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

. 2025 May 22 ; 16 (1) : 4778. [epub] 20250522

Jazyk angličtina Země Anglie, Velká Británie Médium electronic

Typ dokumentu časopisecké články, metaanalýza

Perzistentní odkaz   https://www.medvik.cz/link/pmid40404615

Grantová podpora
G032717N Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)
G082421N Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)
1288923N Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)
BOF-BAS Universiteit Gent (UGent)

Odkazy

PubMed 40404615
PubMed Central PMC12098884
DOI 10.1038/s41467-025-59542-3
PII: 10.1038/s41467-025-59542-3
Knihovny.cz E-zdroje

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.

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Food and Agriculture Organization of the United Nations. The Impact of Disasters and Crises on Agriculture and Food Security: 2021 (Food & Agriculture Organization, 2021).

Pandey, P., Irulappan, V., Bagavathiannan, M. V. & Senthil-Kumar, M. Impact of combined abiotic and biotic stresses on plant growth and avenues for crop improvement by exploiting physio-morphological traits. Front. Plant Sci.8, 537 (2017). PubMed PMC

Oshunsanya, S. O., Nwosu, N. J. & Li, Y. Abiotic stress in agricultural crops under climatic conditions. In Sustainable Agriculture, Forest and Environmental Management (eds Jhariya, M., Banerjee, A., Meena, R. & Yadav, D.) 71–100 (Springer, Singapore, 2019).

Savary, S. et al. The global burden of pathogens and pests on major food crops. Nat. Ecol. Evol.3, 430–439 (2019). PubMed

Zhang, H., Zhao, Y. & Zhu, J.-K. Thriving under stress: how plants balance growth and the stress response. Dev. Cell55, 529–543 (2020). PubMed

Zandalinas, S. I. & Mittler, R. Plant responses to multifactorial stress combination. N. Phytol.234, 1161–1167 (2022). PubMed

Kuromori, T., Fujita, M., Takahashi, F., Yamaguchi-Shinozaki, K. & Shinozaki, K. Inter-tissue and inter-organ signaling in drought stress response and phenotyping of drought tolerance. Plant J.109, 342–358 (2022). PubMed PMC

Li, H., Testerink, C. & Zhang, Y. How roots and shoots communicate through stressful times. Trends Plant Sci.26, 940–952 (2021). PubMed

Singh, A. et al. Tissue specific and abiotic stress regulated transcription of histidine kinases in plants is also influenced by diurnal rhythm. Front. Plant Sci.6, 711 (2015). PubMed PMC

Choudhury, F. K., Devireddy, A. R., Azad, R. K., Shulaev, V. & Mittler, R. Rapid accumulation of glutathione during light stress in Arabidopsis. Plant Cell Physiol.59, 1817–1826 (2018). PubMed

Moore, M., Vogel, M. O. & Dietz, K. J. The acclimation response to high light is initiated within seconds as indicated by upregulation of AP2/ERF transcription factor network in Arabidopsis thaliana. Plant Signal Behav.9, 976479 (2014). PubMed PMC

Kollist, H. et al. Rapid responses to abiotic stress: priming the landscape for the signal transduction network. Trends Plant Sci.24, 25–37 (2019). PubMed

Depaepe, T. et al. At the crossroads of survival and death: the reactive oxygen species–ethylene–sugar triad and the unfolded protein response. Trends Plant Sci.26, 338–351 (2021). PubMed

Depaepe, T. & Van Der Straeten, D. Tools of the ethylene trade: a chemical kit to influence ethylene responses in plants and its use in agriculture. Small Methods4, 1900267 (2020).

Chang, K. N. et al. Temporal transcriptional response to ethylene gas drives growth hormone cross-regulation in Arabidopsis. Elife2, e00675 (2013). PubMed PMC

Anderson, J. P. et al. Antagonistic interaction between abscisic acid and jasmonate–ethylene signaling pathways modulates defense gene expression and disease resistance in Arabidopsis. Plant Cell16, 3460–3479 (2004). PubMed PMC

Van den Broeck, L. et al. From network to phenotype: the dynamic wiring of an Arabidopsis transcriptional network induced by osmotic stress. Mol. Syst. Biol.13, 961 (2017). PubMed PMC

Hossain, M. A. et al. Heat or cold priming-induced cross-tolerance to abiotic stresses in plants: key regulators and possible mechanisms. Protoplasma255, 399–412 (2018). PubMed

Zhang, X., Shen, L., Li, F., Meng, D. & Sheng, J. Arginase induction by heat treatment contributes to amelioration of chilling injury and activation of antioxidant enzymes in tomato fruit. Postharvest Biol. Technol.79, 1–8 (2013).

Chou, T.-S., Chao, Y.-Y. & Kao, C. H. Involvement of hydrogen peroxide in heat shock- and cadmium-induced expression of ascorbate peroxidase and glutathione reductase in leaves of rice seedlings. J. Plant Physiol.169, 478–486 (2012). PubMed

Hossain, M. A., Mostofa, M. G. & Fujita, M. Cross protection by cold-shock to salinity and drought stress-induced oxidative stress in mustard (Brassica campestris L.) seedlings. Mol. Plant Breed.4, 50–70 (2013).

Atkinson, N. J. & Urwin, P. E. The interaction of plant biotic and abiotic stresses: from genes to the field. J. Exp. Bot.63, 3523–3543 (2012). PubMed

Panahi, B., Frahadian, M., Dums, J. T. & Hejazi, M. A. Integration of cross species RNA-seq meta-analysis and machine-learning models identifies the most important salt stress-responsive pathways in microalga. Front. Genet.10, 752 (2019). PubMed PMC

Toro-Domínguez, D. et al. A survey of gene expression meta-analysis: methods and applications. Brief. Bioinform.22, 1694–1705 (2021). PubMed

Meta-analysis in basic biology. Nat. Methods13, 959–959 10.1038/nmeth.4102 (2016).

Gibney, E. Could machine learning fuel a reproducibility crisis in science? Nature608, 250–251 (2022). PubMed

Cortes, C. & Vapnik, V. Support-vector networks. Mach. Learn20, 273–297 (1995).

Winters-Hilt, S. & Merat, S. SVM clustering. BMC Bioinform.8(Suppl. 7), S18 (2007). PubMed PMC

Krishnaveni, N. & Radha, V. Performance evaluation of clustering-based classification algorithms for detection of online spam reviews. In Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems 255–266 (eds Jeena Jacob, I., Kolandapalayam Shanmugam, S., Piramuthu, S. & Falkowski-Gilski, P.) (Springer, Singapore, 2021).

Yan, J. & Wang, X. Unsupervised and semi-supervised learning: the next frontier in machine learning for plant systems biology. Plant J.111, 1527–1538 (2022). PubMed

Codjoe, J. M., Miller, K. & Haswell, E. S. Plant cell mechanobiology: greater than the sum of its parts. Plant Cell34, 129–145 (2022). PubMed PMC

Tenhaken, R. Cell wall remodeling under abiotic stress. Front. Plant Sci.5, 771 (2014). PubMed PMC

Ghosh, D. & Xu, J. Abiotic stress responses in plant roots: a proteomics perspective. Front. Plant Sci.5, 6 (2014). PubMed PMC

Huang, P.-Y., Catinot, J. & Zimmerli, L. Ethylene response factors in Arabidopsis immunity. J. Exp. Bot.67, 1231–1241 (2016). PubMed

Xu, J. & Zhang, S. Regulation of ethylene biosynthesis and signaling by protein kinases and phosphatases. Mol. Plant7, 939–942 (2014). PubMed

Xu, J. et al. Activation of MAPK kinase 9 induces ethylene and camalexin biosynthesis and enhances sensitivity to salt stress in Arabidopsis. J. Biol. Chem.283, 26996–27006 (2008). PubMed

Shin, K. et al. Genetic identification of ACC-RESISTANT2 reveals involvement of LYSINE HISTIDINE TRANSPORTER1 in the uptake of 1-aminocyclopropane-1-carboxylic acid in Arabidopsis thaliana. Plant Cell Physiol.56, 572–582 (2015). PubMed

Zhu, T. et al. Mitochondrial alternative oxidase-dependent autophagy involved in ethylene-mediated drought tolerance in Solanum lycopersicum. Plant Biotechnol. J.16, 2063–2076 (2018). PubMed PMC

Birkenbihl, R. P., Kracher, B., Roccaro, M. & Somssich, I. E. Induced genome-wide binding of three Arabidopsis WRKY transcription factors during early MAMP-triggered immunity. Plant Cell29, 20–38 (2017). PubMed PMC

Shi, J., Drummond, B. J., Wang, H., Archibald, R. L. & Habben, J. E. Maize and Arabidopsis ARGOS proteins interact with ethylene receptor signaling complex, supporting a regulatory role for ARGOS in ethylene signal transduction. Plant Physiol.171, 2783–2797 (2016). PubMed PMC

Samalova, M. et al. Hormone-regulated expansins: expression, localization, and cell wall biomechanics in Arabidopsis root growth. Plant Physiol.194, 209–229 (2024) PubMed PMC

Samalova, M., Gahurova, E. & Hejatko, J. Expansin-mediated developmental and adaptive responses: a matter of cell wall biomechanics? Quant. Plant Biol.3, e11 (2022). PubMed PMC

Xie, Z., Nolan, T. M., Jiang, H. & Yin, Y. AP2/ERF transcription factor regulatory networks in hormone and abiotic stress responses in Arabidopsis. Front. Plant Sci.10, 228 (2019). PubMed PMC

Nakano, T., Suzuki, K., Fujimura, T. & Shinshi, H. Genome-wide analysis of the ERF gene family in Arabidopsis and rice. Plant Physiol.140, 411–432 (2006). PubMed PMC

Zhang, M. & Zhang, S. Mitogen-activated protein kinase cascades in plant signaling. J. Integr. Plant Biol.64, 301–341 (2022). PubMed

Kvint, K., Nachin, L., Diez, A. & Nyström, T. The bacterial universal stress protein: function and regulation. Curr. Opin. Microbiol.6, 140–145 (2003). PubMed

Shaik, R. & Ramakrishna, W. Machine learning approaches distinguish multiple stress conditions using stress-responsive genes and identify candidate genes for broad resistance in rice. Plant Physiol.164, 481–495 (2014). PubMed PMC

Ma, C., Xin, M., Feldmann, K. A. & Wang, X. Machine learning-based differential network analysis: a study of stress-responsive transcriptomes in Arabidopsis. Plant Cell26, 520–537 (2014). PubMed PMC

English, P. J., Lycett, G. W., Roberts, J. A. & Jackson, M. B. Increased 1-aminocyclopropane-1-carboxylic acid oxidase activity in shoots of flooded tomato plants raises ethylene production to physiologically active levels. Plant Physiol.109, 1435–1440 (1995). PubMed PMC

Depaepe, T., Vanhaelewyn, L. & Van Der Straeten, D. UV-B responses in the spotlight: dynamic photoreceptor interplay and cell-type specificity. Plant Cell Environ.46, 3194–3205 (2023). PubMed

Xiong, L. & Zhu, J. K. Molecular and genetic aspects of plant responses to osmotic stress. Plant Cell Environ.25, 131–139 (2002). PubMed

Kim, J. S., Kidokoro, S., Yamaguchi-Shinozaki, K. & Shinozaki, K. Regulatory networks in plant responses to drought and cold stress. Plant Physiol.195, 170–189 (2024). PubMed PMC

Lewandowska, M. et al. Wounding triggers wax biosynthesis in Arabidopsis leaves in an abscisic acid-dependent and jasmonoyl-isoleucine-dependent manner. Plant Cell Physiol.65, 928–938 (2024). PubMed PMC

Prasad, A., Sedlářová, M., Balukova, A., Rác, M. & Pospíšil, P. Reactive oxygen species as a response to wounding: imaging in Arabidopsis thaliana. Front. Plant Sci.10, 1660 (2019). PubMed PMC

Lee, S. & Park, C. M. Regulation of reactive oxygen species generation under drought conditions in Arabidopsis. Plant Signal Behav.7, 599–601 (2012). PubMed PMC

Sato, H., Mizoi, J., Shinozaki, K. & Yamaguchi-Shinozaki, K. Complex plant responses to drought and heat stress under climate change. Plant J.117, 1873–1892 (2024). PubMed

Jahed, K. R., Saini, A. K. & Sherif, S. M. Coping with the cold: unveiling cryoprotectants, molecular signaling pathways, and strategies for cold stress resilience. Front. Plant Sci.14, 1246093 (2023). PubMed PMC

Taji, T. et al. Important roles of drought- and cold-inducible genes for galactinol synthase in stress tolerance in Arabidopsis thaliana. Plant J.29, 417–426 (2002). PubMed

Tan, W.-J. et al. DIACYLGLYCEROL ACYLTRANSFERASE and DIACYLGLYCEROL KINASE modulate triacylglycerol and phosphatidic acid production in the plant response to freezing stress. Plant Physiol.177, 1303–1318 (2018). PubMed PMC

Baez, L. A., Tichá, T. & Hamann, T. Cell wall integrity regulation across plant species. Plant Mol. Biol.109, 483–504 (2022). PubMed PMC

Lamers, J., van der Meer, T. & Testerink, C. How plants sense and respond to stressful environments. Plant Physiol.182, 1624–1635 (2020). PubMed PMC

Kudla, J. et al. Advances and current challenges in calcium signaling. N. Phytol.218, 414–431 (2018). PubMed

Wang, C., Teng, Y., Zhu, S., Zhang, L. & Liu, X. NaCl- and cold-induced stress activate different Ca2+-permeable channels in Arabidopsis thaliana. Plant Growth Regul.87, 217–225 (2019).

Corso, M., Doccula, F. G., de Melo, J. R. F., Costa, A. & Verbruggen, N. Endoplasmic reticulum-localized CCX2 is required for osmotolerance by regulating ER and cytosolic Ca dynamics. Proc. Natl Acad. Sci. USA115, 3966–3971 (2018). PubMed PMC

Li, Z. et al. CCX1, a putative cation/Ca2+ exchanger, participates in regulation of reactive oxygen species homeostasis and leaf senescence. Plant Cell Physiol.57, 2611–2619 (2016). PubMed

Dodd, A. N., Kudla, J. & Sanders, D. The language of calcium signaling. Annu. Rev. Plant Biol.61, 593–620 (2010). PubMed

Zhu, X. et al. Calmodulin-like protein CML24 interacts with CAMTA2 and WRKY46 to regulate ALMT1-dependent Al resistance in Arabidopsis thaliana. N. Phytol.233, 2471–2487 (2022). PubMed

Menke, F. L. H., van Pelt, J. A., Pieterse, C. M. J. & Klessig, D. F. Silencing of the mitogen-activated protein kinase MPK6 compromises disease resistance in Arabidopsis. Plant Cell16, 897–907 (2004). PubMed PMC

Shi, Y. et al. Ethylene signaling negatively regulates freezing tolerance by repressing expression of CBF and type-A ARR genes in Arabidopsis. Plant Cell24, 2578–2595 (2012). PubMed PMC

Hartman, S. et al. Ethylene-mediated nitric oxide depletion pre-adapts plants to hypoxia stress. Nat. Commun.10, 4020 (2019). PubMed PMC

Vaseva, I. I. et al. Ethylene signaling in salt-stressed Arabidopsis thaliana ein2-1 and ctr1-1 mutants—a dissection of molecular mechanisms involved in acclimation. Plant Physiol. Biochem.167, 999–1010 (2021). PubMed

Zhang, X. et al. MAMP-elicited changes in amino acid transport activity contribute to restricting bacterial growth. Plant Physiol.189, 2315–2331 (2022). PubMed PMC

Batista-Silva, W. et al. The role of amino acid metabolism during abiotic stress release. Plant Cell Environ.42, 1630–1644 (2019). PubMed

Chen, Y. & Zhang, J. Multiple functions and regulatory networks of WRKY33 and its orthologs. Gene931, 148899 (2024). PubMed

De Paepe, A., Vuylsteke, M., Van Hummelen, P., Zabeau, M. & Van Der Straeten, D. Transcriptional profiling by cDNA-AFLP and microarray analysis reveals novel insights into the early response to ethylene in Arabidopsis. Plant J.39, 537–559 (2004). PubMed

Kilian, J. et al. The AtGenExpress global stress expression data set: protocols, evaluation and model data analysis of UV-B light, drought and cold stress responses. Plant J.50, 347–363 (2007). PubMed

Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res.43, e47 (2015). PubMed PMC

Kauffmann, A., Gentleman, R. & Huber, W. arrayQualityMetrics—a bioconductor package for quality assessment of microarray data. Bioinformatics25, 415–416 (2009). PubMed PMC

Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc.57, 289–300 (1995).

Gower, J. C. A general coefficient of similarity and some of its properties. Biometrics27, 857 (1971).

Galili, T. dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering. Bioinformatics31, 3718–3720 (2018). PubMed PMC

Yoon, S., Baik, B., Park, T. & Nam, D. Powerful p-value combination methods to detect incomplete association. Sci. Rep.11, 6980 (2021). PubMed PMC

Sharifi, S. et al. Integration of machine learning and meta-analysis identifies the transcriptomic bio-signature of mastitis disease in cattle. PLoS ONE13, e0191227 (2018). PubMed PMC

Roman, I., Santana, R., Mendiburu, A. & Lozano, J. A. In-depth analysis of SVM kernel learning and its components. Neural Comput. Appl.33, 6575–6594 (2021).

Lameski, P., Zdravevski, E., Mingov, R. & Kulakov, A. SVM parameter tuning with grid search and its impact on reduction of model over-fitting. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds) Lecture Notes in Computer Science 464–474 (Springer, Cham, 2015).

Luesse, D. R., Wilson, M. E. & Haswell, E. S. RNA sequencing analysis of the msl2msl3, crl, and ggps1 mutants indicates that diverse sources of plastid dysfunction do not alter leaf morphology through a common signaling pathway. Front. Plant Sci.6, 1148 (2015). PubMed PMC

Bedre, R. & Mandadi, K. GenFam: a web application and database for gene family-based classification and functional enrichment analysis. Plant Direct3, e00191 (2019). PubMed PMC

Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res.47, D607–D613 (2019). PubMed PMC

Grant, C. E., Bailey, T. L. & Noble, W. S. FIMO: scanning for occurrences of a given motif. Bioinformatics27, 1017–1018 (2011). PubMed PMC

Vanderstraeten, L., Sanchez-Muñoz, R., Depaepe, T., Auwelaert, F. & Van Der Straeten, D. Mix-and-match: an improved, fast and accessible protocol for hypocotyl micrografting of Arabidopsis seedlings with systemic ACC responses as a case study. Plant Methods18, 24 (2022). PubMed PMC

De Vylder, J., Vandenbussche, F., Hu, Y., Philips, W. & Van Der Straeten, D. Rosette tracker: an open source image analysis tool for automatic quantification of genotype effects. Plant Physiol.160, 1149–1159 (2012). PubMed PMC

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