Network pharmacology and experimental validation of andrographolide’s renoprotective effects on diabetic nephropathy via AGEs-RAGE signaling and mitophagy pathways

doi:10.21203/rs.3.rs-4621164/v1

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Network pharmacology and experimental validation of andrographolide’s renoprotective effects on diabetic nephropathy via AGEs-RAGE signaling and mitophagy pathways

https://doi.org/10.21203/rs.3.rs-4621164/v1

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Diabetic nephropathy (DN) is a severe diabetic complication, manifested by glomerulosclerosis, renal interstitial fibrosis and inflammation. Andrographolide (AD) is a natural compound with multiple pharmacological effects, such as anti-inflammatory, antioxidant, antidiabetic, etc. However, the mechanism of AD on DN remains elusive. In this study, we integrated network pharmacology and in vitro experiments to investigate the potential targets and pathways of AD on DN, and to confirm its renoprotective effects on renal tubular epithelial cells. We screened 198 potential targets of AD and 3791 related targets of DN from public databases, and obtained 107 common targets of AD-DN. We built the protein-protein interaction network of the common targets, and performed GO and KEGG enrichment analysis, and identified 7 core targets (STAT3, IL6, PIK3CA, JUN, CASP3, MAPK1, ERBB2) and several key pathways (AGEs-RAGE signaling pathway, mitophagy, etc.) of AD-DN. We then used a high-glucose induced mouse glomerular podocyte (MPC-5) model to validate the effects of AD on DN. We found that AD could significantly reduce the cell proliferation, inflammatory factors, ROS levels, inhibit the matrix remodeling, apoptosis and autophagy of cells, and regulate the expression of core target proteins and genes of cells. Moreover, we found that STAT3 played a vital role in the process of AD treating DN, and its blockade by WP1066 could significantly weaken the renoprotective effect of AD. Our study revealed the potential mechanism of AD on DN, and provided new evidence for its clinical application.

Andrographolide

Diabetic nephropathy

AGEs-RAGE signaling

Mitophagy

Network pharmacology

STAT3

Diabetic nephropathy (DN) refers to the kidney damage caused by diabetes, manifested as proteinuria, progressive decline of glomerular filtration rate (GFR), glomerulosclerosis and renal interstitial fibrosis [1]. DN is a common complication of diabetes, and also one of the main causes of end-stage renal disease (ESRD) [2]. It is estimated that there are about 200 million people with diabetes in the world, of which about 40% will develop DN [1]. The disease progression of DN not only seriously affects the quality of life of patients, but also increases the risk and mortality of cardiovascular diseases, bringing heavy burden to patients and society [3]. At present, the treatment of DN mainly relies on controlling blood glucose, blood pressure and blood lipid, as well as using angiotensin converting enzyme inhibitors (ACEI) or angiotensin receptor antagonists (ARB) and other drugs to delay the deterioration of renal function [4, 5]. However, these treatment methods still have many limitations, such as poor efficacy, large side effects, high resistance, etc., which cannot effectively prevent the progression and development of DN. Therefore, it is urgent to find new drugs and mechanisms to improve the treatment effect and prognosis of DN.

Traditional Chinese medicine (TCM) is a kind of traditional medicine with a long history and rich experience, which provides new ideas and choices for the treatment of DN [6]. TCM has the characteristics of multi-component, multi-target and multi-pathway, which can comprehensively regulate the multiple links and factors of DN, thus achieving the effect of synergistic treatment [7]. In recent years, more and more studies have shown that TCM and its active ingredients have significant protective effects on DN, which can improve the structure and function of the kidney, inhibit the inflammation and fibrosis of the kidney, regulate the metabolism and oxidative stress of the kidney, etc [8, 9]. However, the mechanism of action of TCM is often complex and unclear, which is difficult to explain by traditional pharmacology methods, limiting the development and utilization of TCM.

Network pharmacology is a theory based on systems biology, which uses computer and network technology to analyze the complex network of drugs and biological systems, thus revealing the mechanism of action and effective substance basis of drugs [10], which is in line with the concept and characteristics of TCM, and provides a new method and tool for the research of TCM [11]. Andrographolide (AD) is a diterpenoid lactone compound extracted from Andrographis paniculata, which is an important material basis for the pharmacological and pharmacological effects of Andrographis paniculata [12]. A few pharmacological studies have shown that AD has various pharmacological activities, such as anti-inflammatory, antioxidant, anti-tumor, hepatoprotective, choleretic, and protective effects on cardiovascular and nervous systems [13, 14]. Among them, the anti-inflammatory activity of AD is the most prominent, and its anti-inflammatory effect involves multiple inflammation-related targets. It can inhibit the expression and activation of nuclear transcription factor-κB (NF-κB), signal transducer and activator of transcription 3 (STAT3) and other signaling pathways, reduce the synthesis and release of downstream inflammatory mediators, regulate oxidative stress and immune response, and achieve anti-inflammatory effects on various inflammatory diseases [15]. It was reported AD has inhibitory effects on various tumer cells, and can inhibit tumor cell proliferation, block cell cycle, and induce tumor cell apoptosis to exert anti-tumor effects [16]. Some studies have found that AD can improve the renal pathological changes and biochemical indicators of diabetic mice by inhibiting the expression of fibronectin (FN) and NADPH oxidase 4 (NOX-4) in the kidney and regulating glucose metabolism and lowering blood glucose levels [17, 18]. However, these studies did not explore the network and signaling pathways of AD on DN in depth, nor did they systematically analyze and verify its target points. In order to reveal the potential mechanism of AD in the treatment of DN, we first used network pharmacology to screen out potential targets of AD and DN based on the drug-target-disease triad model, and constructed the AD-DN target interaction network. Then, we conducted GO and KEGG enrichment analysis on the core targets in the network to discover the signaling pathway that AD might regulate. Then, a mouse glomerular podocyte MPC-5 model induced by high glucose was used to verify the therapeutic effect and mechanism of AD on DN. Finally, STAT3 inhibitor WP1066 was used to explore the role of STAT3 in the treatment of DN by AD. The flow chart of our study is shown in Figure 1. This study uses network pharmacology and in vitro experiments to reveal the potential mechanism of AD in treating DN, and provide new theoretical basis and direction for its clinical application and new drug development.

2.1 Network pharmacology analysis

2.1.1 Prediction of AD targets and DN disease genes

We searched the chemical structure and molecular properties of AD from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and downloaded its SMILES format file. Using four online databases, namely PharmMapper (http://lilab.ecust.edu.cn/pharmmapper/), SwissTargetPrediction (http://www.swisstargetprediction.ch/), BATMAN-TCM (http://bionet.ncpsb.org/batman-tcm/) and SuperPred (http://prediction.charite.de/), we predicted the possible targets of AD based on its chemical structure, and integrated the results of the four databases, removed the duplicate targets, and obtained the potential target set of AD. To obtain the disease gene information of DN, we used four databases, DrugBank (https://www.drugbank.ca/), PharmGKB (https://www.pharmgkb.org/), GeneCards (https://www.genecards.org/) and OMIM (https://www.omim.org/) for analysis and summary. We entered the keywords “Diabetic Nephropathy” and “Diabetic Nephropathies” into these four databases respectively, obtained the related target information of DN, and used the Uniprot database to convert the protein names of the targets into the corresponding gene names, and performed deduplication and integration, and obtained the final target set of DN.

2.1.2 Intersection targets and network analysis of AD and DN

To obtain the intersection targets of AD and DN, we compared the drug target set of AD and the disease gene set of DN, and obtained the common targets of the two. To analyze the common targets of AD and DN, we used the VennDiagram package of R language, drew the Venn diagram of the targets of AD and DN, and obtained the set of common targets. To construct the protein-protein interaction (PPI) network of the intersection targets of AD and DN, we used the String database to input the intersection targets, queried the PPI information in the database, and set the minimum interaction score to 0.7, obtained the PPI network, and exported it as Cytoscape format, and used Cytoscape software for visualization and analysis. Using Cytoscape software, we used the intersection targets as nodes, and AD and DN as attributes, and drew the network graph. Using the cytoHubba plugin (https://apps.cytoscape.org/apps/cytohubba), according to the target interaction network of AD-DN, we used various algorithms, such as Degree, Betweenness, Closeness, EPC, MCC, etc., to calculate the centrality indicators of each target, and comprehensively considered various indicators, and screened out the core target set of AD-DN. And according to the degree value of the nodes, we used the cytoHubba function module to screen out the 7 core targets with the highest Degree value.

2.1.3 GO and KEGG enrichment analysis.

Using the DAVID database (https://david.ncifcrf.gov/), according to the common target set and core target set of AD-DN, we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and analyzed the biological processes (BP), cellular components (CC), molecular functions (MF) and signaling pathways (Pathway) involved by the common targets and core targets of AD-DN, and drew bar charts, bubble charts, mulberry charts and composite charts and other visualization forms to show the results of enrichment analysis.

2.2 In vitro experiment validation

2.2.1 Cell culture and treatment

Mouse glomerular podocyte MPC-5 cells were purchased from Shanghai Cell Bank and cultured in Dulbeccos Modified Eagle Medium (DMEM) containing 10% fetal bovine serum, and incubated in a 37 °C, 5% CO₂ incubator. The cells were passaged to the 3rd to 5th generation for the experiment. Andrographolide AD was purchased from Shanghai Zhongyao Co., Ltd. of China Pharmaceutical Group, dissolved in dimethyl sulfoxide (DMSO) and diluted to different concentrations of reserve solution, and stored at 8 °C. WP1066 was purchased from Selleck Company in the United States, dissolved in DMSO and diluted to a 10 μM reserve solution, and stored in an 8 °C refrigerator. The cells were divided into the following 6 experimental groups: normal group (NC group), cultured with DMEM containing 5.5 mM glucose; diabetic nephropathy model group (M group), cultured with DMEM containing 30 mM glucose; M-AD 10 μM group (M-10 group), 10 μM of AD was added on the basis of the M group; M-AD 20 μM group (M-20 group), 20 μM of AD was added on the basis of the M group; M-AD 40 μM group (M-40 group), 40 μM of AD was added on the basis of the M group; M-AD 80 μM group (M-80 group), 80 μM of AD was added on the basis of the M group; M-AD 80 μM-WP1066 group (M-80-WP group), 10 μM of WP1066 was added on the basis of the M-80 group. After 48 h of cell culture, the cells and supernatant were collected and subjected to subsequent experiments. The WP1066 (10 μM, this concentration can effectively inhibit the phosphorylation and activation of STAT3, and will not have a significant effect on cell viability) used in this cell experiment is a novel, cell-permeable STAT3 inhibitor, which can inhibit the phosphorylation and activation of STAT3, and thus block the downstream signaling pathways, such as PI3K/AKT, MAPK, ERK, etc.

2.2.2 MTT assay to detect cell proliferation ability

The podocytes were passaged to a 96-well plate, and after 48 h of group intervention, 20 μL of MTT (5 mg/mL) solution was added, incubated at 37 °C for 4 h, the culture medium was removed, 150 μL of DMSO was added, and shaken for 10 min, and the absorbance (OD value) of each well was measured by an enzyme-linked immunosorbent assay (wavelength 490 nm), and the cell proliferation rate was calculated.

2.2.3 ELISA experiment

ELISA method to detect the levels of inflammatory factors (TNF-α, IL-1β), Advanced Glycation End Products (AGEs) and vascular active substances (PGE2, TXB2) in the cell supernatant. The podocytes were passaged to a 6-well plate, and after 48 h of group intervention, the cell supernatant was collected, and operated according to the instructions of the ELISA kit, and the OD value of each well was measured by an enzyme-linked immunosorbent assay (wavelength 450 nm), and the concentration of each indicator was calculated according to the standard curve.

2.2.4 Oxidative stress level detection experiment

DCFH-DA method to detect the level of ROS in cells, GSH/GSSG ratio to detect the level of GSH in cells. The podocytes were passaged to a 6-well plate, and after 48 h of group intervention, the original culture medium was replaced with serum-free culture medium containing 10 μM DCFH-DA, incubated at 37 °C for 30 min, and the original culture medium was replaced with serum-free culture medium containing 10 μM NEM, incubated at 37 °C for 15 min, and the cell lysate was collected, and operated according to the instructions of the GSH/GSSG detection kit, and the OD value of each well was measured by an enzyme-linked immunosorbent assay (wavelength 405 nm), and the data of each indicator was calculated according to the standard curve, and the GSH/GSSG ratio was calculated.

2.2.5 Detection of expression and activity of core targets in cells

Using laser confocal microscopy, we studied the effects of the expression changes of the core target STAT3 in cells on the expression of IL-6 and PIK3CA. The cells were seeded on glass coverslips, 2*10⁴ per well, and cultured for 24 h. After treatment according to the previous method, on the third day after treatment, the cells were fixed with 4% polyformaldehyde for 15 min, permeabilized with 0.1% Triton X-100 for 10 min, blocked with 5% bovine serum for 1 h, and incubated with anti-STAT3, anti-IL-6, and anti-PIK3CA primary antibodies (1:200, Abcam, UK) overnight. Then, the cells were incubated with fluorescently labeled secondary antibodies (1:500, Abcam, UK) for 1 h, stained with DAPI for 10 min, washed three times with PBS, and observed and photographed with a laser confocal microscope (Leica, Germany). The expression and localization of STAT3, IL-6, and PIK3CA in the cells were analyzed. 2.2.6 Immunoprecipitation experiment

Combined with Western blot detection method, we studied whether the expression of STAT3 in cells was strongly correlated with IL-6 and PIK3CA. The cells were collected, lysed with RIPA buffer, centrifuged for 10 min, and the supernatant was taken. The protein concentration was determined by BCA method, and equal amounts of protein were taken. The proteins were incubated with anti-STAT3 primary antibody (1:200, Abcam, UK) overnight, and then protein A/G magnetic beads (Beyotime, China) were added and incubated at room temperature for 2 h. The magnetic beads were adsorbed with a magnetic rack, the supernatant was removed, the proteins were separated by SDS-PAGE, transferred to a membrane, and incubated with anti-IL-6 and anti-PIK3CA primary antibodies (1:1000, Abcam, UK) and corresponding secondary antibodies (1:5000, Abcam, UK). The membrane was developed with ECL luminescent liquid, and the gray value of the bands was analyzed with ImageJ software to reflect the interaction of STAT3 with IL-6 and PIK3CA in the cells.

2.2.7 Protein expression level detection

Western blot method was used to detect the expression changes of AGEs-RAGE signaling pathway-related proteins (RAGE, p38 MAPK, NF-κB), autophagy-related proteins (BINP3, NIX, FUNDC1, PINK1, Parkin) and core target proteins (STAT3, IL6, PIK3CA, JUN, CASP3, MAPK1 and ERBB2) in the cell lysate. The podocytes were passaged to a 6-well plate, and after 48 h of group intervention, the cell lysate was collected, and the protein concentration was measured by the BCA method, and the protein was separated by SDS-PAGE electrophoresis, transferred to the membrane, sealed with 5% skim milk, and added with the corresponding primary antibody (1:1000 dilution) and incubated overnight at 4 °C, and added with HRP-labeled secondary antibody (1:5000 dilution) and incubated at room temperature for 1 h, and colored with ECL luminescent liquid, and photographed with Gel Doc XR+ imaging system, and the gray value was analyzed by Image J software, and the relative protein expression level was calculated with β-actin as the internal reference.

2.2.8 RT-PCR experiment

RT-PCR method was used to detect the transcription level of AGEs-RAGE signaling pathway-related genes (RAGE, p38 MAPK, NF-κB), autophagy-related genes (BINP3, NIX, FUNDC1, PINK1, Parkin) and core targets (STAT3, IL6, PIK3CA, JUN, CASP3, MAPK1 and ERBB2) in the cells. The podocytes were passaged to a 6-well plate, and after 48 h of group intervention, the total RNA of the cells was collected, extracted by Trizol method, and the concentration and purity of RNA were measured by NanoDrop 2000, and reverse transcription was performed by PrimeScript RT reagent Kit to obtain cDNA. Real-time fluorescence quantitative PCR was performed with SYBR Premix Ex Taq II, and the fluorescence signal was detected by ABI 7500 system, and the relative gene expression level was calculated by 2-^ΔΔCt method, with GAPDH as the internal reference. And the primer sequences in this study were shown in Table.1.

2.2.9 Statistical analysis

GraphPad Prism (version 22.0, Inc-La Jolla, USA) was used for statistical analysis. All experiments were performed at least three times, and the gray values of three independent experiments were read by ImageJ-pro software. All data were analyzed by Student’s t-test or one-way analysis of variance (one-way ANOVA), and were normally distributed, and passed the homogeneity of variance test. Data were expressed as mean ± SD of three repeated cell cultures, n=3. In addition, SPSS software (version 19, SPSS Inc., USA) was used for statistical analysis, and principal component analysis was used to define the most important parameters. *, indicates that the data of this group has statistical difference with another control group (P < 0.05); **, indicates that the data of this group has significant statistical difference with other groups (P < 0.01).

3.1 Screening of potential targets of AD and related targets of DN

By searching, screening and integrating public databases, removing duplicate targets, and obtaining the potential target set of AD, as shown in Fig. 2A, we obtained 198 potential targets of AD and 3791 related targets of DN. As shown in Fig. 2B, using Venn diagram, we analyzed the intersection of potential targets of AD and related targets of DN, and obtained the common target set of AD-DN, which was 107, accounting for 54.04% of AD targets and 2.82% of DN targets. This indicates that AD and DN have a high correlation, and AD may exert therapeutic effects by acting on these common targets.

3.2 Network analysis of AD and DN interaction and screening of core targets

We imported the Uniprot ID of these 107 common genes into the STRING database, and constructed the protein-protein interaction (PPI) network of potential targets (Fig. 2C), where nodes represent target proteins, edges represent the interaction relationship between proteins, and the size and color depth of nodes represent the combined score of proteins, which is the sum of the strength of the edges connected to them, reflecting their importance in the network. We used the CytoHubba plugin of Cytoscape software to perform topological attribute analysis on the PPI network, calculated the degree centrality (DC), betweenness centrality (BC) and closeness centrality (CC) and other indicators of each node, to evaluate the core degree and influence of each node in the network. According to the comprehensive ranking of these indicators, we screened out 7 core targets, as shown in Fig.2C and Fig.3A, the core targets are STAT3, IL6, PIK3CA, JUN, CASP3, MAPK1, ERBB2. These core targets may be the key to AD’s therapeutic effect, and are also the focus of subsequent experimental research.

3.3 GO and KEGG enrichment analysis, exploring the pathways and mechanisms of AD

We entered the gene names of the common targets of AD and DN into the DAVID database, and performed GO and KEGG enrichment analysis, and obtained the enriched biological processes (BP), cellular components (CC), molecular functions (MF) and signaling pathways (KEGG). From Fig. 3, it can be seen that the common targets are enriched to BP mainly include: protein phosphorylation, inflammatory response, cell apoptosis, cell cycle, signal transduction, etc. The common targets are enriched to CC mainly include: cell membrane, cell nucleus, cytoplasm, etc. The common targets are enriched to MF mainly include: kinase activity, transcription factor activity, DNA binding, protein binding, etc. The common targets are enriched to KEGG mainly include: AGEs-RAGE signaling pathway, EGFR-related signaling pathway, mitophagy complications, etc. These enrichment results reflect the possible pathways and mechanisms of AD in treating DN.

3.4 GO and KEGG enrichment analysis with Core targets

We used the DAVID database to perform KEGG enrichment analysis on these 7 Core targets, selected the OFFICIAL_GENE_SYMBOL format, set the species to homo sapiens, and performed KEGG_PATHWAY enrichment analysis. We screened out the KEGG pathways with enrichment P value less than 0.05, as shown in Fig. 3. From the results, it can be seen that the enrichment of genes in KEGG pathways such as AGEs-RAGE signaling pathway and mitophagy-related complications, fibrosis, tumor-related signaling pathway, indicating that these KEGG pathways are closely related to the therapeutic effect of the drug and disease studied in this research. And the most two important signaling pathway in the AD-DN shown in Fig.3.D-E.

3.5 Cell proliferation ability detection

MTT method was used to detect cell proliferation ability. As shown in Fig. 4.A, compared with the normal group NC group, the proliferation rate of M group cells decreased, but there was no significant statistical difference. The cell proliferation ability of each subsequent drug administration group remained between 80%-120%, and the difference from the NC group did not exceed 20%, so the experimental concentration design was reasonable and can be used for subsequent experiments.

3.6 Cell oxidative stress level detection

DCFH-DA method was used to detect the level of ROS and AGEs in cells, and GSH/GSSG ratio was used to detect the level of GSH. As shown in Fig.4, compared with the normal group NC group, the AGEs, ROS level in M group cells increased significantly, and the antioxidant substances GSH/GSSG ratio decreased significantly, indicating that high glucose induced oxidative stress and antioxidant ability decline in cells. With the addition of AD, the AGEs and ROS level in cells gradually decreased, and GSH/GSSG ratio gradually increased, indicating that AD could inhibit the oxidative stress and antioxidant ability decline induced by high glucose in cells. However, in the last group M- AD 80 μM-WP1066 (M-80-WP) group, it was found that the addition of STAT3 blocker WP1066 weakened the inhibitory effect of AD.

3.7 Cytokine detection experiment

ELISA method was used to detect the levels of inflammatory factors (TNF-α, IL-1β) and vascular active substances (PGE2, TXB2) in the cell supernatant. As shown in Fig. 4.B, compared with the normal group NC group, the levels of TNF-α, IL-1β, PGE2, and TXB2 in the cell supernatant of M group increased significantly, indicating that high glucose induced the inflammatory response and vascular active substance release of cells. With the addition of AD, the levels of TNF-α, IL-1β, PGE2, and TXB2 in the cell supernatant gradually decreased, indicating that AD could inhibit the inflammatory response and vascular active substance release induced by high glucose in cells. However, in the last group M- AD 80 μM-WP1066 (M-80-WP) group.

3.8 Laser confocal microscopy observation of the effects of core target expression

We used laser confocal microscopy to study the effects of the core target STAT3 on the expression of IL-6 and PIK3CA in cells. The results are shown in Fig.5. Compared with the normal group NC group, the expression of STAT3 in the M group cells was significantly reduced, while the expression of IL-6 and PIK3CA was significantly increased, indicating that high glucose inhibited the expression and activity of STAT3, while promoting the expression and activity of IL-6 and PIK3CA. The expression of STAT3 in the cells of different concentrations of AD treatment groups was higher than that of the M group, while the expression of IL-6 and PIK3CA was lower than that of the M group, and increased or decreased with the increase of AD concentration, indicating that AD could promote the expression and activity of STAT3, while inhibiting the expression and activity of IL-6 and PIK3CA, and showed dose dependence. The expression of STAT3 in the M-80-WP group cells was lower than that of the M-80 group, while the expression of IL-6 and PIK3CA was higher than that of the M-80 group, indicating that the addition of STAT3 inhibitor WP1066 inhibited the promotion of AD on STAT3, while inhibiting the inhibition of AD on IL-6 and PIK3CA, indicating that STAT3 had a significant regulatory effect on the expression of IL-6 and PIK3CA.

3.9 Immunoprecipitation detection of co-expression of core targets

With immunoprecipitation method, we studied whether the expression of STAT3 in cells was strongly correlated with IL-6 and PIK3CA. The results are shown in Fig5 B and E. In WB detection, the corresponding protein expression was detected by IL-6 and PIK3CA antibodies in the input protein and pull-down protein. Secondly, the corresponding protein expression was also detected by IL-6 and PIK3CA antibodies in the protein pulled down by STAT3 antibody. The results met the above two conditions, indicating that there was an interaction between STAT3 and IL-6 and PIK3CA.

3.10 Protein expression analysis results

Western blot method was used to detect the expression changes of AGEs-RAGE and mitophagy related protein and core target proteins. As shown in Fig.6, compared with the normal group NC group, the protein expression of RAGE, p38 MAPK, NF-ΚB, BINP3, NIX, FUNDC1, PINK1, Parkin, IL6, JUN, MAPK1 in the cell lysate of M group increased significantly, and the protein expression of STAT3, CASP3, ERBB2 decreased significantly, indicating that high glucose induced the abnormality of cell matrix remodeling, apoptosis, autophagy and signaling pathway. With the addition of AD, the protein expression of RAGE, p38 MAPK, NF-ΚB, BINP3, NIX, FUNDC1, PINK1, Parkin, IL6, JUN, MAPK1 in the cell lysate gradually decreased, and the protein expression of STAT3, CASP3, ERBB2 gradually increased, indicating that AD could inhibit the abnormality of AGEs-RAGE signaling, apoptosis, autophagy and signaling pathway induced by high glucose. However, in the last group M- AD 80 μM-WP1066 (M-80-WP) group, it was found that the addition of STAT3 blocker WP1066 weakened the effect of AD on the protein expression of each group, indicating that STAT3 played a key role in the process of AD treating DN.

3.11 RT-PCR experiment results

As shown in Fig. 7, compared with the normal group NC group, the gene expression of RAGE, p38 MAPK, NF-ΚB, BINP3, NIX, FUNDC1, PINK1, Parkin, IL6, JUN, MAPK1 in M group cells increased significantly, and the gene expression of STAT3, CASP3, ERBB2 decreased significantly, indicating that high glucose induced the abnormality of cell matrix remodeling, apoptosis, autophagy and signaling pathway. With the addition of AD, the gene expression of RAGE, p38 MAPK, NF-ΚB, BINP3, NIX, FUNDC1, PINK1, Parkin, IL6, JUN, MAPK1 in cells gradually decreased, and the gene expression of STAT3, CASP3, ERBB2 gradually increased, indicating that AD could inhibit the abnormality of cell matrix remodeling, apoptosis, autophagy and signaling pathway induced by high glucose. However, in the last group M- AD 80 μM-WP1066 (M-80-WP) group, it was found that the addition of STAT3 blocker WP1066 weakened the inhibitory effect of AD.

In this study, for the first time, network pharmacology was used to predict the potential targets and related signaling pathways of andrographolide (AD), and to screen out the core targets associated with diabetic nephropathy (DN). We then used a high-glucose induced mouse glomerular podocyte (MPC-5) model to verify the protective effect of AD on renal tubular epithelial cells and its effect on core targets. Finally, we used the STAT3 blocker WP1066 to further confirm the key role of STAT3 in the treatment of diabetic nephropathy by AD. This study reveals the potential mechanism of AD in the treatment of diabetic nephropathy and provides new evidence for its clinical application.

As reported, AD has complicated protective effects on ischemic cardiovascular and cerebrovascular diseases [19, 20]. More and more studies have shown that AD could improve the renal function, structure and tissue damage of DN mice [21]. However, the mechanism of AD on DN is not clear, especially its molecular targets and signaling pathways. Therefore, it is necessary to explore the mechanism of AD on DN, and provide new evidence for its clinical application. Herein, we found that the targets of AD involved multiple signaling pathways related to the pathogenesis of DN, such as AGEs-RAGE signaling pathway and mitophagy in diabetic complications, etc.

AGEs are a kind of stable, non-uniform structure of covalent compounds [22]. Under non-enzymatic conditions, the carboxyl part of reducing sugars and the amino groups of biological macromolecules such as proteins, lipids or nucleic acids react to form endogenous AGEs, also known as Maillard or “browning” reaction [23]. AGEs mainly concentrate in the tissue fibers of liver, kidney, myocardium, etc., which can directly affect the tissue and cell function and participate in the occurrence of diseases, or improve the protein and cell function by reacting with specific receptors, causing pathological changes in the body [24]. RAGE receptor, as a multi-ligand transmembrane receptor, is distributed in various different cells and cells. RAGE is expressed at a low level under normal physiological conditions, but due to RAGE-mediated cell multiple ligands such as AGEs, HMGB1 protein and Aβ binding, a series of damage-related molecular reactions are caused, which can activate different intracellular signaling pathways, such as AGEs-RAGE, p38 MAPK, NF-κB signaling pathway [25]. RAGE and ligand activation play an important role in sepsis, rheumatoid arthritis, diabetic nephropathy, atherosclerosis and nervous system diseases. Due to the state of diabetic nephropathy, glucose provides the main source of carbonyl, and the accumulation and generation of AGEs are very easy, which is also an important cause of diabetes and its complications [26]. Studies have shown that AGEs include sugar or lipid oxidation products, such as carboxymethyl lysine, pentosidine and malondialdehyde-lysine, which accumulate in the glomerular mesangial matrix expansion and glomerular basement membrane thickening in the early diabetic nephropathy and nodular lesions in the late end-stage renal failure [27]. AGEs-RAGE activation can further stimulate RAGE overexpression and aggravate AGEs through ROS signaling pathway conduction and oxidative stress induced [28]. Chronic inflammation infiltration is an important mediator of the occurrence and development of diabetic nephropathy. As a ubiquitous transcription factor, it is the main regulator of inflammatory response. The activation of NF-κB signaling pathway can promote the secretion of various pro-inflammatory cytokines, such as TNF-α, interleukin IL-1β, IL-6 [29]. Studies have shown that the activation of AGEs-RAGE signaling pathway can cause the activation of Caspase-3, which induces cell apoptosis [30]. Therefore, inhibiting the activation of AGEs-RAGE signaling pathway can effectively inhibit oxidative stress, cell apoptosis and the release of inflammatory factors. Mitochondrial autophagy disorder-mediated inflammatory response is closely related to DN. In this research, we found the effects of AD could inhibit the AGEs-RAGE related signaling pathway to improve the progression pf DN.

Mitophagy can clear aging and damaged mitochondria, which plays a very important role in mitochondrial quality control [31]. Mitophagy is mainly initiated by three different autophagy pathways mediated by receptors BNIP3 (B lymphocytoma-2 gene-homology 3)/NIX (Nip-like protein X), FUDNC1 (FUN14 domain containing 1) and ubiquitin-mediated PINK1 (PTEN-induced putative kinase 1)/Parkin [32]. When dysfunctional mitochondria have autophagy defects, it will cause inflammatory response and lead to diabetes development. Studies have shown that PINK1 in skeletal muscle of T2DM patients is inhibited during transcription [33]. In the heart of diabetic rat model, the expression of PINK1 and Parkin is significantly reduced, resulting in mitochondrial autophagy disorder, leading to the loss of mitochondrial quality control [34]. Mitochondrial autophagy plays a major role in maintaining the stable state of pancreatic β-cells and metabolic inflammation. The clearance of autophagy-induced inflammation by mitochondria can alleviate the symptoms of DN, which has a good research prospect. Studies have shown that mice lacking FUNDC1 gene have mitochondrial autophagy dysfunction, mitochondrial quality control disorder, high-fat diet-induced obesity and increased insulin tolerance [35]. Metformin, as a first-line drug for the treatment of T2DM, can reduce mitochondrial-mediated inflammation [33]. At the same time, experiments have shown that metformin can regulate PINK1/Parkin-mediated mitochondrial autophagy, reduce excessive ROS production, and thus reduce inflammation damage caused by diabetes, thereby balancing the function of liver glucose and lipid metabolism [36]. MPC-5 cells are immortalized mouse glomerular podocyte cell line, podocytes are important components of the glomerulus, involved in the maintenance and regulation of glomerular filtration function [37, 38]. Similarly, DN is one of the major complications of diabetes, characterized by glomerulosclerosis, renal tubulointerstitial fibrosis and inflammation, leading to gradual deterioration of renal function, and eventually developing into end-stage renal disease (ESRD) [39]. In this study, we found that AD can protect kidney function by inhibiting autophagy damage to kidney cells caused by high glucose. As high glucose is one of the main causes of DN, high glucose environment can induce a series of pathological changes in podocytes. In the high glucose environment, the expression and activation of STAT3 are inhibited, resulting in the increase of the expression and activation of IL-6 and PIK3CA downstream, thus affecting the processes of inflammation, oxidative stress, autophagy and apoptosis of cells.

STAT3 is an important transcription factor involved in a variety of cell biological processes and is one of the core targets of network pharmacological analysis and prediction in this research. It can be activated by receptor tyrosine kinase, non-receptor tyrosinase kinase, G protein-coupled receptors and other pathways, and it can regulate cell proliferation, differentiation, apoptosis, migration, invasion, angiogenesis, immune response, etc. It plays an important role in a variety of diseases. Such as inflammation, tumor, autoimmune disease, cardiovascular disease and so on. IL-6 is a multifunctional cytokine involved in inflammatory response, immune regulation, cell growth and differentiation. PIK3CA, a catalytic subunit of phosphatidylinositol 3-kinase, is involved in the PI3K/AKT signaling pathway, regulating cell metabolism, proliferation, survival and migration. There are complex interactions and mutual regulation between these molecules, which may affect the occurrence and development of DN through various signaling pathways and molecular mechanisms.

This research found that AD could inhibit the NF-κB、IL-6 and other signaling pathways, to reduce the secretion and release of inflammatory factors and vascular active substances, such as TNF-α, IL-1β, IL-6, PGE2 and TXB2, etc., thus reducing the inflammation and vascular activity of cells, which not only damage the glomerular filtration barrier, increase the permeability of the glomerulus, promote the occurrence of proteinuria, but also attract and activate other inflammatory cells, causing glomerular and interstitial inflammation infiltration, leading to interstitial inflammation edema and chronic tubular atrophy and necrosis. Therefore, the anti-inflammatory and anti-vascular activity effects of AD could protect the glomerular filtration function, reduce the occurrence of proteinuria, and inhibit the glomerular and interstitial inflammation.

In summary, this study is the first to use network pharmacology and in vitro experiments to reveal the potential mechanism of AD in treating DN, and to identify STAT3 as a key target of AD. We found that AD can protect MPC-5 cells from high glucose damage by regulating multiple signaling pathways, such as lipid and atherosclerosis, AGEs-RAGE, EGFR, NF-κB, IL-6, ROS, TGF-β, Wnt/β-catenin, Akt/mTOR, AMPK, etc. Our study provides new evidence and direction for the clinical application and new drug development of AD. However, our study also has some limitations, such as we only used one cell model, and did not perform animal experiments and clinical trials, therefore, the therapeutic effect and safety of AD on DN still need further verification. In the future, we will continue to explore the mechanism of AD on DN, and provide more evidence for its clinical application.

This study combined network pharmacology and in vitro experiments to explore the potential mechanism of AD in treating diabetic nephropathy. We found that AD can protect mouse glomerular podocytes from high glucose-induced damage through various pathways, including inhibiting inflammation and vascular activity, scavenging oxidative stress, inhibiting matrix remodeling, apoptosis and autophagy, and regulating signaling pathways. We also found that STAT3 is the key target of AD in treating diabetic nephropathy, and its blockade can significantly weaken the protective effect of AD. This study provided new evidence for the clinical application of AD, and also provided new ideas for the treatment of diabetic nephropathy.

CRediT authorship contribution statement

Jing-ya Li, Conceptualization, Data curation, Formal analysis.

Xin-xin Hu, Funding acquisition, Investigation.

Ying-chuan Yin, Methodology, Project administration.

Jing He, Resources, Software, Supervision, Validation.

Peng Xu, writing – original draft, Writing – review & editing.

Wang Zhang: Methodology, Project administration, Software, Supervision., Formal analysis, Methodology, Project administration. Xu Peng: Conceptualization, Data

curation, Supervision, Validation, Visualization.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

No data was used for the research described in the article.

Acknowledgment

This work was supported by the Health research project of Anhui Province (No. AHWJ2023BAa20017) and the Applied Medical Research Project of Hefei Municipal Health Commission (No. Hwk2023yb006).

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Network pharmacology and experimental validation of andrographolide’s renoprotective effects on diabetic nephropathy via AGEs-RAGE signaling and mitophagy pathways

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