Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

Podsumowanie

The present protocol describes an integrated strategy for exploring the key targets and mechanisms of Fructus Phyllanthi against hyperlipidemia based on network pharmacology prediction and metabolomics verification.

Streszczenie

Hyperlipidemia has become a leading risk factor for cardiovascular diseases and liver injury worldwide. Fructus Phyllanthi (FP) is an effective drug against hyperlipidemia in Traditional Chinese Medicine (TCM) and Indian Medicine theories, however the potential mechanism requires further exploration. The present research aims to reveal the mechanism of FP against hyperlipidemia based on an integrated strategy combining network pharmacology prediction with metabolomics validation. A high-fat diet (HFD)-induced mice model was established by evaluating the plasma lipid levels, including total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). Network pharmacology was applied to find out the active ingredients of FP and potential targets against hyperlipidemia. Metabolomics of plasma and liver were performed to identify differential metabolites and their corresponding pathways among the normal group, model group, and intervention group. The relationship between network pharmacology and metabolomics was further constructed to obtain a comprehensive view of the process of FP against hyperlipidemia. The obtained key target proteins were verified by molecular docking. These results reflected that FP improved the plasma lipid levels and liver injury of hyperlipidemia induced by a HFD. Gallic acid, quercetin, and beta-sitosterol in FP were demonstrated as the key active compounds. A total of 16 and six potential differential metabolites in plasma and liver, respectively, were found to be involved in the therapeutic effects of FP against hyperlipidemia by metabolomics. Further, integration analysis indicated that the intervention effects were associated with CYP1A1, AChE, and MGAM, as well as the adjustment of L-kynurenine, corticosterone, acetylcholine, and raffinose, mainly involving tryptophan metabolism pathway. Molecular docking ensured that the above ingredients acting on hyperlipidemia-related protein targets played a key role in lowering lipids. In summary, this research provided a new possibility for preventing and treating hyperlipidemia.

Wprowadzenie

Hyperlipidemia is a common metabolic disease with serious impacts on human health, and is also the primary risk factor for cardiovascular diseases¹. Recently, there has been a downward age-related trend for this disease, and younger people have become more susceptible because of long-term irregular lifestyles and unhealthy eating habits². In the clinic, various drugs have been used to treat hyperlipidemia. For example, one of the most commonly used drugs for patients with hyperlipidemia and related atherosclerotic disorders is statins. However, long-term use of statins has side effects that can't be neglected, which lead to a poor prognosis, such as intolerance, treatment resistance, and adverse events^3,4. These shortcomings have become additional pains for hyperlipidemia patients. Therefore, novel treatments for stable lipid-lowering efficacy and fewer side effects should be proposed.

Traditional Chinese Medicine (TCM) has been widely used to treat diseases because of its good efficacy and few side effects⁵. Fructus Phyllanthi (FP), the dried fruit of Phyllanthus emblica Linn. (popularly known as amla berry or Indian gooseberry), is a famous medicine and food homologous material of traditional Chinese and India medicines^6,7. This medicine has been used for clearing heat, cooling blood, and promoting digestion, as per TCM theories⁸. Modern pharmacological studies have shown that FP is rich in bioactive compounds such as gallic acids, ellagic acids, and quercetin⁹, which are responsible for a range of multifaceted biological properties, by acting as an antioxidant, an anti-inflammatory, liver protection, an anti-hypolipidaemic, and so on¹⁰. Recent research has also showed that FP could effectively regulate the blood lipids of patients with hyperlipidemia. For example, Variya et al.¹¹ have demonstrated that FP fruit juice and its main chemical ingredient of gallic acid can decrease plasma cholesterol and reduce oil infiltration in the liver and aorta. The therapeutic efficacy was related to FP's regulation in increasing the expression of peroxisome proliferator-activated receptor-alpha and decreasing hepatic lipogenic activity. However, the underlying mechanism of FP in improving hyperlipidemia should be further investigated, because its bioactive ingredients are quite extensive. We sought to explore the potential mechanism of FP's therapeutic efficacy, which may be beneficial for the further development and utilization of this medicine.

Currently, network pharmacology is regarded as a holistic and efficient technique to study the therapeutic mechanism of TCM. Instead of looking for single disease-causing genes and drugs treating solely an individual target, a complete drug-ingredients-genes-diseases network is constructed to find the multi-target mechanism of the multi-ingredient drug regarding their comprehensive treatment¹². This technique is especially suitable for TCM, as their chemical compositions are massive. Unfortunately, network pharmacology can only be used to forecast targets affected by chemical ingredients in theory. The endogenous metabolites in the disease model should be observed to validate the effectiveness of network pharmacology. The metabolomics method, which emerges with the development of systems biology, is an important tool for monitoring the changes in endogenous metabolites¹³. The changes in metabolites reflect the steady state changes of the host, which is also an important indicator for studying the internal mechanism. Some researchers have successfully integrated network pharmacology and metabolomics to explore the interaction mechanism between drugs and diseases^14,15.

This article explores the mechanistic basis of FP against hyperlipidemia by integrating network pharmacology and metabolomics techniques. Network pharmacology was applied to analyze the relationship between the main active ingredients in FP and molecular targets for hyperlipidemia. Subsequently, metabolomics was performed to observe the change of endogenous metabolites in the animal model, which can explain the medicine actions at the metabolic level. Compared with the application of network pharmacology or metabonomics alone, this integrated analysis provided a more specific and comprehensive research mechanism. Additionally, the molecular docking strategy was used to analyze the interaction between active ingredients and key proteins. In general, this integrated approach could compensate for the lack of experimental evidence for network pharmacology and the lack of an endogenous mechanism for the metabolomics method, and can be used for the therapeutic mechanism analysis of natural medicine. The main schematic flowchart of the protocol is shown in Figure 1.

Protokół

All procedures involving the handling of animals were conducted in accordance with the Chengdu University of Traditional Chinese Medicine Guide for the Care and Use of Laboratory Animals and were approved by the Institutional Ethics Committee of the Chengdu University of Traditional Chinese Medicine (Protocol number 2020-36). Male C57BL/6 mice (20 ± 2 g) were used for the present study. The mice were obtained from a commercial source (see Table of Materials).

1. Network pharmacology-based prediction

NOTE: The network pharmacology is used to predict the active ingredients and their key targets of FP against hyperlipidemia.

1. Selection of active ingredients and key targets
   1. Search the keyword "Phyllanthi Fructus" on the Traditional Chinese Medicine system's pharmacology database (TCMSP; http://tcmspw.com/tcmsp.php) to obtain the list of the candidate active ingredients and targets of FP.
      NOTE: Normally, only ingredients with oral bioavailability (OB) ≥30% and drug-like (DL) values ≥0.18 in the database are included as active ingredients.
   2. Search the keyword "hyperlipidemia" in the GeneCards database (https://www.genecards.org/), the Online Mendelian Inheritance in Man database (OMIM; https://omim.org/), and the therapeutic target database (TTD; http://db.idrblab.net/ttd/) to obtain the respective candidate targets of hyperlipidemia. Download the spreadsheets of disease targets. Delete the repeated targets to obtain the hyperlipidemia targets list.
   3. Copy these lists from steps 1.1.1 and 1.1.2 into a new spreadsheet. Use the "Data - Identify Duplicates" function in the toolbar to get intersection targets. Import the intersection target list into UniProtKB (http://www.uniprot.org/) to standardize the gene and protein names.
      NOTE: These targets are related to both FP and hyperlipidemia. Therefore, predict these intersection targets as the targets of FP against hyperlipidemia.
2. Construction of a protein-protein interaction network
   1. Open the STRING database (https://string-db.org/) 11.5. Paste the intersection target list of FP against hyperlipidemia in the "List of Names" dialog box. Select Homo sapiens in "Organisms" and click on the SEARCH > CONTINUE.
      NOTE: Humans and mice have highly similar genes. Therefore, further experimental verification is carried out with mice.
   2. When the results are available, tick the hide disconnected nodes in the network in "Advanced Settings". Set the highest confidence (0.900) in "minimum required interaction score", then click on the UPDATE button.
   3. Click on Exports in the title bar, and download the short tabular text of the protein-protein interaction (PPI) network in PNG and TSV format.
3. Construction of a drug-component-disease-target network
   1. Open Cytoscape 3.9.1 (see Table of Materials). Import the TSV format file of step 1.2.3. Optimize the color, font, and side of the network nodes through the style bar in the control panel.
   2. Use the "Analyze Network" function for network topology analysis. Obtain hub genes by CytoHubba in Cytoscape. Establish the drug-ingredient-target-disease network.
4. GO and KEGG enrichment analysis
   1. Open DAVID Bioinformatics Resources (https://david.ncifcrf.gov/home.jsp). Click on Start Analysis and paste the target list into the left dialog box. Select OFFICIAL GENE SYMBOL in "Select Identifier". Select Homo sapiens in "Select species". Tick Gene List in "List Type". Click on Submit List.
   2. When the results are available, click on Analyze above gene list with one of DAVID tools. Tick GOTERM_BP_DIRECT, GOTERM_CC_DIRECT, GOTERM_MF_DIRECT in "Gene Ontology" for GO function enrichment analysis. Tick KEGG_Pathway in "Pathways" for KEGG pathway enrichment analysis.
   3. Click on Functional Annotation Chart to display the results.
      ​NOTE: Set the statistical significance threshold of the enrichment analysis at p < 0.05.

2. Experimental design

1. The FP aqueous extract preparation
   NOTE: FP is processed in the laboratory of Professor Lina Xia at the Chengdu University of TCM⁸.
   1. Soak the dried powder of FP (90 g) in 1 L of pure water in a clean 2 L volumetric flask. Use ultrasonic treatment (in a 4 °C water bath, power: 250W, frequency: 35 kHz) to help dissolve for 30 min. Filter the solution to obtain the extract with a double-layer, 1 mm x 1 mm sterile medical gauze. Repeat the above operation three times to ensure the complete dissolution of FP.
   2. Use the rotary evaporation method for further concentration. Set the rotation speed to 50 rpm with a temperature of 60 °C for 4 h. Concentrate the aqueous extract to 100 mL.
   3. Divide the crude extract of FP (0.9 g/mL) evenly into two parts (50 mL). One part is used as the high-dose FP liquid (0.9 g/mL). Add 50 mL of pure water into another part, and consider it as the low-dose FP liquid (0.45 g/mL). Use the high- and low-dose FP aqueous solutions for administration. Store the liquid at -20 °C until use.
2. Animal preparation
   1. House 50 male C57BL/6 mice (20 ± 2 g) in a well-ventilated room at room temperature, with a 12 h light-dark cycle and free access to food and pure water.
   2. Randomly assign the mice to two groups: feed 10 mice with a normal diet and 40 mice with a high-fat diet (see Table of Materials) to induce hyperlipidemia.
      NOTE: After feeding for 8 weeks, the mice were screened for further drug intervention.
   3. In the 8th week, withdraw about 200 µL of blood from each mouse orbit. Centrifuge the blood for 10 min at 5,733 x g at 4 °C to obtain plasma samples. Determine the TC and TG levels with commercially available assay kits (see Table of Materials).
   4. Select six mice with the most normal lipid levels as the no-treatment control (NC) group. Select 24 mice with a significantly higher lipid level as the high-fat diet group, and randomly divide them into four groups: high-fat diet (HFD) group, low-dose FP (FP_L) group, high-dose FP (FP_H) group, and positive control (PC) group.
   5. Administer gastric irrigation to the FP_L and FP_H groups with two dosages of FP (low dose, 4.5 g/kg and high dose, 9 g/kg), respectively; gastric irrigation to the PC group with simvastatin tablets (5 mg/kg; see Table of Materials); and gastric irrigation to the NC and HFD groups with the same volume of physiological saline once a day for 4 weeks.
      NOTE: The current study used the aqueous solutions of FP and simvastatin for treatment.
   6. In the 12th week, after anesthesia by 1% pentobarbital sodium (30 mg/kg), sacrifice the mice of all the groups. Collect ~400 µL blood samples from each mouse's orbital vein.
      NOTE: Stimulate the toes and soles of the mice with tweezers. If there is no reaction, it proves adequate anesthesia.
   7. Centrifuge the blood for 10 min at 5,733 x g at 4 °C to obtain plasma samples, and determine the TC, TG, LDL-C, and HDL-C levels with commercially available assay kits (see Table of Materials). Obtain liver tissue samples¹⁶ and subject them to histopathological analysis. Use the remaining plasma and liver samples for metabolomics analysis (step 3).
      NOTE: All samples are stored at -80 °C until use.
3. Liver histopathological examination
   1. Fix fresh liver tissues with 4% paraformaldehyde solution for more than 24 h. Take out the tissue from the fixative and smooth the target tissues with a scalpel. Place the tissue and the corresponding label into the dehydrator.
   2. Dehydrate in an ethanol gradient: 75% alcohol for 4 h, 85% alcohol for 2 h, 90% alcohol for 2 h, 95% alcohol for 1 h, absolute ethanol for 1 h, xylene for 30 min. Place the tissue cassette in a tissue mold in paraffin wax for three washes, 30 min each¹⁶.
   3. Put the wax-soaked tissues into the tissue embedder (see Table of Materials). Before the wax solidifies, remove the tissues from the dehydrator, put them into the embedded box, and attach the corresponding label.
   4. Cool the wax blocks in a -20 °C freezing table, remove them from the embedded frame, and trim the wax block.
   5. Cut the trimmed wax blocks into 3 µm thick sections using a microtome (see Table of Materials). Float the sections in 40 °C water, remove them from the slides, and bake in a 60 °C oven. After baking with water and dry wax, take it out and keep it at room temperature.
   6. Successively place the sections in xylene I for 10 min, xylene II for 10 min, xylene III for 10 min, absolute ethanol I for 5 min, absolute ethanol II for 5 min, 75% alcohol for 5 min, and wash in water¹⁶.
   7. Stain the sections with hematoxylin staining solution for 4 min, 1% hydrochloric acid alcohol solution (75% alcohol) for differentiation, 1% ammonia water solution back blue, and wash them with water.
   8. Stain the sections with eosin staining solution for 2 min and wash them with water.
   9. Observe the sections using an optical microscope with a magnification of 200x and 400x.
4. Liquid chromatography-mass spectrometry )LC-MS) analysis
   1. Ingredient identification of FP
      NOTE: The analysis is performed using ultra-high-performance liquid chromatography coupled with hybrid quadrupole-orbitrap high-resolution mass spectrometry (UPLC-Q-Orbirap HRMS, LC-MS; see Table of Materials).
      1. Precisely measure 1 g of dried powder of FP and put it into a clean 50 mL volumetric flask.
      2. Add 25 mL of 70% methanol into the volumetric flask and accurately weigh. Use ultrasonic treatment (in a 4 °C water bath, power: 250 W, frequency: 35 kHz) for 30 min to help dissolution. Accurately weigh again to precisely determine the loss after dissolution, and use 70% methanol to make up the loss.
         NOTE: Don't measure the volume, as the scale of the volumetric flask is not accurate, especially after the 4 °C water bath.
      3. Shake up to mix fully. Use a 0.22 µm microporous membrane to filter.
   2. Plasma sample preparation
      1. Precisely add 100 µL of plasma (step 2.2.7) into a double volume (200 µL) of acetonitrile in a 1.5 mL centrifuge tube, and vortex it with a vortex vibrator for at least 30 s. Follow this procedure for all samples.
      2. Centrifuge all samples at 17,200 x g for 10 min at 4 °C. Transfer the supernatants after centrifugation into a new 1.5 mL centrifuge tube. Dry the supernatants under nitrogen. Reconstitute with 200 µL of extraction solvent (acetonitrile:water = 4:1 [v/v]).
      3. Vortex the reconstituted solution for at least 30 s and use ultrasonic treatment for 10 min (in a 4 °C water bath, power: 250 W, frequency: 35 kHz). Centrifuge at 17,200 x g for 10 min at 4 °C.
      4. Filter the supernatants with 0.22 µm filter membranes and keep them at 4 °C for analysis.
   3. Liver sample preparation
      1. Homogenize 90 mg of liver tissue (step 2.2.7) for 1 min in ice-cold methanol-water (1:1, v/v, 1 mL) and centrifuge them at 21,500 x g for 10 min at 4 °C. Transfer the supernatant into 1.5 mL centrifuge tubes. Follow this procedure for all samples.
      2. Extract the precipitates again following the same procedure, and pool the supernatants together into new 1.5 mL centrifuge tubes. Dry the supernatants under nitrogen. Reconstitute with 300 µL of the extraction solvent (methanol:water = 4:1 [v/v]).
      3. Vortex the reconstituted solution for at least 30 s and use ultrasonic treatment for 10 min (in a 4 °C water bath, power: 250 W, frequency: 35 kHz). Centrifuge at 17,200 x g for 15 min at 4 °C.
      4. Filter the supernatants with 0.22 µm filter membranes and keep them at 4 °C for analysis.
         NOTE: The pooled quality control (QC) samples were prepared by mixing 10 µL aliquots from each plasma and liver sample (one per six samples).
   4. Analysis parameters of LC-MS
      NOTE: The mobile phase consists of 0.1% formic acid (solvent A) and acetonitrile (solvent B). Transfer these solvents to a clean glass bottle and connect them with the LC-MS system.
      1. Set the gradient programs of plasma samples in the "inlet file" of the LC-MS system as follows: 1% B (0-1.5 min), 1%-60% B (1.5-13.0 min), 60%-99% B (13.0-20.0 min), maintain at 99% B (20.0-25.0 min), 99%-1% B (25.0-25.1 min), and maintain at 1% B until 27 min.
      2. Set the autosampler conditions of the plasma samples in the "inlet file" of the LC-MS system as follows: the volume of injection, 2 µL; and the flow rate, 0.3 mL/min, for each analysis.
      3. Set the gradient program of liver samples in the "inlet file" of the LC-MS system as follows: 1% B (0-1 min), 1%-53% B (1-15 min), 53%-70% B (15-30 min), 70%-90% B (30-32 min), 90%-95% B (32-40 min), 95%-1% B (40-42 min), and maintain at 1% B until 45 min.
      4. Set the autosampler conditions of the liver samples in the "inlet file" of the LC-MS system as follows: volume of injection, 5 µL; and the flow rate, 0.3 mL/min, for each analysis.
      5. Set the MS detection conditions of both the plasma and liver samples in the "MS tune file" of the LC-MS system. Perform the MS acquisition using both positive and negative ionization modes.
         ​NOTE: The heated electrospray ionization parameters are as follows: spray voltage: 3.5 kV for positive ionization and 3.8 kV for negative ionization; sheath gas flow: 55 arb; auxiliary gas flow: 15 arb; probe heater temperature: 300 °C; and capillary temperature: 350 °C.
      6. Import the collected raw data into Compound Discoverer software, and set the method template following the manufacturer's instructions (see Table of Materials).

3. Metabolomic validation

NOTE: The metabolomic profiling data of plasma and liver metabolites are imported into Compound Discoverer software to perform the metabolic feature extraction by adopting a molecular feature extraction algorithm. Set the parameters as follows: mass deviation, 5 x 10-6; mass range, 100-1,500; signal to noise ratio (SNR) threshold, 3; and retention time deviation, 0.05. Evaluate the stability and repeatability of metabolomics by the relative standard deviation (RSD) of QC peak areas.

1. Use SIMCA-P software (see Table of Materials) for multivariate statistical analysis of the integral values obtained from LC-MS findings. Use orthogonal partial least squares discriminant analysis (OPLS-DA) for the mean-centered data and the modeling of sample classes.
2. After the OPLS-DA test, consider the metabolites, with integral with variable importance in the projection (VIP) values of >1 and a p-value of <0.05 from Student's t-test as the potential differential metabolites.
3. Identify the disturbed metabolites and metabolic pathways by open database sources, including Human Metabolome (HMDB; http://www.hmdb.ca/), Kyoto Encyclopedia of Genes and Genomes (KEGG; https://www.kegg.jp/), and MetaboAnalyst5.0 (https://www.metaboanalyst.ca/).
4. Visualize the result views by MetaboAnalyst5.0 and the 'Wu Kong' platform (https://www.omicsolution.com/wkomics/main/).

4. Molecular docking

1. Download the 3D structure of the selected FP ingredients from the TCMSP database, respectively. Search the ingredient names in the 'Chemical name' search box and download the corresponding 3D structure files in mol2 format.
2. Download the crystal structures of the key targets from the AlphaFold Protein Structure Database (Alphafold DB;, https://alphafold.ebi.ac.uk/). Search the target names in the search box and download the corresponding crystal structures files in pdb format.
3. Import ingredients and target structures file into AutoDockTools software. Click on Edit > Delete Water to delete water molecules. Click on Edit > Hydrogens > Add to add hydrogens. Set the ingredients as the 'ligand' and perform blind docking by selecting the whole targets as the 'receptor'¹⁷.
4. Enter a value in the box behind "center" and "size" to adjust the newly-developed space, making it possible to fully encompass the ligand and the receptor. Save the ligand and receptor files in pdbqt format.
5. Use AutoDock Vina to perform molecular docking. Set the "Receptor" bar to the name of the 'receptor.pdbqt', and the "Ligand" bar to the name of the 'ligand.pdbqt'. Obtain the optimal location for ligand binding to the receptor. Record the binding energy value at the optimal position.
   NOTE: The docking process was calculated by the Genetic Algorithm¹⁴. All docking run options were default values. Docking frames will be automatically ranked from the highest to the lowest binding energy.
6. Import the docking files into PILP (Protein-ligand Interaction Profiler' https://plip-tool.biotec.tu-dresden.de/plip-web/plip/index) to get the visual system model. Download the model files in pse format, and import them into PyMOL software (see Table of Materials) to construct further visualization.

5. Statistical analysis

NOTE: Use SPSS statistical software (see Table of Materials) for data analysis. Consider the value of p < 0.05 as statistically significant.

1. Expresse the values as means ± standard deviation (SD).
2. Perform a one-way ANOVA followed by post hoc least significant difference (LSD), Dunnett (in case of equal variance), or Dunnett's T3 (in case of unequal variance) to test statistical significance among groups.

Wyniki

Network pharmacology
A total of 18 potential ingredients in FP were screened according to their pharmacokinetic and pharmacodynamic properties from the database and LC-MS analysis (the total ion chromatograms are shown in Supplementary Figure 1). Through relevant literature, the content of gallic acid is much higher than other ingredients and is effective in lowering lipids^9,11. Therefore, this ingredient was considered a p...

Dyskusje

In recent years, the incidence rate of hyperlipidemia has been increasing, mainly due to long-term unhealthy eating habits. TCM and its chemical ingredients have various pharmacological activities, which have been widely studied in recent years^37,38. FP is a kind of fruit resource, used both as medicine and food, and has an important potential for treating hyperlipidemia. However, the potential therapeutic mechanism of FP against hyperlipidemia needs further stud...

Materiały

Name	Company	Catalog Number	Comments
101-3B Oven	Luyue Instrument and Equipment Factory	\
80312/80302 Glass Slide	Jiangsu Sitai Experimental Equipment Co., LTD	\
80340-1630 Cover Slip	Jiangsu Sitai Experimental Equipment Co., LTD	\
AccucoreTM C18 (3 mm × 100 mm, 2. 6 μm)	Thermo Fisher Scientific	\
Acetonitrile	Fisher Chemical	A998	Version 1.5.6
ACQUITY UPLC HSS T3 Column (2.1 mm × 100 mm, 1.8 μm)	Thermo Fisher Scientific	\
Aethanol	Fisher Chemical	A995	Version 3.0
Ammonia Solution	Chengdu Cologne Chemicals Co., LTD	1336-21-6	Version 3.9.1
AutoDockTools	Scripps Institution of Oceanography	\
BS-240VT Full-automatic Animal Biochemical Detection System	Shenzhen Mindray Bio-Medical Electronics Co., Ltd.	\
Compound Discoverer	Thermo Fisher Scientific	\
Cytoscape	Cytoscape Consortium	\
DM500 Optical Microscope	Leica	\
DV215CD Electronic Balance	Ohaus Corporation ., Ltd	T15A63
Ethyl Alcohol	Chengdu Cologne Chemicals Co., LTD	64-17-5
Formic Acid	Fisher Chemical	A118
HDL-C Assay Kit	Nanjing Jiancheng Bioengineering Institute	A112-1-1
Hematoxylin Staining Solution	Biosharp	BL700B
High Fat Diet	ENSIWEIER	202211091031
Hitachi CT15E/CT15RE Centrifuge	Hitachi., Ltd.	\
Homogenizer	Oulaibo Technology Co., Ltd	\
Hydrochloric Acid	Chengdu Cologne Chemicals Co., LTD	7647-01-0
Image-forming System	LIOO	\
JB-L5 Freezer	Wuhan Junjie Electronics Co., Ltd	\
JB-L5 Tissue Embedder	Wuhan Junjie Electronics Co., Ltd	\
JK-5/6 Microtome	Wuhan Junjie Electronics Co., Ltd	\
JT-12S Hydroextractor	Wuhan Junjie Electronics Co., Ltd	\
KQ3200E Ultrasonic Cleaner	Kun Shan Ultrasonic Instruments Co., Ltd	\
LDL-C Assay Kit	Nanjing Jiancheng Bioengineering Institute	A113-1-1
Male C57BL/6 Mice	SBF Biotechnology Co., Ltd.	\	Version 2.3.2
Neutral Balsam	Shanghai Yiyang Instrument Co., Ltd	10021190865934
Pure Water	Guangzhou Watson's Food & Beverage Co., Ltd	GB19298
PyMOL	DeLano Scientific LLC	\	Version 14.1
RE-3000 Rotary Evaporator	Yarong Biochemical Instrument Factory ., Ltd	\
RM2016 Pathological Microtome	Shanghai Leica Instruments Co., Ltd	\	Version 26.0
SIMCA-P	Umetrics AB	\
Simvastatin	Merck Sharp & Dohme., Ltd	14202220051
SPSS	International Business Machines Corporation	\
TC Assay Kit	Nanjing Jiancheng Bioengineering Institute	A111-1-1
TG Assay Kit	Nanjing Jiancheng Bioengineering Institute	A110-1-1
UPLC-Q-Exactive Quadrupole Electrostatic Field Orbital Hydrazine High Resolution Mass Spectrometry	Thermo Fisher Scientific	\
Vortex Vibrator	Beijing PowerStar Technology Co., Ltd.	LC-Vortex-P1
Xylene	Chengdu Cologne Chemicals Co., LTD	1330-20-7