Table_3_Integration of Phenomics and Metabolomics Datasets Reveals Different Mode of Action of Biostimulants Based on Protein Hydrolysates in Lactuca sativa L. and Solanum lycopersicum L. Under Salinity.XLSX

Plant phenomics is becoming a common tool employed to characterize the mode of action of biostimulants. A combination of this technique with other omics such as metabolomics can offer a deeper understanding of a biostimulant effect in planta. However, the most challenging part then is the data analysis and the interpretation of the omics datasets. In this work, we present an example of how different tools, based on multivariate statistical analysis, can help to simplify the omics data and extract the relevant information. We demonstrate this by studying the effect of protein hydrolysate (PH)-based biostimulants derived from different natural sources in lettuce and tomato plants grown in controlled conditions and under salinity. The biostimulants induced different phenotypic and metabolomic responses in both crops. In general, they improved growth and photosynthesis performance under control and salt stress conditions, with better performance in lettuce. To identify the most significant traits for each treatment, a random forest classifier was used. Using this approach, we found out that, in lettuce, biomass-related parameters were the most relevant traits to evaluate the biostimulant mode of action, with a better response mainly connected to plant hormone regulation. However, in tomatoes, the relevant traits were related to chlorophyll fluorescence parameters in combination with certain antistress metabolites that benefit the electron transport chain, such as 4-hydroxycoumarin and vitamin K1 (phylloquinone). Altogether, we show that to go further in the understanding of the use of biostimulants as plant growth promotors and/or stress alleviators, it is highly beneficial to integrate more advanced statistical tools to deal with the huge datasets obtained from the -omics to extract the relevant information.

植物表型组学（plant phenomics）现已成为表征生物刺激素（biostimulants）作用模式的常用技术手段。将该技术与代谢组学（metabolomics）等其他组学技术相结合，可更深入解析生物刺激素在植物体内的作用效应。然而，该研究路径中最具挑战性的环节，在于组学数据集的数据分析与结果解读。本研究展示了基于多元统计分析的各类工具，如何助力简化组学数据并提取核心有效信息。我们以不同天然来源的蛋白质水解物（protein hydrolysate, PH）类生物刺激素为研究对象，对其在可控培养条件及盐胁迫环境下的生菜与番茄植株中的作用效果展开验证。该类生物刺激素在两种作物中均可诱导出差异化的表型与代谢组响应。总体而言，在正常培养与盐胁迫条件下，这类生物刺激素均可提升作物生长与光合性能，且在生菜中表现出更优的调控效果。为筛选各处理组的关键性状，本研究采用了随机森林分类器（random forest classifier）。通过该分析方法，我们发现：在生菜中，与生物量相关的参数是评估生物刺激素作用模式的核心性状，其优良响应主要与植物激素调控通路相关。而在番茄中，关键性状则与叶绿素荧光参数及部分有益于电子传递链的抗逆代谢物相关，例如4-羟基香豆素（4-hydroxycoumarin）与维生素K1（phylloquinone，叶绿醌）。综上，本研究证实：若要进一步解析生物刺激素作为植物生长促进剂和/或胁迫缓解剂的应用价值，整合更先进的统计工具以处理组学技术产生的海量数据集并提取有效信息，具有极高的研究价值。