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

  • Nature Microbiology 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.


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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.