Skip to main content
NCBI home page
In Silico Pharmacology logoLink to In Silico Pharmacology
. 2022 Jun 4;10(1):9. doi: 10.1007/s40203-022-00124-2

Investigation of the mechanism of Shen Qi Wan prescription in the treatment of T2DM via network pharmacology and molecular docking

Piaopiao Zhao 1, Xiaoxiao Zhang 1, Yuning Gong 1, Weihua Li 1, Zengrui Wu 1, Yun Tang 1,✉, Guixia Liu 1,✉
PMCID: PMC9167366  PMID: 35673584

Abstract

Shen Qi Wan (SQW) prescription has been used to treat type 2 diabetes mellitus (T2DM) for thousands of years, but its pharmacological mechanism is still unclear. The network pharmacology method was used to reveal the potential pharmacological mechanism of SQW in the treatment of T2DM in this study. Nine core targets were identified through protein-protein interaction (PPI) network analysis and KEGG pathway enrichment analysis, which were AKT1, INSR, SLC2A1, EGFR, PPARG, PPARA, GCK, NOS3, and PTPN1. Besides, this study found that SQW treated the T2DM through insulin resistance (has04931), insulin signaling pathway (has04910), adipocytokine signaling pathway (has04920), AMPK signaling pathway (has04152) and FoxO signaling pathway (has04068) via ingredient-hub target-pathway network analysis. Finally, molecular docking was used to verify the drug-target interaction network in this research. This study provides a certain explanation for treating T2DM by SQW prescription, and provides a certain angle and method for researchers to study the mechanism of TCM in the treatment of complex diseases.

Supplementary information

The online version contains supplementary material available at 10.1007/s40203-022-00124-2.

Keywords: Network pharmacology, SQW, Type 2 diabetes mellitus, Molecular docking

Introduction

Diabetes mellitus is a general term for metabolic disorders characterized by chronic hyperglycemia caused by either impaired insulin secretion or impaired insulin efficacy, or, more commonly, both (Petersmann et al. 2018). In 2019, it was estimated that 463 million people have diabetes. The number is expected to reach 578 million by 2030 and 700 million by 2045, which confirms that diabetes is one of the fastest-growing public health emergencies in the 21st century (Federation 2019). Among all diabetes cases, over 90% are type 2 diabetes mellitus (T2DM) (Bruno et al. 2005; Holman et al. 2015). A series of chronic complications are usually present as T2DM processes, such as nephropathy (Zhu et al. 2011), retinopathy (Pan et al. 2008), cardiovascular disease (Mazzone et al. 2008), diabetic foot (Lauterbach et al. 2010), and neuropathy (Khan et al. 2014), which are the main reasons leading to death in patients with T2DM. Furthermore, the total global healthcare expenditure for people with diabetes aged 18–99 years was estimated at $850 billion in 2017, and this number is expected to increase by 7% to $985 billion in 2045 (Cho et al. 2018). The high incidence of diabetes seriously affects the quality of life and imposes a significant economic burden on families, society, and countries.

At present, there are many medicines on the market to treat T2DM, such as sulfonylureas, thiazolidinediones, α-glucosidase inhibitors, Biguanide, DPP-4 inhibitors, GLP-1 analogs, GPR 119 agonists, SGLT-2 inhibitors, and insulin (DeFronzo et al. 2015). However, there are some adverse effects that existed in these drugs, more or less. For example, the side effects of hypoglycemia and weight gain appear after taking glibenclamide (Chang et al. 2007). Even with the first-line glucose-lowering drug, metformin, side effects such as lactic acidosis and digestive disorders could also occur (Aharaz et al. 2018). Moreover, the high price and the indistinct safety property of some medicines, such as DPP-4 inhibitors, GPR 119 agonists, and SGLT-2 inhibitors, lead to the enormous demand for effective, nontoxic, and affordable drugs of T2DM (Wang et al. 2016).

The knowledge of T2DM treated by traditionary Chinese medicine (TCM) was progressed for over 2000 years. Some studies have shown that TCM can significantly improve the blood glucose levels and clinical indicators of patients with T2DM and effectively delay the progression of diabetes (Tian et al. 2019). Shen Qi Wan (SQW) is a commonly used traditional Chinese medicine formula derived from the Synopsis of Prescriptions of the Golden Chamber wrote by Zhang Zhongjing. It consists of 8 widely used herbs, including cassia twig (guizhi), processed aconite (paofuzi), dried rehmannia (gandihuang), corni fructus (shanzhuyu), yam rhizome (shanyao), poria (fuling), rhizoma alismatis (zexie), and tree peony bark (mudanpi). For centuries, SQW has been effectively employed to treat various diseases associated with kidney yang deficiency syndrome (Zhang et al. 2019). It is recorded that “Men with wasting-thirst and abnormally urinating should be mainly treated with Shen Qi Wan” in the Synopsis of Prescriptions of the Golden Chamber, and diabetes mellitus is called as “wasting-thirst” in TCM. Furthermore, the classic recipe, SQW, is commonly used in clinical trials in diabetes and diabetic complications (Zhao et al. 2006). However, the mechanism of SQW in the treatment of T2DM is not precise.

Network pharmacology, first proposed by Hopkins in 2007, is applied to study the occurrence and development of diseases from the perspective of biological networks, understand the interaction between drugs and the body, and guide the discovery of new drugs (Hopkins 2007). It proposed a paradigm shifted from the “one-target, one-drug, one-disease” strategy to a version of the “multi-targets, multi-compounds, multi-diseases” strategy, which was in line with the tactic of synergistic treatment of diseases with TCM (Hopkins 2008; Li and Zhang 2013). Therefore, network pharmacology can be introduced into the study of TCM to explain the molecular mechanism of the treatment of complex diseases with Chinese herbal medicine compound preparation (Farkas et al. 2011).

In this study, the mechanism of SQW in the treatment of T2DM was explored via the network pharmacology method. Firstly, the ingredients of SQW were collected from TCM-Mesh, TCMID, TCM@taiwan databases, and literature. Then, protein targets of these compounds and T2DM were obtained by collecting from databases and predicting through the balanced substructure-drug-target network-based inference (bSDTNBI) method. After that, protein-protein interaction (PPI) network analysis and KEGG pathway enrichment analysis were conducted to elaborate on the therapeutic mechanism. Finally, the accuracy of the interaction between the compound and the target in this study was verified by molecular docking. This study illustrated the mechanism of SQW in the treatment of T2DM via the ingredient-hub target-pathway network, which provided a method for researchers to study the mechanism of TCM treating complex diseases.

Materials and methods

Ingredients collection and disposition

The chemicals of 8 herbs involved in SQW prescription were obtained from TCM-Mesh (Zhang et al. 2017), traditional Chinese medicine integrative database (TCMID) (Huang et al. 2018), TCM@taiwan databases (Chen 2011), and literature (Gui 2013; Gong et al. 2019; Luan et al. 2020; Wu 2012; Zeng et al. 2018). The PubChem CID number and the canonical SMILES of each compound were obtained from the PubChem database. Then, the duplicate compounds were eliminated. Moreover, the intestinal absorption level of each chemical was calculated by the pipeline pilot, and the compounds whose intestinal absorption level equals 3 (very poor absorption) were removed. Finally, 285 chemicals were obtained, and the detailed ingredient information of each herb can be found in Table 1 and Table S1.

Table 1.

The distribution of compounds obtained in each herb of SQW

Name of herb Number of compounds
Gandihuang 48
Shanyao 28
Shanzhuyu 61
Zexie 79
Fuling 43
Mudanpi 20
Guizhi 32
Paofuzi 5
Open in a new tab

Collection for targets of compounds

In this research, known protein targets for all chemicals of SQW prescription were assembled from databases. Besides, the protein targets of all compounds were predicted using the bSDTNBI method developed by our laboratory.

Acquisition of known protein targets

The known targets for these compounds were obtained via searching from BindingDB (Gilson et al. 2016), ChEMBL (Gaulton et al. 2017), IUPHAR/BPS Guide to Pharmacology (Armstrong et al. 2020), and PDSP Ki databases (Roth et al. 2000), and screening according to the following conditions: (1) The protein target should be Homo Sapiens; (2) The values of Ki, Kd and IC50 should not be greater than 10 µM; (3) When the “Activity Type” is potency, the value should not be greater than 10 µM, and the “Activity Comment” should be active. Finally, 192 known protein targets for chemicals in all herbs of SQW prescription were collected.

Prediction of targets for each compound via bSDTNBI

Known protein targets of only 43 compounds were acquired from the databases, which accounted for 15% of all chemicals in the SQW formula. Hence, it is necessary to predict protein targets for the ingredients, especially for those compounds without known targets. In this study, a network-based method for drug-target interaction prediction, namely bSDTNBI (Wu et al. 2016), was employed to predict protein targets for all compounds collected in the section of ingredients collection and disposition. Firstly, the global drug-target interactions connecting 4428 drugs and 2256 unique human targets, a total of 15,051 terms, were acquired from the literature (Cheng et al. 2019). Then, the optimum parameter values of α, β, and γ were obtained according to the evaluation indicator of the area under the receiver operating characteristic curve (AUC) value by grid search and 10-fold cross-validation. The detailed meanings of parameters can be found in Reference 34. Next, ten protein targets were predicted for each compound using the bSDTNBI method. Finally, 251 predicted protein targets were obtained.

Collection of protein targets associated with T2DM

The T2DM-related human protein targets were collected from Comparative Toxicogenomics Database (CTD) (Davis et al. 2021), Online Mendelian Inheritance in Man (OMIM) (Amberger et al. 2015), ClinVar database in National Center for Biotechnology Information (NCBI) (Landrum et al. 2018), and Open Targets databases (Ochoa et al. 2021). Firstly, we searched the keyword of “diabetes mellitus, type 2” in CTD, screened out “M” or “T” labeled genes, and obtained 234 genes after removing duplication. Then, we chose the term that begins with the symbol “# 125853” after searching the keyword of “Type 2 diabetes mellitus” in OMIM and gained 29 genes. After that, we retrieved the entry of “type 2 diabetes mellitus” from ClinVar and obtained 17 genes by choosing “Pathogenic” of clinical significance and “type 2 diabetes mellitus” under the Condition column. Finally, we searched the keyword of “type 2 diabetes mellitus” in the Open Targets database, screened out the genes meeting the condition of “association_score.overall = 1”, and obtained 173 genes. A total of 297 T2DM-related human genes were acquired after merging the above protein targets and deleting duplications.

Construction of protein-protein interaction network

All genes associated with SQW and T2DM were converted to Gene Symbol, and then the intersection of the above three types of targets (disease targets, predicted targets, and known targets) was taken, which was shown in a Venn diagram (see Fig. 1). In order to understand the protein-protein interactions of these targets, we put the obtained intersection targets into the STRING database and selected the organism of Homo Sapiens. After clicking the search, we got the PPI network diagram. Next, we set the combined_score above 0.4 and exported the file in TSV format. Finally, put the file into Cytoscape software (version 3.6.0) for visualization and calculated the parameter value of each node.

Fig. 1.

Fig. 1

Open in a new tab

The Venn diagram of the drug targets and the disease targets. The blue circle represents disease targets, the green circle represents the predicted targets, and the yellow circle represents known targets

Gene Ontology and KEGG pathway enrichment

In order to more clearly understand the mechanism of SQW prescription in the treatment of diabetes mellitus and biological processes involved in the mutual targets, we put the obtained intersection targets into the DAVID database for Gene Ontology and KEGG pathway enrichment and then retained the terms with p-value less than 0.05.

Molecular docking verification

We downloaded the core protein complexes from the RCSB Protein Data Bank (https://www.pdb.org/), processed the proteins and ligands in the Maestro (Schrödinger Release 2016-1: Maestro, Schrödinger, LLC, New York, NY, 2016.), and generated the docking box for glide docking. The docking results were visualized in PyMOL for further processing.

Results

Analysis of ingredients in SQW prescription

A total of 285 compounds were collected from databases and literature after processing via pipeline pilot. It can be seen from Table 1 and Table S1 that 48, 28, 61, 79, 43, 20, 32, and 5 compounds were obtained from herbs of gandihuang, shanyao, shanzhuyu, zexie, fuling, mudanpi, guizhi, and paofuzi, respectively. Besides, there were 28 chemicals found in at least two herbs, which should be taken seriously.

To comprehensively compare the properties of ingredients in SQW with approved drugs, principal component analysis was performed using seven physicochemical characteristics, which were ALogP, the number of aromatic rings, the number of hydrogen bond acceptors, the number of hydrogen bond donors, molecular weight, the molecular polar surface area and the number of rotatable bonds. It was shown in Fig. 2, the distribution of ingredients in SQW prescription and approved drugs were all compact. Besides, approved drugs almost entirely covered the elements in SQW, which indicated that nearly all components obtained after processing have the potential druggability.

Fig. 2.

Fig. 2

Open in a new tab

Principal component analysis of ingredients and approved drugs orally in SQW and DrugBank database, respectively

To further assess the probability for each ingredient in SQW prescription to be a drug, the weighted quantitative estimate of the drug-likeness (QEDW) value of each chemical was calculated by the module of rdkit.Chem.QED (Table S1) and the frequency distribution diagram of QEDW values were plotted by a python script (Fig. 3). The closer the QEDW value of a compound is to 1, the more favorable drug-likeness properties it has. As shown in Table S1 and Fig. 3, more than 80% of compounds had QEDW values greater than 0.3, which was the optimal operating point of the ROC curve (Bickerton et al. 2012). However, some chemicals with QEDW value less than 0.3 were still retained, because the content of these ingredients was more in herbs of SQW.

Fig. 3.

Fig. 3

Open in a new tab

The frequency distribution diagram of QEDW values for compounds in SQW

Acquisition for protein targets of SQW treating T2DM

In the section of prediction of targets for each compound via bSDTNBI, the optimal parameter values of α, β, and γ were respectively determined to be 0.2, 0.1, and − 0.51 by searching the parameters according to 10-fold cross-validation and grid search. And then, ten protein targets were predicted for each chemical in SQW prescription using the bSDTNBI method. A total of 402 genes were obtained after combining known targets and removing duplication, which can be found in Table S2. Human protein genes related to T2DM were collected from CTD,36 OMIM,37 ClinVar,38 and Open Targets databases,39 and 297 genes were collected after eliminating redundancy, as shown in Table S3.

The drug targets were intersected with the disease targets, and the protein targets of SQW in the treatment of T2DM were obtained, which was shown in Fig. 1. There were 34 protein targets related to SQW in the treatment of T2DM, including 16 known protein targets and 18 predicted targets. The detailed information of intersection targets can be found in Table 2.

Table 2.

The gene symbol, gene name information, and target classification of intersection targets

Gene symbol Gene name Target classification
AMY2A amylase, alpha 2 A Predicted target
SLC2A1 solute carrier family 2 member 1 Known target
HMGCR 3-hydroxy-3-methylglutaryl-CoA reductase Predicted target
PTGS2 prostaglandin-endoperoxide synthase 2 Known target
EGFR epidermal growth factor receptor Known target
PTGS1 prostaglandin-endoperoxide synthase 1 Predicted target
LIPF lipase F, gastric type Predicted target
DPP4 dipeptidyl peptidase 4 Predicted target
WRN Werner syndrome RecQ like helicase Known target
PDE4B phosphodiesterase 4B Predicted target
AKT1 AKT serine/threonine kinase 1 Known target
PDE4A phosphodiesterase 4 A Predicted target
DRD2 dopamine receptor D2 Known target
XDH xanthine dehydrogenase Known target
DRD3 dopamine receptor D3 Known target
DRD4 dopamine receptor D4 Known target
PTPN1 protein tyrosine phosphatase, non-receptor type 1 Predicted target
NOS2 nitric oxide synthase 2 Predicted target
NOS3 nitric oxide synthase 3 Predicted target
VDR vitamin D (1,25- dihydroxyvitamin D3) receptor Predicted target
PDE4D phosphodiesterase 4Dc Predicted target
GAA glucosidase alpha, acid Known target
INSR insulin receptor Known target
PDE4C phosphodiesterase 4 C Predicted target
BRAF B-Raf proto-oncogene, serine/threonine kinase Known target
HBA1 hemoglobin subunit alpha 1 Predicted target
GCK glucokinase Predicted target
NFKB1 nuclear factor kappa B subunit 1 Known target
FKBP1A FK506 binding protein 1 A Predicted target
PDE10A phosphodiesterase 10 A Predicted target
CYP1A2 cytochrome P450 family 1 subfamily A member 2 Known target
PPARG peroxisome proliferator activated receptor gamma Known target
PPARA peroxisome proliferator activated receptor alpha Known target
SHBG sex hormone binding globulin Predicted target
Open in a new tab

Analysis of PPI network for mutual proteins

To understand the mutual effect information of these proteins, the protein-protein interaction network of these 34 proteins was constructed. As shown in Fig. 4, there were 31 genes and 80 interaction pairs after removing genes without interaction. The pink circle represents the predicted targets, and the purple circle represents the known targets. Besides, the tool of NetworkAnalyzer was used to calculate the degree value of each node, and the node size was set according to the degree value. The larger the degree value is, the bigger the node is, indicating that the protein is more important. The herb-ingredient-common target interaction network was also constructed, which was shown in Figure S1. There still were many compounds in herbs acting on mutual protein targets, indicating that traditional Chinese medicine of SQW plays a therapeutic role through the synergy.

Fig. 4.

Fig. 4

Open in a new tab

The PPI network of intersection protein targets. Pink circle: predicted target; purple circle: known target

For the purpose of obtaining more important protein genes, proteins greater than the average value of 5.16 were screened, as shown in Table S4. A total of 11 protein targets were received, which were AKT1, PPARG, NOS2, PTGS2, EGFR, NOS2, SLC2A1, GCK, INSR, PTPN1, and PPARA.

Analysis of biological process and KEGG pathway enrichment

In order to more clearly understand the mechanism of SQW prescription in the treatment of T2DM, we put these 34 genes in the DAVID database to enrich the biological process and KEGG pathway. The bubble plot of the top 20 biological processes was visualized using R script (Fig. 5). As can be seen from Fig. 5, the protein targets of SQW prescription in the treatment of T2DM mainly affect some biological processes, including oxidation-reduction process, negative regulation of blood pressure, glucose transport, cellular response to insulin stimulus, positive regulation of glycogen biosynthetic, and positive regulation of nitric oxide biosynthetic process. The KEGG pathway enrichment result was visualized employing the GlueGO plug-in of Cytoscape (Fig. 6). A total of 33 KEGG pathway terms were obtained (the redder the color is, the more statistically significant the pathway is). Basing on the Kappa score to define the term-term interactions, the terms were clustered into three groups, which were cAMP signaling pathway (86.11%), caffeine metabolism (2.78%), and morphine addiction (11.11%). Most of the KEGG pathways were clustered into cAMP signaling pathways category (Yang and Yang 2016), and these pathways were related to T2DM and its complications.

Fig. 5.

Fig. 5

Open in a new tab

The bubble plots of the biological process enrichment for the mutual targets. The size of the bubble represents the number of genes mapped in this pathway, and the color of the bubble represents the significance level

Fig. 6.

Fig. 6

Open in a new tab

Mutual gene pathway enrichment visualization result. The color of the circle represents the significance level (the darker the color is, the smaller the p-value is), and the size of the circle represents the number of genes mapped to that pathway (the larger the circle is, the more genes are mapped to that pathway)

The pathways closely related to T2DM by searching the literature were chosen (Table 3), which were insulin resistance, insulin signaling pathway, adipocytokine signaling pathway, AMPK signaling pathway, and FoxO signaling pathway. For the purpose of finding the hub targets, the protein targets with a degree greater than 5.16 were intersected with the protein genes in the above pathways, and nine core targets were obtained, namely NOS3, INSR, PTPN1, PPARA, SLC2A1, AKT1, GCK, PPARG, and EGFR. The ingredients and herbs corresponding to the core targets were shown in Table 4. Besides, the ingredient-hub target-pathway network was constructed via Cytoscape software, which was shown in Fig. 7. Therefore, the mechanism of SQW prescription for the treatment of T2DM is that these ingredients (Mol141, Mol16, Mol202, etc.), whose molecule structure can be seen from the Table S1, act on the protein targets, which are NOS3, INSR, PTPN1, PPARA, SLC2A1, AKT1, GCK, PPARG, and EGFR, then affect pathways associated with T2DM, which are insulin resistance, insulin signaling pathway, adipocytokine signaling pathway, AMPK signaling pathway, and FoxO signaling pathway.

Table 3.

The information of pathways closely related to T2DM

Term ID Term description Matching proteins P-value
hsa04931 Insulin resistance NFKB1, NOS3, INSR, PTPN1, PPARA, SLC2A1, AKT1 5.7E-6
hsa04910 Insulin signaling pathway GCK, BRAF, INSR, PTPN1, AKT1 2.8E-3
hsa04920 Adipocytokine signaling pathway NFKB1, PPARA, SLC2A1, AKT1 3.4E-3
hsa04152 AMPK signaling pathway PPARG, HMGCR, INSR, AKT1 1.6E-2
hsa04068 FoxO signaling pathway INSR, AKT1, BRAF, EGFR 2.0E-2
Open in a new tab

Table 4.

The ingredients and herbs corresponding to the core targets

Gene symbol Ligand Herb
NOS3 Mol58, Mol59, Mol73, Mol265 fuling, gandihuang, zexie
INSR Mol182 shanzhuyu
PTPN1 Mol4, Mol5, Mol10, Mol15, Mol64, Mol73, Mol92, Mol99, Mol108, Mol125, Mol128, Mol131, Mol132, Mol174, Mol199, Mol207, Mol208, Mol277 fuling, gandihuang, guizhi, shanzhuyu, zexie
PPARA Mol90, Mol160, Mol197, Mol199, Mol209, Mol275 gandihuang, shanyao, shanzhuyu, zexie
SLC2A1 Mol145 mudanpi
AKT1 Mol6, Mol182 mudanpi, shanzhuyu
GCK Mol202 shanzhuyu
PPARG Mol10, Mol15, Mol22, Mol85, Mol90, Mol106, Mol108, Mol110, Mol112, Mol131, Mol132, Mol160, Mol161, Mol197, Mol199, Mol209, Mol275, Mol277 fuling, gandihuang, guizhi, shanyao, shanzhuyu, zexie
EGFR Mol6, Mol141, Mol182 mudanpi, shanzhuyu
Open in a new tab

Fig. 7.

Fig. 7

Open in a new tab

The ingredient-hub target-pathway network. Green circles represent ingredients, purple diamonds represent target genes, and red triangles represent pathways. Among the drug-target interaction relationships, the sinewave edge represents the known interaction relationship, and the straight line represents the predicted interaction relationship

Validation of drug-target interaction network by molecular docking

In the nine core targets, there were six known targets (AKT1, INSR SLC2A1, EGFR, PPARG, and PPARA) and three forecasted targets (GCK, NOS3, and PTPN1). There were 9 pairs of known drug-target interactions and 45 predicted interaction pairs. In this study, we mainly used molecular docking to validate those 45 predicted drug-target interactions, including five protein targets (PPARG, GCK, NOS3, PPARA, and PTPN1). We first downloaded the protein complexes with high crystal resolution from the RCSB Protein Data Bank, and then processed them using the Protein Preparation Wizard (add hydrogens, create zero-order bonds to metals, create disulfide bonds, fill in missing side chains and loops, remove water molecules, and use the OPLS_2005 force field for energy minimization). Next, the stereochemical structures of small molecules were downloaded from the PubChem database, and then these structures were optimized using the Ligand Preparation in Maestro.

To ensure that the objects studied were suitable for docking on the Maestro, we first performed the self-docking operation to calculate their RMSD values before docking. The RMSD values of these five proteins were shown in Table 5. Except for the protein of PPARA, the RMSD values of the other proteins were all less than 2 Å. Considering that the ligand in the PPARA complex was more flexible, the poses of ligand produced by Maestro were clustered. The representative conformation was chosen to superpose with the crystal conformation, which can be found in Figure S2. It can be seen from Figure S2 that the representative conformation overlapped nicely with the crystal conformation, indicating the protein of PPARA was suitable to dock using Maestro.

Table 5.

The RMSD values of PPARG, GCK, NOS3, PPARA, and PTPN1 genes with their ligands

Gene Symbol PDB Resolution RMSD
PPARG 7AWC 1.74 Å 0.568 Å
GCK 4ISE 1.78 Å 0.241 Å
NOS3 6PP1 1.76 Å 1.673 Å
PPARA 3VI8 1.75 Å 2.662 Å
PTPN1 1C83 1.80 Å 0.368 Å
Open in a new tab

After self-docking, the molecular docking of protein with ligand compounds was carried out. The results of the docking score were listed in Table S5. It can be seen that the binding energy of almost all these compounds with their corresponding proteins are generally less than − 4 kcal/mol, the lowest binding energy is -10.089 kcal/mol, and the highest binding energy is -2.385 kcal/mol. Besides, we put the protein complexes with the lowest docking score (Table 6) into the PLIP for analyzing (Salentin et al. 2015), and then put the analysis results into PyMOL for visualization (Fig. 8). Ligands and residues have robust interactions, such as hydrogen bond interactions, hydrophobic interactions, π-π interaction, and salt bridge, indicating that these small molecules can exist stably in the pockets of the receptors.

Table 6.

The protein complexes with the highest docking score

Protein Ligand Docking score
(kcal/mol)
7AWC Dibenzoyl (-)-p-methoxy-L-tartaric acid -8.179
4ISE 2-[3-[(6-Amino-2-methyl-4-pyrimidinyl) methyl]-4-methyl-5-thiazol-3-iumyl] ethanol -9.551
6PP1 Isoindazole -5.620
3VI8 Labiatenic acid -7.731
1C83 2-(4-Methoxycyclohexa-1,3-dien-1-yl)-2-oxoacetic acid -10.089
Open in a new tab

Fig. 8.

Fig. 8

Open in a new tab

The interaction information of proteins (gray) and ligands (green) whose highest docking score. Figures A ~ E show the interactions of PPARG, GCK, NOS3, PPARA, and PTPN1 proteins with their ligands, respectively (The magenta dotted line represents the hydrogen bond interaction, the yellow dotted line represents the salt bridge, the gray dotted line represents the π-π interaction, and the cyan dotted line represents the hydrophobic interaction)

Discussion

Analysis of main components in SQW prescription

The preparation of SQW has been studied for the treatment of diabetes mellitus since the Song dynasty. And we found that the 285 components of this prescription play a role in the treatment of T2DM via this study. Among these components, 28 compounds were found in more than two herbs, which were bilineurine, gallic acid, 5-hydroxymethylfurfural, 2-furaldehyde phosphate, protocatechuic acid, quercetin, luteolin, apigenin, cinnamic acid, (E)-1, 1-dideuterio-3-phenylprop-2-en-1-ol, uracil, β-adenosine, adenine, paeonol, N-[(4-hydroxy − 3-methoxyphenyl)methyl]-8-methyl-6-nonenamide, crategolic acid, oleanoic acid, 6-deoxyhexose, usolic acid, stigmasterol glucoside, β-Sitosterol 3-O-Beta-D-galactopyranoside, conjugated linoleic acid, ethyl gallate, methyl gallate, pinoresinol, syringaresinol, and myristicin. Previous studies have shown that apigenin can reduce the blood glucose, lipid, and insulin resistance index in obese mice induced by a high-fat diet and ultimately improve their abnormal glucose tolerance. Besides, apigenin can effectively improve their complications, including vascular dysfunction and cognitive decline (Jung et al. 2016; Mao et al. 2015; Zhou et al. 2017). Pinoresinol exerts a hypoglycemic effect by inhibiting α-glucosidase (Wikul et al. 2012). Paeonol can improve glycolipid metabolism in palmitic acid-induced insulin-resistant HepG2 cells by increasing the phosphorylation level of serine/threonine kinase B and upregulating the expression of glucokinase and LDL receptor (Liu et al. 2013; Xu et al. 2019). Oleanolic acid, a pentacyclic triterpenoid found in herbs of fuling and shanzhuyu, has been used as a therapeutic agent in models of diabetes to improve insulin action, inhibit gluconeogenesis, and promote glucose utilization (Ayeleso et al. 2017). Quercetin stimulates glucose uptake by skeletal muscle cells by promoting GLUT4 transport through phosphorylation of both AMPK and serine/threonine kinase B (Dhanya et al. 2017). Luteolin enhances insulin action in adipocytes by directly activating the PPARγ pathway and acting on the insulin signaling cascade (Ding et al. 2010). Protocatechuic acid has antioxidant activity in liver and aortic tissue of type 2 diabetic rats, which can improve hepatic insulin resistance by inhibiting the IRS1/PI3K/AKT2 signaling pathway and restore vascular oxidation state by regulating AGE-RAGE-NOX4 cascade in diabetic rats (Abdelmageed et al. 2021).

Analysis of pathways influenced by SWQ prescription

Pathway enrichment of T2DM-related protein targets acted upon by SQW was conducted to obtain related pathways of SQW in the treatment of T2DM, which were insulin resistance, insulin signaling pathway, adipocytokine signaling pathway, AMPK signaling pathway, and FoxO signaling pathway. Insulin resistance plays a vital role in the pathogenesis and clinical course of type 2 diabetes mellitus, and T2DM occurs when pancreatic β-cell cannot maintain compensatory hyperinsulinemia needed to prevent hyperglycemia (Reaven 2011). The insulin signaling pathway involves a cascade of events initiated by insulin combing with cell surface receptor cascade, followed by the phosphorylation and activation of receptor tyrosine kinase receptor, which results in insulin receptor tyrosine phosphorylation substrates (IRSs). The combination of IRSs and phosphatidylinositol 3 kinase (PI3K) activate 3-phosphoinositide-dependent protein kinase 1 (PDK1), and then PDK1 activates Akt, making glycogen synthase kinase 3 (GSK − 3) inactivated, which lead to glycogen synthase (GYS) activation resulting in glycogen synthesis. The activation of Akt also causes GLUT4 vesicles to move from the intracellular pool to the plasma membrane, allowing glucose to enter the cell (Sano et al. 2003; Taniguchi et al. 2006). Adipose tissue is an essential endocrine organ that secretes various hormones (e.g., adiponectin, leptin, and resistin) and cytokines (e.g., TNF-α and IL-6) (Li et al. 2020). These adipocytokines play a crucial role in regulating energy metabolism, glucose, and lipid metabolism (Jaganathan et al. 2018). AMP-activated protein kinase (AMPK), an evolutionarily conserved serine/threonine kinase, is an energy-sensing enzyme whose activation induces insulin sensitization. AMPK is activated when cellular energy levels are low, thereby stimulating glucose uptake in skeletal muscle, oxidation of fatty acids in fat (and other) tissues, and reducing glucose production in the liver (Coughlan et al. 2014). The FoxO family of proteins can be divided into four different subtypes in mammals, which were FoxO1, FoxO3, FoxO4, and FoxO6. They co-regulate different cell functions, including apoptosis, cell cycle control, glucose metabolism, and oxidative stress. FoxO1 is one of the stimulators of glucose production in the liver because it mediates the expression of genes encoding certain essential enzymes, such as phosphoenolpyruvate carboxykinase and glucose-6-phosphatase (Zhang et al. 2006). Phosphorylated FoxO1 binds to the glucokinase nuclear promoter Sin3A, and then inhibits the expression of the glucokinase gene, thereby inhibiting glucose production ( Langlet et al. 2017).

Analysis the core protein targets acted by SQW prescription

The crucial nodes greater than the mean value of degree were obtained by calculating the degree values of all nodes in the PPI network. After intersecting these nodes with the nodes in the pathway, nine core protein targets were obtained, namely NOS3, INSR, PTPN1, PPARA, SLC2A1, AKT1, GCK, PPARG, and EGFR. Among them, AKT1, INSRS, SLC2A1, EGFR, PPARG, and PPARA were the known targets of SQW, while GCK, NOS3, and PTPN1 were the predicted targets. AKT1 is one of three serine/threonine protein kinases (AKT1, AKT2, and AKT3), known as AKT kinase, which regulates many processes, including metabolism, proliferation, cell survival, growth, and angiogenesis (Hers et al. 2011). AKT regulates glucose uptake by mediating insulin-induced transport of SLC2A4/GLUT4 glucose transporters to the cell surface. INSR, insulin receptor, is a transmembrane glycoprotein that is critical to the action of insulin. Insulin binding to INSRs in the liver, muscle, or adipose tissue triggers multiple intracellular pathways that lead to increased glycogen synthesis and glucose uptake, as well as decreased glucose production in the liver and muscle, resulting in a decrease in blood glucose level (Saltiel 2001; Zhang et al. 2010). Blocking the expression of INSR in mice results in a hyperglycemic phenotype (Michael et al. 2000). SLC2A1, also known as GLUT1, is a glucose transporter family member and provides basic glucose requirements for many cells (Mueckler 1994). Previous studies have found that the expression of GLUT1 is decreased in diabetic muscles (Park et al. 1998). EGFR, epidermal growth factor receptor, is a protein kinase member and encodes a transmembrane glycoprotein. Previous researches have shown that EGFR expression is significantly up-regulated in diabetes samples (Yang et al. 2021). In addition, the inhibition of EGFR tyrosine kinase activity improved insulin resistance (Li et al. 2018). Peroxisome proliferation-activated receptors (PPARs) are ligand-activated transcription factors belonging to the nuclear hormone receptor superfamily. Three subtypes, PPAR-α, PPAR-β, and PPAR-γ, play critical roles in the regulation of lipid and glucose metabolism. Activation of PPAR-γ (PPARG) in insulin-resistant animals or humans leads to inhibition of glucose production in the liver and increases sensitivity to glucose uptake in skeletal muscle (Kim et al. 2004; Zierath et al. 1998). PPAR-α (PPARA) is involved in regulating lipid metabolism and glucose homeostasis by regulating FFAS transport and β-oxidizing protein expression (Blaschke et al. 2006). GCK, hexokinase-4, catalyzes the phosphorylation of hexose, which is primarily expressed in pancreatic beta cells and the liver and constitutes a rate-limiting step in glucose metabolism in these tissues (Cuesta-Muñoz et al. 2004; Takeda et al. 1993). Nitric oxide synthase (NOS3) produces nitric oxide (NO) through a cGMP-mediated signal transduction pathway. It has been shown that in young, healthy individuals, inhibition of NOS3 reduces leg glucose uptake during exercise and that patients with type 2 diabetes are more dependent on NO for glucose uptake during exercise compared to controls (Kingwell et al. 2002). Protein tyrosine phosphatase 1B (PTP1B), also known as PTPN1, plays an essential role in regulating insulin signaling transduction pathways. Some studies showed that mice lacking PTP1B are more sensitive to insulin, thereby increasing tyrosine phosphorylation of insulin receptors in muscle and liver (Zhang and Zhang 2007).

It was found that SQW mainly acted on nine core proteins, NOS3, INSR, PTPN1, PPARA, SLC2A1, AKT1, GCK, PPARG, and EGFR, and played a role in the treatment of T2DM through insulin resistance, insulin signaling pathway, adipocytokine signaling pathway, AMPK signaling pathway, and FoxO signaling pathway, in this research. Furthermore, the molecular docking between the obtained core targets and the components of SQW was conducted to verify the accuracy of the results of this study. However, experiments are still needed to further confirm the mechanism of SQW prescription in the treatment of T2DM.

Conclusions

The mechanism of SQW in the treatment of T2DM through network pharmacology was mainly explored in this study. The ingredients of the SQW, the receptor proteins of the elements, and the related targets of T2DM were collected from the databases and literature. Besides, the bSDTNBI method developed in our laboratory was used to predict targets for the compounds. Then, the intersected targets of compound targets and T2DM-related targets were selected for KEGG pathway enrichment analysis and PPI network analysis, and the core targets of SQW for the treatment of T2DM were obtained, namely NOS3, INSR, PTPN1, PPARA, SLC2A1, AKT1, GCK, PPARG, and EGFR. After pathway enrichment analysis, we found that SQW preparation plays a role in the treatment of T2DM by affecting insulin resistance (has04931), insulin signaling pathway (has04910), adipocytokine signaling pathway (has04920), AMPK signaling pathway (has04152) and FoxO signaling pathway (has04068). Moreover, molecular docking of the core targets with their corresponding compounds verified the accuracy of the compound-target interaction network. In this research, the molecular mechanism of SQW treating T2DM was revealed by using the network pharmacology method, which provided evidence for the treatment of T2DM with the TCM prescription of SQW and a certain direction for the study of the mechanism of TCM treatment of complex diseases.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (1.2MB, docx)
Supplementary Material 2 (78.2KB, xlsx)

Authors’ contributions:

Conceptualization: Piaopiao Zhao, Guixia Liu, Yun Tang and Weihua Li. Methodology: Piaopiao zhao. Formal analysis and investigation: Piaopiao Zhao, Xiaoxiao Zhang and Yunning Gong. Writing - original draft preparation: Piaopiao Zhao. Writing - review and editing: Guixia Liu, Yun Tang and Zengrui Wu. Funding acquisition: no. Resources: no. Supervision: no.

Funding:

We gratefully acknowledged the financial supports from the National Key Research and Development Program of China (Grant 2019YFA0904800) and the National Natural Science Foundation of China (Grant 81872800).

Availability of data and material:

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Code Availability

Not applicable.

Declarations

Conflict of interest

The authors declare that they have no competing interest.

Ethics approval:

Not applicable.

Consent to participate:

Not applicable.

Consent for publication:

Don’t choose open access publishing.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Yun Tang, Email: ytang234@ecust.edu.cn.

Guixia Liu, Email: gxliu@ecust.edu.cn.

References

  1. Abdelmageed ME, Shehatou GS, Suddek GM, Salem HA. Protocatechuic acid improves hepatic insulin resistance and restores vascular oxidative status in type-2 diabetic rats. Environ Toxicol Pharmacol. 2021;83:103577. doi: 10.1016/j.etap.2020.103577. [DOI] [PubMed] [Google Scholar]
  2. Aharaz A, Pottegård A, Henriksen DP, Hallas J, Beck-Nielsen H, Lassen AT. Risk of lactic acidosis in type 2 diabetes patients using metformin: a case control study. PLoS ONE. 2018;13(5):e0196122. doi: 10.1371/journal.pone.0196122. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Amberger JS, Bocchini CA, Schiettecatte F, Scott AF, Hamosh A. OMIM.org: Online Mendelian Inheritance in Man (OMIM (R)), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 2015;43(D1):D789–D798. doi: 10.1093/nar/gku1205. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Armstrong JF, Faccenda E, Harding SD, Pawson AJ, Southan C, Sharman JL, Campo B, Cavanagh DR, Alexander SPH, Davenport AP, Spedding M, Davies JA, NC-IUPHAR The IUPHAR/BPS Guide to PHARMACOLOGY in 2020: extending immunopharmacology content and introducing the IUPHAR/MMV Guide to MALARIA PHARMACOLOGY. Nucleic Acids Res. 2020;48(D1):D1006–D1021. doi: 10.1093/nar/gkz951. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Ayeleso TB, Matumba MG, Mukwevho E. Oleanolic acid and its derivatives: biological activities and therapeutic potential in chronic diseases. Molecules. 2017;22(11):1915. doi: 10.3390/molecules22111915. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Bickerton GR, Paolini GV, Besnard J, Muresan S, Hopkins AL. Quantifying the chemical beauty of drugs. Nat Chem. 2012;4(2):90–98. doi: 10.1038/nchem.1243. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Blaschke F, Takata Y, Caglayan E, Law RE, Hsueh WA. Obesity, peroxisome proliferator-activated receptor, and atherosclerosis in type 2 diabetes. Arterioscler Thromb Vasc Biol. 2006;26(1):28–40. doi: 10.1161/01.ATV.0000191663.12164.77. [DOI] [PubMed] [Google Scholar]
  8. Bruno G, Runzo C, Cavallo-Perin P, Merletti F, Rivetti M, Pinach S, Novelli G, Trovati M, Cerutti F, Pagano G. Incidence of type 1 and type 2 diabetes in adults aged 30–49 years. Diabetes Care. 2005;28(11):2613–2619. doi: 10.2337/diacare.28.11.2613. [DOI] [PubMed] [Google Scholar]
  9. Chang JH, Tseng CF, Wang JY. Hypoglycemia-induced myocardial infarction: an unusual adverse effect of sulfonylureas. Int J Cardiol. 2007;115(3):414–416. doi: 10.1016/j.ijcard.2006.01.062. [DOI] [PubMed] [Google Scholar]
  10. Chen CYC. TCM Database@Taiwan: the world’s largest traditional Chinese medicine database for drug screening in silico. PLoS ONE. 2011;6(1):e15939. doi: 10.1371/journal.pone.0015939. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Cheng F, Kovács IA, Barabási AL. Network-based prediction of drug combinations. Nat Commun. 2019;10:1197. doi: 10.1038/s41467-019-09186-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Cho NH, Shaw JE, Karuranga S, Huang Y, da Rocha Fernandes JD, Ohlrogge AW, Malanda B. IDF Diabetes Atlas: global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract. 2018;138:271–281. doi: 10.1016/j.diabres.2018.02.023. [DOI] [PubMed] [Google Scholar]
  13. Coughlan KA, Valentine RJ, Ruderman NB, Saha AK. AMPK activation: a therapeutic target for type 2 diabetes? Diabetes. Metab Syndr Obes : Targets Ther. 2014;7:241–253. doi: 10.2147/DMSO.S43731. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Cuesta-Muñoz AL, Huopio H, Otonkoski T, Gomez-Zumaquero JM, Näntö-Salonen K, Rahier J, López-Enriquez S, García-Gimeno MA, Sanz P, Soriguer FC, Laakso M. Severe persistent hyperinsulinemic hypoglycemia due to a de novo glucokinase mutation. Diabetes. 2004;53(8):2164–2168. doi: 10.2337/diabetes.53.8.2164. [DOI] [PubMed] [Google Scholar]
  15. Davis AP, Grondin CJ, Johnson RJ, Sciaky D, Wiegers J, Wiegers TC, Mattingly CJ. Comparative Toxicogenomics Database (CTD): update 2021. Nucleic Acids Res. 2021;49(D1):D1138–D1143. doi: 10.1093/nar/gkaa891. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. DeFronzo RA, Ferrannini E, Groop L, Henry RR, Herman WH, Holst JJ, Hu FB, Kahn CR, Raz I, Shulman GI, Simonson DC, Testa MA, Weiss R. Type 2 diabetes mellitus. Nat Rev Dis Primers. 2015;1:15019. doi: 10.1038/nrdp.2015.19. [DOI] [PubMed] [Google Scholar]
  17. Dhanya R, Arya AD, Nisha P, Jayamurthy P. Quercetin, a lead compound against type 2 diabetes ameliorates glucose uptake via AMPK Pathway in skeletal muscle cell line. Front Pharmacol. 2017;8:336. doi: 10.3389/fphar.2017.00336. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Ding L, Jin D, Chen X. Luteolin enhances insulin sensitivity via activation of PPAR gamma transcriptional activity in adipocytes. J Nutr Biochem. 2010;21(10):941–947. doi: 10.1016/j.jnutbio.2009.07.009. [DOI] [PubMed] [Google Scholar]
  19. Farkas IJ, Korcsmáros T, Kovács IA, Mihalik Á, Palotai R, Simkó GI, Szalay KZ, Szalay-Bekő M, Vellai T, Wang S, Csermely P. Network-based tools for the identification of novel drug targets. Sci Signal. 2011;4(173):pt3. doi: 10.1126/scisignal.2001950. [DOI] [PubMed] [Google Scholar]
  20. Federation ID. IDF diabetes atlas ninth. Dunia: IDF; 2019. [Google Scholar]
  21. Gaulton A, Hersey A, Nowotka MA, Bento P, Chambers J, Mendez D, Mutowo P, Atkinson F, Bellis LJ, Cibrián-Uhalte E, Davies M, Dedman N, Karlsson A, Magariños MP, Overington JP, Papadatos G, Smit I, Leach AR. The ChEMBL database in 2017. Nucleic Acids Res. 2017;45(D1):D945–D954. doi: 10.1093/nar/gkw1074. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Gilson MK, Liu T, Baitaluk M, Nicola G, Hwang L, Chong J. BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res. 2016;44(D1):D1045–D1053. doi: 10.1093/nar/gkv1072. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Gong L, Chi J, Wang J, Ren Y, Sun B. Research progress on main functional component and action mechanism of Dioscorea opposita. Sci Technol Food Ind. 2019;40(16):312–319. [Google Scholar]
  24. Gui F (2013) Compounds from Alisma orientalis with inhibitory activities of a-glucosidase. Master Dissertation, Nanchang University
  25. Hers I, Vincent EE, Tavaré JM. Akt signalling in health and disease. Cell Signal. 2011;23(10):1515–1527. doi: 10.1016/j.cellsig.2011.05.004. [DOI] [PubMed] [Google Scholar]
  26. Holman N, Young B, Gadsby R. Current prevalence of type 1 and type 2 diabetes in adults and children in the UK. Diabet Med. 2015;32(9):1119–1120. doi: 10.1111/dme.12791. [DOI] [PubMed] [Google Scholar]
  27. Hopkins AL. Network pharmacology. Nat Biotechnol. 2007;25(10):1110–1111. doi: 10.1038/nbt1007-1110. [DOI] [PubMed] [Google Scholar]
  28. Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 2008;4(11):682–690. doi: 10.1038/nchembio.118. [DOI] [PubMed] [Google Scholar]
  29. Huang L, Xie D, Yu Y, Liu H, Shi Y, Shi T, Wen C. TCMID 2.0: a comprehensive resource for TCM. Nucleic Acids Res. 2018;46(D1):D1117–D1120. doi: 10.1093/nar/gkx1028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Jaganathan R, Ravindran R, Dhanasekaran S. Emerging role of adipocytokines in type 2 diabetes as mediators of insulin resistance and cardiovascular disease. Can J Diabetes. 2018;42(4):446–456e441. doi: 10.1016/j.jcjd.2017.10.040. [DOI] [PubMed] [Google Scholar]
  31. Jung UJ, Cho YY, Choi MS. Apigenin ameliorates dyslipidemia, hepatic steatosis and insulin resistance by modulating metabolic and transcriptional profiles in the liver of high-fat diet-induced obese mice. Nutrients. 2016;8(5):305. doi: 10.3390/nu8050305. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Khan NM, Ahmad A, Tiwari RK, Kamal MA, Mushtaq G, Ashraf GM. Current challenges to overcome in the management of type 2 diabetes mellitus and associated neurological disorders. CNS Neurol Disord : Drug Targets. 2014;13(8):1440–1457. doi: 10.2174/1871527313666141023160448. [DOI] [PubMed] [Google Scholar]
  33. Kim SY, Kim HI, Park SK, Im SS, Li T, Cheon HG, Ahn YH. Liver glucokinase can be activated by peroxisome proliferator-activated receptor-γ. Diabetes. 2004;53:S66–S70. doi: 10.2337/diabetes.53.2007.S66. [DOI] [PubMed] [Google Scholar]
  34. Kingwell BA, Formosa M, Muhlmann M, Bradley SJ, McConell GK. Nitric oxide synthase inhibition reduces glucose uptake during exercise in individuals with type 2 diabetes more than in control subjects. Diabetes. 2002;51(8):2572–2580. doi: 10.2337/diabetes.51.8.2572. [DOI] [PubMed] [Google Scholar]
  35. Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, Chitipiralla S, Gu B, Hart J, Hoffman D, Jang W, Karapetyan K, Katz K, Liu C, Maddipatla Z, Malheiro A, McDaniel K, Ovetsky M, Riley G, Zhou G, Holmes JB, Kattman BL, Maglott DR. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 2018;46(D1):D1062–D1067. doi: 10.1093/nar/gkx1153. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Langlet F, Haeusler RA, Lindén D, Ericson E, Norris T, Johansson A, Cook JR, Aizawa K, Wang L, Buettner C. Selective inhibition of FoxO1 activator/repressor balance modulates hepatic glucose handling. Cell. 2017;171(4):824–835e818. doi: 10.1016/j.cell.2017.09.045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Lauterbach S, Kostev K, Kohlmann T. Prevalence of diabetic foot syndrome and its risk factors in the UK. J Wound Care. 2010;19(8):333–337. doi: 10.12968/jowc.2010.19.8.77711. [DOI] [PubMed] [Google Scholar]
  38. Li Q, Zhao C, Zhang Y, Du H, Xu T, Xu X, Zhang J, Kuang T, Lai X, Fan G, Zhang Y. ) 1H NMR-based metabolomics coupled with molecular docking reveal the anti-diabetic effects and potential active components of berberis vernae on type 2 diabetic rats. Front Pharmacol. 2020;11:932. doi: 10.3389/fphar.2020.00932. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Li S, Zhang B. Traditional Chinese medicine network pharmacology: theory, methodology and application. Chin J Nat Med. 2013;11(2):110–120. doi: 10.3724/SP.J.1009.2013.00110. [DOI] [PubMed] [Google Scholar]
  40. Li Z, Li Y, Overstreet JM, Chung S, Niu A, Fan X, Wang S, Wang Y, Zhang MZ, Harris RC. Inhibition of epidermal growth factor receptor activation is associated with improved diabetic nephropathy and insulin resistance in type 2 diabetes. Diabetes. 2018;67(9):1847–1857. doi: 10.2337/db17-1513. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Liu J, Wang S, Feng L, Ma D, Fu Q, Song Y, Jia X, Ma S. Hypoglycemic and antioxidant activities of paeonol and its beneficial effect on diabetic encephalopathy in streptozotocin-induced diabetic rats. J Med Food. 2013;16(7):577–586. doi: 10.1089/jmf.2012.2654. [DOI] [PubMed] [Google Scholar]
  42. Luan N, Wang X, Zhong X, Zhou N, Juan J, Ji Q, Li J, Shang X. Isolation, identification and neuroprotective effect of bioactive substances from Cornus officinalis. J Food Sci Technol. 2020;39(1):126–136. [Google Scholar]
  43. Mao XY, Yu J, Liu ZQ, Zhou HH. Apigenin attenuates diabetes-associated cognitive decline in rats via suppressing oxidative stress and nitric oxide synthase pathway. Int J Clin Exp Med. 2015;8(9):15506–15513. [PMC free article] [PubMed] [Google Scholar]
  44. Mazzone T, Chait A, Plutzky J. Cardiovascular disease risk in type 2 diabetes mellitus: insights from mechanistic studies. Lancet. 2008;371(9626):1800–1809. doi: 10.1016/S0140-6736(08)60768-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Michael MD, Kulkarni RN, Postic C, Previs SF, Shulman GI, Magnuson MA, Kahn CR. Loss of insulin signaling in hepatocytes leads to severe insulin resistance and progressive hepatic dysfunction. Mol Cell. 2000;6(1):87–97. doi: 10.1016/S1097-2765(05)00015-8. [DOI] [PubMed] [Google Scholar]
  46. Mueckler M. Facilitative glucose transporters. Eur J Biochem. 1994;219(3):713–725. doi: 10.1111/j.1432-1033.1994.tb18550.x. [DOI] [PubMed] [Google Scholar]
  47. Ochoa D, Hercules A, Carmona M, et al. Open targets platform: supporting systematic drug-target identification and prioritisation. Nucleic Acids Res. 2021;49(D1):D1302–D1310. doi: 10.1093/nar/gkaa1027. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Pan H, Zhang H, Chang D, Li H, Sui H. The change of oxidative stress products in diabetes mellitus and diabetic retinopathy. Br J Ophthalmol. 2008;92(4):548–551. doi: 10.1136/bjo.2007.130542. [DOI] [PubMed] [Google Scholar]
  49. Park KS, Ciaraldi TP, Abrams-Carter L, Mudaliar S, Nikoulina SE, Henry RR. Troglitazone regulation of glucose metabolism in human skeletal muscle cultures from obese type II diabetic subjects. J Clin Endocrinol Metab. 1998;83(5):1636–1643. doi: 10.1210/jcem.83.5.4764. [DOI] [PubMed] [Google Scholar]
  50. Petersmann A, Müller-Wieland D, Müller UA, Landgraf R, Nauck M, Freckmann G, Heinemann L, Schleicher E. Definition, classification and diagnosis of diabetes mellitus. Exp Clin Endocrinol Diabetes. 2018;126(7):406–410. doi: 10.1055/a-0584-6223. [DOI] [PubMed] [Google Scholar]
  51. Reaven GM. Relationships among insulin resistance, type 2 diabetes, essential hypertension, and cardiovascular disease: similarities and differences. J Clin Hypertens. 2011;13(4):238–243. doi: 10.1111/j.1751-7176.2011.00439.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Roth BL, Lopez E, Patel S, Kroeze WK. The multiplicity of serotonin receptors: uselessly diverse molecules or an embarrassment of riches? Neuroscientist. 2000;6(4):252–262. doi: 10.1177/107385840000600408. [DOI] [Google Scholar]
  53. Salentin S, Schreiber S, Haupt VJ, Adasme MF, Schroeder M. PLIP: fully automated protein-ligand interaction profiler. Nucleic Acids Res. 2015;43(W1):W443–W447. doi: 10.1093/nar/gkv315. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Saltiel AR. New perspectives into the molecular pathogenesis and treatment of type 2 diabetes. Cell. 2001;104(4):517–529. doi: 10.1016/S0092-8674(01)00239-2. [DOI] [PubMed] [Google Scholar]
  55. Sano H, Kane S, Sano E, Mîinea CP, Asara JM, Lane WS, Garner CW, Lienhard GE. Insulin-stimulated phosphorylation of a Rab GTPase-activating protein regulates GLUT4 translocation. J Biol Chem. 2003;278(17):14599–14602. doi: 10.1074/jbc.C300063200. [DOI] [PubMed] [Google Scholar]
  56. Takeda J, Gidh-Jain M, Xu LZ, Froguel P, Velho G, Vaxillaire M, Cohen D, Shimada F, Makino H, Nishi S. Structure/function studies of human beta-cell glucokinase. Enzymatic properties of a sequence polymorphism, mutations associated with diabetes, and other site-directed mutants. J Biol Chem. 1993;268(20):15200–15204. doi: 10.1016/S0021-9258(18)82456-5. [DOI] [PubMed] [Google Scholar]
  57. Taniguchi CM, Emanuelli B, Kahn CR. Critical nodes in signalling pathways: insights into insulin action. Nat Rev Mol Cell Biol. 2006;7(2):85–96. doi: 10.1038/nrm1837. [DOI] [PubMed] [Google Scholar]
  58. Tian J, Jin D, Bao Q, Ding Q, Zhang H, Gao Z, Song J, Lian F, Tong X. Evidence and potential mechanisms of traditional Chinese medicine for the treatment of type 2 diabetes: a systematic review and meta-analysis. Diabetes Obes Metab. 2019;21(8):1801–1816. doi: 10.1111/dom.13760. [DOI] [PubMed] [Google Scholar]
  59. Wang PC, Zhao S, Yang BY, Wang QH, Kuang HX. Anti-diabetic polysaccharides from natural sources: a review. Carbohydr Polym. 2016;148:86–97. doi: 10.1016/j.carbpol.2016.02.060. [DOI] [PubMed] [Google Scholar]
  60. Wikul A, Damsud T, Kataoka K, Phuwapraisirisan P. (+)-Pinoresinol is a putative hypoglycemic agent in defatted sesame (sesamum indicum) seeds though inhibiting alpha-glucosidase. Bioorg Med Chem Lett. 2012;22(16):5215–5217. doi: 10.1016/j.bmcl.2012.06.068. [DOI] [PubMed] [Google Scholar]
  61. Wu L (2012) Study on the antitumor active compositions of EtOAc extraction of Poria cocos, Master Dissertation, Hubei University of Chinese Medicine
  62. Wu Z, Lu W, Wu D, Luo A, Bian H, Li J, Li W, Liu G, Huang J, Cheng F, Tang Y. In silico prediction of chemical mechanism of action via an improved network-based inference method. Br J Pharmacol. 2016;173(23):3372–3385. doi: 10.1111/bph.13629. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Xu F, Xiao H, Liu R, Yang Y, Zhang M, Chen L, Chen Z, Liu P, Huang H. Paeonol ameliorates glucose and lipid metabolism in experimental diabetes by activating Akt. Front Pharmacol. 2019;10:261. doi: 10.3389/fphar.2019.00261. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Yang H, Yang L. Targeting cAMP/PKA pathway for glycemic control and type 2 diabetes therapy. J Mol Endocrinol. 2016;57(2):R93–R108. doi: 10.1530/JME-15-0316. [DOI] [PubMed] [Google Scholar]
  65. Yang Y, Yao S, Ding JM, Chen W, Guo Y. Enhancer-gene interaction analyses identified the epidermal growth factor receptor as a susceptibility gene for type 2 diabetes mellitus. Diabetes Metab J. 2021;45(2):241–250. doi: 10.4093/dmj.2019.0204. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Zeng M, Zhang L, Li M, Zhang B, Zhou N, Ke Y, Feng W, Zheng X. Estrogenic effects of the extracts from the Chinese yam (Dioscorea opposite Thunb.) and its effective compounds in vitro and in vivo. Molecules. 2018;23(2):11. doi: 10.3390/molecules23020011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Zhang H, Wei J, Xue R, Wu JD, et al. Berberine lowers blood glucose in type 2 diabetes mellitus patients through increasing insulin receptor expression. Metab Clin Exp. 2010;59(2):285–292. doi: 10.1016/j.metabol.2009.07.029. [DOI] [PubMed] [Google Scholar]
  68. Zhang JY, Hong CL, Chen HS, Zhou XJ, Zhang YJ, Efferth T, Yang YX, Li CY. Target identification of active constituents of shen qi wan to treat kidney yang deficiency using computational target fishing and network pharmacology. Front Pharmacol. 2019;10:650. doi: 10.3389/fphar.2019.00650. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Zhang RZ, Yu SJ, Bai H, Ning K. TCM-Mesh: the database and analytical system for network pharmacology analysis for TCM preparations. Sci Rep. 2017;7:2821. doi: 10.1038/s41598-017-03039-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Zhang S, Zhang ZY. PTP1B as a drug target: recent developments in PTP1B inhibitor discovery. Drug Discovery Today. 2007;12(9–10):373–381. doi: 10.1016/j.drudis.2007.03.011. [DOI] [PubMed] [Google Scholar]
  71. Zhang W, Patil S, Chauhan B, Guo S, Powell DR, Le J, Klotsas A, Matika R, Xiao X, Franks R. FoxO1 regulates multiple metabolic pathways in the liver: effects on gluconeogenic, glycolytic, and lipogenic gene expression. J Biol Chem. 2006;281(15):10105–10117. doi: 10.1074/jbc.M600272200. [DOI] [PubMed] [Google Scholar]
  72. Zhao HL, Tong PC, Chan JC. Traditional Chinese medicine in the treatment of diabetes. Nestle Nutr Workshop Ser Clin Perform Programme. 2006;11:15–29. doi: 10.1159/000094399. [DOI] [PubMed] [Google Scholar]
  73. Zhou X, Wang F, Zhou R, Song X, Xie M. Apigenin: a current review on its beneficial biological activities. J Food Biochem. 2017;41(4):e12376. doi: 10.1111/jfbc.12376. [DOI] [Google Scholar]
  74. Zhu C, Liang Q, Hu P, Wang Y, Luo G. Phospholipidomic identification of potential plasma biomarkers associated with type 2 diabetes mellitus and diabetic nephropathy. Talanta. 2011;85(4):1711–1720. doi: 10.1016/j.talanta.2011.05.036. [DOI] [PubMed] [Google Scholar]
  75. Zierath JR, Ryder JW, Doebber T, Woods J, Wu M, Ventre J, Li Z, McCrary C, Berger J, Zhang B, Moller DE. Role of skeletal muscle in thiazolidinedione insulin sensitizer (PPARγ agonist) action. Endocrinology. 1998;139(12):5034–5341. doi: 10.1210/endo.139.12.6364. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (1.2MB, docx)
Supplementary Material 2 (78.2KB, xlsx)

Data Availability Statement

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Not applicable.

Articles from In Silico Pharmacology are provided here courtesy of Springer

RESOURCES