Biosensor-Driven Strain Engineering Reveals Key Cellular Processes for Maximizing Isoprenol Production in Pseudomonas putida

Synthetic biology tools have accelerated the generation of simple mutants, but combinatorial testing remains challenging. High-throughput methods struggle translating from proof-of-principle molecules to advanced bioproducts. We address this challenge with a biosensor-driven strategy for enhanced isoprenol production in Pseudomonas putida , a key precursor for sustainable aviation fuel and platform chemicals. This biosensor leverages P. putida ’s native response to short-chain alcohols via a previously uncharacterized hybrid histidine kinase signaling cascade. Refactoring the biosensor for a conditional growth-based selection enabled identification of competing cellular processes with a ∼16,500-member CRISPRi-library. An iterative combinatorial strain engineering approach yielded an integrated P. putida strain producing ∼900 mg/L isoprenol in glucose minimal medium, a 36-fold increase. Ensemble -omics analysis revealed metabolic rewiring, including amino acid accumulation as key drivers of enhanced production. Techno-economic analysis elucidated the path to economic viability and confirmed the benefits of adding amino acids outweigh the additional costs. This study establishes a robust biosensor driven approach for optimizing other heterologous pathways, accelerating microbial cell factory development.