Clostridium difficile Alters the Structure and Metabolism of Distinct Cecal Microbiomes during Initial Infection To Promote Sustained Colonization

Abstract

Susceptibility to Clostridium difficile infection (CDI) is primarily associated with previous exposure to antibiotics, which compromise the structure and function of the gut bacterial community. Specific antibiotic classes correlate more strongly with recurrent or persistent C. difficile infection. As such, we utilized a mouse model of infection to explore the effect of distinct antibiotic classes on the impact that infection has on community-level transcription and metabolic signatures shortly following pathogen colonization and how those changes may associate with persistence of C. difficile Untargeted metabolomic analysis revealed that C. difficile infection had significantly larger impacts on the metabolic environment across cefoperazone- and streptomycin-pretreated mice, which became persistently colonized compared to clindamycin-pretreated mice, where infection quickly became undetectable. Through metagenome-enabled metatranscriptomics, we observed that transcripts for genes associated with carbon and energy acquisition were greatly reduced in infected animals, suggesting that those niches were instead occupied by C. difficile Furthermore, the largest changes in transcription were seen in the least abundant species, indicating that C. difficile may "attack the loser" in gut environments where sustained infection occurs more readily. Overall, our results suggest that C. difficile is able to restructure the nutrient-niche landscape in the gut to promote persistent infection.IMPORTANCEClostridium difficile has become the most common single cause of hospital-acquired infection over the last decade in the United States. Colonization resistance to the nosocomial pathogen is primarily provided by the gut microbiota, which is also involved in clearing the infection as the community recovers from perturbation. As distinct antibiotics are associated with different risk levels for CDI, we utilized a mouse model of infection with 3 separate antibiotic pretreatment regimens to generate alternative gut microbiomes that each allowed for C. difficile colonization but varied in clearance rate. To assess community-level dynamics, we implemented an integrative multi-omics approach that revealed that infection significantly changed many aspects of the gut community. The degree to which the community changed was inversely correlated with clearance during the first 6 days of infection, suggesting that C. difficile differentially modifies the gut environment to promote persistence. This is the first time that metagenome-enabled metatranscriptomics have been employed to study the behavior of a host-associated microbiota in response to an infection. Our results allow for a previously unseen understanding of the ecology associated with C. difficile infection and provide the groundwork for identification of context-specific probiotic therapies.

Keywords: 16S rRNA gene sequencing; Clostridium difficile; colonization resistance; machine learning; metabolomics; metagenomics; metatranscriptomics; microbial ecology; microbiome; systems biology.