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, Monica Irinia; Gonzalez, Sergio Alberto; Lia, Veronica 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.
Instituto de Biotecnología
Fil: Ribone, Andrés Ignacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Ribone, Andrés Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Fass, Mónica Irina. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Fass, Mónica Irina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Gonzalez, Sergio Alberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnologia y Biología Molecular; Argentina
Fil: Gonzalez, Sergio Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Lia, Veronica Viviana. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Lia, Veronica Viviana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Paniego, Norma Beatriz. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Paniego, Norma Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Rivarola, Maximo Lisandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Rivarola, Maximo Lisandro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fuente
Plants 12 (15) : 2767 (Agosto 2023)
Materia
Transcriptomics
Plant Pathology
Sunflowers
Candidate Genes
Analysis
Transcriptómica
Fitopatología
Girasol
Genes Candidatos
Helianthus annuus
Análisis
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/15470
Acceder
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oai_identifier_str oai:localhost:20.500.12123/15470
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
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, Monica IriniaGonzalez, Sergio AlbertoLia, Veronica VivianaPaniego, Norma BeatrizRivarola, Maximo LisandroTranscriptomicsPlant PathologySunflowersCandidate GenesAnalysisTranscriptómicaFitopatologíaGirasolGenes CandidatosHelianthus annuusAnálisisFungal 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.Instituto de BiotecnologíaFil: Ribone, Andrés Ignacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; ArgentinaFil: Ribone, Andrés Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fass, Mónica Irina. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; ArgentinaFil: Fass, Mónica Irina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gonzalez, Sergio Alberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnologia y Biología Molecular; ArgentinaFil: Gonzalez, Sergio Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Lia, Veronica Viviana. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; ArgentinaFil: Lia, Veronica Viviana. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Paniego, Norma Beatriz. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; ArgentinaFil: Paniego, Norma Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rivarola, Maximo Lisandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; ArgentinaFil: Rivarola, Maximo Lisandro. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaMDPI2023-10-09T09:42:25Z2023-10-09T09:42:25Z2023-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/15470https://www.mdpi.com/2223-7747/12/15/27672223-7747https://doi.org/10.3390/plants12152767Plants 12 (15) : 2767 (Agosto 2023)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-09-04T09:49:59Zoai:localhost:20.500.12123/15470instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-04 09:49:59.854INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
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
Transcriptomics
Plant Pathology
Sunflowers
Candidate Genes
Analysis
Transcriptómica
Fitopatología
Girasol
Genes Candidatos
Helianthus annuus
Análisis
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, Monica Irinia
Gonzalez, Sergio Alberto
Lia, Veronica Viviana
Paniego, Norma Beatriz
Rivarola, Maximo Lisandro
author Ribone, Andrés Ignacio
author_facet Ribone, Andrés Ignacio
Fass, Monica Irinia
Gonzalez, Sergio Alberto
Lia, Veronica Viviana
Paniego, Norma Beatriz
Rivarola, Maximo Lisandro
author_role author
author2 Fass, Monica Irinia
Gonzalez, Sergio Alberto
Lia, Veronica Viviana
Paniego, Norma Beatriz
Rivarola, Maximo Lisandro
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Transcriptomics
Plant Pathology
Sunflowers
Candidate Genes
Analysis
Transcriptómica
Fitopatología
Girasol
Genes Candidatos
Helianthus annuus
Análisis
topic Transcriptomics
Plant Pathology
Sunflowers
Candidate Genes
Analysis
Transcriptómica
Fitopatología
Girasol
Genes Candidatos
Helianthus annuus
Análisis
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.
Instituto de Biotecnología
Fil: Ribone, Andrés Ignacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Ribone, Andrés Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Fass, Mónica Irina. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Fass, Mónica Irina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Gonzalez, Sergio Alberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnologia y Biología Molecular; Argentina
Fil: Gonzalez, Sergio Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Lia, Veronica Viviana. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Lia, Veronica Viviana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Paniego, Norma Beatriz. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Paniego, Norma Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Rivarola, Maximo Lisandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Rivarola, Maximo Lisandro. Consejo Nacional de Investigaciones Científicas y Técnicas; 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 2023-10-09T09:42:25Z
2023-10-09T09:42:25Z
2023-08
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/20.500.12123/15470
https://www.mdpi.com/2223-7747/12/15/2767
2223-7747
https://doi.org/10.3390/plants12152767
url http://hdl.handle.net/20.500.12123/15470
https://www.mdpi.com/2223-7747/12/15/2767
https://doi.org/10.3390/plants12152767
identifier_str_mv 2223-7747
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv Plants 12 (15) : 2767 (Agosto 2023)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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