Abstract
The gastrointestinal tracts of mammals are colonized by hundreds of microbial species that contribute to health, including colonization resistance against intestinal pathogens1. Many antibiotics destroy intestinal microbial communities and increase susceptibility to intestinal pathogens2. Among these, Clostridium difficile, a major cause of antibiotic-induced diarrhoea, greatly increases morbidity and mortality in hospitalized patients3. Which intestinal bacteria provide resistance to C. difficile infection and their in vivo inhibitory mechanisms remain unclear. Here we correlate loss of specific bacterial taxa with development of infection, by treating mice with different antibiotics that result in distinct microbiota changes and lead to varied susceptibility to C. difficile. Mathematical modelling augmented by analyses of the microbiota of hospitalized patients identifies resistance-associated bacteria common to mice and humans. Using these platforms, we determine that Clostridium scindens, a bile acid 7α-dehydroxylating intestinal bacterium, is associated with resistance to C. difficile infection and, upon administration, enhances resistance to infection in a secondary bile acid dependent fashion. Using a workflow involving mouse models, clinical studies, metagenomic analyses, and mathematical modelling, we identify a probiotic candidate that corrects a clinically relevant microbiome deficiency. These findings have implications for the rational design of targeted antimicrobials as well as microbiome-based diagnostics and therapeutics for individuals at risk of C. difficile infection.
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Change history
07 January 2015
A minor change was made to the author list.
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Acknowledgements
E.G.P. received funding from US National Institutes of Health (NIH) grants RO1 AI42135 and AI95706, and from the Tow Foundation. J.B.X. received funding from the NIH Office of the Director (DP2OD008440), NCI (U54 CA148967), and from a seed grant from the Lucille Castori Center for Microbes, Inflammation, and Cancer. C.G.B. was supported by a Medical Scientist Training Program grant from the National Institute of General Medical Sciences of the NIH (award number T32GM07739, awarded to the Weill Cornell/Rockefeller/Sloan-Kettering Tri-Institutional MD-PhD Program).
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C.G.B. and E.G.P. designed the experiments and wrote the manuscript with input from co-authors. C.G.B. performed animal experiments and most analyses. V.B., R.R.S., J.B.X., C.S. and C.G.B. performed microbiota time-series inference modelling and analysis. P.T.M. and C.G.B designed and performed ex vivo experiments. L.L., A.G., A.V. D.N. and M.K. performed 16S amplicon quantification and multiparallel sequencing (454, MiSeq) and contributed to data analysis. M.R.M.v.d.B., R.R.J., Y.T., E.L., C.G.B. and E.G.P. assessed clinical parameters and supervised patient cohort analysis. N.C.T. and C.G.B. performed metagenomic shotgun sequencing analysis. J.R.C. and H.L. developed the metabolomics analysis platform and performed quantification of bile acid species.
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Extended data figures and tables
Extended Data Figure 1 Dynamics of intestinal microbiota structure and C. difficile susceptibility after antibiotic exposure.
a, Strategy for determining C. difficile susceptibility duration post-antibiotic exposure (n = 3 separately-housed mouse colonies per antibiotic arm) and relating infection resistance to microbiota structure. b, Correlation of C. difficile c.f.u. and toxin in intestinal content following infection. c, Intestinal bacterial density of animals before and after antibiotic exposure. d, Relative abundance of bacterial OTUs (≥97% sequence similarity, >0.01% relative abundance) sorted by class (red) and corresponding C. difficile susceptibility (blue) among antibiotic-exposed mice (n = 68) allowed to recover for variable time intervals prior to C. difficile infection challenge. Centre values (mean), error bars (s.e.m.) (c). ND, not detectable.
Extended Data Figure 2 Allo-HSCT patient timelines and C. difficile infection status transitions.
Transitions between C. difficile (tcdB-positive) colonization status in patients receiving allogeneic haematopoietic stem-cell transplantation, as measured by C. difficile 16S rRNA abundance during the period of hospitalization (light grey bars). Time points when C. difficile colonization was determined to be positive (red diamonds) and negative (blue diamonds), and when C. difficile infection was clinically diagnosed (black dots) and metronidazole was administered (dark grey bars), are displayed relative to the time of transplantation per patient.
Extended Data Figure 3 Identification of bacteria conserved across human and murine intestinal microbiota predicted to inhibit C. difficile.
Identification of bacterial OTUs abundant in mice (n = 68) and humans (n = 24) (a) that account for a minority of OTU membership (b) but the majority of the structure of the intestinal microbiota of both host species after antibiotic exposure (c). Subnetworks of abundant OTUs predicted inhibit (blue) or positively associate with (red) C. difficile in murine (d) and human (e) intestinal microbiota.
Extended Data Figure 4 Phylogenetic distribution of resistance-associated intestinal bacteria and isolates selected for adoptive transfer.
The maximum likelihood phylogenetic tree (Kimura model, bootstrap of 100 replicates) was constructed using the MEGA 6.06 package from representative sequences of intestinal bacteria associated with resistance to C. difficile infection (blue), including cultured representatives subsequently used in adoptive transfer experiments (bold). The tree was rooted using intestinal bacteria associated with susceptibility to infection (red) as an outgroup.
Extended Data Figure 5 Adoptive transfer and engraftment of four-bacteria consortium or C. scindens ameliorates intestinal C. difficile cytotoxin load and acute C. difficile-associated weight loss.
a, C. difficile toxin load in antibiotic-exposed animals receiving adoptive transfers 24 h after C. difficile infection challenge. Animals’ weights 48 h after infection challenge and (b) C. difficile c.f.u. 24 h after infection challenge (c). d, Engraftment of bacterial isolates in the intestinal microbiota of antibiotic-exposed animals 2 days after adoptive transfer of B. intestihominis, P. capillosus, B. hansenii, and/or C. scindens. e, Intestinal bacterial density (faeces) from antibiotic-exposed mice administered suspensions containing four bacteria, C. scindens, or vehicle (PBS) as measured by rtPCR of 16S rRNA genes. ****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05; Mann–Whitney (two-tailed) (a, b, d, e), Kruskal–Wallis with Dunn correction (c) (n = 6–10 per group). Centre values, mean; error bars, s.e.m. Results are representative of at least two independent experiments. Numbers under group columns in d denote the number of mice with detectable engraftment of the given bacterium (out of ten possible separately housed animals per group).
Extended Data Figure 6 Adoptive transfer of consortia or C. scindens restores baiCD and the abundance of the gene family responsible for secondary bile acid biosynthesis.
a, PCR-based detection of the 7α-HSDH-encoding baiCD gene in bacterial isolates, intestinal microbiomes (faeces) of animals before antibiotic exposure, and intestinal microbiomes (faeces) of animals that, after antibiotic exposure, remained susceptible to C. difficile or recovered resistance to infection spontaneously or after adoptive transfer of bacterial isolates. b, Reconstituted abundance of the gene family responsible for secondary bile acid biosynthesis, as predicted by PICRUSt, in antibiotic-exposed animals receiving adoptive transfers (n = 10 per group). ***P < 0.001; *P < 0.05; NS, not significant; Mann–Whitney (two-tailed) (b). Centre values, mean; error bars, s.e.m.
Extended Data Figure 7 Impacts of adoptive transfers of bacteria on intestinal abundance of bile acids.
Intestinal abundance of the secondary bile acids LCA (a), ursodeoxycholate (UDCA) (b), and primary bile acids (c–f) in mice after antibiotic exposure and adoptive transfer of bacteria indicated. ****P < 0.0001, *P < 0.05, NS (not significant); Kruskal–Wallis test with Dunn’s correction. Centre values, mean; error bars, s.e.m.
Extended Data Figure 8 C. difficile growth inhibition by secondary bile acids and intestinal content from antibiotic-naive animals.
Addition of the secondary bile acids DCA (a) or LCA (b) to culture media inhibits C. difficile. Bile acid dependent inhibition of C. difficile enumerated by recovery of c.f.u. after inoculation of vegetative C. difficile into cell-free (c) or whole (d) intestinal content harvested from C57BL/6J mice (n = 5 or 6 per group), with or without pre-incubation with cholestyramine. **P < 0.01; Mann–Whitney (two-tailed) (c, d).
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Buffie, C., Bucci, V., Stein, R. et al. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile. Nature 517, 205–208 (2015). https://doi.org/10.1038/nature13828
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DOI: https://doi.org/10.1038/nature13828
