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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

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.

References

  1. 1.

    Lessa, F. C., Winston, L. G. & McDonald, L. C. Burden of Clostridium difficile infection in the United States. N. Engl. J. Med. 372, 2369–2370 (2015).

  2. 2.

    Hryckowian, A. J., Pruss, K. M. & Sonnenburg, J. L. The emerging metabolic view of Clostridium difficile pathogenesis. Curr. Opin. Microbiol. 35, 42–47 (2017).

  3. 3.

    Khanna, S. et al. The epidemiology of community-acquired Clostridium difficile infection: a population-based study. Am. J. Gastroenterol. 107, 89–95 (2012).

  4. 4.

    Ng, K. M. et al. Microbiota-liberated host sugars facilitate post-antibiotic expansion of enteric pathogens. Nature 502, 96–99 (2013).

  5. 5.

    Ferreyra, J. A. et al. Gut microbiota-produced succinate promotes C. difficile infection after antibiotic treatment or motility disturbance. Cell Host Microbe 16, 770–777 (2014).

  6. 6.

    Buffie, C. G. et al. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile. Nature 517, 205–208 (2015).

  7. 7.

    Earle, K. A. et al. Quantitative imaging of gut microbiota spatial organization. Cell Host Microbe 18, 478–488 (2015).

  8. 8.

    Hopkins, M. J. & Macfarlane, G. T. Nondigestible oligosaccharides enhance bacterial colonization resistance against Clostridium difficile in vitro. Appl. Environ. Microbiol. 69, 1920–1927 (2003).

  9. 9.

    Valdes-Varela, L., Hernandez-Barranco, A. M., Ruas-Madiedo, P. & Gueimonde, M. Effect of Bifidobacterium upon Clostridium difficile growth and toxicity when co-cultured in different prebiotic substrates. Front. Microbiol. 7, 738 (2016).

  10. 10.

    Kondepudi, K. K., Ambalam, P., Nilsson, I., Wadstrom, T. & Ljungh, A. Prebiotic-non-digestible oligosaccharides preference of probiotic bifidobacteria and antimicrobial activity against Clostridium difficile. Anaerobe 18, 489–497 2012).

  11. 11.

    Lawley, T. D. et al. Antibiotic treatment of Clostridium difficile carrier mice triggers a supershedder state, spore-mediated transmission, and severe disease in immunocompromised hosts. Infect. Immun. 77, 3661–3669 (2009).

  12. 12.

    Wlodarska, M., Willing, B. P., Bravo, D. M. & Finlay, B. B. Phytonutrient diet supplementation promotes beneficial Clostridia species and intestinal mucus secretion resulting in protection against enteric infection. Sci. Rep. 5, 9253 (2015).

  13. 13.

    Moore, J. H. et al. Defined nutrient diets alter susceptibility to Clostridium difficile associated disease in a murine model. PLoS ONE 10, e0131829 (2015).

  14. 14.

    Lyras, D. et al. Toxin B is essential for virulence of Clostridium difficile. Nature 458, 1176–1179 (2009).

  15. 15.

    Huang, B. et al. Real-time cellular analysis coupled with a specimen enrichment accurately detects and quantifies Clostridium difficile toxins in stool. J. Clin. Microbiol. 52, 1105–1111 (2014).

  16. 16.

    Furuya-Kanamori, L. et al. Asymptomatic Clostridium difficile colonization: epidemiology and clinical implications. BMC Infect. Dis. 15, 516 (2015).

  17. 17.

    Pollock, N. R. Ultrasensitive detection and quantification of toxins for optimized diagnosis of Clostridium difficile infection. J. Clin. Microbiol. 54, 259–264 (2016).

  18. 18.

    Kampmann, C., Dicksved, J., Engstrand, L. & Rautelin, H. Composition of human faecal microbiota in resistance to Campylobacter infection. Clin. Microbiol. Infect. 22, e61–e68 (2016).

  19. 19.

    Ferreira, R. B. et al. The intestinal microbiota plays a role in Salmonella-induced colitis independent of pathogen colonization. PLoS ONE 6, e20338 (2011).

  20. 20.

    Seekatz, A. M. et al. Recovery of the gut microbiome following fecal microbiota transplantation. mBio 5, e00893-14 (2014).

  21. 21.

    Walker, A. W. et al. Dominant and diet-responsive groups of bacteria within the human colonic microbiota. ISME J. 5, 220–230 (2011).

  22. 22.

    Lawley, T. D. et al. Targeted restoration of the intestinal microbiota with a simple, defined bacteriotherapy resolves relapsing Clostridium difficile disease in mice. PLoS Pathog. 8, e1002995 (2012).

  23. 23.

    Schubert, A. M. et al. Microbiome data distinguish patients with Clostridium difficile infection and non-C. difficile-associated diarrhea from healthy controls. mBio 5, e01021-14 (2014).

  24. 24.

    Schubert, A. M., Sinani, H. & Schloss, P. D. Antibiotic-induced alterations of the murine gut microbiota and subsequent effects on colonization resistance against Clostridium difficile. mBio 6, e00974 (2015).

  25. 25.

    Rojo, D. et al. Clostridium difficile heterogeneously impacts intestinal community architecture but drives stable metabolome responses. ISME J. 9, 2206–2220 (2015).

  26. 26.

    Rolfe, R. D. Role of volatile fatty acids in colonization resistance to Clostridium difficile. Infect. Immun. 45, 185–191 (1984).

  27. 27.

    Karlsson, S., Lindberg, A., Norin, E., Burman, L. G. & Akerlund, T. Toxins, butyric acid, and other short-chain fatty acids are coordinately expressed and down-regulated by cysteine in Clostridium difficile. Infect. Immun. 68, 5881–5888 (2000).

  28. 28.

    Kopke, M., Straub, M. & Durre, P. Clostridium difficile is an autotrophic bacterial pathogen. PLoS ONE 8, e62157 (2013).

  29. 29.

    Martin-Verstraete, I., Peltier, J. & Dupuy, B. The regulatory networks that control Clostridium difficile toxin synthesis. Toxins 8, E153 (2016).

  30. 30.

    Kelly, C. J. et al. Crosstalk between microbiota-derived short-chain fatty acids and intestinal epithelial HIF augments tissue barrier function. Cell Host Microbe 17, 662–671 (2015).

  31. 31.

    Carmody, R. N. et al. Diet dominates host genotype in shaping the murine gut microbiota. Cell Host Microbe 17, 72–84 (2015).

  32. 32.

    Sampson, T. R. et al. Gut microbiota regulate motor deficits and neuroinflammation in a model of Parkinson’s disease. Cell 167, 1469–1480.e12 (2016).

  33. 33.

    Smith, P. M. et al. The microbial metabolites, short-chain fatty acids, regulate colonic Treg cell homeostasis. Science 341, 569–573 (2013).

  34. 34.

    Rivera-Chavez, F. et al. Depletion of butyrate-producing Clostridia from the gut microbiota drives an aerobic luminal expansion of Salmonella. Cell Host Microbe 19, 443–454 (2016).

  35. 35.

    Mobarak-Qamsari, E., Kasra-Kermanshahi, R. & Moosavi-Nejad, Z. Isolation and identification of a novel, lipase-producing bacterium, Pseudomnas aeruginosa KM110. Iran. J. Microbiol. 3, 92–98 (2011).

  36. 36.

    Hendrick, J. A., Tadokoro, T., Emenhiser, C., Nienaber, U. & Fennema, O. R. Various dietary fibers have different effects on lipase-catalyzed hydrolysis of tributyrin in vitro. J. Nutr. 122, 269–277 (1992).

  37. 37.

    Rabbani, G. H. et al. Green banana reduces clinical severity of childhood shigellosis: a double-blind, randomized, controlled clinical trial. Pediatr. Infect. Dis. J. 28, 420–425 (2009).

  38. 38.

    Alvarez-Acosta, T. et al. Beneficial role of green plantain [Musa paradisiaca] in the management of persistent diarrhea: a prospective randomized trial. J. Am. Coll. Nutr. 28, 169–176 (2009).

  39. 39.

    Desai, M. S. et al. A dietary fiber-deprived gut microbiota degrades the colonic mucus barrier and enhances pathogen susceptibility. Cell 167, 1339–1353 (2016).

  40. 40.

    Sebaihia, M. et al. The multidrug-resistant human pathogen Clostridium difficile has a highly mobile, mosaic genome. Nat. Genet. 38, 779–786 (2006).

  41. 41.

    Cartman, S. T. & Minton, N. P. A mariner-based transposon system for in vivo random mutagenesis of Clostridium difficile. Appl. Environ. Microbiol. 76, 1103–1109 (2010).

  42. 42.

    Kashyap, P. C. et al. Complex interactions among diet, gastrointestinal transit, and gut microbiota in humanized mice. Gastroenterology 144, 967–977 (2013).

  43. 43.

    Sonnenburg, E. D. et al. Diet-induced extinctions in the gut microbiota compound over generations. Nature 529, 212–215 (2016).

  44. 44.

    Turnbaugh, P. J. et al. The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci. Transl Med. 1, 6ra14 (2009).

  45. 45.

    Etienne-Mesmin, L. et al. Toxin-positive Clostridium difficile latently infect mouse colonies and protect against highly pathogenic C.difficile. Gut https://doi.org/10.1136/gutjnl-2016-313510 (2017).

  46. 46.

    Dell, R. B., Holleran, S. & Ramakrishnan, R. Sample size determination. ILAR J. 43, 207–213 (2002).

  47. 47.

    Sonnenburg, E. D. et al. Specificity of polysaccharide use in intestinal Bacteroides species determines diet-induced microbiota alterations. Cell 141, 1241–1252 (2010).

  48. 48.

    Bachmanov, A. A., Reed, D. R., Beauchamp, G. K. & Tordoff, M. G. Food intake, water intake, and drinking spout side preference of 28 mouse strains. Behav. Genet. 32, 435–443 (2002).

  49. 49.

    Gopinath, S., Lichtman, J. S., Bouley, D. M., Elias, J. E. & Monack, D. M. Role of disease-associated tolerance in infectious superspreaders. Proc. Natl Acad. Sci. USA 111, 15780–15785 (2014).

  50. 50.

    Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012).

  51. 51.

    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).

  52. 52.

    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

  53. 53.

    Caporaso, J. G. et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267 (2010).

  54. 54.

    McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618 (2012).

Download references

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.

Author information

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.

Correspondence to Justin L. Sonnenburg.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figures 1–14

Reporting Summary

Supplementary Tables

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.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Further reading