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Identification of medication–microbiome interactions that affect gut infection

Nature volume 644, pages 506–515 (2025)Cite this article

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Abstract

Most people in the USA manage their health by taking at least one prescription drug, and drugs classified as non-antibiotics can adversely affect the gut microbiome and disrupt intestinal homeostasis1,2. Here we identify medications that are associated with an increased risk of gastrointestinal infections across a population cohort of more than one million individuals monitored over 15 years. Notably, the cardiac glycoside digoxin and other drugs identified in this epidemiological study are sufficient to alter the composition of the microbiome and the risk of infection with Salmonella enterica subsp. enterica serovar Typhimurium (S. Tm) in mice. The effect of digoxin treatment on S. Tm infection is transmissible through the microbiome, and characterization of this interaction highlights a digoxin-responsive β-defensin that alters the microbiome composition and consequent immune surveillance of the invading pathogen. Combining epidemiological and experimental approaches thus provides an opportunity to uncover drug–host–microbiome–pathogen interactions that increase the risk of infections in humans.

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Fig. 1: An analysis of one million individuals over 15 years identifies drugs that increase the risk of infection in humans and mice.
Fig. 2: The effect of digoxin on infection risk is transmissible through the microbiome.
Fig. 3: Digoxin-mediated depletion of SFB increases susceptibility to S. Tm infection in mice.
Fig. 4: A digoxin-inducible, RORγt-dependent β-defensin controls the levels of SFB in the mouse gut.
Fig. 5: Digoxin increases the susceptibility of gnotobiotic mice colonized with human microbial communities to infection with WT S. Tm.

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Data availability

Raw and processed RNA-seq files are available through the NCBI Gene Expression Omnibus (GEO) under accession GSE274850. 16S rRNA-seq files are also available through the NCBI BioProject under accession PRJNA1122171. Metagenomics data for the human gut communities are available under accession PRJEB31790. Source data are provided with this paper.

Code availability

This study did not generate new code.

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Acknowledgements

We thank L. Valle and D. Lazo for assistance with gnotobiotic mouse experiments; the Yale Center for Genome Analysis for sequencing services; the Yale Genome Editing Center for help generating the Vil-Defb39 mouse; M. Graham and the Center for Cellular and Molecular Imaging Electron Microscopy Facility at Yale School of Medicine for assistance with SEM images; T. Moraga for help with statistical analysis in the epidemiological study; C. Kelly and other members of the A.L.G. laboratory for advice and discussion; J. E. Galán for providing strains of S. Tm and CV C57BL/6NTac Nramp1+/+ mice; and K. Honda for providing human TH17-inducing gut bacterial isolates. Support for this work was provided by the National Institutes of Health grants R01DK133798 and R35GM118159 (to A.L.G.) and R01DK098378 and U01AI163069 (to I.I.I.), and by the Canadian Institutes of Health Research grant MOP-111166 (to R.T.).

Author information

Authors and Affiliations

  1. Department of Microbial Pathogenesis and Microbial Sciences Institute, Yale University School of Medicine, New Haven, CT, USA

    Aman Kumar, Ruizheng Sun, Natasha A. Bencivenga-Barry & Andrew L. Goodman

  2. Department of General Surgery, Xiangya Hospital, Central South University, Changsha, China

    Ruizheng Sun

  3. Clinical and Health Informatics Research Group, McGill University, Montreal, Quebec, Canada

    Bettina Habib & Robyn Tamblyn

  4. Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA

    Tong Deng & Noah W. Palm

  5. Department of Microbiology and Immunology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA

    Ivaylo I. Ivanov

  6. Columbia University Digestive and Liver Diseases Research Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA

    Ivaylo I. Ivanov

  7. Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada

    Robyn Tamblyn

  8. Department of Medicine, McGill University Health Center, Montreal, Quebec, Canada

    Robyn Tamblyn

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  1. Aman Kumar
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Contributions

A.K. and A.L.G. conceived and initiated the project and designed experiments. A.K., B.H., R.T. and A.L.G designed the epidemiological study. B.H. and R.T. performed the epidemiological analysis. A.K. performed the experiments and analysed the data. R.S. assisted with digoxin and BD-39 sensitivity assays and data analysis. N.A.B.-B. rederived the Nramp1+/+ mice to the GF state. I.I.I. provided crucial reagents and suggestions. T.D. and N.W.P. assisted with flow cytometry. A.K. and A.L.G. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Andrew L. Goodman.

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Competing interests

A.L.G. serves on the scientific advisory boards of Seres Therapeutics, Taconic Biosciences and Piton Therapeutics. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature thanks Kenya Honda, Gilaad Kaplan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Effects of medications identified in the epidemiological screen on gut microorganisms in mice and in vitro.

a, Details of the epidemiology study design. Examples of drug exposures that are included or excluded from case or control windows are shown. b, Table showing infection risk odds ratio for digoxin is comparable to the odds ratio measured for drug classes and individual drugs expected to increase infection risk. c, PCoA of Bray–Curtis distances between 16S rRNA-seq results from faecal samples from C57BL/6NTac mice before (red) and after treatment (blue). The ellipses in each PCoA plot depicts the 68% confidence marginal relationships among variables in each group generated by an integrated function in the R package “ggplot2”. Each point within the same colour represents an individual mouse. d, Area under the curve (AUC) comparison for growth of representative bacterial taxa under increasing drug concentrations (20, 40, and 80 µM). The tree represents the clustering of taxa based on growth inhibition profiles across all tested drugs. Colour shading represents normalized growth (AUC relative to DMSO control) (n = 3 independent biological replicates/drug). Two-sided Mann–Whitney tests were conducted to test the significance of AUC differences, and P values were corrected by the Benjamini–Hochberg method. Exact P values are provided in the Source Data. * P < 0.05, ** P < 0.01.

Source Data

Extended Data Fig. 2 Digoxin pretreatment before S. Tm ΔinvA infection leads to increased pathogen colonization and dissemination.

a–g, PBS- or digoxin-pretreated C57BL/6NTac mice were infected intragastrically with ~108 CFUs of S. Tm ΔinvA 12 h after the final vehicle or drug dose and infection monitored over time. a, Pathogen burden in faeces at 2 dpi (PBS n = 12, digoxin n = 13). b–e, Faecal pathogen burden at 4 dpi (b). Mice were euthanized at 4 dpi and pathogen burden was enumerated from the ileum (c), caecum (d) and colon (e) contents. f,g, Dissemination of S. Tm ΔinvA to extraintestinal tissues was measured in the liver (f) and spleen (g). In b–g, n = 7 mice per group. h,i, PBS- or digoxin-pretreated C57BL/6NTac mice (n = 5 per group) were infected intraperitoneally (I.P.) with ~105 CFUs of S. Tm ΔinvA and dissemination was measured in liver (h) and spleen (i) at 1 dpi. j,k, C57BL/6NTac Nramp1+/+ mice (n = 5 per group) were pretreated with PBS or digoxin, infected with ~108 CFUs of WT S. Tm, and pathogen burden measured at 4 dpi in GI contents (j) and tissues (k). A two-sided Mann–Whitney test is used to compare two groups. Dotted lines represent the limit of detection. ns, not significant.

Source Data

Extended Data Fig. 3 Digoxin pretreatment decreases ileal AMP expression and increases C. rodentium and VRE pathogen burden in C57BL/6NTac mice.

Mice were pretreated with PBS or digoxin for two days as shown in Fig. 1c; 12 h after the final PBS or drug treatment, mice were infected with C. rodentium (DBS100) or VRE. a,b, Pathogen burden in faeces (a) and attached bacteria in ileal, caecal, and colon tissue (b) at 10 dpi. c, Faecal VRE burden at 12 h after infection and 1 dpi. In a–c, n = 10 mice per group. d, VRE burden in ileal and caecal contents at 1 dpi. (n = 5 per group). e–g, C57BL/6NTac mice were treated with PBS or digoxin for two days. Mice were euthanized 12 h after the final vehicle or drug dose, and tissues were collected for gene-expression measurement by qRT–PCR. Relative gene expression of Reg3b, and Reg3g in ileum (n = 10 per group) (e), caecum (n = 5 per group) (f) and colon (n = 5 per group) (g) tissues is shown. Fold change is measured relative to Gapdh expression. A two-sided Mann–Whitney test is used to compare two groups. Dotted lines represent the limit of detection. ns, not significant, bar represent median in a–d, and geometric mean in e–g.

Source Data

Extended Data Fig. 4 Effect of the duration of digoxin pretreatment on S. Tm infection.

a, Experimental design. CV C57BL/6NTac mice were treated with digoxin or PBS for one dose 2 h before infection (single-dose regimen), twice daily for 2 days followed by a 12-h washout period (standard regimen), or twice daily for 7 days followed by a 12-hour washout period (extended regimen). Mice were then infected with ~108 CFUs of S. Tm ΔinvA and infection monitored over time. b,c, Pathogen burden at 12 h after infection (n = 5 per group) (b) and mortality (c) after single-dose drug or control treatment. d–f, Pathogen burden at 12 h after infection (n = 5 per group) (d), mortality (e) and expression of proinflammatory marker genes in ileum tissue (n = 4 per group) (f) after the extended regimen drug or control treatment. g, Effects of digoxin or PBS treatment on the weight of CV C57BL/6NTac and C57BL/6J mice (n = 10 per group). Data are shown as mean with s.e.m. h, Cohoused C57BL/6J mice were separated from C57BL/6NTac mice prior to PBS or digoxin treatment and were then infected with a high dose (~109 CFUs) of S. Tm ΔinvA. Pathogen burden was enumerated at 3 dpi (n = 5 per group). In f, fold change is measured relative to the mouse housekeeping gene, Gapdh. In b,d,f,h, a two-sided Mann–Whitney test is used to compare two groups; bar represents median. For survival analysis, the Gehan–Breslow–Wilcoxon test is used. ns, not significant.

Source Data

Extended Data Fig. 5 Altered immune responses and pathogen susceptibility are microbiome dependent.

a,b, Characterization of recipient mice after transplantation of gut microbiomes from donor C57BL/6NTac mice treated with digoxin or PBS for 2 days. Gene expression in ileal tissue of recipient mice (PBS n = 4, digoxin n = 3) as measured by qRT–PCR (a) and weight of recipient mice after infection with S. Tm ∆invA (n = 9 per group) (b). c,d, Characterization of recipient mice after transplantation of gut microbiomes from donor C57BL/6NTac mice treated with digoxin or PBS for 7 days. Weight of recipient mice (n = 5 per group) after infection with S. Tm ∆invA (c), and survival of recipient mice after infection with S. Tm ∆invA (d). e–g, Characterization of recipient mice (n = 5 per group) after transplantation of gut microbiomes from donor C57BL/6J mice treated with digoxin or PBS for 2 days. Weight of recipient mice after infection with S. Tm ∆invA (e), pathogen burden in faeces collected from recipient mice at 4 dpi (f) and survival of recipient mice after infection with S. Tm ∆invA (g). In a, fold change is measured relative to the mouse housekeeping gene, Gapdh. In a–c,e,f, a two-sided Mann–Whitney test is used to compare two groups. For survival analysis, the Gehan–Breslow–Wilcoxon test is used. Bar represents geometric mean in a, median in f and s.e.m. in b,c,e. ns, not significant.

Source Data

Extended Data Fig. 6 Effects of digoxin pretreatment on the mouse microbiome and ileal proinflammatory responses.

a, PCoA using weighted UniFrac distance matrices were used to calculate the compositional differences between untreated C57BL/6NTac and C57BL/6J mice. b,c, PCoA plots using weighted UniFrac distance matrices were used to compare the compositional change of untreated, digoxin-treated, or PBS-treated faecal samples in C57BL/6NTac (b), and C57BL/6J mice (c). In a–c, PERMANOVA using the Adonis function with 10,000 permutations was used to calculate the amount of variation. The effect size (R-squared) explains the magnitude of dissimilarities between groups. d, Volcano plot showing differentially abundant taxa in faecal contents in PBS-pretreated and digoxin-pretreated C57BL/6J mice. Statistics were performed using a two-sided Welch’s t-test. A P value cut-off of 0.01 was used to identify enriched/depleted taxa, without adjustments for multiple comparisons. e, Effect of digoxin or PBS treatment (standard regimen; n = 5 per group) on the abundance of Lactobacillus sp. as measured by selective plating on De Man-Rogosa-Sharpe (MRS) agar. A two-sided Mann–Whitney test is used to compare two groups. Multiple comparisons are done using the Bonferroni–Dunn method. f, Relative SFB abundance based on 16S rRNA-seq of faecal samples collected from C57BL/6NTac and C57BL/6J mice 12 h after the final PBS or digoxin dose of a 2-day (standard) treatment regimen. Kruskal–Wallis test was used to compare three or more groups, followed by Dunn’s multiple comparisons test. In the box plot, the centre line is the median, the top and bottom hinges extend from the 25th to 75th percentiles, and the whiskers indicate the minimum and maximum values (n = 5 per group). g, SFB abundance in PBS-pretreated, digoxin-pretreated (5 mg/kg; standard dose), or digoxin-pretreated (0.5 mg/kg) C57BL/6NTac mice (n = 5 per group) relative to total bacteria, as measured by qPCR. Samples were collected 12 h after the last treatment dose. Two-way ANOVA was performed, followed by Dunnett’s multiple comparisons test. h–n, CV C57BL/6NTac mice were treated with PBS or digoxin for two days. Mice were euthanized 12 h after the final vehicle or drug dose, and tissues were collected for gene-expression measurement by qRT–PCR. Relative gene expression of proinflammatory TH17-associated genes Il17a (h) and Il22 (i), SFB-responsive genes Saa1 (j) and Saa2 (k), recruitment markers for neutrophils Cxcl1 (l) and Cxcl2 (m) and the recruitment marker for monocytes Ccl2 (n). In h,i,k–n, n = 5 per group; and in j, n = 10 mice per group were used. Fold change is measured relative to the expression of the housekeeping gene Gapdh. A two-sided Mann–Whitney test is used to compare two groups. In h–n, bar represents geometric mean. ns, not significant, n.d., not detected.

Source Data

Extended Data Fig. 7 Effect of digoxin treatment on SFB abundance in mice.

a–c, GI contents from PBS-pretreated or digoxin-pretreated donor C57BL/6NTac mice were transferred into GF recipient mice. Microbiome analysis was performed from faeces collected from ex-GF recipient mice over time. a, Scree plot showing the percentage of variance explained by each principal coordinate axes. b, PcoA1 plotted against time indicates microbiome stabilization by d7 post transplant. c, Relative SFB abundance over time in the ex-GF recipient mice. P values were calculated using a two-sided Wilcoxon rank-sum test. Error bars are mean with s.e.m. d, SFB-colonized C57BL/6J mice has increased pathogen burden in response to digoxin. SFB colonization was conducted on day (−14) relative to infection, and drugs were administered for two days before infection with S. Tm ΔinvA as in Fig. 1c. Pathogen loads were enumerated at d4 after infection (PBS n = 6, digoxin n = 5). e, SFB abundance over time in faecal samples from C57BL/6NTac mice continuously treated (7 days, 2x/day) with PBS or digoxin. Samples were collected 12 h after the previous treatment dose, and SFB abundance was measured by qPCR and normalized relative to the total bacteria in the sample (n = 9 per group). f, SFB abundance over time in faecal samples from C57BL/6NTac mice (n = 5) intermittently treated with digoxin. Mice were administered digoxin 2x/day on days 1–7, followed by a 7-day rest period (no treatment); digoxin treatment was resumed (2x/day) on days 16-17. SFB abundance was measured as in (e). Error bands represent mean with s.e.m. g,h, C57BL/6NTac mice were treated with vancomycin either intraperitoneally or by oral gavage using the standard two-day treatment regimen. PBS was administered intraperitoneally as a control. (n = 5 mice per group). g, SFB abundance at D(-2) and D0. h, Expression of selected marker genes 12 h after the final treatment dose. A non-parametric Kruskal–Wallis test was used to compare three or more groups, followed by Dunn’s multiple comparisons test. In d,e,g, a two-sided Mann–Whitney test was used to compare two groups. Multiple comparisons using the Bonferroni–Dunn method were performed in e,g. ns, not significant.

Source Data

Extended Data Fig. 8 Characterization of the role of RORγt and enteric β-defensins in digoxin response.

a,b, Effect of PBS or digoxin pretreatment on S. Tm ∆invA infection in SFB-colonized Rorc−/− mice. Pathogen burden (n = 8 mice per group) (a) and mortality (b) is shown. c, Effect of PBS (n = 3 per group) or digoxin pretreatment (ΔILC3 n = 4, RorcSTOP n = 5) on SFB levels in the faeces of SFB-colonized ΔILC3 mice and littermate RorcSTOP controls. d, Effect of PBS or digoxin pretreatment on SFB levels in the ileal content of SFB-monocolonized (ex-GF) Rag1-/- mice (n = 5 per group) and littermate WT SFB-monocolonized (ex-GF) controls (PBS n = 6, digoxin n = 4). A Kruskal–Wallis test followed by Dunn’s multiple comparison test was used for statistical analysis. e, Volcano plot of RNA-seq data from PBS-pretreated and digoxin-pretreated C57BL/6NTac mice. f, Volcano plot of chemokine genes from PBS-pretreated and PBS-pretreated C57BL/6NTac mice. Genes with chemokine activity were identified from the Molecular Signatures Database (MSigDB) (see methods for details). g, Defb39 expression in the ileum, caecum, and colon tissues of C57BL/6NTac mice treated with PBS or digoxin (standard regimen, n = 5 per group). h, Defb39 expression in the ileum tissue of mice (n = 4 per group) treated with PBS or digoxin on day 7 of the extended treatment regimen. In a,c,g,h, a two-sided Mann–Whitney test was used to calculate statistics. In b, the Gehan–Breslow–Wilcoxon test is used. ns, not significant.

Source Data

Extended Data Fig. 9 Characterization of the effect of Defb39 on gut commensal bacteria.

a, Ileal, caecal, and colon tissues were collected from WT and Vil-Defb39 C57BL/6NTac mice (n = 4 per group), and expression of Defb39 was measured relative to the housekeeping gene, Gapdh. b–e, 16S sequencing analysis of faecal samples from WT (n = 4) and Vil-Defb39 (n = 3) mice. b, Scree plot showing the percentage of variance explained by each principal coordinate axes. c, PCoA1 separates samples based on genotype. d, Relative abundance of most abundant taxa. Lactobacillus is significantly reduced in the Vil-Defb39 mice. e, Relative abundance of SFB. f, Effect of varying concentrations of purified BD-39 on the viability of representative human and mouse gut bacterial strains (n = 4 biologically independent samples). Error bars are mean with s.e.m. In a, a two-sided Mann–Whitney test was used to compare the two groups, and in c,e, a two-tailed unpaired t-test is used. In the box plot, the centre line is the median, the top and bottom hinges extend from the 25th to 75th percentiles and the whiskers indicate the minimum and maximum values.

Source Data

Extended Data Fig. 10 Effect of digoxin on mice colonized with human microbial communities.

a–c, CV C57BL/6NTac mice (n = 5 per group) were treated with PBS or dihydrodigoxin (5 mg/kg) (standard regimen). 12 h after the final buffer or drug treatment, mice were infected with ~108 CFUs of S. Tm ΔinvA and infection monitored over time. a, Relative abundance of SFB, normalized to total bacteria, in faecal samples before and after PBS or dihydrodigoxin treatment. b, Pathogen burden at 12 h after infection. c, Survival curve. d, GF C57BL/6NTac Nramp1+/+ mice were colonized with a TH17-inducing defined community, pretreated with PBS (n = 4) or digoxin (n = 5) as in Fig. 1c, euthanized at D0, and ileal expression of select chemokine marker genes was measured relative to Gapdh expression. e, Estimation of cgr2 gene abundance across 29 faecal communities from unrelated human donors, as measured from metagenomic sequencing and ShortBRED analysis or targeted qPCR analysis. f, GF C57BL/6NTac Nramp1+/+ mice (n = 5 per group) colonized with a pooled human community were pretreated with PBS or digoxin as in Fig. 1c, euthanized at d0, and the ileal expression of select marker genes was measured relative to Gapdh expression. g, Volcano plot showing differentially abundant taxa in PBS- or digoxin-pretreated C57BL/6NTac Nramp1+/+ mice colonized with the pooled human-microbiome community. Statistics were performed using a two-sided Welch’s t-test. A P value cut-off of 0.01 was used to identify enriched/depleted taxa without adjustments for multiple comparisons. In a,b,d,f, a two-sided Mann–Whitney test was used to compare the two groups. In c, the Gehan–Breslow–Wilcoxon test is used. Bar represents median values in a,b, and geometric mean in d,f. ns, not significant.

Source Data

Supplementary information

Supplementary Information

This file contains Supplementary Discussion, Supplementary Figures 1 and 2 and additional references.

Reporting Summary

Supplementary Table 1

Characteristics of patients in the study population. This table characterizes the study population, including age, sex, GI illness and other comorbidities of the cohort.

Supplementary Table 2

Drugs and their association with GI illness in the human population. This table provides the raw data for the odds ratio (OR) and the corresponding P values to identify the association between medical drugs and infectious GI events. This table is related to Fig. 1b.

Supplementary Table 3

List of drugs. This table provides the list of all the medications used in this study, along with dosage and PMID references.

Supplementary Table 4

Multivariate analysis of selected drugs. This table lists the association between the selected 21 drugs with increased GI infection risk after performing multivariate analysis and adjusting for the three most expected drug classes associated with GI infections (antibiotics, immunosuppressants and antidiarrhoeals). Statistics were calculated by performing conditional logistic regression and assessing whether the concurrent use of antibiotics, immunosuppressants and/or antidiarrhoeals modified the association by including binary indicators for use (yes/no) in multivariate models after adjusting for the three drug classes. For each drug, multivariate analysis for all three confounders is listed first, followed by each confounder individually.

Supplementary Table 5

List of primers. This table contains all the primers used in the study and associated references.

Supplementary Table 6

Strain list. This table describes all the mouse strains, bacterial strains and plasmids used in the study.

Supplementary Table 7

Metagenomic analysis. This table consists of the ShortBRED analysis performed on the human metagenomic data. This is related to Extended Data Fig. 10e.

Supplementary Table 8

List of community identifiers in the pooled human-microbiome community. This table details the age and sex of the donors whose faecal samples were included in the pooled material used in Fig. 5c.

Supplementary Table 9

AMP gene expression in response to digoxin. This table lists the AMP expression patterns in the RNA-seq data as shown in Fig. 4c. Altered regulation was determined by evaluating log2 fold change (log2FC) and adjusted P value (Padj) of digoxin-treated compared to PBS-treated mice using the Wald test. Genes with log2FC > 1.5 and Padj < 0.05 were considered significantly upregulated, and those with log2FC < −1.5 and Padj < 0.05 were considered significantly downregulated.

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Kumar, A., Sun, R., Habib, B. et al. Identification of medication–microbiome interactions that affect gut infection. Nature 644, 506–515 (2025). https://doi.org/10.1038/s41586-025-09273-8

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