Dynamic substrate preferences predict metabolic properties of a simple microbial consortium.

Area of Science:

• Microbiology
• Systems Biology
• Metabolic Engineering

Background:

• Mixed microbial cultures offer advantages over single-strain engineering for specific biochemical functions.
• Predicting the integrated metabolism of mixed cultures is complex, especially in dynamic environments.
• Exometabolomics provides rich data on resource utilization, aiding predictive modeling.

Purpose of the Study:

• To investigate the predictive power of individual microbial species' substrate utilization for mixed community metabolism.
• To model time-varying substrate depletion in a defined microbial community.
• To identify deviations from predicted metabolism that may indicate inter-species interactions.

Main Methods:

• Exometabolomic profiling of substrate depletion (19 amino acids and glucose) over time.
• Isolation and characterization of Pseudomonas and Bacillus species from groundwater.
• Development and application of predictive models for co-culture substrate utilization.

Main Results:

• Few correlations were found between individual species' resource preferences and growth rate or biomass.
• Most substrate depletion in co-culture largely fit model predictions based on individual species.
• Significant deviations were observed for glucose, histidine, proline, glycine, glutamate, lysine, and arginine, indicating complex interactions.

Conclusions:

• Individual microbial metabolism can predict a substantial portion of a mixed community's function.
• Deviations from predicted substrate usage highlight metabolic pathways influenced by species-species interactions.
• Understanding these interactions can inform strategies to modulate microbial community structure and function.