Antibiotic-induced shifts in the mouse gut microbiome and metabolome increase susceptibility to Clostridium difficile infection

Casey M Theriot, Mark J Koenigsknecht, Paul E Carlson Jr, Gabrielle E Hatton, Adam M Nelson, Bo Li, Gary B Huffnagle, Jun Z Li, Vincent B Young, Casey M Theriot, Mark J Koenigsknecht, Paul E Carlson Jr, Gabrielle E Hatton, Adam M Nelson, Bo Li, Gary B Huffnagle, Jun Z Li, Vincent B Young

Abstract

Antibiotics can have significant and long-lasting effects on the gastrointestinal tract microbiota, reducing colonization resistance against pathogens including Clostridium difficile. Here we show that antibiotic treatment induces substantial changes in the gut microbial community and in the metabolome of mice susceptible to C. difficile infection. Levels of secondary bile acids, glucose, free fatty acids and dipeptides decrease, whereas those of primary bile acids and sugar alcohols increase, reflecting the modified metabolic activity of the altered gut microbiome. In vitro and ex vivo analyses demonstrate that C. difficile can exploit specific metabolites that become more abundant in the mouse gut after antibiotics, including the primary bile acid taurocholate for germination, and carbon sources such as mannitol, fructose, sorbitol, raffinose and stachyose for growth. Our results indicate that antibiotic-mediated alteration of the gut microbiome converts the global metabolic profile to one that favours C. difficile germination and growth.

Figures

Figure 1. Susceptible and resistant states of…
Figure 1. Susceptible and resistant states of C. difficile infection
Each circle represents the state of the intestinal environment for each of the 4 treatment groups. C57BL/6 WT mice (represented at baseline by state R1) were treated with cefoperazone for 10 days. The structural and functional status of the gut was determined two days (S1) or 6 weeks (R3) after discontinuation of the antibiotic. An additional group of mice (R2) was held without any antibiotic treatment to serve as age-matched controls to the R3 animals. Each treatment group was challenged with C. difficile spores to assess if they were resistant or susceptible to colonization and disease. There were 3 states that were fully resistant to colonization and disease (R1, R2, R3) and one susceptible state (S1).
Figure 2. Untargeted metabolomics of the gut…
Figure 2. Untargeted metabolomics of the gut metabolome
(a) A heatmap of the metabolites grouped by KEGG pathway found in the gut metabolome of CDI susceptible and resistant mice from all four-treatment groups (n=8, A–H). Mice susceptible to CDI (S1) showed significant changes in their intestinal metabolome compared to the three groups of mice with intestinal environments that were resistant to C. difficile colonization (R1–R3). (b) A heatmap of the relative amounts of bile acids in the gut metabolome of CDI susceptible and resistant mice from all four-treatment groups. The heatmap scale ranges from −6 to +8 on a log2 scale. (c) A heatmap of carbohydrates in the gut metabolome of CDI susceptible and resistant mice from all four-treatment groups. The heatmap scale ranges from −5 to +10 on a log2 scale.
Figure 3. Targeted metabolomics of gut metabolome
Figure 3. Targeted metabolomics of gut metabolome
(a) Bile acids were analyzed by LC-MS from the cecal content of non-antibiotic treated (R1) and cefoperazone treated mice (S1) (n=4 for each group). Significant changes in the concentrations of taurocholate, cholate and deoxycholate resulted from antibiotic treatment (Mann-Whitney non-parametric t-test). Error bars represent the mean ± SEM. (b) Concentrations of sugar alcohols (mannitol and sorbitol) measured by LC-MS from cecal content of untreated (R1) and cefoperazone-treated mice (S1) (n=4 for each group). Antibiotic treatment significantly changes the levels of these sugar alcohols (Mann-Whitney non-parametric t-test). Error bars represent the mean ± SEM. (c) Short chain fatty acid levels (acetate, propionate, butyrate) were analyzed by GC-MS from cecal content of non-antibiotic treated (R1, n=3), 2 days (S1, n=4) and 6 weeks after cefoperazone treated mice (R3, n=3). Changes in SCFAs were significant between groups R1 and S1 (non-parametric Kruskal-Wallis one-way analysis of variance test followed by Bonferroni-Dunn Multiple Comparison Test). Error bars represent the mean ± SEM.
Figure 4. C. difficile in vitro and…
Figure 4. C. difficile in vitro and ex vivo germination and growth studies
(a) In vitro assays were performed to assess the ability of taurocholate (black bars) and deoxycholate (white bars) to trigger C. difficile spore germination. Spores were incubated in BHIS with each bile salt at the indicated concentrations. Spores were incubated with 0.1% bile salt for 30 minutes or with 0.01% bile salt for 6 hours. Data presented represent mean ± SD of triplicate experiments and were significant (Students t-test). (b) In vitro growth of C. difficile was done in a defined minimal media which included essential amino acids and vitamins required by C. difficile for growth. Carbohydrates that were found to be increased after antibiotic treatment during the CDI susceptible state (S1) were supplemented in the media. C. difficile can utilize many of the carbohydrates in the murine gut after antibiotic treatment. Only carbohydrates that supported growth of C. difficile are shown here (Supplementary Table 1). Error bars represent the mean ± SEM of triplicate experiments. (c) Ex-vivo germination and outgrowth of C. difficile was done measured in cecal contents from untreated and from cefoperazone-treated mice two days after antibiotics. C. difficile VPI 10463 spores inoculated into the antibiotic-treated cecal contents (S1, n=6) were able to germinate and outgrow over a 6 hour period where as spores in the non-antibiotic treated cecal content (R1, n=3) did not. Significance between groups was done by Mann-Whitney non-parametric t-test. Error bars represent the mean ± SEM.
Figure 5. Correlation analysis of the microbiome…
Figure 5. Correlation analysis of the microbiome and metabolome
(a) Spearman’s correlation analysis was done with all 84 OTUs in the microbiome color-coded by phylum and grouped based on unsupervised clustering. All 480 metabolites in the metabolome were similarly clustered and then color-coded by KEGG super pathway. There were four distinct clusters of OTUs that were seen, O1–O4, and two distinct clusters of metabolites, M1–M2. The heatmap scale ranges from +1.0 to −1.0. (b) Relative levels of metabolites from all treatment groups (R1, R2, S1 and R3) depicted in a heatmap but keeping with the same order of metabolites as in (a). The heatmap scale for relative levels of metabolites ranges from +10 to −10 on a log2 scale. (c) Relative abundance of OTUs from all treatment groups (R1, R2, S1 and R3) depicted in a heatmap but keeping with the same order of OTUs as in (a). The heatmap scale ranges from 0 to 100% relative abundance.

References

    1. Hall JC, O'Toole E. Intestinal flora in new-born infants with a description of a new pathogenic anaerobe, Bacillus difficilis. Am J Dis Child. 1935;49:390–402.
    1. Dubberke ER, Olsen MA. Burden of Clostridium difficile on the healthcare system. Clin Infect Dis. 2012;55(Suppl 2):S88–S92.
    1. Lucado J, Gould C, Elixhauser A. HCUP Statistical Brief #124. Rockville, MD: Agency for Healthcare Research and Quality; 2012. Jan, Clostridium difficile Infections (CDI) in Hospital Stays, 2009. (Social & Scientific Systems), (CDC), (AHRQ), .
    1. Freeman J, Wilcox MH. Antibiotics and Clostridium difficile. Microbes Infect. 1999;1:377–384.
    1. Pepin J, et al. Emergence of fluoroquinolones as the predominant risk factor for Clostridium difficile-associated diarrhea: a cohort study during an epidemic in Quebec. Clin Infect Dis. 2005;41:1254–1260.
    1. Antonopoulos DA, et al. Reproducible community dynamics of the gastrointestinal microbiota following antibiotic perturbation. Infect Immun. 2009;77:2367–2375.
    1. Dethlefsen L, Huse S, Sogin ML, Relman DA. The pervasive effects of an antibiotic on the human gut microbiota, as revealed by deep 16S rRNA sequencing. PLoS Biol. 2008;6:e280.
    1. Buffie CG, et al. Profound alterations of intestinal microbiota following a single dose of clindamycin results in sustained susceptibility to Clostridium difficile-induced colitis. Infect Immun. 2012;80:62–73.
    1. Reeves AE, et al. The interplay between microbiome dynamics and pathogen dynamics in a murine model of Clostridium difficile Infection. Gut Microbes. 2011;2:145–158.
    1. Vollaard EJ, Clasener HA. Colonization resistance. Antimicrob Agents Chemother. 1994;38:409–414.
    1. van der Waaij D, Berghuis-de Vries JM, Lekkerkerk L-v. Colonization resistance of the digestive tract in conventional and antibiotic-treated mice. J Hyg (Lond) 1971;69:405–411.
    1. Macfarlane GT, Macfarlane S. Bacteria, colonic fermentation, and gastrointestinal health. J AOAC Int. 2012;95:50–60.
    1. Stecher B, Hardt WD. Mechanisms controlling pathogen colonization of the gut. Curr Opin Microbiol. 2011;14:82–91.
    1. Dai ZL, Wu G, Zhu WY. Amino acid metabolism in intestinal bacteria: links between gut ecology and host health. Front Biosci. 2011;16:1768–1786.
    1. Swann JR, et al. Systemic gut microbial modulation of bile acid metabolism in host tissue compartments. Proc Natl Acad Sci U S A. 2011;108(Suppl 1):4523–4530.
    1. Cho I, et al. Antibiotics in early life alter the murine colonic microbiome and adiposity. Nature. 2012;488:621–626.
    1. Hashimoto T, et al. ACE2 links amino acid malnutrition to microbial ecology and intestinal inflammation. Nature. 2012;487:477–481.
    1. Macfarlane GT, Cummings JH, Allison C. Protein degradation by human intestinal bacteria. J Gen Microbiol. 1986;132:1647–1656.
    1. Ng KM, et al. Microbiota-liberated host sugars facilitate post-antibiotic expansion of enteric pathogens. Nature. 2013;502:96–99.
    1. Sorg JA, Sonenshein AL. Bile salts and glycine as cogerminants for Clostridium difficile spores. J Bacteriol. 2008;190:2505–2512.
    1. Dupuy B, Sonenshein AL. Regulated transcription of Clostridium difficile toxin genes. Mol Microbiol. 1998;27:107–120.
    1. Theriot CM, et al. Cefoperazone-treated mice as an experimental platform to assess differential virulence of Clostridium difficile strains. Gut Microbes. 2011;2:326–334.
    1. Reitman ZJ, et al. Profiling the effects of isocitrate dehydrogenase 1 and 2 mutations on the cellular metabolome. Proc Natl Acad Sci U S A. 2011;108:3270–3275.
    1. Karasawa T, Ikoma S, Yamakawa K, Nakamura S. A defined growth medium for Clostridium difficile. Microbiology. 1995;141(Pt 2):371–375.
    1. Geypens B, et al. Influence of dietary protein supplements on the formation of bacterial metabolites in the colon. Gut. 1997;41:70–76.
    1. Sorg JA, Sonenshein AL. Bile salts and glycine as cogerminants for Clostridium difficile spores. J Bacteriol. 2008;190:2505–2512.
    1. Antunes LC, et al. Effect of antibiotic treatment on the intestinal metabolome. Antimicrob Agents Chemother. 2011;55:1494–1503.
    1. Giel JL, Sorg JA, Sonenshein AL, Zhu J. Metabolism of bile salts in mice influences spore germination in Clostridium difficile. PLoS One. 2010;5:e8740.
    1. Fernandez AS, et al. Flexible community structure correlates with stable community function in methanogenic bioreactor communities perturbed by glucose. Appl Environ Microbiol. 2000;66:4058–4067.
    1. Arumugam M, et al. Enterotypes of the human gut microbiome. Nature. 2011;473:174–180.
    1. Qin J, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464:59–65.
    1. Gill SR, et al. Metagenomic analysis of the human distal gut microbiome. Science. 2006;312:1355–1359.
    1. Verberkmoes NC, et al. Shotgun metaproteomics of the human distal gut microbiota. Isme J. 2009;3:179–189.
    1. Perez J, Springthorpe VS, Sattar SA. Clospore: a liquid medium for producing high titers of semi-purified spores of Clostridium difficile. J AOAC Int. 2011;94:618–626.
    1. Nadkarni MA, Martin FE, Jacques NA, Hunter N. Determination of bacterial load by real-time PCR using a broad-range (universal) probe and primers set. Microbiology. 2002;148:257–266.
    1. Nitsche A, et al. Quantification of human cells in NOD/SCID mice by duplex real-time polymerase-chain reaction. Haematologica. 2001;86:693–699.
    1. Schloss PD, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537–7541.
    1. Cole JR, et al. The Ribosomal Database Project: improved alignments and new tools for rRNA analysis. Nucleic Acids Res. 2009;37:D141–D145.
    1. Nelson AM, et al. Disruption of the Human Gut Microbiota following Norovirus Infection. PLoS One. 2012;7:e48224.
    1. R: A language and environment for statisitcal programing. Vienna, Austria: R Foundation for Statistical Computing; 2010.
    1. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A. 1998;95:14863–14868.
    1. Evans AM, DeHaven CD, Barrett T, Mitchell M, Milgram E. Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. Anal Chem. 2009;81:6656–6667.
    1. Dehaven CD, Evans AM, Dai H, Lawton KA. Organization of GC/MS and LC/MS metabolomics data into chemical libraries. J Cheminform. 2010;2:9.
    1. Hammad LA, Derryberry DZ, Jmeian YR, Mechref Y. Quantification of monosaccharides through multiple-reaction monitoring liquid chromatography/mass spectrometry using an aminopropyl column. Rapid Commun Mass Spectrom. 2010;24:1565–1574.
    1. Balch WE, Wolfe RS. New approach to the cultivation of methanogenic bacteria: 2-mercaptoethanesulfonic acid (HS-CoM)-dependent growth of Methanobacterium ruminantium in a pressureized atmosphere. Appl Environ Microbiol. 1976;32:781–791.

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