<meta charset="utf8"></head><body>Jiawei Shengjiang San, 네트워크 약리학을 사용하여 당뇨병성 신증 해결

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			<td>Glycated serum protein (GSP)</td>
			<td>Nanjing Jiancheng Institute of Biological Engineering</td>
			<td>N/A</td>
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			<td>Transmission electron microscope</td>
			<td>Hitachi, Japan</td>
			<td>H-7650</td>
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			<td>Haematoxylin/eosin (HE) staining solution</td>
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			<td>Image-Pro Plus</td>
			<td>MEDIA CYBERNETICS, USA</td>
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			<td>Irbesartan tablets</td>
			<td>Hangzhou Sanofi Pharmaceuticals</td>
			<td>N/A</td>
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			<td>Isopropanol</td>
			<td>Biosharp, Tianjin, China</td>
			<td>N/A</td>
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			<td>&#160;JWSJS granules</td>
			<td>Guangdong Yifang Pharmaceutical</td>
			<td>N/A</td>
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			<td>Kodak Image Station 2000 MM imaging system</td>
			<td>Kodak, USA</td>
			<td>IS2000</td>
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			<td>Low-density cholesterol (LDL-C)</td>
			<td>Nanjing Jiancheng Institute of Biological Engineering</td>
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			<td>MAPK3/1Primary antibodies&#160;</td>
			<td>Affinity, USA</td>
			<td>AF0155</td>
			<td>Rat</td>
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			<td>Medical Centrifuge</td>
			<td>Hunan Xiangyi Laboratory Instrument Development, China&#160;</td>
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			<td>Mini trans-blot transfer system</td>
			<td>Bio-Rad, USA</td>
			<td>N/A</td>
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			<td>Mini-PROTEAN electrophoresis system</td>
			<td>Bio-Rad, USA</td>
			<td>N/A</td>
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			<td>NanoVue Plus Spectrophotometer</td>
			<td>Healthcare Bio-Sciences AB, Sweden</td>
			<td>111765</td>
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			<td>p-EGFR Primary antibodies&#160;</td>
			<td>Affinity, USA</td>
			<td>AF3044</td>
			<td>Rat</td>
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			<td>Periodic acid-Schiff (PAS) staining solution</td>
			<td>Servicebio, USA</td>
			<td>G1008</td>
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			<td>p-MAPK3/1 Primary antibodies&#160;</td>
			<td>Affinity, USA</td>
			<td>AF1015</td>
			<td>Rat</td>
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			<td>&#160;Santa Cruz, USA</td>
			<td>sc-2357</td>
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			<td>Sigma, USA</td>
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			<td>SureScript First-Strand cDNA Synthesis Kit</td>
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			<td>TriQuick Reagent</td>
			<td>Solarbio, Beijing, China</td>
			<td>R1100</td>
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			<td>Ultra-Clean Workbench</td>
			<td>Suzhou Purification Equipment, China</td>
			<td>SW-CJ-1F&#160;</td>
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antagonistic effect of jiawei shengjiang san on a rat model of diabetic nephropathy: related to egfr/mapk3/1 signaling pathway

Diabetes mellitus (DM) is a chronic disease that affects multiple systems and can cause various complications due to continuous hyperglycemia, such as diabetic nephropathy (DN), retinopathy, and neuropathy1. DN is a serious complication of DM, accounting for about 30%-50% of end-stage renal disease (ESRD)2. Its clinical manifestation is microalbuminuria, which can progress to ESRD characterized by increased glomerular volume, mesangial stromal hyperplasia, and thickened glomerular basement membrane3. The pathogenesis of DN is complex and has not been fully elucidated. Clinical methods such as lowering blood glucose, regulating blood pressure, and reducing proteinuria are mostly used to delay its progress, but the effect is general. 
Currently, no specific drug has been found to treat DN4. For centuries, however, Chinese herbal medicines have been widely used in treating DM and its complications5 and have improved patients' clinical symptoms and delayed disease progression. Due to the advantages of multi-component, multi-target, and multi-pathway effects, Chinese herbal medicines are expected to be an innovative drug source for the treatment of DN6.
"Shengjiang san" originated from the "Wanbing Huichun" by the Ming Dynasty medical doctor Gong Tingxian. The book "Neifu Xianfang" describes the use of Bombyx Batryticatus, Cicadae Periostracum, Curcumaelongae Rhizoma, and Radix Rhei et Rhizome. Based on this, after adding Hedysarum Multijugum Maxim, Epimrdii Herba, and Smilacis Glabrae Rhixoma, it exerts the function of shengjiang san of increasing lucidity, decreasing turbidity, releasing stagnant "heat," and harmonizing "qi" and the blood7,8. It also increases the effect of strengthening the spleen and tonifying the kidneys. Its efficacy is consistent with the pathogenesis of DN's "qi" to rise and fall out of order due to deficiency of "vital energy," excessive dryness and "heat," and stagnation of "heat" caused by a triple energizer7,8.
Previous clinical studies have shown Chinese herbal medicines have been used to treat DM and its complications, and jiawei shengjiang san (JWSJS) has been shown to regulate blood glucose and lipids, reduce proteinuria, and significantly improve the clinical efficacy of patients with early DN7. The ability of JWSJS to reduce urinary protein and blood glucose levels in DN rats has been confirmed by previous studies. This probably happens by inhibiting the TXNIP/NLRP3 and RIP1/RIP3/MLKL signaling pathways, reducing podocyte pyroptosis, and preventing necrotic apoptosis in renal tissues of DN rats, thus achieving renal protection9. JWSJS can upregulate nephrin and podocin protein expression and reduce podocyte injury in DN rats, thus suggesting that JWSJS has an inhibitory effect on podocyte injury. JWSJS has a certain anti-DN effect with good safety profiles, but there is little research on it, and this work mostly focuses on pyroptosis and necrotic apoptosis. The literature is not sufficiently deep or systematic10. Our previous findings have confirmed that JWSJS can reduce proteinuria and alleviate kidney damage in DN rats7. However, there are only a few studies on the mechanism of JWSJS for DN treatment, and these lack depth and systematization. Thus, this study aims to analyze the molecular substances and mechanisms of action of JWSJS for DN treatment using network pharmacology and provide a solid foundation for future research.
Network pharmacology is an emerging method to study the mechanism of drug action, including cheminformatics, network biology, bioinformatics, and pharmacology11,12. Network pharmacology research design is quite similar to the holistic concept of traditional Chinese medicine13,14, and it is an important method to study the mechanism of Chinese herbal medicines. Molecular docking can study interactions between molecules and predict their binding patterns and affinity. Molecular docking has emerged as a critical technique in the field of computer-aided drug research15. Therefore, this study constructed a JWSJS-DN-target interaction network through network pharmacology and molecular docking methods that offers a reliable and theoretical basis for further exploration of DN treatment with JWSJS.

<meta charset="utf8"></head><body>저자 스포트라이트: 당뇨병성 신장 질환에 대한 Jiawei Shengjiang San의 작용을 해독하기 위한 네트워크 약리학 및 분자 도킹

당뇨병성 신병증의 쥐 모델에 대한 Jiawei Shengjiang San의 길항 효과: EGFR/MAPK3/1 신호 경로와 관련

We are exploring how Jiawei Shengjiang San, a traditional herbal plant, can tackle diabetic kidney disease. To address our questions, we study the cell signaling and the cell death numerous in kidney cells. Traditional herbs help manage diabetic kidneys, but the mechanism of action are not clear.We explore network pharmacology and the molecular docking techniques to explore Jiawei Shengjiang San's mechanism of action. Our approach is like JPS for finding the gold staff in herbal remedies. We can easily predict the active components and targets of Jiawei Shengjiang San and construct a molecular drug component target network, which is faster and gives us a clearly picture than any trial and error method.

Read this Scientific Article Protocol about Antagonistic Effect of Jiawei Shengjiang San on a Rat Model of Diabetic Nephropathy: Related to EGFR/MAPK3/1 Signaling Pathway at JoVE.com

We aimed to delve into the mechanisms underpinning Jiawei Shengjiang San's (JWSJS) action in treating diabetic nephropathy and deploying network ...

antagonistic-effect-jiawei-shengjiang-san-on-rat-model-diabetic

Research

JoVE Journal

Medicine

Antagonistic Effect of Jiawei Shengjiang San on a Rat Model of Diabetic Nephropathy: Related to EGFR/MAPK3/1 Signaling Pathway

369 조회수.  Hebei University of Chinese Medicine. 우리는 전통 허브 식물인 Jiawei Shengjiang San이 당뇨병성 신장 질환을 어떻게 해결할 수 있는지 탐구하고 있습니다. 우리의 질문에 답하기 위해, 우리는 신장 세포에서 수많은 세포 신호와 세포 사멸을 연구합니다. 전통적인 허브는 당뇨병성 신장을 관리하는 데 도움이 되지만 작용 메커니즘은 명확하지 않습니다.우리는 네트워크 약리학과 분자 ?...

비디오: 저자 스포트라이트: 당뇨병성 신장 질환에 대한 Jiawei Shengjiang San의 작용을 해독하기 위한 네트워크 약리학 및 분자 도킹

We aimed to delve into the mechanisms underpinning Jiawei Shengjiang San's (JWSJS) action in treating diabetic nephropathy and deploying network pharmacology. Employing network pharmacology and molecular docking techniques, we predicted the active components and targets of JWSJS and constructed a meticulous "drug-component-target" network. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses were utilized to discern the therapeutic pathways and targets of JWSJS. Autodock Vina 1.2.0 was deployed for molecular docking verification, and a 100-ns molecular dynamics simulation was conducted to affirm the docking results, followed by in vivo animal verification. The findings revealed that JWSJS shared 227 intersecting targets with diabetic nephropathy, constructing a protein-protein interaction network topology. KEGG enrichment analysis denoted that JWSJS mitigates diabetic nephropathy by modulating lipids and atherosclerosis, the PI3K-Akt signaling pathway, apoptosis, and the HIF-1 signaling pathway, with mitogen-activated protein kinase 1 (MAPK1), MAPK3, epidermal growth factor receptor (EGFR), and serine/threonine-protein kinase 1 (AKT1) identified as collective targets of multiple pathways. Molecular docking asserted that the core components of JWSJS (quercetin, palmitoleic acid, and luteolin) could stabilize conformation with three pivotal targets (MAPK1, MAPK3, and EGFR) through hydrogen bonding. In vivo examinations indicated notable augmentation in body weight and reductions in glycated serum protein (GSP), low-density lipoprotein cholesterol (LDL-C), uridine triphosphate (UTP), and fasting blood glucose (FBG) levels due to JWSJS. Electron microscopy coupled with hematoxylin and eosin (HE) and Periodic acid-Schiff (PAS) staining highlighted the potential of each treatment group in alleviating kidney damage to diverse extents, exhibiting varied declines in p-EGFR, p-MAPK3/1, and BAX, and increments in BCL-2 expression in the kidney tissues of the treated rats. Conclusively, these insights suggest that the protective efficacy of JWSJS on diabetic nephropathy might be associated with suppressing the activation of the EGFR/MAPK3/1 signaling pathway and alleviating renal cell apoptosis.

Here, we present a protocol describing network pharmacology and molecular docking techniques to explore the mechanism of action of Jiawei Shengjiang San (JWSJS) in treating diabetic nephropathy.

연구

의학

Deciphering Jiawei Shengjiang San Action on Diabetic Nephropathy Using Network Pharmacology

To begin, open the traditional Chinese medicine systems pharmacology database and analysis platform. Enter the medicinal composition for Jiawei Shengjiang San, or JWSJS, and apply the screening criteria. Then using the database, retrieve the targets corresponding to the ingredients.Obtain the active ingredients of Bombyx batryticatus and Cicadidae Periostracum in the traditional Chinese medicine and chemical composition databases. After selecting compounds with chemical abstract service numbers, download 2D structure diagrams of active ingredients from PubChem. With SwissADME, screen the components with high gastrointestinal absorption as drug similarity whose two items or more were yes.Import these into the database for target protein prediction. In the UniProt, set the status as reviewed and species as human and standardize the targets. Search diabetic nephropathy, or DN, through various databases and obtain the targets.After combining and de-duplicating. Using our 4.2.0 software, screen the common targets of JWSJS and DN to draw Venn diagrams. Import the active ingredients and potential targets of JWSJS into Cytoscape 3.8.0 software to build a drug ingredient target network diagram that visualizes the connection between drugs, ingredients, targets, and diseases.To analyze the intersecting genes in STRING platform, set the species to Homo sapiens and the minimum required interaction score to more than 0.9 for constructing the network using a multiple protein analysis mode. Using Cytoscape 3.8.0, analyze the network topologically and calculate the betweenness centrality, closeness centrality, degree centrality, eigenvector centrality, local average connectivity-based method, and network centrality values for each node. Then obtain the IDs of the intersection targets with orghs.eg.db package. Using the cluster profiler org.hs.eg. db, enrichplot and ggplot2 packages, perform enrichment analyses.Screen for functional gene ontology enrichment analysis on the top 10 biological hits with a corrected p-value less than 0.05, and select top 30 pathways with the highest enrichment for Kyoto Encyclopedia of Genes and Genomes Analysis. Search in the PubChem database to obtain the SDF file of the 2D structure of JWSJS core components. Using ChemBio3D Ultra 14.0 software, generate and optimize its 3D structure.Save it in MOL2 format to use it as a ligand file. Find and download the PDB format of the 3D structure of the core targets from the research collaboratory for structural bioinformatics protein data bank database. Using PyMOL 2.4.0 software, remove water molecules and ligands from the protein structure and save it as PDB receptor file.Import the receptor protein file into AutoDock Tools 1.5.7 software for hydrogenation and convert both receptor protein and small molecule ligand into PDBQT format. Set active pocket for receptor protein with spacing coefficient set to one. Using AutoDock Vina 1.2.0 for molecular docking, calculate the binding energy.Visualize the docking results using PyMOL 2.3.0 and LigPlot 2.2.5 software. Conduct molecular dynamic or MD investigations on a trio of the most promising derived ligand complexes, and use SBC aqueous environment with 0.15 molar sodium chloride. Initiate an energy minimization phase for 100 picoseconds using Desmond default parameters.Ensure stable temperature and pressure of 26.85 degrees Celsius and 1.01325 bar across all production systems through the Nose-Hoover chain and Martyna-Tobias-Kline methodologies. Execute the MD simulation for 100 nanoseconds with a time step of two femtoseconds, recording the atomic coordinates at every 100 picoseconds. Open the simulation interactions diagram or SID module and load the simulation result data files into it.Once the analysis is complete, view and interpret the results within the SID module interface. Inject the model group animal with an intraperitoneal injection of streptozotocin. After 72 hours, test the blood sugar levels through tail vein blood sampling.Observe the histopathological changes in the rat kidney to confirm the successful DN model. Then administer appropriate drugs to the rat via oral gavage once daily. Collect blood samples from the anesthetized rat's abdominal aorta.Finally, after euthanizing the rat, collect the kidneys for biochemical and microscopic analyses. The periodic acid-Schiff staining showed that the model group had increased glomerular volumes, thickened basement membrane, and increased mesangial matrix compared to the normal group. These pathological manifestations were significantly alleviated in each drug administered group.Transmission electron microscopy indicated that the glomerular basement membrane in the model group was thickened versus the normal group. Microscopic manifestations of rats in each administration group were relieved to varying extents versus the model group. Compared to the model group, there was a significant reduction in the expression of p-EGFR, p-MAPK3/1, and BAX, and upregulation of BCL-2 expression in rat kidney tissues of each dosing group to varying degrees.