Metabolomics-based profiling of anti-inflammatory compounds from Mentha spicata in shanghe, China

Abstract

Mentha spicata is a popular herb used in foods, cosmetics, and medicines. In the present study, liquid chromatography-mass spectrometry-based metabolomics analysis and the zebrafish model were used to investigate the potential biomarkers of M. spicata growing in Shanghe County (Shandong Province, China) and their anti-inflammatory properties. Network pharmacology and molecular docking were performed to screen the main targets of the characteristic compounds to understand their mechanisms of action. Nine potential markers including sugars (1,2), polyphenolic acids (3-5), and flavonoids (6-9) were identified from the species. The inhibitory effects on leukocyte migration confirmed that compounds 1 and 3-9 played a positive role in the protective effect of Shanghe M. spicata (SM) extract against inflammation. Akt (protein kinase B), EGFR (epidermal growth factor receptor), and MMP9 (matrix metalloproteinase 9) were the core target proteins of the identified compounds in the anti-inflammatory process. The most significant Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment terms were response to abiotic stimulus (Biological Process), carbohydrate derivative binding (Molecular Function), and pathways in cancer. In docking simulations, 3-p-coumaroylquinic acid (3-PC, 4) and cirsimaritin (CN, 7) exhibited the highest potential affinity to the active sites of Akt and EGFR proteins, respectively; additionally, 5-demethylsinensetin (5-DS, 9) and luteolin (LN, 6) were considered the most suitable ligands for the MMP9 protein. The present study highlighted the use of SM resources as functional products with health benefits.

Keywords: Inflammatory disease; Mentha spicata; Metabolomics; Molecular interaction; Signaling pathway.

Graphical abstract

Fig. 1

Metabolic profiling of the LC-MS spectra of SM and BM extracts in negative modes. (A) PCA score plots, (B) OPLS-DA score plots, (C) validation plots of the model, and (D) coefficient-coded loading plots (6 potential biomarkers corresponding to the compounds in Table 1).

Fig. 2

Metabolic profiling of the LC-MS spectra of SM and BM extracts in positive modes. (A) PCA score plots, (B) OPLS-DA score plots, (C) validation plots of the model, and (D) coefficient-coded loading plots (3 potential biomarkers corresponding to the compounds in Table 1).

Fig. 3

SM and BM extracts inhibit CuSO₄-induced inflammatory responses in transgenic zebrafish (**p < 0.01, compared to the model group).

Fig. 4

Effects of the characteristic compounds on leukocyte migration in zebrafish larvae (**p < 0.01, compared to the model group).

Fig. 5

Candidate-target network for the characteristic compounds against the inflammatory process.

Fig. 6

GO and KEGG analyses reveal pathways associated with the targets from the candidate-target network.

Fig. 7

Potential interactions between the ligands and receptors in docking simulations.