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Human gut microbiome viewed across age and geography


Gut microbial communities represent one source of human genetic and metabolic diversity. To examine how gut microbiomes differ among human populations, here we characterize bacterial species in fecal samples from 531 individuals, plus the gene content of 110 of them. The cohort encompassed healthy children and adults from the Amazonas of Venezuela, rural Malawi and US metropolitan areas and included mono- and dizygotic twins. Shared features of the functional maturation of the gut microbiome were identified during the first three years of life in all three populations, including age-associated changes in the genes involved in vitamin biosynthesis and metabolism. Pronounced differences in bacterial assemblages and functional gene repertoires were noted between US residents and those in the other two countries. These distinctive features are evident in early infancy as well as adulthood. Our findings underscore the need to consider the microbiome when evaluating human development, nutritional needs, physiological variations and the impact of westernization.

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Figure 1: Differences in the fecal microbial communities of Malawians, Amerindians and US children and adults.
Figure 2: Bacterial diversity increases with age in each population.
Figure 3: Differences in the functional profiles of fecal microbiomes in the three study populations.
Figure 4: Differences in the fecal microbiota between family members across the three populations studied.

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Data deposits

DNA sequences have been deposited in MG-RAST ( under accession numbers ‘qiime:850’ for Illumina V4 16S rRNA data sets, and ‘qiime:621’ for fecal microbiome shotgun sequencing data sets.


  1. Mueller, S. et al. Differences in fecal microbiota in different European study populations in relation to age, gender, and country: a cross-sectional study. Appl. Environ. Microbiol. 72, 1027–1033 (2006)

    Article  CAS  Google Scholar 

  2. Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010)

    Article  CAS  Google Scholar 

  3. Li, M. et al. Symbiotic gut microbes modulate human metabolic phenotypes. Proc. Natl Acad. Sci. USA 105, 2117–2122 (2008)

    Article  ADS  CAS  Google Scholar 

  4. Kurokawa, K. et al. Comparative metagenomics revealed commonly enriched gene sets in human gut microbiomes. DNA Res. 14, 169–181 (2007)

    Article  CAS  Google Scholar 

  5. Koenig, J. E. et al. Microbes and Health Sackler Colloquium: Succession of microbial consortia in the developing infant gut microbiome. Proc. Natl Acad. Sci. USA 108 (suppl. 1). 4578–4585 (2011)

    Article  ADS  CAS  Google Scholar 

  6. Favier, C. F., Vaughan, E. E., De Vos, W. M. & Akkermans, A. D. Molecular monitoring of succession of bacterial communities in human neonates. Appl. Environ. Microbiol. 68, 219–226 (2002)

    Article  CAS  Google Scholar 

  7. Tannock, G. W. What immunologists should know about bacterial communities of the human bowel. Semin. Immunol. 19, 94–105 (2007)

    Article  CAS  Google Scholar 

  8. Dominguez-Bello, M. G. et al. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc. Natl Acad. Sci. USA 107, 11971–11975 (2010)

    Article  ADS  Google Scholar 

  9. Blaser, M. J. & Falkow, S. What are the consequences of the disappearing human microbiota? Nature Rev. Microbiol. 7, 887–894 (2009)

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  11. De Filippo, C. et al. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc. Natl Acad. Sci. USA 107, 14691–14696 (2010)

    Article  ADS  Google Scholar 

  12. Peach, S., Fernandez, F., Johnson, K. & Drasar, B. S. The non-sporing anaerobic bacteria in human faeces. J. Med. Microbiol. 7, 213–221 (1974)

    Article  CAS  Google Scholar 

  13. Mackie, R. I., Sghir, A. & Gaskins, H. R. Developmental microbial ecology of the neonatal gastrointestinal tract. Am. J. Clin. Nutr. 69, 1035S–1045S (1999)

    Article  CAS  Google Scholar 

  14. 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 

  15. Penders, J. et al. Factors influencing the composition of the intestinal microbiota in early infancy. Pediatrics 118, 511–521 (2006)

    Article  Google Scholar 

  16. Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005)

    Article  CAS  Google Scholar 

  17. Knights, D., Costello, E. K. & Knight, R. Supervised classification of human microbiota. FEMS Microbiol. Rev. 35, 343–359 (2011)

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  ADS  CAS  Google Scholar 

  20. Kristiansson, E., Hugenholtz, P. & Dalevi, D. ShotgunFunctionalizeR: an R-package for functional comparison of metagenomes. Bioinformatics 25, 2737–2738 (2009)

    Article  CAS  Google Scholar 

  21. Kräutler, B. Vitamin B12: chemistry and biochemistry. Biochem. Soc. Trans. 33, 806–810 (2005)

    Article  Google Scholar 

  22. Monsen, A. L., Refsum, H., Markestad, T. & Ueland, P. M. Cobalamin status and its biochemical markers methylmalonic acid and homocysteine in different age groups from 4 days to 19 years. Clin. Chem. 49, 2067–2075 (2003)

    Article  CAS  Google Scholar 

  23. Martens, E. C., Chiang, H. C. & Gordon, J. I. Mucosal glycan foraging enhances fitness and transmission of a saccharolytic human gut bacterial symbiont. Cell Host Microbe 4, 447–457 (2008)

    Article  CAS  Google Scholar 

  24. Hooper, L. V., Xu, J., Falk, P. G., Midtvedt, T. & Gordon, J. I. A molecular sensor that allows a gut commensal to control its nutrient foundation in a competitive ecosystem. Proc. Natl Acad. Sci. USA 96, 9833–9838 (1999)

    Article  ADS  CAS  Google Scholar 

  25. Harzer, G., Franzke, V. & Bindels, J. G. Human milk nonprotein nitrogen components: changing patterns of free amino acids and urea in the course of early lactation. Am. J. Clin. Nutr. 40, 303–309 (1984)

    Article  CAS  Google Scholar 

  26. Metges, C. C. et al. Incorporation of urea and ammonia nitrogen into ileal and fecal microbial proteins and plasma free amino acids in normal men and ileostomates. Am. J. Clin. Nutr. 70, 1046–1058 (1999)

    Article  CAS  Google Scholar 

  27. Millward, D. J. et al. The transfer of 15N from urea to lysine in the human infant. Br. J. Nutr. 83, 505–512 (2000)

    Article  CAS  Google Scholar 

  28. Meakins, T. S. & Jackson, A. A. Salvage of exogenous urea nitrogen enhances nitrogen balance in normal men consuming marginally inadequate protein diets. Clin. Sci. (Lond.) 90, 215–225 (1996)

    Article  CAS  Google Scholar 

  29. Langran, M., Moran, B. J., Murphy, J. L. & Jackson, A. A. Adaptation to a diet low in protein: effect of complex carbohydrate upon urea kinetics in normal man. Clin. Sci. (Lond.) 82, 191–198 (1992)

    Article  CAS  Google Scholar 

  30. Mora, D. et al. Characterization of urease genes cluster of Streptococcus thermophilus. J. Appl. Microbiol. 96, 209–219 (2004)

    Article  CAS  Google Scholar 

  31. Muegge, B. D. et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 332, 970–974 (2011)

    Article  ADS  CAS  Google Scholar 

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

    Article  ADS  CAS  Google Scholar 

  33. Caporaso, J. G. et al. Moving pictures of the human microbiome. Genome Biol. 12, R50 (2011)

    Article  Google Scholar 

  34. Kaufman, L. & Rousseeuw, P. J. Finding Groups in Data: an Introduction to Cluster Analysis Ch. 2 68–125 (Wiley, 1990)

    MATH  Google Scholar 

  35. Rousseeuw, P. J. Silhouettes — a graphical aid to the interpretation and validation of cluster-analysis. J. Comput. Appl. Math. 20, 53–65 (1987)

    Article  Google Scholar 

  36. Tibshirani, R. & Walther, G. Cluster validation by prediction strength. J. Comput. Graph. Statist. 14, 511–528 (2005)

    Article  MathSciNet  Google Scholar 

  37. Teal, T. K. & Schmidt, T. M. Identifying and removing artificial replicates from 454 pyrosequencing data. Cold Spring Harb. Protoc. 2010, pdb.prot5409 (2010)

    Article  Google Scholar 

  38. R Development Core Team. . R: A Language and Envirnoment for Statistical Compuiting (R Foundation for Statistical Computing, 2010)

  39. Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002)

    Google Scholar 

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We thank S. Wagoner and J. Manchester for superb technical assistance, plus B. Muegge, A. Grimm, A. Hsiao, N. Griffin and P. Tarr for suggestions, and M. Ndao, T. Tinnin and R. Mkakosya for patient recruitment and/or technical assistance. This work was supported in part by grants from the National Institutes of Health (DK078669, T32-HD049338), St. Louis Children’s Discovery Institute (MD112009-201), the Howard Hughes Medical Institute, the Crohn’s and Colitis Foundation of America, and the Bill and Melinda Gates Foundation. Parts of this work used the Janus supercomputer, which is supported by National Science Foundation grant CNS-0821794, the University of Colorado, Boulder, the University of Colorado, Denver, and the National Center for Atmospheric Research.

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Authors and Affiliations



T.Y., R.K. and J.I.G. designed the experiments, M.J.M., I.T., M.G.D.-B., M.C., M.M., G.H., A.C.H., A.P.A., R.K., R.N.B., C.A.L., C.L. and B.W. participated in patient recruitment, T.Y. generated the data, T.Y., F.E.R., J.R., J.K., J.G.C., J.C.C., D.K., R.K. and J.I.G. analysed the results, T.Y., R.K. and J.I.G. wrote the paper.

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Correspondence to Jeffrey I. Gordon.

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The authors declare no competing financial interests.

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Yatsunenko, T., Rey, F., Manary, M. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).

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