In vivo continuous evolution via phenotypic sorting to alleviate metabolic bottlenecks in β-alanine production

Area of Science:

• Synthetic biology
• Metabolic engineering
• Enzyme engineering

Background:

• Beta-alanine (β-Alanine) is a key platform chemical with limited biosynthesis efficiency due to pathway bottlenecks.
• Enzyme constraints, particularly with L-aspartate-α-decarboxylase (PanD_bsu), hinder efficient β-alanine production.

Purpose of the Study:

• To overcome limitations in β-alanine biosynthesis by engineering Escherichia coli.
• To enhance the activity and stability of L-aspartate-α-decarboxylase (PanD_bsu) for improved β-alanine production.

Main Methods:

• Systematic engineering of Escherichia coli MG1655 using modular pathway optimization and combinatorial regulation.
• Development of an in vivo evolution platform combining base-editing and biosensor guidance for high-throughput screening.
• Protein engineering of PanD_bsu through site saturation and iterative mutations, followed by structural and functional analysis.

Main Results:

• Generated PanD_bsu variants with enhanced activity using the biosensor-guided evolution platform.
• Identified a PanD_bsu^T4E mutant that increased specific β-alanine production by 62.45% in engineered strain MA31.
• Discovered that the PanD_bsu^T4E mutant exhibits stabilized quaternary structure via a Glu-Lys salt bridge.

Conclusions:

• A scalable strategy was developed to address pathway bottlenecks in microbial cell factories.
• Integrating protein engineering with biosensor-guided evolution is a powerful approach for optimizing metabolic pathways and enhancing chemical production.