Diet rapidly and reproducibly alters the human gut microbiome


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.

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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


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.


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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

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.

Correspondence to Peter J. Turnbaugh.

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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.

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David, L., Maurice, C., Carmody, R. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).

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