(210f) Prediction and Validation of Microbiota-Derived Tryptophan Metabolites with Anti-Inflammatory Properties

The human gastrointestinal (GI) tract is colonized by ~10¹⁴ bacteria belonging to ~ 500 species that are collectively termed the microbiota. Several clinical and in vivo studies have shown that alterations in the microbiota composition and/or function (i.e., its dysbiosis) are correlated to pathologies such as inflammatory bowel disease (IBD) and colitis. The importance of microbiota metabolites is also evident from a metabolomic study of patients with Crohn’s disease where disease severity was correlated with alterations in the levels of tryptophan in feces. Recently, work from our lab has shown that indole, a metabolite derived by the microbiota from tryptophan, attenuates indicators of inflammation intestinal epithelial cell. These studies highlight the potentially important role for tryptophan metabolites in the regulation of inflammation in the GI tract. However, identification of metabolites that exist in the GI tract is very difficult because many of the gut microbiota species cannot be cultured in vitro. As metabolic pathways are highly conserved across different organisms and many of the microbiota metabolites can also be synthesized by host cells, the panel of metabolites that can be exclusively synthesized by the microbiota is largely unknown.

 In this study, we developed a computational approach for predicting possible tryptophan-derived metabolites that can be uniquely ascribed to microbiota using probabilistic pathway analysis. The probabilistic pathway construction algorithm explores the space of possible biochemical transformations in the KEGG database from a starting metabolite (tryptophan) and predicts all possible product metabolites that can be formed from reactions catalyzed by enzymes that are expressed exclusively in bacteria but not in host cells (e.g. human or mouse). Each of these product metabolites, in turn, are used as substrates for the next round of possible biotransformations, and the algorithm continues until a product metabolite that cannot be uniquely ascribed to bacteria is reached. Starting from tryptophan and its immediate metabolites, the algorithm predicted ~10 possible metabolites that can be uniquely ascribed to microbiota reactions. A targeted metabolomics approach was then taken to identify and quantify four of the predicted metabolites – indole-3-acetate, indole-3-pyruvate, indole-3-acetamide, and tryptamine - that were present in murine intestinal tissue and fecal samples using an LC/MS-MS system operating in multiple reaction monitoring (MRM) mode. Preliminary data suggest that the identified metabolites attenuate indicators of inflammation in mucosal cells; thereby, demonstrating the potential and utility of our integrated bioinformatics and metabolomics approach. The identification of novel anti-inflammatory metabolites is expected to lead to impact the development of therapeutic molecules against inflammatory disorders such as IBD and colitis.