Hypoglycemic Effect and Experimental Validation of Scutellariae Radix based on Network Pharmacology and Molecular Docking

: Scutellariae Radix (SR) is a well-known traditional herb that has good pharmacological effects against diabetes. However, the mechanism of SR against diabetes is not clear. In this study, the ingredient–target–pathway relationship and hypoglycemic effect of SR on diabetes were explored using network pharmacology, molecular docking and an animal experiment. The targets of SR and diabetes were mined. The selected targets were studied using Gene Ontology (GO) enrichment analysis and pathway enrichment analysis. The network of active components, targets and pathways was integrated to analyze the ingredient–target–pathway relationship. Then, the correspondence between the active components and targets was veriﬁed using molecular docking. Finally, an animal experiment was used to verify the hypoglycemic effect of SR. There were 52 components and 22 targets for the hypoglycemic effect of SR. We identiﬁed 18 biological processes, 9 cellular components, 15 molecular functions and 25 signaling pathways. Molecular docking results indicated that the targets of diabetes bound strongly to the main components. The animal experiments showed that SR could signiﬁcantly decrease the blood glucose level of diabetic rats ( p ≤ 0.05). This study explored the potential targets and signaling pathways of SR in diabetes, and the results may help to illustrate the hypoglycemic mechanism of SR.


Introduction
Diabetes mellitus is a metabolic disorder caused by a decrease in insulin secretion and/or lower insulin activity [1].The global report of the World Health Organization (WHO) shows that from 1980 to 2019, the number of diabetic patients has almost doubled to 422 million, and it is expected to increase to 693 million by 2045 [2].Although chemotherapy and/or insulin injections could decrease the blood glucose level [3], there is no effective treatment for diabetes.
Nowadays, traditional Chinese medicine has attracted more and more attention due to its remarkable therapeutic effects and low side effects on diabetes [4,5].As a perennial herb in Labiatae, Scutellariae Radix (SR), well known as Huang-Qin in Chinese, is derived from the dry roots of Scutellaria baicalensis Georgi.SR is a famous traditional herb in East Asia, especially in China.Recent experimental studies found that SR has good pharmacological effects against diabetes [6][7][8][9].Zhao Lijuan et al. [6] and Xiao Suwei et al. [7] found that SR could ameliorate hyperglycemia by adjusting the gut microbiota and bile acid metabolism in diabetic rats.Sun Min Park et al. [8] revealed that the extracts of SR had an insulinotropic action in cell experiments of pancreatic islets.Fang Penghua et al. [9] studied the major active ingredients of SR (baicalin and its aglycone baicalein) on metabolic disorders and proposed a novel treatment strategy against hyperglycemia, insulin resistance and type 2 diabetes.However, the hypoglycemic components and mechanism of SR in diabetes are not clear.
Network pharmacology is widely used in drug research.It uses bioinformatics, molecular biology and database systems to study the drug-target-pathway-disease relationship [10].Therefore, as a useful tool, it could help to further study the role of traditional Chinese medicine in the treatment of diseases [10,11].Molecular docking is a method that is used to simulate the interaction between a ligand (active component) and a receptor (biological macromolecule or target).The binding ability between the active components and the target is studied through molecular docking technology [12].Qiqiang Zhang et al. revealed the active components, targets and molecular mechanisms of the Gandhi capsule in treating diabetic nephropathy through network pharmacology and molecular docking technology [12].L. Chen et al. studied the Yangxinshi tablet in the treatment of heart failure using a network pharmacology-molecular docking strategy [11].
In this study, the potential targets and signal pathways of SR in diabetes were identified and the potential underlying mechanisms were uncovered by constructing the ingredienttarget-pathway network.The binding ability of ingredients and targets were analyzed via molecular docking.Furthermore, an animal experiment was used to confirm the hypoglycemic effect of SR.
SR was obtained from Inner Mongolia Province, China.The voucher specimen was deposited at Xinxiang Medical University (Xinxiang, China).About 5.0 g of SR was first decocted with 100 mL water for 1.0 h, and then 50 mL water for 0.5 h.Then, the above extracts were mixed and concentrated to 48 mL.

Chemical Components and Targets
TCMSP (https://www.tcmsp-e.com/,accessed on 1 July 2020) is an open database that can provide information about the chemical composition, chemical structure and clinical application of traditional Chinese medicines.The chemical components of SR were obtained from the TCMSP database.The chemical structures were drawn with ChemOffice software (Version 8.0, CambridgeSoft Corporation, Cambridge, MA, USA) [13].The Batman-tcm database (http://bionet.ncpsb.org.cn/batman-tcm/,accessed on 1 July 2020) is a good bioinformatics analysis tool for studying the molecular mechanisms of traditional Chinese medicine [14].PharmMapper (http://www.lilab-ecust.cn/pharmmapper/,accessed on 1 July 2020) is a useful pharmacophore-matching database for target identification [15].The targets of components were obtained from the TCMSP database, Batman-tcm database and Pharmmapper database.

Selection of Targets for SR in Diabetes
Drugbank (https://go.drugbank.com/,accessed on 1 July 2020) is an online database containing drug and target information.The related targets of diabetes were screened in the DrugBank database.The potential targets of SR were compared with the targets of diabetes to confirm the targets of SR in diabetes.

Network of Targets
The String database (https://cn.string-db.org/,accessed on 1 July 2020) can analyze protein interaction information and calculate the confidence scores of all protein interactions.A score, ranging from 0 to 1, indicates confidence in the interaction between targets, and medium confidence (score ≥ 0.4) was used in this study [16].The targets of SR in diabetes were analyzed in the Homo sapiens species mode.At last, the network of interrelationships between targets was analyzed.

Gene Ontology (GO) and KEGG Pathway Enrichment Analysis
The Metascape database (https://metascape.org/gp/index.html,accessed on 1 July 2020) was used for the target annotation and analysis.First, the targets of SR in diabetes were input into the Metascape database.Then, the parameters were set as follows: Homo sapiens for the species option, 1 for the minimum overlap and 0.05 for the p-value.Finally, the GO analysis and KEGG pathway enrichment analysis were carried out.

Network Analysis
Cytoscape software was used to analyze molecular interactions and integrate targets and biological pathways.In this study, the active ingredients, potential targets and KEGG pathways were integrated to obtain the ingredient-target-pathway relationship network.

Animal Experiment
Sprague Dawley rats (60 adult males weighing 200 to 300 g) were supplied by the Laboratory Animal Centre at Xinxiang Medical University (Xinxiang, China).The rats were raised with sufficient feed and drinking water at 25 ± 2 • C and exposed to light for 12 h a day.After one week of adaptive feeding, the rats were randomly divided into a normal group, a model group, a positive group and an SR group, as shown in Table 1.Then, the rats were raised with different diets for one week.After 12 h starvation and enough water, the rats were administered STZ through an intraperitoneal injection.The STZ (1% m/v) was dissolved in a citric acid-sodium citrate buffer (pH 4.2-4.5).The normal group was treated with the same volume of buffer solution.If the fasting blood glucose of rats exceeded 16.6 mmol/L, it was judged that the diabetic model was successful.Then, 6 eligible rats were randomly selected from each group and administered each day via gavage for 4 weeks.The rats were monitored weekly for blood glucose, water intake and body weight.Four weeks later, the rats were anesthetized with isoflurane first, and then the necks were pressed down while its tail was sharply pulled to the rear.The pancreata of rats were collected and fixed with 4% paraformaldehyde for HE staining.This study was approved by the Ethics Committee of Xinxiang Medical University (no.XXLL-2020055) and followed the internationally accepted principles for laboratory animal use and care, such as the European Community guidelines (EEC Directive of 1986; 86/609/EEC) and Guidelines for the Euthanasia of Animals (2020).

Molecular Docking Analysis
The chembio3D (ChemBio3D, Ultra 14.0) function of ChemOffice software was used to convert the chemical structure of the ingredient into a three-dimensional format.The three-dimensional protein structure of the target was obtained from the PDB database (http://www.rcsb.org/pdb/home/home.do,accessed on 1 July 2020).The target protein was routinely processed (hydrogenation, dehydration, charging, etc.) by using Autodock tools software (version 1.5.6)(La Jolla, CA, USA).Then, the structures of the protein and ingredient were converted into pdqpt format files.Autodock tools software was used to set the molecular docking parameters, and Autodock Vina software (version 1.1.2) was used for molecular docking.Finally, PyMOL software (version 1.7.2.1) (Schrödinger, Inc., New York, NY, USA) was used to analyze the docking results.

Statistical Analysis
In this study, all data were analyzed in the form of mean ± standard deviation using the IBM SPSS Statistics 26 software (Armonk, NY, USA).In the statistical comparison, p < 0.05 indicated there was statistical significance.

Hypoglycemic Ingredients and Targets
A total of 140 active ingredients with 408 related targets were obtained.According to the intersection analysis of the diabetic targets and the SR targets, 22 targets were obtained, as shown in Table 2.There were 52 SR components related to these 22 targets (as listed in Table S1), and 15 components (listed in Table 3) had more than two targets.
Peroxisome proliferator-activated receptor (PPAR) belongs to the nuclear receptor superfamily of regulatory factors, and its function is mainly related to the regulation of genes on glucose and lipid metabolism [17].Studies found that PPARA, PPARG and PPAR signal transduction pathways and the adipocytokine signal pathway were upregulated to prevent hyperglycemia, and PPARG receptors regulated lipid uptake and lipogenesis through gene transcriptional activation [18].On the other hand, PPAR could mediate the induced transcription of the LPL gene [19].LPL is an important part of the synthesis of triglycerides and it plays a key role in lipoprotein and cholesterol metabolism [20].
MGAM can hydrolyze linear α-1,4-linked oligosaccharide substrates and plays an essential role in the production of human intracavity glucose; therefore, it is an effective drug target for the treatment of diabetes and obesity [21,22].
The targets of methyl palmitelaidate, 1,1,6-trimethyl-1,2-dihydronaphthalene and palmitic acid were PTGS1, CYP2B6 and CYP2C8, respectively.These three targets were related to the cyclooxygenase pathway and arachidonic acid epoxygenase activity, and prostaglandin H synthase (PGHS) was a rate-limiting enzyme.Studies showed that the PGHS-2 inhibitors could prevent streptozotocin (STZ)-induced diabetes [23].Studies found that the expression of prostaglandin synthase 2 (COX2) in diabetic patients was significantly higher than that in healthy controls, and the COX-2 signal may lead to high inflammatory levels and apoptosis in diabetes [24,25].Peroxisome proliferator-activated receptor (PPAR) belongs to the nuclear re superfamily of regulatory factors, and its function is mainly related to the regulat genes on glucose and lipid metabolism [17].Studies found that PPARA, PPARG PPAR signal transduction pathways and the adipocytokine signal pathway were up lated to prevent hyperglycemia, and PPARG receptors regulated lipid uptake and genesis through gene transcriptional activation [18].On the other hand, PPAR cou diate the induced transcription of the LPL gene [19].LPL is an important part of th thesis of triglycerides and it plays a key role in lipoprotein and cholesterol metab [20].
MGAM can hydrolyze linear α-1,4-linked oligosaccharide substrates and pla essential role in the production of human intracavity glucose; therefore, it is an eff drug target for the treatment of diabetes and obesity [21,22].
The targets of methyl palmitelaidate, 1,1,6-trimethyl-1,2-dihydronaphthalen palmitic acid were PTGS1, CYP2B6 and CYP2C8, respectively.These three targets related to the cyclooxygenase pathway and arachidonic acid epoxygenase activit prostaglandin H synthase (PGHS) was a rate-limiting enzyme.Studies showed th PGHS-2 inhibitors could prevent streptozotocin (STZ)-induced diabetes [23].S found that the expression of prostaglandin synthase 2 (COX2) in diabetic patient significantly higher than that in healthy controls, and the COX-2 signal may lead t

Molecular Docking Results
As shown in Table 2 and Figure 1, four targets (PTGS1, PPARG, MGAM and GAA) were selected for molecular docking with the main active components of SR.It is generally believed that the smaller the binding free energy (affinity, unit: kcal•mol −1 ) between a molecule and a protein, the more tightly and firmly the two would bind.A binding energy smaller than −5.0 kcal•mol −1 means the active component and protein have good binding activity, and smaller than −7.0 kcal•mol −1 means strong binding activity.The docking results were shown in Figure 2. As shown in Figure 2A, baicalin docked to MGAM through glutamine (GLN) and glycine (GLY) with −8.4 kcal•mol −1 of intermolecular binding energy.Figure 2B indicates the sitoside was bound to GAA targets via glutamate (GLU) and threonine (THR) with −7.8 kcal•mol −1 .As shown in Figure 2C, baicalein was bound to the GAA target via aspartic acid (ASP) and GLY with −7.8 kcal•mol −1 .Figure 2D shows 4-hydroxycinnamic acid docked to PPARG targets through serine (SER) with −6.2 kcal•mol −1 .The molecular docking results illustrated that SR had good pharmacological effects against diabetes.
Figure 2B indicates the sitoside was bound to GAA targets via glutamate (GLU) a onine (THR) with −7.8 kcal•mol −1 .As shown in Figure 2C, baicalein was bound to t target via aspartic acid (ASP) and GLY with −7.8 kcal•mol −1 .Figure 2D show droxycinnamic acid docked to PPARG targets through serine (SER) with −6.2 kc The molecular docking results illustrated that SR had good pharmacologica against diabetes.

GO Analysis, KEGG Analysis and Ingredient-Target-Pathway Network
As shown in Figure 3A and Tables S2-S4, 15 molecular functions were iden ≤ 0.01), including monoamine transmembrane transporter activity, nuclear recept ity, maltose alpha-glucosidase activity and arachidonic acid epoxygenase acti shown in Figure 3B and Table S3, nine cellular components were obtained (p ≤ 0 cluding RNA polymerase II transcription regulator complex, presynaptic membr channel complex and apical plasma membrane.As shown in Figure 3C and Tab biological processes were identified (p ≤ 0.01), including a fatty acid metabolic

GO Analysis, KEGG Analysis and Ingredient-Target-Pathway Network
As shown in Figure 3A and Tables S2-S4, 15 molecular functions were identified (p ≤ 0.01), including monoamine transmembrane transporter activity, nuclear receptor activity, maltose alpha-glucosidase activity and arachidonic acid epoxygenase activity.As shown in Figure 3B and Table S3, nine cellular components were obtained (p ≤ 0.01), including RNA polymerase II transcription regulator complex, presynaptic membrane, ion channel complex and apical plasma membrane.As shown in Figure 3C and Table S2, 18 biological processes were identified (p ≤ 0.01), including a fatty acid metabolic process, maltose metabolic process and positive regulation of lipid localization.As shown in Figure 3D and Table S5, 25 KEGG pathways were obtained (p ≤ 0.05), including the PPAR signaling pathway, arachidonic acid metabolism, galactose metabolism, and starch and sucrose metabolism.Combining the results of the KEGG analysis, molecular docking and relationship between targets, the mechanism of SR in diabetes may be through regulating the metabolism of sugar and resisting the oxidation function.S5, 25 KEGG pathways were obtained (p ≤ 0.05), including the PPAR signaling pathway, arachidonic acid metabolism, galactose metabolism, and starch and sucrose metabolism.Combining the results of the KEGG analysis, molecular docking and relationship between targets, the mechanism of SR in diabetes may be through regulating the metabolism of sugar and resisting the oxidation function.The active ingredients, potential targets and KEGG pathways were integrated to establish the ingredient-target-pathway relationship diagram.As shown in Figure 4, there were 52 chemical components, 22 targets and 25 pathways for SR in diabetes.Through network pharmacology and molecular docking, it was found that baicalin, sitoside, baicalein and 4-hydroxycinnamic acid of SR could exert hypoglycemic effects through PPAR, MGAM, PTGS1 and so on.

Blood Glucose
As listed in Table 4, the blood glucose of rats, except the rats of the normal group, was more than 16.6 mmol/L; therefore, the diabetic rats were successfully induced.After 4 weeks of treatment, the blood glucose levels of the SR and positive control groups were significantly decreased compared with the rats in the model group (p < 0.05), and no significant difference was found between the SR and positive control groups (p > 0.05).Therefore, the results indicated that SR had a good hypoglycemic effect on diabetes.4, the blood glucose of rats, except the rats of the normal group, was more than 16.6 mmol/L; therefore, the diabetic rats were successfully induced.After 4 weeks of treatment, the blood glucose levels of the SR and positive control groups were significantly decreased compared with the rats in the model group (p < 0.05), and no significant difference was found between the SR and positive control groups (p > 0.05).Therefore, the results indicated that SR had a good hypoglycemic effect on diabetes.

Body Weight and Water Drunk
The weights and water drunk of the rats in each group were recorded every week.As shown in Table 4, only the rats of the normal group gained weight, while all the rats of the other three groups lost weight.However, the weight loss level of the SR group and positive control group was significantly smaller than the model group (p < 0.05).The water intake of the diabetic rats was significantly higher than the normal group (p < 0.05).Meanwhile, the amount of water drunk by the SR group was lower than that of the model group (p < 0.05).Therefore, SR had some beneficial effects for diabetic rats in delaying weight loss and reducing water intake.

Effect of SR on the Pancreas in Diabetic Rats
After 4 weeks, the pancreatic tissue was taken out for HE staining.As shown in Figure 5, the islet of normal rats (Figure 5A) was quasi-circular and the cells were closely arranged with a clear nucleus.However, the islet of the diabetic group rats (Figure 5B) changed obviously with an irregular shape; at the same time, the number of islet cells decreased and some islet cells were even completely atrophied with biased nuclei.For the rats of the positive (Figure 5C) and SR groups (Figure 5D), the volume of the pancreas and the number of pancreatic cells were improved, and the cells were arranged neatly with a clear boundary.

Conclusions
In this study, 52 SR components, 22 potential targets and 25 KEGG pathways were identified using network pharmacology.The results of molecular docking increased the reliability of SR in the treatment of diabetes.Finally, an animal experiment showed that SR could significantly decrease the blood glucose level of diabetic rats (p ≤ 0.05).In a future

Conclusions
In this study, 52 SR components, 22 potential targets and 25 KEGG pathways were identified using network pharmacology.The results of molecular docking increased the reliability of SR in the treatment of diabetes.Finally, an animal experiment showed that SR could significantly decrease the blood glucose level of diabetic rats (p ≤ 0.05).In a future study, the hypoglycemic ingredients of an SR extract will be separated and identified.In addition, the targets and signaling pathways will be studied through a cell test and an animal experiment.In summary, further study will be carried out to confirm the ingredienttarget-pathway relationship of SR on diabetes.This study explored the potential targets and pathways of SR in diabetes, and the results may help to illustrate the hypoglycemic mechanisms of SR.

Supplementary Materials:
The following supporting information can be downloaded from https: //www.mdpi.com/article/10.3390/pr10122553/s1.Table S1: The active ingredients and targets for Scutellariae Radix in diabetes.Table S2: The biological process of the therapeutic targets of Scutellariae Radix.Table S3: Cellular component of the therapeutic targets of Scutellariae Radix.Table S4: Molecular function of the therapeutic targets of Scutellariae Radix.Table S5: KEGG analysis results of the therapeutic targets of Scutellariae Radix.
Author Contributions: Study design, J.X., X.L. and Q.C.; sample collection and identification, network pharmacology analysis and animal experiment, X.L., Q.C., X.X., M.L., J.X. and C.L.; writingoriginal draft preparation, review and editing, X.L. and J.X.All authors have read and agreed to the published version of the manuscript.

Data Availability Statement:
The data presented in this study are available in the Supplementary Materials.

Processes 2022 ,Figure 1 .
Figure 1.The interaction network relationships between targets (each node represents a targ each edge represents the proteins that jointly contribute to a shared function).

Figure 1 .
Figure 1.The interaction network relationships between targets (each node represents a target and each edge represents the proteins that jointly contribute to a shared function).

Figure 2 .
Figure 2. Molecular docking results of baicalin with GMAM (A), sitoside with GAA (B), with GAA (C) and 4-hydroxycinnamic acid with PPARG (D).The structures of proteins a using a cartoon model, while the active component is shown using a stick model and am residues are shown using a line model (the number shows the site in the protein).The h bonds between the active component and amino acid residue are represented in yellow lines, with the number showing the distance (Å).

Figure 2 .
Figure 2. Molecular docking results of baicalin with GMAM (A), sitoside with GAA (B), baicalein with GAA (C) and 4-hydroxycinnamic acid with PPARG (D).The structures of proteins are shown using a cartoon model, while the active component is shown using a stick model and amino acid residues are shown using a line model (the number shows the site in the protein).The hydrogen bonds between the active component and amino acid residue are represented in yellow dashed lines, with the number showing the distance (Å).
maltose metabolic process and positive regulation of lipid localization.As shown in Figure 3D and Table

Figure 3 .
Figure 3. Molecular function pathway (A), cellular component pathway (B), biological process pathway (C) and KEGG pathway (D) analysis of the therapeutic targets of Scutellariae Radix in treating diabetes.

Figure 3 .
Figure 3. Molecular function pathway (A), cellular component pathway (B), biological process pathway (C) and KEGG pathway (D) analysis of the therapeutic targets of Scutellariae Radix in treating diabetes.The active ingredients, potential targets and KEGG pathways were integrated to establish the ingredient-target-pathway relationship diagram.As shown in Figure4, there were 52 chemical components, 22 targets and 25 pathways for SR in diabetes.Through network pharmacology and molecular docking, it was found that baicalin, sitoside, baicalein and 4-hydroxycinnamic acid of SR could exert hypoglycemic effects through PPAR, MGAM, PTGS1 and so on.

Figure 4 .
Figure 4.The interaction network of ingredient, target and pathway.The nodes represent the ingredients (orange arrows), the targets (green octagons) and the pathways (purple diamonds), and the edges represent the interactive relationships between two nodes.

Figure 4 .
Figure 4.The interaction network of ingredient, target and pathway.The nodes represent the ingredients (orange arrows), the targets (green octagons) and the pathways (purple diamonds), and the edges represent the interactive relationships between two nodes.

Processes 2022 , 13 Figure 5 .
Figure 5. Histological analysis of pancreata from the normal group (A), diabetic group (B), positive group (C) and Scutellariae Radix group (D) after four weeks of treatment.

Figure 5 .
Figure 5. Histological analysis of pancreata from the normal group (A), diabetic group (B), positive group (C) and Scutellariae Radix group (D) after four weeks of treatment.

Funding:
This research was funded by the scientific and technological projects of Henan Province, grant number 222102310566, and the National College Students Innovation and Entrepreneurship Training Program, grant number S201910472030.

Table 1 .
The protocol of the animal experiment.
* The dosage "1.05 g/kg" was for raw Scutellariae Radix.Before administration, raw Scutellariae Radix was processed according to the method in Section 2.1: Reagents.

Table 2 .
The potential hypoglycemic targets of Scutellariae Radix.

Table 3 .
The chemical information of active ingredients for Scutellariae Radix.

Table 4 .
The levels of blood glucose, body weight and drinking water before and after modeling (mean ± SD).For the blood glucose levels before and after modeling, a significant difference between them is shown as # (p < 0.05); after four weeks of treatment, a significant difference compared with the model group is marked as * (p < 0.05); ↑: weight gain; ↓: weight loss.