Co-expression networks in sunflower : harnessing the power of multi-study transcriptomic public data to identify and categorize candidate genes for fungal resistance
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...
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| Format: | info:ar-repo/semantics/artículo |
| Language: | Inglés |
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MDPI
2023
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| Online Access: | http://hdl.handle.net/20.500.12123/15470 https://www.mdpi.com/2223-7747/12/15/2767 https://doi.org/10.3390/plants12152767 |
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| author | Ribone, Andrés Ignacio Fass, Monica Irinia Gonzalez, Sergio Alberto Lia, Veronica Viviana Paniego, Norma Beatriz Rivarola, Maximo Lisandro |
| author_browse | Fass, Monica Irinia Gonzalez, Sergio Alberto Lia, Veronica Viviana Paniego, Norma Beatriz Ribone, Andrés Ignacio Rivarola, Maximo Lisandro |
| author_facet | Ribone, Andrés Ignacio Fass, Monica Irinia Gonzalez, Sergio Alberto Lia, Veronica Viviana Paniego, Norma Beatriz Rivarola, Maximo Lisandro |
| author_sort | Ribone, Andrés Ignacio |
| collection | INTA Digital |
| 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. |
| format | info:ar-repo/semantics/artículo |
| id | INTA15470 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | INTA154702023-10-09T09:48:28Z 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 Fass, Monica Irinia Gonzalez, Sergio Alberto Lia, Veronica Viviana Paniego, Norma Beatriz Rivarola, Maximo Lisandro Transcriptomics Plant Pathology Sunflowers Candidate Genes Analysis Transcriptómica Fitopatología Girasol Genes Candidatos Helianthus annuus Análisis 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 2023-10-09T09:42:25Z 2023-10-09T09:42:25Z 2023-08 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 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 eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf MDPI Plants 12 (15) : 2767 (Agosto 2023) |
| spellingShingle | Transcriptomics Plant Pathology Sunflowers Candidate Genes Analysis Transcriptómica Fitopatología Girasol Genes Candidatos Helianthus annuus Análisis Ribone, Andrés Ignacio Fass, Monica Irinia Gonzalez, Sergio Alberto Lia, Veronica Viviana Paniego, Norma Beatriz Rivarola, Maximo Lisandro 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 |
| 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_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_sort | co expression networks in sunflower harnessing the power of multi study transcriptomic public data to identify and categorize candidate genes for fungal resistance |
| topic | Transcriptomics Plant Pathology Sunflowers Candidate Genes Analysis Transcriptómica Fitopatología Girasol Genes Candidatos Helianthus annuus Análisis |
| url | http://hdl.handle.net/20.500.12123/15470 https://www.mdpi.com/2223-7747/12/15/2767 https://doi.org/10.3390/plants12152767 |
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