
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

MD-D-23-10241
00086
10.1097/MD.0000000000039598
3
4200
Research Article
Observational Study
Predicting prospective therapeutic targets of Bombyx batryticatus for managing diabetic kidney disease through network pharmacology analysis
Chang Jingsheng PhD changjingshengcjs8@21cn.com
ab
Wang Jue MB wangjuewj@21cn.com
a
Li Xueling MD lixuelinglm@21cn.com
a
https://orcid.org/0009-0003-7798-0860
Zhong Yifei MD, PhD a*
a Department of Nephrology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
b Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
* Correspondence: Yifei Zhong, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Xuhui District, Shanghai 200032, China (e-mail: zhongyifeizyf5@126.com).
13 9 2024
13 9 2024
103 37 e3959816 11 2023
17 5 2024
15 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

We conducted network pharmacology and molecular docking analyses, and executed in vitro experiments to assess the mechanisms and prospective targets associated with the bioactive components of Bombyx batryticatus in the treatment of diabetic kidney disease (DKD). The bioactive components and potential targets of B batryticatus were sourced from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. Using 5 disease databases, we conducted a comprehensive screening of potential disease targets specifically associated with DKD. Common targets shared between the bioactive components and disease targets were identified through the use of the R package, and subsequently, a protein–protein interaction network was established using data from the STRING database. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses pertaining to the identified common targets were conducted using the Database for Annotation, Visualization, and Integrated Discovery. Molecular docking simulations involving the bioactive components and their corresponding targets were modeled through AutoDock Vina and Pymol. Finally, to corroborate and validate these findings, experimental assays at the cellular level were conducted. Six bioactive compounds and 142 associated targets were identified for B batryticatus. Among the 796 disease targets associated with DKD, 56 targets were identified. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed the involvement of these shared targets in diverse biological processes and signaling pathways, notably the PI3K–Akt signaling pathway. Molecular docking analyses indicated a favorable binding interaction between quercetin, the principal bioactive compound in B batryticatus, and RAC-alpha serine/threonine-protein kinase. Subsequently, in vitro experiments substantiated the inhibitory effect of quercetin on the phosphorylation level of PI3K and Akt. The present study provides theoretical evidence for a comprehensive exploration of the mechanisms and molecular targets by which B batryticatus imparts protective effects against DKD.

Bombyx batryticatus
diabetic kidney disease
molecular docking
network pharmacology
PI3K– Akt signaling pathway
National Natural Science Foundation of China 10.13039/501100001809 2019-81903978 and 2019-81973772 Yifei ZhongShanghai Hospital Development centerSHDC2022CRD003 Yifei ZhongOPEN-ACCESSTRUE
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pmc1. Introduction

Diabetic kidney disease (DKD), also referred to as diabetic nephropathy (DN), currently stands as the most prevalent microangiopathy in diabetes and is the leading cause of end-stage renal disease worldwide, presenting a significant threat to public health.[1,2] The worldwide upsurge in individuals afflicted by diabetes mellitus (DM), as evidenced by the diagnosis of 463 million adults aged 20 to 79 years with DM in 2019, is projected to escalate to 700 million by 2045. Notably, approximately 90% of these cases are characterized by type 2 diabetes mellitus (T2DM).[3,4] About 40% of patients with DM are projected to progress to DKD, characterized by sustained elevated proteinuria and diminished renal function.[5] The etiology of DKD is intricate and not fully elucidated, necessitating a multifaceted management approach involving meticulous control of blood sugar, blood pressure, proteinuria, regulation of blood lipids, and other methods, often yielding suboptimal treatment outcomes.[6] Consequently, there is an imperative need to delve deeper into the pathogenesis of DKD and identify effective interventional drugs and targets.

Bombyx batryticatus, known as Jangcan in Chinese, is a renowned traditional Chinese medicine (TCM) with a documented history in “Shen Nong Herbal Classic,” having been used in China for millennia owing to its reliable therapeutic efficacy.[7,8] Extensive studies have revealed the diverse composition of B batryticatus, encompassing proteins, peptides, amino acids, flavonoids, nucleosides, steroids, polysaccharides, and other constituents.[9] Several experiments attest to its multifaceted pharmacological properties, including neuroprotection, clotting control, anti-tumor, anti-inflammatory, anti-oxidative, blood sugar reduction, and other functionalities.[9] Furthermore, B batryticatus demonstrates a favorable safety profile with minimal toxicity and side effects, even at elevated concentrations. It has also been observed to promote the proliferation of HEK293 cells. Notably, certain proteins in B batryticatus have been identified to stimulate the adrenal cortex, thereby enhancing glucose utilization and improving the treatment of T2DM and adult non-insulin-dependent diabetes.[10]

Presently, network pharmacology utilizes analytical data sourced from existing databases to formulate an interactive “drug–target–disease” network. Employing specialized network analysis software, this approach systematically investigates the complex relationships between drug targets and disease targets, thereby predicting the mechanisms and specific targets of drug intervention in a given pathological condition.[11] TCM, distinguished by its multiple components, targets, and pathways, presents a challenge for elucidation through conventional experimental methodologies.[12] Consequently, network pharmacology provides novel insights for the modernization and advancement of TCM.[13]

A network pharmacology approach was used in this study to analyze the pharmacological mechanisms of B batryticatus in DKD. The schematic representation of the analytical workflow is depicted in Figure 1.

Figure 1. The network pharmacology workflow pertaining to diabetic kidney disease and Bombyx batryticatus.

2. Materials and methods

2.1. Bioactive components in B batryticatus

The bioactive components of B batryticatus were searched by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, https://old.tcmsp-e.com/tcmsp.php). This platform systematically delineates the associations among drugs, targets, and diseases, providing a distinctive foundation for the pharmacological exploration of Chinese herbal medicine. Within this database, comprehensive information is available regarding herbal components, encompassing their absorption, distribution, metabolism, and excretion properties, as well as compound–target relationships and target–disease associations. The absorption, distribution, metabolism, and excretion-related properties comprise oral bioavailability (OB), half-life, drug-likeness (DL), Caco-2 permeability, blood–brain barrier, and other aspects.[14] OB refers to the rate and extent of absorption of the bioactive component into the bloodstream, with lower OB values indicative of reduced efficacy. DL denotes the resemblance of a compound to a known drug, and generally, lower DL values suggest a diminished likelihood of the compound serving as the bioactive component in a drug. According to the drug screening criteria recommended by TCMSP databases, chemical compounds with OB ≥ 30% and DL ≥ 0.18 are considered ideal active compounds for further study and analysis. Therefore, we screened for the bioactive components of B batryticatus using the aforementioned criteria. Subsequently, we obtained the 2D structure of these bioactive components was obtained from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) for target predictions.[15]

2.2. Target acquisition and gene name transposition process

The targets were acquired from the TCMSP database, and the names of the target proteins were transposed to gene names as per the UniProt database (https://www.uniprot.org/). This database offers both sequence and functional information for all documented proteins and is accessible without charge.[16]

2.3. Prediction targets of DKD

GeneCards (https://www.genecards.org/) and Online Mendelian Inheritance in Man (OMIM) (https://omim.org/) represent searchable, integrative databases that furnish extensive information on identified and anticipated human genes, genetic phenotypes, and their relationships.[17,18] Pharm GKB (https://www.pharmgkb.org/) is a pivotal element in personalized medicine, scrutinizing the influence of human genetic variability on drug responsiveness to optimize the selection of medications and dosages.[19] The Therapeutic Target Database (TTD) (http://db.idrblab.net/ttd/) is a repository providing details regarding well-established and characterized therapeutic proteins and nucleic acid targets, encompassing information on the targeted disease, associated pathways, and the specific drugs directed at each of these targets.[20] DrugBank (https://go.drugbank.com/releases/latest) holds the distinction of being the world’s largest online repository of drug and drug–target information.[21] In the context of the present study, we used “Diabetic kidney disease” and “Diabetic nephropathy” as keywords to retrieve known therapeutic targets from these 5 databases, specifically for the species “Homo sapiens.”

2.4. Construction of the network

To examine the association between the bioactive components of B batryticatus and their respective targets, we used Cytoscape 3.7.1 software to formulate the bioactive component–target network.

2.5. Construction of the protein–protein interaction network

The R package (library (vennDiagram)) was used to produce a Venn diagram, facilitating the identification of shared targets for B batryticatus and DKD. These common targets were subsequently entered in the STRING database (https://www.string-db.org/) to retrieve protein–protein interaction (PPI) information.[22] The analysis was confined to the species “Homo sapiens,” with a minimum required interaction score set at 0.400. The resulting PPI data was then imported into Cytoscape 3.7.1 software, and topological analysis was conducted using the network analysis module.

2.6. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis

GO and KEGG pathway enrichment analyses for the treatment of DKD with B batryticatus were executed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/home.jsp).[23]

2.7. Molecular docking analysis

Molecular docking has proven to be a pivotal tool in comprehending the interactions between chemical components and their molecular targets, serving as a valuable asset in drug discovery and development.[24] The top 5 common targets, identified through topological analysis, were chosen for molecular docking. These targets comprised RAC-alpha serine/threonine-protein kinase (AKT1), interleukin-6 (IL-6), vascular endothelial growth factor A (VEGFA), tumor necrosis factor (TNF), and caspase-3 (CASP3). The molecular structures of each component were obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov/), subjected to energy minimization under ChemBio3D Ultra 14.0, and subsequently saved in the “mol2” file format.[15] Structural data for the top 5 common targets (AKT1: PDB ID:1UNQ, IL-6: PDB ID: 1ALU, VEGFA: PDB ID: 6ZBR, TNF: PDB ID:5UUI, CASP3: PDB ID:2DKO) were obtained from the Protein Data Bank (PDB) (https://www.pdbus.org/), and the files were downloaded in the “pdb” format.[25] Pymol 2.5 software was used to eliminate water and ligand molecules. Subsequently, AutoDock Tools-1.5.7 was used to hydrogenate the top 5 common targets and convert both the component and target molecules into the “pdbqt” format. The “grid option” tool was then used to set the grid point spacing to 1, adjust the binding pocket volume, center the pocket at the binding site, and save the configuration in the “gpf” format. Finally, AutoDock Vina 1.1.2 was used to conduct molecular docking simulations between the 5 targets and 6 bioactive components. A binding energy of ≤ 0 indicated spontaneous binding between the bioactive component (ligand) and target (receptor), while a binding energy ≤ −5.0 kcal/mol is considered indicative of stable binding between the ligand and receptor. Receptors and ligands demonstrating robust binding-free energies were selected, and their interactions were visualized using Pymol 2.5.[26]

2.8. Cell experiments

2.8.1. Cell culture

The mouse renal podocyte clone 5 (MPC5, normal cells) cells were obtained from the BeNa Culture Collection (BNCC) located in Henan, China. These cells were cultured in RPMI-1640 medium (Gibco, San Diego, CA) supplemented with 10% fetal bovine serum (Gibco, San Diego, CA) and 100 U/mL streptomycin/penicillin. The MPC5 cells were maintained at a temperature of 33 °C and exposed to γ-interferon at a concentration of 10 U/mL. Subsequently, the cells were subjected to differentiation without γ-interferon at 37 °C for 5 days. For subsequent investigations, MPC5 cells were stimulated with high glucose (HG, 30 mM glucose) and normal glucose (NG, 25 mM mannitol + 5 mM glucose), with or without quercetin (1, 5, and 10 µM), and were incubated for a period of 48 hours.

2.8.2. Cell viability assay

Cell viability analysis was conducted using the CCK-8 assay (Biosharp, China) following the instructions provided by the manufacturer. Briefly, cells were seeded into 96-well plates (100 µL, 5 × 104 cells/mL) and allowed to adhere for 12 hours. Glucose (0, 30, 50, 100, 150, and 200 mM) and quercetin (0, 5, 10, 20, 40, and 80 µM) were then added to the cells at various concentrations, followed by incubation at 37 °C for 48 hours. Subsequently, 10 µL of CCK-8 solution was introduced into each well, and cells were further incubated for 2 hours at 37 °C. The optical density was measured at 450 nm using a microplate reader (BioTek Instruments, Inc.).

2.8.3. Western blotting assay

The cells underwent 3 successive washes with ice-cold phosphate-buffered saline (PBS) before being lysed on ice for 30 minutes using radioimmunoprecipitation assay (RIPA) buffer (Thermo Fisher Scientific, USA). The RIPA buffer was supplemented with 1% phenylmethylsulfonyl fluoride and 1% Phosphatase Inhibitor Cocktail. The lysates were subsequently centrifuged at 15,000 × g and 4 °C for 15 minutes. The protein concentration was determined using the bicinchoninic acid method. A quantity of 20 µg of total protein underwent separation through sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and transferred onto polyvinylidene fluoride membranes (Millipore, Bedford, MA). Following blocking with 5% bovine serum albumin (Beyotime Biotechnology, Shanghai, China) for 1 hour at room temperature, the membranes were incubated overnight at 4 °C with primary antibodies including p-PI3K, PI3K, p-AKT, AKT, and β-actin (all from cell signal technique). Subsequently, the membranes were washed 6 times with Tris-buffered saline Tween 20 (TBST, Beyotime Biotechnology, Shanghai, China) for 10 minutes and then incubated with rabbit horseradish-peroxidase-conjugated secondary antibodies (Beyotime Biotechnology, Shanghai, China) in the presence of BSA for 1 hour at room temperature. After another 6 washes with TBST for 10 minutes, the Extreme hypersensitivity electrochemiluminescence (ECL) chemiluminescence kit (Beyotime Biotechnology, Shanghai, China) was used to detect and visualize the protein bands. ImageJ software 1.46 (National Institutes of Health) was used for gray value analysis.

2.9. Statistical analysis

All experiments were conducted independently at least 3 times. The statistical analysis of the data in this study was performed using SPSS 24.0 (IBM Corporation). Quantitative values are presented as mean ± standard deviation. Significant differences were assessed using one-way ANOVA. A significance level of P < .05 was considered indicative of a statistically significant difference.

3. Results

3.1. Bioactive components of B batryticatus and target prediction of the bioactive components

Following the filtration process, a comprehensive analysis revealed 6 bioactive components from the initially identified 11 components of B batryticatus (refer Table 1), and their respective 2D structures were procured from PubChem (depicted in Fig. 2A). A total of 194 targets associated with the bioactive components were identified through the TCMSP database. Additionally, 20,386 targets, representing human species that have been reviewed, were obtained from the Uniprot database. After mapping the 194 targets from TCMSP to the Uniprot database targets and eliminating duplications, a final set of 142 targets was identified.[27]

Table 1 Active ingredients of Bombyx batryticatus and the number of targets.

Mol ID	Molecule name	OB (%)	DL	Number of targets	
MOL000098	Quercetine	46.43	0.28	131	
MOL000359	beta-Sitosterol	36.91	0.75	3	
MOL000422	Kaempferol	41.88	0.24	49	
MOL001525	Daucosterol	36.91	0.75	2	
MOL002224	Aurantiamide acetate	58.38	0.59	3	
MOL009009	(+)-Medioresinol	87.19	0.62	6	

Figure 2. (A) A two-dimensional representation of the active components of Bombyx batryticatus; (B) a network construction of targets and active components of Bombyx batryticatus; and (C) a Venn diagram depicting targets associated with diabetic kidney disease sourced from 5 databases.

3.2. Potential targets of DKD

In order to identify potential targets of DKD, the GeneCards, OMIM, PharmGKB, TTD, and Drug Bank databases were utilized. Within GeneCards, high confidence interactions between diseases and potential targets were determined using relevance scores. The average relevance score was established as a threshold, leading to the screening of 420 genes with relevance scores ≥ 32.23 in GeneCards. Consequently, there were 420 genes identified in the GeneCards database, 254 in the OMIM database, 258 in the PharmGKB database, 27 in the TTD database, and 43 in the Drug Bank database. Subsequently, by eliminating duplicate targets, a total of 796 DKD disease targets were obtained. The R package was then used to generate a Venn diagram for a comprehensive visualization of the overlapping targets (refer Fig. 2C).

3.3. Construction of the network related to targets and bioactive components of B batryticatus

The targets associated with the bioactive components were obtained using the aforementioned methodologies. Subsequently, the components and their corresponding targets were imported into Cytoscape 3.7.1 software to generate a network depicting the bioactive components and targets of B batryticatus (refer Fig. 2B). Within this network, B batryticatus is denoted by a red inverted triangle, the bioactive components are represented by pink diamonds, and the targets are symbolized by blue circles. In this network, quercetine had 131 targets, beta-sitosterol had 3 targets, kaempferol had 49 targets, daucosterol had 2 targets, aurantiamide acetate had 3 targets, and (+)-medioresinol had 6 targets.

3.4. Construction of the PPI network

The intersection between the 142 targets linked to bioactive components and the 796 targets associated with DKD yielded a total of 56 common targets. A Venn diagram illustrating this intersection was generated using the R package (refer Fig. 3A). The identified common targets were subsequently input into the STRING database (https://www.string-db.org/) to retrieve PPI data (refer Fig. 3B). The resulting network comprised 56 nodes and 874 edges, with an average node degree of 31.2 (refer Fig. 6). The PPI data was then imported into Cytoscape 3.7.1 software for topological analysis. The top 30 targets were ranked according to their degree values and depicted in a bar graph (refer Fig. 3C). The results revealed key targets such as AKT1, IL-6, VEGFA, TNF and CASP3, with AKT1 being identified as the most crucial. Detailed results and parameters of the topological analysis are presented in Table 2.

Table 2 Topological analysis of 56 genes shared by Bombyx batryticatus and diabetic kidney disease. Data are ranked by degree.

Name	Degree	Betweenness centrality	Closeness centrality	
 AKT1	51	0.04010077	0.93220339	
VEGFA	50	0.03209810	0.91666667	
   IL-6	50	0.03494325	0.91666667	
  TNF	48	0.02040634	0.88709677	
 CASP3	46	0.02158490	0.85937500	
  TP53	46	0.02794157	0.85937500	
  EGF	45	0.01761005	0.84615385	
  IL1B	45	0.01245086	0.84615385	
  JUN	45	0.01674838	0.84615385	
 EGFR	44	0.01782516	0.83333333	
 PPARG	44	0.02031538	0.83333333	
 PTGS2	44	0.01336826	0.83333333	
 CXCL8	43	0.01020323	0.82089552	
 CCL2	42	0.00934481	0.80882353	
 MMP9	42	0.00644777	0.80882353	
 HIF1A	41	0.01156555	0.79710145	
 ICAM1	40	0.00534055	0.78571429	
 MMP2	40	0.00468362	0.78571429	
 NOS3	39	0.01794851	0.77464789	
SERPINE1	39	0.01271426	0.77464789	
  IL10	39	0.00457349	0.77464789	
HMOX1	38	0.00530073	0.76388889	
VCAM1	38	0.00550980	0.76388889	
 CCND1	37	0.01448640	0.75342466	
 ERBB2	36	0.01168314	0.74324324	
 TGFB1	36	0.00204310	0.74324324	
  IFNG	36	0.00177736	0.74324324	
  CRP	36	0.00730818	0.74324324	
   IL2	33	0.00141833	0.71428571	
  IL1A	33	0.00082529	0.71428571	
  SPP1	33	0.00106528	0.71428571	
  MPO	32	0.00364591	0.70512821	
 MMP3	32	0.00088787	0.70512821	
 MMP1	32	0.00106964	0.70512821	
  SELE	32	0.00129578	0.70512821	
 CAV1	32	0.00900941	0.70512821	
MAPK1	27	0.00742348	0.66265060	
 STAT1	27	0.00038314	0.66265060	
   F3	26	0.00122719	0.65476190	
   AR	24	0.00886526	0.63953488	
  IGF2	23	0.00237597	0.63218391	
 NCF1	21	0.00034810	0.61797753	
SLC2A4	20	0.00264327	0.61111111	
 HSPB1	19	0.00021150	0.60439560	
 THBD	16	0.00002806	0.57291667	
 PTGS1	14	0.00002694	0.57291667	
 ABCG2	13	0.00078947	0.56701031	
AKR1B1	13	0.00013213	0.56701031	
  INSR	11	0.00069818	0.55555556	
CYP3A4	10	0.00154232	0.55000000	
 ADRB2	10	0.00037790	0.55000000	
 PON1	10	0.00050772	0.53398058	
 ODC1	7	0.00000000	0.52380952	
 CHEK2	7	0.00000000	0.52380952	
 BCl-2	6	0.00000000	0.51401869	
 NR3C2	5	0.00007702	0.51401869	
Note: Betweenness centrality: to measure the connectivity of these 56 genes in the gene network, indicating their degree of involvement in gene communication and regulation. Closeness centrality: to measure the distance between these genes and other genes, indicating their integration and regulation speed in the gene regulatory network.

Figure 3. (A) The common targets of Bombyx batryticatus targets and diabetic kidney disease targets; (B) the protein–protein interaction network; (C) the top 30 targets.

3.5. Enrichment analysis using GO and KEGG

To clarify the protective mechanism of B batryticatus against DKD, the common targets were subjected to GO and KEGG pathway enrichment analyses using the DAVID database. The GO enrichment analysis was categorized into 3 groups: biological process (BP), cellular component, and molecular function. GO analysis provides insights into the functional roles of genes based on these categories. Additionally, the KEGG pathway enrichment analysis, a tool commonly applied for functional annotation of differentially expressed genes, was used to comprehend the functions and pathways associated with the identified targets.

A total of 56 common targets were imported into the DAVID database, and subsequent analyses involved retrieving the top 10 enriched terms from the GO database and the top 20 from the KEGG pathway database. To visually represent the findings, the online tool “weishengxin” (http://www.bioinformatics.com.cn/) was used to construct bar and bubble diagrams. In the BP category, the enriched terms included transcriptional regulation, inflammatory response, gene expression, protein phosphorylation, cell proliferation, and apoptotic processes. Within the CC category, the distribution of target proteins was predominantly associated with the extracellular space. In the molecular function category, target proteins were primarily involved in protein binding (refer Fig. 4A). For the KEGG pathway enrichment analysis, target proteins were predominantly enriched in pathways such as hsa04066 (Hypoxia-inducible factor 1 [HIF-1] signaling pathway), hsa04668 (TNF signaling pathway), hsa04151 (PI3K–Akt signaling pathway), hsa04010 (MAPK signaling pathway), and hsa04068 (FoxO signaling pathway) (refer Fig. 4B).

Figure 4. (A) Gene Ontology (GO) enrichment analysis of 56 common targets, including biological process (BP), cellular component (CC), and molecular function (MF); (B) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of 56 common targets.

3.6. Molecular docking between bioactive components and potential targets

The interactions between the bioactive components and targets of B batryticatus were comprehensively elucidated through molecular docking, and the resultant binding-free energy scores and docking parameters are presented in Table 3. AutoDock Vina was used to calculate the affinity energy of receptor and ligand binding, with lower energy values indicating superior binding ability. The binding-free energy scores revealed notable interactions, such as AKT1 exhibiting enhanced binding with beta-sitosterol, IL-6 with quercetin, VEGFA with daucosterol, TNF with quercetin, and CASP3 with aurantiamide acetate. For further analyses, visualizations were generated to provide insights into the binding interactions. It is noteworthy that no binding site was identified between AKT1 and beta-sitosterol, as well as (+)-medioresinol through Pymol 2.5, and therefore, it is not presented in the results. Other visualizations are depicted in Figure 5.

Table 3 The binding-free energies of 6 ingredients with the top 5 targets.

Affinity (kcal/mol)	AKT1	IL-6	VEGFA	TNF	CASP3	
Quercetine	−6.4	−7.0	−7.1	−6.9	−6.7	
beta-Sitosterol	−6.5	−6.1	−6.6	−6.3	−6.3	
Kaempferol	−6.0	−6.9	−7.0	−6.7	−6.9	
Daucosterol	−6.3	−6.7	−8.0	−6.6	−6.9	
Aurantiamide acetate	−6.3	−6.3	−6.3	−6.8	−7.2	
(+)-Medioresinol	−6.4	−6.3	−7.2	−6.5	−7.0	

Figure 5. The results of molecular docking between targets and active compounds. (A) AKT1 is bound to quercetine. (B) IL-6 is bound to quercetine. (C) VEGFA is bound to daucosterol. (D) TNF is bound to quercetine. (E) CASP3 is bound to aurantiamide acetate.

3.7. Effects of quercetin on cell viability

The outcomes of the CCK-8 assay revealed that treatment with glucose concentrations ranging from 0 to 100 mM and quercetin concentrations ranging from 0 to 10 µM for 48 hours did not exhibit significant effects on cell viability (refer Fig. 6A). Therefore, for the subsequent experiments, a glucose concentration of 30 mM and quercetin concentrations ranging from 0 to 10 µM were selected.

Figure 6. (A) Impact of glucose and quercetin on the viability of the MPC5 cell line. ****P < .0001 versus control; (B) (C) Impact of quercetin on the phosphorylation level of PI3K and Akt in high glucose-induced MPC5 cells. The cells were starved for 12 hours, then stimulated with high glucose (30 mM) and pretreated with different concentrations of quercetin (1, 5, and 10 µM) for 48 hours. (B) Following various treatments, the protein levels of p-PI3K, PI3K, p-Akt, Akt, and β-actin were determined using western blot analysis and (C) the results were statistically analyzed. *P < .05, **P < .01.

3.8. Quercetin inhibited the PI3K/Akt pathway in high glucose-induced MPC5 cells

Based on the results of PPI analysis, AKT1 have been screened as the most crucial target, and the PI3K/Akt signaling pathway as the principal pathway associated with the therapeutic effect of B batryticatus against DKD, as evidenced by the results of degree values in Figure 3C and KEGG pathways in Figure 4B. To further investigate the underlying mechanism of quercetin treatment for DKD, the protein levels of p-PI3K, PI3K, p-Akt, and Akt were assessed in MPC5 cells exposed to a high-glucose environment and subsequently treated with quercetin. Compared with the normal glucose group, there was a significant increase in the phosphorylation levels of PI3K and AKT in the high-glucose group. Of note, a marked decrease of the phosphorylation levels PI3K and AKT could be found after intervention with quercetin in a dose-dependent manner, with 10 μM quercetin demonstrating the most effective inhibitory effect. The phosphorylation levels of PI3K and Akt was significantly elevated in MPC5 cells induced by high glucose compared with the normal glucose group. Treatment with varying concentrations of quercetin significantly inhibited this expression in the high-glucose group, with the treatment featuring 10 μM quercetin demonstrating the most effective inhibitory effect (refer Fig. 6B and C).

4. Discussion

DKD currently stands as the most prevalent microangiopathy in diabetes and is recognized as the leading cause of end-stage renal disease globally. In TCM, DKD is categorized under “edema,” “Shenxiao,” and “consumptive disease.” Presently, there exists a lack of specific and effective drugs for the treatment of DKD, with early application of renin-angiotensin inhibitors (angiotensin converting enzyme inhibitor or angiotensin receptor blocker drugs) either alone or in combination with sodium–glucose co-transporter 2 inhibitors being the primary approach.[28] Therefore, there is an urgent need for more effective treatment options. In recent years, various studies have explored the use of TCM in treating DKD, including herbal remedies such as Fructus Arctii, Astragalus membranaceus, Rhizoma Coptidis, Dendrobium mixture, Liuwei Dihuang pills, among others.[29–33] While herbal treatments for DKD are a focal point of current research, studies on the effects of animal-based medicine on DKD are relatively scarce.

In TCM, DKD is conceptualized as a syndrome characterized by deficiency in origin and excess in superficiality. Although the disease is primarily located in the upper energizer, it is often associated with wind-evil affecting the lower energizer. Renowned TCM professor Yiping Chen from Shanghai suggests that DKD “starts from the upper energizer and finally affects the lower energizer.” The use of wind-dispelling drugs, such as the bioactive components of B batryticatus in the treatment of DKD, aims to address the wind-evil aspect. B batryticatus is known for its effects in relieving convulsions and spasms, dispelling wind and relieving pain, and reducing phlegm while resolving masses. The application of B batryticatus in DKD treatment not only targets upper energizer wind-evil, effectively addressing symptoms such as cough, expectoration, and headache, but also addresses issues in the lower energizer, promoting diuresis to alleviate edema. In clinical settings, B batryticatus has demonstrated effectiveness in reducing proteinuria in patients.[34] Despite the observed clinical benefits, the specific mechanism of action of B batryticatus in treating DKD requires further investigation.

Network pharmacology and molecular docking techniques are important screening tools in modern drug development. Researchers can gain insights into the mechanism of action and potential side effects of drugs or components by constructing drug–target-pathway network and analyzing drug–target interactions. Subsequently, these findings can be further verified through in vivo or in vitro studies to understand the screened target-pathway relationships. Therefore, network pharmacology adn molecular docking techniques combined with pharmacological experiments have been focal points for identifying possible mechanisms and screening drug targets, especially for TCM.[35,36] In this study, we conducted a comprehensive analysis of the potential mechanisms underlying the treatment of DKD by B batryticatus, using network pharmacology and molecular docking analyses. A network diagram depicting the interaction between bioactive components and targets of B batryticatus was systematically constructed. Our investigation identified 6 chemical components with potential therapeutic effects, as they have been widely reported for their blood sugar-reducing properties and protective effects on kidney function, coupled with antioxidant and anti-inflammatory properties. Among the identified bioactive components, quercetin, a bioflavonoid, has garnered attention in biochemical and pharmacological studies for its potent scavenging of reactive oxygen species, potentially reducing the risk of renal diseases.[37] Studies on streptozotocin (STZ)-induced DKD murine model treated with a low dose of quercetin revealed reduced proteinuria and blood glucose levels, ameliorated renal dysfunction, and mitigated hypertriglyceridemia. Quercetin acts as an antioxidant and anti-apoptotic agent, influencing the reduction of superoxide radicals and apoptotic cells.[38] Moreover, it has been observed to reverse proliferation and aggregation in the G1 phase, upregulate NF-κB and monocyte chemotactic protein-1 expressions in human mesangial cells (MCs) induced by high glucose, and inhibit renal fibrosis by suppressing the activation of mTORC1/p70S6K.[39,40]

β-sitosterol, a naturally occurring plant sterol, has been demonstrated the ability to regulate blood sugar levels. Studies using β-sitosterol on rats with T2DM induced by a high-fat diet and sucrose revealed the ameliorated renal dysfunction, reduced blood glucose levels through the activation of the insulin receptor and glucose transporter 4 (Glut4), and inhibition of NF-κB expression, leading to anti-inflammatory effects.[41–43] Kaempferol, a flavanol with diverse properties including antioxidant, antimicrobial, anti-inflammatory, lipolytic, and anticancer effects, has been associated with increased expression of Glut4 and Adenosine 5´-monophosphate (AMP)-activated protein kinase (AMPK), resulting in enhanced fat decomposition, lowered blood glucose, and enhanced glucose tolerance and insulin levels.[44] While daucosterol, a natural sterol, has been reported for its involvement in cancer suppression, promotion of neural stem cell proliferation, and induction of the Th1 immune response, its effect on DKD remains unexplored.[45–47] Similarly, limited studies exist on aurantiamide acetate, with 1 study reporting its ability to suppress the growth of malignant gliomas by blocking autophagic flux.[48] Lastly, (+)-medioresinol, a furofuran-type lignan derivative with established traditional medicinal applications as an analgesic, anti-viral, anti-inflammatory, and diuretic agent, has been employed in the therapeutic management of bruises and edema.[49,50]

In this study, we identified key targets, such as AKT1, IL-6, VEGFA, TNF, and CASP3. AKT1 is a serine/threonine-protein kinase that plays a crucial role in regulating various processes such as metabolism, cell survival, growth, and angiogenesis.[51] It is responsible for mediating insulin-induced translocation of the SLC2A4/Glut4 glucose transporter to the cell surface, thus regulating glucose uptake.[52] Podocyte injury, which is closely linked to the development of DKD, involves the Akt/Bcl-2-associated death promoter (BAD) pathway, and myeloid-derived growth factor, an activator of this pathway, has been shown to inhibit podocyte apoptosis and preserve nephrin expression.[53] Further studies are needed to investigate whether B batryticatus can inhibit podocyte apoptosis through this pathway. IL-6 is associated with inflammatory responses and is prominently involved in the acute stage of DKD, correlating with the risk of the disease.[54,55] Elevated IL-6 levels have been observed in patients with T2DM with nephropathy, particularly in relation to proteinuria.[56] VEGFA induces endothelial cell proliferation, promotes cell migration, inhibits apoptosis, and induces blood vessel permeabilization. Glomerular VEGFA mRNA levels were reported to increase in STZ-induced diabetic rats, highlighting the relevance of VEGFA in the context of DKD.[57] TNF, secreted by macrophages, stimulates cell proliferation and induces cell differentiation. Specifically, TNF-α, a component of TNF, has been extensively researched. Studies have suggested that CRP and TNF-α may act as independent risk factors for chronic kidney disease in patients with T2DM.[58] CASP3, closely linked to apoptosis, has been implicated in the pathogenesis of DKD. Inhibiting CASP3 with Z-DEVD-FMK has demonstrated reduced albuminuria, ameliorated renal dysfunction, and mitigated tubulointerstitial fibrosis in diabetic mice.[59]

The KEGG pathway analysis revealed that the target proteins were primarily linked to the following signaling pathways: HIF-1, TNF, PI3K–Akt, MAPK, and FoxO. Serving as a master regulator of oxygen homeostasis, hypoxia-inducible factor 1 (HIF-1) is a transcription factor. The administration of hirudin in the treatment of DKD in rats and HK-2 human renal tubule epithelial cells resulted in a reduction of renal fibrosis indicators through the inhibition of the HIF-1α/VEGF signaling pathway.[60] TNF can activate numerous intracellular signaling pathways, including those associated with cell survival and death, inflammation, and immunity, due to its status as a key cytokine. The elevation in TNF-α levels resulted in podocyte damage in both STZ-induced diabetic mice and db/db mice.[61] The PI3K–Akt signaling pathway is responsible for governing essential cellular processes like translation, survival, growth, proliferation, and transcription. Treatment with Ginsenoside Rh1 (G-Rh1) not only modulated fasting blood glucose levels but also mitigated the excessive production of advanced glycation end products, a hallmark of DKD, in mice induced with DKD through a high-fat diet combined with STZ. Ginsenoside Rh1 also reduced oxidative indices including superoxide dismutase (SOD), glutathione (GSH), and malondialdehyde, and impeded the synthesis of inflammatory factors that were primarily generated via the AMPK/PI3K/Akt signaling pathway.[62] The MAPK signaling system is involved in numerous BP, including cell migration, differentiation, and proliferation. The progression of DKD is correlated with both inflammation and oxidative stress. Baicalin exhibited noteworthy improvement in both proteinuria and renal histopathological changes. Additionally, it reduced the concentrations of proinflammatory factors (IL-1β, IL-6, monocyte chemotactic protein-1, and TNFα), oxidative stress markers (GSH-PX, superoxide dismutase, CAT, and malondialdehyde), and activated Nrf2 signaling. Furthermore, it upregulated the expressions of downstream antioxidant enzymes (HO-1 and NQO-1). Furthermore, the inhibition of ERK1/2, JNK, and P38 expressions was seen in the MAPK signaling pathway.[63] The expression of genes involved in cellular physiological processes, including apoptosis, cell-cycle regulation, glucose metabolism, and resistance to oxidative stress, is regulated by the FoxO family of transcription factors. An attribute associated with DKD is the proliferation of MCs and the buildup of extracellular matrix proteins. FoxO3a phosphorylation and transcriptional inactivation are both increased by TGF-β, a factor that promotes MC survival and oxidative stress.[64]

We obtained 6 bioactive components and 5 main targets for our study by using the insights obtained from network pharmacology. To further validate the prediction generated by the network pharmacology analysis, molecular docking was performed. Based on our molecular docking investigations, AKT1 formed 6 key hydrogen bonds with quercetine, IL-6 formed 4 key hydrogen bond with quercetine, and a similar principle was observed with VEGFA and daucosterol. CASP3 was bound to aurantiamide acetate via 2 key hydrogen bonds, whereas TNF was connected to quercetine via 7 key hydrogen bonds. Quercetin effectively binds to these 5 essential targets. Numerous studies have explored the potential of quercetin in treating DKD; however, none have conclusively established whether it operates through the PI3K–Akt signaling pathway. In vitro experiments were undertaken to validate the efficacy suggested by network pharmacology.

In conclusion, the application of network pharmacology has significantly contributed to expanding our knowledge of the signaling pathways used by B batryticatus to address DKD. This approach helps predict targets involving the active components of B batryticatus. Molecular docking analyses validated these predictions, and in vitro experiments provided additional evidence of quercetin’s molecular mechanisms in DKD. This study provides theoretical support for conducting comprehensive investigations into the molecular targets and protective mechanisms of B batryticatus against DKD.

Acknowledgments

We would like to acknowledge the hard and dedicated work of all the staff that implemented the intervention and evaluation components of the study.

Author contributions

Conceptualization: Jingsheng Chang, Yifei Zhong.

Data curation: Jingsheng Chang, Jue Wang, Xueling Li.

Formal analysis: Jingsheng Chang, Jue Wang.

Funding acquisition: Yifei Zhong.

Methodology: Jingsheng Chang, Xueling Li.

Writing – original draft: Jingsheng Chang, Jue Wang.

Writing – review & editing: Jingsheng Chang, Xueling Li, Yifei Zhong.

Abbreviations:

AKT1 RAC-alpha serine/threonine-protein kinase

AMP adenosine 5´-monophosphate

AMPK adenosine 5´-monophosphate-activated protein kinase

ARB angiotensin receptor blocker

BP biological process

CASP3 Caspase-3

DKD diabetic kidney disease

DL drug-likeness

DM diabetes mellitus

DN diabetic nephropathy

Glut4 glucose transporter 4

GO Gene Ontology

HIF-1 Hypoxia-inducible factor 1

IL-6 Interleukin-6

KEGG Kyoto Encyclopedia of Genes and Genomes

MC mesangial cell

OB oral bioavailability

PPI protein–protein interaction

STZ streptozocin

T2DM type 2 diabetes mellitus

TCMSP Traditional Chinese Medicine Systems Pharmacology Database

TTD Therapeutic Target Database

TNF tumor necrosis factor

VEGFA vascular endothelial growth factor A

This work was supported by National Natural Science Foundation of China (2019-81903978 and 2019-81973772) and Shanghai Hospital Development center (SHDC2022CRD003).

This study was conducted with approval from the Ethics Committee of Shanghai University of Traditional Chinese Medicine. This study was conducted in accordance with the declaration of Helsinki. Written informed consent was obtained from all participants.

The authors have no conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

How to cite this article: Chang J, Wang J, Li X, Zhong Y. Predicting prospective therapeutic targets of Bombyx batryticatus for managing diabetic kidney disease through network pharmacology analysis. Medicine 2024;103:37(e39598).
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