Computational modeling of the gut microbiota reveals putative metabolic mechanisms of recurrent Clostridioides difficile infection

Abstract

Approximately 30% of patients who have 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 commensal gut bacteria are thought to play an important role. In this study, an in silico pipeline was used to process 16S rRNA gene amplicon sequence data of 225 stool samples from 93 CDI patients into sample-specific models of bacterial community metabolism. Clustered metabolite production rates generated from post-diagnosis samples generated a high Enterobacteriaceae abundance cluster containing disproportionately large 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 16S sequence data of 40 samples from fecal microbiota transplantation (FMT) patients suffering from recurrent CDI and their stool donors, the community modeling method generated a high Enterobacteriaceae abundance cluster with a disproportionate large number of pre-FMT samples. This cluster also was predicted to exhibit reduced secondary bile acid synthesis and elevated aromatic amino acid catabolism. Collectively, these in silico predictions suggest that Enterobacteriaceae may create a gut environment favorable for C. difficile spore germination and/or toxin synthesis.