunol. 174, 1675–1685 (2005).

    CAS  Article  Google Scholar 

  37. 37

    Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).

    CAS  Article  Google Scholar 

  38. 38

    Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    CAS  Article  Google Scholar 

  39. 39

    DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).

    CAS  Article  Google Scholar 

  40. 40

    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).

    CAS  Article  Google Scholar 

  41. 41

    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  Article  Google Scholar 

  42. 42

    Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075 (2013).

    CAS  Article  Google Scholar 

  43. 43

    Boisvert, S., Raymond, F., Godzaridis, É., Laviolette, F. & Corbeil, J. Ray Meta: scalable de novo metagenome assembly and profiling. Genome Biol. 13, R122 (2012).

    Article  Google Scholar 

  44. 44

    Hyatt, D., LoCascio, P. F., Hauser, L. J. & Uberbacher, E. C. Gene and translation initiation site prediction in metagenomic sequences. Bioinformatics 28, 2223–2230 (2012).

    CAS  Article  Google Scholar 

  45. 45

    Zhao, Y., Tang, H. & Ye, Y. RAPSearch2 a fast and memory-efficient protein similarity search tool for next-generation sequencing data. Bioinformatics 28, 125–126 (2012).

    CAS  Article  Google Scholar 

  46. 46

    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).

    CAS  Article  Google Scholar 

  47. 47

    Bustin, S. A. et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 55, 611–622 (2009).

    CAS  Article  Google Scholar 

  48. 48

    Li, H. et al. The outer mucus layer hosts a distinct intestinal microbial niche. Nat. Commun. 6, 8292 (2015).

    CAS  Article  Google Scholar 

  49. 49

    Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH image to ImageJ 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

    CAS  Article  Google Scholar 

  50. 50

    Wang, J. et al. Dietary history contributes to enterotype-like clustering and functional metagenomic content in the intestinal microbiome of wild mice. Proc. Natl Acad. Sci. USA 111, E2703–E2710 (2014).

    CAS  Article  Google Scholar 

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Acknowledgements

The authors thank W.-D. Hardt, J. Heesemann, O. Rossier and members of the Stecher laboratory for discussions. The authors also thank R. Robbiani for help with isolation of bacterial strains and K. Berer and G. Krishnamoorthy for providing mice. The work was supported by grants from the BMBF (Medizinische Infektionsgenomik), DFG Priority program SPP1656, research grants DFG STE 1971/4-1 and STE 1971/7-1, the German Center for Infection Research (DZIF), the Centre for Gastrointestinal Microbiome Research (CEGIMIR), the Austrian Science Fund (P26127-B20, P27831-B28, I2320-B22) and the Vienna Science and Technology Fund (LS12-001).

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Authors

Contributions

B.S., S.B., T.C., A.L. and D.B. conceived and designed the experiments. B.S., S.B., M.B., D.G., D.R., M.D., S.He., Y.L., S.Hu., B.B., K.D.M. and D.B. performed the experiments. B.S., S.B., M.B., C.P., D.G., H.-J.R., S.H., B.B., R.P., D.H.H., P.C.M., A.C.M., T.C., A.L. and D.B. analysed the data. D.H.H., A.C.M., K.D.M., A.J.M., A.L., T.C. and D.B. contributed materials/analysis tools. B.S. coordinated the project, wrote the original draft, and all authors reviewed and edited the draft manuscript.

Corresponding author

Correspondence to Bärbel Stecher.

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The authors declare no competing financial interests.

Supplementary information

Supplementary information

Supplementary Figures 1-8, Supplementary Tables 1-4 and 11, legends for Supplementary Tables 5-10, Supplementary References (PDF 20447 kb)

Supplementary Table 5

Taxonomic composition of faecal microbiota expressed as relative abundances (Fig. 2c). (XLSX 24 kb)

Supplementary Table 6

Taxonomic composition of faecal microbiota expressed as relative abundances (Supplementary Fig. 4). (XLSX 15 kb)

Supplementary Table 7

KEGG module analysis for Oligo-MM12 and ASF8 26. (XLSX 39 kb)

Supplementary Table 8

KEGG module analysis for CON metagenomes and artificial metagenomes of Oligo-MM12 and ASF8 32. (XLSX 30 kb)

Supplementary Table 9

KEGG module analysis for Oligo-MM12, ASF8 and FA3 37. (XLSX 60 kb)

Supplementary Table 10

KEGG module analysis for CON metagenomes and artificial metagenomes of Oligo-MM12, FA3 and ASF8. (XLSX 33 kb)

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Brugiroux, S., Beutler, M., Pfann, C. et al. Genome-guided design of a defined mouse microbiota that confers colonization resistance against Salmonella enterica serovar Typhimurium. Nat Microbiol 2, 16215 (2017). https://doi.org/10.1038/nmicrobiol.2016.215

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