Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Diet rapidly and reproducibly alters the human gut microbiome

Abstract

Long-term dietary intake influences the structure and activity of the trillions of microorganisms residing in the human gut1,2,3,4,5, but it remains unclear how rapidly and reproducibly the human gut microbiome responds to short-term macronutrient change. Here we show that the short-term consumption of diets composed entirely of animal or plant products alters microbial community structure and overwhelms inter-individual differences in microbial gene expression. The animal-based diet increased the abundance of bile-tolerant microorganisms (Alistipes, Bilophila and Bacteroides) and decreased the levels of Firmicutes that metabolize dietary plant polysaccharides (Roseburia, Eubacterium rectale and Ruminococcus bromii). Microbial activity mirrored differences between herbivorous and carnivorous mammals2, reflecting trade-offs between carbohydrate and protein fermentation. Foodborne microbes from both diets transiently colonized the gut, including bacteria, fungi and even viruses. Finally, increases in the abundance and activity of Bilophila wadsworthia on the animal-based diet support a link between dietary fat, bile acids and the outgrowth of microorganisms capable of triggering inflammatory bowel disease6. In concert, these results demonstrate that the gut microbiome can rapidly respond to altered diet, potentially facilitating the diversity of human dietary lifestyles.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Short-term diet alters the gut microbiota.
Figure 2: Bacterial cluster responses to diet arms.
Figure 3: Diet alters microbial activity and gene expression.
Figure 4: Foodborne microbes are detectable in the distal gut.
Figure 5: Changes in the faecal concentration of bile acids and biomarkers for Bilophila on the animal-based diet.

Accession codes

Accessions

Gene Expression Omnibus

Data deposits

RNA-seq data have been deposited in the Gene Expression Omnibus under accession GSE46761; 16S and ITS rRNA gene sequencing reads have been deposited in MG-RAST under accession 6248.

References

  1. 1

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

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  2. 2

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

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  3. 3

    Duncan, S. H. et al. Reduced dietary intake of carbohydrates by obese subjects results in decreased concentrations of butyrate and butyrate-producing bacteria in feces. Appl. Environ. Microbiol. 73, 1073–1078 (2007)

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4

    Ley, R. E., Turnbaugh, P. J., Klein, S. & Gordon, J. I. Microbial ecology: human gut microbes associated with obesity. Nature 444, 1022–1023 (2006)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  5. 5

    Walker, A. W. et al. Dominant and diet-responsive groups of bacteria within the human colonic microbiota. ISME J. 5, 220–230 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6

    Devkota, S. et al. Dietary-fat-induced taurocholic acid promotes pathobiont expansion and colitis in Il10−/− mice. Nature 487, 104–108 (2012)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  7. 7

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

    PubMed  PubMed Central  Google Scholar 

  8. 8

    Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006)

    ADS  PubMed  PubMed Central  Google Scholar 

  9. 9

    Faith, J. J., McNulty, N. P., Rey, F. E. & Gordon, J. I. Predicting a human gut microbiota’s response to diet in gnotobiotic mice. Science 333, 101–104 (2011)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  10. 10

    Russell, W. R. et al. High-protein, reduced-carbohydrate weight-loss diets promote metabolite profiles likely to be detrimental to colonic health. Am. J. Clin. Nutr. 93, 1062–1072 (2011)

    CAS  Google Scholar 

  11. 11

    Cordain, L. et al. Plant-animal subsistence ratios and macronutrient energy estimations in worldwide hunter-gatherer diets. Am. J. Clin. Nutr. 71, 682–692 (2000)

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12

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

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    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)

    ADS  PubMed  PubMed Central  Google Scholar 

  14. 14

    Reddy, B. S. Diet and excretion of bile acids. Cancer Res. 41, 3766–3768 (1981)

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

    Smith, E. A. & Macfarlane, G. T. Enumeration of amino acid fermenting bacteria in the human large intestine: effects of pH and starch on peptide metabolism and dissimilation of amino acids. FEMS Microbiol. Ecol. 25, 355–368 (1998)

    CAS  Google Scholar 

  16. 16

    Smith, E. A. & Macfarlane, G. T. Enumeration of human colonic bacteria producing phenolic and indolic compounds: effects of pH, carbohydrate availability and retention time on dissimilatory aromatic amino acid metabolism. J. Appl. Bacteriol. 81, 288–302 (1996)

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17

    Sinha, R. et al. High concentrations of the carcinogen 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) occur in chicken but are dependent on the cooking method. Cancer Res. 55, 4516–4519 (1995)

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18

    Langille, M. G. I. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nature Biotechnol. 31, 814–821 (2013)

    CAS  Google Scholar 

  19. 19

    Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000)

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Pittard, J. & Wallace, B. J. Distribution and function of genes concerned with aromatic biosynthesis in Escherichia coli. J. Bacteriol. 91, 1494–1508 (1966)

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21

    Hawkes, K., O’Connell, J. F. & Jones, N. G. Hunting income patterns among the Hadza: big game, common goods, foraging goals and the evolution of the human diet. Philos. Trans. R. Soc. Lond. B Biol. Sci. 334, 243–250 (1991)

    ADS  CAS  Google Scholar 

  22. 22

    Bourdichon, F., Berger, B. & Casaregola, S. Safety demonstration of microbial food cultures (MFC) in fermented food products. Bull. Int. Dairy Fed. 455, 1–66 (2012)

    Google Scholar 

  23. 23

    Nychas, G. J. & Arkoudelos, J. S. Staphylococci: their role in fermented sausages. Soc. Appl. Bacteriol. Symp. Ser. 19, 167S–188S (1990)

    CAS  Google Scholar 

  24. 24

    McGavin, W. J. & Macfarlane, S. A. Rubus chlorotic mottle virus, a new sobemovirus infecting raspberry and bramble. Virus Res. 139, 10–13 (2009)

    CAS  Google Scholar 

  25. 25

    Zhang, T. et al. RNA viral community in human feces: prevalence of plant pathogenic viruses. PLoS Biol. 4, e3 (2006)

    PubMed  Google Scholar 

  26. 26

    Yoshimoto, S. et al. Obesity-induced gut microbial metabolite promotes liver cancer through senescence secretome. Nature 499, 97–101 (2013)

    ADS  CAS  Google Scholar 

  27. 27

    Ridlon, J. M., Kang, D. J. & Hylemon, P. B. Bile salt biotransformations by human intestinal bacteria. J. Lipid Res. 47, 241–259 (2006)

    CAS  PubMed  Google Scholar 

  28. 28

    Islam, K. B. et al. Bile acid is a host factor that regulates the composition of the cecal microbiota in rats. Gastroenterology 141, 1773–1781 (2011)

    CAS  Google Scholar 

  29. 29

    Maurice, C. F., Haiser, H. J. & Turnbaugh, P. J. Xenobiotics shape the physiology and gene expression of the active human gut microbiome. Cell 152, 39–50 (2013)

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30

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

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31

    Lewis, S. J. & Heaton, K. W. Stool form scale as a useful guide to intestinal transit time. Scand. J. Gastroenterol. 32, 920–924 (1997)

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32

    National Institutes of Health Diet History Questionnaire Version 2.0 (National Institutes of Health, Applied Research Program, National Cancer Institute, 2010)

    Google Scholar 

  33. 33

    Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl Acad. Sci. USA 108 (suppl. 1). 4516–4522 (2011)

    ADS  CAS  Google Scholar 

  34. 34

    Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35

    DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006)

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36

    Jones, E. et al. SciPy: open source scientific tools for Python. (2001)

  37. 37

    McKinney, W. Data structures for statistical computing in Python. Proc. 9th Python Sci. Conf. 51–56. (2010)

  38. 38

    Strimmer, K. fdrtool: a versatile R package for estimating local and tail areabased false discovery rates. Bioinformatics 24, 1461–1462 (2008)

    CAS  PubMed  Google Scholar 

  39. 39

    Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  40. 40

    Turnbaugh, P. J. et al. Organismal, genetic, and transcriptional variation in the deeply sequenced gut microbiomes of identical twins. Proc. Natl Acad. Sci. USA 107, 7503–7508 (2010)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  41. 41

    Rey, F. E. et al. Dissecting the in vivo metabolic potential of two human gut acetogens. J. Biol. Chem. 285, 22082–22090 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42

    Nelson, K. E. et al. A catalog of reference genomes from the human microbiome. Science 328, 994–999 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43

    Ning, Z., Cox, A. J. & Mullikin, J. C. SSAHA: a fast search method for large DNA databases. Genome Res. 11, 1725–1729 (2001)

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    Abubucker, S. et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLOS Comput. Biol. 8, e1002358 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45

    Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011)

    PubMed  PubMed Central  Google Scholar 

  46. 46

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nature Methods 9, 357–359 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47

    Markowitz, V. M. et al. IMG: the Integrated Microbial Genomes database and comparative analysis system. Nucleic Acids Res. 40, D115–D122 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48

    Martin, J. et al. Optimizing read mapping to reference genomes to determine composition and species prevalence in microbial communities. PLoS ONE 7, e36427 (2012)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  49. 49

    Deplancke, B. et al. Molecular ecological analysis of the succession and diversity of sulfate-reducing bacteria in the mouse gastrointestinal tract. Appl. Environ. Microbiol. 66, 2166–2174 (2000)

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50

    Stewart, J. A., Chadwick, V. S. & Murray, A. Carriage, quantification, and predominance of methanogens and sulfate-reducing bacteria in faecal samples. Lett. Appl. Microbiol. 43, 58–63 (2006)

    CAS  Google Scholar 

  51. 51

    Porter, J. L. et al. Accurate enzymatic measurement of fecal bile acids in patients with malabsorption. J. Lab. Clin. Med. 141, 411–418 (2003)

    CAS  Google Scholar 

  52. 52

    Setchell, K. D., Lawson, A. M., Tanida, N. & Sjovall, J. General methods for the analysis of metabolic profiles of bile acids and related compounds in feces. J. Lipid Res. 24, 1085–1100 (1983)

    CAS  Google Scholar 

  53. 53

    Schoch, C. L. et al. Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi. Proc. Natl Acad. Sci. USA 109, 6241–6246 (2012)

    ADS  CAS  Google Scholar 

  54. 54

    Gardes, M. & Bruns, T. D. ITS primers with enhanced specificity for basidiomycetes application to the identification of mycorrhizae and rusts. Mol. Ecol. 2, 113–118 (1993)

    CAS  Google Scholar 

  55. 55

    White, T. J., Bruns, T., Lee, S. & Taylor, J. in PCR Protocols: A Guide to Methods and Applications (eds Gelfand, D. H., Innis, M. A., Shinsky, J. J. & White, T. J. ) 315–322. (1990)

  56. 56

    Walker, H. K., Hall, W. D., Hurst, J. W., Comstock, J. P. & Garber, A. J. Ketonuria 3rd edn (Butterworths, 1990)

    Google Scholar 

Download references

Acknowledgements

We would like to thank A. Murray, G. Guidotti, E. O’Shea, J. Moffitt and B. Stern for insightful comments; M. Delaney for biochemical analyses; C. Daly, M. Clamp and C. Reardon for sequencing support; N. Fierer for providing ITS primers; A. Luong and K. Bauer for technical assistance; J. Brulc and R. Menon for nutritional guidelines; A. Rahman for menu suggestions; A. Must and J. Queenan for nutritional analysis; and our diet study volunteers for their participation. This work was supported by the National Institutes of Health (P50 GM068763), the Boston Nutrition Obesity Research Center (DK0046200), and the General Mills Bell Institute of Health and Nutrition.

Author information

Affiliations

Authors

Contributions

L.A.D., R.J.D. and P.J.T. designed the study, and developed and prepared the diets. L.A.D., C.F.M., R.N.C., D.B.G., J.E.B., B.E.W. and P.J.T. performed the experimental work. A.V.L., A.S.D., Y.V., M.A.F. and S.B.B. conducted bile acid analyses. L.A.D. and P.J.T. performed computational analyses. L.A.D. and P.J.T. prepared the manuscript.

Corresponding author

Correspondence to Peter J. Turnbaugh.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Study design.

a, b, The plant-based (a) and animal-based (b) diets were fed to subjects for five consecutive days. All dates are defined relative to the start of these diet arms (day 0). Study volunteers were observed for 4 days before each diet (the baseline period, days −4 to −1) and for 6 days after each diet arm (the washout period, days 5 to 10) in order to measure subjects’ eating habits and assess their recovery from each diet arm. Subjects were instructed to eat normally during both the baseline and washout periods. Stool samples were collected daily on both diet arms and 16S rRNA and fungal ITS sequencing was performed on all available samples. Subjects also kept daily diet logs. Several analyses (RNA-seq, SCFAs and bile acids) were performed primarily using only two samples per person per diet (that is, a baseline and diet arm comparison). Comparative sampling did not always occur using exactly the same study days owing to limited sample availability for some subjects. Because we expected the animal-based diet to promote ketogenesis, we only measured urinary ketones on the animal-based diet. To test the hypothesis that microbes from fermented foods on the animal-based diet survived transit through the gastrointestinal tract, we cultured bacteria and fungi before and after the animal-based diet.

Extended Data Figure 2 A vegetarian’s microbiota.

ac, One of the study subjects is a lifelong vegetarian (subject 6). a, Relative abundances of Prevotella and Bacteroides are shown across the plant-based diet for subject 6 (orange circles), as well as for all other subjects (green circles). Consecutive daily samples from subject 6 are linked by dashed lines. For reference, median baseline abundances are depicted using larger circles. b, Relative abundances are also shown for samples taken on the animal-based diet. Labelled points correspond to diet days where subject 6’s gut microbiota exhibited an increase in the relative abundance of Bacteroides. c, A principal-coordinates-based characterization of overall community structure for subject 6, as well as all other subjects. QIIME30 was used to compute microbial β diversity with the Bray–Curtis, unweighted UniFrac and weighted UniFrac statistics. Sample similarities were projected onto two dimensions using principal coordinates analysis. Top, when coloured by subject, samples from subject 6 (green triangles) partition apart from the other subjects’ samples. Bottom, of all of subject 6’s diet samples, the ones most similar to the other subjects’ are the samples taken while consuming the animal-based diet.

Extended Data Figure 3 Subject physiology across diet arms.

a, Gastrointestinal motility, as measured by the initial appearance of a non-absorbable dye added to the first and last lunch of each diet. The median time until dye appearance is indicated with red arrows. Subject motility was significantly lower (P < 0.05, Mann–Whitney U test) on the animal-based diet (median transit time of 1.5 days) than on the plant-based one (1.0 days). b, Range (shaded boxes) and median (solid line) of subjects’ weights over time. Subjects’ weight did not change significantly on the plant-based diet relative to baseline periods, but did decrease significantly on the animal-based diet (asterisks denote q < 0.05, Bonferroni-corrected Mann–Whitney U test). Subjects lost a median of 1.6% and 2.5% of body weight by days 3 and 4, respectively, of the animal-based diet arm. c, Measurements of subjects’ urinary ketone levels. Individual subjects are shown with black dots, and median values are connected with a black solid line. Urinary ketone readings were taken from day 0 of the animal-based diet onwards. Ketone levels were compared to the readings on day 0, and asterisks denote days with significant ketone increases (q < 0.05, Bonferroni-corrected Mann–Whitney U test; significance tests were not carried out for days on which less than four subjects reported their readings.). All subjects on the animal-based diet showed elevated levels of ketones in their urine by day 2 of the diet (≥15 mg dl−1 as compared to 0 mg dl−1 during initial readings), indicating that they experienced ketonuria during the diet arm. This metabolic state is characterized by the restricted availability of glucose and the compensatory extraction of energy from fat tissue56.

Extended Data Figure 4 Baseline Prevotella abundance is associated with long-term fibre intake.

Prevotella fractions were computed by summing the fractional 16S rRNA abundance of all OTUs whose genus name was Prevotella. Daily intake of dietary fibre over the previous year was estimated using the Diet History Questionnaire32 (variable name “TOTAL_DIETARY_FIBER_G_NDSR”). There is a significant positive correlation between subjects’ baseline Prevotella abundance and their long-term dietary fibre intake (Spearman’s ρ = 0.78, P = 0.008).

Extended Data Figure 5 Significant correlations between SCFAs and cluster abundances across subjects.

SCFAs are drawn in rectangles and coloured maroon or green if they are produced from amino acid or carbohydrate fermentation, respectively. Clusters whose members include known bile-tolerant or amino-acid-fermenting bacteria15,16 are coloured maroon, whereas clusters including known saccharolytic bacteria3 are coloured green. Uncoloured clusters and SCFAs are not associated with saccharolytic or putrefactive pathways. Significant positive and negative correlations are shown with black arrows and grey arrows, respectively (q < 0.05; Spearman correlation).

Extended Data Figure 6 Inter-individual microbial community variation according to diet and sequencing technique.

a, b, To measure the degree to which diet influences inter-individual differences in gut microbial gene expression, we clustered RNA-seq profiles from baseline (a) and diet (b) periods. Dots indicate pairs of samples that cluster by subject. The potential for diet to partition samples was measured by splitting trees at the arrowed branches and testing the significance of the resulting 2 × 2 contingency table (diet versus partition; Fisher’s exact test). To avoid skewed significance values caused by non-independent samples, we only clustered a single sample per subject, per diet period. In the case of multiple baseline samples, the sample closest to the diet intervention was used. In the case of multiple diet samples, the last sample during the diet intervention was kept. A single sample was randomly chosen if there were multiple samples from the same person on the same day. No association between diet and partitioning was found for partitions I–VI (P > 0.05). However, a significant association was observed for partition VII (P = 0.003). c, To determine whether diet affects inter-individual differences in gut microbial community structure, we hierarchically clustered 16S rRNA data from the last day of each diet arm. Samples grouped weakly by diet: sub-trees partitioned at the arrowed node showed a minor enrichment for plant-based diet samples in one sub-tree and animal-based diet samples in the other (P = 0.07; Fisher’s exact test). Still, samples from five subjects grouped by individual, not diet (indicated by black nodes), indicating that diet does not reproducibly overcome inter-individual differences in gut microbial community structure.

Extended Data Figure 7 Food-associated microbes and their enteric abundance over time.

a, Major bacterial and fungal taxa found in plant-based diet menu items were determined using 16S rRNA and ITS sequencing, respectively, at the species (s), genus (g) and order level (o). The majority of 16S rRNA gene sequences are Streptophyta, representing chloroplasts from the ingested plant matter. b, One of the fungi from a, Candida sp., showed a significance increase in faecal abundance on the plant-based diet (P < 0.05, Wilcoxon signed-rank test). c, Levels of bacteria and fungi associated with the animal-based diet are plotted over the plant- and animal-based diet arms. Taxa are identified on the genus (g) and species (s) level. The abundance of foodborne bacteria was near our detection limit by 16S rRNA gene sequencing; to minimize resulting measurement errors, we have plotted the fraction of samples in which bacteria are present or absent. Lactococcus lactis, Pediococcus acidilactici and Staphylococcus-associated reads all show significantly increased abundance on the animal-based diet (P < 0.05, Wilcoxon signed-rank test). Fungal concentrations were measured using ITS sequencing and are plotted in terms of log-fractional abundance. Significant increases in Penicillium-related fungi were observed, along with significant decreases in the concentration of Debaryomyces and a Candida sp. (P < 0.05, Wilcoxon signed-rank test). One possible explanation for the surprising decrease in the concentration of food-associated fungi is that the more than tenfold increase in Penicillium levels lowered the relative abundance of all other fungi, even those that increased in terms of absolute abundance.

Extended Data Figure 8 Eukaryotic and viral taxa detected via RNA-seq.

a, Identified plant and other viruses. The most common virus is a DNA virus (lambda phage) and may be an artefact of the sequencing process. b, Identified fungi, protists and other eukaryotes. Taxa that were re-annotated using manually curated BLAST searches are indicated with asterisks and their original taxonomic assignments are shown in parentheses (see Methods for more details).

Extended Data Figure 9 Faecal bile acid concentrations on baseline, plant- and animal-based diets.

a, b, Median bulk bile acid concentrations are shown for all individuals on the plant-based (a) and animal-based (b) diets (error bars denote median absolute deviations). For detailed experimental protocols, see Methods. Bile acid levels did not significantly change on the plant-based diet relative to baseline levels (P > 0.1, Mann–Whitney U test). However, bile acid levels trended upwards on the animal-based diet, rising from 1.48 μmol per 100 mg dry stool during the baseline period to 2.37 μmol per 100 mg dry stool (P < 0.10, Mann–Whitney U test).

Extended Data Figure 10 The dissimilatory sulphate reduction pathway.

a, Microbes reduce sulphate to hydrogen sulphide by first converting sulphate to adenosine 5′-phosphosulphate (APS) via the enzyme ATP sulphurylase (Sat). Next, APS is reduced to sulphite by the enzyme APS reductase (Apr). Finally, the end product hydrogen sulphide is reached by reducing sulphite through the enzyme sulphite reductase (DsrA). This last step of the pathway can be performed by Bilophila and is thought to contribute to intestinal inflammation6. b, No significant changes in apr gene abundance were observed on any diet (P > 0.05, Mann–Whitney U test; n = 10 samples per diet arm). Values are mean ± s.e.m. However, dsrA abundance increased on the animal-based diet (Fig. 5d). NS, not significant.

Supplementary information

Supplementary Information

This file contains the Supplementary Discussion. (PDF 132 kb)

Supplementary Data

This file contains Supplementary Tables 1-23. (XLSX 133 kb)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Cite this article

David, L., Maurice, C., Carmody, R. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014). https://doi.org/10.1038/nature12820

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing