Identifying Pathoadaptation in Pseudomonas aeruginosa Using Glycopolymer Sensor Arrays

TL;DR

A new glycopolymer sensor array can detect phenotypic changes in Pseudomonas aeruginosa, helping identify pathoadaptation during infections more efficiently.

Contribution

A novel glycopolymer-based sensor array is introduced for direct identification of phenotypic changes in P. aeruginosa.

Findings

The sensor array distinguishes phenotypic variations from single-gene defects in P. aeruginosa.

It can differentiate P. aeruginosa isolates from other bacteria in polymicrobial infections.

The platform offers potential for rapid diagnostics based on phenotypic profiles.

Abstract

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
changes 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 resulting phenotype 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 that 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.

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