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Longitudinal multi-omics analyses link gut microbiome dysbiosis with recurrent urinary tract infections in women

Nature Microbiology volume 7, pages 630–639 (2022)Cite this article

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Abstract

Recurrent urinary tract infections (rUTIs) are a major health burden worldwide, with history of infection being a significant risk factor. While the gut is a known reservoir for uropathogenic bacteria, the role of the microbiota in rUTI remains unclear. We conducted a year-long study of women with (n = 15) and without (n = 16) history of rUTI, from whom we collected urine, blood and monthly faecal samples for metagenomic and transcriptomic interrogation. During the study 24 UTIs were reported, with additional samples collected during and after infection. The gut microbiome of individuals with a history of rUTI was significantly depleted in microbial richness and butyrate-producing bacteria compared with controls, reminiscent of other inflammatory conditions. However, Escherichia coli gut and bladder populations were comparable between cohorts in both relative abundance and phylogroup. Transcriptional analysis of peripheral blood mononuclear cells revealed expression profiles indicative of differential systemic immunity between cohorts. Altogether, these results suggest that rUTI susceptibility is in part mediated through the gut–bladder axis, comprising gut dysbiosis and differential immune response to bacterial bladder colonization, manifesting in symptoms.

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Fig. 1: Study design and sample collection for the UMB study.
Fig. 2: Women with rUTI have a distinct gut microbiome.
Fig. 3: Frequent gut–bladder transmission and strain persistence in patients with rUTI.
Fig. 4: Phylogenetic distribution of E. coli strains identified in all stool and urine samples.

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Data availability

Metagenomic sequence data are available from the Sequence Read Archive under Bioproject PRJNA400628. PBMC RNA-seq data are available from the database of Genotypes and Phenotypes (dbGaP) under project no. phs002728. Questionnaire data and output files from MetaPhlan2, Humann2 and StrainGE are available from github.com/cworby/UMB-study. Source data are provided with this paper.

Code availability

Custom R scripts used to analyse outputs are available from github.com/cworby/UMB-study.

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Acknowledgements

This project has been funded in part with Federal funds from the National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services under grant no. U19AI110818 to the Broad Institute, from the National Institutes of Health Mucosal Immunology Studies Team consortium under grant no. U01AI095542 to Washington University and the National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health, Department of Health and Human Services, under Grant Number R01DK121822 to the Broad Institute and Washington University. B.S.O. was supported by grants from the National Institutes of Health, USA (nos. T32GM007067 and T32GM139774). A.L.K. was supported by grants from the National Institutes of Health, Department of Health, USA (no. R01AI165915) and the Doris Duke Charitable Foundation. This work was also supported by funds from the Center for Women’s Infectious Disease Research at Washington University School of Medicine. We thank members of the Broad’s Bacterial Genomics group and H. Vlamakis for helpful conversations. We thank B. Haas for assistance with PBMC RNA-seq analysis, as well as the Multi-Omics Core and Genomics Platform at the Broad Institute for sample processing and data generation.

Author information

Authors and Affiliations

  1. Infectious Disease and Microbiome Program, Broad Institute, Cambridge, MA, USA

    Colin J. Worby, Timothy J. Straub, Lucas R. van Dijk, Ryan A. Bronson, Christopher A. Desjardins, Wen-Chi Chou, Sinéad B. Chapman, Abigail L. Manson & Ashlee M. Earl

  2. Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO, USA

    Henry L. Schreiber IV, Benjamin S. Olson, Jerome S. Pinkner, Chloe L. P. Obernuefemann, Vanessa L. Muñoz, Alexandra E. Paharik, Philippe N. Azimzadeh, Karen W. Dodson & Scott J. Hultgren

  3. Center for Women’s Infectious Disease Research, Washington University School of Medicine, St. Louis, MO, USA

    Henry L. Schreiber IV, Benjamin S. Olson, Andrew L. Kau, Karen W. Dodson & Scott J. Hultgren

  4. Division of Biology & Biological Engineering, California Institute of Technology, Pasadena, CA, USA

    Henry L. Schreiber IV

  5. Delft Bioinformatics Lab, Delft University of Technology, Delft, the Netherlands

    Lucas R. van Dijk

  6. Applied Invention, Cambridge, MA, USA

    Bruce J. Walker

  7. Department of Surgery, Division of Urologic Surgery, Washington University School of Medicine, St. Louis, MO, USA

    Karla Bergeron, Aleksandra Klim & H. Henry Lai

  8. Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA

    Thomas J. Hannan

  9. Department of Medicine, University of Miami, Miami, FL, USA

    Thomas M. Hooton

  10. Department of Medicine, Division of Allergy and Immunology, Washington University School of Medicine, St. Louis, MO, USA

    Andrew L. Kau

  11. Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, USA

    H. Henry Lai

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  1. Colin J. Worby
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Contributions

Study design was undertaken by H.L.S., K.W.D., S.J.H. and A.M.E. Study coordination was carried out by H.L.S., K.B., S.B.C. and A.K. Experiments were performed by H.L.S., J.S.P., C.L.P.O., V.L.M. and A.E.P. Data analysis was undertaken by C.J.W., H.L.S., T.J.S., L.R.v.D., R.A.B., B.S.O., B.J.H., C.A.D. and W.-C.C. Consultation and supervision of analyses were the responsibility of B.J.W., A.L.M., T.J.H., T.M.H., A.L.K., H.H.L., K.W.D., S.J.H. and A.M.E. C.J.W., A.L.M., K.W.D., S.J.H. and A.M.E. prepared the original draft. Review and approval of the final manuscript was provided by all authors.

Corresponding authors

Correspondence to Scott J. Hultgren or Ashlee M. Earl.

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Nature Microbiology thanks John Lee, Alice McHardy and Mark Schembri for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Sex precedes all clinical UTI events.

Survey reports of intercourse frequency in the previous two weeks. Responses are partitioned by (i) control women, (ii) rUTI women at time of UTI, and (iii) rUTI women at non-UTI time points.

Source data

Extended Data Fig. 2 SCFA producing bacteria are depleted in the rUTI gut.

Cumulative relative abundances of (a) butyrate and (b) propionate producing bacterial species in rUTI and control samples. Box plots display the median (center line), 25th and 75th percentiles (box), as well as the 5th and 95th percentiles (whiskers). Within-host average relative abundances of individual species for (c) butyrate and (d) propionate producers are also shown. Horizontal lines denote the mean relative abundance in rUTI (red) and control (blue) women.

Source data

Extended Data Fig. 3 Bray Curtis dissimilarity across stool samples.

(a) For each patient, the distribution of Bray-Curtis dissimilarities between all stool samples, ordered by increasing mean patient values within each cohort. (b) Bray-Curtis distributions between samples taken at the time of UTI vs. healthy time points (red), compared to all pairwise healthy sample comparisons. Box plots show the median (center line), 25th and 75th percentiles (box), as well as the 5th and 95th percentiles (whiskers).

Source data

Extended Data Fig. 4 rUTI dysbiosis is not driven by antibiotic use during the study.

We grouped rUTI women according to their antibiotic exposures at any point during the UMB study; (i) ciprofloxacin (n = 6) (ii) non-ciprofloxacin antibiotics (n = 6); (iii) no antibiotics (n = 3); (iv) any antibiotics (n = 12). Groups were compared against each other and against the control cohort (n = 16) for (a) overall microbial richness and (b) relative abundance of butyrate producers. Crosses represent mean values for individuals, boxplots denote the IQR and 95% central quantiles for each group. Wilcoxon rank sum tests (two-sided) were applied to group pairs to derive p-values. (c) Temporal trends of microbial richness (black) and relative abundance of butyrate producers (red) in all rUTI participants using antibiotics during the study. For each individual, linear models were fit to observations (points) over time; fitted trends are shown, with coefficients & p values reported at the top of each panel. Dashed vertical lines denote antibiotic usage. Participant mean values are represented by horizontal lines.

Source data

Extended Data Fig. 5 Most species depleted in the rUTI gut are also depleted in the IBD gut.

We compared discriminatory taxa in rUTI women to those in IBD patients using data from adult participants in the HMP2 study33. For each study, we fitted mixed effects models to standardized Metaphlan2 relative abundances as a function of categorical disease group (rUTI or IBD respectively, vs. each study’s control cohort), including covariates for race and antibiotic use. The disease group coefficients are plotted against each other for each species, with circle pairs representing the average relative abundance in each study. Species with uncorrected p values <0.05 in either study are labelled. Species not present in at least 10% of samples in either study are excluded. IBD comprises patients with either CD or UC.

Source data

Extended Data Fig. 6 Immunological differences between cohorts.

(a) PCA plot of gene expression across cohorts, based on PBMC RNA Seq data. Samples are partitioned into healthy controls (n = 13), rUTI patient baseline (enrollment; n = 12) and rUTI patient at time of UTI (n = 17). (b) Plasma eotaxin-1 levels in control women, and rUTI women at healthy enrollment and time of UTI. (c) Relative abundance of NK cells in control and rUTI women based on CIBERSORT output. Box plots display the median (center line), 25th and 75th percentiles (box), as well as data points within 1.5 IQR of the upper & lower quartiles (whiskers), and outliers beyond this range (dots).

Source data

Extended Data Fig. 7 Limited relationship between non SCFA-producing taxa with butyrate producers.

For all non SCFA-producing genera detected across all samples, the correlation coefficient between its relative abundance and the relative abundance of butyrate producers was calculated and plotted against its mean relative abundance across (a) control (n = 170) and (b) rUTI (n = 197) samples. Genera with an absolute correlation coefficient greater than 0.25 are labeled, along with Escherichia, represented by the red point.

Source data

Extended Data Fig. 8 E. coli relative abundance around the time of UTI and phylogroup distributions.

For all stool samples taken within 3 days of a UTI event, the log fold change is given relative to (a) the median E. coli relative abundance in the corresponding patient, excluding samples taken at the time of UTI, and (b) the relative abundance of E. coli in the preceding stool sample. ‘X’ denotes samples for which there was no prior sample available. (c) Number of detected E. coli strains by sample type. (d) Number of detected StrainGST reference strains vs. relative abundance of E. coli.

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Extended Data Fig. 9 Strain dynamics in control women.

Strain dynamics within all control participants; analogous to Fig. 3. (a) Phylogenetic tree comprising strains called by StrainGE across all stool and urine samples, colored by phylogroup. Bars show number of unique participants with at least one strain observation; bars are bolded if the strain was identified in at least one urine sample. Each strain identified in control women is uniquely identifiable by the phylogroup (colour) and ID (numeral) indicated right. (b) Each panel represents longitudinal strain dynamics within one patient. Numerals refer to strain identifiers in (a). All fecal strains are connected to their most recent previous observation in fecal samples. Diamonds denote clinical rectal swabs. Strains identified in urine outgrowth depicted if available; otherwise raw urine strains are shown. Fecal or urine samples with no detected E. coli strains represented by open grey symbols. Vertical dashed lines represent self-reported antibiotic use.

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Extended Data Table 1 Cohort Characteristics. Demographic, behavioral and dietary characteristics of the rUTI and control women who completed the year-long study. Fisher’s exact tests (two-sided) were used to compare frequencies between cohorts. * recorded recent consumption of item in at least 50% of questionnaire responses during study
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Worby, C.J., Schreiber, H.L., Straub, T.J. et al. Longitudinal multi-omics analyses link gut microbiome dysbiosis with recurrent urinary tract infections in women. Nat Microbiol 7, 630–639 (2022). https://doi.org/10.1038/s41564-022-01107-x

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