Name: network pharmacology prediction and metabolomics validation of the mechanism of fructus phyllanthi against hyperlipidemia
Uploaded: 2023-04-07T14:10:00.000+00:00
Duration: 11 min 6 s
Description: Watch this Scientific Journal Video about Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia at JoVE.com

This research was supported by the Product Development and Innovation Team of TCM Health Preservation and Rehabilitation (2022C005) and Research on New Business Cross-border Integration of &#34;Health Preservation and Rehabilitation+&#34;.

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 &#177; 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.
<ol>
	<li>Selection of active ingredients and key targets
	<ol>
		<li>Search the keyword &#34;Phyllanthi Fructus&#34; on the Traditional Chinese Medicine system&#39;s pharmacology database (TCMSP;&#160;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) &#8805;30% and drug-like (DL) values &#8805;0.18 in the database are included as active ingredients.</li>
		<li>Search the keyword &#34;hyperlipidemia&#34; 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.</li>
		<li>Copy these lists from steps 1.1.1 and 1.1.2 into a new spreadsheet. Use the &#34;Data - Identify Duplicates&#34; 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.</li>
	</ol>
	</li>
	<li>Construction of a protein-protein interaction network
	<ol>
		<li>Open the STRING database (https://string-db.org/) 11.5. Paste the intersection target list of FP against hyperlipidemia in the &#34;List of Names&#34; dialog box. Select&#160;Homo sapiens in &#34;Organisms&#34; and click on the SEARCH &#62; CONTINUE. 
		NOTE: Humans and mice have highly similar genes. Therefore, further experimental verification is carried out with mice.</li>
		<li>When the results are available, tick the hide disconnected nodes in the network in &#34;Advanced Settings&#34;. Set the highest confidence (0.900) in &#34;minimum required interaction score&#34;, then click on the UPDATE button.</li>
		<li>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.</li>
	</ol>
	</li>
	<li>Construction of a drug-component-disease-target network
	<ol>
		<li>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.</li>
		<li>Use the &#34;Analyze Network&#34; function for network topology analysis. Obtain hub genes by CytoHubba in Cytoscape. Establish the drug-ingredient-target-disease network.</li>
	</ol>
	</li>
	<li>GO and KEGG enrichment analysis
	<ol>
		<li>Open DAVID Bioinformatics Resources (https://david.ncifcrf.gov/home.jsp). Click on&#160;Start Analysis and paste the target list into the left dialog box. Select OFFICIAL GENE SYMBOL in &#34;Select Identifier&#34;. Select Homo sapiens in &#34;Select species&#34;. Tick Gene List in &#34;List Type&#34;. Click on Submit List.</li>
		<li>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 &#34;Gene Ontology&#34; for GO function enrichment analysis. Tick KEGG_Pathway in &#34;Pathways&#34; for KEGG pathway enrichment analysis.</li>
		<li>Click on Functional Annotation Chart to display the results. 
		&#8203;NOTE: Set the statistical significance threshold of the enrichment analysis at p &#60; 0.05.</li>
	</ol>
	</li>
</ol>
2. Experimental design
<ol>
	<li>The FP aqueous extract preparation 
	NOTE: FP is processed in the laboratory of Professor Lina Xia at the Chengdu University of TCM8.
	<ol>
		<li>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 &#176;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.</li>
		<li>Use the rotary evaporation method for further concentration. Set the rotation speed to 50 rpm with a temperature of 60 &#176;C for 4 h. Concentrate the aqueous extract to 100 mL.</li>
		<li>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 &#176;C until use.</li>
	</ol>
	</li>
	<li>Animal preparation
	<ol>
		<li>House 50 male C57BL/6 mice (20 &#177; 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.</li>
		<li>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.</li>
		<li>In the 8th week, withdraw about 200 &#181;L of blood from each mouse orbit. Centrifuge the blood for 10 min at 5,733 x g at 4 &#176;C to obtain plasma samples. Determine the TC and TG levels with commercially available assay kits (see Table of Materials).</li>
		<li>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.</li>
		<li>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.</li>
		<li>In the 12th week, after anesthesia by 1% pentobarbital sodium (30 mg/kg), sacrifice the mice of all the groups. Collect ~400 &#181;L blood samples from each mouse&#39;s orbital vein. 
		NOTE: Stimulate the toes and soles of the mice with tweezers. If there is no reaction, it proves adequate anesthesia.</li>
		<li>Centrifuge the blood for 10 min at 5,733 x g at 4 &#176;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 &#176;C until use.</li>
	</ol>
	</li>
	<li>Liver histopathological examination
	<ol>
		<li>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.</li>
		<li>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.</li>
		<li>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.</li>
		<li>Cool the wax blocks in a -20 &#176;C freezing table, remove them from the embedded frame, and trim the wax block.</li>
		<li>Cut the trimmed wax blocks into 3 &#181;m thick sections using a microtome (see Table of Materials). Float the sections in 40 &#176;C water, remove them from the slides, and bake in a 60 &#176;C oven. After baking with water and dry wax, take it out and keep it at room temperature.</li>
		<li>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.</li>
		<li>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.</li>
		<li>Stain the sections with eosin staining solution for 2 min and wash them with water.</li>
		<li>Observe the sections using an optical microscope with a magnification of 200x and 400x.</li>
	</ol>
	</li>
	<li>Liquid chromatography-mass spectrometry )LC-MS) analysis
	<ol>
		<li>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).
		<ol>
			<li>Precisely measure 1 g of dried powder of FP and put it into a clean 50 mL volumetric flask.</li>
			<li>Add 25 mL of 70% methanol into the volumetric flask and accurately weigh. Use ultrasonic treatment (in a 4 &#176;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&#39;t measure the volume, as the scale of the volumetric flask is not accurate, especially after the 4 &#176;C water bath.</li>
			<li>Shake up to mix fully. Use a 0.22 &#181;m microporous membrane to filter.</li>
		</ol>
		</li>
		<li>Plasma sample preparation
		<ol>
			<li>Precisely add 100 &#181;L of plasma (step 2.2.7) into a double volume (200 &#181;L) 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.</li>
			<li>Centrifuge all samples at 17,200 x g for 10 min at 4 &#176;C. Transfer the supernatants after centrifugation into a new 1.5 mL centrifuge tube. Dry the supernatants under nitrogen. Reconstitute with 200 &#181;L of extraction solvent (acetonitrile:water = 4:1 [v/v]).</li>
			<li>Vortex the reconstituted solution for at least 30 s and use ultrasonic treatment for 10 min (in a 4 &#176;C water bath, power: 250 W, frequency: 35 kHz). Centrifuge at 17,200 x g for 10 min at 4 &#176;C.</li>
			<li>Filter the supernatants with 0.22 &#181;m filter membranes and keep them at 4 &#176;C for analysis.</li>
		</ol>
		</li>
		<li>Liver sample preparation
		<ol>
			<li>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 &#176;C. Transfer the supernatant into 1.5 mL centrifuge tubes. Follow this procedure for all samples.</li>
			<li>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 &#181;L of the extraction solvent (methanol:water = 4:1 [v/v]).</li>
			<li>Vortex the reconstituted solution for at least 30 s and use ultrasonic treatment for 10 min (in a 4 &#176;C water bath, power: 250 W, frequency: 35 kHz). Centrifuge at 17,200 x g for 15 min at 4 &#176;C.</li>
			<li>Filter the supernatants with 0.22 &#181;m filter membranes and keep them at 4 &#176;C for analysis. 
			NOTE: The pooled quality control (QC) samples were prepared by mixing 10 &#181;L aliquots from each plasma and liver sample (one per six samples).</li>
		</ol>
		</li>
		<li>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.
		<ol>
			<li>Set the gradient programs of plasma samples in the &#34;inlet file&#34; 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.</li>
			<li>Set the autosampler conditions of the plasma samples in the &#34;inlet file&#34; of the LC-MS system as follows: the volume of injection, 2 &#181;L; and the flow rate, 0.3 mL/min, for each analysis.</li>
			<li>Set the gradient program of liver samples in the &#34;inlet file&#34; 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.</li>
			<li>Set the autosampler conditions of the liver samples in the &#34;inlet file&#34; of the LC-MS system as follows: volume of injection, 5 &#181;L; and the flow rate, 0.3 mL/min, for each analysis.</li>
			<li>Set the MS detection conditions of both the plasma and liver samples in the &#34;MS tune file&#34; of the LC-MS system. Perform the MS acquisition using both positive and negative ionization modes. 
			&#8203;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 &#176;C; and capillary temperature: 350 &#176;C.</li>
			<li>Import the collected raw data into Compound Discoverer software, and set the method template following the manufacturer&#39;s instructions (see Table of Materials).</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
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.&#160;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.
<ol>
	<li>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.</li>
	<li>After the OPLS-DA test, consider the metabolites, with integral with variable importance in the projection (VIP) values of &#62;1 and a p-value of &#60;0.05 from Student&#39;s t-test as the potential differential metabolites.</li>
	<li>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/).</li>
	<li>Visualize the result views by MetaboAnalyst5.0 and the &#39;Wu Kong&#39; platform (https://www.omicsolution.com/wkomics/main/).</li>
</ol>
4. Molecular docking
<ol>
	<li>Download the 3D structure of the selected FP ingredients from the TCMSP database, respectively. Search the ingredient names in the &#39;Chemical name&#39; search box and download the corresponding 3D structure files in mol2 format.</li>
	<li>Download the crystal structures of the key targets from the AlphaFold Protein Structure Database (Alphafold DB;,&#160;https://alphafold.ebi.ac.uk/). Search the target names in the search box and download the corresponding crystal structures files in pdb format.</li>
	<li>Import ingredients and target structures file into AutoDockTools software. Click on Edit &#62; Delete Water to delete water molecules. Click on Edit &#62; Hydrogens &#62; Add to add hydrogens. Set the ingredients as the &#39;ligand&#39; and perform blind docking by selecting the whole targets as the &#39;receptor&#39;17.</li>
	<li>Enter a value in the box behind &#34;center&#34; and &#34;size&#34; 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.</li>
	<li>Use AutoDock Vina to perform molecular docking. Set the &#34;Receptor&#34; bar to the name of the &#39;receptor.pdbqt&#39;, and the &#34;Ligand&#34; bar to the name of the &#39;ligand.pdbqt&#39;. 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.</li>
	<li>Import the docking files into PILP (Protein-ligand Interaction Profiler&#39;&#160;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&#160;Table of Materials) to construct further visualization.</li>
</ol>
5. Statistical analysis
NOTE: Use SPSS statistical software (see Table of Materials) for data analysis. Consider the value of p &#60; 0.05 as statistically significant.
<ol>
	<li>Expresse the values as means &#177; standard deviation (SD).</li>
	<li>Perform a one-way ANOVA followed by post hoc least significant difference (LSD), Dunnett (in case of equal variance), or Dunnett&#39;s T3 (in case of unequal variance) to test statistical significance among groups.</li>
</ol>

All authors declare that they have no conflict of interest.

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 years37,38. 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...

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&#39;t be neglected, which lead to a poor prognosis, such as intolerance, treatment resistance, and adverse events3,4. 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 medicines6,7. 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&#39;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&#39;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 diseases14,15.
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.

<table><tbody><tr><td>101-3B Oven</td><td>Luyue Instrument and Equipment Factory</td><td>\</td><td></td></tr><tr><td>80312/80302 Glass Slide</td><td>Jiangsu Sitai Experimental Equipment Co., LTD</td><td>\</td><td></td></tr><tr><td>80340-1630 Cover Slip</td><td>Jiangsu Sitai Experimental Equipment Co., LTD</td><td>\</td><td></td></tr><tr><td>AccucoreTM C18 (3 mm &amp;times; 100 mm, 2. 6 &amp;mu;m)</td><td>Thermo Fisher Scientific</td><td>\</td><td></td></tr><tr><td>Acetonitrile</td><td>Fisher Chemical</td><td>A998</td><td>Version 1.5.6</td></tr><tr><td>ACQUITY UPLC HSS T3 Column (2.1 mm &amp;times; 100 mm, 1.8 &amp;mu;m)</td><td>Thermo Fisher Scientific</td><td>\</td><td></td></tr><tr><td>Aethanol</td><td>Fisher Chemical</td><td>A995</td><td>Version 3.0</td></tr><tr><td>Ammonia Solution</td><td>Chengdu Cologne Chemicals Co., LTD</td><td>1336-21-6</td><td>Version 3.9.1</td></tr><tr><td>AutoDockTools</td><td>Scripps Institution of Oceanography</td><td>\</td><td></td></tr><tr><td>BS-240VT Full-automatic Animal Biochemical Detection System</td><td>Shenzhen Mindray Bio-Medical Electronics Co., Ltd.</td><td>\</td><td></td></tr><tr><td>Compound Discoverer</td><td>Thermo Fisher Scientific</td><td>\</td><td></td></tr><tr><td>Cytoscape</td><td>Cytoscape Consortium</td><td>\</td><td></td></tr><tr><td>DM500 Optical Microscope</td><td>Leica</td><td>\</td><td></td></tr><tr><td>DV215CD Electronic Balance</td><td>Ohaus Corporation ., Ltd</td><td>T15A63</td><td></td></tr><tr><td>Ethyl Alcohol</td><td>Chengdu Cologne Chemicals Co., LTD</td><td>64-17-5</td><td></td></tr><tr><td>Formic Acid</td><td>Fisher Chemical</td><td>A118</td><td></td></tr><tr><td>HDL-C Assay Kit</td><td>Nanjing Jiancheng Bioengineering Institute</td><td>A112-1-1</td><td></td></tr><tr><td>Hematoxylin Staining Solution</td><td>Biosharp</td><td>BL700B</td><td></td></tr><tr><td>High Fat Diet</td><td>ENSIWEIER</td><td>202211091031</td><td></td></tr><tr><td>Hitachi CT15E/CT15RE Centrifuge</td><td>Hitachi., Ltd.</td><td>\</td><td></td></tr><tr><td>Homogenizer</td><td>Oulaibo Technology Co., Ltd</td><td>\</td><td></td></tr><tr><td>Hydrochloric Acid</td><td>Chengdu Cologne Chemicals Co., LTD</td><td>7647-01-0</td><td></td></tr><tr><td>Image-forming System</td><td>LIOO</td><td>\</td><td></td></tr><tr><td>JB-L5 Freezer</td><td>Wuhan Junjie Electronics Co., Ltd</td><td>\</td><td></td></tr><tr><td>JB-L5 Tissue Embedder</td><td>Wuhan Junjie Electronics Co., Ltd</td><td>\</td><td></td></tr><tr><td>JK-5/6 Microtome</td><td>Wuhan Junjie Electronics Co., Ltd</td><td>\</td><td></td></tr><tr><td>JT-12S Hydroextractor</td><td>Wuhan Junjie Electronics Co., Ltd</td><td>\</td><td></td></tr><tr><td>KQ3200E Ultrasonic Cleaner</td><td>Kun Shan Ultrasonic Instruments Co., Ltd</td><td>\</td><td></td></tr><tr><td>LDL-C Assay Kit</td><td>Nanjing Jiancheng Bioengineering Institute</td><td>A113-1-1</td><td></td></tr><tr><td>Male C57BL/6 Mice</td><td>&amp;nbsp;SBF Biotechnology Co., Ltd.</td><td>\</td><td>Version 2.3.2</td></tr><tr><td>Neutral Balsam</td><td>Shanghai Yiyang Instrument Co., Ltd</td><td>10021190865934</td><td></td></tr><tr><td>Pure Water</td><td>Guangzhou Watson&#039;s Food &amp;amp; Beverage Co., Ltd</td><td>GB19298</td><td></td></tr><tr><td>PyMOL</td><td>DeLano Scientific LLC</td><td>\</td><td>Version 14.1</td></tr><tr><td>RE-3000 Rotary Evaporator</td><td>Yarong Biochemical Instrument Factory ., Ltd</td><td>\</td><td></td></tr><tr><td>RM2016 Pathological Microtome</td><td>Shanghai Leica Instruments Co., Ltd</td><td>\</td><td>Version 26.0</td></tr><tr><td>SIMCA-P</td><td>Umetrics AB</td><td>\</td><td></td></tr><tr><td>Simvastatin</td><td>Merck Sharp &amp;amp; Dohme., Ltd</td><td>14202220051</td><td></td></tr><tr><td>SPSS</td><td>International Business Machines Corporation</td><td>\</td><td></td></tr><tr><td>TC Assay Kit</td><td>Nanjing Jiancheng Bioengineering Institute</td><td>A111-1-1</td><td></td></tr><tr><td>TG Assay Kit</td><td>Nanjing Jiancheng Bioengineering Institute</td><td>A110-1-1</td><td></td></tr><tr><td>UPLC-Q-Exactive Quadrupole Electrostatic Field Orbital Hydrazine High Resolution Mass Spectrometry</td><td>Thermo Fisher Scientific</td><td>\</td><td></td></tr><tr><td>Vortex Vibrator</td><td>Beijing PowerStar Technology Co., Ltd.</td><td>LC-Vortex-P1</td><td></td></tr><tr><td>Xylene</td><td>Chengdu Cologne Chemicals Co., LTD</td><td>1330-20-7</td><td></td></tr></tbody></table>

network pharmacology prediction and metabolomics validation of the mechanism of fructus phyllanthi against hyperlipidemia

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.

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 lipids9,11. Therefore, this ingredient was considered a p...

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

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.

Hyperlipidemia has become a leading risk factor for cardiovascular diseases and liver injury worldwide. Fructus Phyllanthi (FP) is an effective drug ...

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Pharmacokinetics and Pharmacodynamics

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Pharmacokinetics and Pharmacodynamics: Introduction

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1.9K Views.  Chengdu University of Traditional Chinese Medicine. Ons onderzoek omvat seriële experimentele techniek en het protocol heeft deze details die nodig zijn voor lezers om onze methoden te begrijpen. Onze geïntegreerde methode compenseert het nadeel van netwerkfarmacologie en metabolomica en kan worden gebruikt voor de therapeutische meta-analyse van natuurlijke geneeskunde. Deze methode wordt gebruikt om de actieve verbinding te screenen van het ingrediënt van het bedrijf en het pak voor joggers met een enorme chemische samenstelling, zoals ee...

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

A Simple Fractionated Extraction Method for the Comprehensive Analysis of Metabolites, Lipids, and Proteins from a Single Sample

Understanding of complex biological systems requires the measurement, analysis and integration of multiple compound classes of the living cell, usually determined by transcriptomic, proteomic, metabolomics and lipidomic measurements. In this protocol, we introduce a simple method for the reproducible extraction of metabolites, lipids and proteins from biological tissues using a single aliquot per sample. The extraction method is based on a methyl tert-butyl ether: methanol: water system for liquid: liquid partitioning of hydrophobic and polar metabolites into two immiscible phases along with the precipitation of proteins and other macromolecules as a solid pellet. This method, therefore, provides three different fractions of specific molecular composition, which are fully compatible with common high throughput 'omics' technologies such as liquid chromatography (LC) or gas chromatography (GC) coupled to mass spectrometers. Even though the method was initially developed for the analysis of different plant tissue samples, it has proved to be fully compatible for the extraction and analysis of biological samples from systems as diverse as algae, insects, and mammalian tissues and cell cultures.

A protocol for comprehensive extraction of lipids, metabolites and proteins from biological tissues using one sample is presented.

Understanding of complex biological systems requires the measurement, analysis and integration of multiple compound classes of the living cell, usually ...

Biochemistry

Semi-Targeted Ultra-High-Performance Chromatography Coupled to Mass Spectrometry Analysis of Phenolic Metabolites in Plasma of Elderly Adults

A group of 23 elderly persons was given functional meals (a beverage and a muffin) specially formulated for the prevention of sarcopenia (age-related loss of muscle mass). Plasma samples were taken at the beginning of the intervention and after 30 days of consuming the functional meals. A semi-targeted ultra-high-performance chromatography coupled with tandem mass (UPLC-MS/MS) analysis was carried out to identify phenolic compounds and their metabolites. Plasma proteins were precipitated with ethanol and the samples were concentrated and resuspended in the mobile phase (1:1 acetonitrile: water) before injection into the UPLC-MS/MS instrument. Separation was carried out with a C18 reverse-phase column, and compounds were identified using their experimental mass, isotopic distribution, and fragment pattern. Compounds of interest were compared to those of data banks and the internal semi-targeted library. Preliminary results showed that the major metabolites identified after the intervention were phenylacetic acid, glycitin, 3-hydroxyphenylvaleric acid, and gomisin M2.

The goal of this protocol is to detect phenolic metabolites in plasma using a semi-targeted chromatography-mass spectrometry method.

A group of 23 elderly persons was given functional meals (a beverage and a muffin) specially formulated for the prevention of sarcopenia (age-related loss ...

Differential Effects of Lipid-lowering Drugs in Modulating Morphology of Cholesterol Particles

Treatment of dyslipidemia patients with lipid-lowering drugs leads to a significant reduction in low-density lipoproteins (LDL) level and a low to moderate level of increase in high-density lipoprotein (HDL) cholesterol in plasma. However, a possible role of these drugs in altering morphology and distribution of cholesterol particles is poorly understood. Here, we describe the in vitro evaluation of lipid-lowering drug effects in modulating morphological features of cholesterol particles using the plaque array method in combination with imaging flow cytometry. Image analyses of the cholesterol particles indicated that lovastatin, simvastatin, ezetimibe, and atorvastatin induce the formation of both globular and linear strand-shaped particles, whereas niacin, fibrates, fluvastatin, and rosuvastatin induce the formation of only globular-shaped particles. Next, purified very low-density lipoprotein (VLDL) and LDL particles incubated with these drugs showed changes in the morphology and image texture of cholesterol particles subpopulations. Furthermore, screening of 50 serum samples revealed the presence of a higher level of linear shaped HDL cholesterol particles in subjects with dyslipidemia (mean of 18.3%) compared to the age-matched normal (mean of 11.1%) samples. We also observed considerable variations in lipid-lowering drug effects on reducing linear shaped LDL and HDL cholesterol particles formation in serum samples. These findings indicate that lipid-lowering drugs, in addition to their cell-mediated hypolipidemic effects, may directly modulate morphology of cholesterol particles by a non-enzymatic mechanism of action. The outcomes of these results have potential to inform diagnosis of atherosclerosis and predict optimal lipid-lowering therapy.

The objective of this study was to evaluate in vitro lipid-lowering drug effects in modulating the morphology of cholesterol particles. Comparison of lipid-lowering drugs revealed variations in their effect in modulating the morphological features of cholesterol particles.

Treatment of dyslipidemia patients with lipid-lowering drugs leads to a significant reduction in low-density lipoproteins (LDL) level and a low to ...

A Medical Decision Support Platform for Identifying Thrombospondin 1 in Non-alcoholic Fatty Liver Disease via Immuno-Infiltration Analysis

Non-alcoholic fatty liver disease (NAFLD) and myocardial infarction (MI) are two major health burdens with significant prevalence and mortality. This study aimed to explore the co-expressed genes to understand the relationship between NAFLD and MI and identify potential crucial biomarkers of NAFLD-related MI using bioinformatics and machine learning. Functional enrichment analysis was conducted, a co-protein-protein interaction (PPI) network diagram was constructed, and support vector machine-recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) techniques were employed to identify one differentially expressed gene (DEG), Thrombospondin 1 (THBS1). THBS1 demonstrated strong performance in distinguishing NAFLD patients (AUC = 0.981) and MI patients (AUC = 0.900). Immuno-infiltration analysis revealed significantly lower CD8+ T cells and higher neutrophil levels in patients with NAFLD and MI. CD8+ T cells and neutrophils were effective in distinguishing NAFLD/MI from healthy controls. Correlation analysis showed that THBS1 was positively correlated with CCR (chemokine receptor), MHC class (major histocompatibility complex class), neutrophils, parainflammation, and Tfh (follicular helper T cells), and negatively correlated with CD8+ T cells, cytolytic activity, and TIL (tumor-infiltrating lymphocytes) in NAFLD and MI patients. THBS1 emerged as a novel biomarker for diagnosing NAFLD/MI in comparison to healthy controls. The results indicate that CD8+ T cells and neutrophils could serve as inflammatory immune features for differentiating patients with NAFLD/MI from healthy individuals.

This study investigated the relationships between non-alcoholic fatty liver disease (NAFLD) and myocardial infarction (MI) through co-expressed genes, identifying Thrombospondin 1 (THBS1) as a biomarker. Immuno-infiltration analysis revealed CD8+ T cells and neutrophils as key factors, with THBS1 showing potential as a diagnostic tool for NAFLD and MI.

Non-alcoholic fatty liver disease (NAFLD) and myocardial infarction (MI) are two major health burdens with significant prevalence and mortality. This ...

Bioengineering

Investigating the Protective Effects of Platycodin D on Non-Alcoholic Fatty Liver Disease in a Palmitic Acid-Induced In Vitro Model

The occurrence of non-alcoholic fatty liver disease (NAFLD) has been increasing at an alarming rate worldwide. Platycodon grandiflorum is widely used as a traditional ethnomedicine for the treatment of various diseases and is a typical functional food that can be incorporated into the everyday diet. Studies have suggested that platycodin D (PD), one of the main active ingredients in Platycodon grandiflorum, has high bioavailability and significantly mitigates the progress of NAFLD, but the underlying mechanism of this is still unclear. This study aims to investigate the therapeutic effect of PD against NAFLD in vitro. AML-12 cells were pretreated with 300 &#181;M palmitic acid (PA) for 24 h to model NAFLD in vitro. Then, the cells were either treated with PD or received no PD treatment for 24 h. The levels of reactive oxygen species (ROS) were analyzed using 2&#8242;,7&#8242;-dichloro-dihydro-fluorescein diacetate (DCFH-DA) staining, and the mitochondrial membrane potential was determined by the JC-1 staining method. Moreover, the protein expression levels of LC3-II/LC3-I and p62/SQSTM1 in the cell lysates were analyzed by western blotting. PD was found to significantly decrease the ROS and mitochondrial membrane potential levels in the PA-treated group compared to the control group. Meanwhile, PD increased the LC3-II/LC3-I levels and decreased the p62/SQSTM1 levels in the PA-treated group compared to the control group. The results indicated that PD ameliorated NAFLD in vitro by reducing oxidative stress and stimulating autophagy. This in vitro model is a useful tool for studying the role of PD in NAFLD.

Investigating the Protective Effects of Platycodin D on Non-Alcoholic Fatty Liver Disease in a Palmitic Acid-Induced In Vitro Model

This protocol investigates the protective effects of platycodin D on non-alcoholic fatty liver disease in a palmitic acid-induced in vitro model.

The occurrence of non-alcoholic fatty liver disease (NAFLD) has been increasing at an alarming rate worldwide. Platycodon grandiflorum is widely used as a ...

Behavioral and Network Pharmacology-Based Analyses for the Traditional Mongolian Medicine Zadi-5 in a Rat Model of Depression

Zadi-5 is a traditional Mongolian medicine that is widely used for the treatment of depression and symptoms of irritation. Although the therapeutic effects of Zadi-5 against depression have been indicated in previously reported clinical studies, the identity and impact of the active pharmaceutical compounds present in the drug have not been fully elucidated.&#160;This study used network pharmacology to predict the drug composition and identify the therapeutically active compounds in Zadi-5 pills. Here, we established a rat model of chronic unpredicted mild stress (CUMS) and conducted an open field test (OFT), Morris water maze (MWM) analysis, and sucrose consumption test (SCT) to investigate the potential therapeutic efficacy of Zadi-5 in depression. This study aimed to demonstrate Zadi-5&#39;s therapeutic effects for depression and predict the critical pathway of the action of Zadi-5 against the disorder. The vertical and horizontal scores (OFT), SCT, and zone crossing numbers of the fluoxetine (positive control) and Zadi-5 groups were significantly higher (P &#60; 0.05) than those of the CUMS group rats without treatment. According to the results of network pharmacology analysis, the PI3K-AKT pathway was found to be essential for the antidepressant effect of Zadi-5.

The present protocol describes a method for the behavioral test validation and bioinformatical prediction of the therapeutic efficacy of Zadi-5, a traditional Mongolian medicine, in depression.

Zadi-5 is a traditional Mongolian medicine that is widely used for the treatment of depression and symptoms of irritation. Although the therapeutic ...

Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma

We aimed to study the mechanism of Trichosanthes-Fritillaria thunbergii in treating lung adenocarcinoma (LUAD) based on network pharmacology and experimental verification. The effective components and potential targets of Trichosanthis and Fritillaria thunbergii were collected by high-throughput experiment and reference-guided (HERB) database of traditional Chinese medicine and a similarity ensemble approach (SEA) database, and the LUAD-related targets were queried by the GeneCards and Online Mendelian Inheritance in Man (OMIM) databases. A drug-component-disease-target network was constructed by Cytoscape software. Protein-protein interaction (PPI) network, gene ontology (GO) function, and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were conducted to obtain core targets and key pathways. An aqueous extract of Trichosanthes-Fritillaria thunbergii and A549 cells were used for the subsequent experimental validation. Through the HERB database and literature search, 31 effective compounds and 157 potential target genes of Trichosanthes-Fritillaria thunbergii were screened, of which 144 were regulatory targets of Trichosanthes-Fritillaria thunbergii in the treatment of lung adenocarcinoma. The GO functional enrichment analysis showed that the mechanism of action of Trichosanthes-Fritillaria thunbergii against lung adenocarcinoma is mainly protein phosphorylation. The KEGG pathway enrichment analysis suggested that the treatment of lung adenocarcinoma by Trichosanthes-Fritillaria thunbergii mainly involves the PI3K/AKT signaling pathway. The experimental validation showed that an aqueous extract of Trichosanthes-Fritillaria thunbergii could inhibit the proliferation of A549 cells and the phosphorylation of AKT. Through network pharmacology and experimental validation, it was verified that the PI3K/AKT signaling pathway plays a vital role in the action of Trichosanthes-Fritillaria thunbergii in treating lung adenocarcinoma.

Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma

This study reveals the mechanism of Trichosanthes-Fritillaria thunbergii in treating lung adenocarcinoma based on network pharmacology and experimental verification. The study also demonstrates that the PI3K/AKT signaling pathway plays a vital role in the action of Trichosanthes-Fritillaria thunbergii in treating lung adenocarcinoma.

We aimed to study the mechanism of Trichosanthes-Fritillaria thunbergii in treating lung adenocarcinoma (LUAD) based on network pharmacology and ...

LDL Cholesterol Uptake Assay Using Live Cell Imaging Analysis with Cell Health Monitoring

The regulation of LDL cholesterol uptake through LDLR-mediated endocytosis is an important area of study in various major pathologies including metabolic disorder, cardiovascular disease, and kidney disease. Currently, there is no available method to assess LDL uptake while simultaneously monitoring for health of the cells. The current study presents a protocol, using a live cell imaging analysis system, to acquire serial measurements of LDL influx with concurrent monitoring for cell health. This novel technique is tested in three human cell lines (hepatic, renal tubular epithelial, and coronary artery endothelial cells) over a four-hour time course. Moreover, the sensitivity of this technique is validated with well-known LDL uptake inhibitors, Dynasore and recombinant PCSK9 protein, as well as by an LDL uptake promoter, Simvastatin. Taken together, this method provides a medium-to-high throughput platform for simultaneously screening pharmacological activity as well as monitoring of cell morphology, hence cytotoxicity of compounds regulating LDL influx. The analysis can be used with different imaging systems and analytical software.

This protocol provides an efficient approach to measuring LDL cholesterol uptake with real time influx rates using a live cell imaging system in various cell types. This technique provides a platform to screen the pharmacological activity of compounds affecting LDL influx while monitoring for cell morphology and hence potential cytotoxicity.

The regulation of LDL cholesterol uptake through LDLR-mediated endocytosis is an important area of study in various major pathologies including metabolic ...

Biology

Network Pharmacology and GEO Dataset Analysis of SDG in Anus Eczema Treatment

The Efficacy and Underlying Pathway Mechanisms of ShiDuGao Treatment for Anus Eczema Based on GEO Datasets and Network Pharmacology

Anus eczema is a chronic and recurrent inflammatory skin disease affecting the area around the anus. While the lesions primarily occur in the anal and perianal skin, they can also extend to the perineum or genitalia. ShiDuGao (SDG) has been found to possess significant reparative properties against anal pruritus, exudation control, moisture reduction, and skin repair. However, the genetic targets and pharmacological mechanisms of SDG on anal eczema have yet to be comprehensively elucidated and discussed. Consequently, this study employed a network pharmacological approach and utilized gene expression omnibus (GEO) datasets to investigate gene targets. Additionally, a protein-protein interaction network (PPI) was established, resulting in the identification of 149 targets, of which 59 were deemed hub genes, within the "drug-target-disease" interaction network.
The gene function of SDG in the treatment of perianal eczema was assessed through the utilization of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis. Subsequently, the anti-perianal eczema function and potential pathway of SDG, as identified in network pharmacological analysis, were validated using molecular docking methodology. The biological processes associated with SDG-targeted genes and proteins in the treatment of anus eczema primarily encompass cytokine-mediated responses, inflammatory responses, and responses to lipopolysaccharide, among others. The results of the pathway enrichment and functional annotation analyses suggest that SDG plays a crucial role in preventing and managing anal eczema by regulating the Shigellosis and herpes simplex virus 1 infection pathways. Network pharmacology and GEO database analysis confirms the multi-target nature of SDG in treating anal eczema, specifically by modulating TNF, MAPK14, and CASP3, which are crucial hub targets in the TNF and MAPK signaling pathways. These findings provide a clear direction for further investigation into SDG's therapeutic mechanism for anal eczema while highlighting its potential as an effective treatment approach for this debilitating condition.

Author Spotlight: Exploring ShiDuGao's Multi-Target Approach in Anus Eczema Treatment

This investigative effort sought to elucidate the mechanism of topical drug administration using a synergistic integration of network pharmacology and gene expression omnibus (GEO) datasets. This article evaluated the feasibility, target, and mechanism of ShiDuGao (SDG) in treating anus eczema.

Anus eczema is a chronic and recurrent inflammatory skin disease affecting the area around the anus. While the lesions primarily occur in the anal and ...

Evaluating Salidroside as a Therapeutic Agent for Vascular Calcification Using Network Pharmacology and Experimental Rat Models

Vascular calcification (VC) is a critical pathological condition associated with significant morbidity and mortality. This study employs a hybrid approach of network pharmacology and molecular biology to delineate the therapeutic mechanisms of salidroside (SAL), an active compound from Rhodiola crenulata, against VC. Through database mining and network analysis, 388 SAL targets intersecting with 2871 VC-associated targets were identified, resulting in 208 common targets. A protein-protein interaction (PPI) network constructed via the String database and topological analysis in Cytoscape 3.9.1 pinpointed 10 key targets, including IL6, TNF, TP53, IL1B, HIF1A, CASP3, and STAT3, among others. The identified genes were concentrated in the lipid and atherosclerosis pathways, indicating that the improvement of VC by SAL may occur through the regulation of abnormal expression of lipid and inflammatory factors. It was also found that SAL inhibits the abnormal expression of inflammatory factors, thereby activating the JAK2/STAT3 pathway to intervene in the progression of VC. The JAK2/STAT3 pathway is a key molecular mechanism by which SAL prevents further deterioration of VC. Functional enrichment analyses revealed the involvement of these targets in inflammatory responses and lipid metabolism, pivotal pathways in VC. In vivo studies in rats demonstrated SAL's efficacy in mitigating dyslipidemia and vascular inflammation, with improved serum lipid profiles and reduced vascular calcium deposition. The mechanistic exploration, grounded in Western blot analysis, demonstrated salidroside's ability to regulate the JAK2/STAT3 signaling pathway, highlighting its potential as a modulator in this critical molecular mechanism and offering a potential therapeutic target for VC. The strength of this research lies in its methodological rigor, integrating computational predictions with in vivo validations. This comprehensive approach establishes a robust framework for exploring the therapeutic mechanisms of natural compounds in combating VC.

This study establishes a rat model of vascular calcification induced by a high-fat diet (HFD) combined with vitamin D3 (VD3). The model was used to evaluate the therapeutic efficacy of salidroside in preventing and treating vascular calcification, providing insights into its potential mechanisms of action through network pharmacology and in vivo experiments.

Vascular calcification (VC) is a critical pathological condition associated with significant morbidity and mortality. This study employs a hybrid approach ...

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

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