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Integrating multi-omics data with genome scale metabolic modeling for the analysis of Pseudomonas aeruginosa persister cells

ABSTRACT: Pseudomonas aeruginosa is a gram-negative bacterial pathogen, capable of forming antimicrobial-tolerant subpopulations known as persister cells. These cells are transient phenotypic variants that can tolerate antimicrobial treatment, and have been associated with chronic infections and the development of antibiotic- resistance. While persister cells are classically associated with reduced metabolic activity, the characteristics of their metabolism are not well understood, especially in the context of biocides. In this work, we performed an experimental and computational systems-level analysis to characterize the metabolic state of biocide persister cells. To accomplish this, we conducted in-depth profiling ofdeeply profiled both wild-type and persister samples of P. aeruginosa with transcriptomic sequencing and metabolomic analyses. These analyses revealed a distinct metabolic repertoire in biocide persister cells, marked by an upregulation in genes associated with activity in central metabolism. Integration of both the transcriptomic datasetdata set with a P. aeruginosa genome-scale metabolic network reconstruction (GENRE) provided condition-specific models, which were used to identify metabolic reactions and genes that differentiated the persister phenotype from the untreated. Experimental testing of model predictions revealed metabolic functions, such as pyrimidine synthesis and methionine recycling, which could serve as potential targets for inhibiting persister cell formation.

ORGANISM(S): Pseudomonas aeruginosa

PROVIDER: GSE185914 | GEO | 2025/12/01

REPOSITORIES: GEO

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