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ORIGINAL ARTICLE
Year : 2023 | Volume
: 9
| Issue : 2 | Page : 178-190
Study on the mechanism and molecular docking verification of buyang huanwu decoction in treating diabetic foot
Da-Yuan Zhong1, Lan Li2, Huan-Jie Li3, Ruo-Meng Ma2, Yi-Hui Deng2
1 Neurosurgery of Guangdong Provincial Hospital of Integrated Traditional Chinese and Western Medicine, Foshan, China
2 College of Integrative Medicine, Hunan University of Chinese Medicine, Changsha, China
3 Preventive Treatment Center of Foshan Hospital of Traditional Chinese Medicine, Foshan, China
Correspondence Address:
Dr. Yi-Hui Deng
Hunan University of Traditional Chinese Medicine, Changsha
China
Source of Support: None, Conflict of Interest: None
DOI: 10.4103/2311-8571.370108
Objective: The objective of this study was to investigate the molecular mechanism of Buyang Huanwu decoction (BYHWD) in the treatment of diabetic foot (DF). Methods: The TCMSP, BATMAN, PubChem, PharmMapper, UniProt, GeneCards, Webgestalt, and Kobas databases were used to obtain the structures, targets, main biological functions, and pathways of the active ingredients of BYHWD, and the results were visualized using Cytoscape3.6.1, Ledock, and PyMol software. Results: A total of 82 active components of BYHWD and 193 targets related to BYHWD were identified, and 5295 genes related to DF were identified using the GeneCards database, including 65 key targets of BYHWD in the treatment of DF. GO and KEGG enrichment analyses of the 65 targets for BYHWD treatment of DF showed that 47 GO items were involved in the treatment. It was mainly involved in biological processes, such as biological regulation, metabolism, and stress response. It is primarily involved in protein binding, ion binding, nucleotide binding, and other molecular functions. It is mainly involved in membrane encapsulation, membrane lumen closure, and other biological components and involved in the VEGF, TNF, RAS, RAP1, PI3K-AKT, MAPK, and IL-17 signaling pathways. Most targets were enriched in the PI3K-AKT and MAPK signaling pathways. Molecular docking results showed that the 59 key active components of BYHWD had strong binding activity with 64 key DF targets. Conclusion: The therapeutic effect of BYHWD on DF is based on the pharmacological effects of multiple targets and pathways.
Keywords: Buyang Huanwu decoction, diabetic foot, molecular docking, network pharmacology
Diabetes is a rapidly growing chronic systemic disease. The number of diabetes cases worldwide increased from 285 million adults in 2009 to 382 million in 2013 and is expected to reach 471 million by 2030.[1],[2] Diabetes is classified into type 1 and type 2. Although the two types of diabetes have different pathogeneses, they all lead to hyperglycemia. Once hyperglycemia occurs, both types of diabetes may develop chronic complications. Diabetic foot (DF) is one of the most common complications in elderly patients with diabetes, with an annual incidence rate of approximately 2%. More than 40% of patients will have a DF infection. With high mortality and morbidity, DF infection is often manifested as foot ulcer, infection, and necrosis.[3],[4],[5] Zeng and Tang thought that wound tissue ischemia and hypoxia, infection, and local growth factor quantity reduction are the main causes of difficult wound healing.[6] Therefore, it is important to improve local blood flow, control infection, and promote tissue growth and healing. Although routine disinfection, debridement, and the use of antibiotics can control the progression of patients to a certain extent, the existence of a local blood supply is the key factor that affects the prognosis of DF.[7] Buyang Huanwu decoction (BYHWD) originates from Wang Qingren's Medical Forest correction, which tonifies qi, activates blood circulation, and dredges collaterals and has been recommended for DF treatment.[8] A large number of clinical trials have confirmed the efficacy of BYHWD in improving the condition of DF patients.[9],[10] In addition, Niu et al. found that BYHWD effectively improved the patency rate of blood vessels.[11] Pan et al. found that BYHWD inhibits thrombosis and improves hemodynamics.[12] This evidence shows that BYHWD significantly improved the supply of local blood flow. However, the number of studies on the treatment of DF with BYHWD is small, and the specific molecular mechanism of its efficacy is not understood in depth. In this study, the molecular targets and pathways of BYHWD in the treatment of DF were predicted and preliminarily verified by molecular docking based on network pharmacology research.
Data and MethodsDatabases and software
The TCMSP (https://tcmsp-e.com/),[13] BATMAN (http://bionet.ncpsb.org.cn/batman-tcm/index.php/Home/Index/index),[14] PubChem (https://pubchem.ncbi.nlm.nih.gov/),[15] PharmMapper (http://lilab-ecust.cn/pharmmapper/index.html),[16] UniProt (https://www.uniprot.org/),[17] String (https://string-db.org/),[18] GeneCards (https://www.genecards.org/),[19] Webgestalt (http://www.webgestalt.org/),[20] and Kobas databases (http://kobas.cbi.pku.edu.cn/) were used.[21] The Protein–ligand interaction profiler database (https://plip.biotec.tu-dresden.de/plip-web/plip/index),[22] and Cytoscape 3.6.1,[23] OpenBabel-2.4.1,[24] Ledock,[25] Pymol,[26] and R software (Jasmine Mountain, New Jersey, USA)[27] were used in this study.
Collection and treatment of Buyang Huanwu decoction targets
The active components of BYHWD were identified using the TCMSP database, and the components without structures were eliminated. The 3D structures of the BYHWD active ingredients were obtained from the PubChem database and imported into the PharmMapper database to predict its molecular targets. The predicted targets were annotated using the UniProtKB search function in the UniProt database, and the proteinprotein interaction (PPI) network was constructed using the STRING database. A total of 5295 DF targets were obtained using the GeneCards database, and the targets contained in the PPI network were intersected with DF targets to obtain a common subset. Thus, the targets for BYHWD treatment of DF were obtained.
Construction of the target network
The relationship between traditional Chinese medicine and active ingredients, the relationship between active ingredients and targets, the relationship between targets, and the relationship between DF targets and DF were sorted and imported into Cytoscape 3.6.1 to construct the prescription-drug-active ingredients-target-disease network diagram. Through the network analysis function of Cytoscape 3.6.1, the key active components and key targets of BYHWD and DF treatment were screened to a degree >30.
GO and KEGG enrichment analysis
GO enrichment analysis was performed on the key targets of BYHWD for DF treatment using the Webgestalt database, and KEGG enrichment analysis was performed using the Kobas database. Statistical significance was set at P < 0.05, and visualization was performed using the R software.
Molecular docking
The sdf files for the key active ingredients were downloaded from the PubChem database and the mol files were converted using OpenBabel-2.4.1 software. The PDB file of the key DF target was downloaded from the PDB database and subjected to deligand hydrogenation using Ledock software. Molecular docking energies between key active components and key DF targets were calculated using Ledock and visualized using PyMOL and the Protein-Ligand Interaction Profiler database.
ResultsResearch roadmap
Using literature, the efficacy of BYHWD on DF was clarified. The active ingredients of BYHWD were identified and supplemented using the TCMSP and BATMAN databases. The SDF structure of the active components was obtained from the PubChem database. The targets of the active ingredients were predicted using the PharmMapper database. The targets were annotated using the UniProt database. PPI networks were constructed using the STRING database. Next, using the GeneCards database, potential disease targets of DF were identified. The DF targets interacted with the targets of the active components of BYHWD and DF targets of BYHWD for DF treatment were identified. These DF targets were imported into the Webgestalt database for GO enrichment analysis and into the Kobas database for KEGG enrichment analysis. Finally, molecular docking was performed using Ledock software, and the verification results were visualized using PyMOL software. The specific route is illustrated in [Figure 1].
The collection and screening of Buyang Huanwu decoction active ingredients
The TCMSP database was used with OB >30 and DL >0.2 as screening conditions, and 74 effective components of BYHWD were obtained. Eight active components of earthworms were supplemented using the BATMAN database, and 82 active components were obtained, as shown in [Table 1].
Construction of the protein interaction network
Using Normfit >0.7, 241 targets of BYHWD were screened. The UniProt database annotation was imported into the STRING database to build the PPI network. A combined score >0.7 was used as the condition for optimizing the PPI network, and the correlation network between 193 targets was obtained after excluding unrelated targets, as shown in [Figure 2].
Figure 2: PPI network of BYHWD. PPI: Proteinprotein interaction, BYHWD: Buyang Huanwu decoction3.4 Construction of the Medicine-active ingredient-target-diabetic foot target-diabetic foot network
The 193 targets contained in the PPI network intersected with 5295 DF targets, and 145 targets for DF treatment were obtained. Based on the above results, a Chinese medicine-active ingredients-targets-DF targets-DF' network was constructed, as shown in [Figure 3]. The network contained 347 nodes and 5681 edges, with an average degree of 16.37. With degree >30 as the screening condition, 61 key active components and 65 key DF targets were obtained, as shown in [Table 2].
Figure 3: Medicine-active ingredients-target-DF target-DF network. DF: Diabetic footGO enrichment analysis
GO enrichment analysis was performed on the 65 DF key targets, and 47 GO items were obtained with P < 0.05. It is primarily involved in protein binding, ion binding, nucleotide binding, hydrolase activity, transferase activity, and other molecular functions and mainly involved in membrane encapsulation, membrane-enclosed lumens, nuclei, protein complexes, and other biological components. They are mainly involved in biological regulation, metabolic processes, responses to stimuli, cell communication, and multicellular biological processes, as shown in [Figure 4].
KEGG enrichment analysis
For the KEGG enrichment analysis, 65 DF key targets were with P < 0.05, and 203 KEGG entries were obtained. After excluding irrelevant pathways, the most significant 20 pathways were identified. These include the VEGF, TNF, RAS, RAP1, PI3K-AKT, MAPK, and IL-17 signaling pathways. Among these, the PI3K-AKT and MAPK pathways were the most enriched, as shown in [Figure 5].
Molecular docking verification
Using Ledock software, 61 key active ingredients were identified by molecular docking with 64 key targets (Pdb file of APOA2 was not found). Some studies have suggested that the docking energy is <−5.00 kcal/mol, suggesting that the molecular docking structure is stable.[28] A molecular docking heat map was constructed according to this standard. Less than −5.00 kcal/mol is labeled red and >−5.00 kcal/mol is labeled blue. The results showed that the molecular docking energy of 59 compounds of BYHWD and most DF targets was <−5.00 kcal/mol, except for beta-carotene and flavoxanthin. This suggested that the compound and DF targets were stable, as shown in [Figure 6]. Part of the molecular docking structure diagram is shown in [Figure 7], [Figure 8], [Figure 9], [Figure 10].
Figure 7: Molecular docking structure of HSPA8 with baicalin. Note: The docking energy of HSPA8 and baicalin was −9.78 kcal/mol. This was lower than −5 kcal/mol, indicating that baicalin has a strong affinity for HSPA8. Baicalin occupies the active cavity formed by HSPA8 protein residues (TYR, LYS, ASP, THR, GLU, GLY, and ARG), and forms hydrophobic interactions with TYR15A, LYS271A, and ASP366A. Hydrogen bonds were formed between ASP10A, THR13A, THR14A, THR37A, LYS71A, GLU175A, GLY201A, GLY202A, GLY203A, GLU204A, GLY339A, and ASP366A. A π-cation interaction was formed with TYR15A and π-π stacking was performed using ARG272A
Figure 8: Molecular docking structure of HSPA8 with 6-Hydroxynaringenin. Note: The molecular docking energy of HSPA8 with 6-Hydroxynaringenin was −7.29 kcal/mol. This was lower than −5 kcal/mol, indicating that 6-Hydroxynaringenin had a strong affinity for HSPA8. The 6-Hydroxynaringenin occupied the active cavity formed by HSPA8 protein residues (TYR, ASP, THR, GLU, GLY, and PRO) and formed hydrophobic interactions with TYR15A and ASP366A. Hydrogen bonds were formed with ASP10A, THR13A, THR14A, TYR15A, GLU268A, GLY338A, GLY339A, and PRO365A
Figure 9: Molecular docking structure of MAPK14 with baicalin. Note: The molecular docking energy of MAPK14 with baicalin was −8.38 kcal/mol. This was lower than −5 kcal/mol, and baicalin showed a strong affinity for MAPK14. Baicalin occupies the active cavity formed by MAPK14 protein residues (VAL, ALA, LEU, MET, LEU, PHE, LYS, GLU, ILE, and ASP). Hydrophobic interactions were observed with VAL38A, ALA51A, LEU75A, LEU108A, MET109A, LEU167A, and PHE169A. Hydrogen bonds were formed between LYS53A, GLU71A, ILE166A, and APS168A
Figure 10: Molecular docking structure of HSD11B1 with 6-Hydroxynaringenin. Note: The molecular docking energy of HSD11B1 with 6-Hydroxynaringenin was −7.67 kcal/mol. This was lower than −5 kcal/mol, indicating that 6-Hydroxynaringenin had a strong affinity for HSD11B1.The 6-Hydroxynaringenin occupies the active cavity formed by HSD11B1 protein residues (LYS, ALA, GLY, SER, ARG, THR, MET, GLU, and ASN). Hydrophobic bonds are formed with LYS44A and ALA65A. Hydrogen bonds were formed between GLY41A, SER43A, LYS44A, ARG66A, THR92A, MET93A, GLU94A, THR122A, ASN123A, and LYS138A. ARG66A forms π-cation interactions Discussion DF is caused by different degrees of vascular lesions and distal neuropathy of the lower limbs in patients.[29] Vascular lesions of the lower limbs are a serious complication of diabetes, with an incidence of 22%–46% in diabetic patients.[30] Diabetic lower-extremity vascular disease is characterized by lower-extremity blood vessels, especially the small and medium arteries of chronic and progressive stenosis and occlusion. It is often accompanied by limb sensory nerve damage.[31] In patients with distal limb blood supply differences, infection often leads to DF.[32] If active and effective treatment is not performed at this time, toe gangrene may occur in patients with DF, which leads to amputation, and severe infections caused by DF may be life-threatening life. Therefore, to control infection, it is important to improve the local ischemic state as soon as possible. Owing to the complex pathological process of DF, traditional medical treatment, vascular bypass, interventional surgery, and amputation have certain limitations.[33] Early drug therapy can improve limb ischemia but has little effect on middle- and late-stage patients. Angioplasty and intervention can quickly restore the limb blood supply to patients. Diabetic lower-extremity vascular disease occurs mostly in the elderly who with more basic diseases and complex conditions and the risk of surgery is high.[34] Moreover, the lumen may still exhibit restenosis after interventional therapy.[35],[36] In addition, the distal small vessels in most patients are narrow or occluded, and the efficacy of angioplasty and interventional therapy is poor.[37] Therefore, it is necessary to identify more effective drugs for the treatment of DF. After thousands of years of development, Chinese medicine has left many classic drugs, such as BYHWD, which have been used for the treatment of DF. The BYHWD is composed of Huangqi, Danggui, Chishao, Chuanxiong, Taoren, Honghua, and Dilong. It tonifies qi, activates blood, and dredges collaterals. BYHWD has long been used for the treatment of diseases with qi deficiency and blood stasis as the basic pathogenesis. These include ischemic stroke,[38] angina pectoris,[39] diabetic nephropathy,[40] and DF.[9],[10] The available literature shows that Zhu Feng was the first person to use BYHWD to treat DF.[41]
Network pharmacology is a new model for drug molecular mechanism research using the big data analysis.[42],[43] It can be used to predict the target and mechanism and then guide the basic research direction of BYHWD in the treatment of DF. Through network pharmacology, we found that BYHWD treated DF through 61 key active components and 65 key DF targets. This shows that the therapeutic effect of BYHWD on DF is based on the overall pharmacological effects of multiple components, targets, and pathways. The therapeutic effect of BYHWD on DF is based on the pharmacological effects of multiple targets and pathways.
GO enrichment analysis revealed 47 GO items related to DF treatment. It is mainly involved in biological regulation, metabolism, stress responses, cell communication, and multicellular biological processes. It is primarily involved in protein binding, ion binding, nucleotide binding, hydrolase activity, transferase activity, and other molecular functions. It is mainly involved in membrane encapsulation, membrane-enclosed lumens, nuclei, protein complexes, and other biological components. Most DF patients have abnormal glucose metabolism,[44] which affects DF prognosis to some extent;[45] thus, the body produces a stress response. In addition, changes in material metabolism and internal and external environments are coordinated and unified. An organism must regulate metabolic processes. These are mainly regulated by metabolism at the cellular, hormonal, and overall level.[46] DF symptoms can be improved by adjusting for these metabolic abnormalities. Membrane, encapsulation, membrane-enclosed lumen, nucleus, and protein-containing complexes are basic units of cell function, which plays an important role in protein binding, ion binding, hydrolase activity, transferase activity, and other biological processes. This suggests that the cells and cell components in the body are not isolated. A complete network of criss-cross, coordinated, and unified cells is formed in the body by the full play of cell components and cell functions and the complete production of biological processes. This is consistent with the holistic view of TCM.
KEGG enrichment analysis showed that BYHWD treatment of DF mainly involved the VEGF, TNF, RAS, RAP1, PI3K-AKT, MAPK, and IL-17 signaling pathways. The PI3K-AKT and MAPK signaling pathways were the most enriched targets. These two pathways play major roles downstream of the insulin receptor substrate.[47] The PI3K-AKT signaling pathway can inhibit apoptosis, promote cell growth and proliferation, and mediate cell movement, chemotaxis, adhesion, and other biological processes.[48] DF is mainly involved in the regulation of blood glucose levels and promotes wound healing. The PI3K-AKT signaling pathway plays an important role in hepatic glucose synthesis, uptake, and gluconeogenesis. The PI3K-AKT pathway also promotes fat formation by stimulating insulin expression and regulating fat function.[49],[50] Once the PI3K-AKT pathway is interrupted, it may lead to impaired glucose and lipid metabolism, resulting in insulin resistance in the liver.[51],[52] Platelet-derived growth factor (PDGF) is one of the mature factors that disturb the diabetic state during the differentiation and maturation of wound angiogenesis. PDGF promotes capillary maturation by cultivating and recruiting peripheral cells and delaying vascular regression, thereby promoting vascular cell proliferation.[53] This effect was achieved by activating the PI3K-AKT signaling pathway.[54] Activation of the MAPK signaling pathway leads to insulin resistance and reduced glucose transport. This may be related to the inhibition of insulin receptor substrate phosphorylation and downregulation of GLUT4.[55],[56] This evidence suggests that the PI3K-AKT and MAPK signaling pathways play a role in the prevention and treatment of DF.
Molecular docking results showed that the 59 active components had stable docking structures with 64 key DF targets. It has been suggested that 59 key active ingredients can bind to 64 key DF targets and play a functional role. This verified the accuracy of the results of this study.
ConclusionThe results of this study can provide an important basis for follow-up studies of BYHWD in the treatment of DF, which will provide a valuable reference for the scientific demonstration of the basic theory of traditional Chinese medicine in the future. However, owing to the limitations of network pharmacology research methods, the results of this study may lack rigor. Further cell, animal, and clinical studies are needed for further verification.
Acknowledgments
This study was funded by the National Natural Science Foundation of China (grant number 81874416).
Author contribution
Determination of the direction of the topic: Yi-Hui Deng
Research search: Ruo-Meng Ma and Lan Li.
Data curation: Da-Yuan Zhong and Huan-Jie Li.
Writing original draft: Da-Yuan Zhong.
Article revision: Yi-Hui Deng.
Article translation: Da-Yuan Zhong and Yi-Hui Deng.
Data Availability Statement
Requests for additional data may be granted upon reasonable request by contacting the first author (Da-Yuan Zhong, [email protected]) and corresponding author (Yi-Hui Deng, [email protected]. com).
Financial support and sponsorship
This study was funded by the National Natural Science Foundation of China (grant number 81874416).
Conflicts of interest
There are no conflicts of interest
References
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