Bioprospecting the Metabolome of Plant Urtica dioica L.: A Fast Dereplication and Annotation Workflow in Plant Metabolomics

Abstract

Plants have a pivotal role in ethnopharmacology, and their preparations are in use globally. However, getting down to the structure requires an effective workflow and mostly requires a time-consuming isolation process. Although bioassay-guided approaches are widely popular, they face a massive problem of rediscovery in recent times, especially in plant metabolomics. Mass spectrometry (MS)-based approach incorporated molecular networking via Global Natural Product Social Molecular Networking (GNPS) is considered here for the benefit of the fast screening of secondary metabolites. This study uses direct crude extracts obtained from various parts of the Urtica dioica plant for the characterization of secondary metabolites. The crude extract of the plant initially displayed promising antioxidant and anti-diabetic activities. Then, we employed mass spectrometry-based dereplication to identify the phytochemical components in the extracts. This led to the discovery of 7 unknown and 17 known secondary metabolites, which were further verified with the SIRIUS 4 platform, a computational tool for the annotation of compounds using tandem MS data. On the other hand, chasing the antioxidant activity of methanolic extract of U. dioica leaves, we employed a bioassay-guided isolation approach. With this method, we isolated and characterized compound 13, a known molecule, which possessed strong antioxidant activity without showing much toxicity in the brine shrimp lethality test at the test concentration of 1 mg/mL. With our results, we advocate the MS-based approach as a good starting point for the dereplication of compounds from the complex crude extracts of plants.

Figure 1

A graph showing article numbers published each year since 2016 involving GNPS for compound dereplication.

Figure 2

Metabolomics workflow showing the molecular networking approach and conventional isolation approach for finding structural novelty.

Figure 3

Structure of compound 13 showing chromatographic, spectroscopic, and spectrometric features. (Note. (a) MS2 ionization spectra from HRMS data, (b) UV chromatogram in HPLC system with eluted peak (R_T 4.6 min), (c) maximum UV absorbance curve, and (d) elucidated chemical structure based on NMR spectroscopy).

Figure 4

Molecular networking of MS2 data from U. dioica showing nine clusters including 17 known metabolites. (Note. Each node was identified by the neutral mass of the compound. The three colors green, red, and blue in each node represent the intensity of the compound detected in three different parts of the plant extract; leaf, stem, and root, respectively. The edge thickness in the network represents the similarity extent among the connected nodes).

Figure 5

Molecular networking of MS2 data from U. dioica showing four clusters with seven unknown metabolites. (Note. Each node was identified by the neutral mass of the compound. The ratio of three colors as green, red, and blue in each node represents the relative intensity of the compound detected in three different parts of the plant extract; leaf, stem, and root, respectively. The edge thickness in the network represents the similarity extent among the connected nodes).