Model-driven characterization of functional diversity ofPseudomonas aeruginosaclinical isolates with broadly representative phenotypes

Abstract

AbstractPseudomonas aeruginosais 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 ofP. aeruginosaclinical isolates. To better understand the metabolic repertoire ofP. aeruginosain infection, we deeply profiled a representative set from a library of 971 clinicalP. aeruginosaisolates 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 collectiveP. aeruginosapangenome metabolic repertoire. Characterizing this rich set of clinicalP. aeruginosaisolates 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.Impact statementPseudomonas aeruginosais a leading cause of infections in immunocompromised individuals and in healthcare settings. The treatment of these infections is complicated by the presence of a variety of virulence mechanisms and metabolic uniqueness among clinically relevant strains. This study is an attempt to understand the relationships between isolate phenotypic diversity and the functional metabolic landscape within a representative group ofP. aeruginosaclinical isolates. Characterizing this rich set of clinicalP. aeruginosaisolates allows for a deeper understanding of 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 in infection.