Potential Mechanism of S. baicalensis on Lipid Metabolism Explored via Network Pharmacology and Untargeted Lipidomics

Abstract

Background: S. baicalensis, a traditional herb, has great potential in treating diseases associated with aberrant lipid metabolism, such as inflammation, hyperlipidemia, atherosclerosis and Alzheimer's disease.

Aim of the study: To elucidate the mechanism by which S. baicalensis modulates lipid metabolism and explore the medicinal effects of S. baicalensis at a holistic level.

Materials and methods: The potential active ingredients of S. baicalensis and targets involved in regulating lipid metabolism were identified using a network pharmacology approach. Metabolomics was utilized to compare lipids that were altered after S. baicalensis treatment in order to identify significantly altered metabolites, and crucial targets and compounds were validated by molecular docking.

Results: Steroid biosynthesis, sphingolipid metabolism, the PPAR signaling pathway and glycerolipid metabolism were enriched and predicted to be potential pathways upon which S. baicalensis acts. Further metabolomics assays revealed 14 significantly different metabolites were identified as lipid metabolism-associated elements. After the pathway enrichment analysis of the metabolites, cholesterol metabolism and sphingolipid metabolism were identified as the most relevant pathways. Based on the results of the pathway analysis, sphingolipid and cholesterol biosynthesis and glycerophospholipid metabolism were regarded as key pathways in which S. baicalensis is involved to regulate lipid metabolism.

Conclusion: According to our metabolomics results, S. baicalensis may exert its therapeutic effects by regulating the cholesterol biosynthesis and sphingolipid metabolism pathways. Upon further analysis of the altered metabolites in certain pathways, agents downstream of squalene were significantly upregulated; however, the substrate of SQLE was surprisingly increased. By combining evidence from molecular docking, we speculated that baicalin, a major ingredient of S. baicalensis, may suppress cholesterol biosynthesis by inhibiting SQLE and LSS, which are important enzymes in the cholesterol biosynthesis pathway. In summary, this study provides new insights into the therapeutic effects of S. baicalensis on lipid metabolism using network pharmacology and lipidomics.

Keywords: S. baicalensis; cortex metabolomics; lipid metabolism; molecular docking; network pharmacology.

Figure 1

The structures of the ingredients of S. baicalensis.

Figure 2

The ingredient-target network (A), graph showing the intersections of results from different databases (B). In (A), the red and blue circles represent key targets and ingredients of S. baicalensis, respectively, and the size of the nodes represents the total number of connecting edges. In (B), the Venn diagram of different gene databases with intersection numbers and overlapping targets of enriched pathways is shown. The structures of the ingredients of S. baicalensis.

Figure 3

KEGG enrichment analysis of target genes of S. baicalensis involved in lipid metabolism. (A) biological processes, (B) cellular components, (C) molecular functions, and (D) KEGG pathways, and the size of each node indicates the number of enriched terms.

Figure 4

The UpSet graph coupled with elements of overlapping enriched GO terms. (A) GO biological process and (B) two major GO processes of cellular components and molecular functions.

Figure 5

Graph of pprotein–proteininteractions. (A) General view of the interactions of proteins and clustered proteins. (B) cluster of Cytochrome P450 - arranged by substrate type; (C) cluster of transcriptional regulation of white adipocyte differentiation; (D) cluster of steroid biosynthesis; (E) cluster of activation of gene expression by SREBF (SREBP); (F) cluster of fatty acid biosynthesis and (G) triacylglycerol biosynthesis. The circles represent the target protein, and the line represents the interaction of the target protein.

Figure 6

Overview of metabolites from metabolic pathways that were enriched (A) and the alterations in identified biomarkers (B). (A) Histogram of peak areas of metabolites from LC-MS and (B) histogram of peak areas of metabolites from GC-MS. (C) Bubble diagram of enriched metabolic pathways for metabolites (Data are presented as the means ± SEM, n=10 mice per group. *p-value ≤ 0.05 and **p-value ≤0.01, compared with the control.).

Figure 7

Overview diagram of pathways predicted by network pharmacology and metabolomics. In this overview, blue ovals are target genes predicted by network pharmacology, green ovals are target genes identified from metabolomics, and the pathway in the bottom right corner represents the information obtained from metabolomics coupled with potential screened compounds identified by molecular docking and clinical inhibitors.

Figure 8

Heat map of docking energies.

Figure 9

The interaction profile for baicalin against LSS and SQLE enzyme. (A) and (C) overall structure of LSS and SQLE bound to baicalin; (B) and (D) the two dimensions (2d) binding modes of baicalin present at the active site of LSS and SQLE. Hydrogen bond is indicated by green dotted lines, Pi-Alkyl is indicated by purple dotted lines.