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bioRxiv · 10.1101/2024.01.24.576978

The molecular core of transcriptome responses to abiotic stress in plants: a machine learning-driven meta-analysis

Sanchez-Munoz, R.·Depaepe, T.·Samalova, M.·Hejatko, J.·Zaplana, I.·Van Der Straeten, D.
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Abstract

Understanding how plants adapt their physiology to overcome severe stress conditions is vital in light of the current climate crisis. This remains a challenge given the complex nature of the underlying molecular mechanisms. To provide a full picture of stress mitigation mechanisms, an exhaustive analysis of publicly available stress-related transcriptomic data was conducted. We combined a meta-analysis with an unsupervised machine learning algorithm to identify a core of stress-related genes. To ensure robustness and biological significance of the output, often lacking in meta-analyses, a three-layered biovalidation was incorporated. Our results present a stress gene core, a set of key genes involved in plant tolerance to a multitude of adverse environmental conditions rather than specific ones. In addition, we provide a biologically validated database to assist in design of multi-stress resilience. Taken together, our results pave the way towards future-proof sustainable agriculture. TeaserUsing a machine learning-driven meta-analysis, a plant stress gene core was identified as a hub mediating multi-stress regulation

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Sanchez-Munoz, R., Depaepe, T., Samalova, M., Hejatko, J., Zaplana, I., Van Der Straeten, D.. 2024-01-26. The molecular core of transcriptome responses to abiotic stress in plants: a machine learning-driven meta-analysis. https://doi.org/10.1101/2024.01.24.576978

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