Letter

Diet-induced extinctions in the gut microbiota compound over generations

Received:
Accepted:
Published online:

Abstract

The gut is home to trillions of microorganisms that have fundamental roles in many aspects of human biology, including immune function and metabolism1,2. The reduced diversity of the gut microbiota in Western populations compared to that in populations living traditional lifestyles presents the question of which factors have driven microbiota change during modernization. Microbiota-accessible carbohydrates (MACs) found in dietary fibre have a crucial involvement in shaping this microbial ecosystem, and are notably reduced in the Western diet (high in fat and simple carbohydrates, low in fibre) compared with a more traditional diet3. Here we show that changes in the microbiota of mice consuming a low-MAC diet and harbouring a human microbiota are largely reversible within a single generation. However, over several generations, a low-MAC diet results in a progressive loss of diversity, which is not recoverable after the reintroduction of dietary MACs. To restore the microbiota to its original state requires the administration of missing taxa in combination with dietary MAC consumption. Our data illustrate that taxa driven to low abundance when dietary MACs are scarce are inefficiently transferred to the next generation, and are at increased risk of becoming extinct within an isolated population. As more diseases are linked to the Western microbiota and the microbiota is targeted therapeutically, microbiota reprogramming may need to involve strategies that incorporate dietary MACs as well as taxa not currently present in the Western gut.

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Accessions

Sequence Read Archive

Data deposits

The 16S sequence data have been deposited in the Sequence Read Archive (SRA) under the accession PRJNA303185.

References

  1. 1.

    , & Interactions between the microbiota and the immune system. Science 336, 1268–1273 (2012)

  2. 2.

    , , & Assessing the human gut microbiota in metabolic diseases. Diabetes 62, 3341–3349 (2013)

  3. 3.

    & Starving our microbial self: the deleterious consequences of a diet deficient in microbiota-accessible carbohydrates. Cell Metab. 20, 779–786 (2014)

  4. 4.

    et al. Gut microbiome of the Hadza hunter-gatherers. Nature Commun. 5, 3654 (2014)

  5. 5.

    et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012)

  6. 6.

    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)

  7. 7.

    et al. The microbiome of uncontacted Amerindians. Science Advances 1, e1500183 (2015)

  8. 8.

    et al. Subsistence strategies in traditional societies distinguish gut microbiomes. Nature Commun. 6, 6505 (2015)

  9. 9.

    et al. The gut microbiota of rural Papua New Guineans: composition, diversity patterns, and ecological processes. Cell Rep. 11, 527–538 (2015)

  10. 10.

    , & Ten-year trends in fiber and whole grain intakes and food sources for the United States population: National Health and Nutrition Examination Survey 2001–2010. Nutrients 7, 1119–1130 (2015)

  11. 11.

    , & Trends in dietary fiber intake in the United States, 1999–2008. J. Acad. Nutr. Diet. 112, 642–648 (2012)

  12. 12.

    , , , & Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012)

  13. 13.

    et al. Genetically dictated change in host mucus carbohydrate landscape exerts a diet-dependent effect on the gut microbiota. Proc. Natl Acad. Sci. USA 110, 17059–17064 (2013)

  14. 14.

    , , , & The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 42, D490–D495 (2014)

  15. 15.

    , & Interpreting 16S metagenomic data without clustering to achieve sub-OTU resolution. ISME J. 9, 68–80 (2015)

  16. 16.

    , , , & The abundance and variety of carbohydrate-active enzymes in the human gut microbiota. Nature Rev. Microbiol. 11, 497–504 (2013)

  17. 17.

    Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010)

  18. 18.

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

  19. 19.

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

  20. 20.

    et al. Complex interactions among diet, gastrointestinal transit, and gut microbiota in humanized mice. Gastroenterology 144, 967–977 (2013)

  21. 21.

    et al. The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci. Transl. Med. 1, 6ra14 (2009)

  22. 22.

    et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nature Methods 10, 57–59 (2013)

  23. 23.

    et al. dbCAN: a web resource for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 40, W445–W451 (2012)

  24. 24.

    et al. CDD: Specific functional annotation with the Conserved Domain Database. Nucleic Acids Res. 37, D205–D210 (2009)

  25. 25.

    , , , & Evolution, substrate specificity and subfamily classification of glycoside hydrolase family 5 (GH5). BMC Evol. Biol. 12, 186 (2012)

  26. 26.

    , , , & Dividing the large glycoside hydrolase family 13 into subfamilies: towards improved functional annotations of α-amylase-related proteins. Protein Eng. Des. Sel. 19, 555–562 (2006)

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Acknowledgements

We thank M. St. Onge for technical assistance. This work was funded by a grant from National Institutes of Health NIDDK (R01-DK085025 to J.L.S.), an NSF graduate fellowship (to S.A.S.), a Stanford Graduate Fellowship (to S.A.S.), and the Simons Foundation (to M.T.). J.L.S. holds an Investigators in the Pathogenesis of Infectious Disease Award from the Burroughs Wellcome Fund.

Author information

Author notes

    • Erica D. Sonnenburg
    •  & Samuel A. Smits

    These authors contributed equally to this work.

Affiliations

  1. Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California 94305, USA

    • Erica D. Sonnenburg
    • , Samuel A. Smits
    • , Steven K. Higginbottom
    •  & Justin L. Sonnenburg
  2. Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA

    • Mikhail Tikhonov
  3. Kavli Institute for Bionano Science and Technology, Harvard University, Cambridge, Massachusetts 02138, USA

    • Mikhail Tikhonov
  4. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA

    • Ned S. Wingreen
  5. Department of Molecular Biology, Princeton University, Princeton, New Jersey 08544, USA

    • Ned S. Wingreen

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Contributions

E.D.S. and J.L.S. conceived and designed the project. E.D.S., J.L.S. and S.K.H. designed and supervised the experiments. E.D.S. and S.K.H. performed the experiments. E.D.S., S.A.S. and M.T. analysed the experimental data. N.S.W. designed and supervised data analysis. E.D.S. and S.A.S. wrote the manuscript. All authors discussed the results and commented on the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Justin L. Sonnenburg.

Extended data

Supplementary information

Excel files

  1. 1.

    Supplementary Table 1

    A table of high-confidence OTUs that dropped in abundance by at least two-fold in the first generation diet-switching mice after switch to the low-MAC diet and the control high-MAC diet mice.

  2. 2.

    Supplementary Table 2

    A table of high-confidence OTUs that dropped in abundance by at least two-fold in the first generation diet-switching mice after return to the high-MAC diet and the control high-MAC diet mice.

  3. 3.

    Supplementary Table 3

    A table of high-confidence OTUs lost through generation four while consuming the low MAC diet and after switch to the high MAC diet.

  4. 4.

    Supplementary Table 4

    A comparison of glycoside hydrolases from metagenomic data and imputed from 16S rRNA amplicon sequencing data.

  5. 5.

    Supplementary Table 5

    Table of fold change of glycoside hydrolase families from generation one to generation four in the diet-switching and control groups.

  6. 6.

    Supplementary Table 6

    Table of unique glycoside hydrolase sub-families in diet-switching mice between generations one and four by sampling depth.

  7. 7.

    Supplementary Table 7

    Table of low abundance taxa that were passed or not passed between each of four generations of mice.

  8. 8.

    Supplementary Table 8

    Table of number of high-confidence sub-OTUs present at the start of the experiment and the number of high-confidence sub-OTUs lost by the fourth generation and between the third and fourth generation.

  9. 9.

    Supplementary Table 9

    Table of high-confidence OTUs that were no longer detectable after four generations on the low MAC and return after FMT.

  10. 10.

    Supplementary Table 10

    Table of high-confidence sub-OTUs that were no longer detectable after four generations on the low MAC and return after FMT.