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BackgroundThe leading edge of the global problem of antibiotic resistance necessitates novel therapeutic strategies. This study develops a novel systems biology driven approach for killing antibiotic resistant pathogens using benign metabolites.ResultsControlled laboratory evolutions established chloramphenicol and streptomycin resistant pathogens of Chromobacterium. These resistant pathogens showed higher growth rates and required higher lethal doses of antibiotic. Growth and viability testing identified malate, maleate, succinate, pyruvate and oxoadipate as resensitising agents for antibiotic therapy. Resistant genes were catalogued through whole genome sequencing. Intracellular metabolomic profiling identified violacein as a potential biomarker for resistance. The temporal variance of metabolites captured the linearized dynamics around the steady state and correlated to growth rate. A constraints-based flux balance model of the core metabolism was used to predict the metabolic basis of antibiotic susceptibility and resistance.ConclusionsThe model predicts electron imbalance and skewed NAD/NADH ratios as a result of antibiotics - chloramphenicol and streptomycin. The resistant pathogen rewired its metabolic networks to compensate for disruption of redox homeostasis. We foresee the utility of such scalable workflows in identifying metabolites for clinical isolates as inevitable solutions to mitigate antibiotic resistance.

The quantitative estimation of drugs from biofluids in pharmacokinetic studies requires high-throughput bioanalytical methods. We report a rapid, simple, and efficient method for simultaneous quantitative determination of different pharmaceutical drugs [...]

Background: The leading edge of the global problem of antibiotic resistance necessitates novel therapeutic strategies. This study develops a novel systems biology driven approach for killing antibiotic resistant pathogens using benign metabolites. Results: Controlled laboratory evolutions established chloramphenicol and streptomycin resistant pathogens of Chromobacterium. These resistant pathogens showed higher growth rates and required higher lethal doses of antibiotic. Growth and viability testing identified malate, maleate, succinate, pyruvate and oxoadipate as resensitising agents for antibiotic therapy. Resistant genes were catalogued through whole genome sequencing. Intracellular metabolomic profiling identified violacein as a potential biomarker for resistance. The temporal variance of metabolites captured the linearized dynamics around the steady state and correlated to growth rate. A constraints-based flux balance model of the core metabolism was used to predict the metabolic basis of antibiotic susceptibility and resistance. Conclusions: The model predicts electron imbalance and skewed NAD/NADH ratios as a result of antibiotics – chloramphenicol and streptomycin. The resistant pathogen rewired its metabolic networks to compensate for disruption of redox homeostasis. We foresee the utility of such scalable workflows in identifying metabolites for clinical isolates as inevitable solutions to mitigate antibiotic resistance. [ABSTRACT FROM AUTHOR]

Adaptive metabolic switches are proposed to underlie conversions between cellular states during normal development as well as in cancer evolution. Metabolic adaptations represent important therapeutic targets in tumors, highlighting the need to characterize the full spectrum, characteristics, and regulation of the metabolic switches. To investigate the hypothesis that metabolic switches associated with specific metabolic states can be recognized by locating large alternating gene expression patterns, we developed a method to identify interspersed gene sets by massive correlated biclustering and to predict their metabolic wiring. Testing the method on breast cancer transcriptome datasets revealed a series of gene sets with switch-like behavior that could be used to predict mitochondrial content, metabolic activity, and central carbon flux in tumors. The predictions were experimentally validated by bioenergetic profiling and metabolic flux analysis of 13C-labeled substrates. The metabolic switch positions also distinguished between cellular states, correlating with tumor pathology, prognosis, and chemosensitivity. The method is applicable to any large and heterogeneous transcriptome dataset to discover metabolic and associated pathophysiological states. Significance: A method for identifying the transcriptomic signatures of metabolic switches underlying divergent routes of cellular transformation stratifies breast cancer into metabolic subtypes, predicting their biology, architecture, and clinical outcome., (©2024 The Authors; Published by the American Association for Cancer Research.)

Background: The leading edge of the global problem of antibiotic resistance necessitates novel therapeutic strategies. This study develops a novel systems biology driven approach for killing antibiotic resistant pathogens using benign metabolites., Results: Controlled laboratory evolutions established chloramphenicol and streptomycin resistant pathogens of Chromobacterium. These resistant pathogens showed higher growth rates and required higher lethal doses of antibiotic. Growth and viability testing identified malate, maleate, succinate, pyruvate and oxoadipate as resensitising agents for antibiotic therapy. Resistant genes were catalogued through whole genome sequencing. Intracellular metabolomic profiling identified violacein as a potential biomarker for resistance. The temporal variance of metabolites captured the linearized dynamics around the steady state and correlated to growth rate. A constraints-based flux balance model of the core metabolism was used to predict the metabolic basis of antibiotic susceptibility and resistance., Conclusions: The model predicts electron imbalance and skewed NAD/NADH ratios as a result of antibiotics - chloramphenicol and streptomycin. The resistant pathogen rewired its metabolic networks to compensate for disruption of redox homeostasis. We foresee the utility of such scalable workflows in identifying metabolites for clinical isolates as inevitable solutions to mitigate antibiotic resistance.