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

Abstract

20 21Understanding the plant-microbe interactions are crucial for improving plant productivity and 22 plant protection. The latter aspect is particularly relevant for sustainable agriculture and 23 development of new preventive strategies against the spread of plant diseases. Constraint-based 24 metabolic modeling is providing one of the possible ways to investigate the adaptation to 25 different ecological niches and may give insights into the metabolic versatility of plant 26 20, 2018; pathogenic bacteria. In this study, we present a curated metabolic model of the emerging plant 27 pathogenic bacterium Pectobacterium parmentieri SCC3193. Using flux balance analysis (FBA), 28International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/284968 doi: bioRxiv preprint first posted online Mar.we predict the metabolic adaptation to two different ecological niches, relevant for the 29 persistence and the plant colonization by this bacterium: soil and rhizosphere. We performed in 30 silico gene deletions to predict the set of core essential genes for this bacterium to grow in such 31 environments. We anticipate that our metabolic model will be a valuable element for defining a 32 set of metabolic targets to control infection and spreading of this plant pathogen and a scaffold to 33 interpret future -omics datasets for this bacterium. 34 35 INTRODUCTION 36

Development of a multiplex TaqMan qPCR targeting unique genomic regions for the specific and sensitive detection of Pectobacterium species and P. parmentieri

“…Soft rot bacteria present in soil niches are capable of colonizing healthy potato roots and invading the vascular systems, facilitating the spreading of this pathogen (Charkowski, 2018; Skelsey et al, 2018). In a recent study, metabolic modelling of P. parmentieri SCC3193 revealed that this phytopathogen can adapt to different ecological niches, such as soil and rhizosphere, through reconstitution of its metabolic pathways, allowing its survival on plant debris for large periods without interacting with its natural host (Zoledowska et al, 2019). Following these background studies, we evaluated the specificity of our multiplex qPCR using DNA isolated from soil and the rhizosphere of infected plants (Figure 6c).…”

Uncovering the Role of Metabolism in Oomycete–Host Interactions Using Genome-Scale Metabolic Models

“…In a more recent study based on mass spectrometry analyses of immunoprecipitated effector–host target protein complexes in Nicotiana benthamiana , the deduced protein–protein interaction network revealed the cellular vesicle trafficking machinery as a major effector-targeted process ( Petre et al, 2021 ). In other studies, GEMs have been reconstructed for the bacterial plant pathogens Ralstonia solanacearum , Xanthomonas oryzae , and Pectobacterium parmentieri ( Peyraud et al, 2016 ; Zoledowska et al, 2018 ; Koduru et al, 2020 ), and for the fungus Sclerotinia sclerotiorum ( Peyraud et al, 2019 ). However, despite the abundance of omics data for many plant pathogens, very few have been analyzed from a systems biology perspective.…”

Uncovering oomycete metabolism using systems biology

“…In one study a protein-protein interaction network of Arabidopsis thaliana and various pathogens of different kingdoms uncovered that effectors from different pathogens convergently target the same host proteins (Weßling et al, 2014). In other studies, GEMs have been reconstructed for the bacterial plant pathogens Ralstonia solanacearum, Xanthomonas oryzae, and Pectobacterium parmentieri (Koduru et al, 2020;Peyraud et al, 2016;Zoledowska et al, 2019), and for the fungus Sclerotinia sclerotiorum (Peyraud et al, 2019). However, despite the abundance of omics data for many pathogens, very few have been analyzed from a systems biology perspective.…”