Genome-Scale Reconstruction Of The Metabolic Network In Pectobacterium Carotovorum Subs P.Carotovorum PC1and Its Application In Screening Potential Targets

Pectobacterium carotovorum subs P. carotovorum PC1(formally named Erwinia carotovora subsp. carotovora PC1) is a facultative anaerobic, gram-negative bacterium which is a plant pathogen that infects the vascular bundle system of plants. The main virulence determinants of Pectobacterium are the pectolytic enzymes secreted through the type Ⅱ secretion system, involving pectate lyase, polygalacturonase, and pectin lyase. P. carotovorum PC1causes soft-rot diseases of various plant hosts and can dwell in the soil from spring to autumn, so that it is difficult to cure and resulted in huge economic losses in agriculture. Genome-wide of metabolic networks integrate several types of data which combined genotype and phenotype. The model can provide us a chance to explore the metabolic situation of P. carotovorum PC1from the perspective of genomics and help us find useful fungicide targets. For Pectobacterium and Escherichia coli are closely related members of the family enterobacteriaceae, the genome-scale metabolic model of P. carotovorum PC1was constructed based on its annotated genome, literature-derived information and started with the core gene data set present in E. coli K-12MG1655model iJO1366. Here we first reported the genome-scale reconstruction of the metabolic network network model of P. carotovorum PC1, iPC1209, which composed of2235reactions and1113unique metabolites. The model was also validated using the OmniLog Phenotype MicroArray technology and the overall agreement is approximately81%using iPC1209. We also conducted a COG function analysis to the1209genes, and do a functional classification of the reactions. In order to better understanding of P. carotovorum PC1, we made a comparison beween the four bacterial using different biolog data. After that, we also try to integrate the virulence factors pathways into our metabolic network model. In the end we did gene essentiality simulations to find19candidate drug targets and performed synthetic gene deletion analysis to provide the original data for the follow-up research and development of multi-target drugs.