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Diet outperforms microbial transplant to drive microbiome recovery in mice

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Abstract

A high-fat, low-fibre Western-style diet (WD) induces microbiome dysbiosis characterized by reduced taxonomic diversity and metabolic breadth1,2, which in turn increases risk for a wide array of metabolic3,4,5, immune6 and systemic pathologies. Recent work has established that WD can impair microbiome resilience to acute perturbations such as antibiotic treatment7,8, although little is known about the mechanism of impairment and the specific consequences for the host of prolonged post-antibiotic dysbiosis. Here we characterize the trajectory by which the gut microbiome recovers its taxonomic and functional profile after antibiotic treatment in mice on regular chow (RC) or WD, and find that only mice on RC undergo a rapid successional process of recovery. Metabolic modelling indicates that a RC diet promotes the development of syntrophic cross-feeding interactions, whereas in mice on WD, a dominant taxon monopolizes readily available resources without releasing syntrophic byproducts. Intervention experiments reveal that an appropriate dietary resource environment is both necessary and sufficient for rapid and robust microbiome recovery, whereas microbial transplant is neither. Furthermore, prolonged post-antibiotic dysbiosis in mice on WD renders them susceptible to infection by the intestinal pathogen Salmonella enterica serovar Typhimurium. Our data challenge widespread enthusiasm for faecal microbiota transplant (FMT) as a strategy to address dysbiosis, and demonstrate that specific dietary interventions are, at a minimum, an essential prerequisite for effective FMT, and may afford a safer, more natural and less invasive alternative.

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Fig. 1: Bacterial biomass and taxonomic recovery after antibiotic treatment are impaired in mice on WD.
Fig. 2: Functional recovery is severely impaired in mice on WD.
Fig. 3: Metabolic modelling predicts poor syntrophy in mice on WD.
Fig. 4: Dietary intervention facilitates microbiome recovery from antibiotics.
Fig. 5: Prolonged post-antibiotic dysbiosis in mice on WD impairs colonization resistance to ST.

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Data availability

The data, including all DNA sequencing datasets, that support the findings of this study are available in this Article, the supplementary information and BioProject accession PRJNA992061. Metabolomics data have been deposited in the MassIVE database under ID MSV000097318. Other databases used in this work include KEGG69, PFAMs83, dbCAN70,74, KOFams76, AGORA280 and RAST81. Source data are provided with this paper.

Code availability

The Jupyter Notebooks in which the modelling data were processed and the figures were developed, as well as scripts for all sequencing data analysis, statistical analysis, and figure production, are accessible at Zenodo (https://doi.org/10.5281/zenodo.14977112 (ref. 84)).

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Acknowledgements

The authors thank members of the Chang laboratory for scientific support received. This work was performed with support from US National Institutes of Health (NIH) T32DK007074 (M.S.K.), NIH RC2DK122394 (E.B.C.), NIH T32GM007281 (M.S.K.), InnoHK via the Hong Kong Innovation and Technology Commission, the Host–Microbe and Tissue and Cell Engineering cores of the UChicago DDRCC, Center for Interdisciplinary Study of Inflammatory Intestinal Disorders (C-IID)–(NIDDK P30 DK042086), the Gastrointestinal Research Foundation of Chicago, and The Simons Foundation (J.B.). C.S.H., A.F. and K.B. were supported by the KBase project of the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (DE-AC02-06CH11357).

Author information

Author notes

  1. A. Freiburger

    Present address: Department of Chemical Engineering, Northwestern University, Evanston, IL, USA

  2. M. L. St George

    Present address: Medical Scientist Training Program, University of Illinois Chicago, Chicago, IL, USA

  3. M. Kalski

    Present address: Stritch School of Medicine, Loyola University Chicago, Chicago, IL, USA

  4. These authors jointly supervised this work: J. Bergelson, E. B. Chang

Authors and Affiliations

  1. Medical Scientist Training Program, Pritzker School of Medicine, The University of Chicago, Chicago, IL, USA

    M. S. Kennedy

  2. Department of Ecology & Evolution, The University of Chicago, Chicago, IL, USA

    M. S. Kennedy

  3. Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, USA

    A. Freiburger, K. Beilsmith & C. S. Henry

  4. Department of Medicine, The University of Chicago, Chicago, IL, USA

    M. Cooper, M. L. St George, M. Kalski, C. Cham, O. DeLeon & E. B. Chang

  5. Department of Pathology, The University of Chicago, Chicago, IL, USA

    A. Guzzetta

  6. Microbiota I-Center (MagIC), Department of Medicine and Therapeutics, LKS Institute of Health Science, Institute of Digestive Disease, Center for Gut Microbiota Research, The Chinese University of Hong Kong, Hong Kong SAR, China

    S. C. Ng & F. K. Chan

  7. New Cornerstone Science Laboratory, The Chinese University of Hong Kong, Hong Kong, China

    S. C. Ng

  8. Inflammatory Bowel Disease Center, University of Chicago Medicine, Chicago, IL, USA

    D. Rubin

  9. Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, USA

    J. Bergelson

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Contributions

M.S.K. conceptualized and performed all experiments, data analysis and results interpretation, and wrote the manuscript. A.F., K.B. and C.S.H. developed the metabolic model methodology, analysed the data and interpreted the results. M.C., M.L.S.G., M.K. and C.C. performed WD resilience, intervention and colonization resistance experiments. A.G. performed histopathological analyses for colonization resistance experiments. S.C.N., F.K.C., O.D. and D.R. interpreted results, edited the manuscript and acquired funding. J.B. and E.B.C. mentored the participants, interpreted results, acquired funding and assisted in writing and editing of the manuscript.

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Correspondence to E. B. Chang.

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Nature thanks Jessica Allegretti, Sean Gibbons and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer review reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Western diet impairs microbiome taxonomic and biomass recovery from antibiotics.

(A) Consumption of ABX- or PBS-spiked water per mouse per day did not differ significantly across any treatment groups (RC-ABX n = 6 mice; RC-PBS n = 4; WD-ABX n = 6; WD-PBS n = 4; one-way ANOVA). (B-C) Microbial CFUs plated on anaerobic BHIS media from all (B) female (n = 5-18/group/timepoint; exact n values in Table S1A) and (C) male cohorts (n = 6 mice/group) through Day 14 of recovery post-ABX. Three of six female cohorts and the male cohort did not undergo 16S analysis as in the rest of Fig. 1; these data are therefore excluded from Fig. 1a and Table S1B, but are analyzed separately in Table S1C. (D-F) Comparison of alpha diversity metrics across cohorts over time; n = 1-6 mice/treatment group/cohort/timepoint; exact n values and statistics in Table S2. (D) Faith’s phylogenetic diversity; (E) Shannon index; (F) ASV richness. (G-H) PCoA of 16S-based microbiome taxonomic composition at the genus level using Bray-Curtis dissimilarity for samples from all treatment groups; n = 1-6 mice/treatment/cohort/timepoint; exact n values in Table S2A. (G) Through D14; Cohort 1 only. (H) Through D28; paneled by experimental cohort,. (I) Mean relative abundances of different microbial families for Cohorts 2 and 3 (n = 1-3 mice/treatment/cohort/timepoint, exact n values in Table S2A). (J) Mean Bray-Curtis dissimilarity of antibiotic-treated groups from their respective PBS control groups at each timepoint (****q < 0.0001, two-way ANOVA with multiple post-hoc comparisons and FDR correction; Table S2). For boxplots in panels (A) and (I), the middle line is the median, the upper and lower hinges reflect the first and third quartiles, and the whiskers extend to 1.5*IQR. Data beyond the whiskers are plotted as outlying points. In (B – F), data are presented as mean ± SD.

Source Data

Extended Data Fig. 2 Microbiome metagenomic recovery dynamics differ across dietary treatments.

(A-J) Metagenomic analyses of n = 2-8 mice/treatment/timepoint (RC-ABX D-3: n = 8; RC-ABX D2: n = 2; RC-ABX D4: n = 3; RC-ABX D14: n = 3; RC-ABX D28: n = 3; WD-ABX D-3: n = 8; WD-ABX D2: n = 3; WD-ABX D14: n = 4; WD-ABX D28: n = 3; Table S3). (A) Metagenomic functional richness in fecal samples from mice on RC-ABX (blue) and WD-ABX (red) at the KEGG Category (KCat), KEGG Ortholog (KO), and gene call level as a percentage of functional richness at Day -3 (pre-ABX) (Table S3). Data are presented as mean ± SD. (B) Initial (Day -3) versus final (Day 28) functional redundancy (genes calls per KO) for mice on RC-ABX (blue) and WD-ABX (red). Each dot represents a unique KO, with X and Y axes representing mean functional redundancy for that KO averaged across all mice in the respective treatment group/timepoint. (Table S3). For mice on RC-ABX (C-E) or WD-ABX (F-H), counts of significantly differentially abundant KOs (C, F), and Venn diagrams of depleted (D, G) or enriched (E, H) KOs across timepoints. KEGG Family mapping of significantly depleted KOs in mice on (I) RC-ABX or (L) WD-ABX. Roman numerals indicate the subset of KOs depicted in panels (D) and (G). Relative abundances of significantly enriched KOs in mice on RC-ABX at (J) Day 2 and (K) Day 4 relative to Day -3. Relative abundances of significantly enriched KOs in mice on WD-ABX at (M) Day 2 and (N) Day 14 relative to Day -3. See Table S4 for statistics.

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Extended Data Fig. 3 Metabolomic evaluations show distinct recovery dynamics across diets.

(A) Normalized metabolite abundances for mice on RC-ABX at different timepoints are consistent across mice. Each vertical block represents a different day of recovery. Each column within a block represents samples from a different mouse. Abundances are normalized to Day -3 (pre-ABX) for each mouse. (B) PCoA of fecal metabolomics TMS panel data using Bray-Curtis dissimilarity for samples from RC-ABX and WD-ABX through Day 14 of recovery. (C) PCoA of cecal metabolomics TMS panel data using Bray-Curtis dissimilarity for samples from RC-ABX and WD-ABX through Day 28 of recovery. Cecal samples were used due to availability of material through Day 28. (D – F) Metagenomic gene abundances (left axis, Materials and Methods) and normalized metabolite abundances (right axis) over time for mice on RC (top, blue) and WD (bottom, red). Data are presented as mean ± SD. N = 2-3 mice/group/timepoint for genes, n = 3-6 mice/group/treatment for metabolites; see Table S6 for exact n values and statistics. (D) α-galactosidase genes, melibiose and raffinose abundance. (E) Starch metabolism genes, glucose abundance. (F) Arabinan metabolism genes, arabinose abundance.

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Extended Data Fig. 4 Residual antibiotic concentrations were not significantly different across RC-ABX and WD-ABX groups.

Absolute quantification of fecal (A) vancomycin, (B) neomycin, and (C) cefoperazone from immediately after cessation of antibiotic treatment through Day 7 of recovery. RC-ABX D0: n = 3; RC-ABX D1: n = 3, RC-ABX D2: n = 4, RC-ABX D4: n = 4; RC-ABX D7: n = 2. WD-ABX D0: n = 4; WD-ABX D1: n = 4; WD-ABX D2: n = 4; WD-ABX D4: n = 2; WD-ABX D7: n = 2. Data are presented as mean ± SD. See Table S7 for statistics.

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Extended Data Fig. 5 Strain-metabolite Interaction Probability Profiles (SMIPPs) reveal metabolic specialization.

Heatmaps indicating the probability that a given ASV prGEM (rows) has the capacity to (A) consume or (B) produce the indicated compounds (columns).

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Extended Data Fig. 6 Community flux simulations vary across dietary treatment groups.

(A) Shared and unique functional annotations in ASVset pangenomes and MAGs. (B) Scatterplots of biomass growth vs metabolic flux. Each dot represents a single community member, colored by the time interval. (C) Biomass growth vs metabolic flux for individual community members across diets and timepoints. (D) Total predicted consumption or production flux through each metabolite category in mice on RC (blue, top) or WD (red, bottom) over the indicated recovery interval. As recovery proceeds, mice on RC push more flux through carbohydrate metabolism than mice on WD. (E) Total edges (i.e. metabolic interactions) in the community flux-balance analysis simulation networks across dietary groups at each time interval, broken down by (F) production or consumption edges. The microbiome of mice on RC has more edges at all timepoints, indicating that they have more/broader metabolite interaction (primarily consumption interactions) than in mice on WD. (G) Histograms depicting the distribution of edges per ASV across diet groups at each time interval. Mice on WD have few taxa that interact with a large number of metabolites, whereas in mice on RC, a broader array of taxa interact with an intermediate number of metabolites. (H) Percent of community metabolism conducted by oxygen-consuming ASVs.

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Extended Data Fig. 7 Dietary intervention and microbial transplant effects through Day 28 of recovery.

PCoA plot of 16S-based taxonomic data for mice on all treatment groups at D14 (A) and D28 (B) of recovery. Data is paneled according to pre-ABX diet. (C) ASV richness of all treatment groups through Day 28 of recovery. Data is paneled according to pre-ABX diet and presented as mean ± SD. (D) Mean relative abundances of microbial families at Day 28 across treatment groups. Exact n values for (A-D) are presented in table S9A.

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Extended Data Fig. 8 Supplemental information regarding colonization resistance experiments.

N values for all panels are presented in Table S10A. St CFU counts from female (A) and male (B) cohorts through t = 96 hpi. (C) Log10 transformed Infection AUC for all infected treatment groups. (D) Body weight after infection as a percentage of pre-infection body weight for all treatment groups. (E) St CFU counts across body tissue sites for all infected treatment groups at t = 96 hpi. (F) Cecal and (G) colonic histopathology scoring of all treatment groups at t = 96 hpi broken down by subscore. (H-P) mRNA expression of immune genes in cecal mucosal scrapings at t = 96 hpi based on RT-qPCR. Expression is normalized to the housekeeping gene Actb and the RC-PBS-PBS treatment group. See Table S10 for statistics and additional information. In (A), (B), (D), and (F), data are presented as mean ± SD. For boxplots in (C), (E), and (G - O), the middle line is the median, the upper and lower hinges reflect the first and third quartiles, and the whiskers extend to 1.5*IQR. Data beyond the whiskers are plotted as outlying points.

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Supplementary information

Supplementary Information

Supplementary Methods describing metabolic modelling approach and validation; supplementary discussion including intervention day 28 results; body weight results from colonization resistance experiments.

Reporting Summary

Supplementary Table 1

Analyses of microbial biomass by CFU plating.

Supplementary Table 2

16S analyses of microbiome recovery including comparisons of alpha and beta diversity across treatment groups and timepoints.

Supplementary Table 3

Metagenomic analyses of functional diversity and redundancy across timepoints and RC-Abx and WD-Abx treatment groups.

Supplementary Table 4

Differential abundance analysis of metagenomics data at the KO and pathway level.

Supplementary Table 5

Comparison of metabolomics TMS panel and SCFA panel across treatment groups and timepoints. Information on internal standards used in metabolomic analysis.

Supplementary Table 6

Analysis of select metagenomic gene abundances over time.

Supplementary Table 7

Quantification of residual faecal antibiotic from day 0 of recovery immediately after antibiotic cessation through day 7.

Supplementary Table 8

Metabolic modelling information, including metabolite abbreviations, time intervals, correlations between abundances of metabolites in a sample and the predicted capacity of the taxa in that sample to use them, and flux outputs from community-level flux-balance analysis for each treatment group and timepoint.

Supplementary Table 9

Analyses of alpha and beta diversity across mice in all treatment groups at day 14 of recovery in intervention experiments.

Supplementary Table 10

Statistical comparisons of faecal and tissue ST load, body weight, histopathology, and qPCR inflammatory gene expression across all treatment groups in colonization resistance experiments. Includes information on qPCR primers used for analysis.

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Kennedy, M.S., Freiburger, A., Cooper, M. et al. Diet outperforms microbial transplant to drive microbiome recovery in mice. Nature (2025). https://doi.org/10.1038/s41586-025-08937-9

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