Abstract
Background:
This study aimed to investigate the active components, key targets, and potential molecular mechanisms Huaiqihuang (HQH) in the treatment of diabetic kidney disease (DKD) through network pharmacology, molecular docking, and in vitro experiments.
Methods:
The active components and potential targets of HQH were obtained from the TCMSP and HERB databases. The potential targets of DKD were obtained from the GeneCards, OMIM, DrugBank, and TTD databases. Protein interaction relationships were obtained from the STRING database, and a protein interaction network was constructed using Cytoscape software. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis was performed using the Metascape database. Molecular docking was performed using AutoDock software to verify the binding between key compounds and core target genes. In vitro experiments were conducted using human renal proximal tubular epithelial cells and various methods, such as CCK8, RT-PCR, immunofluorescence, and western blot, to evaluate the effects of HQH on inflammatory factors, key targets, and pathways.
Results:
A total of 48 active ingredients, 168 potential targets of HQH, and 1073 potential targets of DKD were obtained. A total of 118 potential targets, 438 biological processes, and 187 signal pathways were identified for the treatment of DKD. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis indicated that HQH may exert its therapeutic effects on DKD by regulating the expression of inflammatory factors through the nuclear factor kappa B (NF-κB) signaling pathway. The molecular docking results showed that β-sitosterol and baicalein had the highest binding affinity with key targets such as AKT1, IL6, TNF, PTGS2, IL1B, and CASP3, suggesting that they may be the most effective active ingredients of HQH in the treatment of DKD. In vitro experimental results demonstrated that HQH could enhance the viability of human renal proximal tubular epithelial cells inhibited by high glucose, decrease the levels of AKT1, TNF, IL6, PTGS2, IL1B, and CASP3, reduce the expression of NF-κB-P65 (P < .01), inhibit NF-κB-p65 nuclear translocation, and decrease chemokine expression (P < .01).
Conclusion:
HQH may exert its therapeutic effects on DKD by inhibiting the NF-κB signaling pathway, reducing the level of pro-inflammatory cytokines, and alleviating the high glucose-induced injury of renal tubular epithelial cells.
Keywords: diabetic kidney disease, huaiqihuang, inflammation, molecular docking, network pharmacology, nuclear factor-κB
1. Introduction
Diabetic kidney disease (DKD) is the most common microvascular complication of diabetes. It is an endocrine metabolic disorder that primarily manifests as a progressive decline in kidney function, accompanied by excessive and persistent proteinuria, dyslipidemia, and the accumulation of metabolic waste, ultimately resulting in symptoms of uremia.[1] The earliest pathological changes in DKD involve thickening of the glomerular basement membrane and expansion of the mesangial cells, followed by nodular glomerulosclerosis and alterations in the tubulointerstitial area, including infiltration of inflammatory cells, accumulation of activated myofibroblasts, and loss of capillary structure.[2] According to relevant statistical analysis, there are currently approximately 537 million diabetes patients worldwide, and this number is projected to increase to 783 million by 2045, with 30% to 40% of them developing DKD.[3]
Currently, the treatment strategy for DKD emphasizes comprehensive management including lifestyle interventions, glycemic control, blood pressure management, and lipid-lowering. There is evidence to support the use of renin-angiotensin-aldosterone system inhibitors, sodium-glucose co-transporter 2 inhibitors, and finerenone. These medications have shown good effects in reducing proteinuria and delaying the progression of kidney function in DKD patients. However, despite these treatment options, many DKD patients still progress to end-stage renal disease.[4] End-stage renal disease patients require long-term renal replacement therapy, which severely impacts their quality of life and survival time. It also increases the economic burden on society and families. Therefore, there is an urgent need for new therapeutic drugs that target multiple aspects of DKD in order to delay its progression.
Traditional Chinese medicine (TCM) has been extensively utilized for thousands of years in the treatment of kidney diseases. In recent years, the medicinal properties of TCM have revealed its ability to regulate blood glucose and lipid metabolism, mitigate kidney damage, delay kidney disease progression, and prevent glomerulosclerosis and fibrosis.[5] Given the complex pathogenesis and diverse factors implicated in these diseases, the development paradigm of new drugs has shifted from a “single target, single drug” approach to a “multiple targets, multiple components” strategy. TCM satisfies the requirements for multi-target, multi-component, and individualized treatment while ensuring safety and efficacy. Huaiqihuang granules (HQH), a TCM formulation comprising herbal ingredients like Trametes robiniophila Murr (Huaier), Lycium barbarum (Gouqi) and Polygonatum sibiricum (Huangjing), possess remarkable antioxidant, anti-apoptotic, and anti-inflammatory properties.[6] They have demonstrated effectiveness in treating kidney diseases such as IgA nephropathy, allergic purpura nephritis, nephrotic syndrome, asthma, and recurrent respiratory infections.[7] While numerous studies have confirmed the therapeutic and mitigating effects of HQH on chronic kidney disease, comprehensive investigations into its precise underlying molecular mechanisms are still lacking.
In recent years, network pharmacology has emerged as a powerful tool that integrates computer science and medicine to elucidate the mechanisms of drug action. It enables the systematic study and identification of the bioactive compounds present in TCM formulas, facilitating the visualization of their effects on multiple targets and pathways.[8,9] This approach has been widely employed to predict, analyze the pharmacological effects and potential mechanisms of TCM. Additionally, molecular docking, a method that simulates the binding mode and affinity of molecular interactions between receptors and drug molecules, has been utilized in conjunction with network pharmacology as a supplementary tool in drug research.[10]
In this study, we explored the therapeutic effects of HQH on DKD and its underlying mechanisms using network pharmacology and molecular docking technology. We also validated its anti-inflammatory mechanisms in human proximal tubular epithelial cells (HK-2) cells in vitro, aiming to provide reference for mechanism research and clinical application. Our workflow is shown in Figure 1.
Figure 1.
Flow diagram of network pharmacological analysis and experimental verification of HQH for DKD treatment. DKD = diabetic kidney disease, DL = drug-like, GO = gene ontology, HQH = Huaiqihuang, KEGG = Kyoto encyclopedia of genes and genomes, OB = oral bioavailability, PPI = protein–protein interaction.
2. Materials and methods
2.1. Collection of potential active ingredients and related targets of HQH
The TCM Systems Pharmacology Database and Analysis Platform (http://tcmspw.com)[11] was employed to collect information on the potential active ingredients and their related targets of HQH. The search keywords “Huai Er” “Gou Qi Zi” and “Huang Jing” were used to retrieve relevant data. Screening criteria were applied to preliminarily select the drug components, including an oral bioavailability of greater than or equal to 30% and a drug-likeness of greater than or equal to 0.18, based on established pharmacokinetic parameter standards.[12] The HERB database (http://herb.ac.cn/) was also utilized as a supplementary resource.[13] The target names were standardized using the UniProt databas (https://www.uniprot.org/),[14] and gene names were extracted to identify the targets corresponding to the active ingredients of HQH. Finally, the drug-active ingredient-target network was constructed using Cytoscape 3.9.0 software.
2.2. Acquisition of DKD disease targets
To identify targets related to DKD, a keyword search was performed using terms such as “diabetic kidney disease,” “diabetic nephropathy,” “DKD,” and “DN” in multiple databases including GeneCards (http://www.genecards.org/),[15] OMIM (http://www.omim.org/),[16] TTD (http://db.idrblab.net/ttd/),[17] and DrugBank (https://go.drugbank.com/).[18] The results from these databases were consolidated, duplicate entries were eliminated, and the remaining targets were considered as DKD-related targets.
2.3. Protein–protein interaction (PPI) network model construction
To examine the relationships between drugs, targets, and diseases, Venn diagrams were created using the Venny2.1.0 platform.[19] These diagrams analyzed the intersection between the corresponding targets of HQH and the targets associated with DKD. The genes present at the intersections were considered potential targets for treating DKD with HQH. Subsequently, the intersecting targets were uploaded into the STRING database (https://string-db.org/) to explore PPI within the Homo sapiens species.[20] A PPI network was constructed using Cytoscape 3.9.0 software. To identify the core targets, metrics such as Betweenness, Closeness, Degree, and Eigenvector were utilized. These core targets were then screened and visually presented with the aid of the CytoNAC plugin.
2.4. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis
To further analyze the potential targets, enrichment analysis of biological processes, cellular components, and molecular functions was conducted using the GO plugin on the Metascape website (https://metascape.org/gp/index.html).[21] Additionally, KEGG pathway enrichment analysis was performed using the KEGG pathway database. The results obtained from the GO enrichment analysis were presented using bar charts, while the KEGG pathway annotations were visualized using bubble charts (http://www.bioinformatics.com.cn/login/).
2.5. Construction of component-target-pathway network
To illustrate the potential therapeutic effects of HQH in DKD, Cytoscape 3.9.0 software was utilized to construct the “drug-active ingredient-key target-pathway” network. The active ingredients, key targets, and pathways associated with HQH were imported into the software to create this comprehensive network representation. This network visualizes the complex interactions and potential therapeutic mechanisms of HQH in the context of DKD.
2.6. Molecular docking validation of key target proteins
Molecular docking analyses were carried out using AutoDock Vina[22] to investigate the interaction of major molecules and key genes. The selection of main molecules was based on their degree values, and their 3-dimensional structures were obtained from PubChem and saved in SDF file format. Key proteins from the PPI network were chosen based on their degree values and dehydrated using PyMOL. The edited proteins and original ligand structures were saved in PDB format. Both the target proteins and ligands were then converted into PDBQT format using AutoDock tools. The molecular docking simulations were performed using AutoDock Vina, and the interactions between the molecules and targets were visualized using PyMOL software. Heat maps were generated to illustrate the results of the docking process, with a docking affinity score below −5.0 kcal/mol indicating a strong binding between the compound and the target.
2.7. Reagent and drug preparation
Captopril (Cat No. HY-B0368, MCE, NJ, USA), DMEM culture medium (Cat No. 8122466, Gibco, NY, USA), Super Fetal bovine serum (Cat No. SA211, Cellmax, Lanzhou, China), Penicillin-Streptomycin (Cat No. BC-CE-007, Solarbio, Beijing, China), RIPA lysate (Cat No. PRL0001), protease inhibitor mixture (Cat No. PR20032), and phosphatase inhibitor (Cat No. PR20015) were purchased from Proteintech (Wuhan, China). The CCK-8 reagent kit (Cat No. K1018, APExBIO, HOU, USA) was obtained from APExBIO. The BCA protein quantification kit (Cat No. ZJ102) was purchased from Shanghai Epizyme Biomedical Technology Co., Ltd (Epizyme ,Shanghai, China). RNAiso Plus (Cat No. 9180), reverse transcription kit (Cat No. RR047A), and TB Green dye kit (Cat No. RR820A) were obtained from TaKaRa Biomedical Technology Co., Ltd (TaKaRa, Beijing, China). The specific primers for the target genes were purchased from Shanghai Shenggong Biotechnology Co., Ltd (Sangon, Shanghai, China). The antibodies for Nuclear factor-kappa B (NF-κB/p65) (Cat No. D14E12) and p-NF-κB/p65 (ser536) (Cat No. 93H1) were purchased from Cell Signaling Technology (CST, MA, USA, and the GAPDH antibody (Cat No. 60004-1) was obtained from Proteintech (Wuhan, China). The HRP-labeled sheep anti-rabbit secondary antibody (Cat No. BA1054) and sheep anti-mouse secondary antibody (Cat No. BA1050) were purchased from BOSTER (Wuhan, China). The sheep anti-rabbit fluorescent secondary antibody (Cat No. A23220) and ECL luminescent solution (Cat No. BMU102-CN) were purchased from Abbkine (Wuhan, China).
HQH cream was brought by Gaitianli Pharmaceutical Co. LTD (Qidong, Jiangsu, China).The preparation involved dissolving 2 g of HQH in 20 mL of basic culture medium to obtain an original solution with a concentration of 100 mg/mL. Subsequently, the solution was filtered through a 0.22 μm membrane for sterilization and stored at −20°C after sealing.
2.8. Cell culture and grouping
The HK-2 was procured from China Fenghui Biology for use in the study. After resuscitation, HK-2 cells were inoculated into culture bottles. The culture medium used for cell growth was DMEM supplemented with 1% penicillin-streptomycin and 10% fetal bovine serum. The cells were maintained in a 37°C incubator with 5% CO2 for their growth. Once the cells reached 60% to 70% confluency, a single cell suspension was prepared and inoculated into culture bottles or cell plates for continuous cultivation. To synchronize the cells in the logarithmic growth phase, serum-free medium was used for further cultivation for 12 hours. Subsequently, the cells were subjected to drug intervention for 48 hours. The concentration of the intervention drug is determined based on reference literature and CCK8 experiments.[23]
The experimental groups were divided as follows: Normal group (5.5 mmol/L glucose, C) Mannitol group (5.5 mmol/L glucose + 34.5 mmol/L mannitol, MAN); High glucose group (40 mmol/L glucose, HG);HQH medication group (40 mmol/L glucose + different concentrations of HQH, HQH); Captopril group (40 mmol/L glucose + 100 μmol/L Captopril, CAP).In this study, Captopril (CAP) was selected as the positive control. Previous research has indicated that Captopril, an ACE inhibitor, can reduce the pore size of the glomerular filtration membrane, restore its permeability, and subsequently decrease proteinuria and tubular damage.[24]
2.9. CCK8
HK-2 cells were seeded in a 96-well plate at a density of 6 × 103 cells per well and incubated at 37°C with 5% CO2. Once the cell confluency reached 60% to 70%, they were treated with various concentrations of HQH. The experimental groups included a blank group (no cells), a control group (treated with drug-free culture medium), and multiple experimental groups with different mass concentrations of HQH (2, 4, 6, 8, 10, 12, and 14 mg/mL). The intervention lasted for 48 hours, after which 10 µL of CCK-8 solution was added to each well. Following a 1-hour incubation at 37°C, and its absorbance value at 450nm was determined. Cell viability was calculated using the following formula: Cell viability (%) = [A (experimental) - A (blank)]/ [A (control) - A (blank)] × 100%.
2.10. Western blot
Total proteins were extracted from HK-2 cells using pre-cooled RIPA lysis buffer supplemented with protease and phosphatase inhibitors. The concentration of the extracted protein was determined using the BCA protein quantification kit. The protein samples were boiled, cooled, and then separated by gel electrophoresis. Subsequently, they were transferred onto PVDF membranes and blocked with 5% skimmed milk for 2 hours at room temperature. The membranes were incubated overnight at 4°C with the primary antibodies. Following this, the membranes were incubated with the secondary antibody at room temperature for 1 hour. Protein bands were visualized using an enhanced chemiluminescence chromogenic solution and detected using an enhanced chemiluminescence detection system. Grayscale values of the protein bands were analyzed using Image J software, with GAPDH serving as the internal reference control.
2.11. Immunofluorescence
HK-2 cells were seeded onto chamber slides in a 12-well culture plate. After treating the cells with various conditions, the chamber slides were fixed with 4% paraformaldehyde at room temperature for 20 minutes. To ensure permeability, the cells were then treated with 0.3% Triton X-100 for 15 minutes and washed with PBS. Subsequently, nonspecific binding was mitigated by blocking with 5% BSA for 30 minutes. Next, the cells were incubated overnight at 4°C with a primary antibody, such as a p65 rabbit monoclonal antibody. Following the primary antibody incubation, the cells were exposed to a fluorophore-conjugated secondary antibody, like goat anti-rabbit IgG, at room temperature for 1 hour. The nuclei were counterstained with 4’,6-diamino-2-phenylindole. Finally, a fluorescence inverted microscope was used to capture the fluorescence image. The green fluorescence represented positive antibody staining, while the blue fluorescence represented nuclear 4’,6-diamino-2-phenylindole labeling. The level of p65 nuclear translocation was assessed and observed.
2.12. Real-time quantitative RT-PCR
To assess the mRNA expression levels of AKT1, PTGS2, IL1B, TNF, MIP-2, MIP-1β, VCAM-1, and CASP3, total RNA was extracted from HK-2 cells using RNAiso Plus. The concentration and purity of the RNA were determined using a spectrophotometer. Subsequently, the RNA was reverse transcribed into complementary DNA (cDNA) using the PrimeScript RT reagent Kit. Quantitative PCR analysis was conducted on the CFX96 system utilizing the TB Green premium Ex Taq II. The reaction conditions included an initial pre-denaturation at 95°C for 30 seconds, followed by 40 cycles of denaturation at 95°C for 5 seconds, annealing at 60°C for 34 seconds, and extension. The mRNA expression levels were determined using the quantitative cycle (Ct) value, with β-actin chosen as the internal reference gene. The differences in gene expression levels were calculated using the 2−ΔΔCt method, where ΔΔCq = ΔCq(target gene) - ΔCq(internal reference gene). Each experiment was repeated 3 times to ensure reliability. The primer sequences used for PCR amplification are provided in Table 1.
Table 1.
Primers sequences of RT-qPCR.
Gene | Sequences of primers (5′–3′) |
---|---|
VCAM-1 | Forward: GTGACTCCGTCTCATTGACTTGC |
Reverse: AGGATTCATTGTCAGCGTAGATGTG | |
MIP-2 | Forward: GCTGCTGCTCCTGCTCCTG |
Reverse: GGACTTCACCTTCACACTTTGGATG | |
MIP-1β- | Forward: CTCTTGGCAGCCTTCCTGATTTC |
Reverse: GGGTGGAAAGGTTTGGAGTATGTC | |
AKT1 | Forward: AGGATGTGGACCAACGTGAGG |
Reverse: GCAGGCAGCGGATGATGAAG | |
CASP3 | Forward: ACAGACAGTGGTGTTGATGATGAC |
Reverse: ATGGCACAAAGCGACTGGATG | |
PTGS2 | Forward: TTGGTCTGGTGCCTGGTCTG |
Reverse: AGTATTAGCCTGCTTGTCTGGAAC | |
IL-1β | Forward: GGACAGGATATGGAGCAACAAGTGG |
Reverse: CAACACGCAGGACAGGTACAGATTC | |
IL-6 | Forward: GGTGTTGCCTGCTGCCTTCC |
Reverse: GTTCTGAAGAGGTGAGTGGCTGTC | |
TNF-α | Forward: CAATGGCGTGGAGCTGAGAGATAAC |
Reverse: GCGATGCGGCTGATGGTGTG | |
β-actin | Forward: CCTGGCACCCAGCACAAT |
Reverse: GGGCCGGACTCGTCATAC |
2.13. Statistical analysis
Data analysis in this study was conducted using SPSS 26.0 statistical software. Measured data are expressed as the means ± standard deviation. Each experiment was repeated independently at least 3 times to ensure the reliability of the findings. Student t test was employed for comparing 2 groups, while one-way ANOVA was utilized for comparing multiple groups. Statistical significance was considered when the P value was less than 0.05 (P < .05).
2.14. Ethical statement
The experiment was performed using in vitro cell cultures, and no human or animal subjects were involved. All data is obtained from the database. Therefore, there were no ethical considerations or potential ethical concerns associated with this experiment.
3. Results
3.1. Potential targets for HQH
The active ingredients of HQH were identified through screening using the Traditional Chinese Medicine Systems Pharmacology database. Our screening criteria included an oral bioavailability value of ≥ 30% and a drug-likeness value of ≥ 0.18. Applying these criteria, a total of 48 active ingredients were identified in HQH. These active ingredients consisted of 4 species of Huaier, 36 species of Gouqi, 9 species of Huangjing, and 1 common component. Further details regarding these active ingredients can be found in Table 2.
Table 2.
Basic information of active components in HQH.
MOL ID | Molecule name | Degree | Source |
---|---|---|---|
MOL000098 | Quercetin | 152 | Gouqi |
MOL000480 | Genistein | 96 | Huaier |
MOL000358 | Beta-sitosterol | 75 | Huangjing, Gouqi |
MOL000422 | Kaempferol | 65 | Huaier |
MOL002714 | Baicalein | 37 | Huangjing |
MOL000449 | Stigmasterol | 31 | Gouqi |
MOL009650 | Atropine | 28 | Gouqi |
MOL005406 | Atropine | 26 | Gouqi |
MOL008400 | Glycitein | 23 | Gouqi |
MOL002959 | 3′-Methoxydaidzein | 19 | Huangjing |
MOL000546 | Diosgenin | 17 | Huangjing |
MOL004941 | (2R)-7-hydroxy-2-(4-hydroxyphenyl)chroman-4-one | 15 | Huangjing |
MOL001792 | DFV | 12 | Huangjing |
MOL000415 | Rutin | 12 | Huaier |
MOL000384 | Glucuronic acid | 9 | Huaier |
MOL006331 | 4′,5-Dihydroxyflavone | 8 | Huangjing |
MOL001323 | Sitosterol alpha1 | 7 | Gouqi |
MOL009646 | 7-O-Methylluteolin-6-C-beta-glucoside_qt | 6 | Gouqi |
MOL000953 | CLR | 5 | Gouqi |
MOL000359 | Sitosterol | 4 | Huangjing |
MOL001494 | Mandenol | 4 | Gouqi |
MOL001979 | LAN | 4 | Gouqi |
MOL007449 | 24-Methylidenelophenol | 4 | Gouqi |
MOL009604 | 14b-Pregnane | 4 | Gouqi |
MOL009622 | Fucosterol | 4 | Gouqi |
MOL009634 | 31-Norlanosterol | 4 | Gouqi |
MOL009641 | 4α,24-Dimethylcholesta-7,24-dienol | 4 | Gouqi |
MOL009644 | 6-Fluoroindole-7-Dehydrocholesterol | 4 | Gouqi |
MOL009681 | Obtusifoliol | 4 | Gouqi |
MOL001495 | Ethyl linolenate | 3 | Gouqi |
MOL006209 | Cyanin | 3 | Gouqi |
MOL008173 | Daucosterol_qt | 3 | Gouqi |
MOL009618 | 24-Ethylcholesta-5,22-dienol | 3 | Gouqi |
MOL009677 | Lanost-8-en-3beta-ol | 3 | Gouqi |
MOL009678 | Lanost-8-enol | 3 | Gouqi |
MOL009763 | (+)-Syringaresinol-O-beta-D-glucoside | 2 | Huangjing |
MOL003578 | Cycloartenol | 2 | Gouqi |
MOL005438 | Campesterol | 2 | Gouqi |
MOL009617 | 24-Ethylcholest-22-enol | 2 | Gouqi |
MOL009620 | 24-Methyl-31-norlanost-9(11)-enol | 2 | Gouqi |
MOL009621 | 24-Methylenelanost-8-enol | 2 | Gouqi |
MOL009633 | 31-Norlanost-9(11)-enol | 2 | Gouqi |
MOL009635 | 4,24-Methyllophenol | 2 | Gouqi |
MOL009639 | Lophenol | 2 | Gouqi |
MOL009640 | 4α,14α,24-Trimethylcholesta-8,24-dienol | 2 | Gouqi |
MOL009642 | 4α-Methyl-24-ethylcholesta-7,24-dienol | 2 | Gouqi |
MOL009656 | (E,E)-1-ethyl octadeca-3,13-dienoate | 2 | Gouqi |
MOL009665 | Physcion-8-O-beta-D-gentiobioside | 2 | Gouqi |
HQH = Huaiqihuang.
3.2. Construction of drug-component-target network
In our study, we successfully constructed a drug-component-target network model using Cytoscape 3.9.0 software by importing the active ingredients and potential targets. The resulting network, as shown in Figure 2, represents the interactions between target proteins, with nodes representing the proteins and edges representing their connections. Remarkably, this comprehensive network consists of 338 nodes and 730 edges, demonstrating the interactive relationships among various targets. Moreover, our extensive drug-component-target network analysis allowed us to identify the top 5 active ingredients based on their degree values. These highly connected components include Quercetin, Genistein, β-Glucosterol, Kaempferol, and Baicalein.
Figure 2.
“Herb-ingredients–targets” network construction. The orange part represent 3 herbs in HQH, the green nodes represent HQH active ingredients, the blue nodes represent the target of chemical composition interaction, and the gray edge represents the interaction between compound molecule and target. HQH = Huaiqihuang.
3.3. Potential targets for DKD
In this study, we conducted a comprehensive investigation using 4 publicly available disease databases to collect target genes associated with DKD. Our search yielded a total of 694 target genes from the GeneCards database (Relevance score ≥ 30), 533 target genes from the OMIM database, 23 target genes from the TTD database, and 43 target genes from the Drugbank databas. After eliminating duplicates and consolidating the data, we have successfully identified a total of 1073 potential disease targets (Fig. 3).
Figure 3.
Determination of DKD-related targets. DKD = diabetic kidney disease.
3.4. Analysis of drug-disease intersection targets
By conducting a Venn analysis of 286 targets associated with HQH active ingredients and 1073 potential DKD targets, we identified 118 intersection targets that are shared between drugs and diseases (Fig. 4).
Figure 4.
Venn diagram of potential targets of HQH-DKD. Purple represents potential targets where HQH acts and yellow represents targets involved in DKD. DKD = diabetic kidney disease, HQH = Huaiqihuang.
3.5. Construction of a PPI network and analysis of core targets
To further explore the interactions among the 118 identified common target genes, we inputted these genes into the STRING database to obtain their protein–protein interaction (PPI) network. The PPI network was then visualized using Cytoscape 3.9.0 software, as demonstrated in Figure 5. The resulting network consisted of 117 nodes representing the target genes and 2575 edges representing the interactions between these genes. To identify the core targets within this PPI network, we utilized the CytoNAC plugin. By setting the average of “Betweenness, Closeness, Degree, Eigenvector” as the cutoff values, we were able to screen and identify 35 core targets. These core targets include well-known genes such as AKT1, TNF, IL6, PTGS2, IL1B, and CASP3, which have been extensively linked to the pathogenesis of DKD. By analyzing the PPI network and identifying the core targets, we gain an in-depth understanding of the molecular interactions and key players involved in DKD. These findings provide valuable insights into the underlying mechanisms of DKD and open up new avenues for targeted therapeutic interventions.
Figure 5.
PPI network analysis. (A) Preliminary PPI network map of HQH-DKD; (B) final core PPI network map. Nodes represent proteins, Edges represent their interactions. The nodes with a larger area and darker color have a larger degree value. DKD = diabetic kidney disease, HQH = Huaiqihuang, PPI = protein–protein interaction.
3.6. GO and KEGG enrichment analysis
To gain insights into the potential mechanisms of HQH in DKD treatment, we conducted enrichment analysis using the Metascape platform. This analysis aimed to identify the biological processes involved. Our results revealed that the potential targets of HQH treatment were primarily associated with a variety of biological processes, including positive regulation of cell migration, response to hormone cellular, response to chemical stress, inflammatory response, gland development, response to nutrient levels, negative regulation of cell differentiation, and negative regulation of cell population proliferation.
Moreover, KEGG enrichment analysis unveiled a total of 187 signaling pathways. For visualization purposes, we narrowed down the top 20 pathways based on their p-values. Notably, significant pathways included the cancer signaling pathway, AGE-RAGE signaling pathway, HIF-1 signaling pathway, IL-17 signaling pathway, NF-κB signaling pathway, and TGF-β signaling pathway, as evidenced in Figure 6. These pathways are closely linked to inflammatory responses, suggesting that HQH may exert its therapeutic effects in DKD through anti-inflammatory mechanisms.
Figure 6.
GO and KEGG enrichment graphs. (A) Green represents BP, orange represents CC, blue represents MF, and the height of the bar represents the number of enrichment; (B) the size of the bubble represents the number of gene enrichment, the larger the bubble, the more the number of genes; the darker the color, the smaller the P value, the greater the significance of enrichment. BP = biological processes, CC = cellular components, GO = gene ontology, KEGG = Kyoto encyclopedia of genes and genomes, MF = molecular functions.
3.7. “Compound-Target-Pathway” network
The “Compound-Target-Pathway” network for the treatment of DKD with HQH was constructed by importing 5 active compounds from 3 Chinese medicinal herbs in HQH, 15 key target genes obtained from screening, and 11 pathways into the Cytoscape 3.9.0 software. The network consists of 32 nodes and 143 edges, as shown in Figure 7. The results demonstrate that the 5 main active compounds of HQH may exert therapeutic effects on DKD by regulating the expression of target genes such as AKT1, TNF, IL6, PTGS2, IL1B, and CASP3, and acting on multiple pathways simultaneously.
Figure 7.
Network of herb-component-target-pathway. The orange nodes represent the herb component, the blue nodes represent the HQH-KEGG pathway common target, and the yellow round nodes represent the KEGG pathway. HQH = Huaiqihuang, KEGG = Kyoto encyclopedia of genes and genomes.
3.8. Molecular docking
Molecular docking was conducted to investigate the interactions between key targets and active components within the component-target pathway network. Protein structures of PTGS2 (5F19), IL6 (1ALU), AKT1 (7MYX), IL1B (1HIB), CASP3 (1NME), and TNF (1A8M) were obtained from the Protein Data Bank website (https://www.rcsb.org/).[25] Subsequently, these protein structures were subjected to docking simulations with 5 active ingredient ligands from the drugs. The closeness of protein-ligand interactions can be assessed through the binding energy obtained from the docking process. Lower binding energy indicates a more stable binding conformation and a higher likelihood of interaction. The results of the Molecular docking indicated that the core compounds of HQH, including β-Sitosterol and Baicalein, exhibited docking binding energies of ≤−5 kJ/mol with their respective potential targets. These findings suggest that HQH possesses a low conformational energy and demonstrates strong binding activity with the predicted targets. The details of the docking binding energies can be observed in Figure 8. To illustrate the successful binding interactions between the main components and main targets in HQH, a representative Molecular docking diagram was selected and included as a schematic diagram (Fig. 9).
Figure 8.
Thermal diagram analysis of molecular docking fraction.
Figure 9.
Molecular docking mode of active components of HQH with core targets. HQH = Huaiqihuang.
3.9. Effect of HQH on HK-2 cells viability
In order to investigate the potential therapeutic effect of HQH on DKD, we conducted experiments to assess its impact on the viability of HK-2 cells under conditions that simulate high glucose-induced DKD. It is well established that renal tubular epithelial cells play a significant role in DKD. Initially, we observed that the lowest cell proliferation activity occurred at a glucose concentration of 40 mmol/L. Subsequently, we treated the HK-2 cells with varying concentrations of HQH (2, 4, 6, 8, 10, 12, and 14 mg/mL) for 48 hours under high glucose stimulation. Our analysis using the CCK-8 assay demonstrated a concentration-dependent effect of HQH on the viability of the HK-2 cells exposed to high glucose. Remarkably, the most substantial impact on HK-2 cell viability was observed at the concentration of 8 mg/mL HQH (P < .001), as shown in Figure 10. Based on these results, we determined that a concentration of 8mg/mL of HQH would be utilized for subsequent experimental interventions in the HK-2 cells.
Figure 10.
Effect of HQH on viability of HK-2 cells. HK-2 = human proximal tubular epithelial cells, HQH = Huaiqihuang.
3.10. Effect of HQH on high glucose-induced core target mRNA expression in HK-2 cells
To investigate the therapeutic effects of HQH on DKD, we selected key targets, including AKT1, TNF, IL6, PTGS2, IL1B, and CASP3, based on the results of PPI analysis and pathway enrichment analysis. Our objective was to determine if HQH could reduce the expression levels of these targets in HK-2 cells subjected to high glucose-induced injury. Quantitative real-time PCR (qRT-PCR) was employed to assess the mRNA expression levels. The results revealed a significant upregulation of AKT1, TNF, IL6, PTGS2, IL1B, and CASP3 in the high glucose-treated group compared to the control group, with a statistically significant difference (P < .01). However, treatment with 8mg/mL HQH significantly attenuated the expression levels of AKT1, TNF, IL6, PTGS2, IL1B, and CASP3 in the HK-2 cells. These results indicate that HQH protects against high glucose-induced damage and effectively inhibits the activation of key targets in the NF-κB signaling pathway (P < .01). As illustrated in Figure 11.
Figure 11.
Effect of HQH on mRNA expressions of AKT1, TNF, IL-6, PTGS2, IL-1B and CASP3 in HK-2 cells. HK-2 = human proximal tubular epithelial cells, HQH = Huaiqihuang.
3.11. Impact of HQH on high glucose-induced NF-κB signaling pathway expression in HK-2 cells
As in Figure 12, the high glucose group exhibited a notable increase in p-p65 levels compared to the control group, indicating the activation of the NF-κB signaling pathway. Interestingly, both Captopril and HQH effectively reversed the high glucose-induced elevation of p65 phosphorylation levels, while not affecting the expression of total p65 protein. These findings indicate that HQH possesses the ability to inhibit the activation of the NF-κB signaling pathway, thereby exerting a protective effect against kidney injury.
Figure 12.
HQH inhibits NF-κB p65 pathway in HK-2 cells. (A) Representative bands of NF-κB p65 proteins detected by Western blot. (B) Relative protein levels of in each group of HK-2 cells. Data are expressed as mean ± SD (n = 3). **P < .01, compared with the control group; ##P < .01, compared with the HG group. HG = high glucose, HK-2 = human proximal tubular epithelial cells, HQH = Huaiqihuang, NF-κB = nuclear factor-kappa B.
Furthermore, immunofluorescence analysis was conducted to examine the impact of HQH on the nuclear translocation of NF-κB p65 in high glucose-induced HK-2 cells. As demonstrated in Figure 13, high glucose treatment resulted in a significant increase in green fluorescence intensity in the nucleus of HK-2 cells, representing the translocation of NF-κB p65 from the cytoplasm to the nucleus under high glucose stimulation. In contrast, the Captopril and HQH treatment groups exhibited a substantial decrease in green fluorescence intensity, indicating a reduction in the nuclear localization of NF-κB p65. These results suggest that HQH effectively inhibits the nuclear translocation of NF-κB p65 in HK-2 cells stimulated by high glucose, leading to an improvement in the high glucose-induced inflammatory response.
Figure 13.
The fluorescence intensity (green) indicated the expression level of P65 in HK-2 of the C, MAN, HG, HG + HQH and CAP groups by immunofluorescence. Green and blue fluorescence represent P65 and the cell nucleus, respectively. CAP = Captopril, HG = high glucose, HK-2 = human proximal tubular epithelial cells, HQH = Huaiqihuang, MAN = mannitol.
3.12. Effect of HQH on high glucose-induced inflammatory chemokine mRNA expression in HK-2 cells
In our study, we assessed the mRNA expression levels of Mip-1β, Mip-2, VCAM-1, and COX-2 in HK-2 cells exposed to high glucose using PCR. As depicted in Figure 14, compared to the control group, high glucose stimulation significantly increased the expression levels of Mip-1β, Mip-2, VCAM-1, and COX-2 in HK-2 cells, indicating a heightened state of inflammation (P < .01). However, treatment with both CAP and HQH effectively suppressed the high glucose-induced increase in the expression of Mip-1β, Mip-2, and VCAM-1 genes in HK-2 cells (P < .01).
Figure 14.
Effect of HQH on mRNA expressions of Mip-1β, mip-2 and vcam1 in HK-2 cells. HK-2 = human proximal tubular epithelial cells, HQH = Huaiqihuang.
4. Discussion
DKD is a prominent cause of chronic kidney disease and end-stage renal disease globally.[26] Within TCM, DKD is classified under categories such as “Shenxiao,” and “edema.”[27] The primary pathogenesis often involves a combination of deficiency and excess, along with the presence of blood stasis, dampness, and other factors that obstruct the kidney meridians. Consequently, TCM practitioners historically prioritized supplementing qi, nourishing yin, and strengthening the spleen and kidney in the treatment of DKD. HQH is a representative TCM Polypill that primarily focuses on replenishing qi and yin, as well as replenishing qi and blood.[7] Previous studies have demonstrated that HQH effectively controls proteinuria in chronic kidney disease patients and reduces infection rates and recurrence rates.[28] Furthermore, HQH significantly protects podocytes from damage induced by doxorubicin and improves renal tubulointerstitial damage.[29] Sun et al[30] discovered that HQH significantly decreases proteinuria in rats with doxorubicin nephropathy, reduces proteinuria in rats with IgA nephropathy, and diminishes the deposition of IgA in the mesangium area.
TCM has several advantages in terms of improving clinical symptoms, regulating blood sugar, alleviating inflammatory reactions, and delaying the progression of DKD.[31–33] The mechanism of action of TCM interventions is complex, involving multiple components and targets. Network pharmacology combines network analysis and pharmacology to systematically study the active ingredients, targets, and pathways of drugs at the molecular level, enhancing our understanding of the interactions among drug components, targets, and pathways. To further investigate the pharmacological basis and potential biological mechanisms of HQH treatment, we prospectively predicted the bioactive components and potential targets of HQH from a network pharmacology perspective. We identified 49 active components and obtained 118 common targets, constructing a protein interaction network. Target genes such as IL6, TNF, IL1B, and PTGS2 may play key roles in the biological processes underlying HQH’s effects in DKD. The study identified 5 major active components: Quercetin, Genistein, β-Sitosterol, Kaempferol, and Baicalein, suggesting that these compounds significantly contribute to the therapeutic effects of HQH. GO functional enrichment analysis and KEGG pathway enrichment analysis were conducted on the potential targets of HQH in DKD treatment. These core targets may be involved in inflammatory response and oxidative stress, exerting multi-component, multi-target, and multi-network renal protective effects. Molecular docking was performed between the top 5 core components in the component-target-pathway network diagram of TCM and their respective core targets. The results demonstrated stable binding between the main components of HQH and pathway proteins and target proteins, providing further evidence for the feasibility of HQH treatment. The binding energy of TNF and IL1B docking with most components was relatively low, suggesting that HQH may target TNF and IL1B to exert its effects.
Among the core targets identified in this study, IL6 and IL1B are coding genes for the classic inflammatory factor interleukin (IL).[34] TNF-α is a cytokine with significant pro-inflammatory effects, primarily produced by monocytes and macrophages, but also synthesized and secreted by mesangial cells, endothelial cells, and epithelial cells.[35] TNF-α and its receptors TNFR1 and TNFR2 are involved in the synthesis of fibro kinetics, chemokines, growth factors, and extracellular viral matrix proteins. They amplify the inflammatory cascade effects and mediate the inflammatory response of various intrinsic renal cells. IL1B plays a critical role in inflammation and immune response. In DKD, IL-1β can activate and aggregate immune cells, induce the synthesis and release of other inflammatory factors, chemokines, and adhesion molecules, amplify local or systemic inflammatory reactions, and play a significant role in the inflammatory damage to renal tubular epithelial cells. IL1B can also stimulate the occurrence of an inflammatory reaction in HK-2 cells, thereby promoting the progression of DKD.[36]
NF-κB is a dimeric transcription factor that plays a vital role in coordinating gene transcription in various pathological and physiological processes, especially in the inflammatory development of DKD.[36] Pro-inflammatory cytokines such as TNF-α, IL-1β, and lipopolysaccharides can bind to their corresponding receptor subunits and activate the NF-κB signaling pathway. This triggers a signaling cascade that enhances p65 phosphorylation, leading to subsequent nuclear translocation and activation of pro-inflammatory target genes, including Mip-1β, Mip-2, VCAM-1, and COX-2.[37] NF-κB, as an inflammatory mediator, can trigger a series of inflammatory reactions in the kidneys, exacerbating kidney damage and accelerating the occurrence and progression of DKD. Blocking the NF-κB signaling pathway is considered a preferred strategy for treating DKD.
During the inflammatory process, the NF-κB pathway regulates inflammatory mediators such as Mip-2 and Mip-1β, which play important biological roles. HK-2 cells are involved in renal inflammatory responses, and the activation of the NF-κB pathway in these cells triggers inflammation and the release of inflammatory mediators, including Mip-2 and Mip-1β. Mip-2 is a chemokine that attracts white blood cells to the site of inflammation and promotes the proliferation of certain non-immune cells.[38] Its expression levels increase significantly during the inflammatory response and contribute to the recruitment of white blood cells, further intensifying the inflammatory response. Similarly, Mip-1β is an important inflammatory mediator that induces the migration of monocytes, macrophages, and T cells to the site of inflammation. It functions through the activation of the NF-κB pathway, playing a role in enhancing the strength and extent of the inflammatory response.[39] VCAM-1, a member of the immunoglobulin superfamily, is upregulated in DKD and plays a crucial role in immune responses and inflammatory reactions in DKD, garnering increasing attention in DKD research.[40] These molecules act through ligand-receptor interactions and are involved in various physiological and pathological processes, including inflammation, immune responses, blood coagulation, thrombosis formation, wound healing, tumor metastasis, and dissemination. In this study, HQH significantly suppressed the expression levels of VCAM-1, Mip-2, and Mip-1β mRNA in HK-2 cells exposed to high glucose. Therefore, the NF-κB signaling pathway, by coordinating the inflammatory response, is implicated in the development of DKD.
Moreover, HQH inhibits inflammatory reactions in diseases by suppressing the NF-κB signaling pathway.[41] The study also observed that high glucose levels induced nuclear translocation of NF-κB and increased the expression of p-NF-κB p65 protein in HK-2 cells. However, treatment with HQH significantly inhibited NF-κB nuclear translocation and decreased in p-NF-κB p65 protein expression.
This study provides evidence that HQH has a significant inhibitory effect on inflammation in high glucose-induced HK-2 cells, and its mechanism of action and target molecules have been validated through network pharmacology and in vitro experiments. However, there are certain limitations in this study. Currently, network information technology is not comprehensive, and the accuracy and real-time updating of database data need to be improved. Additionally, we only conducted in vitro experiments to further demonstrate the anti-inflammatory effects and molecular mechanisms of HQH, but further in vivo experiments are needed to validate its predicted components, targets, and pathways. In future experimental designs, we will focus on exploring the therapeutic effects of HQH in inhibiting renal inflammation in animal models and its potential mechanisms of action. Based on these findings, this study not only provides strong evidence for the necessity of network pharmacology in TCM research on the prevention and treatment of DKD but also establishes foundational data for the development of HQH. By combining with modern experimental approaches, it enriches the potential application of TCM in clinical practice.
5. Conclusion
In conclusion, our results suggest that HQH may act on key targets such as IL6, TNF, IL1B, and PTGS2 through multiple chemical constituents (quercetin, genistein, β-sitosterol, kaempferol, baicalein, etc.) and reduce the levels of inflammation by modulating the NF-κB signaling pathway, thereby alleviating renal damage. Our findings provide a reference for further investigating the mechanisms of action of HQH in the treatment of DKD.
Author contributions
Data curation: Junwei Wang.
Funding acquisition: Chanjuan Ma.
Supervision: Chanjuan Ma.
Validation: Junwei Wang, Guiqiao Ma, Peipei Zhang, Chaojing Ma, Jing Shao, Liping Wang.
Writing – original draft: Junwei Wang, Guiqiao Ma.
Writing – review & editing: Junwei Wang, Guiqiao Ma.
Abbreviations:
- DKD
- diabetic kidney disease
- GO
- gene ontology
- HK-2
- human proximal tubular epithelial cells
- HQH
- Huaiqihuang
- IL
- interleukin
- KEGG
- Kyoto encyclopedia of genes and genomes
- NF-κB
- Nuclear factor kappa B
- PPI
- protein–protein interaction
- TCM
- Traditional Chinese medicine
The authors have no conflicts of interest to disclose.
CM was supported by a grant from the Shanxi Provincial Administration of Traditional Chinese Medicine (grant no. 2022ZYYC071), Hubei Chen Xiaoping Science and Technology Development Foundation (grant no. CXPJJH122003-52), and Research Project Supported by Shanxi Scholarship Council of China (grant no. 2022-206).
The datasets generated during and/or analyzed during the current study are publicly available.
How to cite this article: Wang J, Ma G, Zhang P, Ma C, Shao J, Wang L, Ma C. Mechanism of Huaiqihuang in treatment of diabetic kidney disease based on network pharmacology, molecular docking and in vitro experiment. Medicine 2023;102:50(e36177).
Contributor Information
Junwei Wang, Email: 1595654084@qq.com.
Guiqiao Ma, Email: mcj5670206@163.com.
Peipei Zhang, Email: 1204419795@qq.com.
Chaojing Ma, Email: mcj5670206@163.com.
Jing Shao, Email: 1196850017@qq.com.
Liping Wang, Email: 1595654084@qq.com.
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