Abstract
Pathogens give rise to a wide range of diseases threatening global health and hence drawing public health agencies' attention to establish preventative and curative solutions. Genome-scale metabolic modeling is ever increasingly used tool for biomedical applications including the elucidation of antibiotic resistance, virulence, single pathogen mechanisms and pathogen-host interaction systems. With this approach, the sophisticated cellular system of metabolic reactions inside the pathogens as well as between pathogen and host cells are represented in conjunction with their corresponding genes and enzymes. Along with essential metabolic reactions, alternate pathways and fluxes are predicted by performing computational flux analyses for the growth of pathogens in a very short time. The genes or enzymes responsible for the essential metabolic reactions in pathogen growth are regarded as potential drug targets, as a priori guide to researchers in the pharmaceutical field. Pathogens alter the key metabolic processes in infected host, ultimately the objective of these integrative constraint-based context-specific metabolic models is to provide novel insights toward understanding the metabolic basis of the acute and chronic processes of infection, revealing cellular mechanisms of pathogenesis, identifying strain-specific biomarkers and developing new therapeutic approaches including the combination drugs. The reaction rates predicted during different time points of pathogen development enable us to predict active pathways and those that only occur during certain stages of infection, and thus point out the putative drug targets. Among others, fatty acid and lipid syntheses reactions are recent targets of new antimicrobial drugs. Genome-scale metabolic models provide an improved understanding of how intracellular pathogens utilize the existing microenvironment of the host. Here, we reviewed the current knowledge of genome-scale metabolic modeling in pathogen cells as well as pathogen host interaction systems and the promising applications in the extension of curative strategies against pathogens for global preventative healthcare.
Highlights
Pathogens give rise to a wide range of diseases threatening global health and drawing public health agencies’ attention to establish preventative and curative solutions (Sweileh, 2017; World Health Organization, 2017)
An important point that need to be considered in the gene essentiality analysis for drug targeting studies, is essential genes be specific to pathogenic microorganisms
The identification of conserved metabolic pathways during pathogen invasion may lead to alternative complementary routes that can be targeted by novel interfering compounds
Summary
Pathogens give rise to a wide range of diseases threatening global health and drawing public health agencies’ attention to establish preventative and curative solutions (Sweileh, 2017; World Health Organization, 2017). An important point that need to be considered in the gene essentiality analysis for drug targeting studies, is essential genes be specific to pathogenic microorganisms Since their emergence two decades ago, GEMs have extended our knowledge toward system-level understanding of pathogenesis of microbial infections. Experimental analyses of essential genes are performed by different methods including random mutagenesis, targeted mutagenesis and knockdown approaches (Rancati et al, 2018; Gonyar et al, 2019) These studies constitute a significant platform for computational gene essentiality predictions by using pathogen-specific GEMs (Joyce and Palsson, 2007). Genomescale metabolic modeling has fulfilled an inevitable rise in the prediction of pathogenic drug targets Within this scope, essential genes and their corresponding products, obtained from in silico analysis of pathogen-specific GEMs, are regarded as putative drug targets to inhibit cell survival. Glucosylceramide (GlcCer) lowering agents or inhibitors of glycosylceramide synthesis can be considered as new opportunities to prevent fungal infections (Rittershaus et al, 2006; Raj et al, 2017)
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