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Back to Journal »Drug Design, Development and Treatment» Volume 15

Explore the potential mechanism of Scutellaria baicalensis Georgi on lipid metabolism through network pharmacology and non-targeted lipidomics

Authors: Ge Ping, Qi YY, Qu SY, Zhao X, Ni Sheng, Yao ZY, Guo Rong, Yang Ni, Zhang Qingqing, Zhu Hexin

Published on May 4, 2021, Volume 2021: 15 pages, 1915-1930

DOI https://doi.org/10.2147/DDDT.S301679

Single anonymous peer review

Editor who approved for publication: Dr. Georgios D. Panos

Ge Pingyuan, 1 Yi Yuqi, 1 Qu Shuyue, 1 Zhao Xin, 1 Sai Jiani, 2 Yao Zengying, 2 Guo Rui, 3 Yang Nianyun, 2 Zhang Qichun, 1, 2 Zhu Huaxu 2 1 Nanjing University of Traditional Chinese Medicine High-tech Research of Traditional Chinese Medicine Formula Jiangsu Province Key Laboratory, Nanjing, People's Republic of China; 2 Jiangsu Provincial Key Laboratory of Pharmacology and Safety Evaluation of Traditional Chinese Medicine, School of Pharmacy, Nanjing University of Traditional Chinese Medicine, Nanjing; 3 Nanjing University of Traditional Chinese Medicine School of Medicine and Integrated Traditional Chinese and Western Medicine, Nanjing, People's Republic of China Correspondent: Zhang Qichun ; Zhu Huaxu Email [email protected]; [email protected] Background: Scutellaria baicalensis Georgi is a traditional herbal medicine used in the treatment of inflammation, hyperlipidemia, atherosclerosis and Alzheimer's disease related to abnormal lipid metabolism. The disease has great potential. Research objective: To clarify the mechanism of Scutellaria baicalensis controlling lipid metabolism, and to explore the medicinal effects of Scutellaria baicalensis at an overall level. Materials and methods: Using network pharmacology methods, the potential active components of Scutellaria baicalensis Georgi and the targets involved in regulating lipid metabolism were determined. Metabolomics is used to compare lipids that have changed after Scutellaria baicalensis treatment, to identify significantly changed metabolites, and to verify key targets and compounds through molecular docking. Results: Steroid biosynthesis, sphingolipid metabolism, PPAR signaling pathway, and glycerolipid metabolism were enriched and predicted as potential pathways for the action of Scutellaria baicalensis Georgi. Further metabolomics analysis revealed that 14 significantly different metabolites were identified as elements related to lipid metabolism. After pathway enrichment analysis of metabolites, cholesterol metabolism and sphingolipid metabolism were identified as the most relevant pathways. According to the results of pathway analysis, the biosynthesis of sphingolipids and cholesterol and the metabolism of glycerophospholipids are considered to be the key pathways of Scutellaria baicalensis involved in regulating lipid metabolism. Conclusion: According to our metabolomics results, Scutellaria baicalensis may exert its therapeutic effects by regulating cholesterol biosynthesis and sphingolipid metabolism pathways. After further analysis of the metabolites altered in some pathways, the downstream substances of squalene were significantly up-regulated; however, the substrate of SQLE was unexpectedly increased. Based on the evidence of molecular docking, we speculate that baicalin, the main component of Scutellaria baicalensis Georgi, may inhibit cholesterol biosynthesis by inhibiting the important enzymes SQLE and LSS in the cholesterol biosynthesis pathway. In conclusion, this study uses network pharmacology and lipidomics to provide new insights into the therapeutic effects of Scutellaria baicalensis Georgi on lipid metabolism. Keywords: Scutellaria baicalensis Georgi, network pharmacology, cortical metabolomics, lipid metabolism, molecular docking

Lipid metabolism is an extremely complex process. It not only participates in the formation of biological membranes, but also participates in many physiological and pathological processes as signals and messengers. 1,2 Under normal physiological conditions, lipid homeostasis is strictly controlled; however, when genetic and environmental factors are disrupted or dysregulated, the dysregulation of lipid metabolism will lead to a high risk of lipid-related metabolic diseases. 3 Since lipids play a vital role in the body, the destruction of several lipid metabolites may make individuals susceptible to many diseases, especially because the presence of the blood-brain barrier (BBB) ​​prevents most lipids from getting from the periphery. Transportation. 4 Lipid metabolism is strictly regulated. Many studies have shown that many diseases, including Alzheimer's disease and cerebrovascular disease, may be caused by changes in lipid metabolism. 5,6 For example, the accumulated genetic, clinical and modern pharmacological evidence indicates that the pathogenesis of Alzheimer's disease is closely related to the disorder of cholesterol and sphingolipid metabolism. 7,8 In addition, chronic inflammation has been reported to be caused by imbalance of endogenous biologically active lipids. 9-12 Therefore, the way to regulate lipid metabolism may represent an important drug axis and treatment method.

Traditional Chinese medicine has been used for more than two thousand years and is considered a promising resource for potential treatment because a large number of species contain active pharmaceutical ingredients with a wide range of medicinal properties. Pharmacological studies have shown that the water extract of Scutellaria baicalensis mainly contains flavonoids, which has various health and clinical effects such as anti-inflammatory, anti-atherosclerosis, and regulating blood lipid levels, which are closely related to these diseases. 13-15 However, the detailed mechanism of Scutellaria baicalensis controlling its metabolism and exerting its therapeutic effects remains to be elucidated. Therefore, our research aims to understand the effects of Scutellaria baicalensis Georgi on regulating lipid metabolism and clarify its mechanism of action.

Metabolomics is an emerging discipline that is usually used to identify changes in specific substrates and product metabolites of metabolic pathways from a comprehensive and holistic perspective, to explore the mechanisms of diseases and evaluate the therapeutic effects of drugs. 16 Because of completeness in metabolomics research, this method has been widely used to reveal the metabolic mechanism of the entire organism, which reflects the physiological and pathological processes. 17 In particular, the best feature of Chinese medicine (TCM) is that its multi-component nature and ambiguous mechanism have clinically proven therapeutic effects. Researchers are trying to use modern analytical equipment to clarify the mechanism of action of traditional Chinese medicine. 18 At present, metabolomics based on chromatogram and mass spectrometry combined with network pharmacology has become an important method to discover the mechanism of action of Chinese medicine from a system perspective and at the molecular level, accelerating the discovery of the mechanism of action of Chinese medicine and the basis of medicinal materials. 19,20 In this study, network pharmacology was initially used to identify potential therapeutic compounds and explore the biological mechanisms of Scutellaria baicalensis controlling lipid metabolism. In addition, metabolomics based on liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) and gas chromatography-mass spectrometry (GC-MS) was also performed to detect metabolic markers and analyze metabolic pathways. Finally, molecular docking was performed to further verify the molecular mechanism of the influence of compounds in Scutellaria baicalensis Georgi on key targets. This study mainly explored the tiny mechanism of Scutellaria baicalensis Georgi in regulating lipid metabolism.

S. baicalensis (200 g) (S. baicalensis Georgi, Lamiaceae; batch number, 1709019024, purchased from Anhui Fuchuntong Chinese Medicine Decoction Pieces Co., Ltd., China) thoroughly decocted and soaked in distilled water in 2000 mL (1:10 v: w) 2 h, then use 1600 mL distilled water (1:8 v: w) to decoct the medicine residue again and filter with gauze. The filtrates were combined and evaporated by vacuum rotary evaporation at 60°C and concentrated to 100 mL for further freeze drying. Finally, 17.5 g of aqueous extract powder was obtained and stored in a desiccator until further drug treatment. The method of Huanglian Jiedu Decoction refers to the technique of Huanglian Jiedu Decoction used in our laboratory to ensure the extraction of active ingredients.

S. baicalensis is analyzed by HPLC to ensure quality and effectiveness (see supplementary materials for details). The results are shown in supporting infographic S1 and tables S1 and S2.

C57BL/6 male mice (18-22 g) (batch number: SCKX, 2019-0002) were purchased from the Qinglongshan Experimental Animal Center in Nanjing, China. All mice are kept in a standard environment (12/12 hours dark/light cycle, 50±10% relative humidity, 22±2°C), and they have free access to standard mouse food and water. After 10 days of acclimation, they were blinded and randomly divided into two groups: Scutellaria baicalensis Georgi group and control group. Animal welfare and experimental procedures strictly follow the guidelines for the care and use of laboratory animals (National Research Council, 1996) and the relevant ethics committee of Nanjing University of Chinese Medicine (No. 202102A006, No. 012071001462) to approve animal experiments. Scutellaria baicalensis lyophilized powder was administered to mice in the Scutellaria baicalensis group at a dose of 100 mg/kg body weight during the experiment. The mice in the control group were intragastrically administered with normal saline. The experiment time was 7 days, and the weight of the mice was recorded every day. On the 7th day of the experiment, 1 hour after the last administration, all mice were killed by cervical dislocation after blood collection from the orbit. The cortical tissue was removed from the brain and stored at -80°C until further metabolomics analysis.

Using bioinformatics to screen and comprehensively identify the potential active ingredients of Scutellaria baicalensis Georgi. The chemical components of Scutellaria baicalensis Georgi were taken from the pharmacological analysis database and analysis platform (http://tcmspw.com/tcmsp.php) of the Traditional Chinese Medicine System, with Scutellaria baicalensis as the key word, and the compounds were taken from TCMSP according to the NCBI database (https://www.ncbi. Manually supplement the literature search in PubMed Central at nlm.nih.gov/). The selected compounds are further filtered by oral bioavailability (OB), drug similarity (DL), and blood-brain barrier (BBB) ​​values. These ADME characteristics are essential for drug discovery and development. The general filtration standards are OB≥30%, DL ≥0.18 and Caco-2≥-0.4. Subsequently, the molecular format of the filtered active compound is converted to the sdf file format, and then these files are uploaded to the PharmMapper Server (http://www.lilab-ecust.cn/pharmmapper/), which is a freely accessible network The server is designed to use pharmacophore mapping methods to identify potential candidate target proteins for specific probe small molecules (drugs, natural products, or other newly discovered compounds whose binding targets have not been determined). Finally, import the targets obtained from the above database search into Venny (http://www.liuxiaoyuyuan.cn/) to merge them to remove duplicates. These retrieved target proteins are calibrated to their official names (official symbols).

Data on lipid-related target metabolism comes from six sources, DisGeNET (http://www.disgenet.org/), CTD (http://ctdbase.org/), NCBI Gene (https://www.disgenet.org) /). ncbi.nlm.nih.gov), OMIM (http://omim.org/) and GENECARD (https://www.genecards.org/). According to the lipid metabolism classification in KEGG Mapper (https://www.kegg.jp/), lipid metabolism-related targets are searched by keywords related to lipid metabolism, and the retrieved results are imported into Excel to merge and remove duplicate targets. The predicted targets of Scutellaria baicalensis Georgi and lipid metabolism-related targets originated from the intersection as potential targets of Scutellaria baicalensis Georgi in lipid metabolism.

Import key targets into the Metascape database (http://metascape.org) to obtain GO (Gene Ontology) related biological processes (BP), cell components (CC), molecular functions (MF) and KEGG pathway information, GO enriched Set and KEGG pathway enrichment analysis to explore the potential mechanism of S. baicalensis in lipid metabolism at the system level. 21 In addition, the obtained target has been submitted to STRING (https://string-db.org/) and Metascape to obtain the protein-protein interaction network. 22 In addition, Cytoscape 3.7.1 software (http://www.cytoscape.org/) was used to construct a Scutellaria baicalensis drug molecule-potential target network that regulates lipid metabolism.

The mice were sacrificed by cervical dislocation on the 7th day after blood collection. The cortical area was removed and stored at -80°C until further metabolomics analysis. Homogenize tissue (~20 mg) with 200 µL of cold methanol in a homogenizer (multi-sample tissue lyser-48, Jingxin Technology, Shanghai, China). Then, add 600 µL of methyl tert-butyl ether (MTBE) and vortex for 1 minute using a vortexer (Multitube vortexer, Targin Technology, Beijing, China), and then incubate at 4°C for 30 minutes to fully extract Lipid metabolites. After incubation, 150 µL of deionized water was added to induce phase separation, and the mixture was centrifuged at 10,000×g for 10 minutes at 4°C to separate the mixture into two phases with a protein interface. Recover the bottom phase (400 µL), then add 800 µL MTBE and repeat the extraction. Combine the organic solvents, dry under nitrogen, and store at -80°C until analysis. The nitrogen-dried sample was re-dissolved in a mixture of acetonitrile (ACN) and isopropanol (IPA) (1:9 v: v, containing 0.1% formic acid and 1 mM ammonium formate), and then centrifuged (15,000 rpm for 10 minutes), Then collect the supernatant for testing. An equal amount of the mixture is extracted from each lipid extract sample and used as a quality control (QC) cell sample for quality control during MS analysis.

An Agilent 5600 Q-TOF LC/MS (Agilent Technologies, Palo Alto, USA) system equipped with a heated electrospray ionization (ESI) probe was used for lipidomics analysis. On an ACQUITY UPLC BEH C18 analytical column (2.1×50 mm, 1.7 µm; Waters Crop), a 5 µL aliquot of each sample was injected into the system at 40°C. A mixture of acetonitrile and deionized water (6:4 v: v, containing 10 mM ammonium formate and 0.1% formic acid, mobile phase A) and a mixture of acetonitrile and IPA (1:9 v: v, containing ammonium formate and 0.1% formic acid) , Mobile phase B) is used as the mobile phase. In order to obtain a better chromatographic separation effect, a linear gradient with a flow rate of 0.4 mL/min was used for 14 minutes: 32% B at 0 minutes, 40% B at 0-1 minutes, and 45 at 1.5-4 minutes % B, 50% B 4-5 minutes, 60% B 5-8 minutes, 70% B 8-11 minutes, 80% B 11-14 minutes. MS signal acquisition is performed in positive and negative scan modes. Quality control (QC samples) are prepared in an equal volume of 5 µL and injected after every 5 analysis samples during the experiment to evaluate the stability of the analysis system and monitor the reproducibility of the data.

The mass spectrometry parameters are set as follows: ESI, cation and anion modes; mass scanning range, m/Z 50–1500; air curtain pressure, 275.8 kPa; atomizing gas pressure, 379.2 kPa; auxiliary gas pressure, 379.2 kPa; ion source temperature, 550 °C; spray voltage, -450 V; and cluster voltage, -100 V.

In order to comprehensively analyze metabolites, especially neutral lipids, it is well known that these substances are difficult to ionize with ESI source. We choose GC-MS to analyze neutral lipids as a supplement to LC-MS that has insufficient neurolipid analysis capabilities. , The purpose is to obtain better identification and separation of metabolites. Two steps are performed before sample analysis, namely contour pre-processing, as described below. The first step is the lipid metabolite extraction protocol, including methanol homogenization and incubation with MTBE and (butylated hydroxytoluene) BHT (0.4 mg/mL) to prevent auto-oxidation of the extracted lipids because MTBE is in the extraction process Sexual lipids, especially sterols, show better performance, including cholesterol and its precursors, instead of chloroform. After vortexing and phase separation, the organic phase was transferred to a new tube and dried under nitrogen. The second processing step involves sample deviation. Comprehensive deviation, optimization of the mixture of N-trimethylsilyl imidazole (TSIM) and N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA) (1:10, v: v) As a derivatization reagent, the reaction time is set to 1 hour at 60°C. During the derivatization process, use a vortex mixer to shake the vial at least twice for 30 seconds to ensure complete sialylation. Finally, centrifuge each sample (15,000 rpm, 10 minutes), and transfer 100 µL of the supernatant to the GC-MS autosampler for analysis.

For GC-MS data analysis, 1 µL of the derivatized sample was injected (splitless mode), and the inlet temperature was kept at 250°C. All samples used for metabolomics analysis were analyzed in Agilent 7890A GC system combined with Agilent 5975C MSD quadrupole mass spectrometer, using electron impact ionization mode (Agilent, Palo Alto, USA). The injection temperature was 280°C, helium was used as the carrier gas, and the flow rate was 1.4 mL/min. Choose an inert 5% benzyl polysiloxane column (HP-5MS 30 m×0.25 mm 0.25 µm, from Agilent) to ensure satisfactory resolution of metabolites. After the sample is injected, the initial column oven temperature is maintained at 180°C for 1 minute, and the temperature is increased at a rate of 20°C/min until it reaches 280°C, maintained for 7 minutes, and finally rises to 300°C at a certain rate 4°C/min . The transfer temperature and ion source temperature are set to 250°C and 230°C, respectively. In the end, each peak was identified and matched by the NIST14 search program.

Use Markerview 1.2.1 software (AB Corp, Milwaukee, WI, USA) to process the raw LC-MS data for t-test, and then import the preprocessed data into SIMCA-P 14.0 (Umetrics AB, Umea, Sweden) software and HMBD (https: //hmdb.ca/) Perform multivariate statistical analysis to obtain and identify potential differentially altered metabolites. Perform principal component analysis (PCA) to achieve separation between experimental groups. The quality of the PLS-DA model is evaluated by permutation test (n=200). Potential lipid biomarkers were extracted from S-plot and VIP-plot with VIP value>1 and P (corr) absolute value greater than 0.58 with mean value filtering, and then the filtered metabolites were screened with Peakview1.2.1 to compare mass spectrometry information. Finally, the independent sample t test was used to analyze the peak area and retention time to distinguish significant differences. All quantitative data are expressed as mean ± SEM. Import the lipid metabolites regulated by S. baicalensis into the online Metabolite 4.0 software to explore the potential mechanism of S. baicalensis regulating lipid metabolism.

Molecular docking has become an important tool for studying the interaction between ligands and receptor macromolecules and predicting their binding mode and affinity, and has been widely used in drug design and discovery. AutoDock 1.5.6 (http://autodock.scripps.edu/) and Vina (http://vina.scripps.edu/) are excellent freely accessible drug discovery platforms with high docking accuracy and speed, integrated Physically-based predictive methods use machine learning techniques to accelerate drug discovery. 23,24 The sdf file format of the active ingredient obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov/) is prepared and optimized by Chem3D for energy minimization and structural optimization. According to the node of network pharmacology Values ​​and changes in metabolomics, squalene monooxygenase (SQLE) and lanosterol synthase (LSS), a total of 10 protein targets were selected as potential targets. Download the PDB files of key protein targets with the best structural resolution and endogenous ligands from the RCSB protein database (https://www.rcsb.org/). Then Discovery Studio 4.5 processes the PDB file of the key target to perform structural optimization for further analysis. The parameter settings of the docking site come from the published report and the PyMOL 2.2.0 plug-in to obtain the grid frame parameters. After setting the grid frame for the active site of the protein, genetic algorithm is performed, and the docking operation options are set as the default parameters; finally, the dpf file is exported. The docking result is visualized using Discovery Studio Visualizer 4.5 to observe the docking mode.

In order to confirm the inhibitory effect of baicalin on SQLE, we conducted in vitro experiments with mouse liver microsomes (LM-XS-02M, purchased from Shanghai Ruide Institute of Liver Diseases). The biochemical assay of baicalin activity was performed on mouse liver microsomes, and 7 concentration gradients of 175, 150, 100, 75, 50, 40 and 20 μM baicalin were added and incubated for 1 h. The detailed information is shown in Supplementary Information Figure S5 and Table S6.

Based on the oral bioavailability (OB), drug similarity (DL), BBB and Caco-2 parameters obtained through ADME, 36 components were screened as potential active compounds of Scutellaria baicalensis Georgi (Supplementary Table S3), and the identified components are shown in figure 1. Using Pharmacy Mapper and TCMSP online database (https://tcmspw.com/index.php), the possible targets of active ingredients were comprehensively explored, and 471 target proteins of Scutellaria baicalensis were predicted. Target genes related to "lipid metabolism" come from GeneCards (https://www.genecards.org/), OMIM (https://omim.org/) and CTD (http://ctdbase.org/) databases, They intersected with 471 active target proteins, resulting in 89 potential targets of active ingredients involved in lipid metabolism. Finally, the predicted targets and predicted targets of the components of Scutellaria baicalensis involved in lipid metabolism were introduced into Cytoscape 3.71 (https://cytoscape.org/) to construct a drug-target cross-linking network as shown in Figure 2. Figure 1 The composition and structure of Scutellaria baicalensis Georgi. Figure 2 Component-target network (A). The graph shows the intersection of results from different databases (B). In (A), the red and blue circles represent the key targets and components of Scutellaria baicalensis Georgi, respectively, and the size of the node represents the total number of connected edges. In (B), Venn diagrams of different gene databases are shown with overlapping targets for cross-numbering and enrichment pathways. The component structure of Scutellaria baicalensis Georgi.

Figure 1 The composition and structure of Scutellaria baicalensis Georgi.

Figure 2 Component-target network (A). The graph shows the intersection of results from different databases (B). In (A), the red and blue circles represent the key targets and components of Scutellaria baicalensis Georgi, respectively, and the size of the node represents the total number of connected edges. In (B), Venn diagrams of different gene databases are shown with overlapping targets for cross-numbering and enrichment pathways. The component structure of Scutellaria baicalensis Georgi.

According to the corresponding number of key targets, different node sizes are assigned to the components to provide selected components and targets for further molecular docking analysis.

We imported 89 target genes involved in lipid metabolism predicted by Scutellaria baicalensis Georgi into the Metascape database. The Metascape database is a comprehensive and powerful online tool that integrates many authoritative gene enrichment analysis databases to obtain biological processes, cellular components, and predictions related to GO. Target rich molecular functions and KEGG pathway. After importing the key prediction targets, the gene enrichment analysis results of the pathways and processes are obtained, and they are colored by clusters. KEGG and GO enrichment analysis results are shown in Figure 3. Figure 3 KEGG enrichment analysis of target genes related to lipid metabolism of Scutellaria baicalensis Georgi. (A) biological process, (B) cell composition, (C) molecular function, and (D) KEGG pathway. The size of each node indicates the number of enrichment items.

Figure 3 Scutellaria baicalensis Georgi lipid metabolism target gene KEGG enrichment analysis. (A) biological process, (B) cell composition, (C) molecular function, and (D) KEGG pathway. The size of each node indicates the number of enrichment items.

In summary, the enriched KEGG pathway analysis shows that 32 pathways and 16 key targets are involved in sphingolipid metabolism, 26 target genes are involved in metabolic pathways, 9 target genes are involved in steroid biosynthesis, and 8 target genes are involved in PPAR. The signal transduction pathway and 7 target genes are involved in glycerolipid metabolism. The results indicate that Scutellaria baicalensis mainly interferes with sphingolipid metabolism and cholesterol biosynthesis pathways. The GO enrichment analysis information is explained from three perspectives. Target genes are mainly involved in lipid biosynthesis processes, including cholesterol metabolism and biosynthesis, sphingolipid biosynthesis and lipid catabolism. Most of these genes are located in the vacuole cavity, lipid droplet and endoplasmic reticulum cavity and play a role. In addition, the biosynthesis process and the two main enriched cellular components and molecular functions were imported into the OmicStudio tool (https://www.omicstudio.cn/tool) for bioinformatics analysis and the UpSet map was obtained (Figure 4). This result provides the correlation and crossover between the pathway and GO enrichment analysis. Figure 4 The UpSet graph plus overlapping elements with rich GO terms. (A) GO biological process and (B) the two main GO processes of cell composition and molecular function.

Figure 4 The UpSet graph plus overlapping elements with rich GO terms. (A) GO biological process and (B) the two main GO processes of cell composition and molecular function.

According to GO and KEGG enrichment analysis, Scutellaria baicalensis Georgi mainly exerts its regulating effect on lipid metabolism by regulating cholesterol biosynthesis. Cholesterol plays an important role in steroid metabolism and sphingolipid metabolism. In addition, in order to mine the core regulatory genes, the predicted target protein of Scutellaria baicalensis was introduced into Metascape (http://metascape.org) STRING (https://string-db.org/) to construct PPI (Figure 5). PPI results show that the clustered proteome is mainly involved in lipid metabolism, lipid biosynthesis and cholesterol biosynthesis. The analysis of protein interaction network is one of the key challenges in biological research. It links genotype with phenotype, and the destruction of PPI often leads to diseases. Figure 5 p protein-protein interaction diagram. (A) A general view of the interaction between proteins and clustered proteins. (B) Cytochrome P450 clusters-arranged by substrate type; (C) Transcriptional regulatory clusters for white adipocyte differentiation; (D) Steroid biosynthesis clusters; (E) SREBF (SREBP) clusters that activate gene expression; (F) Fatty acid biosynthesis cluster and (G) triacylglycerol biosynthesis. The circle represents the target protein, and the line represents the interaction of the target protein.

Figure 5 p protein-protein interaction diagram. (A) A general view of the interaction between proteins and clustered proteins. (B) Cytochrome P450 clusters-arranged by substrate type; (C) Transcriptional regulatory clusters for white adipocyte differentiation; (D) Steroid biosynthesis clusters; (E) SREBF (SREBP) clusters that activate gene expression; (F) Fatty acid biosynthesis cluster and (G) triacylglycerol biosynthesis. The circle represents the target protein, and the line represents the interaction of the target protein.

We perform PCA to reflect the clustering degree of QC samples, verify QC samples, and analyze the reproducibility and repeatability of the analysis equipment. The PCA results showed that the QC samples were tightly aggregated, indicating excellent reproducibility and stability (Supplementary Figures 2 and 3). In addition, a PCA-type model was established to distinguish the analyzed groups, and the control group and the S. baicalensis group were clearly separated. Execute the OPLS-DA model to focus the classification information on the principal components and determine metabolic changes.

R2 and Q2 (positive mode is shown in Supplementary Figure S2, and negative mode is shown in Supplementary Figure S3) indicate the fitness and prediction accuracy of the model. Subsequently, 200 permutations and threshold VIP value predictions were performed, and VIP values ​​greater than 1 and P values ​​less than 0.05 were used to select lipids with significant differential expression. Finally, the ion mass information is imported into HMDB (Human Metabolome Database) and further processed with Peakview 1.1.2 to obtain the identified metabolites. Among the identified lipids, 14 metabolites are significantly different between the control group and the Scutellaria baicalensis group, including sphingomyelin, galactosylceramide (d18:1/9Z-18:1), lactoseceramide (LacCer ) (d18:1/12:0), glucosylceramide (GlcCer) (d18:1/24:0), sulfatide, cholesteryl ester (CE) (16:1(9Z)), phosphatidylcholine, Phosphatidylethanolamine and triacylglycerol (TG) (16:0/16:0).

Fourteen differentially changed endogenous metabolites were identified between the S. baicalensis group and the control group. For detailed information about the changed metabolites and corresponding trends, see Supplementary Table S4. These metabolites are mainly related to cholesterol biosynthesis and sphingolipids, fatty acids and steroid hormones. 25 By introducing these differentially changed metabolites into MetaboAnalyst 4.0 (http://www.metaboanalyst.ca/) for metabolic pathway analysis, in order to further explore the potential role of Scutellaria baicalensis Georgi and its possible mechanism for regulating lipid metabolism. Pathway analysis based on different biomarkers showed that when filtered with a threshold of P≤0.05, Scutellaria baicalensis treatment resulted in significant enrichment of cholesterol de novo biosynthesis pathway and sphingolipid metabolism. Finally, the histogram and bubble map enrichment pathways of lipid metabolites that were significantly alternated after treatment with Scutellaria baicalensis were constructed (Figure 6). The results of metabolomics preliminary support the regulation of Scutellaria baicalensis Georgi on lipid metabolism and demonstrate the main metabolic pathways of Scutellaria baicalensis Georgi. Therefore, a significant difference in endogenous metabolites between Scutellaria baicalensis Georgi and the control group was proposed. Figure 6 Overview of metabolites from enriched metabolic pathways (A) and changes in identified biomarkers (B). (A) LC-MS metabolite peak area histogram and (B) GC-MS metabolite peak area histogram. (C) Bubble chart of metabolic pathways enriched by metabolites (data are expressed as mean ± SEM, n=10 mice per group. *p value ≤ 0.05 and **p value ≤ 0.01, compared with the control.).

Figure 6 Overview of metabolites from enriched metabolic pathways (A) and changes in identified biomarkers (B). (A) LC-MS metabolite peak area histogram and (B) GC-MS metabolite peak area histogram. (C) Bubble chart of metabolic pathways enriched by metabolites (data are expressed as mean ± SEM, n=10 mice per group. *p value ≤ 0.05 and **p value ≤ 0.01, compared with the control.).

We analyzed the correlation between metabolites and possible upstream targets, integrated the results of metabolomics and network pharmacology analysis, and comprehensively explored the underlying mechanisms to further understand the potential treatment of Scutellaria baicalensis Georgi in lipid metabolism And effective substrate. We specifically mentioned the relationship between key pathway metabolites and their related proteins (such as enzymes identified from the human metabolome database). Finally, a network containing the relationships between metabolites, metabolic pathways, enzymes, and target genes was established (Figure 7). This constructed graph combines the results of network pharmacology and metabolomics to provide a general view of the pathways of Scutellaria baicalensis Georgi. Figure 7 Overview of pathways predicted by network pharmacology and metabolomics. In this overview, the blue ellipse is the target gene predicted by network pharmacology, the green ellipse is the target gene identified from metabolomics, and the pathway in the lower right corner represents the information obtained from metabolomics, plus molecular docking and clinical Potential screening compounds for inhibitor identification.

Figure 7 Overview of pathways predicted by network pharmacology and metabolomics. In this overview, the blue ellipse is the target gene predicted by network pharmacology, the green ellipse is the target gene identified from metabolomics, and the pathway in the lower right corner represents the information obtained from metabolomics, plus molecular docking and clinical Potential screening compounds for inhibitor identification.

It is worth noting that the interaction between the drug and the target is the basis of biological effects. Many interaction modes play an important role in the recognition and binding of proteins and drugs. However, due to the difference in binding energy and effective radius, the stability and binding strength are also different. In this case, as mentioned earlier, three main existing interaction modes have been introduced. The hydrogen bond with an average binding energy of 5 kJ/mol is a weak electrostatic attraction between a positively charged hydrogen atom and a negatively charged heteroatom, which plays an important role in the drug-target interaction. 26 In addition, the relatively weak attractive force mediated by van der Waals force widely exists and decays with the increase of the interaction radius. The hydrocarbyl part of the drug molecule forms a hydrophobic interaction with the hydrophobic group of the target, which squeezes out the water molecules originally arranged on the surface of the protein and the drug. This mode of interaction is very important for maintaining the stability and formation of the complex.

Autodock1.5.6 is used to clarify the interaction between active ingredients and identify different lipid-related protein targets. The docking results are shown in Supplementary Table S5. A lower docking energy represents a stronger affinity between the protein and the ligand. In addition, the docking mode is visualized using Discovery Studio 4.5 to predict the bonding mode. According to the results of the target-component interaction network, 9 targets and 10 components are selected for molecular docking, and the target of the crystal structure file is obtained from the PDB bank. The docking results are shown in the heat map (Figure 8). Consider the docking energy and the predicted Ki (inhibition constant). Each target corresponds to the best component with the lowest energy and highest Ki and LSS. SQLE is determined to have the best Ki and binding energy, and is used to predict its interaction mechanism and binding mode with baicalin (as shown in Figure 9) ). In the anatomy of the binding mode predicted in Discovery Studio shown in the 3D and 2D graphs, 6 hydrogen bonds between LSS and baicalin and 4 hydrogen bonds between SQLE coupled with FAD and baicalin were observed. In the complex of OSC and baicalin, TYR 98 (2.09 Å), GLY380 (2.12 Å), CYS456 (3.08 Å), TYR 503 (1.82 Å) and TYR 704 (2.47 Å) interact with the hydroxyl group of baicalin, 5 hydrogen bonds are formed; in addition, TRP192, TRP230, PHE521 and ILE524 form a hydrophobic pocket that is conducive to the stability of the complex. In addition, the alkyl group of baicalin is stacked between LEU324, PHE509, PRO505, VAL506 and LEU509. The alkoxy and hydroxyl groups form hydrogen bonds with TY195 (2.71 Å), PRO415 (1.87 Å), and LEU416 (1.95 Å). Atom (2.9 Å). In addition, the optimal conformation of baicalin is tightly aggregated with the endogenous ligands of LSS and SQLE, indicating that baicalin is accurately located in the active pocket of the target (Supplementary Figure 4). Based on the above results, we concluded that baicalin has a good binding mode and can be used as a potential inhibitor of LSS and SQLE. Figure 8 Docking energy heat map. Figure 9 The interaction curve of baicalin with LSS and SQLE enzymes. (A) and (C) the overall structure of LSS and SQLE that bind baicalin; (B) and (D) the two-dimensional (2d) binding mode of baicalin at the active site of LSS and SQLE. The hydrogen bond is represented by a green dashed line, and Pi-Alkyl is represented by a purple dashed line.

Figure 8 Docking energy heat map.

Figure 9 The interaction curve of baicalin with LSS and SQLE enzymes. (A) and (C) the overall structure of LSS and SQLE that bind baicalin; (B) and (D) the two-dimensional (2d) binding mode of baicalin at the active site of LSS and SQLE. The hydrogen bond is represented by a green dashed line, and Pi-Alkyl is represented by a purple dashed line.

Considering that the SQLE protein is mainly located in the endoplasmic reticulum, we selected mouse liver microsomes for the in vitro inhibition experiment of baicalin. In the dissection of the results of treatment of mouse liver microsomes with seven different concentrations of baicalin, similar but concentration-dependent inhibition was observed (as shown in Supplementary Information Figure S5 and Table S6). Squalene, the substrate of SQLE, gradually increases with the increase of baicalin, which means that the inhibitory effect of baicalin is concentration-dependent. Both mouse liver microsomes and metabolomics results confirmed the molecular docking results.

The effect of Scutellaria baicalensis Georgi on lipid metabolism has been extensively studied and reported, and clinical evidence has also explored its regulatory role in lipid metabolism, involving glucose and lipid metabolism, triglyceride signaling pathways and cholesterol metabolism. 27-30 In many recently published studies, Scutellaria baicalensis Georgi and its main biological activities and active ingredients produced many anti-obesity, hypolipidemic and anti-hepatic steatosis activities, and revealed some potential mechanisms closely related to lipid metabolism . 31-33 As shown in in vitro and in vivo experiments. , Scutellaria and baicalin exhibit anti-obesity and regulate blood cholesterol and triglyceride levels by regulating metabolism. Experiments combined with chemical proteomics have shown that baicalin can directly activate carnitine palmitoyltransferase 1 (CPT1) and accelerate The oxidation of lipids. 33-35 In this study, S. b clarified that aicalensis regulates lipid metabolism by identifying differentially altered lipid metabolites and performing network pharmacology predictions. The results of metabolomics and network pharmacology indicated that the influence of Scutellaria baicalensis Georgi on lipid metabolism involves the de novo biosynthesis of sphingolipids and cholesterol and the regulation of glycerophospholipid metabolism. In addition, 16 goals and 12 components were determined based on the constructed network. The targets involved in cholesterol biosynthesis such as SQLE, LSS, FDFT1 and SOAT are potential targets. Baicalein, baicalein, berberine, epiberberine, wogonin and sitosterol may be potential active components of scutellaria in regulating cholesterol biosynthesis. In addition, as disclosed herein, ARSA, KDSR, GALC, GBA, GLA, NEU, UGCG, UGT8, DEGS1, SPHK1, B4GALT6, GAL3ST1, CERS2, SGPP1, ACER1, SGMS1, AKT2 and MAPK1 are potential targets for sphingolipid metabolism In a word, the various components of Scutellaria baicalensis Georgi exert its regulating effect on lipid metabolism by targeting multiple metabolic pathways. In the end, our research results show that Scutellaria baicalensis Georgi mainly acts on cholesterol biosynthesis and sphingolipid metabolism, which may be the potential mechanism of Scutellaria baicalensis Georgi for its therapeutic effects.

Cholesterol has a variety of basic functions in the human body, especially in the central nervous system. It contains 25% of the body's total cholesterol, which is mainly distributed in neurons, glial cells and myelin sheaths in the form of free cholesterol. 36 Cholesterol not only forms the cell membrane but also performs signal transduction functions. The cholesterol turnover rate of neurons and glial cells may reach an estimated 20% per day. 37 In addition, based on the accumulation of genetic, biochemical, and clinical evidence, changes in cholesterol metabolism may be the cause of susceptibility to many neurodegenerative diseases. 9,37 In addition to cholesterol, many precursors in remote cholesterol biosynthesis, including and starting with squalene and 24-OH cholesterol, have recently become promising drug targets for several diseases. 38,39 For example, it is reported that the accumulation of squalene is 40,41. In addition, a large number of studies have confirmed the relationship between high cholesterol levels and Alzheimer's disease. Cholesterol drugs including statins and efavirenz have been produced Significant curative effect. 42 In this study, after the intervention of S. baicalensis, metabolomics results showed that cholesterol biosynthesis was significantly inhibited. The metabolomics results are consistent with the treatment results of NB598 as a SQLE inhibitor, indicating that squalene is significantly increased, while cholesterol and β-sitosterol are decreased. 43 Using metabolomics and its changing metabolite trend analysis in the upstream and downstream relationship, we speculate that SQLE, or squalene monooxygenase, may be the key target of S. baicalensis. Combining the evidence obtained from molecular docking and network pharmacology, SQLE, one of the rate-limiting enzymes in cholesterol biosynthesis, may be a target. In the analysis of the results of network pharmacology and molecular docking, baicalin with the best binding energy and nodularity was selected as a potential inhibitor of SQLE. Interestingly, SQLE has been shown to be related not only to cholesterol biosynthesis, but also to cancer and tumors. 44 Considering the clinical therapeutic effects of scutellaria and baicalin and the evidence based on our research, we are confident of our conclusions. SQLE is a kind of Ingredient Baicalin is a potentially effective medicinal ingredient that regulates cholesterol biosynthesis by interacting with SQLE. Therefore, further research should be conducted to verify and clarify the detailed mechanism of baicalin's effect on SQLE.

Sphingolipids are a component of all membranes, but they are particularly abundant in myelin sheath. 46 It is reported that the hydrolysis of sphingolipids, which are important biologically active metabolites, is involved in the regulation of many important signal transduction processes. 47 Previous studies have shown that changes in sphingolipid metabolism are associated with a high risk of neurodegenerative diseases. 48 A recent study by Vladimir Rudajev reported that sphingomyelin triggers the oligomerization of Aβ40, which is one of the main biochemical signs of Alzheimer's disease. 49 In addition, previous lipidomics studies on the brains of wild-type mice of different ages showed that the levels of glucosylceramide, glucosphingosine and GM1a increase with age. 50 In this study, after treatment, SM (d18:1/18:0) and glucosylceramide significantly reduced S. baicalensis compared with the normal cohort. Supporting evidence was also obtained for the serum levels of these lipids after Huanglian Jiedu Decoction treatment . 51 As many studies have reported, the increase in SM content is closely related to inflammation and apoptosis. 52,53 From the perspective of cell function, SM is mainly located in the endoplasmic reticulum and is regulated by sphingomyelin phosphodiesterase (SMPD1). It mainly converts sphingomyelin to ceramide to maintain the relative balance of SM. 54 Using Instadock for virtual screening to identify biologically active ingredients and explore its mechanism. S. baicalensis regulates SMPD1.55. Wogonin, norwogonin and coptisine showed good binding activity with SMPD1 in our study, which may help in the lipid group Changes observed in learning.

In this study, based on bioinformatics including network pharmacology, GO and KEGG enrichment analysis, and molecular docking, we screened 10 candidate components and two lipid-related pathways: cholesterol biosynthesis and sphingolipid metabolism. Comprehensive and comprehensive strategies can overcome the shortcomings of traditional Chinese medicine research and determine biological mechanisms more accurately and accurately. The results of lipidomics and network pharmacology analysis showed that SQLE, LSS, SOAT, CPT1A and SMPD1 were predicted to be the lipid-related targets of Scutellaria baicalensis Georgi in lipid metabolism. Interestingly, SQLE and LSS may be the direct interaction target of baicalin to exert its clinical therapeutic effect on lowering cholesterol. In summary, this study preliminarily elucidated the pharmacological mechanism of Scutellaria baicalensis Georgi in regulating lipid metabolism through network pharmacology and lipidomics, which may help Scutellaria baicalensis Georgi for further drug discovery and clinical application in diseases related to lipid metabolism disorders.

PPARs, peroxisome proliferator activated receptor; SQLE, squalene monooxygenase; LSS, lanosterol synthase; BBB, blood-brain barrier; Chinese medicine, traditional Chinese medicine; OB, oral bioavailability; DL, drug Similarity; HPLC, high performance liquid chromatography; GO, gene ontology; BP, biological process; CC, cell composition; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, p protein-protein interaction; PCA , Principal component analysis; PLSDA, partial least square discriminant analysis; LacCer, lactose ceramide; GlcCer, glucosylceramide; CE, cholesterol ester; TG, triacylglycerol; SM, sphingomyelin; SOAT, sterol O-acyltransferase ; CPT1A, carnitine O-palmitoyltransferase 1, liver isoform; SMPD1, sphingomyelin phosphodiesterase; TYR, tyrosinase; GLY, glycine; CYS, cysteine; PHE, phenylalanine Acid; ILE, isoleucine; VAL, valine; LEU, leucine; PRO, proline.

This research was awarded by the National Natural Science Foundation of China (project numbers: 81873027 and 81573635), Jiangsu Qinglan Project, Jiangsu Province Key Laboratory of Pharmacology and Safety Evaluation of Traditional Chinese Medicine Open Project (project number: No.JKLPSE201820), Jiangsu Provincial University Key Discipline Construction Project (PAPD), Nanjing University of Traditional Chinese Medicine Innovation Research Team Project, Six Talent Projects in Jiangsu Province.

The author reported that this work had no conflicts of interest and stated that the research was conducted without any commercial or financial relationships that could be interpreted as potential conflicts of interest.

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