Mitigation of host cell mutations and regime shift during microbial fermentation: a perspective from flux memory

Area of Science:

• Biotechnology
• Metabolic Engineering
• Synthetic Biology

Background:

• Microbial engineering for enhanced production rates strains cellular resources, leading to growth deficiency.
• This metabolic burden creates selective pressure for mutations that revert to original metabolic pathways ('flux memory'), resulting in non-producers.
• Suboptimal fermentation conditions exacerbate this genetic instability by increasing the selection window for such mutations.

Purpose of the Study:

• To explore strategies for mitigating host genetic instability in microbial engineering.
• To understand the role of metabolic suboptimality and flux memory in bioproduction.
• To identify methods for assessing and preventing culture-population shifts during bioprocess scale-up.

Main Methods:

• Laboratory evolution and high-resolution genome resequencing with phenotype screening.
• Engineering mismatch repair proteins and employing advanced synthetic biology tools (oscillators, biosensor regulators).
• Utilizing 13C-metabolic flux analysis to quantify metabolic suboptimality and its drivers (metabolic burden, cultivation stress).

Main Results:

• Identified key strategies including laboratory evolution, genetic engineering, and synthetic biology approaches to combat instability.
• Demonstrated that 13C-metabolic flux analysis can quantify metabolic burdens and cultivation stresses.
• Established the link between metabolic suboptimality and host mutation rates/spectra.

Conclusions:

• Mitigating metabolic burden and understanding flux memory are crucial for stable, high-yield bioproduction.
• Advanced synthetic biology and metabolic flux analysis offer powerful tools to manage host genetic instability.
• Correlating metabolic suboptimality with mutation dynamics enables early risk assessment and strategies to enhance bioproduction.