(474c) Metabolic Modulations during Infection Conform to the Diversity in Clinical Pseudomonas Aeruginosa Isolates

The antibiotic-resistant pathogen Pseudomonas aeruginosa is a leading cause of infections in immunocompromised individuals in healthcare settings. The treatment of these infections is complicated by the presence of a large variety of metabolic and virulence mechanisms among clinical strains. However, these differences in metabolic functions are poorly characterized. We hypothesize that the metabolic differences in P. aeruginosa are dependent on a complex combination of host and pathogen-specific factors, which can be delineated using a combination of genomic and transcriptomic analyses coupled with genome-scale metabolic modeling to identify the core and unique metabolic functions in different clinical isolates.

In our recent study presented in this abstract, we characterized a set of 25 clinical P. aeruginosa isolates that are representative of a large population of isolates collected from the University of Virginia Health System. We performed a multi-faceted analysis of these isolates by employing whole genome sequencing, phenotypic and genotypic clustering, functional annotation and analyses on core, accessory, and unique traits in the P. aeruginosa pan-genome, and genome-scale metabolic reconstruction with analysis of flux samples.

The selected 25 isolates were cultured for whole genome sequencing and comparative genomic analysis using the PA14 strain as the reference genome. The genotypic clustering was compared to the phenotypic clustering generated from a multi-parametric analysis to assess the genotype-phenotype correlation. Each of the complete genomes of the isolates was annotated based on the KEGG biochemical database and a genome-scale metabolic network reconstruction was developed for each isolate through extensive amendment to an existing PA14 reconstruction, iPau21. These network reconstructions show diverse metabolic functionalities and substrate dependencies, as well as enhance the collective P. aeruginosa pangenome metabolic repertoire.

To assess the impact of mucin, five clinical isolates along with the laboratory strain PA14 were selected for transcriptomic sequencing using static cultures in Synthetic Cystic Fibrosis growth medium (SCFM) ± 0.5% MUC5AC. Specific metabolic functions and biological processes modulated by the presence of mucin were identified using differential gene expression analysis. mucin-driven metabolic shifts were observed in various central and peripheral metabolic pathways, which include both shared and unique metabolic functions across the clinical isolates studied. Furthermore, the transcriptomic datasets are analyzed and incorporated into the genome-scale metabolic models of the clinical isolates, allowing for the identification of mucin-driven metabolic changes in the clinical isolates. 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 mucin-induced metabolic modulations during infection.