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Mobile genes in the human microbiome are structured from global to individual scales

Nature volume 535, pages 435439 (21 July 2016) | Download Citation

  • A Corrigendum to this article was published on 22 March 2017


Recent work has underscored the importance of the microbiome in human health, and has largely attributed differences in phenotype to differences in the species present among individuals1,2,3,4,5. However, mobile genes can confer profoundly different phenotypes on different strains of the same species. Little is known about the function and distribution of mobile genes in the human microbiome, and in particular whether the gene pool is globally homogenous or constrained by human population structure. Here, we investigate this question by comparing the mobile genes found in the microbiomes of 81 metropolitan North Americans with those of 172 agrarian Fiji islanders using a combination of single-cell genomics and metagenomics. We find large differences in mobile gene content between the Fijian and North American microbiomes, with functional variation that mirrors known dietary differences such as the excess of plant-based starch degradation genes found in Fijian individuals. Notably, we also observed differences between the mobile gene pools of neighbouring Fijian villages, even though microbiome composition across villages is similar. Finally, we observe high rates of recombination leading to individual-specific mobile elements, suggesting that the abundance of some genes may reflect environmental selection rather than dispersal limitation. Together, these data support the hypothesis that human activities and behaviours provide selective pressures that shape mobile gene pools, and that acquisition of mobile genes is important for colonizing specific human populations.

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We thank our field collaborators in the Fiji Islands: the Wildlife Conservation Society, Fiji, Wetlands International-Oceania, K. Jenkins, S. Korovou, N. Litidamu, and K. Kishore. We thank T. Poon for sample, sequencing, and data coordination, and A. Materna (QIAGEN) for technical assistance. This work was supported by grants from the National Human Genome Research Institute (U54HG003067) to the Broad Institute, the Center for Environmental Health Sciences at MIT, the Center for Microbiome Informatics and Therapeutics at MIT, and the Fijian Ministry of Health. Additional support was provided by a Columbia University Earth Institute Fellowship (I.L.B.); a Broad Institute Lawrence Summers Fellowship (L.X.); a Burroughs Wellcome Fund Career Award at the Scientific Interface (P.C.B.); and an R01 DE020891 funded by the NIDCR and ENIGMA and a Lawrence Berkeley National Laboratory Scientific Focus Area Program supported by the US Department of Energy, Office of Science, Office of Biological and Environmental Research (S.Y. and A.K.S.). Sandia is a multi-program laboratory operated by Sandia Corp., a Lockheed Martin Co., for the United States Department of Energy under Contract DE-AC04-94AL85000.

Author information

Author notes

    • S. Yilmaz
    • , K. Huang
    •  & L. Xu

    These authors contributed equally to this work.


  1. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

    • I. L. Brito
    • , C. S. Smillie
    •  & E. J. Alm
  2. Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02139, USA

    • I. L. Brito
    • , K. Huang
    • , L. Xu
    • , J. R. Wortman
    • , B. W. Birren
    • , R. J. Xavier
    • , P. C. Blainey
    • , D. Gevers
    •  & E. J. Alm
  3. Sandia National Laboratories, Livermore, California 94608, USA

    • S. Yilmaz
    •  & A. K. Singh
  4. Wildlife Conservation Society, Suva, Fiji

    • S. D. Jupiter
    •  & W. Naisilisili
  5. Edith Cowan University, Joondalup, Western Australia 6027, Australia

    • A. P. Jenkins
  6. Department of Aquatic Ecology, Eawag, CH-8600 Dubendorf, Switzerland

    • M. Tamminen
  7. Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland

    • M. Tamminen
  8. Massachusetts General Hospital, Boston, Massachusetts 02114, USA

    • R. J. Xavier
  9. Center for Microbiome, Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

    • R. J. Xavier
    •  & E. J. Alm


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I.L.B. and E.J.A. designed the study. I.L.B., S.D.J., A.P.J. and W.N. oversaw and performed the field collection of FijiCOMP data and samples. I.L.B., L.X., S.Y., and M.T. performed all experimental work. D.G., B.W.B., J.R.W., P.C.B., R.J.X. and A.K.S. oversaw the DNA sequencing production. I.L.B. and K.H. processed the shotgun data and performed alignments. I.L.B., K.H., and D.G. provided new analytical tools. I.L.B., K.H. and C.S.S. performed computational analysis. I.L.B. and E.J.A. wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to E. J. Alm.

Reviewer Information Nature thanks P. Bork, K. Forslund, P. Hugenholtz and C. Rinke for their contribution to the peer review of this work.

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    Supplementary Tables

    This zipped file contains Supplementary Tables 1-10.

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    Supplementary Data

    This file contains the DNA sequences of all horizontally transferred genes observed in this study. A FASTA file containing 37,853 DNA sequences. Sequence identifiers correspond to the cell identifier followed by the contig number and the gene number on that contig.

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