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.
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.
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.
All animals were maintained and used in accordance with the US National Research Council Guide for the Care and Use of Laboratory Animals, 8th Edition, and were reported as recommended in the ARRIVE guidelines16,17. The study was conducted in accordance with the China National Research Council Guide for the Care and Use of Laboratory Animals and was approved by the Animal Ethics Committee of Hebei University of Chinese Medicine (DWLL2019030).
1. JWSJS active ingredients and target collection
2. DN corresponding target collection
3. Molecular docking
4. Molecular dynamic simulation
5. Animal experiment validation
6. Statistical methods
Following the protocol, 90 active ingredients of JWSJS were finally obtained from the analysis after screening and deduplication according to the set standards of OB and DL. These included 20 kinds of Hedysarum Multijugum Maxim, 23 kinds of Epimrdii Herba, 15 kinds of Smilacis Glabrae Rhixoma, 16 kinds of Radix Rhei et Rhizome, four kinds of Curcumaelongae Rhizoma, 15 kinds of Cicadae Periostracum, and six kinds of Bombyx Batryticatus components. Because there are many active ingredients in JWSJS, only 20 are listed (see Table 1). After deduplication, 396 targets corresponding to the active ingredients of JWSJS were finally obtained.
After searching for disease-related genes, 3035 DN-related target genes were finally obtained after merging and deduplication. The corresponding targets of DN were compared and analyzed with the targets of drugs' active ingredients. The intersection was determined, which resulted in 227 intersection targets of JWSJS and DN. A Wayne diagram was then drawn (see Figure 1A). The "drug-ingredient-target" network diagram was built using the Cytoscape 3.8.0 software (see Figure 1B), and topological analysis was performed. Each node was ranked from high to low according to the degree value. The top five drug components were quercetin, palmitoleic acid, luteolin, kaempferol, and naringenin, and the top five targets were PTGS2, PTGS1, NCoA2, AR, and ESR1.
Next, a PPI network for JWSJS and DN intersection targets was developed (196 nodes and 1714 edges with a mean degree value of 17.4). Moreover, seven key targets that may be the key targets of JWSJS for DN treatment were obtained after three screenings: MAPK14, TP53, MAPK3, MYC, HIF1A, ESR1, and MAPK1 (see Figure 1C).
Then, GO enrichment analysis was performed to speculate about the biological characteristics of the target of JWSJS in treating DN. Among BP, CC, and MF, the top ten enriched entries are plotted in Figure 1D. The results show that JWSJS could regulate cell proliferation, differentiation, and apoptosis by binding with transcription factors and protein kinases in membrane rafts and membrane microdomains, thus resulting in a therapeutic effect on DN. KEGG pathway enrichment analysis was performed on the participating targets to explore the potential pathways of action of JWSJS in DN. The top thirty enriched pathways are plotted in Figure 1E. The results show that JWSJS treatment of DN might act on 181 signaling pathways, mainly involving lipid and atherosclerosis, the PI3K-Akt signaling pathway, chemical carcinogenesis, hepatitis, prostate cancer, apoptosis, the HIF-1 signaling pathway, and the TNF signaling pathway.
To further explore the targets of the first 30 KEGG-enriched pathways, we used Cytoscape 3.8.0 software to construct a KEGG signaling pathway relationship network map for JWSJS-treated DN. MAPK1, MAPK3, EGFR, and AKT1 were found to be targets of multiple pathways acting together (see Figure 1F), suggesting that these proteins may play a central role in mediating the effects of JWSJS on DN.
To further validate the potential targets of JWSJS in treating DN, we next screened three key targets, MAPK1, MAPK3, and EGFR, with high degree values in the PPI network and the KEGG signaling pathway network. These were molecularly docked with quercetin, palmitoleic acid, and luteolin-the top three cores' components of JWSJS for DN treatment. The conditions for screening PDB ID are as follows: (1) Be derived from humans; (2) Conformational resolution ≤ 2.5Å, i.e., as small as possible; (3) Conformational sequence to be as complete as possible, i.e., the structural complex must have small molecule ligand information; and (4) Crystalline pH value should be as close as possible to the normal physiological range of the human body35. Finally, the PDB IDs of MAPK1, MAPK3, and EGFR were 4QTA, 4QTB, and 7JXQ.
To verify the reliability of the AutoDock software to the docking system in this study36, we first redocked the three proteins of MAPK1, Mapk3, and EGFR. The ligand conformation in the original crystal structure of the three target proteins was superposed with the ligands after docking (Figure 2A). The root-mean-square deviation (RMSD) values between the ligand conformation post-docking and the original crystal structure were then calculated. The RMSD values were 1.129, 1.201, and 1.877 Å, respectively, all ≤ 2Å. This showed that the docking method was reliable and could be used for the next molecular docking verification. To compare and declare the inhibitory potential of core components, the original ligands of MAPK1, MAPK3, and EGFR were then used as controls. The docking analysis successfully predicted the binding energy between luteolin, palmitoleic acid, and quercetin and the three core targets. These were all negative and less than -637; see Table 2. Of note, the molecular docking between luteolin and MAPK1 has the highest cavity size and the lowest binding energy. Overall, molecular docking results showed that luteolin, palmitoleic acid, and quercetin had good binding activity with three core targets (Figure 2B,C).
The RMSD is utilized to quantify the average displacement changes of a selected group of atoms in relation to a reference frame. Meanwhile, the RMSF effectively characterizes localized alterations along the protein chain. The RMSD plot (Figure 2D) shows that the RMSD values of the compound luteolin-MAPK1 complex and quercetin-MAPK3 complex stabilized within 5 ns. The luteolin-EGFR complex showed a fluctuating RMSD ranging from 0-35 ns; this value stabilized at 35 ns until the end of the 100 ns MD simulation. All complexes stably interacted at the end of the 100 ns MD simulation. The RMSF value was assessed from the MD simulation trajectory: Atoms in the active site and the main chain that fluctuated minimally implied a minimal conformational change, suggesting that the lead compound mentioned is securely anchored within the cavity of the target protein's binding pocket. The RMSF results showcased limited fluctuations in the complex structures.
Finally, we verify the above results in vivo. We first observed that the weight of rats in the model group decreased significantly, and GSP, LDL-C, UTP, and FBG increased significantly (P < 0.05). The body weight of rats increased significantly in the irbesartan group and each JWSJS group versus the model group. GSP, LDL-C, UTP, and FBG decreased to different degrees (P < 0.05; see Table 3).
Then, HE staining showed that the glomeruli in the normal group had a normal size. They were regular and clear in structure and had a smooth basement membrane, neatly arranged renal tubules, and no inflammatory cell infiltration. In the model group, the glomeruli were hypertrophic, the basement membrane was thickened, and the mesangial matrix was increased. The interstitium was infiltrated with inflammatory cells. The pathological manifestations of each administration group were alleviated to different extents compared to the model group (see Figure 3A).
PAS staining showed that the model group had increased glomerular volumes, thickened basement membrane, and increased mesangial matrix versus the normal group. These pathological manifestations were significantly alleviated by each administration group (see Figure 3B). Using transmission electron microscopy, we found that the glomerular basement membrane in the model group was thickened versus the normal group. The mesangial matrix grew, and the podocyte foot processes were extensively fused. Microscopic manifestations of rats in each administration group were relieved to varying extents versus the model group (see Figure 3C).
Finally, the expressions of total EGFR, p-EGFR, MAPK3/1, and p-MAPK3/1 were detected to see whether they changed in JWSJS-treated DN rats. There was no significant change in total EGFR and MAPK3/1 expression in the model groups versus the normal group. The expression of p-EGFR, p-MAPK3/1, and BAX increased significantly, and the BCL-2 expression decreased significantly. Versus the model group, the expression of p-EGFR, p-MAPK3/1, and BAX were significantly reduced, and BCL-2 expression was significantly upregulated to varying degrees in rat kidney tissues in each dosing group (Figure 3D,E). This result was confirmed by immunohistochemistry (Figure 3F,G). This study showed that JWSJS could protect the kidneys in DN rats by regulating the expression of p-EGFR, p-MAPK3/1, BAX, and BCL-2 proteins and reducing cell apoptosis. In addition, qPCR was utilized to assess the expression levels of MAPK3, MAPK1, and EGFR mRNA across various groups. The results revealed that the relative expression in the DN group was significantly higher compared to the control group, as well as the Irbesartan, JWSJS-L, JWSJS-M, and JWSJS-H groups. This finding further corroborates the initial results (Figure 3H).
Figure 1: Network Pharmacology clarified the JWSJS potential targets and signaling pathways in diabetic nephropathy. (A) Venn diagram of the intersecting targets of JWSJS and diabetic nephropathy. Green represents the number of therapeutic targets of JWSJS components, and purple represents the number of relevant targets of DN. The intersecting part represents the number of intersecting targets (i.e., the number of targets of JWSJS for DN). (B) "Drug-ingredient-target" network diagram of JWSJS in the treatment of DN. The light blue represents the relevant targets of JWSJS for DN treatment. Fuchsia represents the active ingredient of Bombyx Batryticatus. Green represents the active ingredients of Hedysarum Multijugum Maxim. Dark blue represents the active ingredients of Radix Rhei et Rhizome. Red represents the active ingredients of Cicadae Periostracum. Purple represents the active ingredients of Epimrdii Herba. Yellow represents the active ingredients of Smilacis Glabrae Rhixoma. Sky blue represents the active ingredients of Curcumaelongae Rhizoma. (C) PPI network of JWSJS and DN intersection targets. (D) The 10 most significant genes identified via ontology analysis of therapy target genes implicated in JWSJS treatment of DN. (E) The 30 most significant genes identified via pathway analysis of therapy target genes implicated in JWSJS treatment of DN. (F) The relationship network of the KEGG signaling pathway in the treatment of DN by JWSJS. Yellow icons represent signaling pathways. Blue icons represent action targets. A larger graph implies that more pathways are connected to it. Please click here to view a larger version of this figure.
Figure 2: Molecular docking and molecular dynamics trajectories visualization of JWSJS in the treatment of diabetic nephropathy. (A) The conformations of the ligand in the original crystal structure of the three target protein complexes 7JXQ (a), 4QTA (b), and 4QTB (c) are compared with the conformations of the ligand after docking. Green, orange, and pink are EGFR, MAPK1, and MAPK3 target proteins, respectively. Gold is the original ligand molecule, and blue is the ligand molecule after docking. (B) 3D molecular docking visualization of JWSJS in the treatment of diabetic nephropathy. Green, orange, and pink are the target proteins 7JXQ, 4QTA, and 4QTB, respectively. 1a-3a represents the interaction diagram of 7JXQ, 4QTA, and 4QTB with the original inhibitor (blue). 1b-3b represents the interaction diagram of 7JXQ, 4QTA, 4QTB, and luteolin (yellow). 1c-3c represents the interaction diagram of 7JXQ, 4QTA, 4QTB, and palmitoleic acid (fuchsia). 1d-3d represents the interaction diagram of 7JXQ, 4QTA, 4QTB, and quercetin (purple). (C) 2D molecular docking visualization of JWSJS in the treatment of diabetic nephropathy. 1a-3a represents the interaction diagram of 7JXQ, 4QTA, and 4QTB with the original inhibitor. 1b-3b represents the interaction diagram of 7JXQ, 4QTA, 4QTB, and luteolin. 1c-3c represents the interaction diagram of 7JXQ, 4QTA, 4QTB, and palmitoleic acid. 1d-3d represents the interaction diagram of 7JXQ, 4QTA, 4QTB, and quercetin. (D) RMSD and RMSF analysis of the molecular dynamics trajectories of (a) luteolin and target protein 7JXQ, (b) luteolin and target protein 4QTA, and (c) quercetin and target protein 4QTB complexes. Please click here to view a larger version of this figure.
Figure 3: JWSJS alleviates kidney injury and suppresses the EGFR/MAPK3/1 signalling pathway activation in vivo experiments. (A) HE staining and (B) periodic acid-Schiff (PAS) staining of rat kidney tissues in different groups (Magnification, 200x; n = 10). (C) Transmission electron microscopy of kidney tissue in different groups of rats (Magnification, 5000x; n = 3). (D) EGFR, p-EGFR, MAPK3/1, p-MAPK3/1, BAX, and BCL-2 in rat kidney tissue of diabetic through western blotting (n = 3). (E) Statistical analysis of p-EGFR, p-MAPK3/1, BAX, and BCL-2 expression. (F) Immunohistochemical analysis of p-EGFR AND p-MAPK3/1 expression in the six groups (n = 6). (G) Statistical analysis of p-EGFR AND p-MAPK3/1 expression. (H) qPCR analysis of MAPK3, MAPK1, and EGFR mRNA expression in six groups. *P < 0.05 vs. control. **P < 0.01 vs. control. ▲P < 0.05 vs. DN. ▲▲P < 0.01 vs. DN. Please click here to view a larger version of this figure.
Table 1: Information of some active ingredients of JWSJS. It gives details about each component and their characteristics or properties. Please click here to download this Table.
Table 2: Table of docking results of core targets and main active ingredients of JWSJS. The table presents the results from docking studies of core targets and main active components of JWSJS. This table shows how these constituents interact with their respective targets, which can provide insight into the mechanism of action of JWSJS. Please click here to download this Table.
Table 3: Effect of JWSJS on body weight and biochemical indexes in rats with diabetic nephropathy (expressed as mean ± standard deviation, n = 10). Compared with the normal group: * P < 0.05. Compared with the model group: # P < 0.05. Please click here to download this Table.
Our study employed a combination of network pharmacology, molecular docking, and in vivo animal models. A critical step was the establishment of the "drug-component-target" network, which was crucial for identifying the potential mechanisms of JWSJS in treating DN, focusing particularly on its interaction with the EGFR/MAPK3/1 signaling pathway.
During this study, we made several modifications, particularly in the molecular docking process, to enhance the accuracy of our predictions. Troubleshooting was mainly focused on optimizing the conditions for the in vivo animal model to ensure its relevance to human DN. By identifying the key active components and targets of JWSJS, this study provides a theoretical basis for further research and development of JWSJS as a potential drug candidate for DN treatment.
The study investigated the potential mechanism of action of JWSJS in reducing renal apoptosis in DN. Molecular docking results showed that quercetin, palmitoleic acid, and luteolin have regular binding activities to key targets, including MAPK1, MAPK3, and EGFR. We selected these targets for further study due to their common action in multiple pathways. In DN rats, JWSJS treatment resulted in a significant decrease in FBG and GSP levels, improvement in UTP and LDL-C, weight gain, and improved renal tissue microscopic manifestations, indicating the potential effectiveness of JWSJS in treating DN.
Apoptosis is a critical mode of cell death, and dysregulation can increase the risk of various diseases38. Previous studies demonstrated that DN rats had increased expression of apoptosis-related proteins BAX and caspase-3, and a significant increase in the number of apoptotic cells in renal tissue39,40. The results showed that JWSJS treatment reversed these changes, suggesting that it can attenuate podocyte apoptosis under diabetic conditions.
Further investigation into the potential mechanism of action of JWSJS showed that it has a multi-target and multi-pathway effect on DN. We focused on studying MAPK1, MAPK3, and EGFR, as they had the largest number of enrichment pathways. The ERK pathway, which is involved in various physiological and pathological processes such as cell growth, proliferation, differentiation, apoptosis, and abnormal steroid secretion, is an essential pathway in the MAPK signal transduction pathway13,41,42,43. It inferred that JWSJS rescued podocyte apoptosis by inhibiting the EGFR/MAPK3/1 signaling pathway.
We also found that JWSJS could regulate the expression of p-EGFR and p-MAPK3/1, resulting in markedly upregulated levels of Bcl-2 but a noticeable down-regulation of Bax. This, in turn, led to reduced renal cell apoptosis. Similar results have been found in previous studies. For instance, Chen reported that EGFR deletion in podocytes attenuated diabetic nephropathy44. Some researchers found that the EGFR/ERK pathway promoted the proliferation and differentiation of porcine intestinal epithelial cells9. Inhibition of EGFR can down-regulate the expression of TGF-β and BAX, thus improving renal fibrosis and apoptosis45. These findings suggest that JWSJS is a traditional Chinese medicine prescription with a significant role in the adjuvant therapy of DN by inhibiting the EGFR/MAPK3/1 signal pathway. However, further studies are needed to fully understand the potential mechanism of action of JWSJS in treating DN.
There were several limitations to this study. First, the reliance on animal models may not fully replicate the human response to JWSJS. Additionally, network pharmacology predictions, while insightful, require further validation through experimental data. Further clinical studies on human subjects are necessary to validate the findings. Second, although network pharmacology is a useful tool for analyzing drug-disease relationships, the results should be interpreted with caution as they are based on predictions rather than experimental data. Thirdly, the mechanism of action of JWSJS for treating DN remains unclear, and the study only focused on the potential targets of MAPK1, MAPK3, and EGFR.
The methods used in this study can be applied to other traditional Chinese medicine formulations, potentially leading to the discovery of new therapeutic options for DN and other complex diseases. Further, this approach lays the groundwork for integrating traditional and modern medicine in future pharmacological research. Further studies are needed to fully elucidate the molecular mechanisms underlying the therapeutic effects of JWSJS. Finally, the study did not investigate the potential side effects or toxicity of JWSJS, which are important considerations in the development of any new drug. Further research is needed to evaluate the safety of JWSJS and ensure its clinical application.
In summary, this study primarily investigates the therapeutic potential of Jiawei Shengjiang San for diabetic nephropathy utilizing network pharmacology, molecular docking, and in vivo animal validation. It emphasizes the procedural aspects, focusing on the methodology employed to understand the interactions between JWSJS and the EGFR/MAPK3/1 signaling pathway and how these interactions potentially reduce renal cell apoptosis in DN.
The authors have nothing to disclose.
This study was supported by the general project of the Natural Science Foundation of Hebei Province, China (No. H2019423037).
2×SYBR Green qPCR Master Mix | Servicebio, Wuhan, China | G3320-05 | |
24-h urine protein quantification (UTP) | Nanjing Jiancheng Institute of Biological Engineering | N/A | |
3,3'-Diaminobenzidine | Shanghai Huzheng Biotech, China | 91-95-2 | |
Automatic biochemical analysis instrument | Hitachi, Japan | 7170A | |
Anhydrous Ethanol | Biosharp, Tianjin, China | N/A | |
BAX Primary antibodies | Affinity, USA | AF0120 | Rat |
BCL-2 Primary antibodies | Affinity, USA | AF6139 | Rat |
BX53 microscope | Olympus, Japan | BX53 | |
Chloroform Substitute | ECOTOP, Guangzhou, China | ES-8522 | |
Desmond software | New York, NY, USA | Release 2019-1 | |
Digital Constant Temperature Water Bath | Changzhou Jintan Liangyou Instrument, China | DK-8D | |
EGFR Primary antibodies | Affinity, USA | AF6043 | Rat |
Embed-812 RESIN | Shell Chemical, USA | 14900 | |
Fasting blood glucose (FBG) | Nanjing Jiancheng Institute of Biological Engineering | N/A | |
FC-type full-wavelength enzyme label analyser | Multiskan; Thermo, USA | N/A | |
GAPDH Primary antibodies | Affinity, USA | AF7021 | Rat |
Glycated serum protein (GSP) | Nanjing Jiancheng Institute of Biological Engineering | N/A | |
Transmission electron microscope | Hitachi, Japan | H-7650 | |
Haematoxylin/eosin (HE) staining solution | Servicebio, USA | G1003 | |
Image-Pro Plus | MEDIA CYBERNETICS, USA | N/A | |
Real-Time PCR Amplification Instrument | Applied Biosystems, USA | iQ5 | |
Irbesartan tablets | Hangzhou Sanofi Pharmaceuticals | N/A | |
Isopropanol | Biosharp, Tianjin, China | N/A | |
JWSJS granules | Guangdong Yifang Pharmaceutical | N/A | |
Kodak Image Station 2000 MM imaging system | Kodak, USA | IS2000 | |
Low-density cholesterol (LDL-C) | Nanjing Jiancheng Institute of Biological Engineering | N/A | |
MAPK3/1Primary antibodies | Affinity, USA | AF0155 | Rat |
Medical Centrifuge | Hunan Xiangyi Laboratory Instrument Development, China | TGL-16K | |
Mini trans-blot transfer system | Bio-Rad, USA | N/A | |
Mini-PROTEAN electrophoresis system | Bio-Rad, USA | N/A | |
NanoVue Plus Spectrophotometer | Healthcare Bio-Sciences AB, Sweden | 111765 | |
p-EGFR Primary antibodies | Affinity, USA | AF3044 | Rat |
Periodic acid-Schiff (PAS) staining solution | Servicebio, USA | G1008 | |
p-MAPK3/1 Primary antibodies | Affinity, USA | AF1015 | Rat |
Secondary antibodies | Santa Cruz, USA | sc-2357 | Rabbit |
Streptozotocin | Sigma, USA | S0130 | |
SureScript First-Strand cDNA Synthesis Kit | GeneCopeia, USA | QP056T | |
TriQuick Reagent | Solarbio, Beijing, China | R1100 | |
Ultra-Clean Workbench | Suzhou Purification Equipment, China | SW-CJ-1F |
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