Metabolomic networks connect host-microbiome processes to human Clostridioides difficile infections

Abstract

Clostridioides difficile infection (CDI) accounts for a substantial proportion of deaths attributable to antibiotic-resistant bacteria in the United States. Although C. difficile can be an asymptomatic colonizer, its pathogenic potential is most commonly manifested in patients with antibiotic-modified intestinal microbiomes. In a cohort of 186 hospitalized patients, we showed that host and microbe-associated shifts in fecal metabolomes had the potential to distinguish patients with CDI from those with non-C. difficile diarrhea and C. difficile colonization. Patients with CDI exhibited a chemical signature of Stickland amino acid fermentation that was distinct from those of uncolonized controls. This signature suggested that C. difficile preferentially catabolizes branched chain amino acids during CDI. Unexpectedly, we also identified a series of noncanonical, unsaturated bile acids that were depleted in patients with CDI. These bile acids may derive from an extended host-microbiome dehydroxylation network in uninfected patients. Bile acid composition and leucine fermentation defined a prototype metabolomic model with potential to distinguish clinical CDI from asymptomatic C. difficile colonization.

Keywords: Amino acid metabolism; Bacterial infections; Diagnostics; Gastroenterology; Infectious disease.

Figure 1. Metabolomic characteristics of the patient cohort.

(A) Histogram showing the distribution of feature richness (number of features present per sample) across all patient specimens. (B) Histogram showing the number of samples within which each unique feature is present. Fecal metabolomes were highly individualistic: among the more than 2000 features detected, most were infrequent. While the resulting data are very sparse overall, the distribution has a relatively heavy tail with a few features present in many samples. (C) Principal component analysis (PCA) score plot across the first 2 components created using log-transformed feature intensities across all metabolomic features. (D) PCA does not appear to reveal dominant modes of variation, with no single component explaining more than 9% of the variance and a long tail of modes each explaining approximately 1% each.

Figure 2. Supervised metabolomic analyses comparing Cx⁺/EIA⁺ with Cx^–/EIA^– samples.

(A) Observed separation of Cx⁺/EIA⁺ and Cx^–/EIA^– samples under sparse partial least squares–discriminatory analysis (sPLS-DA). The data ellipses are drawn around each group of samples (at the 95% level). (B) Penalized logistic regression under repeated 5-fold cross-validation shows how the number of features used relates to the obtained accuracy, yielding high accuracy with a relatively small number of features. The maximum percent predicted is indicated by a star. (C) Using the penalty parameter associated with the maximum percent predicted, penalized logistic regression demonstrates good separation in the distribution of log-odds to be classified Cx⁺/EIA⁺ versus Cx^–/EIA^–. In the log-odds distribution shown here, only the test folds of Cx⁺/EIA⁺ and Cx^–/EIA^– for each randomized cross-validated run are shown (that is, the corresponding distribution of the training set is not shown). For comparison, the corresponding log-odds of the Cx⁺/EIA^– samples are also shown. (D) Logistic regression (without penalty) to classify Cx⁺/EIA⁺ versus Cx^–/EIA^– was performed using only the 6 features most frequently used in the penalized logistic regressions. Fitting to all samples gives 96.7% ROC AUC. The 95% CI of 85.6%–100% AUC was obtained under repeated randomized 5-fold cross-validation using the same 6 features.

Figure 3. Amino acid metabolism in C. difficile.

(A) Stickland metabolism consists of anaerobic amino acid fermentation through coupled oxidation and reduction pathways. In the reductive pathway, amino acids are first deaminated to form 2-hydroxy acids and then reduced to carboxylic acids. In the oxidative pathway, amino acids are deaminated and oxidized with loss of CO₂ to yield a distinct set of carboxylic acids. Depicted here are established Stickland substrates and products identified within patient fecal metabolomes. Stickland substrates include the nonproteinogenic amino acid ornithine. ND, not determined. (B) Heatmap of Stickland precursor and product abundances corresponding to patient fecal metabolomes from the 3 diagnostic groups. Metabolites were organized using unsupervised hierarchical clustering. Metabolites differing significantly (Mann-Whitney U test; *P < 0.05, ***P < 0.001) between Cx^–/EIA^– and Cx⁺/EIA⁺ groups are labeled, along with the direction of the difference relative to the Cx^–/EIA^– control group. Stickland products are labeled according to the color scheme in A. (C) Adjusted and unadjusted (crude) CDI odds ratios and confidence intervals (95%) for Stickland precursors and products. Odds ratios were estimate by fitting logistic regression models to each of 2000 bootstrap samples stratified on Cx/EIA status (Cx^–/EIA^– vs. Cx⁺/EIA⁺). Logistic models containing a single metabolite were fit to obtain crude odds ratios (red). A single logistic model including all metabolites was fit to obtain the adjusted odds ratios (green). Bars represent 95% bootstrap percentile confidence intervals and black dots represent median odds ratios across all bootstrap samples. Stickland products are labeled according to the color scheme in A.

Figure 4. 4-MPA/leucine ratio elevated in CDI.

(A) Dot plots of 4-MPA/leucine product/precursor ratios measured by targeted (SIM) reanalysis of fecal specimens (n = 32 for each group). Patient groups were compared using the Kruskal-Wallis test (P = 1.3 × 10^–8). To further characterize pair-wise differences between groups, Bonferroni-corrected Mann-Whitney U test P values are indicated (3 comparisons; NS: P ≥ 0.05, ***P < 0.001). Ratio thresholds giving perfect specificity (0.0825, black star) or sensitivity (0.00132, white star) for CDI⁺/EIA⁺ are marked as gray dashed lines. (B) Receiver-operator characteristic (ROC) plot distinguishing Cx⁺/EIA⁺ patients from Cx^–/EIA^– patients. The gray region represents the bootstrapped 95% confidence interval for the true-positive rate at each false-positive rate. Thresholds with perfect specificity or sensitivity are marked by stars, as in A.

Figure 5. Isoleucine isomer correlated with C. difficile.

(A) Chemical structures of isoleucine and its diastereomer, allo-isoleucine. (B) Dot plot of allo-isoleucine/isoleucine ratios as measured by SIM (n = 32 for each group). Patient groups were compared using the Kruskal-Wallis test (P = 6.5 × 10^–5). To further characterize pair-wise differences between groups, Bonferroni-corrected Mann-Whitney U test P values are indicated (3 comparisons; NS: P ≥ 0.05, ***P < 0.001). (C) ROC plot showing ability to distinguish Cx⁺/EIA⁺ patients from Cx^–/EIA^– patients. The gray region represents the bootstrapped 95% confidence interval for the true-positive rate at each false-positive rate.

Figure 6. Bile acid transformations in the clinical cohort.

(A) A force-directed network layout illustrates associations between bile acids in the study cohort. Each node represents a bile acid and each connecting line (edge) represents an association between 2 bile acids as 1 of the 5 highest correlations for at least 1 of the corresponding nodes. Edge lengths are determined by the level of correlation between connected bile acids. Nodes are colored by community assignment. (B) Scheme showing metabolic transformations producing bile acids in the network analysis. The central structure highlighted in gray represents a tri-hydroxylated primary bile acid (e.g., cholic acid). Taurine or glycine conjugation forms peptide bonds to the carboxylic acid group (right). Alcohol groups are removed from the bile acid nucleus (dehydroxylation, bottom right) or oxidized to a ketone (top left). Bile acid sulfation involves substitution of an alcohol group with a sulfate (R = SO₄^–) group (bottom left). Desulfation of bile acid sulfates yields unsaturated bile acids (left).

Figure 7. The bile acid distribution in patients with CDI resembles that of a characteristic subgroup of uninfected, hospitalized patients.

(A) Depicted here is a PCA plot of uninfected patients’ bile acid profiles (green, n = 62). Onto this space, we projected the bile acid metabolome of patients with CDI (red, n = 62). Data ellipses are drawn around each group of samples (95% level). Clustering of CDI specimens at high PC1 values is consistent with a favored bile acid distribution among patients with CDI. (B) Dot plot of PC1 scores for each patient sample (n = 62 in each group). Gray dashed line represents optimal PC1 threshold for distinguishing Cx^–/EIA^– from Cx⁺/EIA⁺ samples. This threshold was chosen by maximizing the sum of percent sensitivity and specificity. (C) ROC plot evaluating the ability of PC1 to distinguish CDI patients from controls. The gray region represents the bootstrapped 95% confidence interval for the true-positive rate at each false-positive rate. An asterisk marks the point corresponding to the optimal PC1 threshold depicted in B. (D) PCA loading plot depicting the relative contributions of each bile acid to the distribution of Cx^–/EIA^– samples in A. Abbreviations are indicated in Table 3.

Figure 8. Principal component analysis of GC-MS–defined metabolome in the clinical cohort.

(A) Depicted here is a PCA plot of uninfected patients’ GC-MS metabolomes (green, n = 62), onto which is projected the GC-MS metabolomes of patients with CDI (red, n = 62). Data ellipses are drawn around each group of samples (95% level). The clustering of CDI specimens at high PC1 values is consistent with a favored metabolomic profile among patients with CDI. (B) Dot plot of PC1 scores for each patient (n = 62 in each group). Gray dashed line depicts the PC1 threshold that maximizes the sum of percent sensitivity and specificity for distinguishing Cx^–/EIA^– from Cx⁺/EIA⁺ samples. (C) ROC plot evaluating the ability of PC1 to distinguish between CDI patients and controls. The gray region represents 95% confidence intervals bootstrapped for the true-positive rate at each possible false-positive rate. An asterisk marks the point corresponding to the optimal PC1 threshold depicted in panel B. (D) Plot of PC1 and PC2 loadings for all 2539 GC-MS features. It depicts the relative contributions of each GC-MS feature to the distribution of Cx^–/EIA^– samples in the PCA projection in A. Features in the top or bottom 1% of PC1 loadings tentatively identified as sugars or sugar alcohols are highlighted in blue.

Figure 9. Interrelationships between host- and C. difficile–associated metabolites.

(A) Plotting bile acid PC1 (Figure 7) versus 4-methylpentanoic acid index (Figure 4) reveals that high PC1 score and high 4-methylpentanoic acid index values coincide in patients with CDI compared with control patients (n = 32 for each group). The dashed line marks the dividing line assigned 50% probability of being Cx⁺/EIA⁺ by a logistic regression model incorporating both PC1 and 4-methylpentanoic acid index. (B) Probabilities assigned to each patient by the logistic regression model (n = 32 per group). Higher values indicate higher certainty of Cx⁺/EIA⁺ status. The gray line marks the 50% probability cutoff above which samples are considered Cx⁺/EIA⁺. (C) ROC curve showing the performance of the logistic regression model in discriminating Cx^–/EIA^– patients from Cx⁺/EIA⁺ patients. The gray region represents 95% confidence intervals bootstrapped for the true-positive rate at each possible false-positive rate. The AUC and its 95% confidence interval are also reported. (D) Euler diagram showing the overlap between culture, EIA, and metabolome status. Samples were considered metabolome-positive if assigned a probability above 50% by the logistic regression model.