Rational Design Assisted by Evolutionary Engineering Allows (De)Construction and Optimization of Complex Phenotypes...

Area of Science:

• Synthetic Biology
• Metabolic Engineering
• Microbial Physiology

Background:

• Complex phenotype engineering requires integrating genetic circuits within the host's metabolic and genetic context.
• Standardized, reliable tools are essential for metabolic complexity in phenotype engineering.
• Pseudomonas putida is a key host for metabolic engineering applications.

Purpose of the Study:

• To introduce GENIO (GENome Integration and fitness Optimization platform) for optimizing genetic circuit performance in Pseudomonas putida.
• To characterize chromosomal loci for predictable gene expression and enable fitness improvement.
• To demonstrate the platform's utility in restoring metabolic pathways and engineering complex phenotypes.

Main Methods:

• Utilizing chromosome-location-based differential gene expression analysis.
• Characterizing 10 Pseudomonas putida chromosomal loci (ppLPS) for expression strength and cell-to-cell variation.
• Employing evolutionary engineering for fitness improvement of engineered strains.
• Contextualizing gene expression across genomic loci, integration sites, and plasmids.

Main Results:

• Genome context, not distance to ORI, significantly influences differential gene expression at ppLPS.
• GENIO facilitates comprehensive exploration of the gene expression landscape in P. putida.
• Successful restoration of P. putida's aromatic hydrocarbon metabolism (toluene/m-xylene pathway).
• Demonstrated the need for accurate pathway contextualization for robust complex phenotype engineering.

Conclusions:

• GENIO provides a robust framework for optimizing genetic circuit performance and metabolic pathways in Pseudomonas putida.
• Understanding genome context is crucial for predictable and tunable gene expression.
• The platform supports the engineering of complex phenotypes through pathway optimization and evolutionary strategies.