Fecal Microbiota Transplant (FMT) is an emerging therapy that has had remarkable success in the treatment of infectious diseases like recurrent Clostridioides difficile infection (rCDI) with growing interest in leveraging the treatment for other indications. However, FMT is associated with risks such as the transfer of antibiotic resistant organisms, necessitating the development of a more targeted approach to bacteriotherapy. To address this concern, we developed a novel systems biology pipeline to identify candidate probiotic strains that influence C. difficile pathogenesis. Utilizing metagenomic characterization of human FMT donor samples, we built genome-scale metabolic network reconstructions for candidate probiotic bacteria and used them to model metabolic interactions C. difficile. This revealed high levels of crossfeeding for the amino acids proline, leucine, and phenylalanine in several species most associated with FMT efficacy, which we validated in vitro. Guided by the in silico models we assembled two four member consortia. Treatment with the consortium that was predicted to have increased cross-feeding with C. difficile provided complete protection in a murine model of CDI. This therapy resulted in decreased levels of toxin, recovered gut microbiota diversity, and increased intestinal eosinophil. Our predictive platform uncovered mechanistic connections between the metabolism of the microbiota and C. difficile leading to a rationally designed biotherapeutic for rCDI.
project
|- README # description of content
|- LICENSE # the license for this project
|
|- doc/ # additional documents associated with the study
|
|- data/ # raw and primary data
|
|- code/ # all programmatic code (python & R)
|
|- results/ # all output from workflows and analyses
| |- figures/ # manuscript figures
| +- tables/ # supplementary tables
|
|- notebooks/ # jupyter notebooks for the analyses performed during this study