Charting the Cannabis plant chemical space with computational metabolomics.

INTRODUCTION: The chemical classification of Cannabis is typically confined to the cannabinoid content, whilst Cannabis encompasses diverse chemical classes that vary in abundance among all its varieties. Hence, neglecting other chemical classes within Cannabis strains results in a restricted and biased comprehension of elements that may contribute to chemical intricacy and the resultant medicinal qualities of the plant. OBJECTIVES: Thus, herein, we report a computational metabolomics study to elucidate the Cannabis metabolic map beyond the cannabinoids. METHODS: Mass spectrometry-based computational tools were used to mine and evaluate the methanolic leaf and flower extracts of two Cannabis cultivars: Amnesia haze (AMNH) and Royal dutch cheese (RDC). RESULTS: The results revealed the presence of different chemical compound classes including cannabinoids, but extending it to flavonoids and phospholipids at varying distributions across the cultivar plant tissues, where the phenylpropnoid superclass was more abundant in the leaves than in the flowers. Therefore, the two cultivars were differentiated based on the overall chemical content of their plant tissues where AMNH was observed to be more dominant in the flavonoid content while RDC was more dominant in the lipid-like molecules. Additionally, in silico molecular docking studies in combination with biological assay studies indicated the potentially differing anti-cancer properties of the two cultivars resulting from the elucidated chemical profiles. CONCLUSION: These findings highlight distinctive chemical profiles beyond cannabinoids in Cannabis strains. This novel mapping of the metabolomic landscape of Cannabis provides actionable insights into plant biochemistry and justifies selecting certain varieties for medicinal use.

Studying Plant Specialized Metabolites Using Computational Metabolomics Strategies.

Plant specialized metabolites have diversified vastly over the course of plant evolution, and they are considered key players in complex interactions between plants and their environment. The chemical diversity of these metabolites has been widely explored and utilized in agriculture and crop enhancement, the food industry, and drug development, among other areas. However, the immensity of the plant metabolome can make its exploration challenging. Here we describe a protocol for exploring plant specialized metabolites that combines high-resolution mass spectrometry and computational metabolomics strategies, including molecular networking, identification of structural motifs, as well as prediction of chemical structures and metabolite classes.

Metabolic Circuits in Sap Extracts Reflect the Effects of a Microbial Biostimulant on Maize Metabolism under Drought Conditions.

The use of microbial biostimulants in the agricultural sector is increasingly gaining momentum and drawing scientific attention to decode the molecular interactions between the biostimulants and plants. Although these biostimulants have been shown to improve plant health and development, the underlying molecular phenomenology remains enigmatic. Thus, this study is a metabolomics work to unravel metabolic circuits in sap extracts from maize plants treated with a microbial biostimulant, under normal and drought conditions. The biostimulant, which was a consortium of different Bacilli strains, was applied at the planting stage, followed by drought stress application. The maize sap extracts were collected at 5 weeks after emergence, and the extracted metabolites were analyzed on liquid chromatography-mass spectrometry platforms. The acquired data were mined using chemometrics and bioinformatics tools. The results showed that under both well-watered and drought stress conditions, the application of the biostimulant led to differential changes in the profiles of amino acids, hormones, TCA intermediates, phenolics, steviol glycosides and oxylipins. These metabolic changes spanned several biological pathways and involved a high correlation of the biochemical as well as structural metabolic relationships that coordinate the maize metabolism. The hypothetical model, postulated from this study, describes metabolic events induced by the microbial biostimulant for growth promotion and enhanced defences. Such understanding of biostimulant-induced changes in maize sap pinpoints to the biochemistry and molecular mechanisms that govern the biostimulant-plant interactions, which contribute to ongoing efforts to generate actionable knowledge of the molecular and physiological mechanisms that define modes of action of biostimulants.