Leveraging a Clostridium Difficile Genre and Metagenomics to Identify Candidate Probiotic Bacterial Strains
Conference on Constraint-Based Reconstruction and Analysis (COBRA)
2018
5th Conference on Constraint-Based Reconstruction and Analysis (COBRA 2018)
Poster Session
Poster Session
Sunday, October 14, 2018 - 6:00pm to 7:00pm
Clostridium difficile is a Gram-positive, sporulating anaerobe that has become the leading causing of hospital-acquired infection. Exposure to antibiotics sensitizes hosts to colonization of the gastrointestinal tract by C. difficile through disruption of the healthy resident bacterial community, known as the gut microbiota. Previous studies have strongly supported that colonization resistance to C. difficile is driven by competition for growth nutrients by members of the gut microbiota. As C. difficile is known to colonize numerous host species and is capable of catabolizing a large array of possible growth substrates, it is difficult to determine the bacterial groups in a resistant community that confer this property. In order to more closely study nutrient utilization by C. difficile during infection across distinctly susceptible environments within the complex milieu of the gut microbiome, we employed Flux-Balance Analysis in an in vivo contextualized GENRE of C. difficile strain 630. In consideration of large deficits in nutrient utilization pathways and in an effort to address several challenges with existing reconstructions of C difficile, we generated a de novo reconstruction of C. difficile 630 followed by extensive manual curation of its core metabolism and nutrient acquisition systems. In silico predictions in both rich and minimal medias, as well as transcriptomic and metabolomic data contextualization collected from an animal model of infection, closely mimic experimental characterization across multiple in vitro and in vivo studies. Furthermore, correlations between predicted C. difficile substrate utilization across distinct infection conditions with metagenome-enabled metatranscriptomic results from those microbiotas reveal discrete groups of bacteria which share distinct metabolic functionalities with the pathogen when compared to resistant communities. Ultimately, by identifying metabolic functionalities of resistant/sensitive communities we can design targeted probiotic therapies with the desired community phenotype.