Clinical and Translational Research Open Access
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Diabetes. Jul 15, 2024; 15(7): 1562-1588
Published online Jul 15, 2024. doi: 10.4239/wjd.v15.i7.1562
Network pharmacology and molecular dynamics study of the effect of the Astragalus-Coptis drug pair on diabetic kidney disease
Mo-Yan Zhang, Liaoning University of Traditional Chinese Medicine, Liaoning University of Traditional Chinese Medicine, Shenyang 110847, Liaoning Province, China
Shu-Qin Zheng, Department of Endocrinology, The Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang 110032, Liaoning Province, China
ORCID number: Mo-Yan Zhang (0009-0002-2495-790X); Shu-Qin Zheng (0009-0006-2801-5986).
Author contributions: Zhang MY and Zheng SQ participated in the study design and data collection; Zhang MY performed the data analysis; Zhang MY drafted the manuscript; and Zhang MY and Zheng SQ revised the manuscript; All the authors read and approved the final manuscript.
Conflict-of-interest statement: All the authors report having no relevant conflicts of interest for this article.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Shu-Qin Zheng, MSc, Associate Professor, Department of Endo-crinology, The Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, No. 33 Beiling Street, Huanggu District, Shenyang 110032, Liaoning Province, China. qxjzsq@sina.com
Received: April 3, 2024
Revised: May 13, 2024
Accepted: May 29, 2024
Published online: July 15, 2024
Processing time: 95 Days and 17 Hours

Abstract
BACKGROUND

Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease. The Astragalus-Coptis drug pair is frequently employed in the management of DKD. However, the precise molecular mechanism underlying its therapeutic effect remains elusive.

AIM

To investigate the synergistic effects of multiple active ingredients in the Astragalus-Coptis drug pair on DKD through multiple targets and pathways.

METHODS

The ingredients of the Astragalus-Coptis drug pair were collected and screened using the TCMSP database and the SwissADME platform. The targets were predicted using the SwissTargetPrediction database, while the DKD differential gene expression analysis was obtained from the Gene Expression Omnibus database. DKD targets were acquired from the GeneCards, Online Mendelian Inheritance in Man database, and DisGeNET databases, with common targets identified through the Venny platform. The protein-protein interaction network and the “disease-active ingredient-target” network of the common targets were constructed utilizing the STRING database and Cytoscape software, followed by the analysis of the interaction relationships and further screening of key targets and core active ingredients. Gene Ontology (GO) function and Kyoto Ency-clopedia of Genes and Genomes (KEGG) pathway enrichments were performed using the DAVID database. The tissue and organ distributions of key targets were evaluated. PyMOL and AutoDock software validate the molecular docking between the core ingredients and key targets. Finally, molecular dynamics (MD) simulations were conducted to simulate the optimal complex formed by interactions between core ingredients and key target proteins.

RESULTS

A total of 27 active ingredients and 512 potential targets of the Astragalus-Coptis drug pair were identified. There were 273 common targets between DKD and the Astragalus-Coptis drug pair. Through protein-protein interaction network topology analysis, we identified 9 core active ingredients and 10 key targets. GO and KEGG pathway enrichment analyses revealed that Astragalus-Coptis drug pair treatment for DKD involves various biological processes, including protein phosphorylation, negative regulation of apoptosis, inflammatory response, and endoplasmic reticulum unfolded protein response. These pathways are mainly associated with the advanced glycation end products (AGE)-receptor for AGE products signaling pathway in diabetic complications, as well as the Lipid and atherosclerosis. Molecular docking and MD simulations demonstrated high affinity and stability between the core active ingredients and key targets. Notably, the quercetin-AKT serine/threonine kinase 1 (AKT1) and quercetin-tumor necrosis factor (TNF) protein complexes exhibited exceptional stability.

CONCLUSION

This study demonstrated that DKD treatment with the Astragalus-Coptis drug pair involves multiple ingredients, targets, and signaling pathways. We propose a novel approach for investigating the molecular mechanism underlying the therapeutic effects of the Astragalus-Coptis drug pair on DKD. Furthermore, we suggest that quercetin is the most potent active ingredient and specifically targets AKT1 and TNF, providing a theoretical foundation for further exploration of pharmacologically active ingredients and elucidating their molecular mechanisms in DKD treatment.

Key Words: Astragalus membranaceus, Coptis chinensis Franch, Diabetic kidney disease, Network pharmacology, Molecular docking, Molecular dynamics simulation

Core Tip: Network pharmacology, molecular docking, and molecular dynamics simulation studies have demonstrated that diabetic kidney disease (DKD) treatment with the Astragalus-Coptis drug pair involves a diverse range of active ingredients, targets, and pathways. Additionally, quercetin has been found to exhibit strong affinity and binding stability with AKT serine/threonine kinase 1 and tumor necrosis factor, highlighting its potential therapeutic role in DKD. The findings of this study establish a robust theoretical basis for applying the Astragalus-Coptis drug pair in DKD treatment, and these results may provide a guiding framework for further experiments.



INTRODUCTION

Diabetic kidney disease (DKD) is a form of chronic kidney disease that arises as a consequence of diabetes mellitus and is the primary etiology of end-stage renal disease (ESRD)[1]. Approximately 40% of patients with type 2 diabetes and approximately 30% of patients with type 1 diabetes are prone to developing DKD[2]. As the prevalence of diabetes increases globally, the number of people with DKD is expected to increase by nearly 50% over the next 24 years, from 537 million to 783 million[3]. The pathogenesis of DKD is associated with alterations in renal hemodynamics, oxidative stress, inflammation, renal fibrosis, hypoxia, and hyperactivity of the renin-angiotensin-aldosterone system[4,5]. The current approach to managing DKD primarily revolves around glycemic control and rigorous antihypertensive therapy; however, it fails to effectively halt the progression of DKD to ESRD. The treatment options for end-stage renal failure, such as dialysis and kidney transplantation, are burdened by high costs, frequent adverse reactions, and limited availability of renal sources[6-8].

Traditional Chinese medicine (TCM) has a rich history spanning thousands of years, rendering it uniquely advantageous for ameliorating the clinical symptoms and indicators exhibited by patients with DKD. Consequently, TCM is an indispensable intervention method for the clinical prevention and treatment of this disease[9,10]. The pathogenesis of DKD is relatively intricate. Modern TCM succinctly characterizes it as the syndrome of “deficiency in origin and excess in superficiality”. Several studies have demonstrated that the “deficiency in origin” of DKD is primarily characterized by Qi deficiency, while the “excess in superficiality” is dominated by heat. Among these, Astragalus (Astragalus membranaceus. Huang Qi) and Coptis (Coptis chinensis Franch. Huang Lian) are commonly used for invigorating Qi and clearing heat, respectively[11]. Astragalus was initially described in Benjing (Sheng Nong’s herbal classic) for its functions in tonifying Qi, elevating Yang, consolidating surface and antiperspirant effects, reducing edema, and promoting diuresis, nourishing body fluids and enriching the blood. Coptis is first mentioned in Benjing, highlighting its therapeutic properties, such as heat-clearing and dampness-drying effects, fire-reducing capabilities and detoxification abilities. The antioxidative stress, inhibitory response to inflammation, regulation of glucose and lipid metabolism, protection of podocytes, and en-hancement of insulin sensitivity exhibited by Astragalus can effectively attenuate renal function deterioration in patients with DKD[12]. The administration of Coptis exerts a significant impact on glycemic control, blood pressure regulation, lipid metabolism disorder correction, and proteinuria reduction in patients with DKD, thereby effectively delaying disease progression[13]. The compatibility of Astragalus and Coptis aligns with the therapeutic principle of “supplementing deficiency and reducing actual symptoms” in the Sanbu Jiu hou Lun of Plain Questions, as both herbs serve to tonify Qi and reduce fire. The Astragalus-Coptis drug pair has demonstrated significant efficacy in DKD clinical treatment. However, there is a lack of comprehensive investigations into its beneficial effects on DKD mechanisms, including analyses of its active ingredients and targets, biological processes (BP), and pathways.

Network pharmacology (NP) is an emerging discipline rooted in the principles of systems biology and is employed for identifying targets within TCM ingredients. Aligned with the holistic approach of TCM in treating diseases through multiple ingredients, targets, and pathways, NP enables a comprehensive assessment of drug regulatory effects on biomolecular networks from a systemic perspective. This novel paradigm offers insights into and visualizations of the potential interaction network between TCM and multifactorial diseases[14,15]. The application of molecular docking techniques enables the evaluation of binding surfaces and interaction forces between receptors and ligands, as well as the prediction of binding modes and affinities in receptor-ligand complexes[16]. The structural stability and flexibility of the receptor and ligand, as well as the dynamics of receptor-ligand interactions based on appropriate force fields, are assessed through molecular dynamics (MD) simulations[17].

The present study employed NP to investigate the synergistic effects of multiple active ingredients in the Astragalus-Coptis drug pair on DKD through multiple targets and pathways. Molecular docking technology and MD simulation were employed to validate the results, providing a theoretical basis for subsequent traditional Chinese medicine treatment of DKD and novel drug development.

MATERIALS AND METHODS
Identification of the active ingredients and targets of the Astragalus-Coptidis drug pair

The TCMSP is a highly authoritative platform in Chinese herbal medicine system pharmacology that facilitates the screening of active ingredients in drugs and the analysis of the relationships between drug targets and diseases[18]. The active ingredients were queried in the TCMSP database using “Huangqi” and “Huanglian” as search terms. TCM is primarily administered orally and exerts its effects through gastrointestinal digestion and absorption, blood circulation, and metabolism. Therefore, it is essential to evaluate the absorption, distribution, metabolism, and excretion (ADME) properties of the retrieved active ingredients. The retrieval results were filtered based on the criteria of oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18[19]. The active ingredients were imported from the PubChem database and Novopro platform, consistent with the standard. Canonical SMILESs for each ingredient were obtained and subsequently entered into the SwissADME platform[20]. The GI absorption score was classified as “high”. The screening criteria based on the Lipinski, Ghose, Veber, Egan, and Muegge rules required at least two positive results (“Yes”) for DL. The results were collected and imported into the SwissTargetPrediction database, where “Homo sapiens” was selected as the target species. Predictions of targets for each active ingredient were made, and target data with a probability greater than 0.1 were compiled. The URLs for the database and platform mentioned in this article are presented in Table 1.

Table 1 Databases and platform addresses.
Identification of common targets between DKD targets and active ingredient targets

The Human Gene Database (GeneCards)[21], the Online Mendelian Inheritance in Man database (OMIM)[22], and the DisGeNET platform[23] were utilized to search for “Diabetic Kidney Disease” and “Diabetic Nephropathy” as keywords. The targets obtained from these three databases were then consolidated, eliminating any duplicates, to obtain the targets related to DKD. The Venny2.1 platform was used to identify common targets by identifying the intersection of targets for active ingredients in the Astragalus-Coptis drug pair and DKD targets, and the results were visualized using a Venn diagram. Additionally, gene expression levels in DKD were analyzed by retrieving samples from the Gene Expression Omnibus with “Diabetic Kidney Diseases” as the keyword and “Homo sapiens” as the organism. Gene expression data from DKD and normal kidney samples (GSE1009) were accessed[24]. The gene expression analysis data for GSE1009 were obtained from the GPL8300 platform (source: Affymetrix Human Gene Expression Panel). Thresholds of |log2FC| > 1 and P < 0.05 were applied to identify DEGs[25]. Volcanoes and heatmaps were visualized using the ggplot2 and pheatmap packages in R software.

Construction of the “disease-active ingredient-target” network

The network was constructed using Cytoscape 3.10.1, with a focus on potential key objectives, followed by system analysis of the network parameters[26]. A network diagram of “disease-active ingredient-target” was generated using Cytoscape 3.10.1, incorporating the active ingredients of the Astragalus-Coptis drug pair and common and DKD targets. The topological properties of each node were calculated using the “CytoNCA” functional module in Cytoscape 3.10.1 to determine their significance, with nodes ranked based on degree value from high to low for core active ingredient identification.

Establishment of the protein-protein interaction network

The STRING database systematically compiles and integrates protein-protein interactions, encompassing physical contacts and functional associations[27]. Protein-protein interaction (PPI) analysis of common targets was conducted using the STRING database. Multiple proteins were selected, and Homo sapiens was chosen as the research subject to construct a PPI network diagram of common targets. The TSV file of the interaction network was exported. Cytoscape 3.10.1 was utilized to import the TSV files, and the CentiscaPe 2.2 function module was employed to calculate three network topology parameters: Degree centrality, betweenness centrality, and closeness centrality. Subsequently, the PPI network graph underwent optimization based on thresholds set for degree centrality, betweenness centrality, and closeness centrality values to identify key targets[28]. The MCODE plug-in in Cytoscape 3.10.1 was utilized to identify high-relevance modules in the PPI network based on topological parameters, including degree cutoff = 2, K-core = 2, node score cutoff = 0.2, and maximum depth = 100.

Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes enrichment analysis

The joint utilization of the DAVID enrichment analysis platform is sought to enhance our understanding of the functional significance of common targets[29]. Gene Ontology (GO) biological function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed separately[30]. GO functional enrichment analysis included three categories: Biological process (BP), cellular component (CC), and molecular function (MF). The results of the enrichment analysis were exported, and based on the P value, the top 20 enriched terms in both GO and KEGG were closely examined for their relevance to DKD. A bioinformatics platform will be used for data visualization.

Construction of the “key target-organ” network

The precise mechanisms underlying the in vivo metabolism of the Astragalus-Coptis drug pair remain unclear, and multiple organs and tissues are likely involved in DKD treatment. To further investigate this possibility, microarray analysis was conducted using the BioGPS database to obtain the mRNA levels of key targets for each Astragalus-Coptis drug pair in various organ tissues[31]. Subsequently, a network map depicting the interconnectedness of these “key target-organ” was constructed using Cytoscape 3.10.1.

Construction of the “active ingredient-target-signaling pathway” network

To visualize and elucidate the intricate relationships among active ingredients, pathways, and targets, we imported the data on the core active ingredients of the Astragalus-Coptis drug pair, as well as the key targets and main pathways obtained through KEGG enrichment analysis, into Cytoscape 3.10.1 software for visualization purposes. Subsequently, a network diagram illustrating the “active ingredient-target-signaling pathway” was generated.

Molecular docking verification

The receptor of the Astragalus-Coptis drug pair was utilized as the key target for treating DKD, while its core active ingredient served as the ligand for molecular docking verification to analyze its binding affinity. The molecular structure data of the receptor and ligand proteins were obtained from the RCSB database and TCMSP database, respectively, and converted into pdb format. The receptor and ligand proteins were pretreated using PyMOL software to eliminate water molecules and the original ligands. AutoDock-Tools 1.5.6 software was used to conduct molecular docking between the processed receptors and ligand files, which were saved in “pdbqt” format. AutoDock Vina was used to calculate the minimum binding energy between each target and active ingredient, and a heatmap was generated on a bioinformatics platform for visualization purposes. Finally, PyMOL software was used to visualize the docking results.

MD simulation

Gromacs 2018 was chosen as the software for dynamics simulations, while OPLS was utilized for the force fields of both proteins and small molecules. The TIP3P water model was used to add water molecules to the system, with a 10 nm3 × 10 nm3 × 10 nm3 water box established (ensuring that the edge of the water box was at least 1.2 nm away from the protein). An ion auto-balance system was also incorporated. Electrostatic interactions were handled using the particle-mesh Ewald method in conjunction with the steepest descent energy minimization (with a maximum of 50000 steps). The Coulomb force distance and van der Waals radius cutoff distances were set at 1 nm each. Subsequently, a canonical ensemble and an isothermal-isobaric ensemble were employed to equilibrate the system before conducting MD simulations for 100 ns under room temperature and pressure conditions. A nonbonded interaction cutoff value of 10 Ångstroms was applied. The V-rescale temperature coupling method maintained the simulation temperature at 300 K, while pressure control was achieved using the Berendsen method at 1 bar. Calculating binding free energy allows the assessment of intermolecular interaction strength within receptor-ligand complexes by quantifying contributions from various chemical energies. The Molecular Mechanics Poisson-Boltzmann Surface Area method provides a straightforward means to quantify the binding free energy between receptors and ligands[32]. The binding free energy was computed using the gmx_MMPBSA tool implemented in GROMACS[33].

RESULTS
Active ingredients and potential targets of the Astragalus-Coptis drug pair

The Astragalus-Coptis drug pair was selected from the TCMSP database, resulting in 135 active ingredients. To meet the criteria, these active ingredients had to have an OB equal to or greater than 30%, a DL value equal to or greater than 0.18, a “high” gastrointestinal absorption score according to the SwissADME platform, and at least two positive results for the five principles of drug similarity (Lipinski, Ghose, Veber, Egan, Muegge). After applying these criteria, we identified 27 active ingredients from the Astragalus-Coptis drug pair (Table 2). Subsequently, using the SwissTargetPrediction database for target prediction analysis of these 27 active ingredients resulted in 1646 predicted targets. After removing duplicates, we obtained a final list of 512 unique targets for these active ingredients.

Table 2 Active ingredients of the Astragalus-Coptis drug pair.
TCM
Serial number
MOL ID
Ingredient
OB (%)
DL
Astragalus membranaceusHQ1MOL0003799,10-dimethoxypterocarpan-3-O-β-D-glucoside36.740.92
HQ2MOL000387Bifendate31.100.67
HQ3MOL0004421,7-Dihydroxy-3,9-dimethoxy pterocarpene39.050.48
HQ4MOL0003713,9-di-O-methylnissolin53.740.48
HQ5MOL000380(6aR,11aR)-9,10-dimethoxy-6a,11a-dihydro-6H-benzofurano[3,2-c]chromen-3-ol64.260.42
HQ6MOL000354Isorhamnetin49.600.31
HQ7MOL0003787-O-methylisomucronulatol74.690.30
HQ8MOL000398Isoflavanone109.990.03
HQ9MOL000239Jaranol50.830.29
HQ10MOL000098Quercetin46.430.28
HQ11MOL000438(3R)-3-(2-hydroxy-3,4-dimethoxyphenyl)chroman-7-ol67.670.26
HQ12MOL000417Calycosin47.750.24
HQ13MOL000422Kaempferol41.880.24
HQ14MOL000392Formononetin69.670.21
Coptis chinensis FranchHL1MOL002903(R)-Canadine55.370.77
HL2MOL001454Berberine36.860.78
HL3MOL002894Berberrubine35.740.73
HL4MOL002904Berlambine36.680.82
HL5MOL001458Coptisine30.670.86
HL6MOL002907Corchoroside A_qt104.950.78
HL7MOL002897Epiberberine43.090.78
HL8MOL000622Magnograndiolide63.710.19
HL9MOL008647Moupinamide86.710.26
HL10MOL013352Obacunone43.290.77
HL11MOL000785Palmatine64.600.65
HL12MOL000098Quercetin46.430.28
HL13MOL002668Worenine45.830.87
DKD targets and common targets

The integration of 2961, 1818, and 1189 DKD targets obtained from the GeneCards, OMIM, and DisGeNET databases yielded 4044 unique DKD targets (Figure 1A). By employing the Venny 2.1 tool to compare the active ingredient targets with the DKD targets, a Venn diagram was generated (Figure 1B), revealing a set of 273 common targets. Furthermore, DEG between DKD samples and normal samples in the test group using GSE1009 data identified 34 upregulated genes and 16 downregulated genes (Table 3). Volcano plots and heatmaps illustrating the differential gene expression data for DKD are presented in Figure 1C and D.

Figure 1
Figure 1 Screening of Astragalus-Coptis drug pair common targets. A: Venn diagram illustrating the target genes associated with diabetic kidney disease (DKD); B: Venn diagram depicting the 273 common targets between the active ingredient targets and DKD targets; C: Volcano plot displaying differential gene expression patterns in DKD samples, where red and blue indicate upregulated and downregulated genes, respectively, while gray represents no significant difference; D: Heatmap presenting the expression profiles of these 50 differential gene expression analysis. Columns correspond to sample groups, while rows represent individual genes. The cyan Group A denotes normal samples, whereas the red Group B signifies DKD samples. OMIM: Online Mendelian Inheritance in Man database.
Table 3 Information on the differential gene expression analysis identified from the Gene Expression Omnibus dataset.
Gene symbol
Protein name
Log2FC
P value
Change
PLCE1Phospholipase C epsilon 13.9804210.00000765Upregulated
CLIC5Chloride intracellular channel 54.3809690.00001522Upregulated
PTPROProtein tyrosine phosphatase receptor type O3.9222490.00001810Upregulated
HSPA12AHeat shock protein family A (Hsp70) member 12A4.1941410.00001836Upregulated
AIF1Allograft inflammatory Factor 13.6162730.00002007Upregulated
GMDSGDP-mannose 4,6-dehydratase3.9002180.00002555Upregulated
SEMA5ASemaphorin 5A3.9568680.00002673Upregulated
CEP152Centrosomal protein 1524.0043390.00002683Downregulated
FOXC1Forkhead Box C13.5057150.00003095Upregulated
MMEMembrane metalloendopeptidase3.8379580.00003666Upregulated
FGF1Fibroblast growth Factor 13.7433980.00003695Upregulated
TNNC1Troponin C1, slow skeletal and cardiac type3.6020370.00004852Upregulated
CYP2C8Cytochrome P450 family 2 subfamily C member 8-3.556710.00004895Downregulated
THBS1Thrombospondin 13.1700900.00005034Upregulated
VEGFAVascular endothelial growth Factor A4.2616450.00005391Upregulated
LUNAR1Leukemia-associated noncoding IGF1R activator RNA 13.2651150.00005733Downregulated
HOXD1Homeobox D13.8128930.00005999Upregulated
BMP2bone morphogenetic protein 23.1154850.00006297Upregulated
IQGAP2IQ motif containing GTPase activating protein 22.5404330.00006536Upregulated
ST3GAL6ST3 beta-galactoside alpha-2,3-sialyltransferase 62.8581000.00006870Upregulated
THSD7AThrombospondin type 1 domain containing 7A2.5810300.00006966Upregulated
F2RCoagulation factor II thrombin receptor2.8091480.00007121Upregulated
SLC9A1Solute carrier family 9 member A14.3309250.00007424Downregulated
ADORA2BAdenosine A2b receptor2.5823780.00008127Downregulated
FAM153AFamily with sequence similarity 153 member A3.7743750.00009200Upregulated
ITGB1Integrin subunit beta 13.7743280.00009284Downregulated
CDS1CDP-diacylglycerol synthase 12.6952870.00009577Upregulated
EGR2Early growth response 23.5802850.00010401Downregulated
PLA2R1Phospholipase A2 receptor 13.4851080.00010718Upregulated
GAS1Growth arrest specific 13.6590520.00010882Upregulated
TYRO3TYRO3 protein tyrosine kinase2.8540820.00011003Upregulated
LOC101929500Uncharacterized LOC1019295002.6649310.00011229Upregulated
F3Coagulation factor III, tissue factor3.0356910.00011752Upregulated
XPNPEP2X-prolyl aminopeptidase 23.4548280.00013038Downregulated
FUT6Fucosyltransferase 63.3572470.00013492Downregulated
LCN1Lipocalin 12.6540970.00014868Downregulated
C1orf21Chromosome 1 open reading frame 212.6558850.00015593Upregulated
LOC103344931Uncharacterized LOC1033449312.4238610.00015641Upregulated
FRYFRY microtubule binding protein2.9220640.00016127Upregulated
POU2F2POU class 2 homeobox 2-2.519720.00016162Downregulated
VEGFAVascular endothelial growth Factor A2.8601800.00016446Upregulated
CHRNECholinergic receptor nicotinic epsilon subunit2.9544350.00016654Downregulated
TUBB4ATubulin beta 4A class IVa3.9085270.00016713Downregulated
MGAT5Alpha-1,6-mannosylglycoprotein 6-beta-N-acetylglucosaminyltransferase2.5686780.00017864Upregulated
PTPRDProtein tyrosine phosphatase receptor type D3.0522490.00018095Upregulated
HYAL1Hyaluronidase 1-2.2176400.00018409Downregulated
DPYSL3Dihydropyrimidinase like 33.7822040.00018554Upregulated
UGT2B17UDP glucuronosyltransferase family 2 member B172.6045040.00018633Downregulated
PAMPeptidylglycine alpha-amidating monooxygenase2.255750.00018848Upregulated
RFC4Replication Factor C subunit 43.0941090.00020080Downregulated
“Disease-active ingredient-target” network

The DKD targets, the active ingredients of the Astragalus-Coptis drug pair, and the common targets were imported into Cytoscape 3.10.1 to generate a network diagram of “disease-active ingredient-target”, as depicted in Figure 2, consisting of 303 nodes and 1253 edges. The degree value for each active ingredient was calculated using the “CytoNCA” functional module to determine their topological importance, resulting in a ranking from highest to lowest degree. Following network analysis, active ingredients with a degree ≥ 56 were selected. Among them, 3,9-di-O-methylnissolin, isorhamnetin, jaranol, quercetin, 1-7-dihydroxy-3,9-dime-thoxypterocarpene, (3R)-3-(2-hydroxy-3,4-dimethoxyphenyl)chroman-7-ol, palmatine, obacunone, and moupinamide were found to be associated with 66, 63, 63, 62, 62, 58, 57, and 56 disease targets, respectively, in this study. These active ingredients may serve as core ingredients in the Astragalus-Coptis drug pair for treating DKD (Table 4).

Figure 2
Figure 2 Disease-active ingredient-target network. The yellow octagon nodes represent the Astragalus-Coptis drug pair, while the blue prismatic nodes depict the active ingredients in this drug pair. The purple arrow nodes symbolize diabetic kidney disease, and the pink circular nodes represent the common targets. The edges illustrate the interaction between the active ingredient and its target. DKD: Diabetic kidney disease.
Table 4 Core active ingredients of the Astragalus-Coptis drug pair.
Serial number
MOL ID
Ingredient
Degree
HQ4MOL0003713,9-di-O-methylnissolin66.0
HQ6MOL000354Isorhamnetin63.0
HQ9MOL000239Jaranol63.0
HQ10, HL12MOL000098Quercetin62.0
HQ13MOL0004221,7-Dihydroxy-3,9-dimethoxy pterocarpene62.0
HQ11MOL000438(3R)-3-(2-hydroxy-3,4-dimethoxyphenyl)chroman-7-ol58.0
HL11MOL000785Palmatine58.0
HL10MOL013352Obacunone57.0
HL9MOL008647Moupinamide56.0
Construction and analysis of the protein-protein interaction network

To investigate the mechanism of action of the Astragalus-Coptis drug pair in treating DKD, we input 273 common targets associated with this drug pair into the STRING database for PPI analysis (Figure 3A). The resulting PPI network TSV file was imported into Cytoscape 3.10.1 software to construct a visual representation of the PPI network diagram, excluding nodes that had no interaction with other nodes. Consequently, we obtained a PPI network consisting of 271 nodes and 4129 edges (Figure 3B). Cluster analysis using MCODE was performed to generate a highly connected subnetwork and assign targets to four groups (Figure 3C). By utilizing the CentiscaPe 2.2 functional module, we determined thresholds for DC (degree centrality), BC (betweenness centrality), and CC (closeness centrality) within the common target interaction network as follows: DC = 30.47232472, BC = 303.9409594, and CC = 0.001774064, respectively. Based on topological analysis using these thresholds as screening criteria, we identified a core PPI network comprising 10 key targets: AKT serine/threonine kinase 1 (AKT1) (degree value: 46), epidermal growth factor receptor (EGFR) (degree value: 44), tumor necrosis factor (TNF) (degree value: 43), SRC proto-oncogene, non-receptor tyrosine kinase (SRC) (degree value: 43), Jun proto-oncogene, AP-1 transcription factor subunit (JUN) (degree value: 43), caspase 3 (CASP3) (degree value: 43), mitogen-activated protein kinase 3 (MAPK3) (degree value: 43), heat shock protein 90 alpha family class A member 1 (HSP90AA1) (degree value: 41), signal transducer and activator of transcription 3 (STAT3) (degree value: 40), and estrogen receptor 1 (ESR1) (degree value: 38) (Figure 3D, Table 5). These targets may have significant implications for understanding the pathogenesis and treatment of DKD.

Figure 3
Figure 3 Protein-protein interaction network. A: The protein-protein interaction (PPI) network of common targets; B: The PPI network, commonly targeted, was optimized using Cytoscape 3.10.1; C: Cluster analysis-based construction of a PPI network utilizing MCODE plugin; D: The central core PPI network consisted of 48 nodes and 707 edges, wherein 10 nodes exhibiting an orange-to-purple gradient symbolized key targets associated with Astragalus-Coptis drug pair combination for treating diabetic kidney disease. Node sizes were proportionate to target degrees in the network.
Table 5 Analysis of topological parameters of key targets.
Name
Degree centrality
Betweenness centrality
Closeness centrality
Degree
AKT11556135.220268480020.0025839793281653746
EGFR1272630.661657516480.0023923444976076544
TNF1556936.34372604360.0025706940874035943
SRC1293308.849276429640.0024038461538461543
JUN1171696.429731203890.0023201856148491843
CASP31162086.858122769040.0023364485981308443
MAPK31192232.219294461360.0023310023310023343
HSP90AA11243622.007914583810.0023584905660377341
STAT31231881.067284582540.0023419203747072640
ESR11021938.434414985110.0022421524663677138
GO enrichment analysis

The 273 common targets of the Astragalus-Coptis drug pair in treating DKD were entered into the DAVID database for GO function and KEGG pathway enrichment analysis. A total of 1265 items were obtained through GO functional enrichment analysis, including 937 BP, 122 CC, and 206 MF terms. The respective items within each category were sorted based on their P values from smallest to largest, and the top 20 items were selected for visualization, as depicted in Figure 4. The results revealed that cell BP included protein phosphorylation, negative regulation of apoptotic process, and the inflammatory response. The primary CCs included mitochondrion, extracellular exosome, and membrane raft. The main MF were protein serine/threonine/tyrosine kinase activity, ATP binding, and protein kinase activity.

Figure 4
Figure 4 Gene Ontology enrichment diagram. A: Bubble map illustrating cellular biological processes (BP); B: Bubble plot depicting cellular components (CC); C: Bubble plot representing molecular function (MF). The X- and Y-axes display the fold enrichment and full name, respectively, while the color and size of the bubbles indicate the P value and number of genes, correspondingly; D: Top 10 BP, CC, and MF from Gene Ontology enrichment analysis are visualized as blue, orange, and purple bars, respectively.
KEGG enrichment analysis

KEGG pathway enrichment analysis identified 185 related pathways, which were then ranked by P value from smallest to largest. The top 20 pathways were selected for further analysis (Figure 5A and B; Table 6). The core active ingredients of the Astragalus-Coptis drug pair in the treatment of DKD, along with common targets and the top 20 KEGG signaling pathways, were imported into Cytoscape 3.10.1 software to construct a comprehensive active ingredient-target-signaling pathway network diagram (Figure 5C). This network graph consisted of a total of 80 nodes and 385 edges. The results demonstrated that the Astragalus-Coptis drug pair has synergistic effects on treating DKD through multiple ingredients, targets, and pathways. The P value for the advanced glycation end products (AGE)-receptor for AGEs (AGE-RAGE) signaling pathway in diabetic complications was 2.05E-22, while the gene number was 31. In addition, 40 genes related to the Lipid and atherosclerosis pathway had a P value of 3.17E-20. The Astragalus-Coptis drug pair may play crucial roles in DKD treatment through these two core pathways. Based on the analysis of the differentially expressed genes mentioned above, we reconstructed a KEGG pathway map for the core pathway (Figure 6) and constructed a Sankey diagram using a bioinformatics platform to illustrate the relationships between the targets and pathways (Figure 5D).

Figure 5
Figure 5 Kyoto Encyclopedia of Genes and Genomes pathway enrichment diagram. A: Bubble plot depicting the top 20 pathways identified through Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis; B: Classification of the top 20 pathways based on KEGG enrichment analysis according to their respective KEGG types; C: Diagram illustrating the active ingredient-target-signaling pathway. The cyan node represents the active ingredient, while the purple node signifies the target. The blue node denotes the pathway itself; D: Sankey diagram showcasing KEGG core pathways. The left rectangular node in this sankey plot represents the target, whereas the right rectangular node symbolizes the corresponding KEGG pathway. The lines represent associations between targets and pathways.
Figure 6
Figure 6 Distribution of common targets in the core pathways. A: Distribution of common targets associated with the advanced glycation end products (AGE)-receptor for AGEs signaling pathway in diabetic complications; B: Distribution of relevant common targets in the Lipid and atherosclerosis pathway. Red, green, and gray rectangles represent upregulated and downregulated gene targets, respectively, without any significantly differentially expressed gene targets.
Table 6 Kyoto Encyclopedia of Genes and Genomes enrichment results.
ID
Term
Enrichment
P value
Count
hsa04933AGE-RAGE signaling pathway in diabetic complications10.21070042.05E-2231
hsa05417Lipid and atherosclerosis6.127952223.17E-2040
hsa04151PI3K-Akt signaling pathway4.559179143.19E-1949
hsa04066HIF-1 signaling pathway8.158890527.92E-1727
hsa04510Focal adhesion5.678921241.28E-1635
hsa04014Ras signaling pathway5.163968214.42E-1637
hsa04012ErbB signaling pathway8.912565803.47E-1523
hsa04664Fc epsilon RI signaling pathway10.17195014.44E-1521
hsa04931Insulin resistance7.624477597.90E-1525
hsa04068FoxO signaling pathway6.788695161.03E-1427
hsa04010MAPK signaling pathway4.253549623.49E-1439
hsa04062Chemokine signaling pathway5.318073127.06E-1431
hsa04926Relaxin signaling pathway6.383283565.15E-1325
hsa05418Fluid shear stress and atherosclerosis5.924054532.81E-1225
hsa04668TNF signaling pathway6.645334152.32E-1223
hsa04657IL-17 signaling pathway5.956825901.70E-0817
hsa04922Glucagon signaling pathway4.309611261.88E-0514
hsa04614Renin-angiotensin system10.02453054.72E-057
hsa04960Aldosterone-regulated sodium reabsorption7.121674201.01E-048
hsa04152AMPK signaling pathway4.899829571.15E-0718
Analysis of the “key target-organ network”

We analyzed the mRNA distribution levels across various cells, organs, and tissues for the 10 key targets. Notably, there was a significant increase in the mRNA levels of genes related to 23 specific entities, including the kidney, adrenal gland, adrenal cortex, prostate, thyroid gland, liver, cardiomyocytes, CD4+ T cells, CD8+ T cells and others. To demonstrate the associations between these key targets and organ tissues mentioned above, we constructed a “key target-organ network” consisting of 33 nodes and 130 edges (Figure 7). Network analysis revealed that these organs were primarily closely linked with the kidney and immune-related factors such as CD34+ T cells, CD4+ T cells, and CD8+ T cells. This finding suggested that the Astragalus-Coptis drug pair may exert therapeutic effects on DKD by activating renal function and systemic immunity.

Figure 7
Figure 7 Diagram of the “key target-organ network”. The green nodes represent the key targets, while the yellow nodes depict the organs. The edges symbolize the connections between these key targets and organs.
Molecular docking

The ligands were selected based on the highest degree value in the “disease-active ingredient-target” network analysis, which included 9 core active ingredients. Molecular docking was then performed with the key target receptors AKT1, EGFR, TNF, SRC, JUN, CASP3, MAPK3, HSP90AA1, STAT3, and ESR1. AutoDock software was used to calculate the minimum binding energy, and the results are presented in Figure 8 as a heatmap. A lower binding energy indicates a stronger binding capacity between the ligand and receptor. Specifically, a binding energy ≤ -5.0 kcal/mol suggests moderate binding capacity, while a binding energy ≤ -7.0 kcal/mol indicates high binding capacity[34]. The binding energies of quercetin and obacunone to the key target were below -6.0 kcal/mol, while the binding energies of the other ingredients to the target were mostly lower than -5.0 kcal/mol. This indicates that the Astragalus-Coptis drug pair exhibits strong binding affinity for both the core active ingredients and the key targets, particularly between quercetin, obacunone, and the targets. The ingredient-target interactions with the smallest fraction of binding energy and their corresponding binding modes were visualized using PyMOL 1.7.2.1 and Discovery Studio 2020 (Figure 9). Notably, quercetin displayed the lowest binding energy with the TNF target (-9.95 kcal/mol), which can be attributed to its interaction with GLN-B:27 through hydrogen bonds formed with residues LEU-B:26 and ILE-B:136, a C-H bond formed with residue GLU-B:135, and a hydrophobic interaction involving residue PRO-B:139 of TNF (Figure 9J).

Figure 8
Figure 8 Heatmap of molecular docking binding energies (kcal/mol). The X-axis corresponds to the key target receptor, while the Y-axis represents the core active ingredient. The darker shade of blue in the heatmap indicates a lower binding energy and a stronger binding affinity between the active ingredient and the target.
Figure 9
Figure 9 Docking result diagram. A: Obacunone-mitogen-activated protein kinase 3 (MAPK3); B: Obacunone-AKT serine/threonine kinase 1 (AKT1); C: Obacunone-heat shock protein 90 alpha family class A member 1 (HSP90AA1); D: Quercetin-tumor necrosis factor (TNF); E: Quercetin-AKT serine/threonine kinase 1 (AKT1); F: Quercetin-epidermal growth factor receptor (EGFR); G-L: The different types of two-dimensional bonding modes, respectively.
MD simulation and binding free energy

MD simulation is a crucial method for investigating the stability and dynamics of protein complexes in aqueous solutions. To validate the binding stability of the ligand-receptor protein complex, MD simulations were conducted for 100 ns based on the results obtained from molecular docking studies involving the HL10 (obacunone)-MAPK3, HQ10 (quercetin)-AKT1, and HQ10 (quercetin)-TNF protein complexes.

The atomic root mean square deviation (RMSD) serves as a reliable metric for assessing the conformational stability of both the receptor and ligand while also quantifying the degree to which atomic positions deviate from their initial state. Lower deviations indicate enhanced conformational stability[35]. The Rg value can reflect the compactness of the binding structure and the degree of constraint of the system. Low Rg values indicate dense and compact systems[36]. The system stability can be assessed by measuring the RMSD and Rg. Based on the corresponding RMSD and Rg values for the three protein complexes (Figure 10A and B), it is evident that during the 100 ns MD simulation, the protein complexes exhibited a predominantly stable state with no significant structural alterations.

Figure 10
Figure 10  Molecular dynamics simulations of HL10 (obacunone)-mitogen-activated protein kinase 3, HQ10 (quercetin)-AKT serine/threonine kinase 1, and HQ10 (quercetin)-tumor necrosis factor. A: Root mean square deviation (RMSD) plots of HL10-mitogen-activated protein kinase 3 (MAPK3), HQ10-quercetin-AKT serine/threonine kinase 1 (AKT1), and HQ10-tumor necrosis factor (TNF) protein complexes; B: Rg plot of HL10-MAPK3, HQ10-AKT1, and HQ10-TNF protein complexes; C: Root mean square fluctuation (RMSF) plots of HL10-MAPK3, HQ10-AKT1, and HQ10-TNF protein complexes; D: Number of hydrogen bonds in HL10-MAPK3, HQ10-AKT1, and HQ10-TNF protein complexes.

The root mean square fluctuation (RMSF) can effectively capture the volatility and flexibility of amino acid sites, as demonstrated in Figure 10C. Notably, the RMSF values for all residues within the three systems were consistently below 0.5 nm, except the first terminal residue. This observation strongly suggested that the core structure of the three-protein complex exhibited remarkable rigidity.

Hydrogen bonding is one of the most robust noncovalent binding interactions between an acceptor and a ligand, and a greater number of hydrogen bonds contributes to enhanced binding stability. Analysis of hydrogen bonds (Figure 10D) revealed that in the HQ10-AKT1 system, most time scales exhibited 1 to 3 hydrogen bonds, with a maximum of 4 hydrogen bonds observed. In the HQ10-TNF system, there was a significant increase in frames with a high number of hydrogen bonds (≥ 3), reaching a maximum of 5 hydrogen bonds. Conversely, only one hydrogen bond was present in the HL10-MAPK3 system. The formation of hydrogen bonds between the ligand and receptor plays a crucial role in stabilizing the protein-protein complex.

The overall secondary structure diagram of the protein complex (Supplementary Figure 1) revealed no significant fluctuations in the secondary structures, such as B-sheet, B-bridge, or bend structures, during the 100 ns MD simulation. The solvent-accessible surface area represents the degree of exposure of the protein surface. The results indicated that MAPK3 exhibited greater effects than did AKT1 and TNF. Both TNF and MAPK3 proteins displayed comparable exposure to hydrophobic and hydrophilic surfaces, while AKT1 had a slightly larger hydrophilic surface than a hydrophobic surface.

In parallel, we analyzed the binding free energy between the HL10 (obacunone)-MAPK3, HQ10 (quercetin)-AKT1, and HQ10 (quercetin)-TNF protein complexes. The GGAS parameter represents the free energy of the gas phase, which is determined through a comprehensive calculation involving van der Waals energy (VDWAALS) and electrostatic energy (EEL). Additionally, the nonpolar solvation energy (ESURF), although its value is negligible, and the polar solvation energy (EGB) in GSOLV were examined to assess the influence of polar solvents on binding affinity. These findings are illustrated in Figure 11.

Figure 11
Figure 11  Binding free energy between receptor-ligand protein complexes. A: Binding energy of the HQ10-AKT serine/threonine kinase 1 protein complex; B: Binding energy of the HQ10-tumor necrosis factor protein complex; C: Binding energy of the HL10-mitogen-activated protein kinase 3 protein complex. EEL: Electrostatic energy; EGB: Polar solvation energy; ESURF: Nonpolar solvation energy; GGAS: Total gas phase free energy; GSOLV: Total solvation free energy (overall total solvation free energy, which is calculated as the sum of total gas phase free energy and total gas phase free energy); VDWAALS: Van der Waals energy.

The mean VDWAALS and mean EEL values were negative for the HQ10-AKT1, HQ10-TNF, and HL10-MAPK3 complexes (-12.75 kcal/mol and -0.77 kcal/mol; -14.65 kcal/mol and -10.13 kcal/mol; -1.32 kcal/mol and -0.30 kcal/mol, respectively), indicating a favorable combination of van der Waals energy and EEL in these complexes. However, the average EGB in GSOLV was relatively positive (7.60 kcal/mol, 16.15 kcal/mol and 1.05 kcal/mol, respectively). Although the average ESURF was negative (-1.52 kcal/mol, -1.97 kcal/mol, and -0.18 kcal/mol, respectively), the overall average GSOLV values were positive (6.08 kcal/mol, 14.18 kcal/mol, and 0.87 kcal/mol, respectively), suggesting that polar solvents are unfavorable for binding interactions. The average binding free energies were -7.44 kcal/mol ± 6.35 kcal/mol, -10.60 kcal/mol ± 9.98 kcal/mol, and -0.75 kcal/mol ± 2.97 kcal/mol, respectively. From these results, it can be concluded that all three protein complexes are stable. However, the HQ10-TNF protein complex exhibits stronger binding stability due to its greater content of van der Waals forces and hydrogen bonds.

After conducting MD simulations, we observed alterations in the interaction forces between ligands and receptors within all protein complexes of HQ10-AKT1, HQ10-TNF, and HL10-MAPK3 (Supplementary Figure 2). The combination of HQ10 and AKT1 in Supplementary Figure 2B is stabilized by two conventional hydrogen bonds with LEU347 and GLU341, one carbon-hydrogen bond and a conventional hydrogen bond with ARG346, as well as a Pi-Pi stacking interaction with TYR350. Additionally, van der Waals forces contribute to stabilizing its binding complex through interactions with ARG243, GLY345, and LEU239. The interaction between HQ10 and TNF in Supplementary Figure 2D is mediated by two hydrogen bonds with GLN27 and ASN19, one hydrogen bond with ILE136, one carbon-hydrogen bond with GLU135, and a conventional hydrogen bond. Additionally, there is an alkyl interaction involving LEU26 and PRO129. A pi-alkyl interaction occurs between LEU26 and ALA, which is stabilized through van der Waals forces from ASN46, VAL150, TRP28, ARG138, ASN137, LEU142, ALA22, and GLN21. The interaction between HL10 and MAPK in Supplementary Figure 2F is reinforced by a hydrogen bond and a carbon-hydrogen bond with ARG64, as well as the van der Waals forces contributed by LYS65 and THR66.

DISCUSSION

The therapeutic potential of the Astragalus-Coptis drug pair for DKD treatment has been supported by clinical and experimental studies. However, the active ingredients of the Astragalus-Coptis drug pair and the underlying mechanisms responsible for its therapeutic effects on DKD remain unclear. Therefore, we employed NP approaches, molecular docking, and MD simulations to elucidate the active ingredients, potential targets, and mechanisms of action associated with the Astragalus-Coptis drug pair in DKD.

According to the “disease-active ingredient-target” network topology analysis, the Astragalus-Coptis drug pair used to improve the core of DKD active ingredients include 3,9-di-O-methylnissolin, isorhamnetin, jaranol, quercetin, 1,7-dihydroxy-3,9-dimethoxy pterocarpene, (3R)-3-(2-hydroxy-3,4-dimethoxyphenyl)chroman-7-ol, palmatine, obacunone, and moupinamide. Relevant studies support the therapeutic effects of these active ingredients on DKD. Both quercetin and isorhamnetin mitigate the impact of DKD on renal function through their ability to lower glucose levels, enhance oxidative status, alleviate inflammation, and regulate lipid metabolism and adipocyte differentiation[37,38]. Additionally, quercetin can regulate renal lipid accumulation to prevent and alleviate DKD while also improving diabetic kidney injury through the Nrf2/HO-1 signaling pathway activation, which inhibits deiron[39,40]. Palmatine is a potent inhibitor of lipase that effectively reduces serum cholesterol and blood glucose levels. Furthermore, it exhibits a direct and/or indirect association with DKD[41]. Obacunone can alleviate high glucose-induced oxidative damage in NRK-52E cells by inhibiting oxidative stress and mitochondrial dysfunction through the GSK-3β signaling pathway and plays a protective role in the kidney[42]. The active ingredients in the Astragalus-Coptis drug pair are known to have therapeutic effects on DKD.

PPI network analysis indicated that AKT1, EGFR, TNF, SRC, JUN, CASP3, MAPK3, HSP90AA1, STAT3, and ESR1 are the key targets of the Astragalus-Coptis drug pair and its active ingredients in the treatment of DKD. Furthermore, the synergistic relationship between these targets is crucial for their therapeutic efficacy. Numerous cell and animal model studies have shown that AKT1, a member of the serine/threonine protein kinase subfamily known as AGC protein kinase, participates in various signaling pathways related to DKD. These intricate signaling pathways underscore the diverse regulatory roles of AKT1 in kidney cell processes affected by DKD and contribute to renal protection[43]. EGFR is a member of the tyrosine kinase ErbB receptor family, whose receptors are widely expressed in mammalian kidneys, including podocytes. Selective removal of EGFR from podocytes can significantly improve the progression of glomerular disease and renal tubulointerstitial fibrosis in DKD, reduce proteinuria, maintain the integrity of podocytes, increase autophagy, and reduce inflammation. EGFR inhibitors may reduce DKD by reducing ROS and ER stress. Quercetin can reduce renal fibrosis in vitro and in vivo by inhibiting EGFR signaling activation[44,45]. TNF may serve as an autonomous risk factor for the development of chronic kidney disease in patients diagnosed with type 2 diabetes. Elevated levels of TNF inflammatory mediators can exacerbate renal inflammation in individuals suffering from DKD, and it is plausible that inhibition of TNF could mitigate or prevent the onset of DKD. Quercetin inhibits the release of proinflammatory markers such as IL-1β, IL-4, IL-6, and TNF-α, alleviating renal inflammation in DKD[46-48]. SRC is a tyrosine-protein kinase that governs cellular metabolism, survival, and proliferation. Inhibition of SRC kinase significantly upregulates CKIP-1 expression to ameliorate diabetic renal fibrosis. Therefore, targeting SRC may represent a promising strategy for preventing and treating age-related microangiopathy[49]. The transcription factor signal transduction and activator of transcription 3 (STAT3) plays a crucial role in the pathogenesis of renal fibrosis and inflammation. Inhibition of STAT3 impedes the progression and development of DKD[50,51]. Introns 1 and 2 of the ESR1 may contain functionally sig-nificant regions associated with the risk of type 2 diabetes or ESRD. ESR1 has favorable effects on blood glucose homeostasis. ESR1 regulates VEGFA expression in adipose tissue, promoting angiogenesis, reducing inflammation, and improving adipose tissue functionality. It confers a protective effect on patients with DKD[52-54]. The above studies suggest that these key targets are closely related to the occurrence and development of DKD and that the core ingredients of the Astragalus-Coptis drug pair can regulate these targets to varying degrees.

GO enrichment analysis revealed that the therapeutic effect of the Astragalus-Coptis drug pair on DKD primarily involves protein phosphorylation, endoplasmic reticulum (ER) unfolded protein, negative regulation of apoptotic process and inflammatory response, vascular endothelial growth factor receptor signaling pathway, and other cellular BP. Additionally, this effect is associated with various cell components, such as the ER, mitochondria, and exosomes. The MF included protein serine/threonine/tyrosine kinase activity and protein kinase activity. Various studies have de-monstrated that protein tyrosine phosphatase nonreceptor type 2 (PTPN2) plays a protective role in DKD by ameliorating metabolic disorders and inhibiting renal STAT phosphorylation-dependent microinflammation[55]. Several factors associated with the pathogenesis of DKD, including hyperglycemia, AGEs, angiotensin II, and various cytokines, have been demonstrated to promote the expression of vascular endothelial growth factor (VEGF) in various cell types. In the early stages of DKD, there is increased VEGF and VEGF2 kidney expression. Inhibiting VEGF-A or its receptor can prevent proteinuria and alleviate glomerular injury, thereby exerting a beneficial effect on kidney changes related to DKD[56,57]. ER stress (ERS) refers to the accumulation of misfolded proteins in cells, which induces cellular stress and regulates cell damage bidirectionally. ERSs are regulated by various signaling pathways and are closely associated with the pathogenesis of DKD[58]. The potential mechanisms by which the Astragalus-Coptis drug pair improves DKD are consistent with the GO enrichment results mentioned above.

The KEGG enrichment results included pathways related to the AGE-RAGE signaling pathway in diabetic complications, the Lipid and atherosclerosis, the PI3K-Akt signaling pathway, and the HIF-1 signaling pathway, among others. The active ingredient of the Astragalus-Coptis drug pair can enhance the treatment of DKD by targeting the AGE-RAGE signaling pathway, which is involved in diabetic complications, as well as lipid and atherosclerosis. The development of AGEs associated with hyperglycemia plays a pivotal role in the pathogenesis of DKD, and the interaction between AGE receptors (RAGE) and their ligands initiates oxidative stress, chronic inflammation, and fibrosis in renal tissue, ultimately culminating in renal dysfunction. A reduction in AGE levels or inhibition of RAGE is beneficial to DKD in experimental models. The AGE/RAGE signaling pathway elicits ER stress in DKD, and targeting key targets within this pathway holds promise for treating these and other related kidney disorders[59,60]. Additionally, the heightened occurrence of DKD in individuals with diabetes is linked to lipid and atherosclerosis signaling pathway. Dyslipidemia can exacerbate the progression of DKD in patients with DKD. Quercetin may mitigate atherosclerotic lesions by modulating the gut microbiota and reducing atherogenic lipid metabolites[61-63]. By inhibiting the aberrant activation of the PI3K/Akt signaling pathway, it is possible to reduce podocyte apoptosis, restore podocyte homeostasis, alleviate kidney injury, and improve renal function. Quercetin alleviates chronic kidney failure by targeting the PI3K/Akt pathway. Furthermore, activation of the PI3K/Akt signaling pathway plays a crucial role in mediating extracellular matrix accumulation and contributes to the pathogenesis and progression of DKD[64-66]. The above evidence demonstrates that the Astragalus-Coptis combination exerts synergistic effects by affecting multiple ingredients, targets, and pathways to improve the treatment of DKD.

Finally, molecular docking was used to evaluate 10 key targets and 9 core active ingredients. The docking results showed binding energies ranging from -4.56 kcal/mol to -9.95 kcal/mol, indicating that all the active ingredients are likely to have good docking ability. The binding energies of the obacunone-MAPK3, quercetin-AKT1, and quercetin-TNF protein complexes were the lowest. It has relatively good docking ability. MD simulations were used to investigate the structural stability of the HL10 (obacunone)-MAPK3, HQ10 (quercetin)-AKT1, and HQ10 (quercetin)-TNF protein complexes. The structures of these protein complexes are very stable at 300 K. The binding free energies of the HQ10-AKT1 and HQ10-TNF protein complexes were -7.44 kcal/mol ± 6.35 kcal/mol and -10.60 kcal/mol ± 9.98 kcal/mol, respectively, which were the lowest among the three groups, indicating good binding activity and structural stability. These findings suggest that these active ingredients, together with their targets, play indispensable roles in DKD treatment. Currently, there is a lack of relevant experimental studies investigating the impact of quercetin on AKT1 and TNF in DKD treatment. However, animal studies have demonstrated that quercetin improves renal function in DKD animals, reduces oxidative stress levels, and alleviates inflammatory responses in the kidneys[67]. Furthermore, a NP study predicted that EGFR is a potential physiological target of quercetin. This finding was confirmed by in vitro and in vivo experiments, which showed that quercetin inhibits the activation of the EGFR signaling pathway by reducing the phosphorylation of EGFR and ERK1/2, mitigating podocyte apoptosis, and improving DKD[68]. This study utilized NP, molecular docking, and MD to predict that quercetin binds stably to AKT1 and TNF, identifying them as potential therapeutic targets. AKT1 and TNF are implicated in core pathways such as the AGE-RAGE signaling pathway in diabetic complications and the Lipid and atherosclerosis pathway. Consequently, quercetin may modulate these core pathways by targeting AKT1 and TNF, potentially influencing AGEs and lipid metabolism, thereby enhancing the improvement of DKD. The present study, however, has certain limitations. First, the active ingredient and target data were obtained from the literature and databases, which may introduce potential biases. Additionally, the reliability and accuracy of predictions heavily rely on the quality of the available data. Therefore, further in vivo experiments, in vitro experiments and clinical observations are necessary to validate whether the Astragalus-Coptis drug pair can effectively improve DKD through the ingredients, targets, and pathways.

CONCLUSION

In summary, this study represents the first systematic exploration of the pharmacological and molecular mechanisms underlying the Astragalus-Coptis drug pair treatment for DKD using bioinformatics tools such as NP, molecular docking, and MD simulation. The bioinformatics and computational analysis above demonstrated that quercetin and obacunone are the fundamental active ingredients of the Astragalus-Coptis drug pair in DKD treatment. Their mechanisms of action involve the regulation of key targets, such as AKT1, EGFR, TNF, MAPK3, and HSP90AA1, which subsequently modulate various BP, CCs, and MF. Additionally, the Astragalus-Coptis drug pair can also intervene in the AGE-RAGE signaling pathway in diabetic complications. Moreover, it can modulate various signaling pathways, such as the Lipid and atherosclerosis pathway and the PI3K-Akt pathway, among others, to enhance glucose and lipid metabolism while reducing atherosclerosis. Furthermore, it potentially mitigates podocyte apoptosis, inhibits inflammation, regulates AGEs and ERS, etc, offering therapeutic benefits for DKD. The findings of this study establish a solid theoretical foundation for the use of the Astragalus-Coptis drug pair in the treatment of DKD, thereby providing valuable insights for expanding its clinical applications. Moreover, these results are anticipated to serve as a guiding framework for future advanced experimental investigations.

Footnotes

Provenance and peer review: Unsolicited article; Externally peer reviewed.

Peer-review model: Single blind

Specialty type: Endocrinology and metabolism

Country of origin: China

Peer-review report’s classification

Scientific Quality: Grade B, Grade B, Grade C, Grade C

Novelty: Grade B, Grade B

Creativity or Innovation: Grade B, Grade B

Scientific Significance: Grade B, Grade B

P-Reviewer: Alsindi N, United Arab Emirates; Pennazio M, Italy S-Editor: Chen YL L-Editor: Filipodia P-Editor: Che XX

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