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Microbiota-accessible carbohydrates suppress Clostridium difficile infection in a murine model

Abstract

Clostridium difficile is an opportunistic diarrhoeal pathogen, and C.difficile infection (CDI) represents a major health care concern, causing an estimated 15,000 deaths per year in the United States alone1. Several enteric pathogens, including C.difficile, leverage inflammation and the accompanying microbial dysbiosis to thrive in the distal gut2. Although diet is among the most powerful available tools for affecting the health of humans and their relationship with their microbiota, investigation into the effects of diet on CDI has been limited. Here, we show in mice that the consumption of microbiota-accessible carbohydrates (MACs) found in dietary plant polysaccharides has a significant effect on CDI. Specifically, using a model of antibiotic-induced CDI that typically resolves within 12 days of infection, we demonstrate that MAC-deficient diets perpetuate CDI. We show that C.difficile burdens are suppressed through the addition of either a diet containing a complex mixture of MACs or a simplified diet containing inulin as the sole MAC source. We show that switches between these dietary conditions are coincident with changes to microbiota membership, its metabolic output and C.difficile-mediated inflammation. Together, our data demonstrate the outgrowth of MAC-utilizing taxa and the associated end products of MAC metabolism, namely, the short-chain fatty acids acetate, propionate and butyrate, are associated with decreased C.difficile fitness despite increased C.difficile toxin expression in the gut. Our findings, when placed into the context of the known fibre deficiencies of a human Western diet, provide rationale for pursuing MAC-centric dietary strategies as an alternate line of investigation for mitigating CDI.

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Fig. 1: Dietary MACs toggle the fitness of C.difficile in the gut while engendering distinct microbiota states.
Fig. 2: A diet containing inulin as the sole MAC source reduces C.difficile burdens without increasing microbial diversity.
Fig. 3: Acetate, propionate and butyrate levels are elevated in the caeca of mice fed MACs and affect growth and toxin production in C.difficile.
Fig. 4: Inflammation and C.difficile toxin expression are diet dependent.

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Acknowledgements

We thank S. K. Higginbottom (Department of Microbiology and Immunology, Stanford University) for expertise and technical assistance in all mouse experiments, A. S. Chien (Stanford University Mass Spectrometry Facility) for developing the gas chromatography–mass spectrometry parameters used in this study and C. G. Gonzalez (Department of Chemical and Systems Biology, Stanford University) for assistance with DNA extractions from mouse faeces. This work was funded by a grant from the NIH NIDDK (R01-DK085025 to J.L.S.), an NIH postdoctoral NRSA (5T32AI007328 to A.J.H.), a Stanford University School of Medicine Dean’s Postdoctoral Fellowship (A.J.H.), NSF Graduate Research Fellowships (DGE-114747 to S.A.S and W.V.T), an NIH predoctoral NRSA (5T32AI007328 to N.M.D.) and a Smith Stanford Graduate Fellowship (S.A.S.). J.L.S. received an Investigators in the Pathogenesis of Infectious Disease Award from the Burroughs Wellcome Fund.

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A.J.H., N.M.D. and J.O.G. performed the experiments. D.M.B. conducted blinded scoring, imaging and analysis of the tissue sections. A.J.H., W.V.T., S.A.S., N.M.D., D.M.B. and J.L.S. analysed and interpreted the data, designed the experiments and prepared the display items. A.J.H. and J.L.S. wrote the paper. All authors edited the manuscript prior to submission.

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Correspondence to Justin L. Sonnenburg.

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Supplementary information

Supplementary Information

Supplementary Figures 1–14

Reporting Summary

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Supplementary Tables 1–5

Supplementary Code 1

Commands executed for the 16S rRNA-based bioinformatics analysis.

Supplementary Code 2

Script which imports raw growth curve data, accesses the ‘analyze_growth_curve_SCFA.m’ file to perform analysis of growth curves, plots growth curves, and exports analysed data to an Excel spreadsheet.

Supplementary Code 3

Function that analyses individual growth curves for important metrics, including maximum OD and maximum growth rate. It also performs the function to smooth the data.

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Hryckowian, A.J., Van Treuren, W., Smits, S.A. et al. Microbiota-accessible carbohydrates suppress Clostridium difficile infection in a murine model. Nat Microbiol 3, 662–669 (2018). https://doi.org/10.1038/s41564-018-0150-6

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