Systems biology approach to elucidate complex metabolic dependencies in the evolution of antibiotic resistance

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Project Summary We propose a systems biology approach to investigate the connection between metabolism and the emergence of antimicrobial resistance (AMR) in two prominent human pathogens, Pseudomonas aeruginosa and Staphylococcus aureus, within the context of physiologically-relevant environments. Two of the most serious threats for AMR, P. aeruginosa and S. aureus cause more than 30,000 and 300,000 drug-resistant infections per year in the United States, respectively [4]. The objective of this R01 application is to construct computational metabolic network models of antimicrobial-resistant and -sensitive P. aeruginosa and S. aureus and to experimentally validate model predictions of metabolic genes and pathways supporting their evolution towards resistance. To combat opportunistic and nosocomial infections, antimicrobials have been developed for clinical use; yet with every new antimicrobial introduced, the aforementioned pathogens have quickly evolved resistance [12, 13]. Therefore, new approaches are urgently needed to limit emergence and expansion of AMR in these species. With a focus in this application on Gram-negative (P. aeruginosa) and Gram-positive (S. aureus) bacteria, we will compare metabolic functions supportive of antimicrobial resistance to evaluate whether the metabolic genes and pathways observed to be critical are unique to an organism or a more general mechanism of metabolic adaptation. We posit that a combination of phenotyping, computational modeling, and analysis of clinical antimicrobial-resistant strains will reveal bacterial metabolic processes that are central to the growth of antimicrobial-resistant microbes and can be modulated to select against growth of antimicrobial-resistant populations. Data from our lab and others have recently found that antimicrobial- resistant P. aeruginosa and S. aureus display systems-level differences in metabolism [2, 14-16]. Furthermore, data collected from experiments will enable: novel approaches to integrate gene expression data with metabolic network models for complex environments, ensemble methods that better account for uncertainty in metabolic network reconstructions, and machine learning methods to delineate metabolic states that correlate with AMR in clinical isolates. The significance of the proposed work lies in the mechanistic understanding of essential genes, reactions, and substrate preferences that underlie the development of AMR in two clinically important pathogens. Further, metabolic states will be characterized in clinical isolates to support the relevance of the systematic interrogation of metabolic dependencies in the development of AMR. The knowledge gap this work will address is the mechanistic link between metabolism and the development of AMR. With the successful implementation of the proposed project, we will identify several potential therapeutic targets in clinically important pathogens as well as establish a framework for how such computational model-driven approaches can be applied to other antimicrobial resistant pathogens.