Metabolic modeling of Pectobacterium parmentieri SCC3193 provides insights into metabolic pathways of plant pathogenic bacteria
Sabina Żołędowska , Luana Presta , Marco Fondi , Francesca Decorosi , Luciana Giovannetti , Alessio Mengoni , Ewa Łojkowska
AbstractUnderstanding plant–microbe interactions is crucial for improving plants’ productivity and protection. Constraint-based metabolic modeling is one of the possible ways to investigate the bacterial adaptation to different ecological niches and may give insights into the metabolic versatility of plant pathogenic bacteria. We reconstructed a raw metabolic model of the emerging plant pathogenic bacterium Pectobacterium parmentieri SCC3193 with the use of KBase. The model was curated by using inParanoind and phenotypic data generated with the use of the OmniLog system. Metabolic modeling was performed through COBRApy Toolbox v. 0.10.1. The curated metabolic model of P. parmentieri SCC3193 is highly reliable, as in silico obtained results overlapped up to 91% with experimental data on carbon utilization phenotypes. By mean of flux balance analysis (FBA), we predicted the metabolic adaptation of P. parmentieri SCC3193 to two different ecological niches, relevant for the persistence and plant colonization by this bacterium: soil and the rhizosphere. We performed in silico gene deletions to predict the set of essential core genes for this bacterium to grow in such environments. We anticipate that our metabolic model will be a valuable element for defining a set of metabolic targets to control infection and spreading of this plant pathogen.
|Journal series||Microorganisms, ISSN 2076-2607, (N/A 20 pkt)|
|Publication size in sheets||0.7|
|Keywords in English||Flux Balance Analysis, plant pathogenic bacteria, bacterial adaptation, metabolic reactions|
|License||Journal (articles only); published final; ; with publication|
|Score||= 20.0, 06-12-2019, ArticleFromJournal|
|Publication indicators||= 1; = 1; : 2018 = 4.167 (2)|
|Citation count*||1 (2019-12-10)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.