Data science approaches provide a roadmap to understanding the role of abscisic acid in defence.

Data science approaches provide a roadmap to understanding the role of abscisic acid in defence.
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DOI:
10.1017/qpb.2023.1
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发表时间:
2023
影响因子:
--
通讯作者:
Luna, Estrella
Luna, Estrella
中科院分区:
其他
文献类型:
--
作者:
Stevens, Katie;Johnston, Iain G.;Luna, Estrella

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脱落酸(阿坝)是一种植物激素,具有调节非生物胁迫反应的作用.阿坝在生物防御中的作用也得到了认可,但目前对它是否起着积极或消极的作用缺乏共识。在这里,我们使用监督机器学习来分析阿坝防御作用的实验观察结果,以确定决定疾病表型的最有影响力的因素。阿坝浓度,植物年龄和病原体的生活方式被确定为重要的调制器的防御行为,在我们的计算预测。我们在番茄中进行了新的实验,证明阿坝处理后的表型确实高度依赖于植物年龄和病原体的生活方式。将这些新的结果整合到统计分析中,完善了阿坝影响的定量模型,提出了一个框架,用于提出和利用进一步的研究,以便在这个复杂的问题上取得更多进展。我们的方法提供了一个统一的路线图,以指导未来涉及阿坝在国防中作用的研究。
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.
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