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Dynamics and associations of microbial community types across the human body

Nature volume 509, pages 357–360 (2014)Cite this article

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Abstract

A primary goal of the Human Microbiome Project (HMP) was to provide a reference collection of 16S ribosomal RNA gene sequences collected from sites across the human body that would allow microbiologists to better associate changes in the microbiome with changes in health1. The HMP Consortium has reported the structure and function of the human microbiome in 300 healthy adults at 18 body sites from a single time point2,3. Using additional data collected over the course of 12–18 months, we used Dirichlet multinomial mixture models4 to partition the data into community types for each body site and made three important observations. First, there were strong associations between whether individuals had been breastfed as an infant, their gender, and their level of education with their community types at several body sites. Second, although the specific taxonomic compositions of the oral and gut microbiomes were different, the community types observed at these sites were predictive of each other. Finally, over the course of the sampling period, the community types from sites within the oral cavity were the least stable, whereas those in the vagina and gut were the most stable. Our results demonstrate that even with the considerable intra- and interpersonal variation in the human microbiome, this variation can be partitioned into community types that are predictive of each other and are probably the result of life-history characteristics. Understanding the diversity of community types and the mechanisms that result in an individual having a particular type or changing types, will allow us to use their community types to assess disease risk and to personalize therapies.

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Figure 1: Analysis of stool samples reveals four community types.
Figure 2: Community-type associations are strongest within a body region, but also exist between stool and the oral cavity.
Figure 3: Dynamics of community types at various body sites indicates that community type stability is correlated with the diversity of the community type.

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References

  1. Peterson, J. et al. The NIH Human Microbiome Project. Genome Res. 19, 2317–2323 (2009)

    Article  Google Scholar 

  2. The Human Microboime Consortium. A framework for human microbiome research. Nature 486, 215–221 (2012)

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

  4. Holmes, I., Harris, K. & Quince, C. Dirichlet multinomial mixtures: generative models for microbial metagenomics. PLoS ONE 7, e30126 (2012)

    Article  CAS  ADS  Google Scholar 

  5. Aagaard, K. et al. The Human Microbiome Project strategy for comprehensive sampling of the human microbiome and why it matters. FASEB J. 27, 1012–1022 (2013)

    Article  CAS  Google Scholar 

  6. Turnbaugh, P. J. et al. A core gut microbiome in obese and lean twins. Nature 457, 480–484 (2009)

    Article  CAS  ADS  Google Scholar 

  7. Costello, E. K. et al. Bacterial community variation in human body habitats across space and time. Science 326, 1694–1697 (2009)

    Article  CAS  ADS  Google Scholar 

  8. Koren, O. et al. A guide to enterotypes across the human body: meta-analysis of microbial community structures in human microbiome datasets. PLOS Comput. Biol. 9, e1002863 (2013)

    Article  CAS  Google Scholar 

  9. Arumugam, M. et al. Enterotypes of the human gut microbiome. Nature 473, 174–180 (2011)

    Article  CAS  Google Scholar 

  10. Wu, G. D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011)

    Article  CAS  ADS  Google Scholar 

  11. Quince, C. et al. The impact of Crohn’s disease genes on healthy human gut microbiota: a pilot study. Gut 62, 952–954 (2013)

    Article  Google Scholar 

  12. Brotman, R. M. et al. Association between Trichomonas vaginalis and vaginal bacterial community composition among reproductive-age women. Sex. Transm. Dis. 39, 807–812 (2012)

    Article  Google Scholar 

  13. Gajer, P. et al. Temporal dynamics of the human vaginal microbiota. Sci. Transl. Med. 4, 132ra152 (2012)

    Article  Google Scholar 

  14. Ravel, J. et al. Vaginal microbiome of reproductive-age women. Proc. Natl Acad. Sci. USA 108 (suppl. 1). 4680–4687 (2011)

    Article  CAS  ADS  Google Scholar 

  15. Statnikov, A. et al. Microbiomic signatures of psoriasis: feasibility and methodology comparison. Sci. Rep. 3, 2620 (2013)

    Article  Google Scholar 

  16. Arumugam, M. et al. Addendum: Enterotypes of the human gut microbiome. Nature 506, 516 (2014)

    Article  CAS  ADS  Google Scholar 

  17. Moeller, A. H. et al. Chimpanzees and humans harbour compositionally similar gut enterotypes. Nature Commun. 3, 1179 (2012)

    Article  ADS  Google Scholar 

  18. Palmer, C., Bik, E. M., Digiulio, D. B., Relman, D. A. & Brown, P. O. Development of the human infant intestinal microbiota. PLoS Biol. 5, e177 (2007)

    Article  Google Scholar 

  19. Koenig, J. E. et al. Succession of microbial consortia in the developing infant gut microbiome. Proc. Natl Acad. Sci. USA 108 (suppl. 1). 4578–4585 (2011)

    Article  CAS  ADS  Google Scholar 

  20. Pantoja-Feliciano, I. G. et al. Biphasic assembly of the murine intestinal microbiota during early development. ISME J. 7, 1112–1115 (2013)

    Article  Google Scholar 

  21. Fierer, N., Hamady, M., Lauber, C. L. & Knight, R. The influence of sex, handedness, and washing on the diversity of hand surface bacteria. Proc. Natl Acad. Sci. USA 105, 17994–17999 (2008)

    Article  CAS  ADS  Google Scholar 

  22. Markle, J. G. et al. Sex differences in the gut microbiome drive hormone-dependent regulation of autoimmunity. Science 339, 1084–1088 (2013)

    Article  CAS  ADS  Google Scholar 

  23. Ma, B., Forney, L. J. & Ravel, J. Vaginal microbiome: rethinking health and disease. Annu. Rev. Microbiol. 66, 371–389 (2012)

    Article  CAS  Google Scholar 

  24. Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009)

    Article  CAS  Google Scholar 

  25. Schloss, P. D., Gevers, D. & Westcott, S. L. Reducing the effects of PCR amplification and sequencing artifacts on 16S rRNA-based studies. PLoS ONE 6, e27310 (2011)

    Article  CAS  ADS  Google Scholar 

  26. Jumpstart Consortium Human Microbiome Project Data Generation Working Group. Evaluation of 16S rDNA-based community profiling for human microbiome research. PLoS ONE 7, e39315 (2012)

  27. Quince, C., Lanzen, A., Davenport, R. J. & Turnbaugh, P. J. Removing noise from pyrosequenced amplicons. BMC Bioinform. 12, 38 (2011)

    Article  Google Scholar 

  28. Schloss, P. D. A high-throughput DNA sequence aligner for microbial ecology studies. PLoS ONE 4, e8230 (2009)

    Article  ADS  Google Scholar 

  29. Schloss, P. D. Secondary structure improves OTU assignments of 16S rRNA gene sequences. ISME J. 7, 457–460 (2013)

    Article  CAS  Google Scholar 

  30. Pruesse, E. et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 35, 7188–7196 (2007)

    Article  CAS  Google Scholar 

  31. Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011)

    Article  CAS  Google Scholar 

  32. 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)

    Article  CAS  Google Scholar 

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Acknowledgements

We thank J. Crabtree of the HMP Data Analysis and Coordination Center for his assistance in obtaining the sequencing and metadata files. The analysis described in this study was supported by grants from the National Institutes of Health (R01HG005975, R01GM099514 and P30DK034933).

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Authors and Affiliations

  1. Department of Microbiology and Immunology, 1500 W. Medical Center, University of Michigan, Ann Arbor, 48109, Michigan, USA

    Tao Ding & Patrick D. Schloss

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  1. Tao Ding
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Contributions

T.D. and P.D.S. designed and executed the analysis and prepared the manuscript.

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Correspondence to Patrick D. Schloss.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Comparison of community type assignments for non-metric dimensional scaling (NMDS) ordination of Jensen–Shannon divergence values between stool samples using DMM (a) and PAM-based clustering (b).

The stress computed for this ordination was 0.19 and the R2 between the input distance matrix and the distance matrix calculated between the points in the ordination was 0.90.

Extended Data Figure 2 The frequency of community types for body sites where there was a significant association with the subject’s gender.

a–c, Percentage of female and male tongue communities that affiliated with each of the tongue (a; n = 288 unique individuals; median P = 2 × 10−3), right retroauricular crease (b; n = 268 unique individuals; median P = 9 × 10−5) and right antecubital fossa community types (c; n = 136 unique individuals; median P = 3 × 10−5).

Extended Data Figure 3 The frequency of vaginal community types among women with and without a college degree.

a–c, Percentage of women with and without a college degree whose vaginal communities affiliated with the vaginal introitus (a; n = 74 unique individuals; median P = 2 × 10−3), mid-vagina (b; n = 64 unique individuals; median P = 8 × 10−4) and posterior fornix (c; n = 61 unique individuals; median P = 4 × 10−4) community types.

Extended Data Table 1 Most common characteristics of the individuals included in the HMP healthy cohort
Full size table
Extended Data Table 2 Comparison of PAM- and DMM-based approaches to assigning samples to community types
Full size table
Extended Data Table 3 Average contingency table of stool and saliva community types
Full size table

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Ding, T., Schloss, P. Dynamics and associations of microbial community types across the human body. Nature 509, 357–360 (2014). https://doi.org/10.1038/nature13178

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