Co-Expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal Resistance

Autores
Ribone, Andrés Ignacio; Fass, Mónica Irina; González, Sergio Alberto; Lia, Verónica Viviana; Paniego, Norma Beatriz; Rivarola, Maximo Lisandro
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Fungal plant diseases are a major threat to food security worldwide. Current efforts to identify and list loci involved in different biological processes are more complicated than originally thought, even when complete genome assemblies are available. Despite numerous experimental and computational efforts to characterize gene functions in plants, about ~40% of protein-coding genes in the model plant Arabidopsis thaliana L. are still not categorized in the Gene Ontology (GO) Biological Process (BP) annotation. In non-model organisms, such as sunflower (Helianthus annuus L.), the number of BP term annotations is far fewer, ~22%. In the current study, we performed gene co-expression network analysis using eight terabytes of public transcriptome datasets and expression-based functional prediction to categorize and identify loci involved in the response to fungal pathogens. We were able to construct a reference gene network of healthy green tissue (GreenGCN) and a gene network of healthy and stressed root tissues (RootGCN). Both networks achieved robust, high-quality scores on the metrics of guilt-by-association and selective constraints versus gene connectivity. We were able to identify eight modules enriched in defense functions, of which two out of the three modules in the RootGCN were also conserved in the GreenGCN, suggesting similar defense-related expression patterns. We identified 16 WRKY genes involved in defense related functions and 65 previously uncharacterized loci now linked to defense response. In addition, we identified and classified 122 loci previously identified within QTLs or near candidate loci reported in GWAS studies of disease resistance in sunflower linked to defense response. All in all, we have implemented a valuable strategy to better describe genes within specific biological processes.
Fil: Ribone, Andrés Ignacio. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Fass, Mónica Irina. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: González, Sergio Alberto. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Lia, Verónica Viviana. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Paniego, Norma Beatriz. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Rivarola, Maximo Lisandro. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; Argentina
Materia
CANDIDATE GENES
CO-EXPRESSION NETWORKS
FUNGAL PATHOGENS
META-ANALYSIS
MULTI-STUDY ANALYSIS
PLANT PATHOLOGY
SUNFLOWER
TRANSCRIPTOMICS
WRKY
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/228697
Acceder
id CONICETDig_36f615ce82e2fb40d4c56bc478808930
oai_identifier_str oai:ri.conicet.gov.ar:11336/228697
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Co-Expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal ResistanceRibone, Andrés IgnacioFass, Mónica IrinaGonzález, Sergio AlbertoLia, Verónica VivianaPaniego, Norma BeatrizRivarola, Maximo LisandroCANDIDATE GENESCO-EXPRESSION NETWORKSFUNGAL PATHOGENSMETA-ANALYSISMULTI-STUDY ANALYSISPLANT PATHOLOGYSUNFLOWERTRANSCRIPTOMICSWRKYhttps://purl.org/becyt/ford/4.4https://purl.org/becyt/ford/4Fungal plant diseases are a major threat to food security worldwide. Current efforts to identify and list loci involved in different biological processes are more complicated than originally thought, even when complete genome assemblies are available. Despite numerous experimental and computational efforts to characterize gene functions in plants, about ~40% of protein-coding genes in the model plant Arabidopsis thaliana L. are still not categorized in the Gene Ontology (GO) Biological Process (BP) annotation. In non-model organisms, such as sunflower (Helianthus annuus L.), the number of BP term annotations is far fewer, ~22%. In the current study, we performed gene co-expression network analysis using eight terabytes of public transcriptome datasets and expression-based functional prediction to categorize and identify loci involved in the response to fungal pathogens. We were able to construct a reference gene network of healthy green tissue (GreenGCN) and a gene network of healthy and stressed root tissues (RootGCN). Both networks achieved robust, high-quality scores on the metrics of guilt-by-association and selective constraints versus gene connectivity. We were able to identify eight modules enriched in defense functions, of which two out of the three modules in the RootGCN were also conserved in the GreenGCN, suggesting similar defense-related expression patterns. We identified 16 WRKY genes involved in defense related functions and 65 previously uncharacterized loci now linked to defense response. In addition, we identified and classified 122 loci previously identified within QTLs or near candidate loci reported in GWAS studies of disease resistance in sunflower linked to defense response. All in all, we have implemented a valuable strategy to better describe genes within specific biological processes.Fil: Ribone, Andrés Ignacio. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; ArgentinaFil: Fass, Mónica Irina. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; ArgentinaFil: González, Sergio Alberto. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; ArgentinaFil: Lia, Verónica Viviana. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; ArgentinaFil: Paniego, Norma Beatriz. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; ArgentinaFil: Rivarola, Maximo Lisandro. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; ArgentinaMDPI2023-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/228697Ribone, Andrés Ignacio; Fass, Mónica Irina; González, Sergio Alberto; Lia, Verónica Viviana; Paniego, Norma Beatriz; et al.; Co-Expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal Resistance; MDPI; Plants; 12; 15; 7-2023; 1-172223-7747CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2223-7747/12/15/2767info:eu-repo/semantics/altIdentifier/doi/10.3390/plants12152767info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:24:22Zoai:ri.conicet.gov.ar:11336/228697instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:24:22.662CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Co-Expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal Resistance
title Co-Expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal Resistance
spellingShingle Co-Expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal Resistance
Ribone, Andrés Ignacio
CANDIDATE GENES
CO-EXPRESSION NETWORKS
FUNGAL PATHOGENS
META-ANALYSIS
MULTI-STUDY ANALYSIS
PLANT PATHOLOGY
SUNFLOWER
TRANSCRIPTOMICS
WRKY
title_short Co-Expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal Resistance
title_full Co-Expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal Resistance
title_fullStr Co-Expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal Resistance
title_full_unstemmed Co-Expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal Resistance
title_sort Co-Expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal Resistance
dc.creator.none.fl_str_mv Ribone, Andrés Ignacio
Fass, Mónica Irina
González, Sergio Alberto
Lia, Verónica Viviana
Paniego, Norma Beatriz
Rivarola, Maximo Lisandro
author Ribone, Andrés Ignacio
author_facet Ribone, Andrés Ignacio
Fass, Mónica Irina
González, Sergio Alberto
Lia, Verónica Viviana
Paniego, Norma Beatriz
Rivarola, Maximo Lisandro
author_role author
author2 Fass, Mónica Irina
González, Sergio Alberto
Lia, Verónica Viviana
Paniego, Norma Beatriz
Rivarola, Maximo Lisandro
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv CANDIDATE GENES
CO-EXPRESSION NETWORKS
FUNGAL PATHOGENS
META-ANALYSIS
MULTI-STUDY ANALYSIS
PLANT PATHOLOGY
SUNFLOWER
TRANSCRIPTOMICS
WRKY
topic CANDIDATE GENES
CO-EXPRESSION NETWORKS
FUNGAL PATHOGENS
META-ANALYSIS
MULTI-STUDY ANALYSIS
PLANT PATHOLOGY
SUNFLOWER
TRANSCRIPTOMICS
WRKY
purl_subject.fl_str_mv https://purl.org/becyt/ford/4.4
https://purl.org/becyt/ford/4
dc.description.none.fl_txt_mv Fungal plant diseases are a major threat to food security worldwide. Current efforts to identify and list loci involved in different biological processes are more complicated than originally thought, even when complete genome assemblies are available. Despite numerous experimental and computational efforts to characterize gene functions in plants, about ~40% of protein-coding genes in the model plant Arabidopsis thaliana L. are still not categorized in the Gene Ontology (GO) Biological Process (BP) annotation. In non-model organisms, such as sunflower (Helianthus annuus L.), the number of BP term annotations is far fewer, ~22%. In the current study, we performed gene co-expression network analysis using eight terabytes of public transcriptome datasets and expression-based functional prediction to categorize and identify loci involved in the response to fungal pathogens. We were able to construct a reference gene network of healthy green tissue (GreenGCN) and a gene network of healthy and stressed root tissues (RootGCN). Both networks achieved robust, high-quality scores on the metrics of guilt-by-association and selective constraints versus gene connectivity. We were able to identify eight modules enriched in defense functions, of which two out of the three modules in the RootGCN were also conserved in the GreenGCN, suggesting similar defense-related expression patterns. We identified 16 WRKY genes involved in defense related functions and 65 previously uncharacterized loci now linked to defense response. In addition, we identified and classified 122 loci previously identified within QTLs or near candidate loci reported in GWAS studies of disease resistance in sunflower linked to defense response. All in all, we have implemented a valuable strategy to better describe genes within specific biological processes.
Fil: Ribone, Andrés Ignacio. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Fass, Mónica Irina. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: González, Sergio Alberto. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Lia, Verónica Viviana. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Paniego, Norma Beatriz. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Rivarola, Maximo Lisandro. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; Argentina
description Fungal plant diseases are a major threat to food security worldwide. Current efforts to identify and list loci involved in different biological processes are more complicated than originally thought, even when complete genome assemblies are available. Despite numerous experimental and computational efforts to characterize gene functions in plants, about ~40% of protein-coding genes in the model plant Arabidopsis thaliana L. are still not categorized in the Gene Ontology (GO) Biological Process (BP) annotation. In non-model organisms, such as sunflower (Helianthus annuus L.), the number of BP term annotations is far fewer, ~22%. In the current study, we performed gene co-expression network analysis using eight terabytes of public transcriptome datasets and expression-based functional prediction to categorize and identify loci involved in the response to fungal pathogens. We were able to construct a reference gene network of healthy green tissue (GreenGCN) and a gene network of healthy and stressed root tissues (RootGCN). Both networks achieved robust, high-quality scores on the metrics of guilt-by-association and selective constraints versus gene connectivity. We were able to identify eight modules enriched in defense functions, of which two out of the three modules in the RootGCN were also conserved in the GreenGCN, suggesting similar defense-related expression patterns. We identified 16 WRKY genes involved in defense related functions and 65 previously uncharacterized loci now linked to defense response. In addition, we identified and classified 122 loci previously identified within QTLs or near candidate loci reported in GWAS studies of disease resistance in sunflower linked to defense response. All in all, we have implemented a valuable strategy to better describe genes within specific biological processes.
publishDate 2023
dc.date.none.fl_str_mv 2023-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/228697
Ribone, Andrés Ignacio; Fass, Mónica Irina; González, Sergio Alberto; Lia, Verónica Viviana; Paniego, Norma Beatriz; et al.; Co-Expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal Resistance; MDPI; Plants; 12; 15; 7-2023; 1-17
2223-7747
CONICET Digital
CONICET
url http://hdl.handle.net/11336/228697
identifier_str_mv Ribone, Andrés Ignacio; Fass, Mónica Irina; González, Sergio Alberto; Lia, Verónica Viviana; Paniego, Norma Beatriz; et al.; Co-Expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal Resistance; MDPI; Plants; 12; 15; 7-2023; 1-17
2223-7747
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2223-7747/12/15/2767
info:eu-repo/semantics/altIdentifier/doi/10.3390/plants12152767
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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