Identifying predictive features of Clostridium difficile infection recurrence before, during, and after primary antibiotic treatment

Abstract

Background: Colonization by the pathogen Clostridium difficile often occurs in the background of a disrupted microbial community. Identifying specific organisms conferring resistance to invasion by C. difficile is desirable because diagnostic and therapeutic strategies based on the human microbiota have the potential to provide more precision to the management and treatment of Clostridium difficile infection (CDI) and its recurrence.

Methods: We conducted a longitudinal study of adult patients diagnosed with their first CDI. We investigated the dynamics of the gut microbiota during antibiotic treatment, and we used microbial or demographic features at the time of diagnosis, or after treatment, to predict CDI recurrence. To check the validity of the predictions, a meta-analysis using a previously published dataset was performed.

Results: We observed that patients' microbiota "before" antibiotic treatment was predictive of disease relapse, but surprisingly, post-antibiotic microbial community is indistinguishable between patients that recur or not. At the individual OTU level, we identified Veillonella dispar as a candidate organism for preventing CDI recurrence; however, we did not detect a corresponding signal in the conducted meta-analysis.

Conclusion: Although in our patient population, a candidate organism was identified for negatively predicting CDI recurrence, results suggest the need for larger cohort studies that include patients with diverse demographic characteristics to generalize species that robustly confer colonization resistance against C. difficile and accurately predict disease relapse.

Fig. 1

Hierarchically clustered heatmaps showing weighted UniFrac distances (β-diversity) between patient samples prior to antibiotic treatment (a), following antibiotic treatment (b), prior to discharge from hospital (c), and following discharge from hospital (d). Light purple indicates samples that are similar to one another, while dark purple shows highly dissimilar samples. The colored bars next to each row indicate disease severity (healthy, moderate CDI, and severe CDI). Colored bars above columns indicate CDI recurrence

Fig. 2

Boxplots show distributions of Shannon’s diversities (α-diversity) for patients that did or did not show CDI recurrence across multiple time points (pre- and post-treatment and pre- and post-discharge). The only time point when there was a significant difference in Shannon’s diversity between recurrent and non-recurrent patients was pre-treatment

Fig. 3

Principal Coordinate Analysis (PCoA) plots showing β-diversity differences between recurrent and non-recurrent patient samples at the pre-treatment (a), post-treatment (b), pre-discharge (c), and post-discharge (d) time points. The only time point when there was a significant difference in community structure (β-diversity) between recurrent and non-recurrent patients was pre-treatment

Fig. 4

Relative abundances of bacterial phyla in recurrent vs. non-recurrent patients across the different sampling time points

Fig. 5

Bar plots show the relative abundance of Veillonella dispar OTU (predictive of CDI recurrence in our random forest model) in recurrent vs. non-recurrent patients across the different sampling time points. A significant difference in relative abundance of Veilonella dispar was observed between recurrent (n = 10) and non-recurrent (n = 21) patients (Mann-Whitney U test, adjusted p value = 0.026) at the pre-treatment time. All the p values were adjusted using the FDR correction