Othibeng Kgalaletso, Nephali Lerato, Tugizimana Fidele
Department of Biochemistry, University of Johannesburg, Johannesburg, South Africa.
International Research and Development Division, Omnia Group, Ltd., Johannesburg, South Africa.
Methods Mol Biol. 2023;2642:163-177. doi: 10.1007/978-1-0716-3044-0_9.
Biostimulants show potentials as sustainable strategies for improved crop development and stress resilience. However, the cellular and molecular mechanisms, in particular the signaling and regulatory events, governing the agronomically observed positive effects of biostimulants on plants remain enigmatic, thus hampering novel formulation and exploration of biostimulants. Metabolomics offers opportunities to elucidate metabolic and regulatory processes that define biostimulant-induced changes in the plant's biochemistry and physiology, thus contributing to decode the modes of action of biostimulants. Here, we describe an application of metabolomics to elucidate biostimulant effects on crop plants. Using the case study of a humic substance (HS)-based biostimulant applied on maize plants, under normal and nutrient-starved stress conditions, this chapter proposes key methodological guidance and considerations of computational metabolomics approach to investigate metabolic and regulatory reconfiguration and networks underlying biostimulant-induced physiological changes in plants. Computational metabolome mining tools, in the Global Natural Products Social Molecular Networking (GNPS) ecosystem, are highlighted as well as metabolic pathway and network analysis for biological interpretation of the data.
生物刺激素显示出作为促进作物生长发育和增强胁迫抗性的可持续策略的潜力。然而，生物刺激素对植物产生农学上观察到的积极作用的细胞和分子机制，特别是信号传导和调控事件，仍然是个谜，这阻碍了生物刺激素的新型配方研发和探索。代谢组学为阐明定义生物刺激素诱导的植物生物化学和生理学变化的代谢和调控过程提供了机会，从而有助于解读生物刺激素的作用模式。在此，我们描述了代谢组学在阐明生物刺激素对作物植物影响方面的应用。本章以在正常和营养饥饿胁迫条件下应用于玉米植株的基于腐殖质（HS）的生物刺激素为例，提出了计算代谢组学方法的关键方法指导和注意事项，以研究生物刺激素诱导的植物生理变化背后的代谢和调控重排及网络。重点介绍了全球天然产物社会分子网络（GNPS）生态系统中的计算代谢组挖掘工具，以及用于数据生物学解释的代谢途径和网络分析。
