The active compounds of Guizhi were searched through Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and the PubChem database was used for confirmation. The PubChem ID, Molecular Formula, Canonical SMILES and other information of each compound were collected, and the main active compounds were screened by oral bioavailability (OB) ≥ 30%, drug-likeness (DL) ≥ 0.18, and cell permeability (Caco-2) ≥ −0.4. HitPick, SEA and Swiss Target Prediction databases were used to make target prediction of the main active components, and the duplicates were deleted after merging the predicted targets of the three databases.

The targets of NS were predicted using DisGeNET, GeneCards, OMIM databases. After deduplication, Venny 2.1.0 was used to take the intersection with the drug targets, and further identification of intersecting target was carried out through the UniProt database.

The active ingredients and the intersecting targets were input into Cytoscape software to construct the active component-target network. Among them, the node size of active ingredient was based on degree value. The larger the node is, the greater the degree value is. Edges were used to indicate the link between the active ingredients and the targets.

The PPI network was constructed by String database and Cytoscape software. The intersecting targets were import into the String database, the parameter of Organism was set as Homo Sapiens, and the protein interaction was obtained. Finally, the PPI network was drawn using the Cytoscape software, and the bar chart of the top 10 intersecting targets was drawn according to the degree values.

The GO enrichment and KEGG pathway analysis of the intersecting targets were performed using DAVID database. The intersecting targets were imported into the DAVID database, and the threshold was set as p < 0.05. After the GO items (biological process (BP), cellular component (CC), molecular function (MF)) and KEGG signaling pathways were filtered, the corresponding bubble diagram of the top 10 BP, CC, MF items and top 30 KEGG signaling pathways was drawn according to the p values.

The Guizhi and its active compounds, top 30 KEGG signaling pathways and its targets, as well as NS were used to construct the overall network by Cytoscape.

The top five intersecting targets, selected from the PPI network, and the main active compounds of Guizhi were selected for the molecular docking through Autodock Vina. The smaller the binding energy (affinity), the more stable the interaction between target and active component is.

LPS and CA were purchased from Sigma-Aldrich, Inc. (St. Louis, United States). DXMS was purchased from Shanghai Yuanye Biotechnology Co., LTD. Griess kit and ELISA kits were purchased from MULTISCIENCES (LIANKE) BIOTECH, CO., LTD. (Hangzhou, China). Antibody: NF-κB p65, p-NF-κB p65, p38 MAPK and p-p38 MAPK were purchased from Cell Signaling Technology, Inc. (Danvers, Massachusetts, United States).

RAW264.7 cells were cultured in DMEM high glucose medium with 10% fetal bovine serum at 37°C, 5% CO₂ atmosphere. Experiments were carried out when the cells had grown to about 80%.

MTT assay was used to evaluate the cytotoxicity and cell proliferation ability of C on RAW264.7 cells. After inoculated into 96-well plates at a density of 5 × 10³ cells /mL overnight, RAW264.7 cells were pretreated with or without CA of different concentrations and DXMS (10 μg/ ml) for 2 h, and then co-incubated with or without LPS (1 μg/ ml) for 24 h. After 20 μL MTT solution was added to each well for 4 h, the supernatant was removed, and 150 μL DMSO was added. The OD value was measured by a microplate analyzer at 570 nm. Each group had five replicates and repeated for three times.

RAW264.7 cells were seeded in 6-well plates at a density of 3 × 10⁵ cells per well. After overnight incubation, cells were pretreated with or without various concentrations of CA and DXMS (10 μg/ ml) for 2 h, and then co-incubated with or without LPS (1 μg/ ml) for 24 h. The levels of NO in cell culture supernatants were measured using Griess kit according to the manufacturer’s instructions. OD value was detected at 450nm, and standard curve was made to obtain the content of the above factor.

The concentrations of IL-1β, IL-6, and TNF-α in the cell culture medium were detected by ELISA kits. After cell intervention, the supernatant was taken for detection according to the kit instructions, and then detected the OD value and made the standard curve.

After intervention for 24 h, total protein was extracted from each group, and the protein concentration was measured by BCA method. 10 μL of sample loading was taken from each group, 10% SDS PAGE gel electrophoresis was performed, PVDF film transfer, milk powder sealed for 2h, TBST film washing, primary antibody (1:1000) was incubated overnight at 4°C, TBST film washing, secondary antibody (1:1000) was incubated for 4h, TBST film washing, exposure.

Adriamycin (ADR) was purchased from Shanghai Wokai Chemical Reagent Co., Ltd (shanghai, China); CA was purchased from Sigma-Aldrich, Inc (St. Louis, United States); Benazepril hydrochloride was purchased from Beijing Novartis Pharmaceutical Co., Ltd (beijing, China); Urea nitrogen kit and creatinine kit were purchased from CHANGCHUN HUILI BIOTECH CO., Ltd (changchun, China); Urinary protein quantitative kit was purchased from Nanjing Jiancheng Bioengineering Institute (nanjing, China); HE staining fluid was purchased from Wuhan Google Biotechnology Co. Ltd (wuhan, China); Primary antibody: p38 MAPK, p-p38 MAPK and GAPDH were purchased from Cell Signaling Technology, Inc. (Danvers, Massachusetts, United States); Secondary antibody was purchased from Beyotime Biotechnology Co., Ltd (shanghai, China).

A total of 36 male SD rats were randomly divided according to body weight, including six rats in the normal group, and the other 30 rats were injected with ADR (3 mg/ kg) via tail vein on day 1 and 8, respectively, to create a model of ADR-NS in SD rats. After successful modeling, they were randomly divided into six groups as follows: control (CON), ADR, CA low-dose + ADR (CA-L), CA middle-dose + ADR (CA-M), CA high-dose + ADR (CA-H) and Benazepril + ADR (Benazepril), with six rats in each group. Each group was given corresponding drugs at 10 mg/ kg dose, and CON and ADR groups were given equal volume of normal saline once a day for 28 days.

Urine of rats in each group was collected in the metabolic cage for 24 h, and the specific urine volume was determined. The urine was centrifuged for supernatant, and the urine protein content was detected according to the kit instructions.

Blood samples from abdominal aorta were centrifuged and plasma creatinine and urea nitrogen were detected by automatic biochemical analyzer.

The kidney was removed and frozen in a −80°C refrigerator. After protein quantification using BCA, the protein expressions of P38 MAPK and P-P38 MAPK were detected by Western blot.

GraphPad Prism version 8.0.1was used for descriptive statistical analyses. Data were analyzed by One-way ANOVA, Tukey method, Kruskal-Wallis H rank sum test and the T² test, and p<0.05 was considered to be statistically significant.

A total of 220 compounds of Guizhi were included in TCMSP database, among them, seven main active compounds were preliminarily screened out using ADME parameters, as OB ≥ 30%, DL ≥ 0.18 and Caco-2 ≥ -0.4. cinnamaldehyde (CA), with OB = 31.99%, DL = 0.02, Caco-2 = 1.35, was not in the main active compounds. However, our previous study found that CA might be an important bioactive component of Guizhi (Zhang et al., 2019). Therefore, CA was selected as a candidate active component in this study, as shown in Table 1. Eight major active components of Guizhi were input into Hitpick, SEA and Swiss Target Prediction database, and 7, 89, 261 targets were collected, respectively. After merging the predicted targets from the three databases, the duplicates were deleted, and a total of 317 potential targets were screened out.

With “Nephrotic syndrome” as the key words, 384, 1179 and 619 related disease targets were predicted in DisGeNET, GeneCards, OMIM databases, respectively. The three sets of data were combined and the duplicates were removed. A total of 1846 targets were obtained, and Venny 2.1.0 was used to intersect disease targets with drug targets. Finally, 63 intersecting targets of Guizhi that might act on NS were obtained (Figure 2A). All the 63 intersecting targets were further confirmed by UniProt database, and the results were shown in Table 2.

63 intersecting targets were input into Cytoscape software to construct the active component-target network (Figure 2B). Nodes represented active components or intersecting targets (deep purple circles represented active components, light purple inverted triangles represented intersecting targets), and the degree value was represented by the node size, the larger the node, the greater the degree value. The edges represented the connection between active components and intersecting targets. As can be seen from the figure, different active components corresponded to different targets, which reflected the characteristics of multi-component, multi-target of Guizhi. Among them, the degree values of beta-sitosterol, sitosterol, cinnamaldehyde and peroxyergosterol were 28, 27, 26 and 24, respectively, which indicated that they were the most important active components in the network (Table 3).

A total of 317 drug targets of Guizhi were screened out in Hitpick, SEA and Swiss Target Prediction database, 384, 1179 and 619 disease targets were predicted in DisGeNET, GeneCards, OMIM databases, respectively. Venny 2.1.0 was used to intersect the drug targets with disease targets. Finally, 63 intersecting targets were obtained. Then, the 63 intersecting targets were input into Cytoscape software to construct the active component-target network. The deep purple circles represented active compounds, light purple inverted triangles represented intersecting targets, and the degree value was represented by the node size. Among them, the degree values of beta-sitosterol, sitosterol, cinnamaldehyde and peroxyergosterol were 28, 27, 26, and 24, respectively, which indicated that they were the most important active components in the network.

The 63 intersecting targets were imported into the String database, and the obtained information was imported into Cytoscape software to construct PPI network (Figure 3A). Nodes represented intersecting targets and edges represented the interactions between targets. The figure involved a total of 63 nodes and 370 edges. The size and color of the nodes reflected the values of degree. A bar chart of the top 10 intersecting targets was drawn according to the degree value (Figure 3B). Among them, the degree values of VEGFA, SRC, PTGS2, TLR4 and APP were 39, 32, 27, 26, and 24, respectively, which were the key nodes of the network, suggesting that Guizhi might play an effective role against NS through them.

63 intersecting targets were imported into DAVID database to screen GO entries (BP, CC, MF) and KEGG signaling pathways of p < 0.05. A total of 150 BP related items were found by GO functional enrichment analysis, which mainly involved negative regulation of apoptotic process, response to drug and other biological processes. There were 31 items related to CC, mainly involving cell surface, protein complex and other cellular components. 46 items related to MF, mainly involved enzyme binding, nitric-oxide synthase activity and other molecular functions (Figure 4A). The results indicated that NS, as a complex disease, involves a variety of biological processes, and Guizhi might play a protective role against NS by regulating these biological processes. KEGG signaling pathway analysis showed that the intersecting targets mapped 35 signaling pathways, mainly involving NF-Kappa B, MAPK and other signaling pathways. We further found that most of the key intersecting target were involved in the NF-Kappa B and MAPK signaling pathways (Figure 4B).

To further investigate the molecular mechanism of CA against NS, an overall network was constructed based on the top 30 significant KEGG signaling pathways and their corresponding targets (Figure 5). 83 nodes (1 drug, eight compound, 43 targets, 30 pathways and 1disease) were contained in this network. The color represented the degree value, and it changes from yellow to green indicated the greater degree value. In the network, CA is the top one active compound with the highest degree value, and in these pathways, NF-Kappa B and MAPK were the most important signaling pathways with the highest degree value. Therefore, the network analysis suggesting that the protective role in kidney of Guizhi might be related to NF-Kappa B and MAPK signaling pathways.

Molecular docking was performed with the main compounds of Guizhi and the top five important targets in the above PPI network, namely VEGFA, SRC, PTGS2, TLR4 and APP (Table 4). The smaller the affinity value in the docking results, the more stable the interaction between the targets and the active component was. Through molecular docking, it was found that the above five important targets had good binding activities with the active components of Guizhi (Table 5) (Figure 6). Among them, CA showed good binding activity to VEGFA, TLR4 and APP with binding energy of −6.8,−9.6, and−9.6, respectively. Peroxyergosterol showed the best binding activity to SRC with binding energy of −8.7, while (+)-catechin showed good binding activity to PTGS2 for 7.6. The compounds and targets displayed diverse binding patterns at the active sites, including hydrogen bonds, H-π and π-π interactions. And these compounds bind to the targets through interacting with various amino acid residues, such as HIS-436, GLU-324, HIS-386, ALA-199, TYR-385, SER-247, GLN-285. The binding interactions and the binding site of compounds-targets were shown in Figure 7. Interestingly, the docking results between CA and the five key targets revealed that CA has higher binding affinities compared with the other compounds.

The results of MTT assays were shown in Figure 8. And it suggested that CA had no inhibitory effect on RAW264.7 cells at the concentration of 5 μg/ml, but had cytotoxicity on RAW264.7 cells at the concentration of 10 μg/ml. Therefore, CA with a concentration of 0.3125–5 μg/ml was chosen to evaluate the proliferation ability of CA on LPS challenged RAW264.7 cells. Even though LPS showed marked cytotoxicity to RAW264.7 cells, treatment with CA significantly enhanced the proliferation capacity in LPS challenged RAW264.7 cells.

To investigate the anti-inflammatory of CA, LPS was used to stimulate the release of NO in the RAW264.7 cells to mimic the chronic inflammatory environment. The results (Figure 9A) showed that LPS exposure activated RAW264.7 cells inflammation reflection, as NO secretion in the supernatants significantly enhanced after LPS stimulation for 24 h, and pre-treatment with various concentrations of CA in prior to LPS challenge notably attenuated the enhancement of the cytokine secretions.

The secretion of pro-inflammatory cytokines in LPS challenged RAW264.7 cells were also measured (Figures 9B–D). The levels of IL-1β, IL-6 and TNF-α in cell culture supernatants stimulated with LPS (1 μg/ ml) significantly increased compared to the control group, which indicated that the model of inflammation was successfully established in vitro. Administration of CA significantly attenuated the secretion of IL-1β, IL-6 and TNF-α in LPS challenged RAW264.7 cells in a concentration dependent manner. These results indicated that CA exerted anti-inflammatory activity via the suppression of NO production and pro-inflammatory cytokines IL-1β, IL-6 and TNF-α in LPS challenged RAW264.7 cells.

The major proteins of NF-κB and MAPK signaling pathways were analyzed in LPS challenged RAW264.7 cells. The results showed that NF-κB p65, p-NF-κB p65 and p-p38 MAPK protein levels in LPS stimulated RAW264.7 cells were significantly increased compared to control group. It suggested that NF-κB and MAPK signaling pathways were activated. Treatment with CA significantly decreased the protein expression of NF-κB p65, p-NF-κB p65 and p-p38 MAPK compared to LPS challenged control group (Figure 10). NF-κB p65 and p38 MAPK were mainly localized in the cytosol of RAW264.7 cells, while stimulation with LPS remarkably promoted NF-κB p65 and p38 MAPK translocation into the nucleus. Treatment with CA dramatically lowered the overall translocation of NF-κB p65 and p38 MAPK into the nuclei.

Compared with CON group, 24 h urine volume in ADR group was decreased (p <0.01) (Figure 11A), and the urine protein content was increased (p <0.01) (Figure 11B). Compared with ADR group, urinary protein in CA-L, CA-M and CA-H groups were decreased (p <0.05 or p <0.01).

Compared with CON group, Cr and BUN of ADR group were increased (p < 0.01). Compared with ADR group, Cr (Figure 12A) and BUN (Figure 12B) of CA-L, CA-M, and CA-H groups were decreased (p<0.05 or p<0.01).

In western blot analysis, compared with the CON group, p-p38 MAPK in ADR group were increased. Compared with ADR group, p-p38 MAPK were decreased in CA-L, CA-M and CA-H groups (Figures 13A,B).