Less Genome, More Gain: Genome Reduction Enhances Transaminase-Producing E. coli in a Scale-Down Bioreactor

Area of Science:

• Biotechnology and Bioprocessing
• Metabolic Engineering
• Microbial Physiology

Background:

• Large-scale bioprocesses often create heterogeneous environments due to mixing limitations and design constraints.
• These conditions subject microbial cells to fluctuating external factors, potentially reducing performance compared to lab-scale cultivation.
• Genome-reduced microbial strains are being explored for improved performance in industrial bioprocessing.

Purpose of the Study:

• To evaluate the performance of a genome-reduced Escherichia coli strain (RM214) in producing a heterologous transaminase (TA).
• To compare the productivity of RM214 against a wild-type strain under simulated large-scale bioprocess conditions.
• To assess the robustness of genome-reduced strains in heterogeneous environments.

Main Methods:

• Utilized a STR-PFR (Stirred Tank Reactor - Plug Flow Reactor) scale-down system to mimic industrial bioprocess conditions.
• Subjected both a genome-reduced strain (RM214) and a wild-type Escherichia coli strain to cycles of glycerol limitation/starvation and oxygen limitation.
• Quantified biomass increase, final volumetric activity, substrate uptake rates, and respiratory parameters.

Main Results:

• The genome-reduced RM214 strain significantly outperformed the wild-type strain in transaminase production.
• RM214 exhibited a +53% increase in biomass and a +65% increase in final volumetric activity.
• Reduced biomass-specific substrate uptake and respiratory parameters were observed in RM214, indicating higher efficiency.

Conclusions:

• Genome-reduced Escherichia coli strains, like RM214, demonstrate enhanced robustness and performance in heterogeneous large-scale bioprocessing environments.
• The lower maintenance coefficient of RM214 contributes to its superior biomass yield and productivity.
• These findings support the applicability of genome-reduced strains for optimizing industrial bioprocesses facing challenging conditions.