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Interleukin-22-mediated host glycosylation prevents Clostridioides difficile infection by modulating the metabolic activity of the gut microbiota

Nature Medicine volume 26, pages 608–617 (2020)Cite this article

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Abstract

The involvement of host immunity in the gut microbiota-mediated colonization resistance to Clostridioides difficile infection (CDI) is incompletely understood. Here, we show that interleukin (IL)-22, induced by colonization of the gut microbiota, is crucial for the prevention of CDI in human microbiota-associated (HMA) mice. IL-22 signaling in HMA mice regulated host glycosylation, which enabled the growth of succinate-consuming bacteria Phascolarctobacterium spp. within the gut microbiome. Phascolarctobacterium reduced the availability of luminal succinate, a crucial metabolite for the growth of C. difficile, and therefore prevented the growth of C. difficile. IL-22-mediated host N-glycosylation is likely impaired in patients with ulcerative colitis (UC) and renders UC-HMA mice more susceptible to CDI. Transplantation of healthy human-derived microbiota or Phascolarctobacterium reduced luminal succinate levels and restored colonization resistance in UC-HMA mice. IL-22-mediated host glycosylation thus fosters the growth of commensal bacteria that compete with C. difficile for the nutritional niche.

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Fig. 1: Healthy human microbiota-driven IL-22 prevents C. difficile blooms in the gut.
Fig. 2: IL-22-mediated succinate pathway drives colonization of C. difficile in healthy microbiota.
Fig. 3: Host glycosylation regulated by IL-22 influences the growth of Phascolarctobacterium species.
Fig. 4: N-glycan-related glycosyltransferase gene expression and CDI risk in patients with UC.
Fig. 5: C. difficile utilizes luminal succinate for its growth in patients with UC.
Fig. 6: Restoration of luminal metabolites reduces the risk of C. difficile infection.

Data availability

Source data for all Figures and Extended Data Figures are provided with the paper. The microbiome data in this study are available at the NCBI Sequence Read Archive under BioProject PRJNA594915. The metabolome data are available at the NIH Common Fund’s Data Repository and Coordinating Center website (supported by NIH grant, U01-DK097430), the Metabolomics Workbench (http://www.metabolomicsworkbench.org) where it has been assigned project ID PR000882 (anti-IL-22 antibody experiment in Fig. 2b and Extended Data Fig. 4), PR000881 (FMT experiment in Fig. 6f and Extended Data Fig. 9) and PR000869 (Phascolarctobacterium administration in Fig. 6i).

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Acknowledgements

The authors thank A. B. Shreiner for technical assistance, the University of Michigan Center for Gastrointestinal Research (DK034933), Host Microbiome Initiative, the GF Animal Facility and the Michigan Regional Comprehensive Metabolomics Resource Core (MRC2) (DK097153) for core services and I. L. Bergin from the In Vivo Animal Core at the University of Michigan Unit for Laboratory Animal Medicine for histological assessment. This work was supported by the National Institutes of Health (NIH) DK110146 and DK108901 (N.K.), Crohn’s and Colitis Foundation of America (N.K and H.N.-K.), Young Investigator Grant from the Global Probiotics Council (N.K.), University of Michigan Center for Gastrointestinal Research (DK034933) (N.K.), Joint Usage/Research Program of Medical Mycology Research Center Chiba University 18-1 (N.K. and Y.G.), Japan Society for the Promotion of Science Postdoctoral Fellowship for Research Abroad (H.N.-K. and S.K.), the Uehara Memorial Foundation Postdoctoral Fellowship Award (S.K.), Clinical and Translational Science Awards Program (S.K.), Prevent Cancer Foundation (S.K.), Japan Society for the Promotion of Science KAKENHI grants 16H04901, 17H05654 and 18H04805 (S.F.), Japan Science and Technology Agency PRESTO grant JPMJPR1537 (S.F.), Japan Science and Technology Agency ERATO grant JPMJER1902 (S.F.), Advanced Research and Development Programs for Medical Innovation CREST program grant JP19gm1010009 (S.F.), the Takeda Science Foundation (S.F.) and the Food Science Institute Foundation (S.F.). MS analysis of glycans was performed by the Swedish Infrastructure for Biological Mass Spectrometry, supported by the Swedish Research Council.

Author information

Author notes

  1. Jhansi L. Leslie

    Present address: The University of Virginia, Washington, VA, USA

  2. Anna M. Seekatz

    Present address: Clemson University, Columbia, SC, USA

Authors and Affiliations

  1. Division of Gastroenterology, University of Michigan Medical School, Ann Arbor, MI, USA

    Hiroko Nagao-Kitamoto, Sho Kitamoto, Merritt G. Gillilland III, Peter Kuffa, John Y. Kao, Peter D. R. Higgins & Nobuhiko Kamada

  2. Division of Infectious Disease, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA

    Jhansi L. Leslie, Anna M. Seekatz & Vincent B. Young

  3. Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA

    Jhansi L. Leslie, Eric C. Martens, Kathryn A. Eaton & Vincent B. Young

  4. Institute of Biomedicine, Department of Medical Biochemistry, University of Gothenburg, Gothenburg, Sweden

    Chunsheng Jin, Kristina A. Thomsson & Niclas G. Karlsson

  5. Division of Molecular Immunology, Medical Mycology Research Center, Chiba University, Chiba, Japan

    Yoshiyuki Goto

  6. Division of Mucosal Symbiosis, International Research and Development Center for Mucosal Vaccines, Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Yoshiyuki Goto

  7. AMED-PRIME, Japan Agency for Medical Research and Development, Tokyo, Japan

    Yoshiyuki Goto

  8. Department of Genomic Medicine, Division of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston TX, USA

    Robert R. Jenq

  9. Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan

    Chiharu Ishii, Akiyoshi Hirayama & Shinji Fukuda

  10. Intestinal Microbiota Project, Kanagawa Institute of Industrial Science and Technology, Ebina, Japan

    Shinji Fukuda

  11. Transborder Medical Research Center, University of Tsukuba, Tsukuba, Japan

    Shinji Fukuda

  12. PRESTO, Japan Science and Technology Agency, Kawaguchi, Japan

    Shinji Fukuda

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  1. Hiroko Nagao-Kitamoto
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Contributions

H.N.-K. and N.K. conceived and designed experiments. H.N.-K. conducted most of the experiments with help from J.L.L., S.K., P.K. and A.M.S. M.G.G. performed microbiome analysis. C.I., A.H. and S.F. performed metabolome analysis. K.A.E. helped with GF animal experiments. P.D.R.H. provided human stool samples. C.J., K.A.T and N.G.K. conducted glycan analysis. Y.G., R.R.J., V.B.Y., E.C.M. and J.Y.K. helped with critical advice and discussion. H.N.-K. and N.K. analyzed the data. H.N.-K. and N.K. wrote the manuscript with contributions from all authors.

Corresponding author

Correspondence to Nobuhiko Kamada.

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Extended data

Extended Data Fig. 1 Healthy human microbiotas prevent C. difficile infection.

a, (Left) GF B6 mice were colonized with healthy control (HC) microbiotas for 2 weeks (human microbiota-associated (HMA) mice). GF or HC-HMA mice (GF: n = 9, HC#1:n = 8 and HC#2:n = 3, biologically independent animals) were then infected with C. difficile VPI 10463 spores (103 spores/mouse). C. difficile load in feces was determined on indicated days post-infection. Dots represent individual mice. Bars indicate median. Data were pooled from 2 independent experiments. (Right) The mortality of C. difficile infected GF or HC-HMA mice. Statistical significance was assessed by Log-rank test (two-sided). (b and c) (b) HC-HMA mice were treated with a cocktail of antibiotics or regular water and then infected with C. difficile VPI 10463 spores (n = 5, biologically independent animals). (Left) CFU in feces. Dots represent individual mice. Bars indicate median. Data were pooled from 2 independent experiments. Statistical significance was assessed by 2-way ANOVA with Bonferroni post-hoc test (two-sided). (Right) The mortality of mice. Statistical significance was assessed by Log-rank test (two-sided). c, Representative histological images and associated histological scores. Scale bar is 100 µm. Data are presented as mean ± s.e.m. from pooled two independent experiments. Statistical significance was assessed by Mann-Whitney U test (two-sided).

Source data

Extended Data Fig. 2 IL-22 neutralization inhibits the expression of antimicrobial proteins.

Reg3b and Reg3g mRNA levels in control or αIL-22 antibody-treated HC-HMA Rag1−/− mice were determined by qPCR. Expression was normalized to that of the murine Actb gene. Dots represent individual mice. Data are presented as mean ± s.e.m. (n = 8, biologically independent animals) from pooled 2 independent experiments. Statistical significance was assessed by Mann-Whitney U test (two-sided).

Source data

Extended Data Fig. 3 IL-22 shapes the gut microbial community.

HC-HMA-Rag1−/− mice were treated with control or αIL-22 antibody twice, on day -5 and day -3, before fecal samples were collected. Bacterial 16 S rRNA sequences were analyzed. a, Shannon index (α-diversity) and number of OTUs (richness) of control and αIL-22 antibody-treated HMA-Rag1−/− mice. Dots represent individual mice. Data are presented as mean ± s.e.m. (n = 3, biologically independent animals). Data are representative of 2 independent experiments. Statistical significance was assessed by Mann-Whitney U test (two-sided). b, Microbial community structures were analyzed using the Yue and Clayton dissimilarity distance metric (θYC) and are shown in a nonmetric, multidimensional scaling plot. c, Bacterial taxonomy at the family level in the feces.

Source data

Extended Data Fig. 4 Luminal metabolomic analysis in IL-22–neutralized HMA mice.

a, Fecal samples were collected after treatment with αIL-22 antibody, twice and before CDI (day 0). A heat map showing the quantified metabolic profiles of control or αIL-22 antibody-treated HC-HMA-Rag1−/− mice. All concentrations of quantified metabolites were transformed into Z-scores and clustered according to their Euclidean distance. Gray areas in the heat map indicate that respective metabolites were not detected. b, Principal component analysis of the metabolome data. c, A loading scatter plot of the principal component analysis. d, Luminal metabolites were analyzed by CE-TOF/MS. Dots represent individual mice. Data are presented as mean ± s.e.m. (n = 13, biologically independent animals) from pooled 3 independent experiments. Statistical significance was assessed by Mann-Whitney U test (two-sided).

Source data

Extended Data Fig. 5 C. difficile growth on succinate.

In vitro growth of WT JIR8094 or Cd-CD2344- mutant C. difficile in a minimal medium supplemented with glucose or succinate. Data are presented as mean ± s.d. (n = 3 technical replicates). Data are representative of 3 independent experiments. Statistical significance was assessed by 2-way ANOVA with Bonferroni post-hoc test (two-sided).

Source data

Extended Data Fig. 6 Succinate is not required for the growth of C. difficile in germ-free mice.

GF C57BL/6 mice were infected with WT JIR8094 or Cd-CD2344− mutant C. difficile strains. C. difficile load in feces was determined on indicated days post-infection (n = 5, biologically independent animals). Dots represent individual mice. Bars indicate median. Data were from pooled 2 independent experiments. Statistical significance was assessed by 2-way ANOVA with Bonferroni post-hoc test (two-sided).

Source data

Extended Data Fig. 7 Gene expression profiles in UC patient cohorts.

a, The mRNA expression of IL22 mRNA in the colonic tissue from control subjects (n = 11), patients with inactive UC (n = 23) and patients with active UC (n = 74). Data were derived from GEO data set GSE75214. Dots represent biologically independent subjects. Data are presented as mean ± s.e.m.. Statistical significance was assessed by Kruskal-Wallis test with Dunn posttest (two-sided). (b and d) The mRNA expression of IL22, IL22RA, MGAT4A and MGAT4B mRNA in the colonic tissue from control subjects and patients with active UC. (c and e) Correlation between MGAT4A/MGAT4B and IL22RA mRNA expression in 3 groups. Statistical significance was measured by Pearson correlation test (two-sided). Dots represent biologically independent subjects. Data were derived from GEO data sets GSE16879 (control: n = 8 and active UC: n = 24) and GSE73661 (control: n = 12 and active UC: n = 67). Data are presented as mean ± s.e.m.. Statistical significance was assessed by Mann-Whitney U test (two-sided). (b and d). Statistical significance was measured by Pearson correlation test (two-sided) (c and e).

Source data

Extended Data Fig. 8 FMT restores the microbial composition in UC-HMA mice.

Significantly altered bacteria in pre–C. difficile infected UC-HMA mice with or without FMT were identified by LEfSe analysis. UC-HMA mice–enriched taxa have a positive LDA score (red, n = 6, biologically independent animals), and FMT-treated UC-HMA mice–enriched taxa have a negative LDA score (green, n = 6, biologically independent animals). A P value of < 0.05 and a score ≥ 2.0 were considered significant in Kruskal–Wallis and pairwise Wilcoxon tests (two-sided), respectively.

Extended Data Fig. 9 Luminal metabolomic analysis in FMT-treated UC-HMA mice.

a, Fecal samples were collected after treatment with FMT (day -3) and before CDI (day 0). A heat map showing the quantified metabolic profiles of UC-HMA or FMT-treated UC-HMA mice. All concentrations of quantified metabolites were transformed into Z-scores and clustered according to their Euclidean distance. Gray areas in the heat map indicate that respective metabolites were not detected. b, The principal component analysis of the metabolome data (n = 9, biologically independent animals). c, A loading scatter plot of the principal component analysis. d, Luminal metabolites were analyzed by CE-TOF/MS. Dots represent individual mice. Data are presented as mean ± s.e.m. (n = 9, biologically independent animals) from pooled 2 independent experiments. Statistical significance was assessed by Mann-Whitney U test (two-sided).

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Extended Data Fig. 10 Phascolarctobacterium inoculation protect mice from CDI.

SPF C57BL/6 mice were treated with cefoperazone (0.5 mg/mL in drinking water). After 8 days, the mice were switched to regular water and allowed to recover for 2 days before being infected with C. difficile VPI spores. Mice were treated with P. faecium JCM 30894 and P. succinatutens JCM 16074 (106 CFU each strain) or with its culture medium by oral gavage, once, before C. difficile inoculation (1 day prior to CDI, pre-treatment) or 4 times post-inoculation (1, 3, 7, 10 days post-infection, post-treatment). Dots represent individual mice (n = 5, biologically independent animals). The mortality of C. difficile–infected mice was assessed. Statistical significance was assessed by Log-rank test (two-sided) from pooled 2 independent experiments.

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Nagao-Kitamoto, H., Leslie, J.L., Kitamoto, S. et al. Interleukin-22-mediated host glycosylation prevents Clostridioides difficile infection by modulating the metabolic activity of the gut microbiota. Nat Med 26, 608–617 (2020). https://doi.org/10.1038/s41591-020-0764-0

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