2025/03/01 by Blas Blázquez, Juan Nogales · 2 voices
Biochemistry, Genetics and Molecular Biology · #Microbial Metabolic Engineering and Bioproduction #CRISPR and Genetic Engineering #Bacterial Genetics and Biotechnology
paper · pdf · doi:10.1111/1751-7915.70132
openalex publication_date 2025/03/01 · openalex created_date 2025/03/26 · openalex updated_date 2026/07/31
Beyond the rational construction of genetic determinants to encode target functions, complex phenotype engineering requires the contextualisation of their expression within the metabolic and genetic background of the host strain. Furthermore, wherever metabolic complexity is involved, phenotype engineering demands standard, reliable, plug-and-play tools. We introduce GENIO (GENome Integration and fitness Optimization platform for Pseudomonas putida), a framework to optimise genetic circuit performance by means of (i) chromosome-location-based differential gene expression and (ii) subsequent fitness improvement through evolutionary engineering if needed. Using gene expression strength and cell-to-cell variation, we characterised 10 P. putida chromosomal loci (ppLPS) to show that genome context rather than distance to ORI is the main factor driving differential expression performance. We further contextualised ppLPS gene expression against well-known chromosomal integration sites and plasmids displaying different copy numbers. GENIO supports comprehensive exploration of the gene expression space across P. putida's genome while unlocking performance optimization of complex heterologous metabolic pathways through evolutionary engineering. To demonstrate the usability of GENIO, we restored P. putida's aromatic hydrocarbon metabolism by (de)constructing the toluene/m-xylene catabolic pathway coded in the pWW0 plasmid. We also showed that engineering complex phenotypes requires accurate contextualisation of the synthetic pathways involved, a process that benefits from biological robustness.