Recurrent Clostridium difficile infection associates with distinct bile acid and microbiome profiles

Abstract

Background: The healthy microbiome protects against the development of Clostridium difficile infection (CDI), which typically develops following antibiotics. The microbiome metabolises primary to secondary bile acids, a process if disrupted by antibiotics, may be critical for the initiation of CDI.

Aim: To assess the levels of primary and secondary bile acids associated with CDI and associated microbial changes.

Methods: Stool and serum were collected from patients with (i) first CDI (fCDI), (ii) recurrent CDI (rCDI) and (iii) healthy controls. 16S rRNA sequencing and bile salt metabolomics were performed. Random forest regression models were constructed to predict disease status. PICRUSt analyses were used to test for associations between predicted bacterial bile salt hydrolase (BSH) gene abundances and bile acid levels.

Results: Sixty patients (20 fCDI, 19 rCDI and 21 controls) were enrolled. Secondary bile acids in stool were significantly elevated in controls compared to rCDI and fCDI (P < 0.0001 and P = 0.0007 respectively). Primary bile acids in stool were significantly elevated in rCDI compared to controls (P < 0.0001) and in rCDI compared to fCDI (P = 0.02). Using random forest regression, we distinguished rCDI and fCDI patients 84.2% of the time using bile acid ratios. Stool deoxycholate to glycoursodeoxycholate ratio was the single best predictor. PICRUSt analyses found significant differences in predicted abundances of bacterial BSH genes in stool samples across the groups.

Conclusions: Primary and secondary bile acid composition in stool was different in those with rCDI, fCDI and controls. The ratio of stool deoxycholate to glycoursodeoxycholate was the single best predictor of disease state and may be a potential biomarker for recurrence.

Figure 1

Figure 2

Shannon Entropy Analysis of Microbial Ecological Diversity. P-values from Wilcoxon-Man-Whitney test. Adjusted p-values collected for multiple hypothesis testing using the method of Benjamini-Hochberg (22). *Denotes adjusted-p-value <0.05 **Denotes adjusted-p-value < 0.001

Figure 3

Relative abundances of microbial phyla in each subject (Control subjects n=21, First time subjects n=20, Recurrent subjects n=19).

Figure 4

Beta diversity of gut microbiome in all subjects. Beta-diversity values were calculated using the unweighted Unifrac dissimilarity measure, to assess differences in overall microbial community structure. Overall community structure differed significantly between all groups, considered pairwise (adjusted p-values = 0.001), using Analysis of Molecular Variance for statistical hypothesis testing.

Figure 5

Random forest model accurately assigns patients to groups based on bile acid part-to-whole ratios, (a) ROC curve for the random forest regression using bile acid part-to-whole ratios as independent variables where false positive rate is the incidence of assigning rCDI as fCDI and true positive rate is the incidence of assigning rCDI as rCDI. (b) ratio of deoxycholate to glycoursodeoxycholate and deoxycholate, which was found to be the most important predictor variable in the random forest model.