Optimized identification of therapeutic bacterial strains in ulcerative colitis

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PROJECT SUMMARY Ulcerative colitis (UC) is a chronic gut inflammatory condition thought to be caused by a combination of genetic and environmental factors. Therapeutic options are only partially effective in inducing and maintaining remission, and there is currently no cure for UC. Recent studies have found the microbiome of UC patients is distinct compared to that of healthy controls, suggesting that the microbiome could be a promising therapeutic target. Fecal microbiota transplant (FMT) has been extremely successful in the treatment of colitis caused by Clostridium difficile infection, further demonstrating the potential for bacterial therapeutics. The recently completed FOCUS (Faecal Microbiota Transplantation in Ulcerative Colitis) trial has demonstrated that FMT from healthy donors can induce clinical remission in 27% of UC patients compared to only 8% in the placebo group. However, we still lack an understanding of the exact mechanisms by which FMT is able to modulate disease pathogenesis. The overarching hypothesis of this proposal is that UC remission after FMT is induced by specific bacteria, and that accurate identification of bacterial strains will be paramount to develop microbial therapeutics for UC. To test this hypothesis, we will first develop a novel ensemble method to identify bacterial species and strains from deep metagenomic data. This method combines software tools using a voting algorithm that is weighted based on the accuracy of each tool. The accuracy of this method will be tested using microbial culture collections from UC patients, in silico simulations, and a bacterial mock community as gold standards. We will then apply this approach to metagenomic data generated from the FOCUS study in order to identify bacterial strains that are associated with UC remission. This will be performed by using established supervised learning techniques, as well as through a novel method based on co- occurrence network analysis that identifies clusters of bacteria that act synergistically. Based on these results, we will finally validate our findings using a gnotobiotic model of colitis. The prophylactic and/or therapeutic potential of candidate bacteria will be tested by inoculating them to gnotobiotic mice colonized with the microbiota of UC patients and with colitis induced through the transfer of naïve T cells. Our proposal will be the first to address the question of how specific bacteria modulate disease progression in a randomized trial of FMT in UC through the development of more sensitive and accurate methods for strain characterization that can be experimentally validated. The rational design of our strategy to identify therapeutic bacteria will produce highly translational results that can be used to guide future clinical trials in UC.