Mulaudzi Musiwalo Samuel, Nephali Lerato Pertunia, Tugizimana Fidele
Research Centre for Plant Metabolomics, Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg, South Africa.
International Research and Development Division, Omnia Group, Ltd., Bryanston, Johannesburg, 2021, South Africa.
Plant Cell Rep. 2025 Sep 11;44(10):211. doi: 10.1007/s00299-025-03600-z.
The study provides actionable insights into modes of action of the three microbial biostimulants on maize plants under field conditions. The postulated framework indicates a divergence model involving (i) photoprotection, structural reinforcement, and defense priming, (ii) systemic metabolic reprograming for growth and defense, and (iii) hormonal signalling modulation for stress response. These insights offer a data-driven blueprint for the next generation of sustainable, effective, and field-ready bioformulations. Microbial consortia are currently recognized as a promising strategy for sustainable agriculture due to their ability to enhance plant growth, improve soil health, and mitigate environmental stresses. However, the biochemical and molecular mechanisms governing these beneficial effects on crops under field conditions remain poorly understood, and differential effects due to different microbial formulations are enigmatic. This study, therefore, aims to unravel the metabolic alterations, in maize plants, induced by three microbial biostimulants, under field conditions at different growth stages. Leaves from biostimulant-treated and untreated control maize plants were harvested at different time points. Metabolites were extracted using methanol. The extracts were analyzed on LC-MS/MS system. Computational metabolomics workflows and AI-driven strategies such as molecular networking and machine learning methods (PCA and OPLS-DA) were applied to mine and interpret spectral data. Machine learning models revealed the common and unique significant metabolites among the consortia at the vegetative stage. One of the key findings was that hydroxycinnamic acid (HCA) derivatives are the discriminatory metabolites differentiating the effects of the three microbial consortia on maize plants. Moreover, the results showed that consortia application significantly impacted primary and secondary maize metabolism, reprogramming biological pathways such as phenylalanine, tyrosine, and tryptophan biosynthesis, tyrosine metabolism, the citrate cycle (TCA cycle), flavone and flavonol biosynthesis, and flavonoid biosynthesis. These pathways are associated with plant defense, priming and development. Thus, this study sheds light on the complex molecular interactions between maize and microbial biostimulants under real-world conditions. It reveals that distinct microbial formulations differentially influence plant metabolism by reprogramming defense- and growth-related pathways. These actionable insights establish a foundational framework for functionally characterizing microbial consortia and pave the way for the rational design of next generation biostimulants tailored to specific crop needs and growth stages.
该研究为三种微生物生物刺激素在田间条件下对玉米植株的作用模式提供了可付诸实践的见解。所提出的框架表明了一种分歧模型，涉及（i）光保护、结构强化和防御引发，（ii）用于生长和防御的系统代谢重编程，以及（iii）用于应激反应的激素信号调节。这些见解为下一代可持续、有效且适用于田间的生物制剂提供了一个数据驱动的蓝图。微生物群落目前被认为是可持续农业的一种有前景的策略，因为它们能够促进植物生长、改善土壤健康并减轻环境压力。然而，在田间条件下，控制这些对作物有益作用的生化和分子机制仍知之甚少，不同微生物制剂产生的差异效应也令人费解。因此，本研究旨在揭示三种微生物生物刺激素在田间不同生长阶段对玉米植株诱导的代谢变化。在不同时间点收获经生物刺激素处理和未处理的对照玉米植株的叶片。使用甲醇提取代谢物。提取物在液相色谱 - 串联质谱系统上进行分析。应用计算代谢组学工作流程和人工智能驱动的策略，如分子网络和机器学习方法（主成分分析和正交偏最小二乘法判别分析）来挖掘和解释光谱数据。机器学习模型揭示了营养生长阶段各群落间常见和独特的显著代谢物。其中一个关键发现是羟基肉桂酸（HCA）衍生物是区分三种微生物群落对玉米植株作用的鉴别性代谢物。此外，结果表明群落的应用显著影响了玉米的初级和次级代谢，重新编程了生物途径，如苯丙氨酸、酪氨酸和色氨酸的生物合成、酪氨酸代谢、柠檬酸循环（TCA循环）、黄酮和黄酮醇生物合成以及类黄酮生物合成。这些途径与植物防御、引发和发育相关。因此，本研究揭示了现实条件下玉米与微生物生物刺激素之间复杂的分子相互作用。它表明不同的微生物制剂通过重新编程与防御和生长相关的途径来差异影响植物代谢。这些可付诸实践的见解为功能表征微生物群落建立了一个基础框架，并为根据特定作物需求和生长阶段合理设计下一代生物刺激素铺平了道路。
