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

  • Nature Microbiology volume 2, Article number: 16215 (2016)
  • doi:10.1038/nmicrobiol.2016.215
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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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Change history

  • Corrected online 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.


  1. 1.

    , & 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).

  2. 2.

    & Antibiotics, microbiota, and immune defense. Trends Immunol. 33, 459–466 (2012).

  3. 3.

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

  4. 4.

    , , & The mouse gut microbiome revisited: from complex diversity to model ecosystems. Int. J. Med. Microbiol. 306, 316–327 (2016).

  5. 5.

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

  6. 6.

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

  7. 7.

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

  8. 8.

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

  9. 9.

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

  10. 10.

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

  11. 11.

    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).

  12. 12.

    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).

  13. 13.

    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.

    , , & 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).

  15. 15.

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

  16. 16.

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

  17. 17.

    , , & Draft genome sequences of the altered schaedler flora, a defined bacterial community from gnotobiotic mice. Genome Announc. 2, e00287 (2014).

  18. 18.

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

  19. 19.

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

  20. 20.

    , & Efficiency of various intestinal bacteria in assuming normal functions of enteric flora after association with germ-free mice. Infect. Immun. 2, 376–386 (1970).

  21. 21.

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

  22. 22.

    , , , & Mechanisms that control bacterial populations in continuous-flow culture models of mouse large intestinal flora. Infect. Immun. 39, 676–685 (1983).

  23. 23.

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

  24. 24.

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

  25. 25.

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

  26. 26.

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

  27. 27.

    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).

  28. 28.

    , , & Role of intestinal anaerobic bacteria in colonization resistance. Eur. J. Clin. Microbiol. Infect. Dis. 7, 107–113 (1988).

  29. 29.

    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).

  30. 30.

    , , & The Vibrio cholerae type VI secretion system displays antimicrobial properties. Proc. Natl Acad. Sci. USA 107, 19520–19524 (2010).

  31. 31.

    , , & Isolation of anaerobic bacteria from human gingiva and mouse cecum by means of a simplified glove box procedure. Appl. Microbiol. 17, 568–576 (1969).

  32. 32.

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

  33. 33.

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

  34. 34.

    , , & Akkermansia muciniphila gen. nov., sp. nov., a human intestinal mucin-degrading bacterium. Int. J. Syst. Evol. Microbiol. 54, 1469–1476 (2004).

  35. 35.

    & Aromatic-dependent Salmonella typhimurium are non-virulent and effective as live vaccines. Nature 291, 238–239 (1981).

  36. 36.

    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).

  37. 37.

    , , & Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).

  38. 38.

    , , , & Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

  39. 39.

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

  40. 40.

    et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).

  41. 41.

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

  42. 42.

    , , & QUAST quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075 (2013).

  43. 43.

    , , , & Ray Meta: scalable de novo metagenome assembly and profiling. Genome Biol. 13, R122 (2012).

  44. 44.

    , , & Gene and translation initiation site prediction in metagenomic sequences. Bioinformatics 28, 2223–2230 (2012).

  45. 45.

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

  46. 46.

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

  47. 47.

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

  48. 48.

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

  49. 49.

    , & NIH image to ImageJ 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

  50. 50.

    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).

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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).

Author information

Author notes

    • Markus Beutler
    • , Carina Pfann
    •  & Debora Garzetti

    These authors contributed equally to this work.


  1. Max von Pettenkofer Institute of Hygiene and Medical Microbiology, Ludwig-Maximilians-University of Munich, 80336 Munich, Germany

    • Sandrine Brugiroux
    • , Markus Beutler
    • , Debora Garzetti
    • , Diana Ring
    • , Manuel Diehl
    • , Simone Herp
    • , Saib Hussain
    • , Philipp C. Münch
    •  & Bärbel Stecher
  2. Division of Microbial Ecology, Department of Microbiology and Ecosystem Science, Research Network Chemistry meets Microbiology, University of Vienna, A-1090 Vienna, Austria

    • Carina Pfann
    • , Alexander Loy
    •  & David Berry
  3. German Center for Infection Research (DZIF); Partner Site Munich

    • Debora Garzetti
    •  & Bärbel Stecher
  4. Center for Bioinformatics, University of Tübingen, 72076 Tübingen, Germany

    • Hans-Joachim Ruscheweyh
    •  & Daniel H. Huson
  5. Institute of Microbiology, ETH Zürich, 8093 Zürich, Switzerland

    • Yvonne Lötscher
  6. DSMZ – German Collection of Microorganisms and Cell Cultures, 38124 Braunschweig, Germany

    • Boyke Bunk
    •  & Rüdiger Pukall
  7. Computational Biology of Infection Research, Helmholtz Centre for Infection Research, 38124 Braunschweig, Germany

    • Philipp C. Münch
    •  & Alice C. McHardy
  8. Department of Algorithmic Bioinformatics, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany

    • Alice C. McHardy
  9. Maurice Müller Laboratories, Department of Clinical Research (DKF), UVCM, University Hospital, 3010 Bern, Switzerland

    • Kathy D. McCoy
    •  & Andrew J. Macpherson
  10. ZIEL Institute for Food and Health, Technische Universität München, 85354 Freising, Germany

    • Thomas Clavel


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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.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Bärbel Stecher.

Supplementary information

PDF files

  1. 1.

    Supplementary information

    Supplementary Figures 1-8, Supplementary Tables 1-4 and 11, legends for Supplementary Tables 5-10, Supplementary References

Excel files

  1. 1.

    Supplementary Table 5

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

  2. 2.

    Supplementary Table 6

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

  3. 3.

    Supplementary Table 7

    KEGG module analysis for Oligo-MM12 and ASF8 26.

  4. 4.

    Supplementary Table 8

    KEGG module analysis for CON metagenomes and artificial metagenomes of Oligo-MM12 and ASF8 32.

  5. 5.

    Supplementary Table 9

    KEGG module analysis for Oligo-MM12, ASF8 and FA3 37.

  6. 6.

    Supplementary Table 10

    KEGG module analysis for CON metagenomes and artificial metagenomes of Oligo-MM12, FA3 and ASF8.