This study reveals that persister cells retain active metabolism and exhibit a metabolic program distinct from that of untreated cells, and integrated transcriptomics, metabolomics, and genome-scale metabolic modeling to identify specific metabolic pathways that are critical for persister survival.
Abstract
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 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. IMPORTANCE 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. 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.
Background/Objectives: Antimicrobial resistance (AMR) poses a major global health challenge, particularly in opportunistic pathogens such as Pseudomonas aeruginosa. This study aimed to identify metabolic adaptations associated with antibiotic resistance by integrating transcriptomic data from drug-resistant clinical isolates with a genome-scale metabolic model (GEM) of P. aeruginosa under four antibiotic treatments: ceftazidime (CAZ), ciprofloxacin (CIP), meropenem (MEM), and tobramycin (TOB). Methods: Transcriptomic data from 414 clinical isolates were integrated with the iPau21 genome-scale metabolic model (GEM) of P. aeruginosa. Differential gene expression analysis was performed using DESeq2, and differentially expressed genes (DEGs) were identified using a false discovery rate (FDR)-adjusted p-value < 0.05 and a fold-change threshold of ≥2 or ≤0.5. Reporter metabolites (RMs) were identified using the Reporter Metabolite algorithm with an FDR-adjusted p-value < 0.05. Pathway enrichment analysis was performed to characterize condition-specific metabolic alterations, and pathway significance was determined using the Benjamini–Hochberg procedure with an adjusted p-value < 0.05. Results: The analysis revealed predominantly antibiotic-specific transcriptional responses, with limited overlap in DEGs across treatment conditions. Reporter metabolite and pathway enrichment analyses identified distinct metabolic adaptations associated with biofilm formation, virulence, and stress response pathways. Several metabolites, including propionic acid, acetic acid, L-inositol, glutamine, glutarate, fumarate, and melatonin, were computationally prioritized candidate metabolites for future metabolite-based adjuvant strategies aimed at enhancing antibiotic efficacy. Conclusions: This systems biology approach provides a comprehensive framework for identifying metabolic vulnerabilities associated with AMR in P. aeruginosa. The identified metabolites represent candidate antibiotic adjuvant molecules generated through computational prioritization and should be regarded as hypotheses for future experimental validation rather than validated therapeutic interventions. These findings provide a foundation for future studies exploring metabolism-based strategies to improve antibiotic efficacy and combat antimicrobial resistance.
Ceyda Kula, Rabia Cankul Kerek, K. Arğa· Antibiotics· 0 citations
Pseudomonas aeruginosa is a major opportunistic pathogen, responsible for healthcare-associated urinary tract infections. Its metabolic flexibility and genomic plasticity promote its survival and adaptation in complex environments. Here, we conducted an in-depth analysis of three pairs of sequential P. aeruginosa urinary isolates, named “early” and “late”, from three patients to investigate metabolic and phenotypic changes during urinary tract adaptation. An integrated multi-omics approach combining RNA sequencing and metabolomics was performed on isolates grown in human urine (HU) and trypticase soy (TS) medium, and compared with previously published proteomics data. Late isolates showed down-regulation of genes encoding type VI secretion system in both media, while oxidative phosphorylation associated-genes were up-regulated in HU. These late isolates also displayed significant down-regulation of amino-acid metabolism suggesting an increased use of carbon sources available in urine. As previously observed in proteomics, siderophore- and iron-related genes were significantly down-regulated for all late isolates in HU but not in TS, supporting convergent adaptation to the low-iron urinary environment. Metabolomic profiles of HU supernatants from late isolates clustered together, showing common metabolite production in HU. The differential metabolic profile between early and late isolates included phosphatidylcholines, acylcarnitines, biogenic amines and amino-acids, highlighting their importance in urinary adaptation. While long-term survival in HU and TS was similar between isolates, biofilm formation was reduced or lost in late isolates in line with the down-regulation of biofilm-related genes. These findings highlight the transcriptomic and metabolic reprogramming as well as the phenotypic changes occurring during P. aeruginosa adaptation to the urinary tract. Importance Pseudomonas aeruginosa is a major opportunistic bacterial pathogen responsible for healthcare difficult-to-treat urinary tract infections. Understanding how this bacterium adapts and persists in the human urinary tract is essential for improving the management of persistent infections. Through an integrated analysis of six sequential clinical isolates from three patients, this study shows that P. aeruginosa undergoes coordinated metabolic and phenotypic changes that promote survival in the urinary environment. During adaptation, the bacterium reduces traits involved in bacterial competition and biofilm formation while reprogramming its metabolism to the nutrients available in urine. These changes emerged independently in different patients, revealing convergent adaptation to this challenging environment. By identifying biological processes consistently associated with urinary tract persistence, this work provides new insight into the mechanisms that support long-term colonization and highlights adaptive metabolic pathways that may represent future targets for therapeutic intervention.
Caroline Martin-Duval, S. Dahyot, A. Tebani et al.· bioRxiv· 0 citations
Rec reconstructed genome-scale metabolic models of 44 Pseudomonas strains from various environments and investigated their capabilities to metabolize different carbon sources and metabolic intermediaries, demonstrating how GEM-predicted capabilities can differentiate between strains and that high metabolic versatility is associated with the predicted ability of the strains to remove toxic compounds while maintaining core functionalities.
C. Fócil-Espinosa, Christopher Dalldorf, Diego Martinez et al.· Computational and Structural...· 0 citations
Abstract Background and Objective Pseudomonas aeruginosa is a ubiquitous Gram-negative pathogen notorious for causing infections with high mortality rates. Its large genome supports extensive metabolic diversity and promotes its adaptation to diverse environments. The survival and persistence of P. aeruginosa in clinical settings is further facilitated by a vast arsenal of strategies that contribute to its tolerance or resistance to antibiotics. Here, we aimed to systematically identify the genetic determinants that affect its susceptibility to antibiotics. Methods The ordered PA14 transposon mutant library was grown in the presence of sub-MIC concentrations of a panel of antibiotics of various classes, and the growth of each mutant was quantified to generate susceptibility scores. Results We observed a dense network of genes affecting antibiotic susceptibility in P. aeruginosa, where a large portion of genes modulated susceptibility to various classes of antibiotics. Not surprisingly, efflux and outer membrane permeability were key contributors, but we also identified genes that are not typically associated with antibiotic susceptibility. Conclusions The data provide a genome-wide view of the antibiotic susceptibility network in P. aeruginosa. Overall, our approach deepens our understanding of antibiotic susceptibility and opens new avenues for developing strategies against multidrug-resistant P. aeruginosa.
Océane Goncalves, Jean-Philippe Côté· Journal of Antimicrobial Che...· 0 citations
The results demonstrate that iModulons provide a genome-scale framework for comparing transcriptional regulation across closely related organisms, revealing regulatory innovations that are not apparent from genome comparisons alone.
Heera Bajpe, Ying Hefner, R. Szubin et al.· bioRxiv· 0 citations
These findings deepen the understanding of Microcystis’ phycosphere functioning and demonstrate the value of multi-omics systems biology approaches, while suggesting that metabolic complementarity between species and across phycospheres could play a role in bloom-associated microbiome structure.
Juliette Audemard, Nicolas Creusot, Julie Leloup et al.· ISME Communications· 0 citations