Biosensor-driven strain engineering reveals key cellular processes for maximizing isoprenol production in Pseudomonas...

Area of Science:

• Synthetic biology
• Metabolic engineering
• Biotechnology

Background:

• Synthetic biology produces many designs, but screening them is a bottleneck.
• High-throughput methods are needed to analyze large combinatorial libraries.
• Isoprenol is a potential biofuel precursor, requiring efficient production methods.

Purpose of the Study:

• To develop a biosensor-driven selection strategy for isoprenol production in Pseudomonas putida.
• To identify and overcome host limitations in microbial isoprenol synthesis.
• To engineer Pseudomonas putida for enhanced production of aviation fuel precursors.

Main Methods:

• Developed a growth-coupled biosensor for isoprenol.
• Utilized a pooled CRISPR interference (CRISPRi) library for selection.
• Performed iterative combinatorial strain engineering.
• Integrated omics analysis (genomics, transcriptomics, metabolomics).

Main Results:

• Discovered and characterized a novel noncanonical signaling pathway involving a hybrid histidine kinase and alcohol dehydrogenase.
• Identified key host limitations through CRISPRi screening.
• Achieved a 36-fold increase in isoprenol titer to ~900 mg/L through strain engineering.
• Demonstrated that metabolic rewiring toward amino acid catabolism is critical for improved production.

Conclusions:

• The developed biosensor and selection strategy effectively optimize complex heterologous pathways.
• The workflow enables the discovery of emergent host biology for metabolic engineering.
• Metabolic rewiring towards amino acid catabolism enhances biofuel precursor production, validated by technoeconomic analysis.