Computational Modeling of the Gut Microbiota Predicts Metabolic Mechanisms of Recurrent Clostridioides difficile Infection

Abstract

Approximately 30% of patients who have a Clostridioides difficile infection (CDI) will suffer at least one incident of reinfection. While the underlying causes of CDI recurrence are poorly understood, interactions between C. difficile and other commensal gut bacteria are thought to play an important role. In this study, an in silico metagenomics pipeline was used to process taxa abundance data from 225 CDI patient stool samples into sample-specific models of bacterial community metabolism. The predicted metabolite production capabilities of each community were shown to provide improved recurrence prediction compared to direct use of taxa abundance data. More specifically, clustered metabolite synthesis rates generated from post-diagnosis samples produced a high Enterobacteriaceae cluster with disproportionate numbers of recurrent samples and patients. This cluster was predicted to have significantly reduced capabilities for secondary bile acid synthesis but elevated capabilities for aromatic amino acid catabolism. When applied to 40 samples from fecal microbiota transplantation (FMT) patients and their donors, community modeling generated a high Enterobacteriaceae cluster with a disproportionate number of pre-FMT samples. This cluster also was predicted to exhibit reduced secondary bile acid synthesis and elevated aromatic amino acid catabolism. Because clustering of CDI and FMT samples did not identify statistical differences in C. difficile abundances, these model predictions support the hypothesis that Enterobacteriaceae may create a gut environment favorable for C. difficile spore germination and toxin synthesis. ImportanceClostridioides difficile is an opportunistic human pathogen responsible for acute and sometimes chronic infections of the colon. Elderly individuals who are immunocompromised, frequently hospitalized and recipients of antibiotics are particular susceptible to infection. Approximately 30% of treated patients will suffer at least one episode of reinfection, commonly termed recurrence. The objective of the current study was to utilize computational metabolic modeling to investigate the hypothesis that recurrent infections are related to the composition of the gut bacterial community within each patient. Our model predictions suggest that patients who have high compositions of the bacterial family Enterobacteriaceae during antibiotic treatment are more likely to develop recurrent infections due to a metabolically-disrupted gut environment. Successful treatment of recurrent patients with transplanted fecal matter is predicted to correct this metabolic disruption, suggesting that interactions between C. difficile and Enterobacteriaceae are worthy of additional study.