Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis
Abstract Understanding how plants adapt their physiology to overcome severe and often multifactorial stress conditions in nature is vital in light of the climate crisis. This remains a challenge given the complex nature of the underlying molecular mechanisms. To provide a comprehensive picture of st...
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| Format: | Article |
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Nature Portfolio
2025-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-59542-3 |
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| author | Raul Sanchez-Munoz Thomas Depaepe Marketa Samalova Jan Hejatko Isiah Zaplana Dominique Van Der Straeten |
| author_facet | Raul Sanchez-Munoz Thomas Depaepe Marketa Samalova Jan Hejatko Isiah Zaplana Dominique Van Der Straeten |
| author_sort | Raul Sanchez-Munoz |
| collection | DOAJ |
| description | Abstract Understanding how plants adapt their physiology to overcome severe and often multifactorial stress conditions in nature is vital in light of the climate crisis. This remains a challenge given the complex nature of the underlying molecular mechanisms. To provide a comprehensive picture of stress-mitigation mechanisms, an exhaustive analysis of publicly available stress-related transcriptomic data has been conducted. We combine a meta-analysis with an unsupervised machine-learning algorithm to identify a core of stress-related genes active at 1-6 h and 12-24 h of exposure in Arabidopsis thaliana shoots and roots. To ensure robustness and biological significance of the output, often lacking in meta-analyses, a triple validation is incorporated. We present a ‘stress gene core’: a set of key genes involved in plant tolerance to ten adverse environmental conditions and ethylene-precursor supplementation rather than individual conditions. Notably, ethylene plays a key regulatory role in this core, influencing gene expression and acting as a critical factor in stress tolerance. Additionally, the analysis provides insights into previously uncharacterized genes, key genes within large families, and gene expression dynamics, which are used to create biologically validated databases that can guide further abiotic stress research. These findings establish a strong framework for advancing multi-stress-resilient crops, paving the way for sustainable agriculture in the face of climate challenges. |
| format | Article |
| id | doaj-art-25c2ecf2fe104ad2bbe67f4737262d65 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-25c2ecf2fe104ad2bbe67f4737262d652025-08-20T03:48:18ZengNature PortfolioNature Communications2041-17232025-05-0116112110.1038/s41467-025-59542-3Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in ArabidopsisRaul Sanchez-Munoz0Thomas Depaepe1Marketa Samalova2Jan Hejatko3Isiah Zaplana4Dominique Van Der Straeten5Laboratory of Functional Plant Biology, Department of Biology, Faculty of Sciences, Ghent UniversityLaboratory of Functional Plant Biology, Department of Biology, Faculty of Sciences, Ghent UniversityDepartment of Experimental Biology, Faculty of Science, Masaryk UniversityCEITEC - Central European Institute of Technology, Masaryk UniversityInstitute of Industrial and Control Engineering (IOC), Universitat Politècnica de Catalunya - BarcelonaTech (UPC)Laboratory of Functional Plant Biology, Department of Biology, Faculty of Sciences, Ghent UniversityAbstract Understanding how plants adapt their physiology to overcome severe and often multifactorial stress conditions in nature is vital in light of the climate crisis. This remains a challenge given the complex nature of the underlying molecular mechanisms. To provide a comprehensive picture of stress-mitigation mechanisms, an exhaustive analysis of publicly available stress-related transcriptomic data has been conducted. We combine a meta-analysis with an unsupervised machine-learning algorithm to identify a core of stress-related genes active at 1-6 h and 12-24 h of exposure in Arabidopsis thaliana shoots and roots. To ensure robustness and biological significance of the output, often lacking in meta-analyses, a triple validation is incorporated. We present a ‘stress gene core’: a set of key genes involved in plant tolerance to ten adverse environmental conditions and ethylene-precursor supplementation rather than individual conditions. Notably, ethylene plays a key regulatory role in this core, influencing gene expression and acting as a critical factor in stress tolerance. Additionally, the analysis provides insights into previously uncharacterized genes, key genes within large families, and gene expression dynamics, which are used to create biologically validated databases that can guide further abiotic stress research. These findings establish a strong framework for advancing multi-stress-resilient crops, paving the way for sustainable agriculture in the face of climate challenges.https://doi.org/10.1038/s41467-025-59542-3 |
| spellingShingle | Raul Sanchez-Munoz Thomas Depaepe Marketa Samalova Jan Hejatko Isiah Zaplana Dominique Van Der Straeten Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis Nature Communications |
| title | Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis |
| title_full | Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis |
| title_fullStr | Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis |
| title_full_unstemmed | Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis |
| title_short | Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis |
| title_sort | machine learning meta analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in arabidopsis |
| url | https://doi.org/10.1038/s41467-025-59542-3 |
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