Computational modeling of the gut microbiota reveals putative metabolic mechanisms of recurrent Clostridioides difficile infection, Henson, 2021

Andy · Feb 23, 2021

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.

Author summary
Clostridioides difficile is an opportunistic human pathogen responsible for acute and sometimes chronic infections of the colon. Elderly individuals who are immunocompromised, frequently hospitalized and antibiotic recipients are particular susceptible to C. difficile infection (CDI). Approximately 30% of CDI 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 modeling results suggest that patients who have high abundances of the bacterial family Enterobacteriaceae following 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 computationally predicted to correct this metabolic disruption, suggesting that interactions between C. difficile and Enterobacteriaceae are worthy of additional study.
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Open access, https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008782

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Computational modeling of the gut microbiota reveals putative metabolic mechanisms of recurrent Clostridioides difficile infection, Henson, 2021

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Computational modeling of the gut microbiota reveals putative metabolic mechanisms of recurrent Clostridioides difficile infection, Henson, 2021

Andy Committee Member

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