Moving beyond microbiome-wide associations to causal microbe identification

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

Abstract

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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: MMb mice have more severe colitis than HMb mice.
Figure 2: Microbe–phenotype triangulation reveals that the bacterial family Lachnospiraceae is associated with survival from colitis.
Figure 3: C. immunis protects MMb mice from colitis.
Figure 4: R. gnavus and L. reuteri induce intestinal expression of Reg3γ.

Accession codes

Primary accessions

European Nucleotide Archive

NCBI Reference Sequence

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.

References

  1. 1

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

    CAS  Article  Google Scholar 

  2. 2

    Surana, N. K. & Kasper, D. L. Deciphering the tête-à-tête between the microbiota and the immune system. J. Clin. Invest. 124, 4197–4203 (2014)

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3

    Sze, M. A. & Schloss, P. D. Looking for a signal in the noise: revisiting obesity and the microbiome. MBio 7, e01018–16 (2016)

    Article  Google Scholar 

  4. 4

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

    ADS  CAS  Article  Google Scholar 

  5. 5

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

    CAS  Article  Google Scholar 

  6. 6

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

    ADS  CAS  Article  Google Scholar 

  7. 7

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

    Article  Google Scholar 

  8. 8

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

    CAS  Article  Google Scholar 

  9. 9

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

    CAS  Article  Google Scholar 

  10. 10

    Mazmanian, S. K., Round, J. L. & Kasper, D. L. A microbial symbiosis factor prevents intestinal inflammatory disease. Nature 453, 620–625 (2008)

    ADS  CAS  Article  Google Scholar 

  11. 11

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

    CAS  Article  Google Scholar 

  12. 12

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

    CAS  Article  Google Scholar 

  13. 13

    Baumgart, M. 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)

    CAS  Article  Google Scholar 

  14. 14

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

    ADS  CAS  Article  Google Scholar 

  15. 15

    Kang, S. 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)

    Article  Google Scholar 

  16. 16

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

    ADS  CAS  Article  Google Scholar 

  17. 17

    Cash, H. L., Whitham, C. V., Behrendt, C. L. & Hooper, L. V. Symbiotic bacteria direct expression of an intestinal bactericidal lectin. Science 313, 1126–1130 (2006)

    ADS  CAS  Article  Google Scholar 

  18. 18

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

    CAS  Article  Google Scholar 

  19. 19

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

    Article  Google Scholar 

  20. 20

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

    CAS  Article  Google Scholar 

  21. 21

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

    Google Scholar 

  22. 22

    Wirtz, S., Neufert, C., Weigmann, B. & Neurath, M. F. Chemically induced mouse models of intestinal inflammation. Nat. Protocols 2, 541–546 (2007)

    CAS  Article  Google Scholar 

  23. 23

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

    CAS  Article  Google Scholar 

  24. 24

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

    CAS  Article  Google Scholar 

  25. 25

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

    CAS  Article  Google Scholar 

  26. 26

    Vázquez-Baeza, Y., Pirrung, M., Gonzalez, A. & Knight, R. EMPeror: a tool for visualizing high-throughput microbial community data. Gigascience 2, 16 (2013)

    Article  Google Scholar 

  27. 27

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

    Article  Google Scholar 

  28. 28

    Kaneuchi, C., Watanabe, K., Terada, A., Benno, Y. & Mitsuoka, T. 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)

    Google Scholar 

  29. 29

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

    CAS  Article  Google Scholar 

  30. 30

    Joensen, K. G. 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)

    Article  Google Scholar 

  31. 31

    Cosentino, S., Voldby Larsen, M., Møller Aarestrup, F. & Lund, O. PathogenFinder—distinguishing friend from foe using bacterial whole genome sequence data. PLoS ONE 8, e77302 (2013)

    ADS  CAS  Article  Google Scholar 

Download references

Acknowledgements

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

Affiliations

Authors

Contributions

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.

Corresponding authors

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

Ethics declarations

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.

Additional information

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.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Figure 1 Individual MWAS reveal a large number of differentially abundant taxa.

Linear discriminant analysis effect size was used to identify differentially abundant taxa in the faecal microbiota of various mice. Taxa coloured red and green were more abundant in that particular group of mice. Taxa coloured yellow did not statistically differ in abundance between groups. Each ring of the cladogram represents a different taxonomic level, starting with kingdom in the centre and ending with genus in the outer ring. a, Comparison of HMb and MMb. b, Comparison of MMb and SPF. c, Comparison of MMb and MMbHMb-1d. d, Comparison of HMb and HMbMMb-1d. The family Lachnospiraceae is indicated by the symbols c4 (a), a6 (b), a1 (c), and a9 (d). Source data

Extended Data Figure 2 Several taxa that are differentially present in HMb and MMb mice do not augment colitis severity.

a, Survival of MMb mice (n = 2 mice) and MMb mice orally receiving P. clara (n = 4 mice) or B. uniformis (n = 4 mice) and subjected to DSS-induced colitis. b, Survival of HMb mice (n = 2 mice) and HMb mice orally receiving L. reuteri (n = 4), R. gnavus (n = 4 mice), or SFB (n = 4 mice) and subjected to DSS-induced colitis. Source data

Extended Data Figure 3 Culture of MMb faeces on semi-selective medium does not enrich for Lachnospiraceae.

The relative abundance of bacterial families present in MMb faeces before (left) and after (right) culture is shown. Source data

Extended Data Figure 4 MMb mice given MMb cx and MMb mice given HMb cx have distinct microbiotas.

Weighted principal components analysis of the faecal microbiota of MMb mice before and after gavage with MMb cx or HMb cx is shown. The arrow indicates an MMb mouse that received HMb cx but died after being challenged with DSS. Source data

Extended Data Figure 5 The HMb cx bacterial consortium is sufficient to protect mice from colitis-associated death.

The survival of germ-free mice orally receiving HMb cx (n = 10 mice) and subjected to DSS-induced colitis is shown. Source data

Extended Data Figure 6 Several taxa that are present in MMb mice and absent in HMb mice do not induce Reg3γ expression.

qPCR analysis of ileal Reg3γ expression in HMb mice receiving no organisms (n = 4 mice) and in HMb mice receiving orally administered A. stercoricanis (n = 4 mice), M. intestinale (n = 4 mice), or L. vaginalis (n = 4 mice). Reg3γ expression was normalized to germ-free mice (n = 3 mice). Individual (dots) and mean (bars) values are shown. Source data

Extended Data Table 1 List of bacterial taxa associated with Reg3γ induction

Supplementary information

PowerPoint slides

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Surana, N., Kasper, D. Moving beyond microbiome-wide associations to causal microbe identification. Nature 552, 244–247 (2017). https://doi.org/10.1038/nature25019

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.