Multidimensional Clinical Surveillance of Pseudomonas aeruginosa Reveals Complex Relationships between Isolate Source, Morphology, and Antimicrobial Resistance

Molecular epidemiology and carbapenem resistance mechanisms of Pseudomonas aeruginosa isolated from a hospital in Fujian, China

The worldwide spread of Pseudomonas aeruginosa, especially carbapenem-resistant P. aeruginosa (CRPA), poses a serious threat to global public health. In this research, we collected and studied the clinical prevalence, molecular epidemiology, and resistance mechanisms of CRPA in Fujian, China. Among 167 non-duplicated P. aeruginosa isolates collected during 2019-2021, strains from respiratory specimens and wound secretions of older males in the intensive care unit dominated. Ninety-eight isolates (58.7 %) were resistant to at least one tested antibiotic, among which 70 strains were carbapenem-resistant. Moleclar typing of the CRPA isolates revealed they were highly divergent, belonging to 46 different sequence types. It is noteworthy that two previously reported high risk clones, ST1971 specific to China and the globally prevalent ST357, were found. Several carbapenem resistance-related characteristics were also explored in 70 CRPA isolates. Firstly, carbapenemase was phenotypically positive in 22.9 % of CRPA, genetically predominant by metallo-β-lactamase (MBL) and co-carrige of different carbapenemase genes. Then, mutations of the carbapenem-specific porins oprD and opdP were commonly observed, with frequencies of 97.1% and 100.0%, respectively. Furthermore, the biofilm formation and relative transcription levels of 8 multidrug efflux pump genes were also found to be increased in 48.6 % and 72.9 % of CRPA isolates compared to the reference strain PAO1. These findings will help fill the data gaps in molecular characteristics of CRPA on the southeastern coast of China and emphasize the urgent need for data-based specific stewardship for antipseudomonal practices to prevent the dissemination of CRPA.

Model-driven characterization of functional diversity of Pseudomonas aeruginosa clinical isolates with broadly representative phenotypes

Pseudomonas aeruginosa is a leading cause of infections in immunocompromised individuals and in healthcare settings. This study aims to understand the relationships between phenotypic diversity and the functional metabolic landscape of P. aeruginosa clinical isolates. To better understand the metabolic repertoire of P. aeruginosa in infection, we deeply profiled a representative set from a library of 971 clinical P. aeruginosa isolates with corresponding patient metadata and bacterial phenotypes. The genotypic clustering based on whole-genome sequencing of the isolates, multilocus sequence types, and the phenotypic clustering generated from a multi-parametric analysis were compared to each other to assess the genotype-phenotype correlation. Genome-scale metabolic network reconstructions were developed for each isolate through amendments to an existing PA14 network reconstruction. These network reconstructions show diverse metabolic functionalities and enhance the collective P. aeruginosa pangenome metabolic repertoire. Characterizing this rich set of clinical P. aeruginosa isolates allows for a deeper understanding of the genotypic and metabolic diversity of the pathogen in a clinical setting and lays a foundation for further investigation of the metabolic landscape of this pathogen and host-associated metabolic differences during infection.

Model-driven characterization of functional diversity of Pseudomonas aeruginosa clinical isolates with broadly representative phenotypes

Pseudomonas aeruginosa is a leading cause of infections in immunocompromised individuals and in healthcare settings. This study aims to understand the relationships between phenotypic diversity and the functional metabolic landscape of P. aeruginosa clinical isolates. To better understand the metabolic repertoire of P. aeruginosa in infection, we deeply profiled a representative set from a library of 971 clinical P. aeruginosa isolates with corresponding patient metadata and bacterial phenotypes. The genotypic clustering based on whole-genome sequencing of the isolates, multi-locus sequence types, and the phenotypic clustering generated from a multi-parametric analysis were compared to each other to assess the genotype-phenotype correlation. Genome-scale metabolic network reconstructions were developed for each isolate through amendments to an existing PA14 network reconstruction. These network reconstructions show diverse metabolic functionalities and enhance the collective P. aeruginosa pangenome metabolic repertoire. Characterizing this rich set of clinical P. aeruginosa isolates allows for a deeper understanding of the genotypic and metabolic diversity of the pathogen in a clinical setting and lays a foundation for further investigation of the metabolic landscape of this pathogen and host-associated metabolic differences during infection.