Erzhi pill for diabetic nephropathy: validation of integrated network pharmacology, molecular docking, proteomics, and transcriptomics

Erzhi pill for diabetic nephropathy: validation of integrated network pharmacology, molecular docking, proteomics, and transcriptomics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Erzhi pill for diabetic nephropathy: validation of integrated network pharmacology, molecular docking, proteomics, and transcriptomics Wei Xie, Wei Li, Liu-Bin Xu, Yu-Xin Yan, Jin-Xian Huang, Hui-Fang Kuang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5308470/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract OBJECTIVE: The study aimed to investigate the mechanism of Erzhi Pill in treating diabetic nephropathy (DN) using network pharmacology and molecular docking, with animal experiments providing additional validation. METHODS: Initially, the molecular basis and potential mechanisms of Erzhi Pill were examined through network pharmacology, followed by molecular docking between its core components and key potential targets to corroborate the network pharmacology findings. These results were then validated through experimental approaches. RESULTS: Network pharmacology analysis and HPLC fingerprinting identified Specnuezhenide and wedelolactone as primary components, demonstrating effective binding between the core components and key targets, thus confirming the predictions. In vivo experiments revealed that Erzhi Pill markedly improved blood glucose, lipid profiles, insulin resistance, and kidney function in db/db mice, while also reversing renal cell pathological changes. Transcriptomics and proteomics analyses of KEGG-enriched differential proteins suggested that the preventive and therapeutic effects of Erzhi Pill on DN may operate through AGE-RAGE, PPAR, and other related signaling pathways. CONCLUSION: Overall, the combined findings from network pharmacology, molecular docking, and experimental validation elucidate the mechanism by which Erzhi Pill inhibits renal fibrosis in DN. Biological sciences/Molecular biology Health sciences/Endocrinology Health sciences/Medical research Health sciences/Nephrology Er Zhi Pill1 diabetic nephropathy2 network pharmacology3 molecular docking4 transcriptomics5 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Diabetic nephropathy (DN) is a prevalent microvascular complication among patients with diabetes and is the leading cause of end-stage renal disease (ESRD) in China [1] , contributing significantly to mortality in this population. The majority of patients with diabetes in China are affected by type 2 diabetes mellitus (T2DM), and as its prevalence continues to rise, so does the incidence of DN. A 2019 meta-analysis reported that the prevalence of DN in China was 21.8% [2], making it a major chronic kidney disease that significantly impairs quality of life and reduces life expectancy. Early clinical manifestations of DN are often subtle, typically presenting as proteinuria and a decline in glomerular filtration rate (GFR) [3], with the disease gradually progressing to ESRD. Current clinical management relies on symptomatic treatments such as controlling blood glucose and blood pressure and enhancing circulation to slow the progression of kidney damage, but the results remain suboptimal [4]. In contrast, traditional Chinese medicine (TCM) offers distinctive benefits in the treatment of DN. Various forms of herbal medicine, including formulas, single herbs, extracts, and compounds, have demonstrated anti-inflammatory effects in both animal and cellular models of DN [5-7]. Network pharmacology is an emerging field that leverages network analysis to study drug actions within cellular networks, offering deeper insights into the complex mechanisms of drug efficacy. This approach holds promise for drug discovery, particularly in the treatment of multifaceted diseases [8]. In this study, network pharmacology, molecular docking, and transcriptomic analysis were employed to investigate the mechanism of action of Erzhi Pill in treating DN, aiming to provide evidence-based support for the use of traditional Chinese medicine in DN management. Recent studies [9] have demonstrated that Erzhi Pill effectively reduces proteinuria, suppresses renal inflammation, combats oxidative stress, and improves glomerular and podocyte functions, with Specnuezhenide and wedelolactone being particularly capable of targeting renal tissues. As a classic traditional Chinese medicine prescription composed of Ecliptae and Ligustri Lucidi Fructus, Erzhi Pill has long been recognized for its clinical efficacy in DN treatment, based on the knowledge accumulated from experienced practitioners. However, while its effectiveness has been clinically validated, the precise mechanisms underlying its therapeutic effects still require further elucidation. 2. Results 2.1 Network pharmacology analysis The components and targets of Erzhi Pill were screened, yielding nine active ingredients from Ligustri Lucidi Fructus and eight from Ecliptae, supplemented by Specnuezhenide according to the literature. After refining the list, a total of 15 key active ingredients were identified (Table 1). Through GeneCards, OMIM, TTD, DrugBank, and PharmGKB databases, 210 targets corresponding to these active ingredients were obtained, after removing duplicates. Additionally, 3245 DN-related targets were identified, with 3004 from GeneCards, 253 from OMIM, 74 from PharmGKB, 33 from DrugBank, and 24 from TTD, as depicted in Fig. 1(a). By mapping the targets of the active ingredients to DN-related targets, 138 key targets for DN treatment through TCM were identified (Fig. 1(b)). The key targets were imported into the STRING database to construct the PPI network, consisting of 138 nodes and 534 edges, with an average node degree of 7.74. Higher node connectivity indicated greater influence. Further analysis using Cytoscape 3.9.1 identified key nodes through Betweenness values, with the top 50 shown in this study (Table 2). The most significant nodes were MAPK1, RXRA, CYP3A4, HSP90AA1, TP53, and AKT1, highlighting their pivotal role in the therapeutic effects of the core components of Erzhi Pill on DN (Fig. 1(c)). GO and KEGG enrichment analyses were performed on the 138 key targets using the Metascape platform. GO enrichment yielded 1701 BP entries, 149 MF entries, and 79 CC entries, with the top ten most significant ones in each category presented in this study (Fig. 3(B)). KEGG pathway enrichment identified 196 pathways, with the top 20 visualized (Fig. 3(A)). The analysis indicated that the most relevant pathways included those involved in cancer, lipid and atherosclerosis, hepatitis B, phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT), mitogen-activated protein kinase (MAPK), and AGE-RAGE signaling pathways, as well as the hepatitis C pathway. 2.2 Molecular docking Molecular docking revealed that the binding energies of the two active ingredients with six core proteins were all ≤ -8.0 kcal/mol, indicating favorable binding affinities [10](Table 3). Both Specnuezhenide and wedelolactone exhibited efficient binding to P53, JNK, EGR1, AKT, PKC, and P38. Visualization of these docking results is shown in Fig. 2. 2.3 Comparison of fasting blood glucose, fasting insulin, blood lipids, and kidney function levels in mice Glycolipid metabolism indexes: Compared to the MOD group, fasting blood glucose levels in the EZW-H, EZW-L, and CAP groups were significantly reduced (P < 0.001, P < 0.05, P < 0.01, respectively). The fasting insulin (FINS) levels in the EZW-H, EZW-L, and CAP groups also showed a significant reduction (P < 0.001). Additionally, high-density lipoprotein cholesterol (HDL-C) levels were significantly elevated in both the EZW-H and EZW-L groups (P 0.05). Low-density lipoprotein cholesterol (LDL-C) levels in the EZW-H, EZW-L, and CAP groups were significantly lower compared to the MOD group (P < 0.001, P < 0.01, P < 0.05, respectively). Total cholesterol (TC) levels in these groups were also significantly reduced (P < 0.001, P < 0.001, P < 0.01). Triglyceride (TG) levels in the EZW-H and EZW-L groups were significantly lower than those in the MOD group (P < 0.001), whereas the CAP group showed no statistically significant reduction in TG levels (P = 0.06). (Fig. 4(a-f)). Renal function indexes: In terms of renal function, serum creatinine (SCr) levels in the EZW-H and EZW-L groups were significantly lower than those in the MOD group (P < 0.001). Blood urea nitrogen (BUN) levels in the CAP and EZW-H groups were also significantly reduced compared to the MOD group (P < 0.001, P 0.05).(Fig. 4(g-h)). 2.4 Comparison of histopathological results in mice HE staining revealed that the glomerular and tubular structures in control mice were intact, with a normal extracellular matrix and no signs of thylakoid proliferation. In contrast, the model group exhibited mild thylakoid proliferation, along with vacuolar degeneration and loss of the brush border in tubular epithelial cells within the tubular mesenchymal region, indicating tubular damage. In the EZW-H (high-dose), EZW-L (low-dose), and CAP groups, renal pathology improved significantly, as evidenced by reduced glomerular and tubular lesions and a lower tubular injury index compared to the model group. PAS staining showed that control mice had no PAS-positive deposits in the glomeruli, maintaining normal structure, whereas the model group displayed substantial glycogen deposition in the glomerular thylakoid zone. This deposition was markedly reduced in the EZW-H, EZW-L, and CAP groups. Masson staining further demonstrated normal kidney structure in control mice, with evenly distributed collagen fibers. Conversely, the model group showed a pronounced increase in collagen accumulation in the glomerular and tubulointerstitial regions. Treatment with EZW-H, EZW-L, and CAP led to a significant reduction in collagen accumulation, suggesting these interventions may help inhibit the progression of renal fibrosis.(Fig. 5). 2.5 Transcriptomics analysis As illustrated in Fig. 6(A) the model group displayed 1,618 differentially expressed genes compared to the normal group, with 699 genes up-regulated and 919 down-regulated. In contrast, the TCM treatment group identified 368 differentially expressed genes compared to the model group, consisting of 111 up-regulated and 257 down-regulated genes. Fig. 6(B) present volcano plots comparing differential gene expression between the TCM and model groups, as well as between the model and blank groups. The horizontal axis represents the log2-transformed expression ratio between groups, where a log2 (ratio) > 0 indicates highly expressed genes, and < 0 denotes lowly expressed genes, with the distribution being symmetrical. Fig. 6(C) shows clustering analysis of differentially expressed genes, where genes exhibiting similar expression patterns across samples are grouped together. Gene Ontology (GO) enrichment analysis revealed that, compared to the model group, the top five enriched biological processes (BPs) in the kidney tissues of the TCM group included regulation of triglyceride catabolic processes, positive regulation of triglyceride catabolic processes, neutral lipolytic metabolic processes, acylglycerol catabolic processes, and regulation of triglyceride metabolic processes. The top five cellular components (CCs) enriched were chylomicron particles, very low-density lipoprotein particles, triglyceride-rich plasma lipoprotein particles, high-density lipoprotein particles, and plasma lipoprotein particles. As for molecular functions (MFs), the top five enriched terms were nuclear receptor activity, ligand-activated transcription factor activity, steroid hormone receptor activity, lipase inhibitor activity, and glucocorticoid receptor binding (Fig.7(A)). KEGG pathway enrichment analysis, sorted by P-value, demonstrated that, compared to the model group, differentially expressed genes in the kidney tissues of the TCM group were primarily enriched in pathways such as the PPAR signaling pathway, fat digestion and absorption, cholesterol metabolism, vitamin digestion and absorption, lipid atherosclerosis, adipocytokine signaling, and the P53 signaling pathway (Fig.7(B)). 2.6 Proteomic analysis Differential proteins between groups were identified using univariate analysis, with selection criteria of Fold Change (FC) > 1.2 and P-value < 0.05. This analysis revealed 397 differential proteins in the MOD group, comprising 274 up-regulated and 123 down-regulated proteins compared to the NOR group. In the TCM group, 727 differential proteins were identified, with 330 up-regulated and 397 down-regulated proteins in comparison to the MOD group (Fig.8). The corresponding volcano plots illustrate differential protein expression, with log2 ratios on the horizontal axis, indicating highly expressed proteins (log2 (ratio) > 0) and lowly expressed proteins (log2 (ratio) < 0) (Fig. 9). GO enrichment analysis of differential proteins revealed the top 10 BPs enriched in the TCM group’s kidney tissues, including the thioester metabolic process, ribose phosphate metabolic process, purine-containing compound metabolic process, peptide metabolic process, oxoacid metabolic process, regulation of lipid metabolic process, modulation of cellular ketone body metabolic process, steroid metabolic process, positive modulation of small-molecule metabolic process, and organophosphorus metabolic process. For CC, the top enrichments were protein complex, peroxisome, organelle membrane, organelle inner membrane, mitochondria, mitochondrial protein complex, mitochondrial membrane, mitochondrial membrane, microsomal lumen, and membrane-bound organelles. In terms of MF, the top categories were unfolded protein binding, small-molecule binding, ribonucleotide binding, pyrophosphatase activity, purine ribonucleotide binding, purine ribonucleotide binding, oxidizing reductase activity, isomerase activity, hydrolase activity, and catalytic activity (Fig. 10(A)). KEGG pathway enrichment analysis, ranked by P-value, showed that differential proteins in the TCM group were predominantly enriched in the PPAR signaling pathway, butyrate metabolism, peroxisomal and endoplasmic reticulum protein processing, fatty acid biosynthesis, unsaturated fatty acid biosynthesis, drug metabolism (other enzymes), aflatoxin biosynthesis, cytochrome P450-related drug metabolism, steroid hormone biosynthesis, propionate metabolism, β-alanine metabolism, and fatty acid degradation pathways (Fig. 10(B)). 2.7 Fingerprint results HPLC fingerprint analysis of the three batches of sachets revealed 12 distinct peaks (Fig. 11). The similarity evaluation results are summarized in Table 4. Comparison with the control identified peak 4 as Rhodiola rosea glycosides, peak 10 as Specnuezhenide, and peak 12 as wedelolactone. 3. Materials and methods 3.1 Animal grouping and intervention Forty-eight SPF-grade, 8-week-old male db/db mice were housed in a controlled environment at 23±3°C with appropriate humidity and a 12-hour light-dark cycle. The mice had free access to food and water. After a one-week acclimatization period, the 48 db/db mice were randomly assigned to groups using Excel 2016. The RAND function was applied to generate random numbers, which were sorted using the RANK function, and group assignments were determined by dividing the ranks into four groups using the ROUNDUP function. This process resulted in four groups of 12 mice each: the model group (MOD), low-dose TCM group (EZW-L), high-dose TCM group (EZW-H), and captopril group (CAP). Additionally, 12 wild-type mice (C57BL6), from the same litter, served as the normal control group (NOR). The animals were obtained from Beijing Huafukang Bio-technology Co., Ltd (Animal Production License No. SCXK (Beijing) 2019-0008; Animal Licence No. 110322230102178525). The CAP group received 5 mg/kg of captopril, while the EZW-L and EZW-H groups were administered 4 g/kg and 8 g/kg of Erzhi Pill, respectively. All treatments were administered via gastric gavage at a volume of 10 mL/kg of body weight, once daily for 12 consecutive weeks. Mice in the control and model groups were gavaged with an equivalent volume of distilled water. During the treatment period, bedding was changed daily to maintain a clean environment, and fasting blood glucose and body weight were recorded weekly. After 12 weeks, the animals were sacrificed by isofluorane asphyxiation. Blood samples were collected under isoflurane anesthesia, followed by centrifugation at 4°C, 3500 r/min for 10 minutes. The serum was harvested and stored at -80°C for further analysis. The animal protocol was approved by the Laboratory Animal Ethical Review Committee of the Shenzhen Zhongzuan Precision Medicine Research Institute (approval number ZXJZ202306280003) and carried out in strict according to the Guide for the Care and Use of Laboratory Animals of National Institute of Health (NIH) (Bethesda, MD, USA). 3.2 Experimental herbs reagents and instruments Granules of Ecliptae and Ligustri Lucidi Fructus were purchased from Shenzhen Hospital of Traditional Chinese Medicine, with the manufacturer being Chongqing Tianjiang Party Pharmaceutical Company Limited. The batch numbers for Ligustri Lucidi Fructus and Ecliptae were 21080081 and 21100181, while the control reagent kit and other assay kits were sourced from Nanjing Jiancheng Bioengineering Institute. These included the Urine Protein Quantification Test Kit (C035-2), Urea Nitrogen Test Kit (C013-2), Mouse Microalbumin Elisa Kit (H127-1), Mouse Insulin Elisa Kit (H203-1), LDL Cholesterol Determination Kit (A113-1), Triglyceride Determination Kit (A110-1), HDL Cholesterol Determination Kit (A112-1), and Total Cholesterol Determination Kit (A111-1). Additional chemicals and reagents, such as paraformaldehyde (Sinopharm Chemical Reagent Co., Ltd., 80096618), anhydrous ethanol (Sinopharm Chemical Reagent Co., Ltd., 10009218), eosin Y (Sia Reagent, D12621), and hematoxylin (Sigma, H9627-25G), were also utilized. Other materials included neutral gum (Solarbio, G8590), and Masson’s stain (Servicebio, G1006). Laboratory equipment used for the study included an enzyme labeling instrument (USCNK, SMR16.1), benchtop centrifuge (Shanghai Anting Scientific Instrument Factory, TGL-16c), freezing centrifuge (Hunan Xiangyi Laboratory Instruments, TGL-16), a general optical microscope (OLYMPUS, CX21), orthogonal white light photomicrograph microscope (OLYMPUS, CX31), imaging system (Q-IMAGING, MicroPublisher), and an inverted white light/fluorescence photomicroscope (OLYMPUS, IX51). 3.3 Experimental sampling After 12 weeks of treatment, blood was collected from the orbital vein following a 10-12 hour fasting period and anesthesia. The blood was left at room temperature for 2 hours, then centrifuged for 10 minutes at 4°C at 3500 r/min. The serum was separated from the upper layer and stored in a freezer at -80°C. Following blood collection and after the mice were sacrificed, kidney tissues were rapidly excised and stored at -80°C. Additionally, a portion of the kidney tissue was fixed in 4% paraformaldehyde and embedded in paraffin for histopathological analysis. 3.4 Serum index test Serum indices were detected following the protocols and procedures outlined in the respective assay kits. 3.5 Histopathology Mouse kidney tissues were fixed in 4% paraformaldehyde for 48 hours, dehydrated with ethanol, cleared using xylene, embedded in paraffin, and then sectioned for histological analysis. The tissue sections were stained with hematoxylin-eosin (HE), periodic acid-silver (PAS), and Masson’s stain. Observations and imaging were performed under a microscope. 3.6 Transcriptome sequencing After RNA extraction with TRIZOL, magnetic beads containing Oligo (dT) were employed to enrich eukaryotic mRNA from the total RNA by binding to the polyA tail of mRNA through A-T complementary pairing. The mRNA was then fragmented into short fragments by adding a fragmentation buffer. The first cDNA strand was synthesized using six-base random hexamers as primers, with the mRNA serving as the template. The second cDNA strand was synthesized by adding dNTPs, RNase H, and DNA polymerase I. Following the synthesis of the first strand, buffer, dNTPs, RNase H, and DNA polymerase I were added for the synthesis of the second strand. Subsequently, end repair and 3' end A-tailing were performed on the double-stranded cDNA. Sequencing adapters were ligated, and the cDNA was purified using Hieff NGS® DNA Selection Beads, which were also used for fragment selection. The purified and fragment-selected junction products underwent PCR amplification. Qubit fluorescence quantification and enzyme labeling were employed for quality control and quantification of the libraries. The double-stranded target region libraries were then denatured, cyclized, and digested to obtain single-stranded circular DNA. The single-stranded circular DNA was amplified through Rolling Circle Amplification (RCA), resulting in the formation of DNA Nano Balls (DNB). After constructing the library, Qubit was used again for quantitative quality control, and the library was deemed suitable for sequencing on the DNBSEQ-T7 platform. 3.7 TMT quantitative proteomic analysis For protein analysis, total protein was extracted from tissue samples using a lysis buffer. Proteins were precipitated using the acetone precipitation method, and protein concentration was determined by the Bradford assay after treatment with DTT and IAA. Trypsin was then added to the samples at a 1:50 mass ratio for enzymatic digestion. The digested protein samples were labeled using TMT labeling and separated via high-pH reversed-phase chromatography. Mass spectrometry analysis was conducted using the Orbitrap Exploris™ 480 mass spectrometer coupled with the UltiMate™ 3000 liquid chromatography system. The acquired mass spectrometry data were processed using MaxQuant software with the Andromeda algorithm, and the proteome reference database was constructed. 3.8 Exploration of network pharmacological mechanisms Using the TCMSP (V2.3) database (Ru et al., 2014) (http://tcmspw.com/tcmsp.php), the chemical composition of Erzhi Pill was screened based on oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18[12]. Additional target identification was performed using the PharmMapper database [13] (http://www.lilab-ecust.cn/). According to the 2020 edition of the Chinese Pharmacopoeia, Specnuezhenide were used as quality control markers for Erzhi Pills, while wedelolactone was the indicator for Ecliptae. Once the active ingredients were identified, the targets associated with each compound were collected, and target-gene matching was completed using the Uniprot database [14] (https://www.uniprot.org). This process was further verified through databases such as GeneCards [15] (https://www.genecards.org/), OMIM [16] (https://www.omim.org/), TTD [17] (http://db.idrblab.net/ttd/), DrugBank [18] (https://go.drugbank.com/), and PharmGKB [19] (https://www.pharmgkb.org/). For the disease analysis, the keyword "chronic glomerulonephritis" was used to gather relevant disease targets. Venny software was employed to map the overlap between the drug and disease targets, identifying key targets for the treatment of DN. Cytoscape 3.9.1 was utilized to construct a "disease-drug-component-target" interaction network. The STRING platform [20] (https://string-db.org/) was applied to analyze protein-protein interactions (PPI) among the key targets, while Metascape [21] (http://metascape.org) was used to perform GO and KEGG pathway enrichment analyses of the PPI network. 3.9 Molecular docking The 3D structures of human oncogene P53, C-Jun N-terminal kinase (JNK), protein kinase C (PKCβII), mitogen-activated protein kinase 14 (P38), and AKT were obtained from the PDB database (http://www.rcsb.org/) [22]. The 2D structures of the active compounds Specnuezhenide and wedelolactone were retrieved from the PubChem database (http:// pubchem.ncbi.nlm.nih.gov/) [23]. These 2D structures were then converted into 3D models using ChemBio3D 14.0 software. Subsequently, the 3D structures of the six proteins and compounds were transformed into the "pdbqt" format using AutoDockTools 1.5.6. Molecular docking simulations were carried out using AutoDock Vina 1.1.2. The docking results were analyzed and visualized through Pymol software for further interpretation and visualization of binding interactions. 3.10 Fingerprint mapping studies 3.10.1 Sample preparation Preparation of test solution: Formulated granules equivalent to 10 g each of Ecliptae and Ligustri Lucidi Fructus were weighed and mixed with 100 mL of 75% methanol. The mixture was ultrasonicated for 30 minutes and filtered through a 0.2 μm microporous filter membrane before being analyzed via HPLC. Preparation of Control Solution: Rhodiola rosea glycoside,Specnuezhenide, and Wedelolactone were weighed and dissolved in methanol to prepare a mixed solution with mass concentrations of 0.26, 0.83, and 0.18 mg/mL, respectively. 3.10.2 Chromatographic conditions A Thermo Acclaim 120 C18 column (250×4.6 mm, 5 μm) was used. The mobile phase consisted of 0.1% phosphoric acid in water (A) and acetonitrile (B), with a gradient elution profile: 0–30 min, 8% B; 30–40 min, 8–16% B; 40–60 min, 16% B; 60–80 min, 16–18% B; 80–100 min, 18–28% B; 100–115 min, 28–40% B; 115–118 min, 40–95% B; 118–126 min, 95% B; 126–127 min, 95–8% B; and 127–137 min, 8% B. The flow rate was set at 0.8 mL/min. Detection was performed at 256 nm for 0–100 min and 351 nm for 100–137 min. The column temperature was maintained at 30°C, and the injection volume was 10 μL. 3.10.3 Methodological investigation of HPLC fingerprints Precision test: The test solution was prepared following the method outlined in "2.1.1," and chromatographic conditions were applied according to "2.2." The sample was injected six times consecutively, and chromatograms were recorded. Using peak 10 (Specnuezhenide) as the reference, the relative retention time and relative peak area of the main peaks were evaluated. Results showed that the relative standard deviation (RSD) for the retention times of the main peaks was less than 1%, while the RSD for the relative peak areas was less than 5%, indicating the high precision of the instrument. Repeatability test: Six test solutions were prepared in parallel following the method in "2.1.1" and analyzed using the established chromatographic conditions. Chromatograms were recorded, and the relative retention time and relative peak area of the main peaks were again evaluated using Specnuezhenide (peak 10) as the reference. The results demonstrated that the RSD for the relative retention times of the main peaks was below 1%, and the RSD for the relative peak areas was below 5%, confirming the reproducibility of the method. Stability test: Batch 1 prescription tablets were prepared following the method described in “2.1.1” to generate the test solutions. Chromatographic determinations were conducted at various time intervals (0, 2, 4, 6, 8, 12, 18, and 24 hours) under the chromatographic conditions outlined in “2.2.” At each time point, chromatograms were recorded, and the relative retention time and peak area of the main shared peaks were analyzed using Specnuezhenide (peak 10) as the reference peak. The results indicated that the RSD for the retention times of the main peaks was less than 1%, and the RSD for the relative peak areas was less than 5%. These results confirm that the test solution remained stable within 24 hours, as evidenced by consistent retention times and peak areas across all time points. 3.10.4 Establishment of fingerprint profiles The fingerprint profiles of three batches of different beverages were analyzed using the "Chinese Medicine Chromatographic Fingerprint Similarity Evaluation System (2004A)." HPLC fingerprint profiles for the three batches were generated through the median method and automatic matching, along with the construction of common pattern control profiles. 3.11 Statistical analysis Statistical analysis was performed using GraphPad Prism 8.0 software. Given the small sample size in this study, the normality of numerical variables was assessed using the Shapiro-Wilk test. At a significance level of α = 0.05, if P > 0.05, the null hypothesis was not rejected, indicating that the data followed a normal distribution. For variables following a normal distribution, the mean ± standard deviation (SD) was used for statistical description. For variables that did not follow a normal distribution, the median (interquartile range) was used instead. For multi-group comparisons of normally distributed variables, one-way ANOVA was applied. Bartlett's test was conducted first to assess the homogeneity of variances. If the F-test indicated significant differences in overall means, the LSD method was used for pairwise comparisons in the case of chi-square tests. If variances were unequal, Welch's test was applied for overall mean comparison, and Dunnett's T3 test was used for pairwise chi-square comparisons. 4. Discussion 4.1 Analyzing the mechanism of action of EZW for diabetic kidney disease (DKD) based on network pharmacology and molecular docking In this study, 9 active components of Ligustri Lucidi Fructus and 8 active components of Ecliptae were identified, along with 210 active component targets after refinement, and 3,245 DN-related targets. These results indicate that the core prescription for DN treatment involves multiple components, targets, and pathways. According to the Chinese Pharmacopoeia, Specnuezhenide and wedelolactone serve as the index components for Ligustri Lucidi Fructus and Ecliptae, respectively, and the results of HPLC fingerprinting were consistent with these components. Animal studies demonstrated that Erzhi Pill effectively reduces urinary protein levels and repairs diabetes-induced kidney damage by upregulating CD2AP and podocin expression in podocyte slit membrane proteins, thereby protecting renal function. Additionally, Erzhi Pill reduced the expression levels of p-AMPK, p-PI3K, p-Akt, and p-FoxO3a proteins in the heart tissues of rats with diabetic cardiomyopathy. Modern research has shown [9] that Erzhi Pill plays a key role in reducing proteinuria, inhibiting renal inflammation, and protecting against renal oxidative damage, thereby improving glomerular function and increasing the levels of functional podocyte proteins, particularly Specnuezhenide and wedelolactone, which are capable of reaching renal tissues. Specnuezhenide, a cleaved cyclic enol ether terpene glycoside derived from Ligustrum officinale, has demonstrated antioxidant, anti-inflammatory, and hypoglycemic properties in DN models [24, 25]. Additionally, Specnuezhenide has been shown to inhibit inflammatory responses in vascular tissues by suppressing NF-κB expression in diabetic rats [26], indicating its potential for managing DN and other related complications. Wedelolactone, a natural coumarin found in the Araliaceae family, exhibits immunomodulatory, antifibrotic, anti-inflammatory, and antioxidant properties [27, 28]. It has demonstrated protective effects against kidney injury by mitigating inflammation and oxidative stress in podocytes via downregulation of the NF-κB pathway [28]. Wedelolactone may also reduce lipopolysaccharide-induced inflammatory cytokine secretion and apoptosis in HK-2 cells by increasing PTPN2 levels, which alleviates renal cell injury and provides nephroprotective effects in sepsis-induced renal damage by regulating the p38 MAPK/NF-κB signaling pathway. The pathogenesis of DN is heavily influenced by podocyte injury, inflammation, and oxidative stress. Through PPI analysis, key targets such as MAPK1, RXRA, CYP3A4, HSP90AA1, TP53, AKT1, P53, EGR1, PKC, and P38 were identified. Both Specnuezhenide and wedelolactone showed efficient binding to P53, JNK, EGR1, AKT, PKC, and P38, suggesting their central role in the therapeutic effects on DN. KEGG analysis of the intersecting genes revealed that the AGE-RAGE, MAPK, and PI3K/AKT signaling pathways are primarily involved in DN. Advanced glycation end products (AGEs) and their receptors (RAGE) are critical components in the development of diabetes and its complications, often arising from a high-fat diet or hyperglycemia. The interaction between AGEs and RAGE triggers multiple biological effects, leading to microvascular and macrovascular complications such as neuropathy, nephropathy, retinopathy, cardiomyopathy, and atherosclerosis. This interaction impacts cellular functions, motility, and metabolism, while AGEs can directly cause cellular and tissue damage through inflammation and oxidative stress. The AGE-RAGE axis activates several signaling cascades, including PKC, PI3K/Akt, MAPK/ERK, Src/RhoA, JAK/STAT, and NADPH oxidase pathways. This complex signaling increases the production of nuclear factor κB (NF-κB), Egr-1, and other transcription factors, as well as reactive oxygen species (ROS) production [29]. The consequences include heightened inflammation, oxidative damage, impaired cell motility, and disruptions in cellular metabolism. Moreover, elevated oxidative stress can further amplify AGE production and trigger RAGE signaling, perpetuating abnormal cellular function [30, 31]. MAPKs, a family of protein kinases including JNK, extracellular signal-regulated kinases (ERKs), and p38 MAPK, are central to this process. Activation of the MAPK pathway accelerates cellular proliferation, contributing to kidney injury and fibrosis, with p38 phosphorylation playing a particularly important role. Numerous studies have demonstrated significant activation of the MAPK pathway during the progression of DN. Disorders in glucose metabolism and insulin resistance are closely linked to abnormal activation of the PI3K/Akt and MAPK pathways, which regulate cell growth, metabolism, and survival through gene expression and mitophagy associated with glucose metabolism [32]. The p38 MAPK signaling pathway is a critical junction in cellular signaling, responsive to various stimuli such as high glucose, inflammatory factors, ROS, angiotensin II, and glycation end products. These stimuli can alter the cellular redox state, disrupting cellular function and playing a pivotal role in diabetes. In summary, the activation of the MAPK, AGE-RAGE, and lipid atherosclerosis pathways represents key therapeutic targets for managing glucose and lipid metabolism disorders in DN. Further investigation into the mechanisms of Erzhi Pill's action on these pathways will be conducted in the next phase of the study. 4.2 Analyzing the mechanism of action of EZW for DKD based on multi-omics technology After 12 weeks of EZW treatment in db/db mice, significant reductions were observed in serological markers such as INS, FBG, LDL-C, TC, TG, Scr, and BUN, while HDL-C levels significantly increased. Histopathological analysis further confirmed the protective effect of Erzhi Pill on diabetic kidneys, showing marked improvements in DKD-related changes, including reduced glomerular thickening and collagen deposition. Previous network pharmacology studies suggested that Erzhi Pill may act through the PI3K/AKT, MAPK, and AGE-RAGE signaling pathways. Proteomics and transcriptomics analysis further indicated that the core mechanism of Erzhi Pill in DN involves key pathways such as PPAR and P53. Notably, the differential protein solute carrier family 22 member 26 (SLC22A26), part of the SLC family, emerged as a potential target, although its role in DKD progression and regulation remains unexplored. Previous research [33] has shown that molecular variants of the SLC22A transporter associated with renal diseases can affect systemic drug clearance, potentially leading to altered pharmacokinetics and adverse effects due to drug accumulation. Peroxisome proliferator-activated receptors (PPARs), which include PPAR-α, PPAR-δ, and PPAR-γ, are known to play a protective role in DKD by regulating glycemic control and lipid metabolism. Increasing evidence highlights the significance of PPARs in glucose regulation and lipid metabolism in the pathogenesis of DKD [34]. For example, the PPARα agonist fenofibrate has been shown to lower fasting glucose and insulin resistance, reduce urinary albumin excretion, alleviate glomerular hypertrophy, inhibit oxidative stress, and decrease inflammation in diabetic models [35-37]. Although PPARs were not directly enriched in the PI3K/AKT, MAPK, or AGE-RAGE pathways, these results provide further support for the mechanisms underlying the preventive and therapeutic effects observed in this study. AGEs are key contributors to the production of ROS and pro-inflammatory cytokines in diabetes, ultimately leading to kidney damage. The activation of the AGE-RAGE axis in the kidneys triggers fibrotic responses through intracellular pathways, including NF-кB, MAPK/ERK, and PI3K/Akt [38]. Activation of PPAR-γ has been shown to inhibit AGE-RAGE-mediated oxidative stress and inflammation in diabetic models [39]. Erythrartin, a natural PPAR-γ agonist, exerts anti-inflammatory effects by inhibiting JNK, ERK, p38 kinase, and cytokine production. It also demonstrates antidiabetic properties by improving hyperglycemia, reducing insulin resistance, and mitigating oxidative stress. Chronic hyperglycemia in diabetes elevates circulating AGEs, which bind to RAGE and initiate a cascade of signaling events. The interaction between AGEs and RAGE activates multiple downstream effectors, such as MAPK, p38, stress-activated protein kinase/JNK (SAPK/JNK), Ras-mediated ERK1/2, and the JAK/STAT pathways, leading to sustained activation of transcription factors like NF-κB, STAT3, HIF-1α, and AP-1[40-42]. Several studies in both animals and humans have confirmed that stimulation of the AGE-RAGE axis and its downstream pathways is involved in the pathogenesis of diabetes and its complications, including cardiomyopathy, nephropathy, retinopathy, and neurodegeneration [43]. These findings align with previous network pharmacology insights. In conclusion, our experiments suggest that Erzhi Pill significantly improves glucose and lipid metabolism, likely by modulating the AGE-RAGE and PPAR signaling pathways, which in turn reduce oxidative stress and inflammation. SLC22A26 may represent a novel target closely associated with the progression of DKD. Further research is needed to elucidate the precise mechanisms by which Erzhi Pill exerts its therapeutic effects in the treatment of DN. Declarations 1 Author Contributions Wei Xie :Writing–original draft,Writing–review and editing. Wei Li : Writing–review and editing. Liu-Bin Xu : Writing–review and editing.Yu-Xin Yan : Writing–review and editing. Jin-Xian Huang: Writing–review and editing.Hui-Fang Kuang: Writing–review and editing.Wen-Jie Li:Writing–review and editing. Wei-Ge Li : Writing–review and editing. Qian Wang : Writing–review and editing.Jin-Hua Li *: Writing–original draft, Writing–review and editing. Xue-Mei Liu *: Writing–original draft, Writing–review and editing. De-Liang Liu *: Writing–original draft, Writing–review and editing. All authors have read and approved the final version of the manuscript. 2 Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work has been supported by the National Natural Science Foundation of China (82274419). 3 Acknowledgments We thank Bullet Edits Limited for the linguistic editing and proofreading of the manuscript. 4 ARRIVE guidelines statement: The study was designed and conducted in accordance with the ARRIVE guidelines. 5 Ethics statement This animal experiment was approved by Shenzhen Following Precision Medical Research Institute for ethical review. This study was conducted in accordance with local legal and institutional requirements. 6 Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest . 7 Supplementary Material The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author. 8 Data Availability Statement All raw data and materials are available from the corresponding author on reasonable request. References Zhang, L., et al., Trends in Chronic Kidney Disease in China. N Engl J Med, 2016. 375(9): p. 905-6. Zhang, X.X., J. Kong and K. Yun, Prevalence of Diabetic Nephropathy among Patients with Type 2 Diabetes Mellitus in China: A Meta-Analysis of Observational Studies. J Diabetes Res, 2020. 2020: p. 2315607. 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Tables Table 1 Active ingredients of core Chinese medicines MOLID Ingredient Name OB(%) DL MOL000358 Betasitosterol 36.91 0.75 MOL000422 kaempferol 41.88 0.24 MOL004576 taxifolin 57.84 0.27 MOL005147 Lucidumoside D_qt 54.41 0.47 MOL005190 eriodictyol 71.79 0.24 MOL005212 Olitoriside_qt 103.23 0.78 MOL000006 luteolin 36.16 0.25 MOL000098 quercetin 46.43 0.28 MOL005188 Specnuezhenide 19.30 0.50 MOL001689 acacetin 34.97 0.24 MOL002975 butin 69.94 0.21 MOL003389 3'-O-Methylorobol 57.41 0.27 MOL003398 Pratensein 39.06 0.28 MOL003402 demethylwedelolactone 72.13 0.43 MOL003404 wedelolactone 49.60 0.48 Table 2 Betweenness values of key targets Gene BC value MAPK1 1733.464759 RXRA 1621.072699 CYP3A4 1599.4 HSP90AA1 1556.756597 ESR1 1237.059267 CYP1A1 1232.480825 F2 1198.463892 CES1 1150 TP53 1045.41738 AKT1 1007.994614 TNF 971.4449598 MPO 934 PPARA 749.6788927 RELA 728.8170545 CAV1 696.9967105 CYP19A1 566.8996818 MAPK8 526.1592809 IGFBP3 494.8636065 PRKCA 491.9193089 PON1 472 IL6 471.4494478 MAPK14 450.8685923 EGFR 388.8684945 MYC 385.1069446 IL10 351.0440302 NR1I2 319.6197659 VEGFA 318.0551019 MMP2 313.9399281 AHR 303.2421587 MMP1 302.0317958 NCOA1 282.7540861 STAT1 278.89665 EGF 257.6404576 VCAM1 257.5538079 MMP3 255.5558177 F3 248.0538462 APOB 238 AR 211.8878549 CASP8 201.5987723 HIF1A 189.7408197 PRKCB 154.7222104 CDKN1A 152.423573 NCF1 141.872779 RB1 140.0938467 GSK3B 140.057239 CCND1 137.9464031 IL2 129.3606351 CASP3 109.9004882 IL4 107.638245 RAF1 98.405444 Table 3 Protein target binding energy (kcal/mol) Targets Compounds Binding energy(kcal/mol) P53 Specnuezhenide -8.3 JNK Specnuezhenide -8.3 EGR1 Specnuezhenide -11.8 AKT Specnuezhenide -9.4 PKC Specnuezhenide -9.1 P38 Specnuezhenide -8.1 P53 Wedelolactone -7.9 JNK Wedelolactone -8.0 EGR1 Wedelolactone -12.0 AKT Wedelolactone -8.2 PKC Wedelolactone -8.9 P38 Wedelolactone -8.4 Table 4 Evaluation results of similarity of prescriptions in each batch S1 S2 S3 Control mapping (R) S1 1 0.929 0.868 0.927 S2 0.929 1 0.929 0.994 S3 0.868 0.929 1 0.935 Control mapping (R) 0.927 0.994 0.935 1 Additional Declarations No competing interests reported. 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HE-stained section of mouse kidney tissue in each group (200x); (B) PAS-stained section of mouse kidney in each group (200x); (C) Masson-stained section of mouse kidney in each group (200x); NOR is the blank group; MOD is the model group; EZW-H is the high-dose group of Dizitsuguwan; EZW-L is the low-dose group of Dizitsuguwan; and CAP is the captopril group.\u003c/p\u003e","description":"","filename":"Figure513.png","url":"https://assets-eu.researchsquare.com/files/rs-5308470/v1/21f0b14bec5a0c1889de4e90.png"},{"id":69910089,"identity":"1b2ad123-905d-4b4a-b8e8-3ff85e0813b7","added_by":"auto","created_at":"2024-11-26 13:44:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3760408,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential gene Wayne diagram (A); differential gene volcano diagram (B); differential gene clustering diagram (C)\u003c/p\u003e","description":"","filename":"Figure614.png","url":"https://assets-eu.researchsquare.com/files/rs-5308470/v1/738203f24c34b24fc7ccaad0.png"},{"id":69910724,"identity":"d825171e-1aa0-4658-a48b-178492dd0646","added_by":"auto","created_at":"2024-11-26 13:52:53","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2008399,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential gene GO enrichment analysis (A); differential gene KEGG enrichment analysis (B)\u003c/p\u003e","description":"","filename":"Figure78.png","url":"https://assets-eu.researchsquare.com/files/rs-5308470/v1/1f26f532293a51e27e5759cc.png"},{"id":69909174,"identity":"10c9dd61-480a-4fdb-b6ca-350952a09fe6","added_by":"auto","created_at":"2024-11-26 13:36:53","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":576711,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential protein amount statistics of each group\u003c/p\u003e","description":"","filename":"Figure86.png","url":"https://assets-eu.researchsquare.com/files/rs-5308470/v1/4bb276a59a024194680edc9d.png"},{"id":69909175,"identity":"f6e211e4-1e3c-44ae-bfc3-4dcca903e2b3","added_by":"auto","created_at":"2024-11-26 13:36:53","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3392136,"visible":true,"origin":"","legend":"\u003cp\u003eQuantitative statistics of differential proteins in each group\u003c/p\u003e\n\u003cp\u003eNote: A and B are heat maps of the TCM and model groups, and model and blank groups, respectively; C and D are volcano maps of differential proteins of the TCM and model groups, and model and blank groups, respectively.\u003c/p\u003e","description":"","filename":"Figure95.png","url":"https://assets-eu.researchsquare.com/files/rs-5308470/v1/7840fd1b0e2f9ebd6b705cd7.png"},{"id":69909171,"identity":"0762e4fd-dfb0-44ab-8bb6-6378a45e998b","added_by":"auto","created_at":"2024-11-26 13:36:53","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":4048511,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential protein GO-enriched bubble plot (A); differential protein KEGG-enriched bubble plot (B).\u003c/p\u003e","description":"","filename":"Figure.10.png","url":"https://assets-eu.researchsquare.com/files/rs-5308470/v1/3c6ace0ec3dbb9782c70bb63.png"},{"id":69909173,"identity":"41ffced6-2e64-4ab0-954b-a567653bd608","added_by":"auto","created_at":"2024-11-26 13:36:53","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":777690,"visible":true,"origin":"","legend":"\u003cp\u003eFingerprint profile (A) and shared pattern profile (B) of 3 batches of prescription\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-5308470/v1/49cb08d53cef9208926067d9.png"},{"id":72700206,"identity":"bfd19c84-1d4d-4f58-822a-1c6a6789e045","added_by":"auto","created_at":"2024-12-31 12:02:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":81101371,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5308470/v1/da7e7644-6609-4e98-903c-741a9c82246a.pdf"},{"id":69909165,"identity":"51fa52bb-7744-48f4-b2e0-03da75b4efc5","added_by":"auto","created_at":"2024-11-26 13:36:53","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":56927,"visible":true,"origin":"","legend":"","description":"","filename":"DataSheet1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5308470/v1/30a6bd10f593844155494c48.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Erzhi pill for diabetic nephropathy: validation of integrated network pharmacology, molecular docking, proteomics, and transcriptomics","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDiabetic nephropathy (DN) is a prevalent microvascular complication among patients with diabetes and is the leading cause of end-stage renal disease (ESRD) in China\u0026nbsp;[1]\u0026nbsp;, contributing significantly to mortality in this population. The majority of patients with diabetes in China are affected by type 2 diabetes mellitus (T2DM), and as its prevalence continues to rise, so does the incidence of DN. A 2019 meta-analysis reported that the prevalence of DN in China was 21.8%\u0026nbsp;[2], making it a major chronic kidney disease that significantly impairs quality of life and reduces life expectancy. Early clinical manifestations of DN are often subtle, typically presenting as proteinuria and a decline in glomerular filtration rate (GFR)\u0026nbsp;[3], with the disease gradually progressing to ESRD. Current clinical management relies on symptomatic treatments such as controlling blood glucose and blood pressure and enhancing circulation to slow the progression of kidney damage, but the results remain suboptimal\u0026nbsp;[4]. In contrast, traditional Chinese medicine (TCM) offers distinctive benefits in the treatment of DN. Various forms of herbal medicine, including formulas, single herbs, extracts, and compounds, have demonstrated anti-inflammatory effects in both animal and cellular models of DN\u0026nbsp;[5-7].\u003c/p\u003e\n\u003cp\u003eNetwork pharmacology is an emerging field that leverages network analysis to study drug actions within cellular networks, offering deeper insights into the complex mechanisms of drug efficacy. This approach holds promise for drug discovery, particularly in the treatment of multifaceted diseases\u0026nbsp;[8]. In this study, network pharmacology, molecular docking, and transcriptomic analysis were employed to investigate the mechanism of action of Erzhi Pill in treating DN, aiming to provide evidence-based support for the use of traditional Chinese medicine in DN management.\u003c/p\u003e\n\u003cp\u003eRecent studies [9] have demonstrated that Erzhi Pill effectively reduces proteinuria, suppresses renal inflammation, combats oxidative stress, and improves glomerular and podocyte functions, with Specnuezhenide and wedelolactone being particularly capable of targeting renal tissues. As a classic traditional Chinese medicine prescription composed of Ecliptae and Ligustri Lucidi Fructus, Erzhi Pill has long been recognized for its clinical efficacy in DN treatment, based on the knowledge accumulated from experienced practitioners. However, while its effectiveness has been clinically validated, the precise mechanisms underlying its therapeutic effects still require further elucidation.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cp\u003e\u003cstrong\u003e2.1 Network pharmacology analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe components and targets of Erzhi Pill were screened, yielding nine active ingredients from Ligustri Lucidi Fructus and eight from Ecliptae, supplemented by \u0026nbsp;Specnuezhenide according to the literature. After refining the list, a total of 15 key active ingredients were identified (Table 1). Through GeneCards, OMIM, TTD, DrugBank, and PharmGKB databases, 210 targets corresponding to these active ingredients were obtained, after removing duplicates. Additionally, 3245 DN-related targets were identified, with 3004 from GeneCards, 253 from OMIM, 74 from PharmGKB, 33 from DrugBank, and 24 from TTD, as depicted in Fig. 1(a). By mapping the targets of the active ingredients to DN-related targets, 138 key targets for DN treatment through TCM were identified (Fig. 1(b)). The key targets were imported into the STRING database to construct the PPI network, consisting of 138 nodes and 534 edges, with an average node degree of 7.74. Higher node connectivity indicated greater influence. Further analysis using Cytoscape 3.9.1 identified key nodes through Betweenness values, with the top 50 shown in this study (Table 2). The most significant nodes were MAPK1, RXRA, CYP3A4, HSP90AA1, TP53, and AKT1, highlighting their pivotal role in the therapeutic effects of the core components of Erzhi Pill on DN (Fig. 1(c)).\u003c/p\u003e\n\u003cp\u003eGO and KEGG enrichment analyses were performed on the 138 key targets using the Metascape platform. GO enrichment yielded 1701 BP entries, 149 MF entries, and 79 CC entries, with the top ten most significant ones in each category presented in this study (Fig.\u0026nbsp;3(B)). KEGG pathway enrichment identified 196 pathways, with the top 20 visualized (Fig.\u0026nbsp;3(A)). The analysis indicated that the most relevant pathways included those involved in cancer, lipid and atherosclerosis, hepatitis B, phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT), mitogen-activated protein kinase (MAPK), and AGE-RAGE signaling pathways, as well as the hepatitis C pathway.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Molecular docking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMolecular docking revealed that the binding energies of the two active ingredients with six core proteins were all \u0026le; -8.0 kcal/mol, indicating favorable binding affinities\u0026nbsp;[10](Table 3). Both Specnuezhenide and wedelolactone exhibited efficient binding to P53, JNK, EGR1, AKT, PKC, and P38. Visualization of these docking results is shown in Fig. 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Comparison of fasting blood glucose, fasting insulin, blood lipids, and kidney function levels in mice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGlycolipid metabolism indexes: Compared to the MOD group, fasting blood glucose levels in the EZW-H, EZW-L, and CAP groups were significantly reduced (P \u0026lt; 0.001, P \u0026lt; 0.05, P \u0026lt; 0.01, respectively). The fasting insulin (FINS) levels in the EZW-H, EZW-L, and CAP groups also showed a significant reduction (P \u0026lt; 0.001). Additionally, high-density lipoprotein cholesterol (HDL-C) levels were significantly elevated in both the EZW-H and EZW-L groups (P \u0026lt; 0.001). Although HDL-C levels in the CAP group showed a slight increase, this difference was not statistically significant (P\u0026gt;0.05). Low-density lipoprotein cholesterol (LDL-C) levels in the EZW-H, EZW-L, and CAP groups were significantly lower compared to the MOD group (P \u0026lt; 0.001, P \u0026lt; 0.01, P \u0026lt; 0.05, respectively). Total cholesterol (TC) levels in these groups were also significantly reduced (P \u0026lt; 0.001, P \u0026lt; 0.001, P \u0026lt; 0.01). Triglyceride (TG) levels in the EZW-H and EZW-L groups were significantly lower than those in the MOD group (P \u0026lt; 0.001), whereas the CAP group showed no statistically significant reduction in TG levels (P = 0.06). (Fig.\u0026nbsp;4(a-f)).\u003c/p\u003e\n\u003cp\u003eRenal function indexes: In terms of renal function, serum creatinine (SCr) levels in the EZW-H and EZW-L groups were significantly lower than those in the MOD group (P \u0026lt; 0.001). Blood urea nitrogen (BUN) levels in the CAP and EZW-H groups were also significantly reduced compared to the MOD group (P \u0026lt; 0.001, P \u0026lt; 0.05, respectively), while the BUN levels in the EZW-L group did not show a statistically significant difference (P\u0026gt;0.05).(Fig.\u0026nbsp;4(g-h)).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Comparison of histopathological results in mice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHE staining revealed that the glomerular and tubular structures in control mice were intact, with a normal extracellular matrix and no signs of thylakoid proliferation. In contrast, the model group exhibited mild thylakoid proliferation, along with vacuolar degeneration and loss of the brush border in tubular epithelial cells within the tubular mesenchymal region, indicating tubular damage. In the EZW-H (high-dose), EZW-L (low-dose), and CAP groups, renal pathology improved significantly, as evidenced by reduced glomerular and tubular lesions and a lower tubular injury index compared to the model group. PAS staining showed that control mice had no PAS-positive deposits in the glomeruli, maintaining normal structure, whereas the model group displayed substantial glycogen deposition in the glomerular thylakoid zone. This deposition was markedly reduced in the EZW-H, EZW-L, and CAP groups. Masson staining further demonstrated normal kidney structure in control mice, with evenly distributed collagen fibers. Conversely, the model group showed a pronounced increase in collagen accumulation in the glomerular and tubulointerstitial regions. Treatment with EZW-H, EZW-L, and CAP led to a significant reduction in collagen accumulation, suggesting these interventions may help inhibit the progression of renal fibrosis.(Fig.\u0026nbsp;5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Transcriptomics analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs illustrated in Fig.\u0026nbsp;6(A)\u0026nbsp;the model group displayed 1,618 differentially expressed genes compared to the normal group, with 699 genes up-regulated and 919 down-regulated. In contrast, the TCM treatment group identified 368 differentially expressed genes compared to the model group, consisting of 111 up-regulated and 257 down-regulated genes.\u003c/p\u003e\n\u003cp\u003eFig.\u0026nbsp;6(B)\u0026nbsp;\u0026nbsp;present volcano plots comparing differential gene expression between the TCM and model groups, as well as between the model and blank groups. The horizontal axis represents the log2-transformed expression ratio between groups, where a log2 (ratio) \u0026gt; 0 indicates highly expressed genes, and \u0026lt; 0 denotes lowly expressed genes, with the distribution being symmetrical.\u003c/p\u003e\n\u003cp\u003eFig.\u0026nbsp;6(C)\u0026nbsp;shows clustering analysis of differentially expressed genes, where genes exhibiting similar expression patterns across samples are grouped together. Gene Ontology (GO) enrichment analysis revealed that, compared to the model group, the top five enriched biological processes (BPs) in the kidney tissues of the TCM group included regulation of triglyceride catabolic processes, positive regulation of triglyceride catabolic processes, neutral lipolytic metabolic processes, acylglycerol catabolic processes, and regulation of triglyceride metabolic processes. The top five cellular components (CCs) enriched were chylomicron particles, very low-density lipoprotein particles, triglyceride-rich plasma lipoprotein particles, high-density lipoprotein particles, and plasma lipoprotein particles. As for molecular functions (MFs), the top five enriched terms were nuclear receptor activity, ligand-activated transcription factor activity, steroid hormone receptor activity, lipase inhibitor activity, and glucocorticoid receptor binding (Fig.7(A)). KEGG pathway enrichment analysis, sorted by P-value, demonstrated that, compared to the model group, differentially expressed genes in the kidney tissues of the TCM group were primarily enriched in pathways such as the PPAR signaling pathway, fat digestion and absorption, cholesterol metabolism, vitamin digestion and absorption, lipid atherosclerosis, adipocytokine signaling, and the P53 signaling pathway (Fig.7(B)).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Proteomic analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferential proteins between groups were identified using univariate analysis, with selection criteria of Fold Change (FC) \u0026gt; 1.2 and P-value \u0026lt; 0.05. This analysis revealed 397 differential proteins in the MOD group, comprising 274 up-regulated and 123 down-regulated proteins compared to the NOR group. In the TCM group, 727 differential proteins were identified, with 330 up-regulated and 397 down-regulated proteins in comparison to the MOD group (Fig.8). The corresponding volcano plots illustrate differential protein expression, with log2 ratios on the horizontal axis, indicating highly expressed proteins (log2 (ratio) \u0026gt; 0) and lowly expressed proteins (log2 (ratio) \u0026lt; 0) (Fig.\u0026nbsp;9).\u003c/p\u003e\n\u003cp\u003eGO enrichment analysis of differential proteins revealed the top 10 BPs enriched in the TCM group\u0026rsquo;s kidney tissues, including the thioester metabolic process, ribose phosphate metabolic process, purine-containing compound metabolic process, peptide metabolic process, oxoacid metabolic process, regulation of lipid metabolic process, modulation of cellular ketone body metabolic process, steroid metabolic process, positive modulation of small-molecule metabolic process, and organophosphorus metabolic process. For CC, the top enrichments were protein complex, peroxisome, organelle membrane, organelle inner membrane, mitochondria, mitochondrial protein complex, mitochondrial membrane, mitochondrial membrane, microsomal lumen, and membrane-bound organelles. In terms of MF, the top categories were unfolded protein binding, small-molecule binding, ribonucleotide binding, pyrophosphatase activity, purine ribonucleotide binding, purine ribonucleotide binding, oxidizing reductase activity, isomerase activity, hydrolase activity, and catalytic activity (Fig.\u0026nbsp;10(A)).\u003c/p\u003e\n\u003cp\u003eKEGG pathway enrichment analysis, ranked by P-value, showed that differential proteins in the TCM group were predominantly enriched in the PPAR signaling pathway, butyrate metabolism, peroxisomal and endoplasmic reticulum protein processing, fatty acid biosynthesis, unsaturated fatty acid biosynthesis, drug metabolism (other enzymes), aflatoxin biosynthesis, cytochrome P450-related drug metabolism, steroid hormone biosynthesis, propionate metabolism, \u0026beta;-alanine metabolism, and fatty acid degradation pathways (Fig.\u0026nbsp;10(B)).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Fingerprint results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHPLC fingerprint analysis of the three batches of sachets revealed 12 distinct peaks (Fig. 11). The similarity evaluation results are summarized in Table 4. Comparison with the control identified peak 4 as Rhodiola rosea glycosides, peak 10 as Specnuezhenide, and peak 12 as wedelolactone.\u003c/p\u003e"},{"header":"3. Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e3.1 Animal grouping and intervention\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eForty-eight SPF-grade, 8-week-old male db/db mice were housed in a controlled environment at 23\u0026plusmn;3\u0026deg;C with appropriate humidity and a 12-hour light-dark cycle. The mice had free access to food and water. After a one-week acclimatization period, the 48 db/db mice were randomly assigned to groups using Excel 2016. The RAND function was applied to generate random numbers, which were sorted using the RANK function, and group assignments were determined by dividing the ranks into four groups using the ROUNDUP function. This process resulted in four groups of 12 mice each: the model group (MOD), low-dose TCM group (EZW-L), high-dose TCM group (EZW-H), and captopril group (CAP). Additionally, 12 wild-type mice (C57BL6), from the same litter, served as the normal control group (NOR). The animals were obtained from Beijing Huafukang Bio-technology Co., Ltd (Animal Production License No. SCXK (Beijing) 2019-0008; Animal Licence No. 110322230102178525). The CAP group received 5 mg/kg of captopril, while the EZW-L and EZW-H groups were administered 4 g/kg and 8 g/kg of Erzhi Pill, respectively. All treatments were administered \u003cem\u003evia\u003c/em\u003e gastric gavage at a volume of 10 mL/kg of body weight, once daily for 12 consecutive weeks. Mice in the control and model groups were gavaged with an equivalent volume of distilled water. During the treatment period, bedding was changed daily to maintain a clean environment, and fasting blood glucose and body weight were recorded weekly. After 12 weeks,\u0026nbsp;the animals were sacrificed by isofluorane asphyxiation. Blood samples were collected under isoflurane anesthesia, followed by centrifugation at 4\u0026deg;C, 3500 r/min for 10 minutes. The serum was harvested and stored at -80\u0026deg;C for further analysis. The animal protocol was approved by the Laboratory Animal Ethical Review Committee of the Shenzhen Zhongzuan Precision Medicine Research Institute (approval number ZXJZ202306280003)\u0026nbsp;and carried out in strict according to the Guide for the Care and Use of Laboratory Animals of National Institute of Health (NIH) (Bethesda, MD, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Experimental herbs reagents and instruments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGranules of Ecliptae and Ligustri Lucidi Fructus were purchased from Shenzhen Hospital of Traditional Chinese Medicine, with the manufacturer being Chongqing Tianjiang Party Pharmaceutical Company Limited. The batch numbers for Ligustri Lucidi Fructus and Ecliptae were 21080081 and 21100181, while the control reagent kit and other assay kits were sourced from Nanjing Jiancheng Bioengineering Institute. These included the Urine Protein Quantification Test Kit (C035-2), Urea Nitrogen Test Kit (C013-2), Mouse Microalbumin Elisa Kit (H127-1), Mouse Insulin Elisa Kit (H203-1), LDL Cholesterol Determination Kit (A113-1), Triglyceride Determination Kit (A110-1), HDL Cholesterol Determination Kit (A112-1), and Total Cholesterol Determination Kit (A111-1). Additional chemicals and reagents, such as paraformaldehyde (Sinopharm Chemical Reagent Co., Ltd., 80096618), anhydrous ethanol (Sinopharm Chemical Reagent Co., Ltd., 10009218), eosin Y (Sia Reagent, D12621), and hematoxylin (Sigma, H9627-25G), were also utilized. Other materials included neutral gum (Solarbio, G8590), and Masson\u0026rsquo;s stain (Servicebio, G1006). Laboratory equipment used for the study included an enzyme labeling instrument (USCNK, SMR16.1), benchtop centrifuge (Shanghai Anting Scientific Instrument Factory, TGL-16c), freezing centrifuge (Hunan Xiangyi Laboratory Instruments, TGL-16), a general optical microscope (OLYMPUS, CX21), orthogonal white light photomicrograph microscope (OLYMPUS, CX31), imaging system (Q-IMAGING, MicroPublisher), and an inverted white light/fluorescence photomicroscope (OLYMPUS, IX51).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Experimental sampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter 12 weeks of treatment, blood was collected from the orbital vein following a 10-12 hour fasting period and anesthesia. The blood was left at room temperature for 2 hours, then centrifuged for 10 minutes at 4\u0026deg;C at 3500 r/min. The serum was separated from the upper layer and stored in a freezer at -80\u0026deg;C. Following blood collection and after the mice were sacrificed, kidney tissues were rapidly excised and stored at -80\u0026deg;C. Additionally, a portion of the kidney tissue was fixed in 4% paraformaldehyde and embedded in paraffin for histopathological analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Serum index test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerum indices were detected following the protocols and procedures outlined in the respective assay kits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Histopathology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMouse kidney tissues were fixed in 4% paraformaldehyde for 48 hours, dehydrated with ethanol, cleared using xylene, embedded in paraffin, and then sectioned for histological analysis. The tissue sections were stained with hematoxylin-eosin (HE), periodic acid-silver (PAS), and Masson\u0026rsquo;s stain. Observations and imaging were performed under a microscope.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Transcriptome sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter RNA extraction with TRIZOL, magnetic beads containing Oligo (dT) were employed to enrich eukaryotic mRNA from the total RNA by binding to the polyA tail of mRNA through A-T complementary pairing. The mRNA was then fragmented into short fragments by adding a fragmentation buffer. The first cDNA strand was synthesized using six-base random hexamers as primers, with the mRNA serving as the template. The second cDNA strand was synthesized by adding dNTPs, RNase H, and DNA polymerase I. Following the synthesis of the first strand, buffer, dNTPs, RNase H, and DNA polymerase I were added for the synthesis of the second strand. Subsequently, end repair and 3\u0026apos; end A-tailing were performed on the double-stranded cDNA. Sequencing adapters were ligated, and the cDNA was purified using Hieff NGS\u0026reg; DNA Selection Beads, which were also used for fragment selection. The purified and fragment-selected junction products underwent PCR amplification. Qubit fluorescence quantification and enzyme labeling were employed for quality control and quantification of the libraries. The double-stranded target region libraries were then denatured, cyclized, and digested to obtain single-stranded circular DNA. The single-stranded circular DNA was amplified through Rolling Circle Amplification (RCA), resulting in the formation of DNA Nano Balls (DNB). After constructing the library, Qubit was used again for quantitative quality control, and the library was deemed suitable for sequencing on the DNBSEQ-T7 platform.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 TMT quantitative proteomic analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor protein analysis, total protein was extracted from tissue samples using a lysis buffer. Proteins were precipitated using the acetone precipitation method, and protein concentration was determined by the Bradford assay after treatment with DTT and IAA. Trypsin was then added to the samples at a 1:50 mass ratio for enzymatic digestion. The digested protein samples were labeled using TMT labeling and separated \u003cem\u003evia\u003c/em\u003e high-pH reversed-phase chromatography. Mass spectrometry analysis was conducted using the Orbitrap Exploris\u0026trade; 480 mass spectrometer coupled with the UltiMate\u0026trade; 3000 liquid chromatography system. The acquired mass spectrometry data were processed using MaxQuant software with the Andromeda algorithm, and the proteome reference database was constructed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.8 Exploration of network pharmacological mechanisms\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the TCMSP (V2.3) database (Ru et al., 2014) (http://tcmspw.com/tcmsp.php), the chemical composition of Erzhi Pill was screened based on oral bioavailability (OB) \u0026ge; 30% and drug-likeness (DL) \u0026ge; 0.18[12]. Additional target identification was performed using the PharmMapper database [13] (http://www.lilab-ecust.cn/). According to the 2020 edition of the Chinese Pharmacopoeia, Specnuezhenide were used as quality control markers for Erzhi Pills, while wedelolactone was the indicator for Ecliptae. Once the active ingredients were identified, the targets associated with each compound were collected, and target-gene matching was completed using the Uniprot database [14] (https://www.uniprot.org). This process was further verified through databases such as GeneCards [15] (https://www.genecards.org/), OMIM [16] (https://www.omim.org/), TTD [17] (http://db.idrblab.net/ttd/), DrugBank [18] (https://go.drugbank.com/), and PharmGKB [19] (https://www.pharmgkb.org/). For the disease analysis, the keyword \u0026quot;chronic glomerulonephritis\u0026quot; was used to gather relevant disease targets. Venny software was employed to map the overlap between the drug and disease targets, identifying key targets for the treatment of DN. Cytoscape 3.9.1 was utilized to construct a \u0026quot;disease-drug-component-target\u0026quot; interaction network. The STRING platform [20] (https://string-db.org/) was applied to analyze protein-protein interactions (PPI) among the key targets, while Metascape [21] (http://metascape.org) was used to perform GO and KEGG pathway enrichment analyses of the PPI network.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.9 Molecular docking\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 3D structures of human oncogene P53, C-Jun N-terminal kinase (JNK), protein kinase C (PKC\u0026beta;II), mitogen-activated protein kinase 14 (P38), and AKT were obtained from the PDB database (http://www.rcsb.org/) [22]. The 2D structures of the active compounds Specnuezhenide and wedelolactone were retrieved from the PubChem database (http:// pubchem.ncbi.nlm.nih.gov/) [23]. These 2D structures were then converted into 3D models using ChemBio3D 14.0 software. Subsequently, the 3D structures of the six proteins and compounds were transformed into the \u0026quot;pdbqt\u0026quot; format using AutoDockTools 1.5.6. Molecular docking simulations were carried out using AutoDock Vina 1.1.2. The docking results were analyzed and visualized through Pymol software for further interpretation and visualization of binding interactions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.10 Fingerprint mapping studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.10.1 Sample preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePreparation of test solution: Formulated granules equivalent to 10 g each of Ecliptae and Ligustri Lucidi Fructus were weighed and mixed with 100 mL of 75% methanol. The mixture was ultrasonicated for 30 minutes and filtered through a 0.2 \u0026mu;m microporous filter membrane before being analyzed \u003cem\u003evia\u003c/em\u003e HPLC. Preparation of Control Solution: Rhodiola rosea glycoside,Specnuezhenide, and Wedelolactone were weighed and dissolved in methanol to prepare a mixed solution with mass concentrations of 0.26, 0.83, and 0.18 mg/mL, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.10.2 Chromatographic conditions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA Thermo Acclaim 120 C18 column (250\u0026times;4.6 mm, 5 \u0026mu;m) was used. The mobile phase consisted of 0.1% phosphoric acid in water (A) and acetonitrile (B), with a gradient elution profile: 0\u0026ndash;30 min, 8% B; 30\u0026ndash;40 min, 8\u0026ndash;16% B; 40\u0026ndash;60 min, 16% B; 60\u0026ndash;80 min, 16\u0026ndash;18% B; 80\u0026ndash;100 min, 18\u0026ndash;28% B; 100\u0026ndash;115 min, 28\u0026ndash;40% B; 115\u0026ndash;118 min, 40\u0026ndash;95% B; 118\u0026ndash;126 min, 95% B; 126\u0026ndash;127 min, 95\u0026ndash;8% B; and 127\u0026ndash;137 min, 8% B. The flow rate was set at 0.8 mL/min. Detection was performed at 256 nm for 0\u0026ndash;100 min and 351 nm for 100\u0026ndash;137 min. The column temperature was maintained at 30\u0026deg;C, and the injection volume was 10 \u0026mu;L.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.10.3 Methodological investigation of HPLC fingerprints\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrecision test: The test solution was prepared following the method outlined in \u0026quot;2.1.1,\u0026quot; and chromatographic conditions were applied according to \u0026quot;2.2.\u0026quot; The sample was injected six times consecutively, and chromatograms were recorded. Using peak 10 (Specnuezhenide) as the reference, the relative retention time and relative peak area of the main peaks were evaluated. Results showed that the relative standard deviation (RSD) for the retention times of the main peaks was less than 1%, while the RSD for the relative peak areas was less than 5%, indicating the high precision of the instrument.\u003c/p\u003e\n\u003cp\u003eRepeatability test: Six test solutions were prepared in parallel following the method in \u0026quot;2.1.1\u0026quot; and analyzed using the established chromatographic conditions. Chromatograms were recorded, and the relative retention time and relative peak area of the main peaks were again evaluated using Specnuezhenide (peak 10) as the reference. The results demonstrated that the RSD for the relative retention times of the main peaks was below 1%, and the RSD for the relative peak areas was below 5%, confirming the reproducibility of the method.\u003c/p\u003e\n\u003cp\u003eStability test: Batch 1 prescription tablets were prepared following the method described in \u0026ldquo;2.1.1\u0026rdquo; to generate the test solutions. Chromatographic determinations were conducted at various time intervals (0, 2, 4, 6, 8, 12, 18, and 24 hours) under the chromatographic conditions outlined in \u0026ldquo;2.2.\u0026rdquo; At each time point, chromatograms were recorded, and the relative retention time and peak area of the main shared peaks were analyzed using Specnuezhenide (peak 10) as the reference peak. The results indicated that the RSD for the retention times of the main peaks was less than 1%, and the RSD for the relative peak areas was less than 5%. These results confirm that the test solution remained stable within 24 hours, as evidenced by consistent retention times and peak areas across all time points.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.10.4 Establishment of fingerprint profiles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe fingerprint profiles of three batches of different beverages were analyzed using the \u0026quot;Chinese Medicine Chromatographic Fingerprint Similarity Evaluation System (2004A).\u0026quot; HPLC fingerprint profiles for the three batches were generated through the median method and automatic matching, along with the construction of common pattern control profiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.11 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis was performed using GraphPad Prism 8.0 software. Given the small sample size in this study, the normality of numerical variables was assessed using the Shapiro-Wilk test. At a significance level of \u0026alpha; = 0.05, if P \u0026gt; 0.05, the null hypothesis was not rejected, indicating that the data followed a normal distribution. For variables following a normal distribution, the mean \u0026plusmn; standard deviation (SD) was used for statistical description. For variables that did not follow a normal distribution, the median (interquartile range) was used instead. For multi-group comparisons of normally distributed variables, one-way ANOVA was applied. Bartlett\u0026apos;s test was conducted first to assess the homogeneity of variances. If the F-test indicated significant differences in overall means, the LSD method was used for pairwise comparisons in the case of chi-square tests. If variances were unequal, Welch\u0026apos;s test was applied for overall mean comparison, and Dunnett\u0026apos;s T3 test was used for pairwise chi-square comparisons.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cstrong\u003e4.1 Analyzing the mechanism of action of EZW for diabetic kidney disease (DKD) based on network pharmacology and molecular docking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, 9 active components of Ligustri Lucidi Fructus and 8 active components of Ecliptae were identified, along with 210 active component targets after refinement, and 3,245 DN-related targets. These results indicate that the core prescription for DN treatment involves multiple components, targets, and pathways. According to the Chinese Pharmacopoeia, Specnuezhenide and wedelolactone serve as the index components for Ligustri Lucidi Fructus and Ecliptae, respectively, and the results of HPLC fingerprinting were consistent with these components. Animal studies demonstrated that Erzhi Pill effectively reduces urinary protein levels and repairs diabetes-induced kidney damage by upregulating CD2AP and podocin expression in podocyte slit membrane proteins, thereby protecting renal function. Additionally, Erzhi Pill reduced the expression levels of p-AMPK, p-PI3K, p-Akt, and p-FoxO3a proteins in the heart tissues of rats with diabetic cardiomyopathy. Modern research has shown\u0026nbsp;[9]\u0026nbsp;that Erzhi Pill plays a key role in reducing proteinuria, inhibiting renal inflammation, and protecting against renal oxidative damage, thereby improving glomerular function and increasing the levels of functional podocyte proteins, particularly Specnuezhenide and wedelolactone, which are capable of reaching renal tissues. Specnuezhenide, a cleaved cyclic enol ether terpene glycoside derived from Ligustrum officinale, has demonstrated antioxidant, anti-inflammatory, and hypoglycemic properties in DN models\u0026nbsp;[24, 25]. Additionally, Specnuezhenide has been shown to inhibit inflammatory responses in vascular tissues by suppressing NF-\u0026kappa;B expression in diabetic rats\u0026nbsp;[26], indicating its potential for managing DN and other related complications. Wedelolactone, a natural coumarin found in the Araliaceae family, exhibits immunomodulatory, antifibrotic, anti-inflammatory, and antioxidant properties\u0026nbsp;[27, 28]. It has demonstrated protective effects against kidney injury by mitigating inflammation and oxidative stress in podocytes \u003cem\u003evia\u003c/em\u003e downregulation of the NF-\u0026kappa;B pathway\u0026nbsp;[28]. Wedelolactone may also reduce lipopolysaccharide-induced inflammatory cytokine secretion and apoptosis in HK-2 cells by increasing PTPN2 levels, which alleviates renal cell injury and provides nephroprotective effects in sepsis-induced renal damage by regulating the p38 MAPK/NF-\u0026kappa;B signaling pathway. The pathogenesis of DN is heavily influenced by podocyte injury, inflammation, and oxidative stress.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThrough PPI analysis, key targets such as MAPK1, RXRA, CYP3A4, HSP90AA1, TP53, AKT1, P53, EGR1, PKC, and P38 were identified. Both\u0026nbsp;Specnuezhenide\u0026nbsp;and wedelolactone showed efficient binding to P53, JNK, EGR1, AKT, PKC, and P38, suggesting their central role in the therapeutic effects on DN.\u003c/p\u003e\n\u003cp\u003eKEGG analysis of the intersecting genes revealed that the AGE-RAGE, MAPK, and PI3K/AKT signaling pathways are primarily involved in DN. Advanced glycation end products (AGEs) and their receptors (RAGE) are critical components in the development of diabetes and its complications, often arising from a high-fat diet or hyperglycemia. The interaction between AGEs and RAGE triggers multiple biological effects, leading to microvascular and macrovascular complications such as neuropathy, nephropathy, retinopathy, cardiomyopathy, and atherosclerosis. This interaction impacts cellular functions, motility, and metabolism, while AGEs can directly cause cellular and tissue damage through inflammation and oxidative stress. The AGE-RAGE axis activates several signaling cascades, including PKC, PI3K/Akt, MAPK/ERK, Src/RhoA, JAK/STAT, and NADPH oxidase pathways. This complex signaling increases the production of nuclear factor \u0026kappa;B (NF-\u0026kappa;B), Egr-1, and other transcription factors, as well as reactive oxygen species (ROS) production\u0026nbsp;[29]. The consequences include heightened inflammation, oxidative damage, impaired cell motility, and disruptions in cellular metabolism. Moreover, elevated oxidative stress can further amplify AGE production and trigger RAGE signaling, perpetuating abnormal cellular function\u0026nbsp;[30, 31]. MAPKs, a family of protein kinases including JNK, extracellular signal-regulated kinases (ERKs), and p38 MAPK, are central to this process. Activation of the MAPK pathway accelerates cellular proliferation, contributing to kidney injury and fibrosis, with p38 phosphorylation playing a particularly important role. Numerous studies have demonstrated significant activation of the MAPK pathway during the progression of DN. Disorders in glucose metabolism and insulin resistance are closely linked to abnormal activation of the PI3K/Akt and MAPK pathways, which regulate cell growth, metabolism, and survival through gene expression and mitophagy associated with glucose metabolism\u0026nbsp;[32]. The p38 MAPK signaling pathway is a critical junction in cellular signaling, responsive to various stimuli such as high glucose, inflammatory factors, ROS, angiotensin II, and glycation end products. These stimuli can alter the cellular redox state, disrupting cellular function and playing a pivotal role in diabetes.\u003c/p\u003e\n\u003cp\u003eIn summary, the activation of the MAPK, AGE-RAGE, and lipid atherosclerosis pathways represents key therapeutic targets for managing glucose and lipid metabolism disorders in DN. Further investigation into the mechanisms of Erzhi Pill\u0026apos;s action on these pathways will be conducted in the next phase of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Analyzing the mechanism of action of EZW for DKD based on multi-omics technology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter 12 weeks of EZW treatment in db/db mice, significant reductions were observed in serological markers such as INS, FBG, LDL-C, TC, TG, Scr, and BUN, while HDL-C levels significantly increased. Histopathological analysis further confirmed the protective effect of Erzhi Pill on diabetic kidneys, showing marked improvements in DKD-related changes, including reduced glomerular thickening and collagen deposition.\u003c/p\u003e\n\u003cp\u003ePrevious network pharmacology studies suggested that Erzhi Pill may act through the PI3K/AKT, MAPK, and AGE-RAGE signaling pathways. Proteomics and transcriptomics analysis further indicated that the core mechanism of Erzhi Pill in DN involves key pathways such as PPAR and P53. Notably, the differential protein solute carrier family 22 member 26 (SLC22A26), part of the SLC family, emerged as a potential target, although its role in DKD progression and regulation remains unexplored. Previous research\u0026nbsp;[33]\u0026nbsp;has shown that molecular variants of the SLC22A transporter associated with renal diseases can affect systemic drug clearance, potentially leading to altered pharmacokinetics and adverse effects due to drug accumulation. Peroxisome proliferator-activated receptors (PPARs), which include PPAR-\u0026alpha;, PPAR-\u0026delta;, and PPAR-\u0026gamma;, are known to play a protective role in DKD by regulating glycemic control and lipid metabolism. Increasing evidence highlights the significance of PPARs in glucose regulation and lipid metabolism in the pathogenesis of DKD\u0026nbsp;[34]. For example, the PPAR\u0026alpha; agonist fenofibrate has been shown to lower fasting glucose and insulin resistance, reduce urinary albumin excretion, alleviate glomerular hypertrophy, inhibit oxidative stress, and decrease inflammation in diabetic models\u0026nbsp;[35-37]. Although PPARs were not directly enriched in the PI3K/AKT, MAPK, or AGE-RAGE pathways, these results provide further support for the mechanisms underlying the preventive and therapeutic effects observed in this study.\u003c/p\u003e\n\u003cp\u003eAGEs are key contributors to the production of ROS and pro-inflammatory cytokines in diabetes, ultimately leading to kidney damage. The activation of the AGE-RAGE axis in the kidneys triggers fibrotic responses through intracellular pathways, including NF-кB, MAPK/ERK, and PI3K/Akt\u0026nbsp;[38]. Activation of PPAR-\u0026gamma; has been shown to inhibit AGE-RAGE-mediated oxidative stress and inflammation in diabetic models\u0026nbsp;[39]. Erythrartin, a natural PPAR-\u0026gamma; agonist, exerts anti-inflammatory effects by inhibiting JNK, ERK, p38 kinase, and cytokine production. It also demonstrates antidiabetic properties by improving hyperglycemia, reducing insulin resistance, and mitigating oxidative stress. Chronic hyperglycemia in diabetes elevates circulating AGEs, which bind to RAGE and initiate a cascade of signaling events. The interaction between AGEs and RAGE activates multiple downstream effectors, such as MAPK, p38, stress-activated protein kinase/JNK (SAPK/JNK), Ras-mediated ERK1/2, and the JAK/STAT pathways, leading to sustained activation of transcription factors like NF-\u0026kappa;B, STAT3, HIF-1\u0026alpha;, and AP-1[40-42]. Several studies in both animals and humans have confirmed that stimulation of the AGE-RAGE axis and its downstream pathways is involved in the pathogenesis of diabetes and its complications, including cardiomyopathy, nephropathy, retinopathy, and neurodegeneration\u0026nbsp;[43]. These findings align with previous network pharmacology insights.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our experiments suggest that Erzhi Pill significantly improves glucose and lipid metabolism, likely by modulating the AGE-RAGE and PPAR signaling pathways, which in turn reduce oxidative stress and inflammation. SLC22A26 may represent a novel target closely associated with the progression of DKD. Further research is needed to elucidate the precise mechanisms by which Erzhi Pill exerts its therapeutic effects in the treatment of DN.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e1 \u0026nbsp;Author Contributions\u003c/h1\u003e\n\u003cp\u003eWei Xie :Writing\u0026ndash;original draft,Writing\u0026ndash;review and editing. Wei Li : Writing\u0026ndash;review and editing. Liu-Bin Xu : Writing\u0026ndash;review and editing.Yu-Xin Yan : Writing\u0026ndash;review and editing. Jin-Xian Huang: Writing\u0026ndash;review and editing.Hui-Fang Kuang: Writing\u0026ndash;review and editing.Wen-Jie Li:Writing\u0026ndash;review and editing. Wei-Ge Li : Writing\u0026ndash;review and editing. Qian Wang : Writing\u0026ndash;review and editing.Jin-Hua Li *: Writing\u0026ndash;original draft, Writing\u0026ndash;review and editing. Xue-Mei Liu *: Writing\u0026ndash;original draft, Writing\u0026ndash;review and editing. De-Liang Liu *: Writing\u0026ndash;original draft, Writing\u0026ndash;review and editing. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003ch2\u003e2 \u0026nbsp;Funding\u003c/h1\u003e\n\u003cp\u003eThe author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work has been supported by the National Natural Science Foundation of China (82274419).\u003c/p\u003e\n\u003ch2\u003e3 \u0026nbsp;Acknowledgments\u003c/h1\u003e\n\u003cp\u003eWe thank Bullet Edits Limited for the linguistic editing and proofreading of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4 \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eARRIVE guidelines\u003c/strong\u003e statement: The study was designed and conducted in accordance with the ARRIVE guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5 \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis animal experiment was approved by Shenzhen Following Precision Medical Research Institute for ethical review. This study was conducted in accordance with local legal and institutional requirements.\u003c/p\u003e\n\u003ch2\u003e6 \u0026nbsp;Conflict of Interest\u003c/h1\u003e\n\u003cp\u003e\u003cem\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest\u003c/em\u003e.\u003c/p\u003e\n\u003ch2\u003e7 \u0026nbsp;Supplementary Material\u003c/h1\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003ch2\u003e8 \u0026nbsp;Data Availability Statement\u003c/h1\u003e\n\u003cp\u003eAll raw data and materials are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eZhang, L., et al., Trends in Chronic Kidney Disease in China. 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Open Med (Wars), 2019. 14: p. 740-747.\u003c/li\u003e\n \u003cli\u003eChen, B., et al., Fructus Ligustri Lucidi in Osteoporosis: A Review of its Pharmacology, \u0026nbsp;Phytochemistry, Pharmacokinetics and Safety. Molecules, 2017. 22(9).\u003c/li\u003e\n \u003cli\u003eAli, F., B.A. Khan and S. Sultana, Wedelolactone mitigates UVB induced oxidative stress, inflammation and early \u0026nbsp;tumor promotion events in murine skin: plausible role of NFkB pathway. Eur J Pharmacol, 2016. 786: p. 253-264.\u003c/li\u003e\n \u003cli\u003eZhu, M.M., et al., Wedelolactone alleviates doxorubicin-induced inflammation and oxidative stress \u0026nbsp;damage of podocytes by IkappaK/IkappaB/NF-kappaB pathway. Biomed Pharmacother, 2019. 117: p. 109088.\u003c/li\u003e\n \u003cli\u003eAsadipooya, K. and E.M. Uy, Advanced Glycation End Products (AGEs), Receptor for AGEs, Diabetes, and Bone: \u0026nbsp;Review of the Literature. J Endocr Soc, 2019. 3(10): p. 1799-1818.\u003c/li\u003e\n \u003cli\u003eLitwinoff, E., et al., Emerging Targets for Therapeutic Development in Diabetes and Its Complications: \u0026nbsp;The RAGE Signaling Pathway. Clin Pharmacol Ther, 2015. 98(2): p. 135-44.\u003c/li\u003e\n \u003cli\u003eWautier, M.P., P.J. Guillausseau and J.L. Wautier, Activation of the receptor for advanced glycation end products and consequences \u0026nbsp; on health. Diabetes Metab Syndr, 2017. 11(4): p. 305-309.\u003c/li\u003e\n \u003cli\u003eKlimontov, V.V., O.V. Saik and A.I. Korbut, Glucose Variability: How Does It Work? Int J Mol Sci, 2021. 22(15).\u003c/li\u003e\n \u003cli\u003eRoth, M., A. Obaidat and B. Hagenbuch, OATPs, OATs and OCTs: the organic anion and cation transporters of the SLCO and \u0026nbsp;SLC22A gene superfamilies. Br J Pharmacol, 2012. 165(5): p. 1260-87.\u003c/li\u003e\n \u003cli\u003eLiu, K., et al., Design, synthesis, and biological evaluation of a novel dual peroxisome \u0026nbsp;proliferator-activated receptor alpha/delta agonist for the treatment of diabetic \u0026nbsp; kidney disease through anti-inflammatory mechanisms. Eur J Med Chem, 2021. 218: p. 113388.\u003c/li\u003e\n \u003cli\u003eZuo, N., et al., Fenofibrate, a PPARalpha agonist, protect proximal tubular cells from albumin-bound \u0026nbsp;fatty acids induced apoptosis via the activation of NF-kB. Int J Clin Exp Pathol, 2015. 8(9): p. 10653-61.\u003c/li\u003e\n \u003cli\u003ePark, C.W., et al., Accelerated diabetic nephropathy in mice lacking the peroxisome \u0026nbsp;proliferator-activated receptor alpha. Diabetes, 2006. 55(4): p. 885-93.\u003c/li\u003e\n \u003cli\u003eYaribeygi, H., et al., Fenofibrate improves renal function by amelioration of NOX-4, IL-18, and p53 \u0026nbsp;expression in an experimental model of diabetic nephropathy. J Cell Biochem, 2018. 119(9): p. 7458-7469.\u003c/li\u003e\n \u003cli\u003eNowotny, K., et al., Advanced glycation end products and oxidative stress in type 2 diabetes mellitus. Biomolecules, 2015. 5(1): p. 194-222.\u003c/li\u003e\n \u003cli\u003eRani, N., et al., Chrysin, a PPAR-gamma agonist improves myocardial injury in diabetic rats through \u0026nbsp;inhibiting AGE-RAGE mediated oxidative stress and inflammation. Chem Biol Interact, 2016. 250: p. 59-67.\u003c/li\u003e\n \u003cli\u003eGasiorowski, K., et al., RAGE-TLR Crosstalk Sustains Chronic Inflammation in Neurodegeneration. Mol Neurobiol, 2018. 55(2): p. 1463-1476.\u003c/li\u003e\n \u003cli\u003eSergi, D., et al., The Role of Dietary Advanced Glycation End Products in Metabolic Dysfunction. Mol Nutr Food Res, 2021. 65(1): p. e1900934.\u003c/li\u003e\n \u003cli\u003e Yan, S.F., et al., Glycation, inflammation, and RAGE: a scaffold for the macrovascular complications \u0026nbsp;of diabetes and beyond. Circ Res, 2003. 93(12): p. 1159-69.\u003c/li\u003e\n \u003cli\u003eKhalid, M., G. Petroianu and A. Adem, Advanced Glycation End Products and Diabetes Mellitus: Mechanisms and \u0026nbsp;Perspectives. Biomolecules, 2022. 12(4).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Active ingredients of core Chinese medicines\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4123%;\"\u003e\n \u003cp\u003eMOLID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6456%;\"\u003e\n \u003cp\u003eIngredient Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4231%;\"\u003e\n \u003cp\u003eOB(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.519%;\"\u003e\n \u003cp\u003eDL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4123%;\"\u003e\n \u003cp\u003eMOL000358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6456%;\"\u003e\n \u003cp\u003eBetasitosterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4231%;\"\u003e\n \u003cp\u003e36.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.519%;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4123%;\"\u003e\n \u003cp\u003eMOL000422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6456%;\"\u003e\n \u003cp\u003ekaempferol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4231%;\"\u003e\n \u003cp\u003e41.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.519%;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4123%;\"\u003e\n \u003cp\u003eMOL004576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6456%;\"\u003e\n \u003cp\u003etaxifolin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4231%;\"\u003e\n \u003cp\u003e57.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.519%;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4123%;\"\u003e\n \u003cp\u003eMOL005147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6456%;\"\u003e\n \u003cp\u003eLucidumoside D_qt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4231%;\"\u003e\n \u003cp\u003e54.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.519%;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4123%;\"\u003e\n \u003cp\u003eMOL005190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6456%;\"\u003e\n \u003cp\u003eeriodictyol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4231%;\"\u003e\n \u003cp\u003e71.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.519%;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4123%;\"\u003e\n \u003cp\u003eMOL005212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6456%;\"\u003e\n \u003cp\u003eOlitoriside_qt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4231%;\"\u003e\n \u003cp\u003e103.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.519%;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4123%;\"\u003e\n \u003cp\u003eMOL000006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6456%;\"\u003e\n \u003cp\u003eluteolin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4231%;\"\u003e\n \u003cp\u003e36.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.519%;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4123%;\"\u003e\n \u003cp\u003eMOL000098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6456%;\"\u003e\n \u003cp\u003equercetin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4231%;\"\u003e\n \u003cp\u003e46.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.519%;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4123%;\"\u003e\n \u003cp\u003eMOL005188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6456%;\"\u003e\n \u003cp\u003eSpecnuezhenide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4231%;\"\u003e\n \u003cp\u003e19.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.519%;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4123%;\"\u003e\n \u003cp\u003eMOL001689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6456%;\"\u003e\n \u003cp\u003eacacetin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4231%;\"\u003e\n \u003cp\u003e34.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.519%;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4123%;\"\u003e\n \u003cp\u003eMOL002975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6456%;\"\u003e\n \u003cp\u003ebutin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4231%;\"\u003e\n \u003cp\u003e69.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.519%;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4123%;\"\u003e\n \u003cp\u003eMOL003389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6456%;\"\u003e\n \u003cp\u003e3\u0026apos;-O-Methylorobol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4231%;\"\u003e\n \u003cp\u003e57.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.519%;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4123%;\"\u003e\n \u003cp\u003eMOL003398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6456%;\"\u003e\n \u003cp\u003ePratensein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4231%;\"\u003e\n \u003cp\u003e39.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.519%;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4123%;\"\u003e\n \u003cp\u003eMOL003402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6456%;\"\u003e\n \u003cp\u003edemethylwedelolactone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4231%;\"\u003e\n \u003cp\u003e72.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.519%;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.4123%;\"\u003e\n \u003cp\u003eMOL003404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.6456%;\"\u003e\n \u003cp\u003ewedelolactone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4231%;\"\u003e\n \u003cp\u003e49.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.519%;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2 Betweenness values of key targets\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eGene\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eBC value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eMAPK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e1733.464759\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eRXRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e1621.072699\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eCYP3A4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e1599.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eHSP90AA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e1556.756597\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eESR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e1237.059267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eCYP1A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e1232.480825\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eF2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e1198.463892\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eCES1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e1150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e1045.41738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eAKT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e1007.994614\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTNF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e971.4449598\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eMPO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003ePPARA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e749.6788927\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eRELA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e728.8170545\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eCAV1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e696.9967105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eCYP19A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e566.8996818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eMAPK8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e526.1592809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eIGFBP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e494.8636065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003ePRKCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e491.9193089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003ePON1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e472\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eIL6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e471.4494478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eMAPK14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e450.8685923\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eEGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e388.8684945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eMYC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e385.1069446\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eIL10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e351.0440302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eNR1I2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e319.6197659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eVEGFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e318.0551019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eMMP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e313.9399281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eAHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e303.2421587\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eMMP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e302.0317958\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eNCOA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e282.7540861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eSTAT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e278.89665\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eEGF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e257.6404576\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eVCAM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e257.5538079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eMMP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e255.5558177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e248.0538462\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eAPOB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e211.8878549\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eCASP8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e201.5987723\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eHIF1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e189.7408197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003ePRKCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e154.7222104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eCDKN1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e152.423573\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eNCF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e141.872779\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eRB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e140.0938467\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eGSK3B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e140.057239\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eCCND1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e137.9464031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eIL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e129.3606351\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eCASP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e109.9004882\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eIL4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e107.638245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eRAF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e98.405444\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3 Protein target binding energy (kcal/mol)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eTargets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eCompounds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eBinding energy(kcal/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eSpecnuezhenide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e-8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eJNK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eSpecnuezhenide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e-8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eEGR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eSpecnuezhenide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e-11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eAKT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eSpecnuezhenide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e-9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003ePKC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eSpecnuezhenide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e-9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eP38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eSpecnuezhenide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e-8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eWedelolactone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e-7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eJNK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eWedelolactone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e-8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eEGR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eWedelolactone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e-12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eAKT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eWedelolactone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e-8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003ePKC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eWedelolactone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e-8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eP38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eWedelolactone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e-8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable\u0026nbsp;4\u0026nbsp;Evaluation results of similarity of prescriptions in each batch\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"463\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.5195%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5022%;\"\u003e\n \u003cp\u003eS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7186%;\"\u003e\n \u003cp\u003eS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7186%;\"\u003e\n \u003cp\u003eS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5411%;\"\u003e\n \u003cp\u003eControl mapping (R)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.5195%;\"\u003e\n \u003cp\u003eS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5022%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7186%;\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7186%;\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5411%;\"\u003e\n \u003cp\u003e0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.5195%;\"\u003e\n \u003cp\u003eS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5022%;\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7186%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7186%;\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5411%;\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.5195%;\"\u003e\n \u003cp\u003eS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5022%;\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7186%;\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7186%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5411%;\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.5195%;\"\u003e\n \u003cp\u003eControl mapping (R)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5022%;\"\u003e\n \u003cp\u003e0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7186%;\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7186%;\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5411%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Er Zhi Pill1, diabetic nephropathy2, network pharmacology3, molecular docking4, transcriptomics5","lastPublishedDoi":"10.21203/rs.3.rs-5308470/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5308470/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOBJECTIVE: The study aimed to investigate the mechanism of Erzhi Pill in treating diabetic nephropathy\u0026nbsp;(DN)\u0026nbsp;using network pharmacology and molecular docking, with animal experiments providing additional validation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMETHODS: Initially, the molecular basis and potential mechanisms of Erzhi Pill were examined through network pharmacology, followed by molecular docking between its core components and key potential targets to corroborate the network pharmacology findings. These results were then validated through experimental approaches.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRESULTS: Network pharmacology analysis and HPLC fingerprinting identified Specnuezhenide\u0026nbsp;and wedelolactone as primary components, demonstrating effective binding between the core components and key targets, thus confirming the predictions. In vivo\u0026nbsp;experiments revealed that Erzhi Pill markedly improved blood glucose, lipid profiles, insulin resistance, and kidney function in db/db mice, while also reversing renal cell pathological changes. Transcriptomics and proteomics analyses of KEGG-enriched differential proteins suggested that the preventive and therapeutic effects of Erzhi Pill on DN\u0026nbsp;may operate through AGE-RAGE, PPAR, and other related signaling pathways.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCONCLUSION: Overall, the combined findings from network pharmacology, molecular docking, and experimental validation elucidate the mechanism by which Erzhi Pill inhibits renal fibrosis in DN.\u003c/p\u003e","manuscriptTitle":"Erzhi pill for diabetic nephropathy: validation of integrated network pharmacology, molecular docking, proteomics, and transcriptomics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-26 13:36:49","doi":"10.21203/rs.3.rs-5308470/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"059e7388-5794-417f-82d7-53547f45d38c","owner":[],"postedDate":"November 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40379024,"name":"Biological sciences/Molecular biology"},{"id":40379025,"name":"Health sciences/Endocrinology"},{"id":40379026,"name":"Health sciences/Medical research"},{"id":40379027,"name":"Health sciences/Nephrology"}],"tags":[],"updatedAt":"2024-12-31T11:53:45+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-26 13:36:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5308470","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5308470","identity":"rs-5308470","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}