Stevens Katie, Johnston Iain G, Luna Estrella
School of Biosciences, University of Birmingham, Birmingham, United Kingdom.
Department of Mathematics, University of Bergen, Bergen, Norway.
Quant Plant Biol. 2023 Feb 8;4:e2. doi: 10.1017/qpb.2023.1. eCollection 2023.
Abscisic acid (ABA) is a plant hormone well known to regulate abiotic stress responses. ABA is also recognised for its role in biotic defence, but there is currently a lack of consensus on whether it plays a positive or negative role. Here, we used supervised machine learning to analyse experimental observations on the defensive role of ABA to identify the most influential factors determining disease phenotypes. ABA concentration, plant age and pathogen lifestyle were identified as important modulators of defence behaviour in our computational predictions. We explored these predictions with new experiments in tomato, demonstrating that phenotypes after ABA treatment were indeed highly dependent on plant age and pathogen lifestyle. Integration of these new results into the statistical analysis refined the quantitative model of ABA influence, suggesting a framework for proposing and exploiting further research to make more progress on this complex question. Our approach provides a unifying road map to guide future studies involving the role of ABA in defence.
脱落酸（ABA）是一种众所周知的调节植物非生物胁迫反应的植物激素。ABA在生物防御中的作用也得到认可，但目前对于它发挥的是积极作用还是消极作用尚无定论。在此，我们使用监督式机器学习来分析关于ABA防御作用的实验观察结果，以确定决定疾病表型的最具影响力的因素。在我们的计算预测中，ABA浓度、植株年龄和病原体生活方式被确定为防御行为的重要调节因子。我们通过在番茄上开展的新实验对这些预测进行了探索，结果表明ABA处理后的表型确实高度依赖于植株年龄和病原体生活方式。将这些新结果整合到统计分析中，完善了ABA影响的定量模型，为提出并利用进一步的研究以在这个复杂问题上取得更多进展提供了一个框架。我们的方法提供了一个统一的路线图，以指导未来涉及ABA在防御中作用的研究。
