Exploring the Underlying Mechanism of Shenyankangfu Tablet in the Treatment of Glomerulonephritis Through Network Pharmacology, Machine Learning, Molecular Docking, and Experimental Validation.

PURPOSE: This study aimed to explore the underlying mechanisms of Shenyankangfu tablet (SYKFT) in the treatment of glomerulonephritis (GN) based on network pharmacology, machine learning, molecular docking, and experimental validation.
METHODS: The active ingredients and potential targets of SYKFT were obtained through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, the targets of GN were obtained through GeneCards, etc. Perl and Cytoscape were used to construct an herb-active ingredient-target network. Then, the clusterProfiler package of R was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. We also used the STRING platform and Cytoscape to construct a protein-protein interaction (PPI) network, as well as the SwissTargetPrediction server to predict the target protein of the core active ingredient based on machine-learning model. Molecular-docking analysis was further performed using AutoDock Vina and Pymol. Finally, we verified the effect of SYKFT on GN in vivo.
RESULTS: A total of 154 active ingredients and 255 targets in SYKFT were screened, and 135 targets were identified to be related to GN. GO enrichment analysis indicated that biological processes were primarily associated with oxidative stress and cell proliferation. KEGG pathway analysis showed that these targets were involved mostly in infection-related and GN-related pathways. PPI network analysis identified 13 core targets of SYKFT. Results of machine-learning model suggested that STAT3 and AKT1 may be the key target. Results of molecular docking suggested that the main active components of SYKFT can be combined with various target proteins. In vivo experiments confirmed that SYKFT may alleviate renal pathological injury by regulating core genes, thereby reducing urinary protein.
CONCLUSION: This study demonstrated for the first time the multicomponent, multitarget, and multipathway characteristics of SYKFT for GN treatment.

Introduction

Glomerulonephritis (GN) is a group of glomerular diseases with proteinuria, hematuria, and hypertension as the basic clinical manifestations and is currently considered as immune-related diseases. In China, GN is a common cause of chronic kidney disease (CKD),1 and proteinuria remains a risk factor for all-cause mortality, cardiovascular mortality, CKD progression, and renal failure.2,3 CKD is an important factor leading to morbidity and mortality from noncommunicable diseases and serves as a risk factor for cardiovascular disease. CKD, associated with an increased risk of cardiovascular disease mortality, may be risky to patients diagnosed with hypertension and diabetes. Almost one-third of CKD patients live in two countries: China (130 million) and India (120 million). CKD ranked as the 12th leading cause of death in 2017.4

Common drugs for lowering proteinuria include glucocorticoids, immunosuppressants, angiotensin-converting enzyme inhibitors, and angiotensin II receptor blockers, among which the use of glucocorticoids and immunosuppressants is often associated with a series of side effects. Shenyankangfu tablet (SYKFT) comprises 13 kinds of Chinese herbal medicines, including Renshen, Xiyangshen, Dihuang, etc. and is one of the most common Chinese medicines for clinical use in treating GN and CKD. SYKFT is listed in the National Basic Medical Insurance Drug Catalogue for Work Injury Insurance and Maternity Insurance (2017). A controlled trial under random selections organized by multiple centers has confirmed that SYKFT could reduce proteinuria concentration in patients with primary GN. The use of combined SYKFT and losartan potassium could exert stronger effects of proteinuria reduction than the use of losartan potassium.5

However, the potential mechanism of SYKFT for GN treatment remains unclear. The study aimed to predict the targets and signaling pathways of SYKFT for GN treatment under network pharmacology and to further clarify the potential mechanism of SYKFT for GN treatment. Furthermore, molecular-docking technique was utilized for validation to provide theoretical basis for further research and the rational clinical application of SYKFT for GN treatment. The workflow is shown in Figure 1.

Figure 1

Workflow of this study.

Materials and Methods

Screening of Active Ingredients and Target Prediction for SYKFT

SYKFT primarily comprises 13 Chinese herbal medicines, including Xiyangshen, Renshen, Dihuang, Duzhong, Shanyao, Baihuasheshecao, Heidou, Tufuling, Yimucao, Danshen, Zexie, Baimaogen, and Jiegeng. The main active ingredients were researched through the Traditional Chinese Medicine Database and Analysis Platform (), with oral bioavailability ≥ 30% and drug-like properties ≥ 0.18 as the main screening conditions. The targets of the active ingredients were collected and had their proteins converted into the corresponding gene symbols through the UniProt database ().

GN-Disease Target Prediction

GN-related target proteins could be found in the GeneCards (), OMIM (), PharmGkb (), TTD (), and Drugbank () databases by searching the keyword “glomerulonephritis,” and the retrieved target proteins were combined. All target proteins were then converted to gene symbols by using the UniProt database.

Construction of SYKFT-Active Ingredient–Potential Action Target Network

The interaction between the active ingredient targets and disease targets was extracted as the potential targets of SYKFT for GN treatment. Perl was used to identify the herbs and active ingredients that act on these targets, and nodes were utilized for single herbs, active ingredients and targets, and edges to represent the interconnection between nodes. Then, the interaction network of herb-active ingredient–potential targets was established. The files were imported into Cytoscape software to draw the herb-active ingredient–potential target network. The degree value of each node, displayed in node size, was calculated.

Enrichment Analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathways for Potential Targets

GO function and KEGG pathway-enrichment analyses were performed using clusterProfiler R package for the potential targets of GN treatment with SYKFT, with p set at <0.01 and FDR set at <0.05. The result was visualized in the form of a bubble chart.

Construction and Analysis of Protein-Interaction Networks of Potential Targets

The potential targets were imported into the STRING database. The lowest interaction score was set at “highest confidence (0.9)” to obtain the protein interaction and delete the isolated nodes to build up the initial network, which was then imported to Cytoscape software to draw the protein–protein interaction (PPI) network of the potential targets. The CytoNCA plugin was used to perform two filters to identify core targets by using the median of the topological properties (including closeness centrality, eigenvector centrality, betweenness centrality, local average connectivity-based method, and network centrality) of the initial network nodes as criteria.

Machine-Learning Models Predicting the Target Protein of the Active Ingredient

The machine-learning model was used to predict the target proteins of core active ingredients. This process verified the results of network pharmacology and aided the identification of potential targets for GN treatment with SYKFT. SwissTargetPrediction displayed strong performance in target prediction for small molecules. Previous tests have shown that the achievement of at least one correct human target in the top 15 predictions accounted for more than 70% of external compounds. A series of natural products was submitted to SwissTargetPrediction server for target prediction (). This website enabled the estimation of the most probable macromolecule targets of a small molecule with assumed bioactivity characteristic. The projection was established on a combined 2D and 3D machine-learning model similar to a library of 370,000 known activities on over 3000 proteins from three different species.

Molecular Docking

The 3D structure of the target protein was retrieved and downloaded in PDB file format from the RSCB PDB database. PyMoL software was utilized to remove water molecules and ligands. AutoDockTools software was used to add nonpolar hydrogen, calculate the Gasteiger charge, and set the atom type to assign AD4 type. The file was saved in PDBQT file format as the pair acceptor. The SDF format file of the 2D structure of the small-molecule ligand was retrieved and downloaded from PubChem and then converted into the mol2 format file of the 3D structure with the help of ChemBio3D software. Then, the ligand mol2 file was integrated into AutoDockTools when the number of rotatable chemical bonds was clarified. The file was output into a PDBQT format file as the docking ligand. After identifying the active pocket for the acceptor, AutoDock Vina (1.1.2) software was used for molecular-docking analysis with parameters set at “num modes=20, energy range=5, exhaustiveness=8”. PyMOL software was also used to visualize the molecular-docking results.

Therapeutic Effects of SYKFT on the Experimental Mesangial Proliferative GN

Anti-Thy-1 nephritis is the classic animal model of mesangial proliferative GN, which is a common cause of primary GN in China. Experimental mesangial proliferative GN was induced in male SD rats through a single intravenous injection of anti-Thy 1 antibody (2.5 mg/kg).6 Sprague–Dawley rats weighing 180–220 g were obtained from Charles River (Beijing, China). All experimental protocols were conducted in compliance with the guidelines of animal welfare of the World Organization for Animal Health and were approved by the Animal Care Committee of the Beijing-Chaoyang Hospital. Rats used in this study were divided into three groups: normal control (n=6), experimental GN (n=6), and SYKFT treatment (n=6). Rats in the normal control group were injected with a fixed volume of normal saline. In the preliminary experiment, anti-Thy1 nephritis rats were treated with three doses of SYKFT respectively (400, 600, and 800 mg/kg·d), and the renal pathological changes of rats at day 7 after anti-Thy1 antibody injection were observed. Finally, 600 mg/kg·d was determined as the therapeutic dose. Twenty-four hours after the injection of anti-Thy1, the rats in the SYKRT treatment group were treated orally with SYKFT (600 mg·kg–1·d–1 per 1 mL of saline solution). Rats in the control and GN groups were treated orally with 1 mL of saline solution. After injection of anti-Thy1 antibody, mesangial dissolution occurred at 3 d, mesangial proliferation occurred at 5 d, and severe mesangial cell proliferation, and extracellular-matrix accumulation occurred at 7 d. Mesangial proliferation began to subside at 10 d–and significantly subsided at 14 d. Then, rats were killed on days 3, 5, 7, 10, and 14 after the injection of anti-Thy1 antibody. Renal tissues were examined by periodic acid-Schiff staining. Histological examinations were performed independently in a blinded manner by two observers. The glomerular score was evaluated as follows: score 0, no changes in capillaries; score 1, capillary dilatation <25% of glomerular tuft area; score 2, capillary dilatation >25% of glomerular tuft area or capillary aneurysm <50% of glomerular tuft area; score 3, capillary aneurysm comprising 50–75% of glomerular tuft area; and score 4, capillary aneurysm comprising >75% of the glomerular tuft area.7 The level of serum creatinine and urine protein was tested using a Chemray 240 Automatic Biochemical Analyzer (Rayto Life and Analytical Sciences Co., Ltd., Shenzhen, China). All animals were kept in a pathogen-free environment and offered ad lib feeding. The procedures for the care and use of animals were approved by the Ethics Committee of the Beijing-Chaoyang Hospital, and all applicable institutional and governmental regulations concerning the ethical use of animal in experiments were strictly observed.

Total RNA was extracted from renal tissues by using TRIzol (Invitrogen, CA, USA) with reference to the manufacturer’s instructions. Total RNA was reverse transcribed into cDNA. Reverse transcriptase polymerase chain reaction (RT-PCR) was performed on an ABI 7500 system under the following parameters: 95 °C for 30 s, 40 cycles of 95 °C for 15 s, 55 °C for 30 s, and 72 °C for 30 s. The primer sequences are listed in . GAPDH was used as a reference gene, and comparison in the expression between groups was performed through the 2-ΔΔCT method.

Results

Active Ingredients and Potential Targets of SYKFT

After the preliminary pharmacokinetic screening, 182 active ingredients in total were obtained (11 in Xiyangshen, 22 in Renshen, 2 in Dihuang, 28 in Duzhong, 16 in Shanyao, 7 in Baihuasheshecao, 13 in Heidou, 15 in Tufuling, 8 in Yimucao, 65 in Danshen, 10 in Zexie, 4 in Beimaogen, and 7 in Jiegeng) (). After removing the active ingredients lacking potential target information, 154 active ingredients and 255 targets were identified ().

GN-Related Targets

By searching the GeneCards, OMIM, PharmGkb, TTD, and Drugbank databases, 1844, 0, 59, 7, and 81 GN-related targets were identified, respectively. After integrating all targets and deleted duplicate values, a total of 1952 GN-related target proteins were obtained (Figure 2A).

Figure 2

Potential targets of SYKFT in GN treatment. (A) Venn analysis diagram of GN-related targets. (B) Venn analysis diagram of targets in SYKFT–GN. (C) Herb-active ingredient–target network of SYKFT in GN treatment.

Herb-Active Ingredient–Target Network Construction

A total of 135 overlapping targets between the targets of SYKFT and GN-related targets were recognized as the therapeutic targets of SYKFT in GN treatment (Figure 2B). Perl was used to map the 154 active ingredients and 255 targets, integrating the mapping results into Cytoscape software to construct a visualized network of “herb-active ingredient–drug targets” (Figure 2C and ). Quercetin boosts the most potential targets, followed by kaempferol, arachidonic acid, luteolin, and naringenin, suggesting that these active ingredients played a crucial role in the working mechanism of SYKFT when GN treatment was conducted.

GO and KEGG Pathway-Enrichment Analysis

The above 135 targets were assessed for GO functional and KEGG pathway-enrichment analyses and screened at p<0.05. A total of 2708 GO items were identified, among which 2492 entries were identified in biological process, including response to lipopolysaccharide, molecule of bacterial origin, oxidative stress, reactive oxygen species metabolic process and nutrient levels; cellular response to chemical stress, muscle cell proliferation, regulation of smooth muscle cell proliferation, cellular response to oxidative stress, smooth muscle cell proliferation, etc. A total of 64 entries were for cellular components, including membrane raft, membrane microdomain, membrane region, caveola, plasma membrane raft, external side of plasma membrane, RNA polymerase II transcription regulator complex, etc. A total of 152 entries were for molecular functions, including RNA polymerase II-specific DNA-binding transcription factor binding, DNA-binding transcription factor, nuclear receptor activity, ligand-activated transcription factor activity, cytokine receptor binding, cytokine activity, catecholamine binding, etc. (Figure 3A).

Figure 3

GO enrichment analysis (A) and KEGG pathway-enrichment analysis (B).

Based on the KEGG pathway-enrichment analysis, 174 items were identified, including AGE-RAGE signaling pathway in diabetic complications, Lipid and atherosclerosis, Fluid shear stress and atherosclerosis, Hepatitis B, TNF signaling pathway, IL-17 signaling pathway, Kaposi sarcoma-associated herpesvirus infection, etc. (Figure 3B).

Construction of Main Active Components–GN-Core Target Network

This PPI network comprised 122 nodes and 580 edges (Figures 4 and 5A), and sub-network 1 was acquired through filtration scoring higher than the median performance, consisting of 37 nodes and 239 edges (Figure 5A). After secondary filtration, the core network consisting of 13 nodes and 60 edges was established (Figure 5A). The core targets included RELA, TP53, STAT3, MAPK14, IL2, MAPK1, AKT1, TNF, IL6, MYC, MAPK8, JUN, and FOS. The herb-active ingredient–core target network was constructed again to screen the core active ingredients (including cryptotanshinone, quercetin, luteolin, naringenin, arachidonic acid, kaempferol, beta-carotene, diosgenin, ginsenoside rh2, astilbin, tanshinone iia, acacetin, taxifolin, isorhamnetin, hirsutin, syringetin, and beta-sitosterol) (Figure 5B). As shown in Figure 5B, quercetin had the most edges, followed by luteolin, Kaempferol, tanshinone iia, etc. Then, we made the KEGG pathway analysis on these 13 core targets, and found that they were enriched in infection-related pathways (including Hepatitis B, Kaposi sarcoma-associated herpesvirus infection, Human T-cell leukemia virus 1 infection, etc.) and immune-related pathways (MAPK signaling pathway, Toll-like receptor signaling pathway, etc.) (Figure 6A). Due to the large number of core targets enriched in MAPK signaling pathway and Toll-like receptor signaling pathway, we showed the specific information of enrichment in Figure 6B and C, and the red represented that core targets played a positive regulatory role in the pathway.

Figure 4

PPI network of targets generated using STRING. Nodes represent proteins. Edges represent PPIs.

Figure 5

Topological analysis of the protein–protein interaction network to identify the core targets (A). Herb-active ingredients–target network of core targets (B).

Figure 6

KEGG pathway-enrichment analysis of core targets: (A) Barplot and bubble chart. (B) MAPK signaling pathway. (C) Toll-like receptor signaling pathway.

Machine-Learning Model Predicting the Target Protein of the Core Active Ingredient

In the present study, predictions on 17 core active ingredients by using the SwissTargetPrediction server were conducted (). For core active ingredients, 1111 target-prediction results in total were obtained (). To increase the accuracy, the most promising target for each compound based on the probability score rank given by the SwissTargetPrediction server was summarized (Table 1). Notably, the AKT1 and STAT3 of these 13 core targets of SYKFT were the target proteins predicted by the machine-learning model. Next, molecular-docking analysis was performed to explore the binding patterns and binding affinity of molecules to targets with the greatest potentials.

Table 1

Top 15 Potential Target of Natural Products Predicted by SwissTargetPrediction

Molecule	Top 15 Targetsa	Max Probability Target	Max Probability	Min Probability Target	Min Probabilityb
Yacacetin	CYP1B1, CYP19A1, CA7, CA12, CA4, CBR1, ABCC1, ESR2, ABCB1, NOX4, TNKS2, TNKS, CDK5R1 CDK5, XDH, CCNB3 CDK1 CCNB1 CCNB2	CYP1B1	100.00%	ACHE	17.20%
Arachidonic acid	FABP4, PPARG, PPARA, PPARD, ALOX5, FFAR1, FABP3, PTGS1, CNR1, FAAH, TERT, FABP5, FABP1, TOP1, PTGES	FABP4	74.56%	SCD	11.15%
Astilbin	PTGS1, CA12, MMP13, MMP12, ABCB1, TDP1, CYP19A1, MMP2, CA2, CA1, CA5B, SLC5A1, MAPT, DYRK1A, KCNH2	PTGS1	10.65%	APP	10.65%
Beta-carotene	RBP4, ADORA1, ADORA2A, ADORA3, AR, CYP19A1, ESR1, ESR2, RORC, HTR2B, RXRB, ADRA2B, RARG, RXRG, RARB	RBP4	8.69%	RARA	0.00%
Beta-sitosterol	HMGCR, CYP51A1, AR, NPC1L1, NR1H3, CYP17A1, RORC, CYP19A1, ESR2, ESR1, SHBG, SREBF2, CYP2C19, SLC6A2, BCHE	HMGCR	68.10%	RORA	12.28%
Cryptotanshinone	AKR1B1, ACHE, CES1, CES2, STAT3, PTPN6, PTPN11, IDO1, TDP2, MALT1, KDM4E, CDC25B, CNR2, CHRM2, CHRM1	AKR1B1	100.00%	CHRM3	11.15%
Diosgenin	IL2, NR1H3, ALK, NR1H4, PTPN1, NR1H2, MDM2, KCNA3, CCR1, SMO, TRPV1, SHH, PLK1, CRHR1, PRCP	IL2	10.62%	CYP51A1	10.62%
Ginsenoside rh2	STAT3, IL2, HSP90AA1, PTAFR, PSEN2 PSENEN NCSTN APH1A PSEN1 APH1B, VEGFA, FGF1, FGF2, HPSE, ATP1A1, BCL2L1, S1PR1, RORC, LGALS4, LGALS3	STAT3	10.31%	LGALS8	7.46%
Hirsutin	TRPA1, CYP19A1, TDP2, PARP1, PARP3, PARP2, PARP4, TNKS, HSD11B1, SRD5A1, SRD5A2, HSD3B1, CA2, CA1, CA12	TRPA1	4.15%	CA9	3.12%
Isorhamnetin	XDH, CA2, CA7, CA12, CA4, CYP1B1, ABCC1, NOX4, AKR1B1, ABCG2, ACHE, ALOX15, ALOX12, IGF1R, EGFR	XDH	100.00%	AVPR2	25.99%
Kaempferol	NOX4, AKR1B1, XDH, TYR, FLT3, CA2, ALOX5, CA7, HSD17B2, ABCC1, HSD17B1, AHR, CA12, ESRRA, ABCB1	NOX4	100.00%	CYP1B1	100.00%
Luteolin	NOX4, AKR1B1, CDK5R1 CDK5, XDH, MAOA, FLT3, CA2, CCNB3 CDK1 CCNB1 CCNB2, ALOX5, ADORA1, CA7, GLO1, APP, SYK, GSK3B	NOX4	100.00%	PARP1	100.00%
Taxifolin	No target	No	No	No	No
Naringenin	CYP19A1, CA7, ABCC1, HSD17B1, CA12, SHBG, CA4, CYP1B1, CBR1, ESR1, ESR2, PTGS1, MAOB, ADORA1, ADORA3	CYP19A1	91.29%	ABCG2	12.51%
Quercetin	NOX4, AVPR2, AKR1B1, XDH, MAOA, IGF1R, FLT3, CYP19A1, EGFR, F2, CA2, PIM1, ALOX5, AURKB, DRD4	NOX4	100.00%	ADORA1	100.00%
Syringetin	AKR1B1, CYP1B1, XDH, CA2, CA7, CA12, CA4, BACE1, ABCC1, NOX4, ADORA3, CYP19A1, ABCB1, HSD17B2, F2	AKR1B1	62.45%	IGF1R	12.23%
Tanshinone iia	AKR1B1, CES1, TERT, PTPN6, CES2, PTPN11, EED SUZ12 EZH2, RBBP4 RBBP7 EED SUZ12 EZH2, CDC25C, MPI, CDC25A, PTPRC, CDC25B, ALOX5, HTR6	AKR1B1	100.00%	PDE10A	11.15%

Notes: aSome molecule predicted target numbers less than 15. b“Min Probability” means No. 15 target probability.

Molecular docking intends to simulate the docking of small-molecule ligands and large-molecule proteins, and the docking results are evaluated by binding energy (affinity). Smaller affinity delivers better molecular-docking binding effect. Generally, affinity < −7 kcal·mol–1 refers to better binding effect, and affinity < −9 kcal·mol–1 means very good binding effect. Through docking simulations, the docking results of SYKFT active molecule and core target protein were obtained (Table 2). Their binding energies were all less than −5 kJ mol, meaning that all of them can bind very well. AKT1 was the target of SYKFT active molecule predicted by machine learning, and the molecular-docking results suggested that it had good interbinding with the active molecule. Detailed information on the molecular-docking targets and corresponding active compounds is shown in Figure 7.

Table 2

Results of Molecular Docking

Core Component	Target	PDB ID	Binding Energy (kcal/mol)	Combination Possibility
Cryptotanshinone	STAT3	6NUQ	−8.1	1
	TNF	7ATB	−10.8
	RELA	3QXY	−10.4
Quercetin	IL2	3INK	−9.0
	MAPK1	4FV5	−8.7
	AKT1	3O96	−9.9	1
	TNF	7ATB	−10.0
	IL6	4O9H	−11.9
	RELA	3QXY	−9.2
	TP53	7BWN	−8.9
	MYC	2OR9	−6.8
	JUN	5T01	−8.6
	FOS	1FOS	−10.2
Luteolin	IL2	3INK	−8.8
	MAPK1	4FV5	−9.0
	AKT1	3O96	−10.1	0.279037062
	TNF	7ATB	−10.3
	IL6	4O9H	−11.5
	RELA	3QXY	−9.0
	TP53	7BWN	−8.7
	JUN	5T01	−9.0
Naringenin	MAPK1	4FV5	−8.8
	AKT1	3O96	−10.0
	RELA	3QXY	−8.9
Arachidonic acid	MAPK1	4FV5	−5.1
	RELA	3QXY	−6.3
Kaempferol	AKT1	3O96	−9.9	0.40264338
	TNF	7ATB	−9.9
	RELA	3QXY	−9.3
	MAPK8	2NO3	−8.3
	JUN	5T01	−8.1
Beta-carotene	AKT1	3O96	−8.9
	MYC	2OR9	−6.2
Diosgenin	AKT1	3O96	−13.3
	RELA	3QXY	−8.6
	TP53	7BWN	−9.8
	IL2	3INK	−9.9	0.106165761
	MAPK14	3MPA	−9.9	0.106165761
Ginsenoside rh2	TNF	7ATB	−9.9
	STAT3	6NUQ	−7.7	0.10307426
	IL2	3INK	−8.5	0.074564954
Astilbin	TNF	7ATB	−10.2
Tanshinone iia	RELA	3QXY	−11.2
	TP53	7BWN	−9.4
	MYC	2OR9	−7.8
	FOS	1FOS	−8.6
	JUN	5T01	−8.9
Acacetin	RELA	3QXY	−9.2
	TP53	7BWN	−8.7
Taxifolin	RELA	3QXY	−9.8
Isorhamnetin	RELA	3QXY	−9.2
	MAPK14	3MPA	−8.5
	AKT1	3O96	−9.9	0.259910104
Hirsutin	MAPK14	3MPA	−4.6
Syringetin	MAPK14	3MPA2OR9	−8.4
	AKT1	3O96	−10.1	0.113979815
Beta-sitosterol	JUN	5T01	−8.0

Notes: Binding energy calculated by AutoDock Vina; Combination possibility predicted by SwissTargetPrediction.

Figure 7

Molecular-docking interaction of active ingredients with the binding site of the target protein.

Treatment Effect of SYKFT on Experimental Mesangial Proliferative GN

Compared with the control group, experimental GN induced by anti-Thy 1 showed that mesangial dissolution was detected at day 3 after anti-Thy1 antibody injection. On days 5 and 7 (proliferative period), glomerular cell number significantly increased and mesangial area markedly expanded. Then, mesangial cell proliferation gradually bounced back and nearly returned to the normal level on days 10 and 14 (proliferation decline period). Proteinuria significantly increased on day 3 and then decreased progressively on days 5, 7, 10, and 14. No significant differences existed between serum creatinine and serum uric acid levels. Compared with the GN group, the SYKFT group improved in terms of the pathological changes in the kidney and the sharp reduction in urinary protein levels (Figure 8A and B).

Figure 8

SYKFT improved the pathological changes in the kidney (A) Periodic acid-Schiff staining, 400x) and significantly reduced levels of urinary protein (B); mRNA expression of core targets among the control, GN, and SYKFT groups (C). (*p<0.05 versus the control group; #p<0.05 versus the model group).

Expression Levels of Core Targets Against SYKFT Treatment of GN

The mRNA expression levels of 13 core targets were detected by RT-PCR in the control, GN, and SYKFT groups, respectively. Compared with the control group, the expression levels of RELA, TP53, STAT3, MAPK14, IL2, MAPK1, AKT1, TNF, IL6, MYC, MAPK8, JUN, and FOS were significantly upregulated in the GN group. Compared with the GN group, the expression levels of TP53, MAPK14, MAPK1, AKT1, TNF, MYC, MAPK8, JUN, and FOS were substantially downregulated in the SYKFT group (Figure 8C).

Discussion

The complexity of mechanism of traditional Chinese medicine action lies in its multiple components and targets. Given that the pathogenesis of the disease has not yet been determined, the mechanism of action of traditional Chinese medicine is difficult to analyze. Network pharmacology with the combination of system network analysis and engineering enables the systematic study of active ingredients, as well as targets and pathways of drugs at the molecular level. It also helps researchers find effective active ingredients, targets, and pathways. Previous studies have shown that SYKFT can alleviate proteinuria, and the effect improves with the prolongation of medication time, indicating that the SYKFT mechanism differs from that of RAS inhibitors.5

In the present study, herb-target network analysis on the active ingredient indicated that quercetin, kaempferol, arachidonic acid, luteolin, naringenin, and other active ingredients could act as multiple targets in the network and may play important roles in GN treatment under the SYKFT mechanism. Quercetin possesses the most potential targets; kaempferol comes second, followed by arachidonic acid, luteolin, and naringenin. Studies have proven that quercetin could ameliorate glucose-induced renal injury, acute kidney injury induced by the SARS-CoV-2 virus, renal ischemia-reperfusion injury, and kidney fibrosis.8–11 Studies also show that quercetin can resist inflammation and oxidative stress by inhibiting TNF, IL6, and MAPKs, thereby attenuating kidney damage.12–14 Moreover, the present study confirmed that quercetin had high binding energy with TNF, IL6, and MAKP1 through molecular docking. Kaempferol can also ameliorate kidney damage by inhibiting oxidative stress through action on AKT1 and TNF15,16 and deliver high binding energy with AKT1 and TNF through molecular docking. Luteolin could mitigate drug nephrotoxicity, ischemia-reperfusion kidney injury, and acute kidney injury through its antioxidant, anti-inflammatory, and antiapoptotic effects. Given that the targets of SYKFT’s active ingredients interact with the disease targets of GN, the unique edge in traditional Chinese medicine, to the best of its ability, could benefit GN treatment with multiple compounds and targets.

GO enrichment analysis showed that the potential targets of SYKFT treatment on GN were mostly abundant in the biological process of oxidative stress and cell proliferation, as well as in the cell component of cell membrane that was correlated with the mechanism of drug action on cell-membrane targets, the binding of transcription factors, cytokines and active substances with molecular functions, and the activation of receptor ligands, cytokines and transcription factors. KEGG enrichment analysis suggested that the potential targets of SYKFT in GN treatment were mostly concentrated in the infection-related pathway, including Hepatitis B, Chagas disease, Kaposi sarcoma-associated herpesvirus infection, Human T-cell leukemia virus 1 infection, and Yersinia infection, as well as GN-related pathways, such as Toll-like receptor signaling pathway, T cell receptor signaling pathway, Th17 cell differentiation, TNF signaling pathway, IL-17 signaling pathway, and MAPK signaling pathway, etc. Studies have demonstrated that autoimmune GN is the product of auto-antibodies and produces the effector functions of T cells targeting autoantigens.17 Toll-like receptor 9 can aggravate SLE-associated GN,18 and Toll-like Receptor 3 is associated with hepatitis C-associated GN.19 The Toll-like receptor 3 signaling pathway contributes to regional neutrophil recruitment in cultured human glomerular endothelial cells. Therefore, the intervention of glomerular TLR3 signaling may work as a suitable therapeutic strategy for treating GN.20 The IL-17 signaling pathway can amplify kidney damage in an animal model of antibody-induced GN,21 and Interleukin-17 cytokines have played critical parts in the development of fatal lupus GN.22 The MAPK pathway, as a classic pathway in the pathogenesis of mesangial proliferative GN, enables various cell functions, including cell proliferation, cell survival, differentiation and migration, and inflammation.23 Notably, the potential targets are also enriched in the Coronavirus disease 2019 (COVID-19) pathway, suggesting that SYKFT may also exert a potential therapeutic effect on COVID-19-related kidney injury, indicating the necessity of further studies. Overall, enrichment analysis suggested that the SYKFT treatment of GN primarily involved infection, inflammation, or immunity processes. Considering that GN was primarily correlated with infection or autoimmune abnormalities, SYKFT may exert therapeutic effects by regulating pathways involved in immunity, inflammation, and oxidative stress.

Furthermore, correlation network analysis showed that RELA, TP53, STAT3, MAPK14, IL2, MAPK1, AKT1, TNF, IL6, MYC, MAPK8, JUN, and FOS may be the key targets of SYKFT in GN treatment. Among them, JUN had the most nodules, followed by STAT3, MAPK1, MAPK14, RELA, TP53, and other targets. The Herb-Active ingredient-Core target network showed that quercetin had the most edges with core targets, followed by luteolin, kaempferol, tanshinone iia, etc., suggesting that quercetin, luteolin, kaempferol, tanshinone iia might be the important active ingredients of SYKFT that plays the therapeutic role on GN. According to the herb-active ingredient-target network, quercetin was the active ingredient of Baihuasheshecao, Duzhong, Tufuling and Yimucao. Luteolin was the active ingredient of Danshen and Jiegeng. Kaempferol was the active ingredient of Duzhong, Renshen and Yimucao. Tanshinone iia was the active ingredient of Danshen. It fully reflects the advantages of traditional Chinese medicine in multi-target therapy through the synergistic effect of multi-components. Notably, given that the concentrations of JUN, STAT3, MAPK1, MAPK14, and RELA were all enriched in the MAPK pathway of GN, the theoretical basis of multicomponent, multitarget, and multidisease traditional Chinese medicine could be clarified. The core active ingredients were identified through the herb-active ingredient–core target network, and machine-learning model was used to predict the target proteins of core active ingredients. Results of machine-learning model analysis suggested that STAT3, AKT1, and IL2 may be the key targets of SYKFT, which was also confirmed by in vivo experiments, indicating that the accuracy of machine-learning model was relatively high. Docking results showed that these compounds could stably bind to the active pockets of core proteins. Results of in vivo experiments also proved that SYKFT may relieve renal pathological injury through the regulation of core genes, thereby decreasing the concentration of urinary proteins.

Through network pharmacology, machine learning, and molecular-docking analysis, the clinical application of traditional Chinese medicine has become evidence based. SYKFT could be used to assist glucocorticoids/immunosuppressive agents/ACEI/ARB to improve proteinuria and also be used solely for patients with contraindications to the above drugs.

Conclusion

This study clarified the potential mechanism of SYKFT in GN treatment through network pharmacology and machine learning. Our findings showed that active ingredients such as quercetin, kaempferol, arachidonic acid, luteolin, and naringenin could deliver key therapeutic effects by affecting target proteins, such as STAT3, AKT1, and IL2. Molecular-docking studies have also demonstrated that the main active ingredients could dock well with the target proteins, providing an important basis for further research. In vivo animal experiments verified the effect and potential mechanism of SYKFT on GN. Therefore, by combining network pharmacology analysis, machine learning, molecular-docking analysis, and experimental validation, we found that the pharmacodynamics of SYKFT was through its multiple active molecules acting on target proteins to exert therapeutic effects, and its pharmacokinetic characteristics require further exploration. This study also had certain deficiencies, and more pharmacological and clinical investigations are needed to verify our conclusions.