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Metabolic Modeling of Pectobacterium parmentieri SCC3193 Provides Insights into Metabolic Pathways of Plant Pathogenic Bacteria
Understanding 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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6518042/ https://www.ncbi.nlm.nih.gov/pubmed/30959803 http://dx.doi.org/10.3390/microorganisms7040101 |
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author | Zoledowska, Sabina Presta, Luana Fondi, Marco Decorosi, Francesca Giovannetti, Luciana Mengoni, Alessio Lojkowska, Ewa |
author_facet | Zoledowska, Sabina Presta, Luana Fondi, Marco Decorosi, Francesca Giovannetti, Luciana Mengoni, Alessio Lojkowska, Ewa |
author_sort | Zoledowska, Sabina |
collection | PubMed |
description | Understanding 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. |
format | Online Article Text |
id | pubmed-6518042 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-65180422019-05-31 Metabolic Modeling of Pectobacterium parmentieri SCC3193 Provides Insights into Metabolic Pathways of Plant Pathogenic Bacteria Zoledowska, Sabina Presta, Luana Fondi, Marco Decorosi, Francesca Giovannetti, Luciana Mengoni, Alessio Lojkowska, Ewa Microorganisms Article Understanding 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. MDPI 2019-04-05 /pmc/articles/PMC6518042/ /pubmed/30959803 http://dx.doi.org/10.3390/microorganisms7040101 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zoledowska, Sabina Presta, Luana Fondi, Marco Decorosi, Francesca Giovannetti, Luciana Mengoni, Alessio Lojkowska, Ewa Metabolic Modeling of Pectobacterium parmentieri SCC3193 Provides Insights into Metabolic Pathways of Plant Pathogenic Bacteria |
title | Metabolic Modeling of Pectobacterium parmentieri SCC3193 Provides Insights into Metabolic Pathways of Plant Pathogenic Bacteria |
title_full | Metabolic Modeling of Pectobacterium parmentieri SCC3193 Provides Insights into Metabolic Pathways of Plant Pathogenic Bacteria |
title_fullStr | Metabolic Modeling of Pectobacterium parmentieri SCC3193 Provides Insights into Metabolic Pathways of Plant Pathogenic Bacteria |
title_full_unstemmed | Metabolic Modeling of Pectobacterium parmentieri SCC3193 Provides Insights into Metabolic Pathways of Plant Pathogenic Bacteria |
title_short | Metabolic Modeling of Pectobacterium parmentieri SCC3193 Provides Insights into Metabolic Pathways of Plant Pathogenic Bacteria |
title_sort | metabolic modeling of pectobacterium parmentieri scc3193 provides insights into metabolic pathways of plant pathogenic bacteria |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6518042/ https://www.ncbi.nlm.nih.gov/pubmed/30959803 http://dx.doi.org/10.3390/microorganisms7040101 |
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