Human gut microbiome viewed across age and geography

Journal name:
Nature
Volume:
486,
Pages:
222–227
Date published:
DOI:
doi:10.1038/nature11053
Received
Accepted
Published online

Abstract

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.

At a glance

Figures

  1. Differences in the fecal microbial communities of Malawians, Amerindians and US children and adults.
    Figure 1: Differences in the fecal microbial communities of Malawians, Amerindians and US children and adults.

    a, UniFrac distances between children and adults decrease with increasing age of children in each population. Each point shows the average distance between a child and all adults unrelated to that child but from the same country. Results are derived from bacterial V4 16S rRNA data sets. b, Large interpersonal variations are observed in the phylogenetic configurations of fecal microbial communities at early ages. Malawian and Amerindian (Amr) children and adults are more similar to one another than to US children and adults. UniFrac distances were defined from bacterial V4 16S rRNA data generated from the microbiota of 181 unrelated adults (≥18 years old) and 204 unrelated children (n = 31 Malawians 0.03–3 years old, 21 3–17 years old; 30 Amerindians 0.08–3 years old, 29 3–17 years old; 31 US residents 0.08–3 years old, 62 sampled at 3–17 years of age). *P<0.05, **P<0.005 (Student’s t-test with 1,000 Monte Carlo simulations). See Supplementary Table 3 for a complete description of the statistical significance of all comparisons shown. c, PCoA of unweighted UniFrac distances for the fecal microbiota of adults. PC, principal coordinate.

  2. Bacterial diversity increases with age in each population.
    Figure 2: Bacterial diversity increases with age in each population.

    ac, The number of observed OTUs sharing≥97% nucleotide sequence identity plotted against age for all subjects (a), during the first 3 years of life (b), and adults (c). Mean±s.e.m. are shown in c. *P<0.05, **P<0.005 (ANOVA with Bonferroni post-hoc test).

  3. Differences in the functional profiles of fecal microbiomes in the three study populations.
    Figure 3: Differences in the functional profiles of fecal microbiomes in the three study populations.

    Examples of KEGG ECs that showed the largest differences, as determined by Random Forests and ShotgunFunctionalizeR analyses, in proportional representation between US and Malawian/Amerindian populations. Shown are the relative abundances of genes encoding the indicated ECs (normalized by Z-score across all data sets). a, UPGMA (unweighted pair group method with arithmetic mean) clustering of 10 US, 10 Malawian and 6 Amerindian baby fecal microbiomes. b, UPGMA clustering of 16 US, 5 Malawian and 5 Amerindian adult fecal microbiomes.

  4. Differences in the fecal microbiota between family members across the three populations studied.
    Figure 4: Differences in the fecal microbiota between family members across the three populations studied.

    UniFrac distances between the fecal bacterial communities of family members were calculated (n = 19 Amerindian, 34 Malawian families, and 54 US families with teenage twins). DZ, dizygotic; MZ, monozygotic. Meanands.e.m. values are plotted. The UniFrac matrix was permuted 1,000 times; P values represent the fraction of times permuted differences were greater than real differences. m, months; NS (not significant; P>0.05), *P<0.05, **P<0.005.

References

  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, 10271033 (2006)
  2. Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 5965 (2010)
  3. Li, M. et al. Symbiotic gut microbes modulate human metabolic phenotypes. Proc. Natl Acad. Sci. USA 105, 21172122 (2008)
  4. Kurokawa, K. et al. Comparative metagenomics revealed commonly enriched gene sets in human gut microbiomes. DNA Res. 14, 169181 (2007)
  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). 45784585 (2011)
  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, 219226 (2002)
  7. Tannock, G. W. What immunologists should know about bacterial communities of the human bowel. Semin. Immunol. 19, 94105 (2007)
  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, 1197111975 (2010)
  9. Blaser, M. J. & Falkow, S. What are the consequences of the disappearing human microbiota? Nature Rev. Microbiol. 7, 887894 (2009)
  10. Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7, 335336 (2010)
  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, 1469114696 (2010)
  12. Peach, S., Fernandez, F., Johnson, K. & Drasar, B. S. The non-sporing anaerobic bacteria in human faeces. J. Med. Microbiol. 7, 213221 (1974)
  13. Mackie, R. I., Sghir, A. & Gaskins, H. R. Developmental microbial ecology of the neonatal gastrointestinal tract. Am. J. Clin. Nutr. 69, 1035S1045S (1999)
  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)
  15. Penders, J. et al. Factors influencing the composition of the intestinal microbiota in early infancy. Pediatrics 118, 511521 (2006)
  16. Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 82288235 (2005)
  17. Knights, D., Costello, E. K. & Knight, R. Supervised classification of human microbiota. FEMS Microbiol. Rev. 35, 343359 (2011)
  18. Arumugam, M. et al. Enterotypes of the human gut microbiome. Nature 473, 174180 (2011)
  19. Wu, G. D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105108 (2011)
  20. Kristiansson, E., Hugenholtz, P. & Dalevi, D. ShotgunFunctionalizeR: an R-package for functional comparison of metagenomes. Bioinformatics 25, 27372738 (2009)
  21. Kräutler, B. Vitamin B12: chemistry and biochemistry. Biochem. Soc. Trans. 33, 806810 (2005)
  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, 20672075 (2003)
  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, 447457 (2008)
  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, 98339838 (1999)
  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, 303309 (1984)
  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, 10461058 (1999)
  27. Millward, D. J. et al. The transfer of 15N from urea to lysine in the human infant. Br. J. Nutr. 83, 505512 (2000)
  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, 215225 (1996)
  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, 191198 (1992)
  30. Mora, D. et al. Characterization of urease genes cluster of Streptococcus thermophilus. J. Appl. Microbiol. 96, 209219 (2004)
  31. Muegge, B. D. et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 332, 970974 (2011)
  32. Turnbaugh, P. J. et al. A core gut microbiome in obese and lean twins. Nature 457, 480484 (2009)
  33. Caporaso, J. G. et al. Moving pictures of the human microbiome. Genome Biol. 12, R50 (2011)
  34. Kaufman, L. & Rousseeuw, P. J. Finding Groups in Data: an Introduction to Cluster Analysis Ch. 2 68125 (Wiley, 1990)
  35. Rousseeuw, P. J. Silhouettes — a graphical aid to the interpretation and validation of cluster-analysis. J. Comput. Appl. Math. 20, 5365 (1987)
  36. Tibshirani, R. & Walther, G. Cluster validation by prediction strength. J. Comput. Graph. Statist. 14, 511528 (2005)
  37. Teal, T. K. & Schmidt, T. M. Identifying and removing artificial replicates from 454 pyrosequencing data. Cold Spring Harb. Protoc. 2010, pdb.prot5409 (2010)
  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, 1822 (2002)

Download references

Author information

Affiliations

  1. Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, Missouri 63108, USA

    • Tanya Yatsunenko,
    • Federico E. Rey &
    • Jeffrey I. Gordon
  2. Department of Pediatrics, Washington University School of Medicine, St Louis, Missouri 63110, USA

    • Mark J. Manary,
    • Indi Trehan &
    • Barbara Warner
  3. Department of Community Health, University of Malawi College of Medicine, Blantyre, Malawi

    • Mark J. Manary
  4. Department of Paediatrics and Child Health, University of Malawi College of Medicine, Blantyre, Malawi

    • Indi Trehan
  5. Department of Biology, University of Puerto Rico - Rio Piedras, Puerto Rico 00931-3360

    • Maria Gloria Dominguez-Bello
  6. Venezuelan Institute of Scientific Research (IVIC), Carretera Panamericana, Km 11, Altos de Pipe, Venezuela

    • Monica Contreras
  7. Amazonic Center for Research and Control of Tropical Diseases (CAICET), Puerto Ayacucho 7101, Amazonas, Venezuela

    • Magda Magris &
    • Glida Hidalgo
  8. Division of Gastroenterology and Nutrition, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA

    • Robert N. Baldassano
  9. Department of Psychiatry, Washington University School of Medicine, St Louis, Missouri 63110, USA

    • Andrey P. Anokhin &
    • Andrew C. Heath
  10. Department of Chemistry and Biochemistry, University of Colorado, Boulder 80309, USA

    • Jens Reeder,
    • Justin Kuczynski,
    • Catherine A. Lozupone,
    • Christian Lauber,
    • Jose Carlos Clemente,
    • Dan Knights &
    • Rob Knight
  11. Department of Computer Science, Northern Arizona University, Flagstaff, Arizona 86001, USA

    • J. Gregory Caporaso
  12. Howard Hughes Medical Institute, University of Colorado, Boulder 80309, USA

    • Rob Knight

Contributions

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.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

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

Author details

Supplementary information

PDF files

  1. Supplementary Information (8.5M)

    This file contains Supplementary Text, Supplementary References, Supplementary Figures 1-20 and Legends for Supplementary Tables 1-11.

Zip files

  1. Supplementary Tables (1.3M)

    This file contains supplementary Tables 1-11.

Additional data