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Identifying Metabolic Inhibitors to Reduce Bacterial Persistence.

ABSTRACT: Bacterial persisters are rare phenotypic variants that are temporarily tolerant to high concentrations of antibiotics. We have previously discovered that stationary-phase-cell subpopulations exhibiting high redox activities were less capable of producing proteins and resuming growth upon their dilution into fresh media. The redox activities of these cells were maintained by endogenous protein and RNA degradation, resulting in self-inflicted damage that transiently repressed the cellular functions targeted by antibiotics. Here, we showed that pretreatment of stationary-phase cells with an ATP synthase inhibitor, chlorpromazine hydrochloride (CPZ), significantly reduced stationary-phase-redox activities and protein degradation, and yielded cells that were more susceptible to cell death when exposed to antibiotics in fresh media. Leveraging this knowledge, we developed an assay integrating a degradable fluorescent protein system and a small library, containing FDA-approved drugs and antibiotics, to detect medically relevant drugs that potentially target persister metabolism. We identified a subset of chemical inhibitors, including polymyxin B, poly-L-lysine and phenothiazine anti-psychotic drugs, that were able to reduce the persistence phenotype in Escherichia coli. These chemical inhibitors also reduced Pseudomonas aeruginosa persistence, potentially verifying the existence of similar mechanisms in a medically relevant organism.

SUBMITTER: Mohiuddin SG

PROVIDER: S-EPMC7118205 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identifying Metabolic Inhibitors to Reduce Bacterial Persistence.

Mohiuddin Sayed Golam SG Hoang Thuy T Saba Adesola A Karki Prashant P Orman Mehmet A MA

Frontiers in microbiology 20200327

Bacterial persisters are rare phenotypic variants that are temporarily tolerant to high concentrations of antibiotics. We have previously discovered that stationary-phase-cell subpopulations exhibiting high redox activities were less capable of producing proteins and resuming growth upon their dilution into fresh media. The redox activities of these cells were maintained by endogenous protein and RNA degradation, resulting in self-inflicted damage that transiently repressed the cellular function ...[more]

PMID: 32292393

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Project description:BackgroundMetabolic interactions involve the exchange of metabolic products among microbial species. Most microbes live in communities and usually rely on metabolic interactions to increase their supply for nutrients and better exploit a given environment. Constraint-based models have successfully analyzed cellular metabolism and described genotype-phenotype relations. However, there are only a few studies of genome-scale multi-species interactions. Based on genome-scale approaches, we present a graph-theoretic approach together with a metabolic model in order to explore the metabolic variability among bacterial strains and identify and describe metabolically interacting strain communities in a batch culture consisting of two or more strains. We demonstrate the applicability of our approach to the bacterium E. coli across different single-carbon-source conditions.ResultsA different diversity graph is constructed for each growth condition. The graph-theoretic properties of the constructed graphs reflect the inherent high metabolic redundancy of the cell to single-gene knockouts, reveal mutant-hubs of unique metabolic capabilities regarding by-production, demonstrate consistent metabolic behaviors across conditions and show an evolutionary difficulty towards the establishment of polymorphism, while suggesting that communities consisting of strains specifically adapted to a given condition are more likely to evolve. We reveal several strain communities of improved growth relative to corresponding monocultures, even though strain communities are not modeled to operate towards a collective goal, such as the community growth and we identify the range of metabolites that are exchanged in these batch co-cultures.ConclusionsThis study provides a genome-scale description of the metabolic variability regarding by-production among E. coli strains under different conditions and shows how metabolic differences can be used to identify metabolically interacting strain communities. This work also extends the existing stoichiometric models in order to describe batch co-cultures and provides the extent of metabolic interactions in a strain community revealing their importance for growth.

Dataset Information

Identifying Metabolic Inhibitors to Reduce Bacterial Persistence.

Publications

Identifying Metabolic Inhibitors to Reduce Bacterial Persistence.

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