Metabolic Modelling Facilitates the Design of Synthetic Communities by Simplifying Natural Microbial Communities.

Natural microbial communities generally have complex compositions and unclear metabolic interactions, posing constraints on their applications. Clarifying these intricate interactions within microbial communities is challenging for traditional experiment-based methods. Here, we developed a simulation-based approach to design synthetic communities (SynComs) by simplifying complex microbial communities through metabolic modelling. We constructed genome-scale metabolic models (GSMMs) and curated them based on data obtained from straightforward experiments, ensuring these models precisely characterized metabolic features of each strain. By simulations utilizing multi-strain metabolic models encompassing various strain combinations, we identified helper strains capable of enhancing the degradation efficiency of degrader strains and predicted optimal strain combinations that achieved a simplified community structure while maintaining high pollutant-degrading efficiency. The simulations also unravelled cross-feeding of glucosamine, amino acids and organic acids between the degrader and helper strains, which boosted the pollutant-degrading efficiency of SynComs. Furthermore, helper strains rapidly degraded the toxicant intermediate, thereby alleviating its inhibitory effect on degrader strains. These predictions were further verified experimentally, demonstrating the accuracy and feasibility of metabolic model-based simulations. Our study establishes a framework for designing simplified SynComs without sacrificing degradation efficiency and highlights the often-underestimated role of microbial interactions in biodegradation.