Integrating multi-omics data with network modeling to characterize Pseudomonas aeruginosa persister cell metabolism

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 system-level analysis to characterize the metabolic state of biocide persister cells. To accomplish this, we conducted in-depth profiling of 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 data 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.

Bacterial persister cells represent a transient subpopulation that can survive lethal antimicrobial treatments and are a major barrier to eliminating chronic infections, yet their formation and maintenance of this state remain poorly understood. By examining Pseudomonas aeruginosa treated with the industrial biocide benzisothiazolinone, this study reveals that persister cells retain active metabolism and exhibit a metabolic program distinct from that of untreated cells. We integrated transcriptomics, metabolomics, and genome-scale metabolic modeling to identify specific metabolic pathways, notably pyrimidine biosynthesis and methionine recycling, that are critical for persister survival. These findings provide insight into persister cell biology and highlight metabolism as a promising target for strategies aimed at preventing or eliminating these tolerant bacterial subpopulations.