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

Abstract

AbstractUnderstanding 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