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Clostridium Difficile Colonizes Alternative Nutrient Niches

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Clostridium Difficile Colonizes Alternative Nutrient Niches bioRxiv preprint doi: https://doi.org/10.1101/092304; this version posted December 20, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 Clostridium difficile colonizes alternative nutrient 2 niches during infection across distinct murine gut 3 environments 4 Authors: Matthew L. Jenior, Jhansi L. Leslie, Vincent B. Young, and Patrick D. Schloss* 5 Abstract 6 Clostridium difficile infection (CDI) has grown to be the most prevalent cause of hospital 7 acquired infection in the United States. Susceptibility to CDI is induced by recent 8 antibiotic exposure, which is known to alter the structure of the gut microbiome and to 9 affect the availability of growth nutrients in the gut. We hypothesized that C. difficile is a 10 generalist that adapts its physiology to the nutrients available within the gut. We orally 11 challenged C57BL/6 mice that previously received one of three antibiotics with C. 12 difficile and demonstrated that it was able to colonize the cecum within 18 hours of 13 infection. However, levels of both spore and toxin production, which are known to be 14 affected by nutrient availability, varied between each antibiotic treatment group. To 15 more closely investigate the specific responses of C. difficile as it colonized the cecum, 16 we performed in vivo transcriptional analysis of C. difficile from cecal content of infected 17 mice. This approach revealed variation in expression of genes that drive life-cycle 18 switches as well as metabolic pathways associated with catabolizing a variety of carbon 19 sources such as carbohydrates, amino acids, and amino sugars. To assess which 20 substrates C. difficile was most likely exploiting in each antibiotic-perturbed microbiome, 1 bioRxiv preprint doi: https://doi.org/10.1101/092304; this version posted December 20, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 21 we developed a novel metabolite scoring algorithm within the genome-scale bipartite 22 metabolic network of C. difficile that incorporated both network topology and transcript 23 abundance to infer the likelihood that a given metabolite was acquired from the 24 environment. Applying this approach, we found that C. difficile indeed occupies 25 alternative nutrient niches across each antibiotic-perturbed microbiome and that the 26 highlighted metabolites support significant growth, in vitro. In silico findings were further 27 validated using in vivo metabolomic analysis to compare resitant and susceptible cecal 28 conditions. These results support the hypothesis that consumption of N-acetyl-D- 29 glucosamine and Stickland fermentation substrates are central components of C. 30 difficile's metabolic strategy and pathogenesis. This work has implications for 31 elucidating specifics of the nutrient niche of C. difficile during infection and may lead to 32 the discovery of targeted measures to prevent C. difficile colonization including potential 33 pre- or probiotic therapies. 34 Significance 35 Clostridium difficile infection has grown to be the largest single cause of hospital- 36 acquired infection in the United States. A compromised gut microbiota, typically through 37 previous antibiotic treatment, is a prerequisite feature of C. difficile colonization 38 susceptibility. This loss of resistance has also been described across classes of 39 antibiotics, in which many result in distinct gut communities and each presumably 40 presents individual metabolic challenges to C. difficile as it colonizes. In this study we 41 demonstrate that not only does C. difficile alter its pathogenesis between differentially 42 sensitized hosts, but also exploits separate available nutrient niches across these 43 environments. We accomplish this through an in vivo murine model of infection, 2 bioRxiv preprint doi: https://doi.org/10.1101/092304; this version posted December 20, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 44 characterized using transcriptomic-enabled genome-scale metabolic modeling in 45 combination with untargeted metabolomic analysis. Our results support that C. difficile 46 possesses a highly plastic nutrient niche space, allowing it to successfully infect 47 numerous different hosts and ultimately cause disease. This work also provides 48 evidence that C. difficile virulence may be driven by the concentrations of specific 49 carbohydrate sources that are accessible for growth during each unique infection. 50 Introduction 51 Infection by the Gram-positive, spore-forming bacterium Clostridium difficile has 52 increased in both prevalence and severity across numerous countries during the last 53 decade (1). In the United States, C. difficile was estimated to have caused >500,000 54 infections and resulted in ~$4.8 billion worth of acute care costs in 2014 (2). C. difficile 55 infection (CDI) causes an array of toxin-mediated symptoms ranging from abdominal 56 pain and diarrhea to the more life-threatening conditions pseudomembraneous colitis 57 and toxin megacolon. Prior treatment with antibiotics is the most common risk factor 58 associated with susceptibility to CDI (3). It has been shown that antibiotic therapy alters 59 the structure and function of the gut microbiota making it susceptible to colonization by 60 C. difficile (4). This is referred to as colonization resistance, in which the gut microbiota 61 inhibits the persistence or growth of a number of pathogenic bacteria. Colonization 62 resistance can be achieved by multiple mechanisms including competition for physical 63 space or growth nutrients (5). 64 Mouse models have been an effective tool for studying the mechanisms of colonization 65 resistance. Use of distinct antibiotic classes to vary the structure of the microbiota which 66 have been shown to result in susceptibility C. difficile colonization (6–8). In this case, 3 bioRxiv preprint doi: https://doi.org/10.1101/092304; this version posted December 20, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 67 the antibiotics chosen significantly impact the structure and diversity of the cecal 68 microbiome (Fig. S1a & S1b). It has been further demonstrated that at 18 hours after 69 being introduced to a cefoperazone treated mouse, C. difficile reached its maximum 70 vegetative cell density in the cecum (9). This provided a single timepoint to measure the 71 largest population of metabolically active C. difficile. Building upon these results, others 72 have shown that many of these antibiotic classes also alter the gut metabolome, 73 increasing the concentrations of known C. difficile growth substrates (7, 10–12). Taken 74 together these results are a strong indication that the healthy gut microbiota inhibits the 75 growth of C. difficile through limitation of substrates it needs to grow. The ability of an 76 intact gut community to exclude C. difficile colonization is suggestive of the nutrient- 77 niche hypothesis in which an organism must be able to utilize a subset of available 78 resources better than all competitors to colonize the intestine (13, 14). 79 Based on its genome sequence and in vitro growth characteristics, C. difficile appears 80 able to fill multiple nutrient niches. C. difficile has a relatively large and mosaic genome, 81 it is amenable to a variety of growth substrates, and is able to colonize a diverse array 82 of hosts suggesting that that it is a bacterial generalist (15–17). The ability to metabolize 83 a variety of substrates is important since these substrates affect the regulation of genes 84 involved in C. difficile’s pathogenesis. For example, in vitro transcriptomic analysis 85 suggests that high concentrations of easily metabolized carbon sources, such as 86 glucose or amino acids, inhibit toxin gene expression and sporulation (18, 19). These 87 genes are regulated by DNA-binding sigma factors, such as the pleiotropic regulator 88 ccpA, which are under the control of environmental nutrient concentrations, especially 89 carbohydrates (20, 21). Downstream effects of this regulation likely have enormous 4 bioRxiv preprint doi: https://doi.org/10.1101/092304; this version posted December 20, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 90 impact on the lifestyle and metabolic strategy of C. difficile when colonizing across 91 sensitive hosts. 92 Previous transcriptomic studies of C. difficile have mainly focused on transcription of 93 virulence factors, in vitro (22, 23), with some work characterizing transcription during 94 colonization of germfree mice (24, 25). More relevant to nutrient acquisition, C. difficile 95 up-regulated several phosphotransferase systems (PTS) and ABC transporters in 96 germfree mice, alluding to metabolic adaptation to nutrient availability in vivo (25). 97 Although these analyses are informative, they are either primarily directed toward the 98 expression of virulence factors or lack the context of the gut microbiota which C. difficile 99 must compete against for substrates. Metabolomic analyses have also been used to 100 more directly assay changes in bacterial metabolism as they relate to CDI (7, 12); 101 however, these methods cannot focus on C. difficile-specific metabolites and more 102 closely resemble echoes of metabolism, not currently active processes. In contrast to 103 these approaches, in vivo C.
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