Constraint-based genome-scale computational modeling of human enterotype-associated gut commensal metabolism: Focus on the systems metabolic picture of bacterium Prevotella copri

A constraint-based, computational genome-scale metabolic model is a comprehensive set of metabolic reactions in an organism, which enables quantitative prediction of metabolic fluxes and cell growth rates under various conditions. Our proposed research aims to harness the power of this modeling for elucidating the metabolic capabilities of a less characterized but representative microorganism inside the human gut, Prevotella copri.

The human gut microbiota plays an important role in human health and disease. Given the complexity of the gut microbiota, the stratification of microbiota compositions has been of interest. Despite debate, the stratified composition types, called enterotypes, have been recurrently proposed across studies and Prevotella is the bacterial genus enriched in a certain enterotype. Among such Prevotella species, P. copri is the most abundant, particularly in association with plant-based diets. However, P. copri also shows seemingly contradictory effects on host glucose homeostasis and is implicated in the risk of autoimmune diseases. Yet, the predominant P. copri studies have been based on the genomic and metagenomic analyses.

A major stepping point is to elucidate functions and mechanisms with detailed systems modeling and culture-based approaches. Here, we propose to develop the high-quality, validated, computable genome-scale model of P. copri metabolism by the state-of-the-art integration of predictive computational modeling, in vitro culture, and metabolomic/transcriptomic profiling. This modeling aims to unveil the detailed global metabolic functionality of P. copri and to predict growth-limiting nutrients of this organism towards the rational design of nutrient supplementation for the microbiota composition intervention.

First, we will assemble the high-quality genome-scale metabolic network of P. copri from genomic, biochemical, and literature information. The initially constructed model will undergo in vitro experimental validation and further refinement. In parallel, we will perform metabolomic and transcriptomic profiling and thereby establish a comprehensive set of secreted metabolites and transcript expressions and enhance the completeness of the model. Lastly, we will simulate the multi-species system including our well-curated P. copri model in nutrient conditions reminiscent of different human diets to characterize interspecies metabolic interactions and predict the repertoire of growth-limiting nutrients of these interacting organisms. We expect that the mechanistic insights and predicted candidate compounds for nutrient supplementation from our work could contribute towards the development of precision healthcare.