Genome-Scale Metabolic Modeling for Unraveling Molecular Mechanisms of High Threat Pathogens

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.

Antibiotic resistance: Time of synthesis in a post-genomic age

“…All the above-mentioned multi-omics technologies are increasingly used for the global analysis of AR. They simultaneously provide measurements of the expression of genes, proteins and metabolites that can be used to compare the physiology of susceptible and resistant pathogens at different time points [26] . However, information from these types of analysis is frequently disconnected, and the integration of this information may benefit from the establishment of cell models.…”

“…However, information from these types of analysis is frequently disconnected, and the integration of this information may benefit from the establishment of cell models. The availability of an increasing amount of sequenced genomes and transcriptomes, together with experimental work on the associated phenotypes, metabolic fluxes and biochemical studies, has allowed the reconstruction of genome-scale metabolic models (GSMMs) to elucidate the organisms' metabolisms and thus deduce metabolic capabilities for different species, several of them causing infections [26] ( Fig. 2 ).…”

“…Thereby, they predict metabolic responses linking gaps between transcriptional and phenotypic responses, a feature particularly relevant to understand the bacterial response to antibiotics [259] , [260] , [261] , [262] , [263] , [264] , [265] , [266] . In this regard, GSMMs together with multi-omics data are the first step to a quantitative analysis of pathogens physiology, including their metabolism, resistance and virulence mechanisms, hence constituting novel approaches for metabolism-based drug target studies [26] .…”

“…Therefore, in order to have a complete understanding of AR mechanisms and the consequences of acquiring resistance for bacterial physiology, bioinformatics tools and techniques able to analyse the increasing data generated in molecular biology through multi-omics technologies - including transcriptomics, proteomics and metabolomics - have become essential. The main objective of the information acquired through “-omics” is to create knowledge from data, providing simultaneous information on the presence and identification of alleles of thousands of genes, analysis of their genetic linkage with other genes, and measurements of their levels of expression as well as of the proteins they encode and cell metabolites [26] . To get this goal, system-scale models able to collect the huge amount of experimental information into mathematical models have been developed to further understand the bacterial cell as a whole [27] , [28] .…”

Model-driven design of Mycoplasma as a vaccine chassis

“…As of 2019, 6239 GEMs exist, of which 5897 are of bacterial organisms, 127 of archaea and 215 of eukaryotes 182 . Relevant applications of GEMs include strain development for the production of bio-based chemicals and materials [183][184][185][186][187] , drug targeting in pathogens [188][189][190][191][192][193][194] , prediction of enzyme functions [195][196][197][198][199][200] and interactions among organisms [201][202][203][204] .…”