The glycopatterns of Pseudomonas aeruginosa as a potential biomarker for its carbapenem resistance

Abstract

Multidrug-resistant (MDR) Pseudomonas aeruginosa infections pose a significant challenge to effective treatment. Although carbapenems were once considered the primary therapeutic option for MDR P. aeruginosa , the clinical use of these antibiotics has become increasingly limited, and the exploration of alternative antimicrobial strategies remains necessary. Bacterial surface glycans are critical in response to antibiotics and represent an attractive therapeutic target. However, the understanding of the role of glycan structures in bacterial resistance is currently quite limited. In this study, we used lectin microarrays to analyze the differences in glycan alterations between 53 drug-sensitive P. aeruginosa (DSPA) strains and 57 carbapenem-resistant P. aeruginosa (CRPA) strains obtained from clinical isolates, with the goal of identifying important glycopatterns associated with carbapenem resistance. The results revealed significant differences in the expression levels of glycan structures [e.g., Fucα1–6GlcNAc, α- d -Man, and Fucα1–3(Galβ1–4)GlcNAc] recognized by 20 lectins (e.g., LCA, PSA, and AAL) on the bacterial surface. Furthermore, we applied K -fold cross-validation to determine the optimal parameters and constructed the DSPA and CRPA models using gradient boosting decision tree (GBDT) algorithm. The GBDT model presented the best performance in an independent test cohort, demonstrating that the screened glycopatterns could serve as potential biomarkers for differentiating bacterial resistance. Our study is the first to apply lectin microarrays to monitor carbapenem resistance in clinical P. aeruginosa isolates, and we hope that the methodology used in this work will serve as an important tool for exploring the association of bacterial resistance with glycosylation mechanisms. IMPORTANCE 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.

N-Glycan Profiles of Neuraminidase from Avian Influenza Viruses

“…The same steps were also applied to the negative allantoic fluid. All the above collections were used for further SDS-PAGE analysis [ 20 , 21 ].…”

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

“…Machine learning based on protein sequences successfully predicted antimicrobial resistance genes in Gram-negative bacteria with over 90% accuracy [22,23]. Researchers investigating biomarkers for Pseudomonas aeruginosa's carbapenem resistance implemented a learning model using a gradient boosting decision tree (GBDT) algorithm to screen for important glycan structures associated with resistant strains [24]. A machine learning model was successfully implemented in research for the classification of resistant genomes in Pseudomonas aeruginosa.…”