Letter | Published:

Moving beyond microbiome-wide associations to causal microbe identification

Nature volume 552, pages 244247 (14 December 2017) | Download Citation

  • An Erratum to this article was published on 17 January 2018

This article has been updated


Microbiome-wide association studies have established that numerous diseases are associated with changes in the microbiota1,2. These studies typically generate a long list of commensals implicated as biomarkers of disease, with no clear relevance to disease pathogenesis1,2,3,4,5. If the field is to move beyond correlations and begin to address causation, an effective system is needed for refining this catalogue of differentially abundant microbes and to allow subsequent mechanistic studies1,4. Here we demonstrate that triangulation of microbe–phenotype relationships is an effective method for reducing the noise inherent in microbiota studies and enabling identification of causal microbes. We found that gnotobiotic mice harbouring different microbial communities exhibited differential survival in a colitis model. Co-housing of these mice generated animals that had hybrid microbiotas and displayed intermediate susceptibility to colitis. Mapping of microbe–phenotype relationships in parental mouse strains and in mice with hybrid microbiotas identified the bacterial family Lachnospiraceae as a correlate for protection from disease. Using directed microbial culture techniques, we discovered Clostridium immunis, a previously unknown bacterial species from this family, that—when administered to colitis-prone mice—protected them against colitis-associated death. To demonstrate the generalizability of our approach, we used it to identify several commensal organisms that induce intestinal expression of an antimicrobial peptide. Thus, we have used microbe–phenotype triangulation to move beyond the standard correlative microbiome study and identify causal microbes for two completely distinct phenotypes. Identification of disease-modulating commensals by microbe–phenotype triangulation may be more broadly applicable to human microbiome studies.

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Change history

  • 17 January 2018

    Please see accompanying Erratum (http://doi.org/10.1038/nature25471). In Fig. 2c, the labels for the green, blue and red lines were corrected from: ‘MMb’, ‘MMbHMb−1d’ and ‘MMbHMb−3d’ to ‘HMb’, ‘HMbMMb−1d’ and ‘HMbMMb−3d’, respectively.


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  1. 1.

    & The human intestinal microbiome in health and disease. N. Engl. J. Med. 375, 2369–2379 (2016)

  2. 2.

    & Deciphering the tête-à-tête between the microbiota and the immune system. J. Clin. Invest. 124, 4197–4203 (2014)

  3. 3.

    & Looking for a signal in the noise: revisiting obesity and the microbiome. MBio 7, e01018–16 (2016)

  4. 4.

    et al. Microbiome-wide association studies link dynamic microbial consortia to disease. Nature 535, 94–103 (2016)

  5. 5.

    et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe 15, 382–392 (2014)

  6. 6.

    et al. Treg induction by a rationally selected mixture of Clostridia strains from the human microbiota. Nature 500, 232–236 (2013)

  7. 7.

    et al. Gut bacteria that prevent growth impairments transmitted by microbiota from malnourished children. Science 351, aad3311 (2016)

  8. 8.

    et al. Induction of intestinal Th17 cells by segmented filamentous bacteria. Cell 139, 485–498 (2009)

  9. 9.

    et al. Functional characterization of IgA-targeted bacterial taxa from undernourished Malawian children that produce diet-dependent enteropathy. Sci. Transl. Med. 7, 276ra24 (2015)

  10. 10.

    , & A microbial symbiosis factor prevents intestinal inflammatory disease. Nature 453, 620–625 (2008)

  11. 11.

    et al. Immunoglobulin A coating identifies colitogenic bacteria in inflammatory bowel disease. Cell 158, 1000–1010 (2014)

  12. 12.

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

  13. 13.

    et al. Culture independent analysis of ileal mucosa reveals a selective increase in invasive Escherichia coli of novel phylogeny relative to depletion of Clostridiales in Crohn’s disease involving the ileum. ISME J. 1, 403–418 (2007)

  14. 14.

    et al. Molecular-phylogenetic characterization of microbial community imbalances in human inflammatory bowel diseases. Proc. Natl Acad. Sci. USA 104, 13780–13785 (2007)

  15. 15.

    et al. Dysbiosis of fecal microbiota in Crohn’s disease patients as revealed by a custom phylogenetic microarray. Inflamm. Bowel Dis. 16, 2034–2042 (2010)

  16. 16.

    et al. The antibacterial lectin RegIIIγ promotes the spatial segregation of microbiota and host in the intestine. Science 334, 255–258 (2011)

  17. 17.

    , , & Symbiotic bacteria direct expression of an intestinal bactericidal lectin. Science 313, 1126–1130 (2006)

  18. 18.

    et al. Mining the human gut microbiota for immunomodulatory organisms. Cell 168, 928–943 (2017)

  19. 19.

    Molecular Koch’s postulates applied to microbial pathogenicity. Rev. Infect. Dis. 10 (Suppl. 2), S274–S276 (1988)

  20. 20.

    & Sequence-based identification of microbial pathogens: a reconsideration of Koch’s postulates. Clin. Microbiol. Rev. 9, 18–33 (1996)

  21. 21.

    Untersuchungen über Bakterien: V. Die Ätiologie der Milzbrand-Krankheit, begründet auf die Entwicklungsgeschicte des Bacillus anthracis. Cohns Beitr. Biol. Pflanz. 2, 277–310 (1876)

  22. 22.

    , , & Chemically induced mouse models of intestinal inflammation. Nat. Protocols 2, 541–546 (2007)

  23. 23.

    et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012)

  24. 24.

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

  25. 25.

    et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618 (2012)

  26. 26.

    , , & EMPeror: a tool for visualizing high-throughput microbial community data. Gigascience 2, 16 (2013)

  27. 27.

    et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011)

  28. 28.

    , , , & Taxonomic study of Bacteroides clostridiiformis subsp. clostridiiformis (Burri and Ankersmit) Holdeman and Moore and of related organisms: proposal of Clostridium clostridiiformis (Burri and Ankersmit) comb. nov. and Clostridium symbiosum (Stevens) comb. nov. Int. J. Syst. Evol. Microbiol. 26, 195–204 (1976)

  29. 29.

    et al. Identification of acquired antimicrobial resistance genes. J. Antimicrob. Chemother. 67, 2640–2644 (2012)

  30. 30.

    et al. Real-time whole-genome sequencing for routine typing, surveillance, and outbreak detection of verotoxigenic Escherichia coli. J. Clin. Microbiol. 52, 1501–1510 (2014)

  31. 31.

    , , & PathogenFinder—distinguishing friend from foe using bacterial whole genome sequence data. PLoS ONE 8, e77302 (2013)

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We thank C. Couter for technical assistance; S. Edwards, J. Ramos, and T. Sherpa for assistance with gnotobiotic mice; R. Bronson for review of histology; J. McCoy for editorial assistance; and members of the Kasper laboratory for discussions. Support for this work was provided by a Career Development Award from Boston Children’s Hospital (N.K.S.) and National Institutes of Health grants K08 AI108690 (N.K.S.) and U19 AI109764 (N.K.S. and D.L.K.).

Author information


  1. Division of Immunology, Department of Microbiology and Immunobiology, Harvard Medical School, Boston, Massachusetts 02115, USA

    • Neeraj K. Surana
    •  & Dennis L. Kasper
  2. Division of Infectious Diseases, Department of Medicine, Boston Children’s Hospital, Boston, Massachusetts 02115, USA

    • Neeraj K. Surana


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N.K.S. conceived the study, designed and performed experiments, and analysed all data. D.L.K. supervised all aspects of the project. N.K.S. and D.L.K. wrote the paper.

Competing interests

N.K.S. and D.L.K. are inventors on patent application numbers 17/38680, 62/581372 and 62/523330 submitted by Harvard University that cover the therapeutic use of C. immunis.

Corresponding authors

Correspondence to Neeraj K. Surana or Dennis L. Kasper.

Reviewer Information Nature thanks J. Faith, M. Lathrop, A. Macpherson and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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