Bacterial surface glycans are an attractive therapeutic target in response to antibiotics; however, current knowledge of the corresponding mechanisms is rather limited. Antimicrobial susceptibility testing, genome sequencing, and MALDI-TOF MS, commonly used in recent years to analyze bacterial resistance, are unable to rapidly and efficiently establish associations between glycans and resistance. The discovery of new antimicrobial strategies still requires the introduction of promising analytical methods. In this study, we applied lectin microarray technology and a machine-learning model to screen for important glycan structures associated with carbapenem-resistant P. aeruginosa. This work highlights that specific glycopatterns can be important biomarkers associated with bacterial antibiotic resistance, which promises to provide a rapid entry point for exploring new resistance mechanisms in pathogens.
Keywords: bacterial cell surface; carbapenem-resistant P. aeruginosa (CRPA); glycan structures; lectin microarrays; machine learning.