Construction of a genome‐scale metabolic network of the plant pathogen Pectobacterium carotovorum provides new strategies for bactericide discovery

Abstract

a b s t r a c tWe reconstructed the first genome-scale metabolic network of the plant pathogen Pectobacterium carotovorum subsp. carotovorum PC1 based on its genomic sequence, annotation, and physiological data. Metabolic characteristics were analyzed using flux balance analysis (FBA), and the results were afterwards validated by phenotype microarray (PM) experiments. The reconstructed genome-scale metabolic model, iPC1209, contains 2235 reactions, 1113 metabolites and 1209 genes. We identified 19 potential bactericide targets through a comprehensive in silico gene-deletion study. Next, we performed virtual screening to identify candidate inhibitors for an important potential drug target, alkaline phosphatase, and experimentally verified that three lead compounds were able to inhibit both bacterial cell viability and the activity of alkaline phosphatase in vitro. This study illustrates a new strategy for the discovery of agricultural bactericides.

Metabolic flux analyses of Pseudomonas aeruginosa cystic fibrosis isolates

“…FBA investigations of pathogenic organisms have been used to search for novel drug targets and have pointed to potential metabolic targets not affected by current therapeutics, such as amino acid production or fatty acid metabolism [53][54][55][56][57][58][59][60]. Oberhardt and colleagues have constructed a genome-based model of metabolism and transport in P. aeruginosa [61], and used flux balance analysis (FBA) with transcript data from two CF clinical strains to investigate P. aeruginosa's metabolic capabilities and potential metabolic changes during prolonged infection [62].…”

Metabolic modeling of Pectobacterium parmentieri SCC3193 provides insights into metabolic pathways of plant pathogenic bacteria

“…iLPI245 is highly reliable, as in silico obtained results overlap in 91% with experimentally obtained data on carbon utilization phenotypes, a value that perfectly fits with the currently accepted standard for genome-scale metabolic reconstructions 41,42 . For example, a previously described genome-scale metabolic reconstruction of Pectobacterium aroidearum PC1, a monocotyledonous plant pathogenic bacterium, showed the agreement of 80.4% between in silico simulations and Phenotype Microarray™ (Biolog) experiments 25 . This difference in accuracy of the models was probably related to the fact, that the metabolic model of P. aroidearum PC1 was constructed on the template of the latest version of E. coli K12 MG1655 metabolic model iJO1366, whereas in our case E. coli genome was used only for biomass estimation, and identification of orthologues genes, in consequence producing a more P. parmentieri -specific model.…”

“…Constraints-based approaches, and in particular Flux Balance Analysis (FBA) 19 have been shown to infer growth phenotypes and are claimed to provide a systems biology view on multi-omics data, possibly allowing to predict physiological changes and evolution of bacterial populations 20,21 . Recently, genome-scale metabolic model (GEM) reconstruction and FBA have been used for deciphering the metabolic adaption of environmental microbes following ecological parameters variation 22 , ecological niche shift 23 , as well as for providing insights into the metabolic adaptation in human and bacterial plant pathogens 24,25 . To the best of our knowledge only in a few cases, FBA has been applied in understanding the metabolic adaptation of specific plant bacterial pathogens e .…”

Network Analyses in Plant Pathogens

“…campestris ( Xcc ) (Schatschneider et al, 2013 ). Another example, includes the study of metabolic precursors of lipopolysaccharides in Pectobacterium carotovorum because of their role in antimicrobial resistance (Wang et al, 2014a ). These two studies highlight the importance of virulence factors in the relocation of resources for pathogen growth and their potential use as drug targets.…”