Perturbations 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 hours 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 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.
project
|- README # the top level description of content
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|- doc/ # documentation for the study
| |- notebook/ # preliminary analyses (dead branches of analysis)
| +- paper/ # manuscript(s), whether generated or not
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|- data # raw and primary data, are not changed once created
| |- references/ # reference files to be used in analysis
| |- raw/ # raw data, will not be altered
| +- process/ # cleaned data, will not be altered once created
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|- code/ # any programmatic code
|- results # all output from workflows and analyses
| |- tables/ # text version of tables to be rendered with kable in R
| |- figures/ # graphs, likely designated for manuscript figures
| +- pictures/ # diagrams, images, and other non-graph graphics
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|- scratch/ # temporary files that can be safely deleted or lost
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|- study.Rmd # executable Rmarkdown for this study, if applicable
|- study.md # Markdown (GitHub) version of the *Rmd file
|- study.html # HTML version of *.Rmd file
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+- Makefile # executable Makefile for this study, if applicable