Arabidopsis-Based Dual-Layered Biological Network Analysis Elucidates Fully Modulated Pathways Related to Sugarcane Resistance on Biotrophic Pathogen Infection
We assembled a dual-layered biological network to study the roles of resistance gene analogs (RGAs) in the resistance of sugarcane to infection by the biotrophic fungus causing smut disease. Based on sugarcane-Arabidopsis orthology, the modeling used metabolic and protein-protein interaction (PPI) d...
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doaj-99db5405415944509f9fa9866b6f10622021-08-19T14:40:45ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2021-08-011210.3389/fpls.2021.707904707904Arabidopsis-Based Dual-Layered Biological Network Analysis Elucidates Fully Modulated Pathways Related to Sugarcane Resistance on Biotrophic Pathogen InfectionHugo V. S. Rody0Luis E. A. Camargo1Silvana Creste2Marie-Anne Van Sluys3Loren H. Rieseberg4Claudia B. Monteiro-Vitorello5Departamento de Genética, Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo, Piracicaba, BrazilDepartamento de Genética, Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo, Piracicaba, BrazilCentro de Cana, IAC-Apta, Ribeirão Preto, BrazilDepartamento de Botânica, Instituto de Biociências, Universidade de São Paulo, São Paulo, BrazilDepartment of Botany, University of British Columbia, Vancouver, BC, CanadaDepartamento de Genética, Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo, Piracicaba, BrazilWe assembled a dual-layered biological network to study the roles of resistance gene analogs (RGAs) in the resistance of sugarcane to infection by the biotrophic fungus causing smut disease. Based on sugarcane-Arabidopsis orthology, the modeling used metabolic and protein-protein interaction (PPI) data from Arabidopsis thaliana (from Kyoto Encyclopedia of Genes and Genomes (KEGG) and BioGRID databases) and plant resistance curated knowledge for Viridiplantae obtained through text mining of the UniProt/SwissProt database. With the network, we integrated functional annotations and transcriptome data from two sugarcane genotypes that differ significantly in resistance to smut and applied a series of analyses to compare the transcriptomes and understand both signal perception and transduction in plant resistance. We show that the smut-resistant sugarcane has a larger arsenal of RGAs encompassing transcriptionally modulated subnetworks with other resistance elements, reaching hub proteins of primary metabolism. This approach may benefit molecular breeders in search of markers associated with quantitative resistance to diseases in non-model systems.https://www.frontiersin.org/articles/10.3389/fpls.2021.707904/fullbiological networksdata miningdata integrationtranscriptomebiotrophic pathogensSaccharum |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hugo V. S. Rody Luis E. A. Camargo Silvana Creste Marie-Anne Van Sluys Loren H. Rieseberg Claudia B. Monteiro-Vitorello |
spellingShingle |
Hugo V. S. Rody Luis E. A. Camargo Silvana Creste Marie-Anne Van Sluys Loren H. Rieseberg Claudia B. Monteiro-Vitorello Arabidopsis-Based Dual-Layered Biological Network Analysis Elucidates Fully Modulated Pathways Related to Sugarcane Resistance on Biotrophic Pathogen Infection Frontiers in Plant Science biological networks data mining data integration transcriptome biotrophic pathogens Saccharum |
author_facet |
Hugo V. S. Rody Luis E. A. Camargo Silvana Creste Marie-Anne Van Sluys Loren H. Rieseberg Claudia B. Monteiro-Vitorello |
author_sort |
Hugo V. S. Rody |
title |
Arabidopsis-Based Dual-Layered Biological Network Analysis Elucidates Fully Modulated Pathways Related to Sugarcane Resistance on Biotrophic Pathogen Infection |
title_short |
Arabidopsis-Based Dual-Layered Biological Network Analysis Elucidates Fully Modulated Pathways Related to Sugarcane Resistance on Biotrophic Pathogen Infection |
title_full |
Arabidopsis-Based Dual-Layered Biological Network Analysis Elucidates Fully Modulated Pathways Related to Sugarcane Resistance on Biotrophic Pathogen Infection |
title_fullStr |
Arabidopsis-Based Dual-Layered Biological Network Analysis Elucidates Fully Modulated Pathways Related to Sugarcane Resistance on Biotrophic Pathogen Infection |
title_full_unstemmed |
Arabidopsis-Based Dual-Layered Biological Network Analysis Elucidates Fully Modulated Pathways Related to Sugarcane Resistance on Biotrophic Pathogen Infection |
title_sort |
arabidopsis-based dual-layered biological network analysis elucidates fully modulated pathways related to sugarcane resistance on biotrophic pathogen infection |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Plant Science |
issn |
1664-462X |
publishDate |
2021-08-01 |
description |
We assembled a dual-layered biological network to study the roles of resistance gene analogs (RGAs) in the resistance of sugarcane to infection by the biotrophic fungus causing smut disease. Based on sugarcane-Arabidopsis orthology, the modeling used metabolic and protein-protein interaction (PPI) data from Arabidopsis thaliana (from Kyoto Encyclopedia of Genes and Genomes (KEGG) and BioGRID databases) and plant resistance curated knowledge for Viridiplantae obtained through text mining of the UniProt/SwissProt database. With the network, we integrated functional annotations and transcriptome data from two sugarcane genotypes that differ significantly in resistance to smut and applied a series of analyses to compare the transcriptomes and understand both signal perception and transduction in plant resistance. We show that the smut-resistant sugarcane has a larger arsenal of RGAs encompassing transcriptionally modulated subnetworks with other resistance elements, reaching hub proteins of primary metabolism. This approach may benefit molecular breeders in search of markers associated with quantitative resistance to diseases in non-model systems. |
topic |
biological networks data mining data integration transcriptome biotrophic pathogens Saccharum |
url |
https://www.frontiersin.org/articles/10.3389/fpls.2021.707904/full |
work_keys_str_mv |
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