Clostridioides difficile infection (CDI) remains a significant cause of morbidity and mortality in the health care setting and in the community (Guh et al., 2020). Antibiotic treatments, among other risk factors associated with weakened colonization resistance, increase susceptibility to CDI (Dubberke and Olsen, 2012; Eze et al., 2017). C. difficile residence in the human gastrointestinal (GI) tract may result in a spectrum of clinical manifestations, from asymptomatic colonization to severe CDI-related colitis and fatal toxic megacolon (Crobach et al., 2018). Diagnosis of CDI relies on detection of the protein toxin, most commonly by enzyme immunoassay (EIA), or the detection of the toxin-encoding genes tcdA and tcdB, by nucleic acid amplification test (NAAT). These diagnostic tools serve as rough benchmarks for assessing the severity of disease. Discrepancies between the results of these assays, as in the case of patients with clinically significant diarrhea (CSD) who are EIA negative (EIA-) but NAAT positive for toxigenic C. difficile (Cx+), highlight the complexity of states in which C. difficile can exist in the GI tract. Because CDI is a multi-factorial interaction between the host, pathogen, and microbiome, clarifying the differences in biological correlates between asymptomatic colonization (Cx+/EIA-) and CDI (Cx+/EIA+) is critical for identifying mechanisms of colonization resistance, and for defining novel probiotic or prebiotic avenues for treatment or prevention of CDI (Kondepudi et al., 2012; Rätsep et al., 2017).

C. difficile enters the GI tract as a spore, germinates in the presence of primary bile acids, and replicates through consumption of amino acids and other microbiota or host-derived nutrients (Hryckowian et al., 2017). Notably, many of these metabolic cues are characteristic of a perturbed microbiome (Nagao-Kitamoto et al., 2020; Battaglioli et al., 2018). The hallmark of C. difficile pathogenesis is the expression of the toxin locus encoded on the tcd operon; this locus is tightly regulated by nutrient levels (Martin-Verstraete et al., 2016). Correspondingly, it is hypothesized that an environment replete of nutrients induces toxinogenesis, allowing C. difficile to restructure the gut environment and acquire nutrients through inflammation (Fletcher et al., 2018; Fletcher et al., 2021). The instances of patients who are colonized but have no detectable C. difficile toxin in their stool suggests that these patients’ microbiomes may be less permissive towards CDI development. Identification of metabolic traits within the microbiome of asymptomatic, C. difficile-colonized patients could reveal a number of potential therapeutic pathways toward precise amelioration of symptomatic C. difficile disease.

A multitude of probiotic and prebiotic approaches have demonstrated efficacy to curb C. difficile proliferation in vivo (Rätsep et al., 2017; Chen et al., 2020; Pereira et al., 2020). While restoration of the microbiota through fecal microbiota transplantation can provide colonization resistance (Laffin et al., 2017), the molecular mechanisms of how this resistance is conferred remain unclear. Recent studies using a murine model of infection have indicated that the administration of carbohydrates (both complex and simple) in the diet can be used to curb or prevent CDI (Mefferd et al., 2020; Schnizlein et al., 2020; Hryckowian et al., 2018). Paradoxically, integrated metabolomics and transcriptomics data collected during murine C. difficile colonization indicates that simple carbohydrates are imperative for pathogen replication (Fletcher et al., 2018). It is critical to understand the mechanism by which catabolism of specific carbohydrates could inhibit C. difficile proliferation in the human GI tract.

Here, we perform a multi-level investigation of two relevant patient populations, those colonized with C. difficile but EIA negative (asymptomatically colonized) and those who are EIA positive (CDI) to understand the microbial and metabolic features that may underlie protection from CDI. First, we use microbiome analyses to identify a number of non-C. difficile, clostridial species that are negatively correlated with C. difficile in asymptomatically colonized individuals. Secondly, interrogation of a metabolomics dataset from the same patient population (Robinson et al., 2019) reveals increased abundance of a number of carbohydrate metabolites in asymptomatically colonized patients. Finally, we show that some metabolites enriched in asymptomatically colonized individuals are largely non-utilizable by C. difficile isolates. Together, these datasets reveal that asymptomatically colonized patients are defined by an interaction of clostridial species and carbohydrate metabolites that may serve as a last-line of resistance against CDI in colonized patients.

The clinical manifestation of C. difficile colonization in a host gastrointestinal tract is determined by a multi-factorial interaction between the host, their microbiome, and the pathogen. We hypothesized that, among these factors, natural variation in C. difficile strains infecting patients might differentiate asymptomatic from CDI patients (Dubberke et al., 2018). Through retrospective analysis of a human cohort of 124 patients (Supplementary file 1) with clinically significant diarrhea (CSD) and stool submitted for C. difficile toxin testing, we defined two cohorts: those diagnosed with CDI (Cx+/EIA+) or those asymptomatically colonized (Cx+/EIA-) (Robinson et al., 2019). EIA status (EIA+ or EIA-) was determined by the result of the clinical toxin EIA performed on the stool specimen, and a positive toxigenic culture (a C. difficile isolate with with tcdA and/or tcdB; Cx+) (Dubberke et al., 2018). In-depth analysis of C. difficile isolate factors related to EIA status was performed on the isolates corresponding to the 102 metagenomic samples analyzed (see Materials and Methods, Supplementary file 2). Multiplex PCR was used to identify isolates with cdtAB, the binary toxin locus (Cowardin et al., 2016). Notably, there was a significant enrichment of isolates with cdtAB in the stools of patients with CDI (Figure 1A; p = 0.0012, Fisher’s exact test). Additionally, there were differences in the distribution of C. difficile strains associated with the two patient cohorts; CDI patients were more likely to be infected by a C. difficile isolate of the ribotype 027 lineage (Figure 1A; p = 0.0058, Fisher’s exact test), a clade likely to contain virulent members (Merrigan et al., 2010). Interestingly, of the isolates positive for cdtAB (22 out of 102 isolates), 36% were considered a ribotype 027 strain. Given these genetic indicators of potential differences in virulence, we asked if strains from both groups were capable of producing toxin, using culture supernatants from in vitro broth culture. We found that 56% of isolates expressed detectable TcdA/B, with no significant different (p = 0.86) in the capacity of strains from Cx+/EIA- stools (24 out of 54 isolates) or Cx+/EIA+ (24 out of 48 isolates) to elaborate toxin (Figure 1—figure supplement 1A). Predictably, differences in genetic indicators of strain virulence (as indicated by prevalence of both a prominent ribotype and a second toxin locus) were significant correlates of EIA status.

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Raw absorbance value for in vitro toxin ELISA of 102 C.difficile isolates.

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As antibiotics are a well-known risk factor for CDI, we analyzed previous inpatient antibiotic orders (within one month prior to diagnosis) for patients in the Cx+/EIA- and Cx+/EIA+ cohort, as a proxy for antibiotic exposure (Table 1). Fitting antibiotic exposure to a logistic regression model (McFadden’s R₂ = 0.306) revealed that CDI was significantly associated with cephalosporin exposure. Analysis of potential antibiotic exposures in our patient cohort confirms the risk that antibiotics pose for CDI development (Mullish and Williams, 2018; Webb et al., 2020).

Antibiotics increase susceptibility to CDI through disruption of colonization resistance, mainly conferred to the host via the gut microbiome (Theriot et al., 2014). To determine the microbial correlates of disease state, we performed shotgun metagenomic sequencing on patient stool samples from the asymptomatic (n = 54) and CDI (n = 48) groups, and classified species using MetaPhlAn2. Given the strong association with antibiotic exposure in our CDI cohort, we hypothesized that our asymptomatically colonized patients would have increased microbiome-mediated colonization resistance relative to CDI patients. We examined community structure in stool metagenomes and found that there was no significant difference in Faith’s diversity (Faith and Baker, 2007), a measure of alpha-diversity that incorporates phylogenetic relationships, between patient groups (Figure 1—figure supplement 1B, Wilcoxon rank-sum test, p = 0.1602). There were no significant differences in beta-diversity, as measured by weighted Unifrac distance, between EIA status (p = 0.233, permutational analysis of variance test [PERMANOVA]) (Figure 1B). Although we hypothesized that increased virulence (through additional toxin allele or ribotype) associated with EIA+ could affect microbiome structure, we found no significant association between beta-diversity and cdtAB presence (Figure 1—figure supplement 1C; p = 0.799, PERMANOVA) or ribotype distribution (Figure 1—figure supplement 1D; p = 0.982, PERMANOVA). Previous comparative microbiome studies have revealed phylum-level differences in Bacteroides and Firmicutes in CDI cases versus controls not colonized with C. difficile (Kachrimanidou and Tsintarakis, 2020). In contrast, we found no significant differences in relative abundance of bacterial phyla between asymptomatically colonized patients and patients with CDI (Figure 1—figure supplement 1E). These data indicate that there were no gross differences in microbiome structure related to either EIA status or pathogen features.

Instead, we hypothesized that differences between these states may manifest at higher resolution. We used a multivariable regression model, as implemented by MaAslin2 (Mallick et al., 2021) to identify microbial taxa predictive of either group. Interestingly, species from class Clostridia were most enriched in taxa significantly altered by EIA status (Fisher’s exact test, p = 0.0022). C. difficile was the strongest predictor of CDI state, whereas non-C. difficile clostridial taxa were predictive of asymptomatic state (Figure 1C, FDR < 0.25). Correspondingly, we saw increased C. difficile relative abundance in CDI patients and increased levels of a number of non-C. difficile clostridial species, including Eubacterium spp., Dorea spp., and Lachnospiraceae spp. in asymptomatic patients (Figure 1—figure supplement 1F). Given our inability to detect C. difficile in all sequenced stools (70 out of 102 culture-positive stool samples), we utilized an alternative metagenomic species classifier Kraken (Wood and Salzberg, 2014), to validate our findings. Using Kraken, we detected C. difficile in nearly all stool metagenomes (101 out of 102). Using the identical linear mixed modeling approach (MaAsLin2), we recapitulated data indicating that C. difficile abundance was the strongest predictor of EIA status and increased in Cx+/EIA+ patients. Additionally, a number of commensal clostridial taxa from the Eubacterium genus and Anaerostipes genus were strongly associated with EIA- status, confirming prior MetaPhlAn2 predictions (Figure 1—figure supplement 2A,B).

Using our microbiome data, we examined the association between C. difficile levels and pathogen markers previously associated with EIA status. We found that C. difficile relative abundance was not significantly different when stratified by isolate CDT status (Figure 1—figure supplement 2C; p = 0.3, Wilcoxon rank sum) or isolate ribotype (p = 0.78, Kruskal-Wallis). Notably, there was a slight, yet insignificant increase in C. difficile abundance in microbiomes associated with a ribotype 027 isolate (Figure 1—figure supplement 2D) relative to microbiomes associated with C. difficile isolates of other ribotypes. We also interrogated taxonomic features that were predictive of antibiotic exposure. Expectedly, we found that taxonomic features predictive of CDI state were also associated with antibiotic exposure (Figure 1D). Our data indicate that patients with asymptomatic C. difficile colonization or CDI do not have grossly different gut microbiome community structures but instead have distinctive alterations in a subset of species from class Clostridia and class Bacilli in the microbiota.

C. difficile pathogenesis is heavily affected by carbohydrate, amino acid, and bile acid levels in the gastrointestinal tract, related to the metabolism of competitive commensals (Sorbara and Pamer, 2019). To identify metabolic pathways in other clostridia that might enable them to outcompete C. difficile, we defined metabolic potential in patient microbiomes using HUMAnN2 to quantify microbial pathway abundances. We found no significant differences in alpha- or beta-diversity between overall metabolic pathway composition in the two patient microbiome groups (Figure 2—figure supplement 1A,B; p = 0.2393, Wilcoxon rank sum and p = 0.054, PERMANOVA). Therefore, we trained an elastic net model to identify specific pathways associated with EIA status (Figure 2A). We found a number of carbohydrate degradation pathways and amino acid biosynthetic pathways associated with the asymptomatically-colonized (Cx+/EIA-) patients, including sucrose degradation III and fucose and rhamnose degradation. Investigation of the genera that encode such pathways revealed that the sucrose degradation III pathway was increased in asymptomatic patients, largely due to Blautia spp. and Faecalibacterium spp. of the class Clostridia (Figure 2B). Interestingly, the fucose and rhamnose degradation pathways were entirely defined by Escherichia spp., presumably E. coli. This suggests that metabolic functions such as fucose and rhamnose degradation may be confined to a smaller number of taxa than carbohydrate degradation pathways such as sucrose degradation. Using the HUMAnN2 (Franzosa et al., 2018) gene family information, we used linear mixed modeling to identify carbohydrate-active enzymes differentially associated with EIA status (Figure 2C). Supporting the pathway analysis, we found an increased abundance of a subset of glycoside hydrolase genes, specifically involved in sucrose and starch metabolism in the asymptomatically colonized patients. Our metabolic pathway analyses highlight differentially abundant carbohydrate degradation processes in clostridial taxa that could contribute to colonization resistance against C. difficile in patient microbiomes.

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We hypothesized that differences in metabolic potential of fecal microbiome communities might be reflected in metabolomic profiles, and therefore sought to identify metabolites that are altered in CDI patients relative to those asymptomatically colonized with C. difficile (Robinson et al., 2019). Ordination of Euclidean distances between Cx+/EIA- and Cx+/EIA+ stool metabolomes revealed no significant differences in metabolome structure (Figure 2—figure supplement 1C, PERMANOVA = 0.426). We again used MaAslin2 to determine metabolites associated with each disease state. Consistent with previous analysis, a number of end-product Stickland fermentation metabolites (4-methypentanoic acid and 5-aminovalerate) were associated with CDI patients. While 4-hydroxyproline was the strongest predictor of asymptomaticallycolonized patients, many of the significant metabolites that were associated with asymptomatic patients were predicted to be carbohydrates (Figure 3A, FDR < 0.25; Supplementary file 3). Putative metabolite identities were initially annotated by matching metabolite spectra to the NIST14 GC-MS spectral library. The preponderance of carbohydrates in asymptomatically colonized patients and the substantial similarity of carbohydrate spectra prompted us to rigorously validate the identities of these metabolites by comparing EI spectra and GC retention times against authentic standards, where commercially available (Supplementary file 3, Figure 3—figure supplement 1). These data reveal a carbohydrate signature that is depleted in CDI patients. Notably, fructose and rhamnose are either substrates or products of the sucrose degradation III and fucose and rhamnose degradation pathways, which we found to be enriched in asymptomatically colonized patients. The co-occurrence of these microbial pathways and their corresponding metabolites in asymptomatically colonized patients suggests that a commensal carbohydrate catabolism may contribute to suppression of C. difficile pathogenesis.

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Growth curve data for C. difficile isolates.

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Our examination of taxa, metabolic pathways, and metabolites revealed a number of carbohydrates which we predict are undigestible by C. difficile or are end-products of a more complex commensal metabolism that is exclusionary to C. difficile. Using a set of clinical C. difficile isolates cultured from this patient cohort (8 isolates representing six different ribotypes), we examined growth of C. difficile on carbohydrates associated with asymptomatically colonized patients. Using a defined minimal media (CDMM)( Karasawa et al., 1995) to test nutrient utilization, we found that C. difficile isolates grew robustly on fructose as expected (median maximum A₆₀₀ of 0.90), but did not proliferate on rhamnose or lactulose (median maximum A₆₀₀ of 0.19 and 0.24, respectively). Notably, in the case of sorbitol, we found that a subset of strains, including the reference strain C. difficile 630 and C. difficile VPI10643, grew to a maximum A₆₀₀ of greater than 0.47 (Figure 3B). Given that we had found sucrose degradation as a metabolic pathway enriched in asymptomatically colonized patients, we hypothesized that C. difficile would be unable to use this carbohydrate. Indeed, when grown on sucrose as the sole carbon source, strains achieved a median maximum A₆₀₀ ~4.7-fold less than that of growth on fructose. C. difficile’s restricted carbohydrate metabolism, coupled with the presence of commensal Clostridia could hamper progression to CDI.

We hypothesized that the differential abundance of identified stool metabolites in these patient cohorts is related to the metabolism of specific microbes or host processes. We performed a sparse partial least-squares-discriminatory analysis (sPLS-DA) with the mixOmics package to define relationships between the most predictive features of patient metabolomes and microbiomes. We optimized the number of latent components (Figure 4—figure supplement 1A) and number of variables (Figure 4—figure supplement 1B). Our final model contained two latent components, with the first one composed of 15 metabolites and 25 microbial species. Of the largest metagenomic variable weights, four out of five species (C. difficile, a Lachnospiraceae spp., Anaerostipes hadrus, and Clostridium clostridioforme) were also significantly associated with an EIA state (Figure 1). Of the metabolomic variable weights (Figure 4A), the 10 highest-weighted metabolites were also discovered by previous analyses (Figure 2). The predictive value of each of the components per block was greater that an area under the curve (AUC) of 0.85, with the second metagenomic block component having the best performance (AUC = 0.94, Figure 4—figure supplement 1C). The strong performance of the latent components in classifying samples via EIA status validated our previous findings. Using the variables defining the first latent component, we performed correlational analyses (Figure 4B) and found a number of striking correlations. C. difficile abundance was positively correlated with a number of well-known Stickland metabolites (5-amino-valeric acid and 4-methylpentanoic acid, rho = 0.48 and 0.36, respectively)(Robinson et al., 2019), whereas C. difficile had negative correlations with fructose, rhamnose, and hydroxyproline (rho = –0.27, 0.36, and –0.34, respectively). Given our metagenomic data suggesting that Kraken metagenomic profiling yielded more sensitive estimates of C. difficile abundance, we performed an independent multi-omics analysis on the same dataset using the Kraken metagenomic data. Using a similar process of model building as above, the final model consisted of two latent components, with 15 metabolites and 15 microbes in the first component (Figure 4—figure supplement 1C). C. difficile was also most positively correlated to 5-amino-valeric acid and 4-methylpentanoic acid, with corresponding negative correlations to fructose, rhamnose, and hydroxyproline (Figure 4—figure supplement 1D). These microbe-metabolite relationships highlight the known pathophysiology of CDI, and identify novel C. difficile-carbohydrate relationships that define asymptomatic colonization.

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Given the anticorrelation between C. difficile and rhamnose, we sought to explain the enrichment of this carbohydrate in asymptomatically colonized patients. Though C. difficile cannot grow on rhamnose as the sole carbohydrate, in other organisms rhamnose has substantial transcriptional influence over carbon catabolite gene clusters (Egan and Schleif, 1993; Hirooka et al., 2015). We wanted to rule out the possibility that rhamnose may impact C. difficile through possibly cryptic transcriptional reprogramming, perhaps contributing to C. difficile repression in vivo. Accordingly, we performed whole transcriptome RNA sequencing on C. difficile cultures exposed to a metabolizable substrate, fructose, or a non-metabolizable substrate, rhamnose. In the presence of fructose, we found 555 genes significantly altered (adjusted p-value < 0.05 and |fold-change| > 2) (Supplementary file 4). Some of the most altered genes were indicative of carbon catabolite repression of sugar transport and upregulation of glycolytic processes to metabolize fructose. In contrast, we found only three genes significantly increased in the rhamnose condition. The lack of striking systems-level or targeted (toxin expression, sporulation) regulation by rhamnose, and C. difficile’s inability to utilize it, leads us to conclude that its association with asymptomatically colonized patients’ microbiomes is not through direct interaction or suppression of C. difficile. Instead, we speculate that rhamnose may be the byproduct of a complex commensal metabolism of other dietary polysaccharide substrates, which could exclude C. difficile from the GI tract.

Metagenomics reads were deposited under BioProject accession number PRJNA748262 and RNA sequencing reads were deposited under BioProject accession number PRJNA748261.

1. 1. Fishbein SRS

   (2021) NCBI BioProject

   ID PRJNA748262. Fecal metagenomes of C. difficile colonized patients.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJNA748262
2. 1. Fishbein SRS

   (2021) NCBI BioProject

   ID PRJNA748261. C. difficile carbohydrate transcriptomics.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJNA748261

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Author details

1. Skye RS Fishbein

   1. The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, United States
   2. Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9554-1170

2. John I Robinson

   Center for Women’s Infectious Disease Research, Division of Infectious Diseases, Department of Internal Medicine, Washington University School of Medicine, St Louis, United States

   Contribution

   Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3107-0047

3. Tiffany Hink

   Division of Infectious Diseases, Washington University School of Medicine, St. Louis, United States

   Contribution

   Data curation, Methodology

   Competing interests

   No competing interests declared

4. Kimberly A Reske

   Division of Infectious Diseases, Washington University School of Medicine, St. Louis, United States

   Contribution

   Data curation, Methodology, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

5. Erin P Newcomer

   1. The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, United States
   2. Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

6. Carey-Ann D Burnham

   1. Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, United States
   2. Department of Molecular Microbiology, Washington University School of Medicine, St Louis, United States
   3. Department of Pediatrics, Washington University School of Medicine, St. Louis, United States

   Contribution

   Data curation, Investigation, Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

7. Jeffrey P Henderson

   Center for Women’s Infectious Disease Research, Division of Infectious Diseases, Department of Internal Medicine, Washington University School of Medicine, St Louis, United States

   Contribution

   Conceptualization, Funding acquisition, Investigation, Methodology, Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

8. Erik R Dubberke

   Division of Infectious Diseases, Washington University School of Medicine, St. Louis, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Resources, Supervision, Writing – review and editing

   For correspondence

   edubberk@wustl.edu

   Competing interests

   E.R.D. has received research support from Synthetic Biologics, Pfizer and Ferring, and has been a consultant for Summit, Merck, Ferring, Pfizer and Seres Therapeutics, all unrelated to this study

9. Gautam Dantas

   1. The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, United States
   2. Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, United States
   3. Department of Molecular Microbiology, Washington University School of Medicine, St Louis, United States
   4. Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, United States

   Contribution

   Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – review and editing

   For correspondence

   dantas@wustl.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0455-8370

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