Identification of medication-microbiome interactions that affect gut infection.

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Published in final edited form as: Nature. 2025 Jul 16;644(8076):506–515. doi: 10.1038/s41586-025-09273-8 Search in PMC Search in PubMed View in NLM Catalog Add to search Identification of medication-microbiome interactions that impact gut infection Aman Kumar Aman Kumar 1 Department of Microbial Pathogenesis and Microbial Sciences Institute, Yale University School of Medicine, New Haven, CT, USA Find articles by Aman Kumar 1 , Ruizheng Sun Ruizheng Sun 1 Department of Microbial Pathogenesis and Microbial Sciences Institute, Yale University School of Medicine, New Haven, CT, USA 2 Department of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China Find articles by Ruizheng Sun 1, 2 , Bettina Habib Bettina Habib 3 Clinical and Health Informatics Research Group, McGill University, Montreal, Canada Find articles by Bettina Habib 3 , Tong Deng Tong Deng 4 Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA Find articles by Tong Deng 4 , Natasha A Bencivenga-Barry Natasha A Bencivenga-Barry 1 Department of Microbial Pathogenesis and Microbial Sciences Institute, Yale University School of Medicine, New Haven, CT, USA Find articles by Natasha A Bencivenga-Barry 1 , Noah W Palm Noah W Palm 4 Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA Find articles by Noah W Palm 4 , Ivaylo I Ivanov Ivaylo I Ivanov 5 Department of Microbiology and Immunology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA 6 Columbia University Digestive and Liver Diseases Research Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA Find articles by Ivaylo I Ivanov 5, 6 , Robyn Tamblyn Robyn Tamblyn 3 Clinical and Health Informatics Research Group, McGill University, Montreal, Canada 7 Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada 8 Department of Medicine, McGill University Health Center, Montreal, Canada Find articles by Robyn Tamblyn 3, 7, 8 , Andrew L Goodman Andrew L Goodman 1 Department of Microbial Pathogenesis and Microbial Sciences Institute, Yale University School of Medicine, New Haven, CT, USA Find articles by Andrew L Goodman 1, * Author information Article notes Copyright and License information 1 Department of Microbial Pathogenesis and Microbial Sciences Institute, Yale University School of Medicine, New Haven, CT, USA 2 Department of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China 3 Clinical and Health Informatics Research Group, McGill University, Montreal, Canada 4 Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA 5 Department of Microbiology and Immunology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA 6 Columbia University Digestive and Liver Diseases Research Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA 7 Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada 8 Department of Medicine, McGill University Health Center, Montreal, Canada Author 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 analyzed the data; R.S. assisted with digoxin and BD-39 sensitivity assays and data analysis; N.A.B. rederived the Nramp1 +/+ mice to the GF state; I.I.I. provided crucial reagents and helpful suggestions; and T.D. and N.W.P. provided assistance with flow cytometry. A.K. and A.L.G. wrote the manuscript with input from all authors. * Correspondence: [email protected] Issue date 2025 Aug. PMC Copyright notice PMCID: PMC13092348  NIHMSID: NIHMS2155260  PMID: 40670788 The publisher's version of this article is available at Nature Previous version available: This article is based on a previously available preprint posted on Research Square on October 18, 2024: " Impacts of Medications on Microbiome-mediated Protection against Enteric Pathogens ". Summary The majority of people in the U.S. manage health through at least one prescription drug, and drugs classified as non-antibiotics can adversely affect the gut microbiome and disrupt intestinal homeostasis 1 , 2 . Here, we identified medications associated with an increased risk of GI infections across a population cohort of more than 1 million individuals monitored over 15 years. Notably, the cardiac glycoside digoxin and other drugs identified in this epidemiological study are sufficient to alter microbiome composition and risk of Salmonella enterica serovar Typhimurium ( S . Tm) infection in mice. The impact of digoxin treatment on S. Tm infection is transmissible via the microbiome, and characterization of this interaction highlights a digoxin-responsive β-defensin that alters 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 infection risk in humans. Gut microbiome disruption is linked to a wide range of consequences depending on the individual and the environment, including inflammatory bowel diseases, autoinflammatory disease, colon cancer, and gastrointestinal (GI) infections 3 – 6 . However, these diseases are individually rare, and the specific triggers and resulting microbiome changes that alter risk in susceptible individuals remain largely unknown. Large-scale, prospective epidemiological studies (of tens to hundreds of thousands of individuals) are a critical strategy to identify risk factors for rare pathologies in human populations, but this approach is challenging in the context of the gut microbiome because available cohorts that are sufficiently powered to include rare events do not include microbiome data captured before and after the onset of disease. One of the most widespread consequences of microbiome disruption is the loss of colonization resistance in the GI tract, wherein the gut microbiome loses its ability to work in concert with the host immune system to prevent the expansion of pathogens in the gut. Notably, the use of prescription medications is commonly associated with GI side effects, including infections 6 – 8 . These side effects contribute to drug-related hospitalizations and can limit drug tolerance 9 . Although both oral and non-oral medical drugs can alter microbiome composition and gut microbes can metabolize medical drugs 2 , 10 – 13 , potential connections between non-antibiotic prescription drug use, microbiome disruption, and infection risk are largely unknown. Given that the majority of the U.S. population maintains health through the use of one or more prescription medications and diarrheal diseases kill over 1 million people globally each year 1 , 14 – 16 , such interactions have widespread implications. Although microbiome data is not directly available in large-scale, longitudinal epidemiological datasets, we reasoned that clinical diagnosis codes reporting GI infection could serve as an indirect indicator of microbiome disruption in a large human population. In this report, we analyzed prescription drug use and clinical diagnosis codes from over 1 million individuals over 15 years to identify drugs associated with an elevated risk of GI infection. We determined that many of these drugs, including the cardiac glycoside digoxin, alter gut microbiome composition and colonization resistance in a mouse model of Salmonella enterica serovar Typhimurium ( S . Tm) infection. We demonstrate that digoxin induces RORγt-dependent expression of a previously uncharacterized family of enteric β-defensins, which in turn selectively decreases commensal microbes that promote immune surveillance against enteropathogens. Integrating epidemiological and experimental approaches thus provides a strategy to identify triggers of microbiome disruption in human populations that alter the risk for pathologies recorded in clinical diagnosis codes. Drug-infection links in 1M human cohort To identify drugs associated with increased infection risk in humans, we examined medical services, hospitalization, and pharmacy claims data within a dynamic cohort of over one million anonymized individuals spanning over 15 years. We conducted a case-crossover study design to perform this analysis, which allowed us to use each case as their own control, effectively adjusting for various known and unknown individual characteristics that could influence the underlying risk of GI infection ( Fig. 1a ) 17 . To this end, we identified the first recorded infectious GI event for each individual within the 15-year time period ( Supplementary Table 1 ). Next, we established a ~60-day case window immediately preceding the infectious GI event and a ~60-day control window immediately prior to the case window and assessed exposure to prescription drugs in both case and control windows ( Extended Data Fig. 1a ; see methods for details). Utilizing conditional logistic regression, we identified drug classes that had higher odds of having been dispensed in case windows compared to control windows, indicating an association between the drug classes and the risk of GI infection. Because the relationship between drug exposure and microbiome disruption is expected to vary between drugs, we assumed that recency would be important in establishing potential associations. We used a 60-day window to allow for some degree of medication non-adherence and possible lagged effects of exposure. A sensitivity analysis found similar associations when a shorter window of 30 days was used. Fig. 1|. Analysis of 1M individuals over 15 years identifies drugs that increase infection risk in humans and mice. Open in a new tab a, Design of a case-crossover epidemiological study to identify associations between prescription medications and infectious gastrointestinal (GI) events across >1M individuals. Case and control windows are defined relative to infectious GI events; for each drug, an odds ratio is calculated as the number of individuals (N) taking the drug in case periods relative to control periods. b, Epidemiological study results. 21 drugs (with names listed) were identified for further study based on the number of patients with drug dispensations within each class, odds ratio > 1.5, and P -value < 0.05. Letters (a-ba) indicate drug classes, and numbers (1–231) indicate individual drugs ( Supplementary Table 2 ). Multivariate analysis was performed for the selected 21 drugs to adjust for potential confounders (shaded area: anti-microbial agents, immunosuppressants, antidiarrheals) of increased infection risk ( Supplementary Table 4 ). c, Experimental design to study colonization resistance in mice. d, Drug-dependent microbiome compositional differences measured using principal coordinate analysis on Bray-Curtis dissimilarity before and after drug treatment (left panel) and between drug- and control-treated animals in the same cohort (right panel). Permutational Multivariate Analysis of Variance (PERMANOVA) analysis was used to calculate the amount of variation. The effect size (R-squared) explains the magnitude of dissimilarities between groups. Drug and vehicle names are colored by mouse cohort. e, Multiple drugs identified in the epidemiological screen impact S. Tm Δ invA pathogen burden in C57BL/6NTac mice. Statistical significance is calculated using the non-parametric Kruskal-Wallis test followed by Dunn’s multiple comparisons test ( n = 5 mice/drug group; n = 25 mice for vehicle group). In the box plot, the center line is the median, the top and bottom hinges extend from the 25 th to 75 th percentiles, and the whiskers indicate the minimum and maximum values. This analysis identified several expected drug classes, such as antibacterials, immunosuppressants, and antidiarrheals, suggesting that the case-crossover strategy can successfully identify drug classes that increase GI infection risk ( Extended Data Fig. 1b ). Notably, this approach also highlighted specific non-antibiotic drug classes that increase GI infection disease risk to a similar or greater extent as observed with the expected drug classes ( Fig. 1b , Extended Data Fig. 1b , Supplementary Table 2 ). Next, we performed an unbiased analysis of individual drugs from both significant and non-significant drug classes and identified several drugs that were individually associated with increased infection risk. We selected medications meeting specific criteria within each class (prescription number >100, odds ratio >1.5, P -value < 0.05) and identified 21 candidate drugs for further studies in mice ( Fig. 1b , Supplementary Table 2 , 3 ). P values after adjustment for potential confounders ( e.g., concurrent use of antibiotics, antidiarrheals, and immunosuppressants) are provided in Supplementary Table 4 ). Validation in mice We next examined the impact of the 21 drugs identified in the epidemiological study on microbiome composition and infection risk in mice challenged with the model enteropathogen Salmonella enterica serovar Typhimurium ( S . Tm). Colonization resistance is a critical factor in determining pathogen infection in this model, and most studies rely on streptomycin treatment to disrupt the microbiome prior to infection 18 . We administered each of the 21 drugs ( Supplementary Table 3 ), plus vehicle controls in each mouse cohort, twice daily to separate groups of conventional (CV) C57BL/6NTac mice (Taconic Biosciences) for two days with fecal sampling for 16S rRNA sequencing before treatment initiation and 12 hours after the final dose. Wildtype (WT) S . Tm contains two Type III secretion systems (T3SS): T3SS-1 mediates invasion of non-phagocytic cells and induces widespread gut inflammation, while T3SS-2 enables systemic spread and survival within the host 19 . Because inflammation further disrupts the microbiome, we conducted the initial drug screen using an S. Tm Δ invA strain, which lacks an integral structural component of T3SS-1 and is attenuated for colitis but encodes a functional T3SS-2 19 , 20 . After a 12-hour washout period following the final drug dose, we infected each animal with S. Tm Δ invA and monitored infection over time ( Fig. 1c ). To quantify the impact of drug treatment on the gut microbiome, we evaluated beta diversity within groups before and after drug treatment and between drug- and control-treated groups from the same mouse cohort. Several drugs induced significant compositional changes in the fecal microbiome in at least one of these comparisons ( Fig. 1d ; Extended Data Fig. 1c ); digoxin and donepezil significantly altered microbiome composition in both comparisons. Additionally, four drugs (digoxin, clonazepam, pantoprazole, and quetiapine) significantly increased pathogen burden in mice 12 hours post-infection ( Fig. 1e ). These drugs did not exhibit widespread antibiotic activity when tested on representative human gut bacterial isolates ( Extended Data Fig. 1d ). Digoxin disrupts colonization resistance Because digoxin significantly altered microbiome composition and risk of S . Tm infection in mice, and its interaction with the microbiome is well-appreciated 21 , 22 , we selected this drug for further study. Independent replication of S. Tm Δ invA infection in digoxin-pretreated mice confirmed the significant increase in pathogen burden in fecal samples collected 12 hours, 2 days, and 4 days post infection, as well as in gastrointestinal contents and extraintestinal tissues 4 days post infection ( Fig. 2a , Extended Data Fig. 2a - g ). Consistent with this increased pathogen burden, digoxin-pretreated mice exhibited significantly increased mortality compared to control mice treated with PBS prior to S. Tm Δ invA infection ( Fig. 2b ). Digoxin pre-treatment had no impact on bacterial loads in the liver and spleen of mice infected with S. Tm Δ invA via intraperitoneal infection, indicating that increased mortality results from higher pathogen burden in the gut and subsequent dissemination and suggesting that digoxin plays a key role in altering the gut microenvironment to cause increased pathogenesis ( Extended Data Fig. 2h - i ). Fig. 2|. The impact of digoxin on infection risk is transmissible via the microbiome. Open in a new tab a, b, C57BL/6NTac mice were orally gavaged with PBS (in 5% dimethyl sulfoxide, DMSO) or digoxin (5mg/kg) and infected with S . Tm Δ invA as in Fig. 1c . a, Fecal pathogen burden in PBS-pretreated ( n = 11) or digoxin-pretreated ( n = 13) mice 12 hr post-infection (p.i.). b, Mortality of PBS-pretreated or digoxin-pretreated C57BL/6NTac mice after S. Tm Δ invA infection or mock infection. c, Pathogen burden in PBS-pretreated ( n = 22) or digoxin-pretreated ( n = 23) C57BL/6NTac Nramp1 +/+ mice 12 hr after infection with WT S. Tm. d, Survival of PBS-pretreated or digoxin-pretreated C57BL/6NTac Nramp1 +/+ mice after infection with WT S. Tm. e-f, Impact of digoxin on infection of C57BL/6J mice. Fecal pathogen burden ( n = 5/group) ( e ) and mortality ( f ) in non-cohoused, PBS-treated or digoxin-pretreated C57BL/6J mice after S. Tm Δ invA infection is shown. g, Impact of PBS-pretreatment or digoxin-pretreatment on S. Tm infection in C57BL/6J mice that were cohoused with C57BL/6NTac mice for 14 days. Animals were separated prior to PBS or drug administration and infection. h, Schematic of gut microbiome transplantation experiments. i-j, Impact of transplantation of gastrointestinal contents from PBS-pretreated or digoxin-pretreated C57BL/6NTac donor mice on S. Tm pathogen burden (PBS n = 9, digoxin n = 8) ( i ) and mortality ( j ) in ex-GF recipient mice colonized with either microbiome prior to infection. In a , c , e , i , Bar represents median values, and dotted lines represent the limit of detection. A two-sided Mann-Whitney test was used for comparison. In b , d , f , g , j , P- value was calculated using the Gehan-Breslow-Wilcoxon test. ns, not significant. C57BL/6NTac mice rapidly succumb to infection by WT S. Tm because they encode a nonfunctional allele of Nramp1 , which is essential for the bactericidal activity of macrophages; C57BL/6NTac Nramp1 +/+ mice exhibit increased resistance to WT S. Tm 23 . Pretreatment of C57BL/6NTac Nramp1 +/+ mice with digoxin prior to infection with WT S. Tm increases pathogen burden and mortality in these animals compared to controls treated with PBS prior to WT S. Tm infection ( Fig. 2c - d , Extended Data Fig. 2j - k ). To examine the effect of digoxin on other pathogens, we infected digoxin-pretreated C57BL/6NTac mice with Citrobacter rodentium , an attaching and effacing pathogen commonly used as a murine surrogate for human pathogens such as enterohemorrhagic E. coli and enteropathogenic E. coli, or Vancomycin-resistant Enterococcus faecium (VRE), a human pathogen associated with nosocomial infection. Notably, digoxin pretreatment significantly increased C. rodentium burden (measured at the peak of infection (d10 p.i.)) in feces, and also increased the levels of C. rodentium attached to the intestinal epithelium ( Extended Data Fig. 3a - b ). Similarly, mice pretreated with digoxin had an increased VRE burden in feces as well as in gastrointestinal contents ( Extended Data Fig. 3c - d ). Together, these results suggest that digoxin pretreatment is associated with increased infection risk in humans independent of concurrent use of other drugs and is sufficient to increase infection risk in multiple mouse models of infection. Digoxin can alter the activity of retinoic acid-related orphan receptor (RORγt, encoded by the Rorc gene) 24 , 25 . RORγt is essential for the differentiation of naïve T cells into proinflammatory helper T (Th17) cells that produce interleukin-17 (IL-17) and interleukin-22 (IL-22) cytokines 24 , 25 ; other cell types ( e.g. group 3 innate lymphoid cells (ILC3)) also produce these cytokines in a RORγt dependent manner 26 . These cytokines act on epithelial cells to upregulate the expression of anti-microbial lectins such as Reg3g and Reg3b and induce expression of chemokines that promote neutrophil and monocyte recruitment to the mucosal surface 27 – 29 . This orchestrated innate immune response contributes significantly to the establishment of a gut barrier, which is vital for sustaining gut-immune homeostasis and conferring protection against enteric pathogens, and the presence of these factors in the ileum is implicated in resistance against S . Tm colonization 27 , 30 . Notably, expression of IL-17/22-dependent anti-microbial lectins such as Reg3b and Reg3g was downregulated in digoxin-pretreated mice, specifically in ileal tissue but not in the cecum and colon ( Extended Data Fig. 3e - g ). We next assessed whether the direct effect of digoxin on the host is likely to explain the differences in susceptibility to infection outcome irrespective of the effect of digoxin on the microbiome. To this end, we first determined whether other digoxin treatment regimens altered the risk of infection. We assessed S. Tm Δ invA infection in C57BL/6NTac mice treated with a single dose of digoxin (2 hours before infection) or given an extended treatment regimen (twice-daily treatment for 7 days, prior to a 12-hour washout before infection) (Extended Data Fig. 4a) . Unlike the 2-day treatment group, neither of these treatment strategies altered infection outcome relative to control animals treated with PBS prior to infection ( Extended Data Fig. 4b - e ). Consistent with this result, proinflammatory S . Tm resistance genes that were significantly downregulated after 2 days of digoxin treatment returned to baseline levels after 7 days of drug administration (Extended Data Fig. 4f) . These distinct phenotypes, depending on the timing of digoxin treatment, suggest that the impact of digoxin on the host may not be sufficient to explain the increased S . Tm susceptibility. Because digoxin treatment alters microbiome composition ( Fig. 1d , Extended Data Fig. 1c ), we next examined whether mice with different microbiomes exhibit similar digoxin responses. While both C57BL/6NTac mice and C57BL/6J mice (obtained from Jackson Laboratories) exhibited a slight reduction in weight upon digoxin treatment, pretreatment with the drug did not cause increased pathogen burden or altered mortality in C57BL/6J mice infected with S. Tm Δ invA ( Fig. 2e - f , Extended Data Fig. 4g ). Cohousing these animals with Taconic (C57BL/6NTac) mice for two weeks to allow for microbiome exchange altered this phenotype: C57BL/6J mice cohoused with C57BL/6NTac mice were significantly more susceptible to infection after digoxin pre-treatment compared to cohoused controls that were not treated with digoxin prior to infection ( Fig. 2f - g ). Despite this increased mortality, pathogen burden was similar at 3d p.i., potentially because of the high dose of S. Tm used in this experiment ( Extended Data Fig. 4h ). To determine whether the impact of digoxin on pathogen susceptibility is transmissible via the microbiome, we next treated C57BL/6NTac donor animals with digoxin or PBS for 2 days as above; after a 12-hour washout period, we transplanted the gut contents of each donor animal into a germ-free (GF) recipient mouse. Seven days after microbiome transplantation, recipient mice were sacrificed (to measure proinflammatory markers) or infected with S. Tm Δ invA ( Fig. 2h ). Recipient mice carrying the microbiomes of digoxin-treated donor animals exhibited significantly reduced expression of proinflammatory markers prior to infection and increased pathogen burden, weight loss, and mortality after infection compared to recipient animals colonized with the microbiomes of PBS-treated animals ( Fig. 2i - j , Extended Data Fig. 5a - b ). By contrast, transplantation of gut contents from C57BL/6NTac donor animals treated for 7 days with digoxin or PBS, or C57BL/6J donor animals treated for two days with digoxin or PBS, into GF recipient mice resulted in no difference in S. Tm Δ invA susceptibility between recipient groups ( Extended Data Fig. 5c - g ; as above, gut contents were collected from donor animals 12 hours after the last digoxin or PBS dose). Together, these data suggest that the direct interaction between digoxin and host cells (including any trace carryover from microbiome transplantation after the 12-hour washout in donor animals and 7 days in recipient animals) is not sufficient to increase susceptibility to S . Tm infection. Digoxin decreases SFB abundance Because the impact of digoxin on infection was transmissible via the microbiome, we next evaluated digoxin-mediated microbiome changes in C57BL/6NTac and C57BL/6J mice. Digoxin treatment significantly altered gut microbiome composition in both C57BL/6NTac and C57BL/6J mice ( Extended Data Fig. 6a - c ), including decreased relative abundance of multiple Lactobacillus taxa ( Fig. 3a , Extended Data Fig. 6d - e ). Digoxin treatment also led to a marked depletion of Candidatus savagella in C57BL/6NTac mice; this microbe was absent in C57BL/6J animals ( Fig. 3a , Extended Data Fig. 6f ). C. savagella is a member of the segmented filamentous bacteria (SFB) group of spore-forming, Gram-positive bacteria. SFB attaches to the ileal epithelium and triggers a cascade of host immune responses, including upregulation of the serum amyloid A (SAA1/2) pathway and activation of RORγt, which results in a proinflammatory gut environment that protects against invading pathogens 27 , 31 . Consistent with the 16S rRNA sequencing results, targeted (qPCR) measurement of SFB levels in C57BL/6NTac and C57BL/6NTac Nramp1 +/+ mice indicated a significant reduction in fecal SFB levels after digoxin pretreatment ( Fig. 3b - c ). Scanning electron microscopy (SEM) analyses of ileal tissue from digoxin- and vehicle-pretreated mice reveal that digoxin pretreatment nearly completely eliminates SFB from the ileal epithelium ( Fig. 3d ). Mice pretreated with 0.5 mg/kg digoxin also show significant reduction in SFB as measured by qPCR and SEM, indicating that 2 days of digoxin treatment alters the ability of SFB to attach to the ileal epithelium or colonize the gut across the range of digoxin doses used in the literature 24 , 25 ( Fig. 3d , Extended Data Fig. 6g ). Interestingly, the other drugs selected from the epidemiological screen do not affect SFB abundance in mice, suggesting additional mechanisms of drug-mediated loss of colonization resistance ( Supplementary Fig. 1 ). Fig. 3|. Digoxin-mediated depletion of segmented filamentous bacteria (SFB) increases susceptibility to S . Tm infection in mice. Open in a new tab a, Volcano plot showing differentially abundant taxa in fecal contents of PBS-pretreated and digoxin-pretreated C57BL/6NTac mice. b, c, SFB abundance in PBS-pretreated or digoxin-pretreated C57BL/6NTac ( n = 5/group) ( b ) and C57BL/6NTac Nramp1 +/+ ( n = 8/group) ( c ) mice relative to total bacteria, as measured by quantitative PCR (qPCR). d, Scanning electron microscopy of the terminal ileum of mice pretreated with PBS or digoxin. Representative images are shown from two independent experiments. e, Representative flow cytometry plots showing ileal CD4⁺ IL-17A + T cells (top panel), ileal neutrophils (middle panel), and ileal Ly-6C hi monocytes/macrophages (bottom panel) from PBS- or digoxin-pretreated mice. CD4⁺ T cells were gated as B220⁻NK1.1⁻TCRβ⁺CD4⁺CD8⁻. Neutrophils were gated as B220⁻NK1.1⁻TCRβ⁻CD11b⁺Ly-6G⁺. Monocytes/macrophages were gated as B220⁻NK1.1⁻TCRβ⁻CD11b⁺Ly-6G⁻Ly-6C hi . f-h, Frequency of IL-17A⁺ CD4⁺ T cells after ex vivo stimulation with PMA/ionomycin ( f ), neutrophils ( g ), or Ly-6C hi monocytes/macrophages ( h ) from the ileal lamina propria of PBS- or digoxin-pretreated mice ( n = 5 animals/group). i, C57BL/6J mice cohoused with C57BL/6NTac animals acquire SFB, which is reduced upon digoxin (Dig) treatment. Animals were separated prior to PBS or drug administration ( n = 5/group). Data are shown as mean with SEM. j, Relative abundance of SFB in ex-GF recipient mice after transplantation of gastrointestinal contents from PBS-pretreated or digoxin-pretreated C57BL/6NTac donor animals ( n = 5/group). Data are presented as geometric mean, and error bars represent geometric SD. k, l, SFB colonization is sufficient to alter the response of C57BL/6J mice to digoxin. SFB colonization was conducted on day (−14) relative to infection, and drugs were administered for two days before infection as in Fig. 1c . Relative abundance of SFB after PBS ( n = 8) or digoxin ( n = 6) pretreatment ( k ) and mortality after S. Tm Δ invA infection ( l ) is shown. The P- value was calculated using the Gehan-Breslow-Wilcoxon test. In b-c , f-h , and i-k , a two-sided Mann-Whitney test was used for comparison. ns, not significant. Four lines of evidence further implicate digoxin control of immunomodulatory microbes such as SFB as a key step in determining the impact of the drug on pathogen susceptibility. First, these SFB-depleted animals exhibit a significant decrease in the proportion and total numbers of IL-17A producing CD4 T cells, neutrophils, and Ly6C hi monocytes/macrophages in the small intestine (SI) lamina propria ( Fig. 3e - h , Supplementary Fig. 2a - e ). Congruently, expression of IL17a , neutrophil, and monocyte marker genes was significantly downregulated in digoxin pre-treated mice ( Extended Data Fig. 6h - n ). Second, digoxin treatment of SFB-negative C57BL/6J mice has no impact on S. Tm infection; C57BL/6J mice cohoused with C57BL/6NTac animals become colonized with SFB and digoxin treatment of these mice after cohousing decreases their SFB levels and increases S. Tm pathogenesis relative to PBS-treated controls ( Fig. 2e - g , 3i ). Third, SFB levels are significantly reduced in ex-GF recipient mice that were engrafted for 7 days (to allow for microbiome equilibration) with the microbiomes of digoxin-treated C57BL/6NTac donor mice compared to recipients engrafted with microbiomes from PBS-treated donors ( Fig. 3j , Extended Data Fig. 7a - c ). Finally, we directly colonized C57BL/6J mice with fecal material from SFB-monoassociated gnotobiotic mice; these animals became robustly colonized and digoxin treatment resulted in a significant depletion in the SFB population ( Fig. 3k ) and increased pathogen burden and infection susceptibility ( Fig. 3l , Extended Data Fig. 7d ). Consistent with the observation that the impact of digoxin on infection susceptibility and proinflammatory marker genes is transient ( Extended Data Fig. 4b - f ), SFB levels recover in mice treated with digoxin for 5 days or more ( Extended Data Fig. 7e ). A brief drug discontinuation period is sufficient to render the system digoxin-sensitive again ( Extended Data Fig. 7f ). Finally, we treated C57BL/6NTac mice with vancomycin to understand whether treatment with antibiotics that reduce SFB levels is sufficient to recapitulate the impact of digoxin on SFB-dependent proinflammatory marker genes. Indeed, oral vancomycin reduced SFB and expression of these genes ( Extended Data Fig. 7g - h ). Together, these results suggest that treatments that reduce SFB levels for two days are sufficient to alter gut immune homeostasis. Digoxin-induced BD-39 controls SFB levels Because digoxin binds the master transcription factor RORγt 24 , we next examined whether the digoxin-dependent decrease in SFB is RORγt dependent. Digoxin pretreatment of SFB-colonized Rorγ −/− mice did not result in reduced SFB levels or increased susceptibility to infection ( Fig. 4a , Extended Data Fig. 8a - b ), suggesting that 1) digoxin does not directly kill SFB; and 2) digoxin reduces SFB levels in a RORγt-dependent manner. Although RORγt is expressed in both Th17 and ILC3 cells, mice specifically lacking ILC3 cells 32 exhibit SFB reduction in response to digoxin as observed in WT animals, while mice lacking both Th17 and ILC3 cells are insensitive ( Extended Data Fig. 8c ). We also examined Rag1 −/− mice, which lack mature T cell and B cells: digoxin treatment of SFB-monocolonized Rag1 −/− mice has no impact on SFB abundance in these animals, while SFB-monocolonized WT control animals exhibit a significant decrease in SFB abundance upon digoxin treatment ( Extended Data Fig. 8d ). Together, these observations suggest that digoxin controls SFB levels in a Th17-dependent manner. Fig. 4|. A digoxin-inducible, RORγt-dependent β-defensin controls SFB levels in the mouse gut. Open in a new tab a, Impact of PBS or digoxin pretreatment on SFB levels in SFB-colonized Rorγ −/− mice ( n = 8/group) and their WT littermate controls ( n = 7/group). b-c, Relative expression of select genes encoding anti-microbial peptides (AMPs) in ileal tissue of C57BL/6NTac mice with and without digoxin pretreatment (Exact n and P values are provided in the Source Data) ( b ) and RNA-seq ( c ). A two-tailed unpaired t test was used. In b, FC indicates fold change. d-f, defb39 gene expression in ileal tissue of SFB-colonized Rorγ −/− mice ( n = 4/group) ( d ), GF mice ( n = 5/group) ( e ), and SFB-monocolonized ex-GF mice ( f ) after PBS ( n = 4) or digoxin ( n = 6) pretreatment. g, Absolute SFB abundance in the ileum content of ex-GF mice mono-colonized with SFB and treated with PBS ( n = 5) or digoxin ( n = 6). h, Expression of SFB-responsive ( SAA1, Reg3g ) genes in ileal tissue of ex-GF mice mono-colonized with SFB and treated with PBS ( n = 4) or digoxin ( n = 6). i, SFB abundance in fecal samples from vil-defb39 transgenic mice and WT controls ( n = 3/group). j, Schematic for measuring antimicrobial activity of purified BD-39 against SFB. k, Absolute SFB abundance over time in ex-GF mice ( n = 3/group). A two-sample Welch t test was used to compare the two groups. In b , d , e , f , h , fold change is measured relative to the mouse housekeeping gene, Gapdh . In a , d , e , f , g , h , i , a two-sided Mann-Whitney test is used to compare two groups. In a , multiple comparisons are made using Bonferroni-Dunn method. Bar represent median values in a , g , and geometric mean in d , e , f , h . Error bars in i , k represent s.e.m. ns, not significant; * P <0.05, ** P <0.01, *** P <0.001. Because anti-microbial peptides (AMPs) such as Reg3g and α-defensins are linked with reduced SFB abundance in the gut 33 , 34 , we measured the expression of a panel of AMPs in ileal tissue of digoxin- and PBS-pretreated C57BL/6NTac mice. Digoxin pretreatment generally had no effect or resulted in downregulation of most of the measured AMPs; however, drug treatment resulted in significant upregulation of the gene encoding β-defensin 39 (BD-39) ( Fig. 4b ). RNA sequencing of ileal tissue (from a separate cohort of digoxin- and PBS-pretreated C57BL/6NTac mice) confirmed the digoxin-dependent defb39 upregulation observed in the targeted gene expression study, and further identified a second member of this β-defensin family (BD-37) as potentially upregulated in digoxin-pretreated compared to PBS-treated animals ( Fig. 4c , Extended Data Fig. 8e ). Consistent with the digoxin-dependent reduction in SFB levels and prior targeted gene expression measurements, these RNA-seq measurements validated digoxin-induced downregulation of SFB-dependent genes ( SAA1, SAA2 ) 27 as well key proinflammatory factors involved in control of S. Tm infection ( IL-17a , IL-22 , Reg3b , Reg3g, CXCL1, CXCL2, CCL2 ; Extended Data Fig. 3e , 6h - n , 8e - f ). Digoxin-dependent induction of defb39 was specific to the ileum ( Extended Data Fig. 8g ), which is the primary site of SFB attachment; digoxin-dependent repression of Reg3b and Reg3g in SFB-positive mice was also specific to the ileum ( Extended Data Fig. 3e - g ). Notably, digoxin pretreatment of SFB-colonized Rorγ −/− mice does not alter defb39 expression in these animals, and WT mice administered digoxin for an extended (7 day) interval return defb39 expression to untreated levels ( Fig. 4d , Extended Data Fig. 8h ). Additionally, GF mice, which lack Th17 cells in the SI lamina propia 27 , also do not overexpress defb39 in the ileum upon digoxin treatment ( Fig. 4e ). Further, digoxin treatment of SFB-monoassociated GF mice resulted in significantly increased expression of defb39, reduced SFB abundance, and decreased expression of canonical SFB-regulated genes ( SAA1 , Reg3g ) ( Fig. 4f - h ). Together, these results suggest that other members of the microbiota are not necessary for digoxin-dependent defb39 upregulation or SFB reduction. To determine whether defb39 upregulation is sufficient to modulate SFB levels, we generated transgenic mice that constitutively express defb39 under the control of the intestinal epithelium-specific Villin (encoded by Vil1 gene) promoter. These mice overexpress defb39 throughout the length of the gut ( Extended Data Fig. 9a ) and exhibit robust microbiome changes, including a significant decrease in Lactobacillus ( Extended Data Fig. 9b - d ). Notably, transgenic vil-defb39 expression significantly reduces SFB levels compared to WT C57BL/6NTac controls; indeed, SFB levels in vil-defb39 animals are similar to digoxin-pretreated, WT mice ( Fig. 3b , 4i , Extended Data Fig. 9e ). We next purified recombinant BD-39 and assessed its activity against a panel of commensal species spanning the major gut phyla. Notably, certain species ( e.g., Clostridium sporogenes, S . Tm) are insensitive to BD-39, while others (including Lactobacilli strains) show moderate or high levels of sensitivity ( Extended Data Fig. 9f ). Efflux pumps and outer membrane barrier likely contribute to BD-39 resistance, because an E. coli Δ bamB Δ tolC strain exhibits significantly increased sensitivity compared to the WT strain ( Extended Data Fig. 9f ). To test whether BD-39 exhibits direct antimicrobial activity against SFB (which is not readily cultured in vitro ), we incubated cecal contents from SFB-monoassociated mice with purified BD-39 or control buffer under anaerobic conditions, inoculated the incubated samples into GF mice, and measured SFB viability in the inoculum by quantifying SFB levels over time in recipient animals. Notably, SFB levels are significantly reduced in mice colonized with the BD-39 incubated samples as compared to the buffer control-incubated samples, indicating that BD-39 can kill SFB directly ( Fig. 4j - k ). These data suggest that digoxin pretreatment induces defb39 expression in a RORγt-dependent manner and that defb39 expression is sufficient to reduce SFB levels and consequent immune responses in mice. Digoxin and infection in human-microbiome mice Although SFB is rare in humans 35 , individuals exhibit extensive interpersonal variation in other Th17-inducing gut microbes 36 . Additionally, human microbiomes exhibit interpersonal variation in digoxin metabolism (inactivation), which is mediated by the microbial enzyme Cgr2 22 . Oral administration of the Cgr2-dependent digoxin metabolite, dihydrodigoxin, does not alter SFB levels or S . Tm infection in mice ( Extended Data Fig. 10a - c ). To determine whether Th17-inducing human gut microbes render mice susceptible to digoxin-mediated loss of colonization resistance, we rederived C57BL/6NTac Nramp1 +/+ mice to the germfree state, colonized these animals with a defined 20-strain consortium of human gut species known to induce a Th17 response 36 ( Supplementary Table 6 ), and measured digoxin response in these animals (this consortium does not include cgr2- positive species). Notably, digoxin pretreatment reduced the expression of proinflammatory cytokines in the ileum, as observed in C57BL/6NTac (SFB-positive) mice ( Fig. 5a - b , Extended Data Fig. 10d ). To extend these results to complete human gut communities, we reasoned that a pooled human microbiome sample would likely include Th17-inducing taxa. We first estimated cgr2 gene abundance across microbiomes of 30 human donors 12 using targeted and metagenomic approaches; approximately half of the individuals lacked detectable cgr2 ( Fig. 5c , Extended Data Fig. 10e ). We colonized GF C57BL/6NTac Nramp1 +/+ mice with a pooled sample from eight cgr2- negative human donors, and measured digoxin response in these animals. Colonization of GF Nramp1 +/+ animals with these human communities resulted in robust expression of IL22 ( Fig. 5d ). Notably, digoxin pretreatment reduced the expression of IL22 and IL17a, increased the levels of defb39 in the ileum, and variably affected the expression of other proinflammatory marker genes ( Fig. 5d - f , Extended Data Fig. 10f ). Additionally, digoxin treatment altered the microbiome, including depletion of Lachnospiraceae and Bacteroides ( Extended Data Fig. 10g ). Further, infection with WT S. Tm resulted in significantly increased pathogen burden in digoxin-treated mice compared to PBS-treated controls carrying the same pooled human community ( Fig. 5g ). Consistent with this increased pathogen burden, digoxin-pretreated, S. Tm-infected mice exhibit elevated expression of S. Tm-responsive inflammatory marker genes 19 , 37 ( Fig. 5h ). Collectively, these studies suggest that the impact of digoxin pretreatment on infection risk is conserved in the context of SFB-negative human microbiomes. Fig. 5|. Digoxin increases the susceptibility of gnotobiotic mice colonized with human microbial communities to WT S . Tm infection. Open in a new tab a, Experimental design. A Th17-inducing defined community of human gut isolates or pooled human fecal samples were used to colonize GF C57BL/6NTac Nramp1 +/+ recipient mice. Mice were treated with PBS ( n = 4) or digoxin ( n = 5) as in Fig. 1c ; 12 hours after the final treatment dose, mice were euthanized or infected with WT S. Tm. b, Ileal expression of select marker genes at d0 in mice carrying the Th17-inducing defined community. c, Schematic for pooling human microbiome samples. d, Ileal expression of IL22 at d0 in mice carrying Th17-inducing pooled human gut microbiomes (GF n = 5, PBS n =4, digoxin n = 4). An ordinary one-way ANOVA followed by Tukey’s multiple comparisons test was used to compare multiple groups. e, Expression of IL17a from digoxin ( n = 4) or PBS-pretreated ( n = 4) ex-GF mice. f , Expression of defb39 from digoxin ( n = 5) or PBS-pretreated ( n = 5) ex-GF mice. g-h, Ex-GF Nramp1 +/+ mice pretreated with PBS ( n = 5) or digoxin ( n = 5) were infected with WT S . Tm, and pathogen burden in feces, contents of ileum, cecum, and colon ( g ) is shown. h, Gene expression of S. Tm-responsive inflammatory marker genes in ileal tissue from mice colonized with pooled human communities at day 4 post infection (d4 p.i.). A two-sided Mann-Whitney test is used to compare two groups in b , e , f , g , h . Bars represent geometric mean in b , d , e , f , h, and median in g . Discussion Multiple GI diseases are linked to the microbiome. Here, we used large-scale epidemiological human data to identify prescription medications that represent risk factors for infectious GI diseases. Nearly half of the non-antibiotic medications identified in the epidemiological study altered microbiome composition, susceptibility to infection, or both in mice. This study focuses on digoxin, which alters infection risk in mice by inducing expression of beta-defensins that reconfigure the gut microbiome, leading to immune reprogramming and ameliorated pathogen response (see Supplementary Discussion ). In the context of a mouse gut microbiome, immune reprogramming is mediated by SFB; gnotobiotic mice colonized with IL-17a/IL-22-inducing human communities lacking SFB exhibit similar responses, suggesting a conserved response across mice and humans. BD-39, an ortholog of human BD-1, is a member of a family of beta-defensins that are conserved across mammals; humans encode more than 30 family members 38 . Human BD-1 is expressed in the gastrointestinal tract, including the colon and ileum, and is induced in the presence of pathogens or other signals 39 , 40 . While infection and inflammation at the mucosal surface are generally thought to be the triggers for induction of beta-defensins 41 , our data indicate that xenobiotic compounds such as digoxin also serve as inducing signals. Notably, BD-39 expression returns to baseline levels upon extended digoxin treatment. While the specific attenuating mechanism requires further study, induction of regulatory T cells can promote immunogenic tolerance against compounds such as food antigens 42 , 43 . In mice subjected to an extended digoxin treatment regimen, temporary discontinuation followed by resumption of digoxin treatment re-sensitizes animals to the impact of digoxin on BD-39 expression, SFB levels, and consequent immune responses. The epidemiological study likely included patients who initiated digoxin treatment at the end of the case window, as well as individuals who were temporarily noncompliant (missed doses), which could explain why digoxin increases infection risk in patients despite attenuation over time in mice. Notably, ~25% of digoxin patients are typically noncompliant due to under-dosing 44 . Whether digoxin-dependent BD-39 expression and consequent microbiome remodeling is adaptive or accidental remains unclear. Like many medications, digoxin is derived from a poisonous plant (foxglove), and several examples of microbiome-mediated response to plant compounds are observed in nature 45 . Other factors, including environmental exposures, diet, and pathogen infection, can also disrupt microbiome composition; epidemiological studies track each of these events in large human cohorts 46 – 50 . Further, medical records document diverse health outcomes with potential links to microbiome disruption, including non-infectious colitis, colon cancer, and drug efficacy and toxicity 51 – 54 . The combination of epidemiological and experimental approaches described here could be similarly applied to define mechanistic relationships between other causes and consequences of microbiome disruption in humans, and potentially identify individuals at risk as well as therapeutic interventions. Strengths and limitations. In this study, we examined the association between drugs and infectious GI events over 15 years across a population of over 1 million people who had public insurance coverage for all medical care and prescription medications. The epidemiological study design was chosen for efficiency, which allowed us to model a large number of drugs. However, several important limitations should be noted, including the high frequency of chronic medications prescribed in both case and control windows and the necessity for follow-up experimental studies in mice (see Supplementary Discussion ). Future studies will also be necessary to define how the associations found in this cohort extend to other populations. We also note that while multiple lines of evidence highlight the role of BD-39 in mediating the impacts of digoxin on colonization resistance in mice, the role of orthologous proteins in humans was not tested in this study. Methods Epidemiology study design and analysis Epidemiological design and data source. To investigate therapeutic drug classes and individual drugs that may impact colonization resistance, we conducted a case cross-over study in a population-based dynamic cohort. The case cross-over design was selected as it allowed us to use each case as their own control, thereby adjusting for known and unknown individual characteristics that may influence the outcome of gastrointestinal infection 17 , 55 , 56 . The population-based dynamic cohort was comprised of a population-based 25% random sample of the 4.1 million residents of Montreal, Quebec, followed over a period of 15 years from 1999 to 2014. The cohort is dynamically updated each year to account for in- and out-migration. Each member of the cohort is provided with universal health insurance for all medical and hospital care through the provincial health insurer (RAMQ). In addition, all essential drugs are covered for residents 65 years of age or older, those receiving income security and those without drug insurance through their employer (approximately 50% of Quebec residents). For each member of the cohort, the RAMQ provided the research group with all medical services received including the date and location of the service, patient and physician identification, primary diagnosis, type of visit, and reimbursement provided, as well as records of all RAMQ-covered drugs dispensed through community pharmacies (date, drug, patient and pharmacy identification). These data were linked by patient identification to the provincial birth and death registry, which records the date, time and cause of death, as well as to the Ministry of Health database on all hospitalizations in the province (patient identification, dates of admission and discharge, primary and secondary discharge diagnoses, and procedures). Case and control period definition and sampling relative to drug exposure. Within this cohort of 1,434,375 individuals, we identified all individuals who had a physician claim or hospital admission with the primary diagnosis of a gastrointestinal (GI) problem suspected to be of infectious origin (events). The code set of diagnostic codes (indicated by relevant International Classification of Disease, Ninth Revision and Tenth Revision (ICD-9 and ICD-10) codes) was developed based on previously validated code sets 57 , 58 and included ICD9 (001x, 002x, 003x, 004x, 005x, 0060, 0061, 0062, 0069, 007x, 008x, 009x) and ICD10 codes (A00x, A01x, A02x, A03x, A04x, A05x, A060, A061, A062, A069, A07x, A08x, A09). If an individual had multiple visits for an infectious GI problem, we selected the first visit. Prescription drug use in the 60 days (~2 months) prior to the visit for an infectious GI problem was assessed (case period). To avoid misclassification of the timing of drug exposure (i.e., the drug was prescribed to treat the GI problem), we excluded drugs prescribed in the 3-day period directly prior to the GI visit. For each patient, we selected a control date that was ~2 months before the first GI visit. Drug use was assessed in the 2 months prior to the control date, excluding the same 3-day gap period prior to the control date to assess drug use (Extended Data Fig. 1a) . In a sensitivity analysis, we used a shorter window of 30 days for each of the case and control periods. Measurement of Prescription Drug Use. In each 2-month case and control period, we created a drug-by-day matrix using the date of dispensing and the duration of supply for each prescription filled by a patient. Drugs were classified by ingredient and therapeutic class using the American Hospital Formulary System 59 . Analysis and visualization of epidemiological data. Descriptive statistics were used to characterize the study population. Conditional logistic regression was used to evaluate the association between GI infection risk with each therapeutic class and drugs frequently prescribed within each therapeutic class. Several drug classes were considered expected associations (including anti-microbial agents, immunosuppressive agents, antidiarrhea agents, analgesics and antipyretics, antiemetics, and cathartics and laxatives) and were ruled out from further investigation. Based on the selected inclusion criteria (>100 dispensations, odds ratio >1.5, P value <0.05), a subset of drugs were identified for further investigation in mouse models ( Fig. 1b , Supplementary Table 2 ). Drugs that were not well-tolerated in mice ( e.g. , risperidone) were excluded, constituting a final list of 21 drugs. Epidemiological data were visualized using a complex heatmap 60 to illustrate the criteria used for drug selection ( Fig. 1b ). For the final list of 21 drugs that were selected for further investigation in mouse models, we assessed whether the concurrent use of antibiotics, immunosuppressants, and/or antidiarrheals modified the association by including binary indicators for use (yes/no) in multivariate models ( Supplementary Table 4 ). Chemicals and primers Drugs for animal studies were purchased individually, and drug dosages were determined based on previous studies ( Supplementary Table 3 ). All primers used in the study are listed in Supplementary Table 5 . Microbiological culture and growth measurements Bacterial strains. All bacterial strains and isolates used in this study are listed in Supplementary Table 6 . Aerobic growth conditions. Routine aerobic culturing was conducted in Luria Bertani (LB) broth using shaking conditions (220 RPM) at 37°C for 16 hours. Stocks were prepared from 16-hour cultures using autoclaved glycerol (20% final concentration) and stored at −80°C. Anaerobic growth conditions. All anaerobic microbiology steps were performed in a flexible anaerobic chamber (Coy Laboratory Products) containing 20% CO 2 , 10% H 2 , and 70% N 2 using pre-reduced media and materials. Routine anaerobic culturing was conducted using liquid mega medium 61 or Brucella blood agar plates. Stocks were prepared under anaerobic conditions from 16-hour cultures using autoclaved glycerol (20% final concentration) and stored in single-use aliquot vials at −80°C. In vitro growth assays. To measure the impact of medical drugs on the growth of representative gut microbial species, growth measurements were conducted at three relevant drug concentrations (20, 40, 80 μM) as described in previous publications 10 . To this end, 384-well plates were freshly prepared with each of the 21 identified drugs ( Fig. 1b ) at each concentration in modified Gifu Anaerobic Medium (mGAM; HyServe) and stored in the anaerobic chamber for 24 hours before bacterial inoculation. Twelve representative human gut isolates, S . Tm, E. coli , and E. coli Δ bamB Δ tolC 62 ( Supplementary Table 6 ) were cultured for 16 hours and inoculated in quadruplicate into the 384-well plate at an initial optical density (OD 600 ) of 0.025. Plates were incubated anaerobically at 37°C for 24 hours, and OD 600 was recorded every 15 minutes after mixing for 1 minute prior to measurement. Media controls were used as blanks. The area under the curve (AUC) was calculated to evaluate bacteria growth under different drug concentrations, which were compared with the AUC for the corresponding vehicle concentrations (DMSO controls). Double-sided Mann–Whitney U tests were conducted to test the significance of AUC differences, and P values were corrected by the Benjamini-Hochberg method with P < 0.05 after multiple hypothesis testing considered significant. AUC differences were converted into a heatmap using the R package “pheatmap” 63 , and clustering was performed based on bacterial growth AUC. Animal Experiments Conventional and gnotobiotic husbandry. All mice experiments were performed using protocols approved by the Yale University Institutional Animal Care and Use Committee (IACUC), and all mice were kept in a 12-hour light/dark cycle. For housing conditions, temperature is maintained between 68–79°F and humidity between 30–70%. 6–8-week-old, conventional (CV) mice were used in these studies unless stated otherwise. No statistical methods were used to predetermine sample sizes, but sample sizes are similar to those reported in previous publications studying colonization resistance 64 , 65 . Female mice were used for colonization resistance screening and co-housing experiments. Both sexes were used for all other experiments. Conventional mice were housed under specific pathogen-free conditions with unlimited access to water and food (Telkad Global 16% Protein Rodent Diet, Envigo). Germ-free (GF) animals were housed in gnotobiotic isocages (Sentry Sealed Positive Pressure (SPP) isolation cage system (Allentown, Inc., Allentown, NJ, USA)). In experiments using gnotobiotic mice carrying fecal microbiome transplants from individual donor animals or pooled human fecal samples, gnotobiotic recipient mice were individually housed in flexible plastic gnotobiotic isolators. Gnotobiotic mice were fed a standard, autoclaved mouse chow (5K67 LabDiet, Purina) ad libitum and autoclaved water for the duration of the experiment. GF status was routinely monitored by 16S rRNA-targeted PCR of fecal DNA and culturing of fecal samples under aerobic and anaerobic conditions. Investigation of candidate drugs in mice. Groups of 6–8 week old female C57BL/6NTac mice (Taconic Biosciences) were treated with each of the 21 candidate drugs identified in the epidemiological analysis ( Fig. 1b ; n=5/group) in cohorts of 4–5 drugs per cohort. All mice within a cohort were obtained concurrently, and a vehicle control group was included in each cohort. All drugs were dissolved in 90% Phosphate Buffered Saline (PBS), 5% DMSO, and 5% Tween 80 to create a consistent treatment condition between the groups. Drug dosage was determined based on the literature ( Supplementary Table 3 ), and drugs that were not completely dissolved were administered as a slurry; each drug was prepared fresh prior to gavage. Mice were orally gavaged with individual drugs twice daily for two days (four doses total). The first drug dose was administered in the evening (Zeitgeber time ZT12), with the three subsequent doses at 12-hour intervals. Fecal samples were collected for microbiome assessment immediately before the 1 st drug treatment (D(−2)) and 12 hours after the final dose (D0; also ZT12). For infections, approximately 10 8 colony-forming units (CFUs) of S. Tm Δ invA 20 , prepared as described below, were used for infection by oral gavage at the D0 time-point (ZT12, 12 hours after the final drug dose). S. Tm ΔinvA CFUs were enumerated from feces 12h, D1, D2, and D4 after infection. Pathogen infection. WT 66 and ΔinvA (SB1901) 20 genotypes of S. Tm strain SL1344 were grown in LB broth overnight at 37 ºC with shaking at 220 rpm. After 16 hours of growth, bacteria were washed twice and resuspended in autoclaved PBS. Mice were infected with ~10 8 CFUs by oral gavage unless otherwise stated and were weighed at the same time daily before and after infection. For S . Tm infections, animals were euthanized 4 days post-infection or monitored for survival as described below. For intraperitoneal infections, mice were infected with ~10 5 CFUs, liver and spleen were collected at d1pi. For fecal S. Tm CFU quantification, feces were weighed, homogenized by vortexing in 1 mL of sterile PBS, serially diluted in PBS, and plated on agar plates supplemented with 200μg/ml streptomycin. Plates were incubated for ≥ 16 hours at 37 ºC prior to CFU enumeration, and CFU was reported per gram of feces for each mouse. S. Tm-selective CFU plating was also conducted prior to infection to confirm the absence of streptomycin-resistant aerobic taxa in each microbiome. For Citrobacter rodentium ( Cr ; strain DBS100: ATCC51459) infections, strains were grown in LB media as described above. Mice were infected with ~10 8 CFUs intragastrically, animals were euthanized at the peak of infection (d10 post-infection) 67 and fecal samples were collected to measure Cr DNA abundance as described later. Intestinal tissues were dissected, washed thoroughly with PBS to remove non-adherent bacteria, and then vigorously vortexed to obtain tissue-associated bacterial CFUs, which were enumerated by plating on MacConkey agar plates. For Vancomycin-resistant Enterococcus faecium (VRE) infections, strains were grown in Brain heart infusion-supplemented (BHIS) broth overnight at 37 ºC with shaking at 220 rpm. Mice were infected with ~10 8 CFUs/mouse via oral gavage, and animals were euthanized at d1 post infection. Feces and gut contents were plated on Enterococcal agar (BD #212205) plates supplemented with 8μg/ml vancomycin and 100μg/ml streptomycin. Survival analysis. Mice were observed twice daily during the experimental period, and those with ≥ 20% weight loss and/or clear morbidity (lethargy, huddling, shivering, hunched posture) were euthanized immediately. Digoxin administration. Digoxin was freshly prepared in PBS with 5% DMSO before each gavage and administered (5 mg/kg or 0.5 mg/kg) as an oral suspension to mice. PBS 5% DMSO (referred to as PBS controls) was used as vehicle controls for all digoxin experiments in mice. The dose of 5mg/kg of mouse body weight was selected based on previous literature 24 , 25 , 68 . This dose led to a slight reduction in weight in C57BL/6NTac, C57BL/6J, and Rorγ −/− mouse strains as previously observed 69 (Extended Data Fig. 4g) , but did not cause other observed distress, morbidity, or mortality to mice. While both 5 mg/kg and 0.5 mg/kg doses significantly reduced SFB levels and increased S. Tm infection risk in mice, we used the higher dose unless otherwise indicated in order to reduce the number of animals required in light of the intrinsic variability of S. Tm infection in mice that are not receiving streptomycin pretreatment prior to infection. In some experiments, mice were sacrificed at the D0 time point (12 hours after the final drug dose, prior to pathogen infection) for measurement of host gene expression. Vancomycin administration. 6–8 week-old female C57BL/6NTac mice were treated either with vancomycin orally (25 mg/kg) or intraperitoneally (2.5 mg/kg to account for the reported 10% bioavailability of oral vancomycin 70 ) for two days (2 doses/day), and intraperitoneally administered PBS was used as a control. Mice were sacrificed 12 hours after the final drug dose, and ileum tissues were collected for gene expression analysis. Feces were collected pre- and post-drug treatment for SFB enumeration. Co-housing experiments. 3–4 week old female C57BL/6J and C57BL/6NTac mice were purchased from the Jackson Laboratory and Taconic Biosciences, respectively. Mice were tagged and co-housed (two randomly selected C57BL/6J and two C57BL/6NTac mice/cage) for two weeks and then segregated into new cages based on vendor origin. Segregated C57BL/6J mice were housed together, and digoxin administration and pathogen infection were conducted as described above with a high dose of ~10 9 CFU/mouse. Experiments with non-cohoused control animals grouped in cages were concurrently performed. Studies using CV and GF C57BL/6NTac Nramp1 +/+ mice. CV C57BL/6NTac Nramp1 +/+ mice 71 were generously provided by Dr. Jorge Galán (Yale University) and were bred in-house at the Yale Animal Resources Center (YARC). 6–10 week old male and female animals were used for drug administration and infection studies as described above. GF C57BL/6NTac Nramp1 +/+ mice were re-derived to the germ-free state by sterile hysterectomy of the CV C57BL/6NTac Nramp1 +/+ animals. Briefly, donor CV C57BL/6NTac Nramp1 +/+ mice were time-mated along with GF Swiss Webster dams used for fostering. 40μl of progesterone (NDC #00143972501) was subcutaneously injected on the back near the rump into the pregnant donor mice for three consecutive days before the C-section. On the day of the C-section, the pregnant donor mouse is sacrificed via cervical dislocation and submerged in hot (~40°C) Virkon S (Lanxess #57811373) to disinfect. The pup-loaded uterus was removed and submerged in a 50-mL tube filled with Virkon S. The tube with pups was inserted into the germ-free isolator containing the GF Swiss Webster foster female. The pups are removed from their amniotic sacs, cleaned with Q-tips, placed with the foster female, and monitored until they are accepted. Sterilizing levels of chlorine dioxide (CLIDOX-S: 1:3:1 (Pharmacal, #95120F)) were used to maintain sterility during the procedure. Germ-free status was monitored as described above. Rorγ −/− mice. Heterozygous B6.129P2-Rorctm1Litt/J were obtained from the Jackson Laboratory (Strain #007571) 72 . Mice were separated according to genotype at weaning, and heterozygous animals were used to maintain the colony. Because this strain is SFB-negative, homozygous ( Rorγ −/− ) and their WT littermate controls were colonized with SFB prior to drug and infection experiments. To this end, cecal contents from SFB-monocolonized mice (provided by Dr. Ivaylo I. Ivanov, Columbia University) were used to colonize GF C57BL/6NTac mice; after 4 weeks, gastrointestinal contents from 5 animals were collected under sterile anaerobic conditions. All subsequent steps prior to −80 o C storage were also conducted under sterile anaerobic conditions. Contents were weighed, resuspended in 15 mL of sterile, pre-reduced PBS per gram gut contents, vortexed, and passed through a 70μm cell strainer (Fisher #08–771-2). An equal volume of pre-reduced 40% glycerol (in PBS + cysteine) was added to the flow-through and mixed by inverting. These “biobanked” samples were stored in single-use aliquots in sealed Wheaton vials (Fisher #03–140-390) at −80ºC. These aliquots were used to colonize Rorγ −/− mice and their WT littermates by daily oral gavage beginning at 4–6 weeks of age and continuing for 3 days, followed by an 11-day rest period to allow for SFB expansion and stabilization. SFB engraftment was confirmed using qPCR with SFB-specific primers ( Supplementary Table 5 ). Digoxin administration and pathogen infection was conducted as described above. ΔILC3 mice. Rorγ STOP , Cd4-cre + mice (which retain Rorγ expression in all T cells but lack Rorγ expression in ILC3 cells) 32 were bred with Rorγ STOP , Cd4-cre - mice (which lack Rorγ expression in both ILC3 cells and T cells) 32 to obtain littermate animals that either specifically lack ILC3 cells (designated ΔILC3) or lack both ILC3 and Th17 cells (designated Rorγ STOP ). SFB colonization and digoxin administration were conducted as described above. Rag1 −/− mice. GF Rag1 −/− 73 and WT GF control animals were colonized with one dose of SFB cecal contents and administered digoxin or PBS as described above. Mice were sacrificed and ileum contents were collected, weighed, and SFB DNA was quantified. Transgenic C57BL/6NTac vil-defb39 mice. Transgenic mouse generation was conducted at the Yale Genome Editing Center. The defb39 gene insert was obtained as a gBlock (Integrated DNA Technologies). The plasmid 12.4kbVillin-ΔATG 74 was obtained as a gift from Deborah Gumucio through Addgene (Addgene plasmid 19358). The defb39 insert was cloned into the 12.4kbVillin-ΔATG plasmid by InFusion cloning to yield p12.4kbVillin-ΔATG-defb39 ( Supplementary Table 6 ). The villin promoter and transgene was excised from the plasmid backbone and microinjected into zygote pronuclei as described 75 . Embryos were transferred to the oviducts of pseudopregnant CD-1 foster females as described 75 . The presence of the transgene was confirmed by PCR using primers defb39_F and defb39_R ( Supplementary Table 5 ). Although the transgenic mice did not exhibit obvious pathology, they did have defects in generating offspring with a functionally overexpressing defb39 gene, consistent with the reported role of defb39 in reproductive physiology 76 and the activation of the villin promoter in reproductive tissues 77 , 78 . Microbiome transplantation. C57BL/6NTac or C57BL/6J donor mice were treated with digoxin or PBS control (standard 2-day treatment regimen or extended treatment regimens, including a 12-hour drug washout period after the final dose). Gastrointestinal contents were biobanked as described above, except biobanks were established from individual animals without pooling. Male and female GF C57BL/6NTac recipient mice were singly housed in flexible plastic gnotobiotic isolators, with separate isolators for recipients of digoxin-treated and PBS-treated biobanked samples. Recipient mice were colonized with biobanked gut microbiomes from digoxin-treated or PBS-treated donor animals in a 1:1 ratio (individual donor to individual recipient). After 7 days, recipient mice were sacrificed to measure host gene expression or infected with S . Tm Δ invA as described above. SFB abundance was measured during the course of the transplant by qPCR. Mouse weight, S. Tm Δ invA CFU, and survival was measured as described above. SFB monoassociation experiments. GF C57BL/6NTac mice were colonized by oral gavage with a single dose of biobanked material from SFB-monoassociated mice (described above). After 14 days, SFB-monoassociated mice were treated with digoxin or PBS as described above. At the D0 timepoint (12 hours after the final digoxin/PBS dose), mice were sacrificed and ileum tissues and contents collected for gene expression studies and SFB abundance quantification, respectively. Identification of cgr2-negative human gut communities. Fecal samples from 28 healthy, unrelated human donors were previously obtained, aliquoted, and stored at −80 o C under anaerobic conditions under the Yale University Human Investigation Committee protocol number 1106008725 79 . Previously published and deposited metagenomic data from each donor 79 was analyzed for cgr2 gene abundance using Shortbred 80 . To this end, Cgr2 protein sequences were downloaded from NCBI in .fasta format and metagenomic data was retrieved from the European Nucleotide Archive (accession ID: PRJEB31790). Markers were created using the shortbred-identify function and the Uniref90 reference database 81 . Cgr2 abundance was quantified using the created reference markers with shortbred_quantify function using built-in USEARCH v.11.0.667 82 . In addition, cgr2 gene abundance was directly measured in aliquots of these samples by qPCR using gene-specific primers ( Supplementary Table 5 ). The resulting tables for each sample were merged into one summary table ( Supplementary Table 7 ). We selected communities from 8 human donors with cgr2 gene levels below the level of detection by either method ( Supplementary Table 8 ) for gnotobiotic mouse studies. These communities were also screened for SFB using qPCR and were SFB-negative. Colonization of gnotobiotic mice with human microbiome samples. GF C57BL/6NTac Nramp1 +/+ mice (described above) were colonized with either the Th17-inducing consortium of 20 human gut isolates 36 , kindly provided by Dr. Kenya Honda (two strains that failed to grow were replaced with the corresponding type strain as indicated in Supplementary Table 6 ) or the pooled human microbiome community by oral gavage. After 14 days, mice were treated with digoxin or PBS for two days as described above. At the D0 time point (12 hours after the final drug dose), mice were either sacrificed to assess host gene expression using qRT-PCR or infected with ~10 8 CFUs of WT S . Tm. Fecal pathogen burden was enumerated at indicated time points, and mice were sacrificed at 4 days post-infection for enumeration of pathogen loads in different gut compartments, and measurements of ileal gene expression by qRT-PCR. DNA extraction from mouse feces. DNA extraction from fecal samples was performed as previously described 83 . Briefly, fecal pellets were collected directly from mice into pre-weighed 2 mL sterile cryotubes at the designated times. The fecal pellets were frozen at −20°C or directly processed. The fecal pellets were resuspended in 500 μL CP buffer (Omega), 250 μL SDS 20%, 550 μL of 25:24:1 phenol:chloroform:isoamyl alcohol mixture (Sigma), 250 μL of 0.1mm Zirconia silica beads (Biospec), and 1 sterilized 5/32” diameter low-carbon steel ball (McMaster-Carr Supply Co). The samples were subsequently subjected to disruption using a BeadBeater for 2 cycles of 2 minutes, centrifuged at 4000 rpm at 4°C, and 200 μL of supernatant was used for DNA extraction using the EZ-96 Cycle Pure kit (Omega). Eluted DNA was quantified and diluted 1:100 in sterile water before 16S rRNA sequencing or qPCR analysis. Determining bacterial abundance using quantitative PCR (qPCR) The abundance of SFB, and Citrobacter rodentium ( Cr ) relative to total bacteria was measured using primers SFB_F and SFB_R (for SFB), EspA_F, EspA_R (for Cr ) and EU_F and EU_R (for total bacteria) 34 ( Supplementary Table 5 ). qPCR was performed using a CFX96 instrument (BioRad) and SYBR FAST universal master mix (KAPA Biosystems). Relative abundance was calculated using the ΔCq method. Assessment of SFB sensitivity to BD-39 antibacterial activity BD-39 cloning and purification. To purify BD-39 (an ortholog of human BD-1) 84 , mouse cDNA was used as a template, and the active defb39 sequence was amplified using Gibson primers ( Supplementary Table 5 ). Bands were gel-excised and cloned into a pET21_NESG vector with a C-terminal His-tag for protein expression and purification. BD-39 expression. E. coli (BL21) carrying pET21_NESG_defb39 ( Supplementary Table 6 ) was inoculated into 5mL LB broth containing ampicillin and grown for 16 hours at 37 o C with shaking. The culture was diluted 200-fold in 1L LB medium containing ampicillin and grown at 37 o C with shaking to the mid-exponential phase (OD 600 0.6–0.8). 1mM Isopropyl β-D-1-thiogalactopyranoside (IPTG) was added to induce BD-39 expression. After 3 hours of induction, the culture was centrifuged, and cell pellets were collected and stored at −80 o C. Purification, dialysis, and concentration of BD-39. Bacterial pellets were resuspended in lysis buffer [50mM Phosphate buffer, 300mM NaCl, 10mM Imidazole, 1XBugBuster buffer (Fisher, #709223), 1:3000 diluted lysonase bioprocessing reagent (Millipore, #71230) and protease Inhibitor (Thermo, #A32955)]. The lysate was incubated for 30 minutes at room temperature under mild shaking conditions. The lysate was centrifuged, and the supernatant was collected and incubated with IMAC-Ni resin (Thermo, #A50584) for 2 hours at 4 o C. The mixture was then loaded onto polypropylene columns (Qiagen, #34964), washed with 20 column volumes of wash buffer (50mM Phosphate buffer, 300mM NaCl, and 34mM imidazole), and eluted with 30mL of elution buffer (50mM Phosphate buffer, 300mM NaCl, 250mM imidazole). The eluates were transferred into 3kDa Centricon columns (Sigma, #UFC700308) and dialyzed with 10mM Tris buffer (pH 8.0) three times. The concentrated protein was then stored at −80 o C in 10mM Tris buffer. The concentration of purified BD39 was measured using the BCA protein assay kit (ThermoFisher, #23225). Control buffer was prepared as above, except E. coli pET21_NESG (lacking the defb39 insert) was used in the initial step. In vitro killing assay against representative bacterial strains. Overnight anaerobically grown bacterial strains were resuspended with PBS + cysteine, diluted in 10mM Tris buffer (pH 8.0), treated with varying concentrations of purified BD-39 or control (buffer-treated), and incubated anaerobically in quadruplicate. The inoculum for the untreated control groups for all strains was diluted to obtain approximately 10 6 CFU/ml of live bacterial cells after 3 hours of incubation. Bacterial cells were plated on de Man-Rogosa-Sharpe (MRS) agar ( e.g. , Lactobacillus strains), LB agar plates ( e.g. , E. coli and S . Tm), BHI blood agar plates (for Bacteroides ), and Tryptic Soy blood agar plates (for other anaerobic bacteria) and colonies were enumerated after growing at 37°C aerobically (for E. coli and S . Tm) or anaerobically (for other bacterial strains). Viability (determined by CFU) of BD-39 treated cultures was normalized to the untreated controls to determine the sensitivity of each strain to recombinant BD-39. Ex vivo SFB killing assay. All ex vivo steps were performed under anaerobic conditions. Cecal contents of three independent SFB-monoassociated mice were collected, diluted 100-fold in 10mM Tris buffer, and incubated with 10μM BD-39 or control buffer for 24 hours at 37 o C anaerobically. Two groups of GF C57BL/6NTac mice (3 mice/group; age- and weight-matched) were orally gavaged with 200 μL of each incubated sample. Fecal samples were collected and weighed on indicated days. Fecal DNA was extracted as described above, and SFB amounts were quantified using qPCR with SFB-specific primers Cecal DNA from SFB-monoassociated mice was used for the standard curve and to obtain absolute SFB DNA concentration. 16S rRNA sequencing Amplification and sequencing. 16S rRNA sequencing was conducted on samples collected at the D(−2) timepoint (prior to drug/PBS administration) and the D0 timepoint (12h after the final drug dose). After DNA extraction as described above, the V4 hypervariable region of the bacterial 16S rRNA gene was amplified and sequenced as previously described 85 . Briefly, input genomic DNA was quantified (Quant-IT PicoGreen dsDNA assay kit), normalized to 5 ng/μL, and amplified using barcoded primers 86 and AccuPrime Pfx SuperMix. PCR products were cleaned and normalized (SequalPrep, Invitrogen), pooled in sets of 384 samples, and sequenced on an Illumina MiSeq instrument (2×250 bp, dual 8bp indexing, 15% PhiX spike-in) at the Yale Center for Genome Analysis. The 16S sequencing reported in Extended Data Fig. 7a - c , Fig. 9b - e , and Fig. 10g was conducted at SeqCoast Genomics (Portsmouth, NH, USA). Briefly, genomic DNA was extracted as described above and was sent for sequencing. Samples were prepared for 16S V3/V4 amplicon sequencing using the Zymo Quick-16S Plus NGS Library Prep Kit (#D6421), which uses unique dual indexes. The forward primer 341f (a mixture of two sequences: CCTACGGGDGGCWGCAG, CCTAYGGGGYGCWGCAG) and the reverse primer 806r (GACTACNVGGGTMTCTAATCC) were used for amplification. The amplification PCR conditions: 95°C (10min), 95°C (30sec), 55°C (30sec), 72°C (3min) followed by 35 cycles of three previous steps and extension for 72°C (6min) and hold at 4°C. Sequencing was performed on the Illumina NextSeq2000 platform using a 600-cycle flow cell kit to produce 2×300bp paired reads. 30–40% PhiX control (unindexed) was spiked into the library pool to support optimal base calling of low diversity libraries on patterned flow cells. Read demultiplexing, adapter trimming, and run analytics were performed using DRAGEN v4.2.7, an on-board analysis software on the NextSeq2000. Primer trimming was performed prior to analysis. Pre-processing. 16S rRNA sequencing analysis was performed using QIIME2 87 . When required, barcode extraction was performed using QIIME v1.8, and QIIME v2024.2 was used for subsequent analysis, using emp-paired for the demultiplexing step and DADA2 for truncation and denoising 88 . Differential abundance analysis. RESCRIPt was used to prepare a QIIME 2 compatible SSU SILVA reference database based on the curated NR99 (version 138.1) database 89 – 91 . The classifier was trained based on the V4-targeted or V3/V4-targeted 16S primers as noted above and applied to the sequences. The relative abundance of a given taxon (genus or species level, as indicated) was calculated from the feature count of that taxon in a given sample. Differential abundance analysis was performed using Aldex2 92 . To identify significantly altered taxa ( P < 0.01), a Welch’s t test was used. Beta-diversity analysis. The Bray-Curtis distance matrix was used ( Fig. 1d , and Extended Data Fig. 1c ) for principal coordinate analyses (PCoA) of drug-treated and vehicle-treated mice within the same experimental cohort on Day 0, or between D(−2) and D0 timepoints within each mouse group. The weighted UniFrac distance matrix was also utilized to incorporate phylogenetic distance between sequences in estimates of compositional differences ( Extended Data Fig. 6a - c ) 93 . The amount of variation using both distance matrices was calculated using Permutational Multivariate Analysis of Variance (PERMANOVA) analysis using the Adonis function with 10,000 permutations. The effect size (R-squared) explains the magnitude of dissimilarities between groups and measures the proportion of microbiome changes that can be explained by the drug (for comparisons between PBS- and drug-treated animals within an experimental cohort) or the timepoint (for comparisons between timepoints within each mouse group). P values < 0.05 were considered significant. Adonis analysis and visualization were conducted using the R platform (version 4.3.0) and corresponding packages 94 , 95 . Scanning electron microscopy CV C57BL/6NTac mice were treated with digoxin (5 mg/kg or 0.5 mg/kg) or PBS for 2 days as described above. At the D0 timepoint (12 hours after the final drug dose), mice were sacrificed, and ileum tissues were collected for scanning electron microscopy. Briefly, mice were dissected, and ~2mm ileum tissue was placed in 4% paraformaldehyde (PFA). Tissues were changed into a fresh 4% PFA solution after 30 min and incubated at 4ºC overnight, and samples were imaged at the Electron Microscopy Facility at Yale School of Medicine. The samples were pinned open onto silicone pads, and the dissected tissues were refixed with 2.5% glutaraldehyde in 0.1M sodium cacodylate buffer pH 7.4 for 1 hour. Next, samples were rinsed in 0.1M sodium cacodylate buffer and post-fixed in 2% osmium tetroxide in 0.1M sodium cacodylate buffer pH 7.4. These were rinsed in buffer and dehydrated through an ethanol series from 30% to 100%. The samples were dried using a Leica 300 critical point dryer with liquid carbon dioxide as transitional fluid. The samples were glued to aluminum stubs using a carbon graphite adhesive, and sputter coated with 4nm platinum/palladium using a Cressington 208HR coating unit. Digital images were acquired in Zeiss CrossBeam 550 between 1.5–2kV at a working distance of 8–12m. RNA extraction and qRT-PCR PBS or digoxin-treated mice were sacrificed at indicated time points. Mice were dissected, and approximately 2 cm of different tissues, including terminal ileum, cecum, and colon, were collected. Tissues were flushed to remove gastrointestinal contents, rinsed in PBS, transferred into RNAProtect (Qiagen), and stored at −80ºC. RNA was extracted using the RNeasy Plus Universal mini kit (Qiagen #73404) as per the manufacturer’s instructions. RNA concentration was measured using a plate reader (Take3, Biotek). Quantitative reverse transcription-PCR (qRT-PCR) was performed as previously described 96 . Briefly, 2 μg of diluted extracted RNA was converted to cDNA with addition of Superscript II (Invitrogen), random primers (Invitrogen), DTT, and dNTPs. For qRT-PCR, validated primers ( Supplementary Table 5 ) and SYBR FAST universal master mix (KAPA Biosystems) were used on a CFX96 instrument (BioRad). Data were collected and normalized to endogenous Gapdh levels. Fold change was calculated using the ΔΔCq method. A P value of less than 0.05 was considered significant. RNA sequencing Sample preparation and RNA extraction. 6–8 week old female C57BL/6NTac mice (n=3/group) were treated with digoxin or PBS for 2 days as described above. At the D0 timepoint, mice were sacrificed, the small intestine was dissected, and the Peyer’s patches were carefully removed. Approximately 2 cm of the distal small intestine (ileum) was collected, and tissues were flushed and scraped to remove luminal content, stored in RNAProtect tissue reagent (Qiagen), and stored at −80ºC until RNA extraction. RNA was extracted using the QIAzol™-chloroform method and the Qiagen RNeasy Plus Universal mini kit (#73404) as per the manufacturer’s instructions. RNA-seq quality control. Total RNA quality was determined by estimating the A260/A280 and A260/A230 ratios on a Nanodrop instrument. RNA integrity was determined using an Agilent Bioanalyzer or Fragment Analyzer gel to measure the ratio of ribosomal peaks. Samples with RIN values of 7 or greater were used for library preparation. RNA-seq library preparation. mRNA was purified from approximately 1000 ng of total RNA with oligo-dT beads and sheared by incubation at 94 ° C in the presence of Mg 2+ (Kapa mRNA HyperPrep). Following first-strand synthesis with random primers, second strand synthesis and A-tailing were performed with dUTP for generating strand-specific sequencing libraries. Adapters containing 3’ dTMP overhangs were ligated to library insert fragments, and library amplification was used to select and amplify fragments carrying the appropriate adapter sequences at both ends. Strands marked with dUTP are not amplified. Indexed libraries that meet appropriate cut-offs were quantified by qRT-PCR using a commercially available kit (KAPA Biosystems) and insert size distribution determined with the LabChip GX or Agilent TapeStation. Samples with a yield of ≥0.5 ng/μl were used for sequencing. Flow cell preparation and sequencing. Sample concentrations were normalized to 2.0 nM and loaded onto an Illumina NovaSeq X plus flow cell at a concentration that yields 30 million post-filtering clusters per sample. Samples were sequenced using 100bp paired-end sequencing according to Illumina protocols. The 10 bp unique dual index is read during additional sequencing reads that automatically follow the completion of read 1. A positive control (PhiX library) provided by Illumina was included in every lane at a concentration of 0.3% to monitor sequencing quality in real time. Data analysis. Signal intensities were converted to individual base calls using Real Time Analysis (RTA) software (Illumina). Primary analysis (sample de-multiplexing and alignment to the mouse genome) was performed using Illumina’s CASAVA 1.8.2 software suite. Low-quality reads were trimmed, and adaptor contamination was removed using Trim Galore v0.5.0. Trimmed reads were mapped to the mouse reference genome (GRCm38) using HISAT2 v2.1.0 97 . Gene expression levels were quantified using StringTie v1.3.3b 98 with gene models (M15) from the GENCODE project. Differentially expressed genes were identified using DESeq2 99 . Volcano plots were generated using EnhancedVolcano v1.16.0. Measurement of anti-microbial peptide gene/chemokine expression For targeted measurements, independent groups of mice (separate from those used for RNA-seq analysis) were treated with digoxin or PBS control as described above; at the D0 timepoint, ileum tissue was collected and host mRNA extracted. Expression levels of genes encoding targeted AMPs was measured by qRT-PCR using gene-specific primers ( Supplementary Table 5 ). Genes encoding anti-microbial peptides (AMPs) were curated from the mouse REACTOME database 100 ( Supplementary Table 9 ). Genes encoding chemokines were curated from the mouse GOMF_CHEMOKINE_ACTIVITY gene set in the Molecular Signatures Database (MSigDB) 101 , 102 . Altered regulation was determined by evaluating log 2 fold change (log 2 FC) and adjusted P value (p adj ) of digoxin-treated compared to PBS-treated animals using DESeq2 99 . Genes with log 2 FC > 1.5 and p adj < 0.05 were considered significantly upregulated, and those with log 2 FC < −1.5 and p adj < 0.05 were considered significantly downregulated. Flow cytometry Isolation of ileal lamina propria lymphocytes. Peyer’s patches and mesenteric fat were carefully removed from the small intestine. The distal one-third of the small intestine was designated as the ileum. Ileal tissues were incubated in PBS containing 5 mM EDTA and freshly prepared DTT (final concentration of 1 mM) at 37 °C for 15 min with rotation (250 rpm), followed by vigorous manual shaking for 2 min to remove mucus and epithelial cells. The tissues were then washed with PBS by incubation at 37 °C for 10 min with rotation (250 rpm). For lamina propria lymphocyte isolation, the tissues were minced and digested in R-2 buffer (RPMI supplemented with 2% fetal calf serum (FCS) and 10 mM HEPES) containing 2 mg ml⁻ 1 collagenase VIII (Sigma) and 200 μg ml⁻ 1 DNase I (Sigma) at 37 °C for 30 min with rotation (120 rpm). Digested tissues were further broken down by pipetting up and down 10 times, and the enzymatic reaction was quenched with ice-cold R-2 buffer. The tissue suspension was passed through a 70 μm nylon mesh. Mononuclear cells were enriched at the interface of a 40%–80% Percoll gradient by centrifugation at 2,300 rpm for 23 min at room temperature with acceleration and brake off. Ileal lamina propria (LP) lymphocytes were collected from the interphase, washed with ice-cold R-2 buffer, and counted. Cell viability and number were assessed using Trypan blue exclusion and a hemocytometer. Flow Cytometry Analysis of surface markers and cytokines. Cell numbers were normalized to 2×10 6 per sample before dividing into two equal groups: one for cytokine and surface analysis (stimulated group) and the other for surface markers only (unstimulated group). The stimulated group was incubated for 4 h at 37 °C in complete T cell media (RPMI supplemented with 10% FCS (R&D), 10 mM HEPES, 2 mM L-glutamine, 1× Penicillin-Streptomycin, and 0.05 mM 2-mercaptoethanol) with Cell Activation Cocktail (with Brefeldin A) (Biolegend) in a 5% CO₂ incubator, while the unstimulated group proceeded to next steps without stimulation. For flow cytometric analysis, cells from both groups were stained for viability and surface markers, followed by fixation, permeabilization, and intracellular cytokine staining according to the manufacturer’s protocol (Cytofix/Cytoperm buffer set, BD Biosciences). Flow cytometry was performed on a Cytek Aurora (Cytek) using SpectroFlo (v3.3.0) and analyzed with FlowJo 10.10.0 software. Gating for Ly6C hi monocytes/macrophages and neutrophils was performed using the unstimulated group, while other gating strategies were applied to the stimulated group as shown in Supplementary Fig. 2a - b . The following monoclonal antibodies were purchased from BD Biosciences, BioLegend, or eBioscience and used for flow cytometry analysis. CD4 BUV395 (GK1.5), BD Biosciences 563790, 1:200; TCRβ BUV737 (H57–597), BD Biosciences 612821, 1:200; Ly-6C BUV563 (HK1.4.rMAb), BD Biosciences 755198, 1:200; Ly-6G BUV615 (1A8), BD Biosciences 751263, 1:200; CD45.2 Pacific Blue (104), BioLegend 109820, 1:200; CD8a BV510 (53–6.7), BioLegend 100751, 1:200; CD11b BV605 (M1/70), BioLegend 101237, 1:200; CD11c AF700 (N418), BioLegend 117319, 1:200; CD45R FITC (RA3–6B2), BioLegend 103206, 1:200; CD90.2 PerCP (53–2.1), BioLegend 140315, 1:200; NK1.1 (CD161) APC-Cy7 (PK136), BioLegend 108724, 1:200; IL-17A PE (eBio17B7), eBioscience 50–112-9633, 1:200. Live/dead fixable blue (Thermo Fisher L-34962) was used to exclude dead cells. Surface markers were stained with the addition of Brilliant Stain Buffer (Thermo Fisher 00–4409-75) according to the manufacturer’s protocol. Statistics and reproducibility All data were analyzed in GraphPad Prism v10.0.2 and R v4.3.0. Mice were randomized for experiments before being allocated to study groups and respective cages. Statistical significance was calculated using the two-sided non-parametric Mann-Whitney test for comparison between two groups unless otherwise indicated; for experiments involving more than two groups, a non-parametric Kruskal-Wallis test followed by Dunn’s multiple comparisons test was used unless otherwise stated. In some experiments, with sample sizes less than 5, a two-tailed unpaired t test was used to compare two groups, and one-way ANOVA was used to compare more than two groups. A bar was used to represent either median or geometric mean values, as indicated in the respective figure legends. Data are representative of at least two independent experiments. For survival analysis, the P- value was calculated using the Gehan-Breslow-Wilcoxon test. For identifying significantly altered taxa from 16S data, a Welch’s t test was used ( P < 0.01). All tests are two-sided. n represents the number of mice. ns, not significant. Extended Data Extended Data Fig. 1|. Impact of medications identified in the epidemiological screen on gut microbes in mice and in vitro . Open in a new tab 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, Principal coordinate analysis (PCoA) of Bray-Curtis distances between 16S rRNA sequencing results from fecal 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 color represents an individual mouse. d, Area under the curve (AUC) comparison for growth of representative bacterial taxa under increasing drug concentrations (20, 40, and 80μM). The tree represents the clustering of taxa based on growth inhibition profiles across all tested drugs. Color 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. Extended Data Fig. 2|. Digoxin pretreatment prior to S. Tm Δ invA infection leads to increased pathogen colonization and dissemination. Open in a new tab a-g, PBS- or digoxin-pretreated C57BL/6NTac mice were infected intragastrically with ~10 8 CFUs of S. Tm Δ invA 12 h after the final vehicle or drug dose and infection monitored over time. a, Pathogen burden in feces at d2 post infection (p.i.) (PBS n = 12, digoxin n = 13). b, Fecal pathogen burden at d4 p.i.. Mice were sacrificed at d4 p.i. and pathogen burden was enumerated from the ileum ( c ), cecum ( d ), and colon ( e ) contents. Dissemination of S. Tm Δ invA to extraintestinal tissues was measured in the liver ( f ) and spleen ( g ). In b-g , n = 7 animals/group). h-i, PBS- or digoxin-pretreated C57BL/6NTac mice ( n = 5/group) were infected intraperitoneally (I.P.) with ~10 5 CFUs of S. Tm Δ invA and dissemination was measured in liver (h) and spleen (i) at d1 p.i.. j-k, C57BL/6NTac Nramp1 +/+ mice ( n = 5/group) were pretreated with PBS or digoxin, infected with ~10 8 CFUs of WT S . Tm, and pathogen burden measured at d4 p.i. in gastrointestinal contents ( a ) and tissues ( b ). A two-sided Mann-Whitney test is used to compare two groups. Dotted lines represent the limit of detection. ns, not significant. Extended Data Fig. 3|. Digoxin pretreatment decreases ileal antimicrobial peptide expression and increases Citrobacter rodentium ( Cr ) and Vancomycin resistant Enterococcus faecium (VRE) pathogen burden in C57BL/6NTac mice. Open in a new tab Mice were pretreated with PBS or digoxin for two days as shown in Fig. 1c ; 12h after the final PBS or drug treatment, animals were infected with Cr (DBS100) or VRE. a-b, Pathogen burden in feces ( a ) and attached bacteria in ileal, cecal, and colon tissue ( b ) at d10 p.i.. c, Fecal VRE burden at 12hr p.i and d1 p.i.. In a-c , n = 10 animals/group. ( d ), VRE burden in ileal and cecal contents at d1 p.i. ( n = 5/group). e-g , C57BL/6NTac mice were treated with PBS or digoxin for two days. Mice were sacrificed 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/group) ( e ), cecum ( n = 5/group) ( f ), and colon ( n = 5/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 . Extended Data Fig. 4|. Impact of digoxin pretreatment duration on S. Tm infection. a, Open in a new tab Experimental design. CV C57BL/6NTac mice were treated with digoxin or PBS for one dose 2hr prior to infection (single dose regimen), twice daily for 2 days followed by a 12-hour washout period (standard regimen), or twice daily for 7 days followed by a 12-hour washout period (extended regimen). Mice were then infected with ~10 8 CFUs of S . Tm Δ invA and infection monitored over time. b-c, Pathogen burden at 12hr p.i. ( n = 5/group). ( b ) and mortality ( c ) after single-dose drug or control treatment. d-f, Pathogen burden at 12hr p.i. ( n = 5/group). ( d ), mortality ( e ), and expression of proinflammatory marker genes in ileum tissue ( n = 4/group) ( f ) after the extended regimen drug or control treatment. g, Impact of digoxin or PBS treatment on the weight of CV C57BL/6NTac and C57BL/6J mice (n = 10/group). Data are shown as mean with SEM. h , Co-housed C57BL/6J mice were separated from C57BL/6NTac mice prior to PBS or digoxin treatment and were then infected with a high dose (~10 9 CFUs) of S . Tm Δ invA. Pathogen burden was enumerated at d3 p.i. ( n = 5/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. Extended Data Fig. 5|. Altered immune responses and pathogen susceptibility are microbiome dependent. Open in a new tab a-b, Characterization of recipient mice after transplantation of gut microbiomes from donor C57BL/6NTac animals 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 Δ invA ( n = 9/group) ( b ). c-d, Characterization of recipient mice after transplantation of gut microbiomes from donor C57BL/6NTac animals treated with digoxin or PBS for 7 days. Weight of recipient mice ( n = 5/group) after infection with S. Tm Δ invA ( c ), and survival of recipient mice after infection with S. Tm Δ invA ( d ). e-g, Characterization of recipient mice ( n = 5/group) after transplantation of gut microbiomes from donor C57BL/6J animals treated with digoxin or PBS for 2 days. Weight of recipient mice after infection with S. Tm Δ invA ( e ), pathogen burden in feces collected from recipient mice at d4 p.i. ( f ), and survival of recipient mice after infection with S. Tm Δ invA ( g ) In a , fold change is measured relative to the mouse housekeeping gene, Gapdh . In a , b , 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 standard error of mean in b , c , and e . ns, not significant. Extended Data Fig. 6|. Impact of digoxin pretreatment on the mouse microbiome and ileal proinflammatory responses. Open in a new tab a , Principal coordinate analysis (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 fecal samples in C57BL/6NTac ( b ), and C57BL/6J mice ( c ). In a-c, Permutational Multivariate Analysis of Variance (PERMANOVA) analysis 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 fecal 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, Impact of digoxin or PBS treatment (standard regimen; n = 5/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 sequencing of fecal samples collected from C57BL/6NTac and C57BL/6J mice 12h 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 center line is the median, the top and bottom hinges extend from the 25 th to 75 th percentiles, and the whiskers indicate the minimum and maximum values ( n = 5/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/group) relative to total bacteria, as measured by qPCR. Samples were collected 12 hours 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 sacrificed 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/group; and in j , n = 10 animals/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. Extended Data Fig. 7|. Impact of digoxin treatment on SFB abundance in mice. Open in a new tab a-c , Gastrointestinal contents from PBS-pretreated or digoxin-pretreated donor C57BL/6NTac mice were transferred into GF recipient mice. Microbiome analysis was performed from feces collected from ex-GF recipient mice over time. a, Scree plot showing the percentage of variance explained by each principal component. b , Principal component analysis 1 (PCoA1) plotted against time indicates microbiome stabilization by d7 post transplant. c, Relative SFB abundance over time in the ex-GF recipient animals. P -values were calculated using a two-sided Wilcoxon rank-sum test. Error bars are mean with SEM. 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 Δ invA as in Fig. 1c . Pathogen loads were enumerated at d4 after infection (PBS n = 6, digoxin n = 5). e, SFB abundance over time in fecal samples from C57BL/6NTac mice continuously treated (7 days, 2x/day) with PBS or digoxin. Samples were collected 12 hours after the previous treatment dose, and SFB abundance was measured by qPCR and normalized relative to the total bacteria in the sample ( n = 9/group). f, SFB abundance over time in fecal 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 SEM. 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 animals/group). g, SFB abundance at D(−2) and D0. h, Expression of select marker genes 12h 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. Extended Data Fig. 8|. Characterization of the role of RORγt and enteric β-defensins in digoxin response. Open in a new tab a-b, Impact of PBS or digoxin pretreatment on S. Tm Δ invA infection in SFB-colonized Rorγ −/− mice. Pathogen burden ( n = 8 mice/group) ( a ) and mortality ( b ) is shown. c, Impact of PBS ( n = 3/group) or digoxin pretreatment (ΔILC3 n = 4, Rorγ STOP n = 5) on SFB levels in the feces of SFB-colonized ΔILC3 mice and littermate Rorγ STOP controls. d, Impact of PBS or digoxin pretreatment on SFB levels in the ileal content of SFB-monocolonized (ex-GF) Rag1 − /− mice ( n = 5/group) and littermate WT SFB mono-colonized (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 sequencing 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, cecum, and colon tissues of C57BL/6NTac mice treated with PBS or digoxin (standard regimen, n = 5/group). h , Defb39 expression in the ileum tissue of mice ( n = 4/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. Extended Data Fig. 9|. Characterization of the impact of defb39 on gut commensal bacteria. Open in a new tab a , Ileal, cecal, and colon tissues were collected from WT and vil-defb39 C57BL/6NTac mice ( n = 4/group), and expression of defb39 was measured relative to the housekeeping gene, Gapdh . b-e, 16S sequencing analysis of fecal samples from WT ( n = 4) and vil-defb39 ( n = 3) mice. b, Scree plot showing the percentage of variance explained by each principal component. b, Principal component 1 (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 SEM. 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, and in the box plot, the center line is the median, the top and bottom hinges extend from the 25 th to 75 th percentiles, and the whiskers indicate the minimum and maximum values. Extended Data Fig. 10|. Impact of digoxin on mice carrying human microbial communities. Open in a new tab a-c, CV C57BL/6NTac mice ( n = 5/group) were treated with PBS or dihydrodigoxin (5 mg/kg) (standard regimen). 12 hours after the final buffer or drug treatment, mice were infected with ~10 8 CFUs of S . Tm Δ invA and infection monitored over time. a , Relative abundance of SFB, normalized to total bacteria, in fecal samples before and after PBS or dihydrodigoxin treatment. b , Pathogen burden at 12 hr post infection. c , Survival curve. d , GF C57BL/6NTac Nramp1 +/+ mice were colonized with a Th17-inducing defined community, pre-treated 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 fecal 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/group) colonized with a pooled human community were pre-treated 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. Supplementary Material Supplementary Information NIHMS2155260-supplement-Supplementary_Information.docx (3.3MB, docx) Supplementary Table 1 NIHMS2155260-supplement-Supplementary_Table_1.xlsx (10.6KB, xlsx) Supplementary Table 3 NIHMS2155260-supplement-Supplementary_Table_3.xlsx (10.7KB, xlsx) Supplementary Table 4 NIHMS2155260-supplement-Supplementary_Table_4.xlsx (13.1KB, xlsx) Supplementary Table 2 NIHMS2155260-supplement-Supplementary_Table_2.xlsx (28.1KB, xlsx) Supplementary Table 5 NIHMS2155260-supplement-Supplementary_Table_5.xlsx (14.5KB, xlsx) Supplementary Table 6 NIHMS2155260-supplement-Supplementary_Table_6.xlsx (12.6KB, xlsx) Supplementary Table 7 NIHMS2155260-supplement-Supplementary_Table_7.xlsx (11.3KB, xlsx) Supplementary Table 8 NIHMS2155260-supplement-Supplementary_Table_8.xlsx (9.2KB, xlsx) Supplementary Table 9 NIHMS2155260-supplement-Supplementary_Table_9.xlsx (13.7KB, xlsx) Acknowledgments We thank L. Valle and D. Lazo for their assistance with gnotobiotic mouse experiments, the Yale Center for Genome Analysis for sequencing services, the Yale Genome Editing Center for their help in generating the vil-defb39 mouse, and M. Graham and the Center for Cellular and Molecular Imaging, Electron Microscopy Facility at Yale School of Medicine for assistance with SEM images. We thank T. Moraga for helping with the statistical analysis of the epidemiological study. We thank C. Kelly and other members of the Goodman lab for helpful advice and discussion. We thank J. E. Galán for providing strains of S . Tm and CV C57BL/6NTac Nramp1 +/+ mice. We also thank 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.), R01DK098378 and U01AI163069 (to I.I.I.), and Canadian Institutes of Health Research grant number MOP-111166 (to R.T.). Footnotes Code Availability Statement This study did not generate new codes. Ethics declarations The epidemiology study received ethics approval from the McGill University Research Ethics Board, study number A01-E03–13B. All mice experiments were performed using protocols approved by the Yale University Institutional Animal Care and Use Committee (IACUC). Competing interests A.L.G. serves on the scientific advisory boards of Seres Therapeutics, Taconic Biosciences, and Piton Therapeutics. All other authors declare no competing interests. Data availability Raw and processed RNA sequencing files are available on NCBI’s Gene Expression Omnibus (GEO) under accession GSE274850 . 16S rRNA sequencing files are also available on NCBI’s BioProject under PRJNA1122171. Metagenomics data for the human community are available using accession ID PRJEB31790. Source data for Figures 1 – 5 and Extended Data Figures 1 - 10 are provided with this paper. References 1. 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[ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Supplementary Information NIHMS2155260-supplement-Supplementary_Information.docx (3.3MB, docx) Supplementary Table 1 NIHMS2155260-supplement-Supplementary_Table_1.xlsx (10.6KB, xlsx) Supplementary Table 3 NIHMS2155260-supplement-Supplementary_Table_3.xlsx (10.7KB, xlsx) Supplementary Table 4 NIHMS2155260-supplement-Supplementary_Table_4.xlsx (13.1KB, xlsx) Supplementary Table 2 NIHMS2155260-supplement-Supplementary_Table_2.xlsx (28.1KB, xlsx) Supplementary Table 5 NIHMS2155260-supplement-Supplementary_Table_5.xlsx (14.5KB, xlsx) Supplementary Table 6 NIHMS2155260-supplement-Supplementary_Table_6.xlsx (12.6KB, xlsx) Supplementary Table 7 NIHMS2155260-supplement-Supplementary_Table_7.xlsx (11.3KB, xlsx) Supplementary Table 8 NIHMS2155260-supplement-Supplementary_Table_8.xlsx (9.2KB, xlsx) Supplementary Table 9 NIHMS2155260-supplement-Supplementary_Table_9.xlsx (13.7KB, xlsx) Data Availability Statement Raw and processed RNA sequencing files are available on NCBI’s Gene Expression Omnibus (GEO) under accession GSE274850 . 16S rRNA sequencing files are also available on NCBI’s BioProject under PRJNA1122171. Metagenomics data for the human community are available using accession ID PRJEB31790. Source data for Figures 1 – 5 and Extended Data Figures 1 - 10 are provided with this paper. ACTIONS View on publisher site PDF (4.5 MB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

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