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

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We developed GENIO, a platform for optimizing genetic circuits in Pseudomonas putida by controlling gene expression and enhancing fitness. This framework enables robust engineering of complex phenotypes and metabolic pathways.

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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.