Genome-scale modeling of microbial metabolic cross-feeding unravels a dynamic pathway toward equilibrium community structure in microbial fuel cells under variable organic loading

The performance of microbial fuel cells (MFCs) in industrial wastewater treatment is heavily influenced by microbial community dynamics, which can be disturbed by fluctuating industrial operations. While specific microbial guilds linked to MFC efficiency have been identified, a comprehensive understanding of the convergent community structure and adaptive pathways remains elusive. In this study, we developed a microbe-microbe interaction genome-scale metabolic model (mmGEM) to investigate microbial alteration in MFCs processing sulfide-rich wastewater from a canned-pineapple factory. Our model incorporated three key microbial guilds: sulfate-reducing bacteria (SRB), methanogens (MET), and sulfideoxidizing bacteria (SOB). Our findings reveal a transition from an SOB-dominant to MET-dominant community as organic loading rates (OLRs) increase, accompanied by a decline in MFC performance. The mmGEM effectively predicted microbial relative abundance at low OLRs (L-OLRs) and community adaptation at high OLRs (H-OLRs). Simulations indicate that SOB growth is constrained at H-OLRs due to decreased sulfate-sulfide (S) cycling and limited acetate cross-feeding with SRB, leading to increased metabolite transfer to MET and fostering their competitive dominance. Further analysis of cross-feeding dynamics under varying OLRs enabled scenario-based simulations to assess the impact of elevated acidity on SOB growth and MFC performance. These insights underscore the critical role of metabolic interactions in microbial community shifts under high OLR conditions and provide a foundation for optimizing MFC technology in industrial wastewater treatment.