Metabolic cross-feeding interactions modulate the dynamic community structure in microbial fuel cell under variable organic loading wastewaters

The efficiency of microbial fuel cells (MFCs) in industrial wastewater treatment is profoundly influenced by the microbial community, which can be disrupted by variable industrial operations. Although microbial guilds linked to MFC performance under specific conditions have been identified, comprehensive knowledge of the convergent community structure and pathways of adaptation is lacking. Here, we developed a microbe-microbe interaction genome-scale metabolic model (mmGEM) based on metabolic cross-feeding to study the adaptation of microbial communities in MFCs treating sulfide-containing wastewater from a canned-pineapple factory. The metabolic model encompassed three major microbial guilds: sulfate-reducing bacteria (SRB), methanogens (MET), and sulfide-oxidizing bacteria (SOB). Our findings revealed a shift from an SOB-dominant to MET-dominant community as organic loading rates (OLRs) increased, along with a decline in MFC performance. The mmGEM accurately predicted microbial relative abundance at low OLRs (L-OLRs) and adaptation to high OLRs (H-OLRs). The simulations revealed constraints on SOB growth under H-OLRs due to reduced sulfate-sulfide (S) cycling and acetate cross-feeding with SRB. More cross-fed metabolites from SRB were diverted to MET, facilitating their competitive dominance. Assessing cross-feeding dynamics under varying OLRs enabled the execution of practical scenario-based simulations to explore the potential impact of elevated acidity levels on SOB growth and MFC performance. This work highlights the role of metabolic cross-feeding in shaping microbial community structure in response to high OLRs. The insights gained will inform the development of effective strategies for implementing MFC technology in real-world industrial environments. Microbial fuel cells (MFCs) use microorganisms to break down waste biomass and produce electricity directly. The technology holds potential for sustainably efficient wastewater treatment that supports the concept of green economy and zero-waste society. However, application of MFCs to the actual industrial wastewaters was shown a decline in electricity generation and treatment efficiency when organic loading rates (OLR) increased. This decline of MFC performance was attributed to alterations in microbial composition. The lack of understanding regarding the impact of OLR on microbial composition has hindered the maintenance of optimal performance and has consequently impeded the practical application of MFC systems. To this end, we developed a metabolic model (GEM) of microbial community (called mmGEM) to investigate the metabolic cross-feeding interactions which is an important mechanism affecting microbial composition dynamics. Simulation of mmGEM showed that the change of common metabolite cross-feedings altered the microbial composition during increasing OLR. Further analysis of mmGEM offer practical recommendations for optimizing MFCs by supporting growth and interaction of electricity-related microbial guild. In addition, our work exhibits the use of mmGEM to simulate scenario-based circumstances of microbial communities in MFCs that effectively facilitate an optimization of MFC to real-world implementation in industrial sector.