Non-model bacteria offer unique metabolic capabilities for sustainable bioproduction, yet their limited genetic accessibility hinders systematic strain development. Here we present conjugation-based serine recombinase-assisted genome engineering (cSAGE), a broad-host-range platform that enables predictable, iterative genomic integration in transformation-resistant bacteria. cSAGE combines conjugative DNA delivery, standardized low-copy vectors, orthogonal recombinases, and modular genetic parts to support rapid pathway assembly and cross-host benchmarking. Using purple nonsulfur bacteria as a testbed, we integrate promoter engineering, multi-payload genome modification, and genome-scale metabolic modeling to empirically evaluate host-dependent pathway performance. Applying this workflow, we identify strain-specific differences in photosynthetic conversion of lignin-derived p-coumarate to the thermoplastic precursor p-vinylphenol. By enabling genome engineering and functional comparison across diverse bacteria using a single plasmid system, cSAGE provides a general framework for non-model strain prototyping and biotransformation discovery.
AI needs a strong data fabric to deliver business value
Artificial intelligence is moving quickly in the enterprise, from experimentation to everyday use. Organizations are deploying copilots, agents, and predictive systems across finance, supply chains,