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Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis

Raul Sanchez Muñoz (UGent) , Thomas Depaepe (UGent) , Marketa Samalova, Jan Hejatko, Isiah Zaplana and Dominique Van Der Straeten (UGent)
(2025) NATURE COMMUNICATIONS. 16(1).
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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.
Keywords
1-AMINOCYCLOPROPANE-1-CARBOXYLIC ACID, DISEASE RESISTANCE, PROTEIN, EXPRESSION, GENES, TOLERANCE, DROUGHT, BIOSYNTHESIS, INVOLVEMENT, SHOOTS

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MLA
Sanchez Muñoz, Raul, et al. “Machine-Learning Meta-Analysis Reveals Ethylene as a Central Component of the Molecular Core in Abiotic Stress Responses in Arabidopsis.” NATURE COMMUNICATIONS, vol. 16, no. 1, 2025, doi:10.1038/s41467-025-59542-3.
APA
Sanchez Muñoz, R., Depaepe, T., Samalova, M., Hejatko, J., Zaplana, I., & Van Der Straeten, D. (2025). Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis. NATURE COMMUNICATIONS, 16(1). https://doi.org/10.1038/s41467-025-59542-3
Chicago author-date
Sanchez Muñoz, Raul, Thomas Depaepe, Marketa Samalova, Jan Hejatko, Isiah Zaplana, and Dominique Van Der Straeten. 2025. “Machine-Learning Meta-Analysis Reveals Ethylene as a Central Component of the Molecular Core in Abiotic Stress Responses in Arabidopsis.” NATURE COMMUNICATIONS 16 (1). https://doi.org/10.1038/s41467-025-59542-3.
Chicago author-date (all authors)
Sanchez Muñoz, Raul, Thomas Depaepe, Marketa Samalova, Jan Hejatko, Isiah Zaplana, and Dominique Van Der Straeten. 2025. “Machine-Learning Meta-Analysis Reveals Ethylene as a Central Component of the Molecular Core in Abiotic Stress Responses in Arabidopsis.” NATURE COMMUNICATIONS 16 (1). doi:10.1038/s41467-025-59542-3.
Vancouver
1.
Sanchez Muñoz R, Depaepe T, Samalova M, Hejatko J, Zaplana I, Van Der Straeten D. Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis. NATURE COMMUNICATIONS. 2025;16(1).
IEEE
[1]
R. Sanchez Muñoz, T. Depaepe, M. Samalova, J. Hejatko, I. Zaplana, and D. 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, vol. 16, no. 1, 2025.
@article{01KAB930JG447E572V8VA6A3RJ,
  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.}},
  articleno    = {{4778}},
  author       = {{Sanchez Muñoz, Raul and Depaepe, Thomas and Samalova, Marketa and Hejatko, Jan and Zaplana, Isiah and Van Der Straeten, Dominique}},
  issn         = {{2041-1723}},
  journal      = {{NATURE COMMUNICATIONS}},
  keywords     = {{1-AMINOCYCLOPROPANE-1-CARBOXYLIC ACID,DISEASE RESISTANCE,PROTEIN,EXPRESSION,GENES,TOLERANCE,DROUGHT,BIOSYNTHESIS,INVOLVEMENT,SHOOTS}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{21}},
  title        = {{Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis}},
  url          = {{http://doi.org/10.1038/s41467-025-59542-3}},
  volume       = {{16}},
  year         = {{2025}},
}
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