BioProject

Carbapenem-resistance is a major concern in the management of antibiotic resistant Pseudomonas aeruginosa infections. More...

Carbapenem-resistance is a major concern in the management of antibiotic resistant Pseudomonas aeruginosa infections. Direct prediction of carbapenem-resistance phenotype from genotype in P.aeruginosa isolates and clinical samples would promote timely antibiotic therapy. While the complex carbapenem-resistance mechanism and the high prevalence of variant-driven carbapenem-resistance in P.aeruginosa make it challenging to predict its carbapenem-resistance phenotype through genotype. In this study, by use of whole genome sequencing (WGS) data of 1622 P.aeruginosa isolates followed by machine learning, we screened out 16 and 31 key gene features associated with carbapenem-resistance of P.aeruginosa, including oprD(HIGH), and constructed the resistance prediction models regarding imipenem (IPM) or meropenem (MEM). The Area Under Curves of IPM- and MEM-resistance prediction models were 0.906 and 0.925. For the direct carbapenem-resistance prediction for P. aeruginosa from clinical samples by those formerly selected key gene features and constructed prediction models, 72 P. aeruginosa-positive sputum samples were collected and sequenced by metagenomic sequencing (MGS) based on next-generation sequencing (NGS) or Oxford Nanopore Technology (ONT). It turned out that the prediction applicability of MGS based on NGS outperformed that of MGS based on ONT. In 72 P. aeruginosa-positive sputum samples, 65.0% (26/40) IPM-insensitive and 63.2% (24/38) MEM-insensitive P. aeruginosa could be directly predicted by NGS-based MGS with PPVs of 0.897 and 0.889. By the direct detection of those selected key gene features associated with carbapenem-resistance of P. aeruginosa, carbapenem-resistance of P. aeruginosa could be directly predicted from cultured isolates by WGS or clinical samples by NGS-based MGS, which could assist the timely treatment and surveillance of carbapenem-resistant P.aeruginosa. Less...