Metagenomic sequencing from human FMT for C. difficile infection

Cycles of recurrent infection by the bacterial pathogen Clostridoides difficile (rCDI) have proven to be not only difficult to effectively treat, but also extremely costly to the healthcare systems of the United States and Europe. Fecal Microbial Transplant (FMT) has been shown to be highly efficacious against rCDI across large international patient cohorts, but has also been associated with the additional transfer for a variety of negative health outcomes, necessitating more targeted approaches to bacteriotherapy. To address this concern, we utilized a complete systems biology approach to understand the metabolic signals from the microbiome that influence C. difficile pathogenesis. Utilizing a combination of metagenomic characterization of successful human FMT samples with in silico simulation of metabolic interactions between candidate probiotic bacteria and C. difficile, supported by in vitro validation and mass spectrometry. Unsupervised inference for levels of competition and cooperation between species led to assembly of a four member consortia with high levels of putative cross-feeding with C. difficile, which allowed for survival of all animals in a murine model lethal CDI. This result was also associated with significantly decreased toxin levels, recovered gut microbiota diversity, and increased intestinal eosinophil populations. Ultimately, these preclinical studies generated a potential treatment for fulminant CDI as well as a predictive platform for the mechanistic connections between the metabolism of the microbiota and pathogen. Similar applications of systems-biology could subsequently be utilized to identify candidate probiotic strains that interact with other pathogens or even host metabolic phenotypes.