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

Area of Science:

• Environmental Microbiology
• Biotechnology
• Metabolic Engineering

Background:

• Microbial fuel cells (MFCs) treat industrial wastewater, but their efficiency depends on microbial communities vulnerable to operational changes.
• Understanding microbial adaptation and community structure in MFCs is crucial for stable performance.

Purpose of the Study:

• To investigate microbial community adaptation in MFCs treating industrial wastewater using a novel metabolic model.
• To elucidate the role of metabolic cross-feeding in shaping microbial structure under varying organic loading rates (OLRs).

Main Methods:

• Development of a microbe-microbe interaction genome-scale metabolic model (mmGEM).
• Modeling of three key microbial guilds: sulfate-reducing bacteria (SRB), methanogens (MET), and sulfide-oxidizing bacteria (SOB).
• Simulation of microbial community responses to low (L-OLRs) and high (H-OLRs) organic loading rates.

Main Results:

• MFC performance declined with increased OLRs, correlating with a shift from SOB-dominant to MET-dominant communities.
• The mmGEM accurately predicted microbial abundance and adaptation patterns.
• Under H-OLRs, SOB growth was limited by reduced sulfur cycling and acetate cross-feeding, while MET benefited from increased cross-fed metabolites from SRB.

Conclusions:

• Metabolic cross-feeding is a key driver of microbial community structure adaptation in MFCs under fluctuating OLRs.
• Insights into cross-feeding dynamics can guide strategies for optimizing MFCs in industrial wastewater treatment.
• The study provides a framework for predicting MFC performance based on microbial interactions and environmental conditions.