Identifying pathoadaptation in Pseudomonas aeruginosa using glycopolymer sensor arrays [dataset]

In-host bacterial evolution presents a major barrier to effective infection management, driving phenotypic adaptations such as antibiotic resistance and altered virulence. Pseudomonas aeruginosa, a key opportunistic pathogen, frequently undergoes rapid evolutionary change during chronic lung infections, complicating diagnosis and treatment. Current strain typing via whole genome sequencing or selective culturing is costly and time-intensive, and the complex relationship between genetic variations and the phenotype displayed makes clinically relevant pathotypes difficult to identify. Here, we report a cross-reactive glycopolymer-based fluorescent sensor array capable of directly identifying phenotypic changes related to in-host evolution in P. aeruginosa. The sensor array can accurately distinguish phenotypic variations arising from single-gene defects and discriminate clinical isolates with known differences in their evolutionary and pathoadaptive trajectories. Notably, our system is also capable of identifying P. aeruginosa isolates as distinct from other bacterial species commonly found in complex polymicrobial lung infections. Our modular platform presents an opportunity to develop sensor arrays which target carbohydrate recognition in a variety of pathogens, offering potential application as a rapid diagnostic tool to inform clinical treatment decisions based on the direct classification of phenotypic profiles.