Abstract

ABSTRACTPerturbations to the gut microbiota can result in a loss of colonization resistance against gastrointestinal pathogens such as Clostridium difficile. Although C. difficile infection is commonly associated with antibiotic use, the precise alterations to the microbiota associated with this loss in function are unknown. We used a variety of antibiotic perturbations to generate a diverse array of gut microbiota structures, which were then challenged with C. difficile spores. Across these treatments we observed that C. difficile resistance was never attributable to a single organism, but rather it was the result of multiple microbiota members interacting in a context-dependent manner. Using relative abundance data, we built a machine learning regression model to predict the levels of C. difficile that were found 24 h after challenging the perturbed communities. This model was able to explain 77.2% of the variation in the observed number of C. difficile per gram of feces. This model revealed important bacterial populations within the microbiota, which correlation analysis alone did not detect. Specifically, we observed that populations associated with the Porphyromonadaceae, Lachnospiraceae, Lactobacillus, and Alistipes were protective and populations associated with Escherichia and Streptococcus were associated with high levels of colonization. In addition, a population affiliated with the Akkermansia indicated a strong context dependency on other members of the microbiota. Together, these results indicate that individual bacterial populations do not drive colonization resistance to C. difficile. Rather, multiple diverse assemblages act in concert to mediate colonization resistance.

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