Network Pharmacology, Machine Learning, and In Vivo Validation of Danzhi Jiangtang Capsule in Diabetic Nephropathy

Summary

Danzhi Jiangtang Capsule (DJC) may attenuate diabetic nephropathy (DN), as evidenced by changes in chemokine signaling and pyroptosis markers, supported by bioinformatics and in vivo validation.

Abstract

Although Danzhi Jiangtang Capsule (DJC) is a traditional Chinese herbal preparation used clinically for diabetes, how it may protect the kidney during diabetic nephropathy (DN) has not been fully clarified. This investigation was designed to explore potential mechanisms by which DJC affects DN, with a focus on the NLR family pyrin domain-containing 3 (NLRP3)/Caspase-1/Gasdermin D (GSDMD) pyroptosis-related signaling cascade.

An integrated strategy combining network pharmacology and machine learning was employed. The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine (BATMAN-TCM) were used to screen bioactive ingredients and their corresponding protein targets of DJC. DN-associated genes were retrieved from Gene Expression Omnibus (GEO), GeneCards, and Online Mendelian Inheritance in Man (OMIM). Key candidate targets were screened and ranked using multiple machine learning algorithms. The binding affinity between DJC’s active ingredients and core targets was assessed via molecular docking. Finally, the therapeutic efficacy and predicted mechanisms were evaluated in db/db diabetic mice.

Network pharmacology analysis identified 599 DJC targets and 68 overlapping genes shared with DN. Using machine learning algorithms, C-C motif chemokine ligand 2 (CCL2) and CASP1 were identified as prioritized candidate targets. Molecular docking predicted possible strong binding affinities between DJC active ingredients and these core proteins. Functional enrichment analyses (GO/KEGG) suggested that DJC modulation is associated with inflammatory responses and the MAPK pathway. In vivo validation showed that DJC treatment attenuated renal injury and fibrosis markers. These findings suggest that DJC may attenuate DN in part through CCL2/C-C motif chemokine receptor 2 (CCR2)-related pyroptosis signaling changes.

Introduction

The global burden of diabetes mellitus (DM) continues to escalate, presenting a formidable public health challenge. An estimated 5.2 million deaths annually stem from DM and its related complications¹. DN counts as a severe microvascular complication and a major contributor to end-stage renal disease (ESRD) development, affecting approximately 30-40% of the diabetic population^2,3. Multiple intertwined factors contribute to DN pathological progression, including metabolic dysfunction, hemodynamic remodeling, and persistent inflammatory responses. Pathological insults, including high glucose and hyperlipidemia, activate the NLRP3/caspase-1/GSDMD-mediated pyroptosis pathway, inducing inflammatory programmed death in renal mesangial and tubular epithelial cells, followed by progressive tubulointerstitial lesions⁴. Pyroptosis is characterized by plasma membrane rupture, which drives the release of IL-1β and IL-18⁵. Such proinflammatory factors increase C-C motif chemokine ligand 2 (CCL2) transcription, promote macrophage infiltration, and further potentiate pyroptotic activation, thereby progressively exacerbating renal injury⁶. In clinical practice, this condition manifests with progressive albuminuria and gradual reduction of glomerular filtration rate, which can culminate in irreversible kidney damage and a marked deterioration in the quality of life for affected individuals^7,8. Despite advancements in medical care, current therapeutic strategies, which mainly focus on strict glycemic and blood pressure control, often fail to completely arrest disease progression. While emerging pharmacotherapies, including SGLT2 blocking agents, GLP-1 agonists, and mineralocorticoid receptor blockers, as well as stem cell therapies and dietary interventions, have garnered significant attention and are becoming more prevalent in practice⁹, the residual risk of DN remains high. Hence, developing innovative therapeutic substances remains an urgent research priority, particularly those from traditional medicine that may target multiple pathological pathways simultaneously.

Danzhi Jiangtang Capsule (DJC), an in-hospital-exclusive formulation developed at the First Affiliated Hospital of Anhui University of Chinese Medicine (Anhui Provincial Medical Institution Preparation Approval No. Z20090006; Patent No. ZL200310112845.1; Batch No. 20220427), is a promising therapeutic candidate. It must be sealed, stored in a dry, ventilated environment, and protected from light. The formula is composed of six traditional Chinese herbs: Pseudostellaria heterophylla (Radix Pseudostellariae), Whitmania pigra (Hirudo), Alisma orientale (Alismatis Rhizoma), Cuscuta chinensis (Cuscutae Semen), Paeonia suffruticosa (Moutan Cortex), and Rehmannia glutinosa (Rehmanniae Radix Praeparata) (5:4:4:3:2:5)¹⁰. In clinical practice, DJC has demonstrated significant hypoglycemic efficacy and the ability to reduce renal injury markers in diabetic patients¹¹. Previous research suggests that DJC may exert renoprotective effects by regulating blood glucose levels and lipid metabolism^12,13,14. Previous studies of DJC have mainly focused on single-pathway validation with unsystematic target selection. Therefore, we integrated network pharmacology and machine learning to screen active components and identify core hub genes, followed by preliminary in vivo validation, to strengthen the analysis.

Network pharmacology offers an effective strategy for dissecting complicated herbal prescriptions. This framework integrates bioinformatic and systematic biological approaches, computational chemistry, and pharmacology to decipher the "multi-component, multi-target, and multi-pathway" mechanisms of drug actions^15,16,17. This holistic approach facilitates a structured examination of the intricate interactions between herbal compounds and disease-specific networks, uncovering the synergistic effects inherent in TCM¹⁸. However, traditional network pharmacology can sometimes generate excessive noise in the data. To overcome this, integrating machine learning algorithms enables precise screening of core characteristic genes from large-scale datasets. Furthermore, molecular docking provides an intuitive, dynamic perspective for predicting potential drug-biomolecule binding affinities¹⁹.

Protocol

Screening of bioactive compounds and potential targets of DJC

Bioactive compounds of the six herbs in DJC, Pseudostellaria heterophylla (Radix Pseudostellariae), Paeonia suffruticosa (Moutan Cortex), Cuscuta chinensis (Cuscutae Semen), Alisma orientale (Alismatis Rhizoma), Rehmannia glutinosa (Rehmanniae Radix Praeparata), and Whitmania pigra (Hirudo) were retrieved from the TCMSP database. Screening was performed based on pharmacokinetic parameters: oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18. For Whitmania pigra, which lacks data in TCMSP, its active compounds and targets were obtained from the BATMAN-TCM, using a score ≥ 20 and a P-value < 0.05 as selection criteria. UniProt was applied to unify all collected targets into a standard gene nomenclature.

Acquisition of DN-related genes

DN-related genes were collected from three sources. First, GeneCards and OMIM were searched with "diabetic nephropathy" as the search term. Genes from GeneCards with a relevance score > 1 were retained. Second, three microarray profiles (GSE30529, GSE104948, GSE96804) were obtained from the GEO repository. The samples in GSE30529 were derived from renal tubules, whereas those in GSE104948 and GSE96804 originated from renal glomeruli. All database searches were conducted on 15 December 2024. Differentially expressed genes (DEGs) between DN patients and healthy controls in the GSE30529 dataset were identified using the limma package in R software. The screening criteria were set at an adjusted p-value < 0.05 and |log2(Fold Change)| > 0.5. Volcano graphs and heatmaps displayed differentially expressed genes.

Identification of putative therapeutic targets of DJC for DN

Venn diagram analysis identified shared loci between the DJC target pool and DN-associated genes from GeneCards, OMIM, and GSE30529 DEGs. These shared loci served as candidate therapeutic genes for all downstream research procedures.

Establishment and profiling of protein interaction (PPI) networks

Putative therapeutic loci were uploaded to STRING to construct a protein-protein interaction network, with an interaction cutoff set above a 0.4 confidence value. Cytoscape processed the network datasets for visual presentation. Topological indices such as Degree were computed via the CytoNCA plugin to identify core nodes throughout the network.

Enrichment analysis

To delineate the biological roles and signaling cascades linked to candidate targets, functional enrichment was analyzed in R with the clusterProfiler package, using org.Hs.eg.db for annotation and ggplot2 for visualization. Gene Ontology (GO) terms were evaluated across biological process (BP), cellular component (CC), and molecular function (MF) categories. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were retained when the enrichment p-value was < 0.05.

Application of the CytoHubba algorithm

The CytoHubba plugin in Cytoscape was used to calculate four topological centrality indices for each network node. Degree, the count of directly connected neighbors, reflects the breadth of each protein's interactions. Maximum neighborhood component (MNC), a metric that quantifies the size of the largest connected component in the node’s neighborhood subgraph, measures local regulatory influence. The edge percolated component (EPC) assesses global structural importance via simulated random edge removal and quantification of the average connected component size. Genes ranking in the top 20 for each index were intersected to yield the final hub genes.

Machine learning-based core gene screening

Three machine learning approaches were applied to expression profiles from the GSE30529 dataset to identify reliable hub genes among candidate targets.

(1) LASSO (Least Absolute Shrinkage and Selection Operator): The regularization parameter λ was tuned via five-fold cross-validation, with the optimal value determined by average performance across folds. Input gene expression features underwent automatic Z-score standardization to eliminate effects of expression magnitude. With the penalty parameter α fixed at 1 for pure LASSO regularization, a predefined panel of 100 λ values was used for screening. The L1 penalty shrank the coefficients of low-relevance features to zero, enabling simultaneous feature selection and complexity control, while the cross-validation-tuned regularization strength mitigated the risk of overfitting.

(2) SVM-RFE (Support Vector Machine-Recursive Feature Elimination): A two-layer 5-fold cross-validation framework was employed to ensure robust feature screening. Five non-overlapping subsets were generated by stochastically dividing the entire dataset, with four used for training and one for testing across five iterations; fold-wise averaging mitigated bias from single-sample splitting. Recursive feature elimination was applied to reduce feature dimensionality and constrain model complexity, alleviating overfitting. Classification error rate and accuracy served as performance metrics to determine the optimal number of features.

(3) Random Forest (RF): An initial 500-tree random forest was constructed, with the optimal tree number determined by the minimum out-of-bag classification error rate to retrain the final model. All remaining hyperparameters, including the random feature subset size at each node split, were kept at default package settings. Gene importance scores from the final model were ranked in descending order to generate the disease signature gene set. The final set of core genes was determined by intersecting the results from these three algorithms.

Independent dataset-based verification of hub gene expression

Two independent external datasets, GSE104948 and GSE96804, were used to validate transcript levels of the hub genes. Gene expression datasets underwent normalization; expression differences between DN cases and healthy subjects were plotted using R’s ggplot2 and ggsignif toolkits.

Molecular docking

The 3D conformations of the hub target proteins (CASP1, PDB ID: 3E4C; CCL2, PDB ID: 7S00) were obtained from the Protein Data Bank. Bioactive compound 3D conformations were fetched via PubChem. Ursolic acid, 3β-hydroxyurs-12-en-28-oic acid, and quercetin were selected for docking because network pharmacology predicted interactions between these active compounds and the core targets CCL2 or CASP1. Protein structures were pretreated by stripping native ligands and water molecules, introducing polar hydrogens, and subsequently subjected to molecular docking using AutoDock Vina. The binding affinity, represented by the docking score (in kcal/mol), served to assess the strength of interactions. A binding energy threshold of −5.0 kcal/mol was set to define stable intermolecular interactions. The docked conformations were visualized using PyMOL.

Animals and experimental design

The present study used 30 8-week-old male db/db mice of specific pathogen-free (SPF) grade, along with 10 age-matched male SPF db/m mice. All experimental mice were reared in an SPF-grade facility under a 12-h light–dark photoperiod and maintained under stable conditions: 22 ± 2 °C, 50–70% relative humidity, and unrestricted access to regular chow and drinking water. Ethical approval for all in vivo experimental protocols was granted by the Anhui University of Chinese Medicine Animal Ethics Committee (Approval No. AHUCM-mouse-2023059).

Rodents underwent a 7-day adaptation phase before random grouping (6 animals each) and 8 weeks of daily intervention. Fasting blood glucose was measured in all experimental animals; animals were allocated using a random-number table, and histology, IHC, and western blot quantification were performed under blinded assessment.

Normal control (CTL) group: db/m mice receiving saline by oral gavage.

Model (M) group: db/db mice receiving saline by oral gavage.

DJC-Medium Dose (DJC-M) group: db/db mice receiving DJC at 0.78 g/kg (equivalent to the clinical daily dose for adults).

DJC-High Dose (DJC-H) group: db/db mice receiving DJC at 1.56 g/kg (twice the clinical daily dose for adults).

Dosing preparation

Human-to-murine dose scaling was derived using the body surface area normalization approach, as outlined in Methodology of Pharmacological Experiments, with a conversion coefficient of 9.1. The clinical daily dose of DJC was 6 g for a 70-kg adult (0.0857 g/kg), yielding mouse equivalent doses of 0.78 g/kg (1-fold clinical dose) and 1.56 g/kg (2-fold clinical dose). All mice received intragastric administration at a constant volume of 10 mL/kg. DJC powder was precisely weighed and suspended in 0.5% CMC-Na solution to form uniform suspensions at 78 mg/mL (medium dose) and 156 mg/mL (high dose).

After 8 weeks of daily administration, anesthesia was induced in all experimental mice using 1% sodium pentobarbital. Blood specimens were collected via the abdominal aorta and centrifuged to isolate the supernatants for kit-based biochemical assays. Metabolic cages were used to collect murine urine samples. Partial renal tissues were fixed in 4% paraformaldehyde for hematoxylin-eosin (HE) and Masson staining. Snap-frozen in liquid nitrogen, residual renal tissues were preserved at −80 °C for downstream analytical assays.

Histological analysis

Following fixation in 4% paraformaldehyde, renal tissues underwent graded ethanol dehydration, clearing, paraffin embedding, and 4-µm serial sectioning for subsequent HE and Masson’s trichrome staining. HE staining was performed following standard procedures (deparaffinization, rehydration, hematoxylin nuclear staining, acidified ethanol differentiation and blueing, eosin counterstaining, dehydration, clearing, and neutral balsam mounting) to observe renal histopathological changes. Sections were immersed in hematoxylin stain for 3 min, followed by eosin counterstaining for 15 s. For Masson’s trichrome staining, sections underwent deparaffinization, rehydration, sequential staining, differentiation, and acetic acid color separation, followed by dehydration, clearing, and mounting to assess renal collagen accumulation and fibrotic changes. Bright-field microscopy was employed to inspect and photograph all stained tissue sections at 200× magnification. HE-stained sections were scored using the Glomerular Sclerosis Index (GSI). For Masson’s trichrome staining, the Masson Trichrome preset was applied to isolate the blue collagen channel, and the ratio of collagen area to red-stained tissue area was calculated.

Immunohistochemistry (IHC)

Following 24-h fixation with 4% paraformaldehyde, tissue specimens were dehydrated through serial ethanol gradients, and then paraffin-embedded and cut into sequential 5 µm sections. After deparaffinization, sections underwent antigen retrieval and endogenous peroxidase blocking. Subsequent to blocking, tissue slides were incubated with diluted primary antibodies at 4 °C overnight in a moist chamber. Slides received a secondary antibody incubation at room temperature after PBS washes, followed by a DAB chromogenic reaction until optimal staining intensity was achieved. The sections then underwent sequential hematoxylin counterstaining, differentiation, bluing, dehydration, clearing, coverslip mounting, and bright-field microscopic imaging. Antigen retrieval was performed via autoclaving for 10 min, followed by natural cooling to room temperature. After blocking with 5% goat serum for 60 min at room temperature, sections were incubated with primary antibody (1:200 dilution) and then HRP-conjugated goat anti-rabbit IgG secondary antibody (1:300 dilution) for 60 min each at room temperature. The DAB chromogenic reaction was conducted for 3–5 min at room temperature, monitored microscopically, and terminated by rinsing with distilled water. Images were acquired at ×200 magnification, and the percentage of positive staining area was quantified using ImageJ.

ELISA detection

Assay steps complied with the official protocols supplied by kit vendors. Washing buffer and serially diluted standards were prepared in advance. Blank, standard, and sample wells were loaded with reagents and incubated in the dark. Plates were washed thoroughly after incubation, then incubated with the enzyme conjugate in the dark. After washing, a chromogenic substrate was added for a light-protected color reaction, which was terminated with stop buffer once distinct blue gradients emerged in the standard wells. The absorbance (OD) of each well was measured at the target wavelength using a microplate reader. Serum IL-1β and IL-18 concentrations were quantified using calibration curves generated from OD values of standard concentrations (undiluted serum samples; absorbance measured at 450 nm; eight technical replicate wells per sample). Urinary creatinine and 24 h urinary protein were measured in undiluted urine samples collected using 24 h metabolic cages (absorbance read at 546 nm; eight technical replicates per specimen).

Real-time quantitative PCR (RT-qPCR)

Total RNA was isolated from renal tissues, and its purity and concentration were determined by spectrophotometry. Purified RNA was reverse-transcribed to generate cDNA templates. RT-qPCR was performed on a real-time PCR instrument with the following thermal cycling program: 5 min pre-denaturation (95 °C), followed by 40 amplification rounds: 15 s at 95 °C, 1 min at 60 °C. RT-qPCR was performed in a 10 µL reaction containing 5 µL of SYBR Green Master Mix, 0.2 µL of forward primer, 0.2 µL of reverse primer, 1 µL of cDNA template, and 3.6 µL of RNase-free water. Reverse transcription for cDNA synthesis was performed in a total reaction volume of 20 µL. Melting-curve analysis was performed to confirm amplification specificity, and each sample was assayed in three technical replicates. Transcript levels were assessed by 2⁻^ΔΔCq normalization to β-actin, with all primer sequences listed in Table 1.

Western blot analysis

Roughly 40 mg renal tissue samples were harvested from each mouse cohort, minced on ice, and fully homogenized in protein lysis buffer. After a 15 min spin at 12,000 rpm at 4 °C, the total protein-containing supernatant from tissue homogenates was collected, aliquoted, and cryopreserved at −80 °C. The protein supernatant was mixed with loading buffer and denatured by boiling in a water bath. Approximately 10 µL of normalized protein sample was loaded per lane and separated on 10% SDS-polyacrylamide gels. Target proteins were transferred to nitrocellulose filters through wet electroblotting. The transfer was performed at a fixed current of 400 mA in an ice bath for 30–45 min. Post-transfer, blots were incubated in cold non-fat milk for 2 h, then incubated overnight at 4 °C with 1:1,000 primary antibodies. Washed blots were covered with matching secondary antibodies (1:10,000) at 4 °C for 2 h, and luminescent band signals were recorded and quantified from blot photos using ImageJ software. For GSDMD-N, Tubulin was used as the loading control because its expected molecular weight (~35 kDa) overlaps with that of GAPDH (~36 kDa). All western blot experiments were performed with three independent biological replicates.

Statistical analysis

Quantitative data are presented as the mean ± standard deviation (SD). Differences between two groups were analyzed with an unpaired Student’s t-test, whereas comparisons among multiple groups were evaluated by one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test. A p-value < 0.05 was considered statistically significant.

Results

DJC bioactive component and target identification

A total of 66 bioactive compounds that met the screening criteria (OB ≥ 30% and DL ≥ 0.18) were identified from the six herbs comprising DJC. These included 26 compounds from Pseudostellaria heterophylla, 6 from Paeonia suffruticosa, 8 from Cuscuta chinensis, 6 from Alisma orientale, 10 from Rehmannia glutinosa, and 10 from Whitmania pigra. Standardization of target labels and duplicate elimination yielded 599 distinct candidate protein targets relevant to DJC. We built a compound-target interaction map to display such interconnections (Figure 1).

Screening of genes associated with DN alongside functional enrichment assessments

From the GeneCards and OMIM databases, 4,495 DN-associated genes were collected. Additionally, exploration of the GSE30529 dataset identified 1,651 DEGs between DN patients and healthy volunteers: 933 upregulated transcripts and 718 downregulated transcripts (Figure 2A,B). By intersecting the 599 DJC targets with the DN-related genes (database genes and DEGs), 68 candidate therapeutic loci were screened for DJC intervention on DN (Figure 2C).

To clarify the biological roles of the 68 candidate loci, GO and KEGG functional enrichment assessments were performed for all target genes. GO analysis revealed significant enrichment in BP, such as 'response to hypoxia' and 'cellular response to chemical stress'; CC, including 'vesicle lumen' and 'membrane raft'; and MF, like 'protein kinase activator activity' and 'transcription factor binding' (Figure 3A). KEGG analysis highlighted several key signaling pathways, including 'Lipid and atherosclerosis', 'HIF-1 signaling pathway', 'AGE-RAGE signaling pathway in diabetic complications', and the 'MAPK signaling pathway', all of which are closely associated with inflammation and DN pathogenesis (Figure 3B).

PPI map construction and core gene screening

68 candidate loci were used to build a PPI map; the resulting network contained 68 nodes linked by 189 edges (Figure 4). Single topological metrics capture only one dimension of network properties, while multi-algorithm integration mitigates this limitation and identifies genes with high connectivity and functional centrality. The CytoHubba plug-in implemented four topological scoring methods: MCC, MNC, EPC, and Degree. The intersection of the top-ranked genes from each algorithm yielded 18 key hub genes that are likely central to DJC's therapeutic effects on DN (Figure 5A-E).

Screening of core genes by machine learning algorithms

To narrow down core genes and identify the most stable therapeutic loci, three machine learning algorithms were applied to expression data for the 18 hub genes in the GSE30529 dataset. The LASSO algorithm identified four key genes (Figure 6A). The SVM-RFE algorithm identified a 10-gene combination that achieved the highest classification accuracy (Figure 6B). The Random Forest algorithm ranked six genes as most important based on their contribution to the model (Figure 6C). Critically, the intersection of the gene sets identified by all three algorithms pinpointed two core genes: CASP1 and CCL2 (Figure 6D). These two genes were therefore prioritized as the most promising therapeutic targets of DJC for subsequent validation.

Validation of core gene expression in external datasets

To validate the clinical relevance of CASP1 and CCL2, their expression levels were examined in two independent GEO datasets. In both the GSE104948 and GSE96804 datasets, CCL2 and CASP1 transcripts were increased in DN samples versus controls, indicating partial relevance to DN lesions (Figure 7A-D).

Molecular docking

Simulated molecular docking was used to assess interactions between selected DJC ingredients and key protein binding sites. The compounds were selected because network pharmacology predicted interactions between these active ingredients and the core targets CCL2 or CASP1. The results suggested relatively strong binding affinities. Notably, ursolic acid exhibited a binding energy of −9.0 kcal/mol with CASP1, and 3β-hydroxyurs-12-en-28-oic acid showed a binding energy of −7.2 kcal/mol with CCL2. Quercetin also displayed a strong interaction with CCL2 (−6.1 kcal/mol) (Figure 8A-C). These negative binding energies suggest potentially stable, spontaneous binding, providing preliminary theoretical support for potential interactions between selected DJC components and these core targets.

Amelioration of renal injury by DJC in db/db mice

To validate the therapeutic efficacy of DJC, we used a db/db mouse model of DN. HE staining revealed that mice in the model (M) group exhibited renal pathological changes, including glomerular hypertrophy and tubular vacuolation, which were moderately ameliorated in both the medium-dose (DJC-M) and high-dose (DJC-H) treatment groups. Masson trichrome staining additionally revealed collagen accumulation (renal fibrosis) within glomeruli and the tubular interstitium of model animals, a phenotype that was moderately alleviated following DJC intervention (Figure 9A-C). Throughout the feeding period, control mice remained in normal general condition with unremarkable food and water intake. Model mice exhibited sluggish locomotion, polydipsia, polyphagia, and progressive body weight gain, whereas DJC treatment improved fur texture and moderately decreased body weight (Figure 10A). Model rodents showed mild increases in glycemic levels relative to control cohorts. DJC intervention markedly reduced blood glucose levels relative to the model group, indicating an ameliorative effect on hyperglycemia in diabetic mice (Figure 10B). Urinary creatinine and 24 h urinary protein levels were determined in mouse urine samples using assay kits. In DN, impaired filtration barrier function may result in urinary protein leakage that exceeds the tubular reabsorptive capacity, presenting as elevated 24 h urinary protein excretion. The reduction in 24 h urinary protein following DJC administration suggests that DJC may alleviate filtration barrier damage. The decrease in urinary creatinine after DJC treatment may reflect changes in diabetes-related urinary biochemical indicators; however, because serum creatinine, blood urea nitrogen, and urinary albumin-to-creatinine ratio were not assessed, these urinary findings should be interpreted cautiously as renal injury-associated indicators rather than definitive evidence of restored renal function (Figure 10C,D). IL-1β and IL-18 serum concentrations were markedly higher in model groups than in control cohorts, whereas both DJC-M and DJC-H treatments markedly reduced these inflammatory cytokines, suggesting that DJC may alleviate serum inflammation and may be linked to the suppression of pyroptosis in DN mice. Collectively, these results indicate a potential renoprotective effect of DJC against structural injury and renal injury-associated biochemical changes in diabetic mice (Figure 10E,F).

Changes in the CCL2/CCR2 axis and pyroptosis markers in the kidney

To examine whether the predicted core genes and signaling markers were altered after DJC treatment, we quantified mRNA and protein expression levels in kidney samples. IHC and RT-qPCR showed elevated CASP1 and CCL2 expression in model mice, and immunoblotting also showed increased CASP1 and CCL2 protein levels; DJC intervention markedly downregulated these markers (Figure 11A–E and Figure 12E,F). Furthermore, CCR2 mRNA expression, corresponding to the receptor for CCL2, was significantly inhibited after DJC treatment (Figure 12A).

Pivotal mediators of the pyroptosis cascade were further assessed. RT-qPCR and western blot analyses showed that the expression levels of NLRP3, cleaved-Caspase-1, and the cleaved, active form of GSDMD (GSDMD-N) were significantly upregulated in the model group, suggesting potential activation of the pyroptotic cascade. DJC treatment substantially reduced the expression of NLRP3, cleaved-Caspase-1, and GSDMD-N (Figure 12B-I).

Overall, these data indicate that DJC administration was associated with reduced renal CCL2/CCR2 activity and lower expression of NLRP3/CASP1/GSDMD-related pyroptosis markers, which is broadly consistent with the bioinformatics predictions.

Data Availability Statement

All raw data have been uploaded to FigShare for public access. (https://doi.org/10.6084/m9.figshare.32744523)

Figure 1. Compound-target network of DJC. The compound-target network shows the relationship between predicted DJC compounds and candidate targets. Abbreviations: DJC = Danzhi Jiangtang Capsule. Please click here to view a larger version of this figure.

Figure 2. Identification of putative DJC targets for DN. (A) Volcano chart for GSE30529 divergent transcripts: red for overexpressed genes, green for suppressed ones. (B) Heat diagram showing hierarchical clustering of the top 50 differentially expressed transcripts. (C) Venn diagram showing the intersection of DJC targets, DN-associated genes from public databases, and DEGs, revealing 68 putative therapeutic targets. Abbreviations: DJC = Danzhi Jiangtang Capsule; DN = diabetic nephropathy; DEGs = differentially expressed genes. Please click here to view a larger version of this figure.

Figure 3. GO and KEGG enrichment analyses. (A) GO enrichment assessment of 68 loci: top 10 enriched entries of BP, CC, and MF. (B) KEGG enrichment bubble graph showing top 20 enriched signaling cascades. Abbreviations: GO = Gene Ontology; BP = biological process; CC = cellular component; MF = molecular function; KEGG = Kyoto Encyclopedia of Genes and Genomes. Please click here to view a larger version of this figure.

Figure 4. PPI map establishment and core gene screening. Interaction maps of 68 candidate loci built by STRING and visualized with Cytoscape. Abbreviations: PPI = protein-protein interaction. Please click here to view a larger version of this figure.

Figure 5. Hub gene identification. (A-D) Top 20 core genes screened by four distinct algorithms via CytoHubba: (A) MCC, (B) MNC, (D) Degree, and (C) EPC. (E) Venn diagram showing the intersection of the four gene sets, resulting in 18 key hub genes. Abbreviations: MCC = maximal clique centrality; MNC = maximum neighborhood component; EPC = edge percolated component. Please click here to view a larger version of this figure.

Figure 6. Identification of core genes using machine learning algorithms. (A) LASSO regression curves for 18 core gene coefficients. (B) SVM-RFE screening ideal gene counts by model predictive precision. (C) Random Forest analysis ranking genes by their importance score (Mean Decrease Gini). (D) Venn diagram showing the intersection of genes selected by LASSO, SVM-RFE, and RF, identifying CASP1 and CCL2 as the final core genes. Abbreviations: LASSO = Least Absolute Shrinkage and Selection Operator; SVM-RFE = Support Vector Machine-Recursive Feature Elimination; RF = Random Forest. Please click here to view a larger version of this figure.

Figure 7. Core transcript level verification with independent cohorts. CCL2 and CASP1 mRNA box plots are shown for (A,B) GSE104948 and (C,D) GSE96804. Data are presented as mean ± SD. * p < 0.05; ** p < 0.01. Abbreviations: CCL2 = C-C motif chemokine ligand 2; CASP1 = cysteine-dependent aspartate-directed protease 1. Please click here to view a larger version of this figure.

Figure 8. Molecular docking. (A) Ursolic acid-CASP1 molecular docking. (B) 3β-hydroxyurs-12-en-28-oic acid-CCL2. (C) 3D and 2D diagrams showing the binding conformation of quercetin with CCL2. Hydrogen bonds are indicated by dashed lines. Abbreviations: CCL2 = C-C motif chemokine ligand 2; CASP1 = cysteine-dependent aspartate-directed protease 1. Please click here to view a larger version of this figure.

Figure 9. HE staining and Masson's trichrome staining. (A) Representative images of HE staining (top row, scale bar = 50 µm) and Masson's trichrome staining (bottom row, scale bar = 50 µm) of kidney sections from the four experimental groups. (B) Quantitative analysis of HE staining. (C) Quantitative analysis of Masson's trichrome staining. Abbreviations: HE = hematoxylin and eosin staining; Masson = Masson's trichrome staining; GSI = Glomerulosclerosis Index. Please click here to view a larger version of this figure.

Figure 10. Effects of DJC on renal injury-associated indicators in model mice. (A) Changes in body weight of mice. (B) Changes in blood glucose levels of mice. (C) CRE. (D) 24 h PRO. (E) Quantification of IL-1β. (F) Quantification of IL-18. Data are presented as mean ± SD, * p < 0.05; ** p < 0.01 (n = 6 per group). Abbreviations: CTL = control; M = model; DJC-M = Danzhi Jiangtang Capsule medium-dose group; DJC-H = Danzhi Jiangtang Capsule high-dose group; CRE = urinary creatinine; PRO = 24-hour urinary protein; IL-1β = interleukin-1β; IL-18 = interleukin-18. Please click here to view a larger version of this figure.

Figure 11. Effects of DJC on core genes CCL2 and CASP1. (A) Representative IHC images of CASP1 and CCL2 expression in kidney tissues (scale bar = 50 µm). (B,C) Quantitative analysis of CASP1 (B) and CCL2 (C) IHC staining. (D,E) Relative mRNA levels of (D) Casp1 and (E) Ccl2. Data are presented as mean ± SD. * p < 0.05; ** p < 0.01 (n = 6 per group). Abbreviations: CTL = control; M = model; DJC-M = Danzhi Jiangtang Capsule medium-dose group; DJC-H = Danzhi Jiangtang Capsule high-dose group; CCL2 = C-C motif chemokine ligand 2; CASP1 = cysteine-dependent aspartate-directed protease 1; IHC = immunohistochemistry. Please click here to view a larger version of this figure.

Figure 12. Effects of DJC on the CCL2/CCR2 axis and pyroptosis pathway. mRNA expression of (A) Ccr2, (B) Nlrp3, and (C) Gsdmd. (D) Representative western blot bands of the indicated proteins. (E-I) Quantitative analysis of (E) CCL2, (F) CASP1, (G) NLRP3, (H) cleaved CASP1, and (I) GSDMD-N protein expression. GAPDH served as the internal reference for most western blots; Tubulin was used for GSDMD-N because its expected molecular weight nearly overlaps that of GAPDH. Data are presented as mean ± SD. * p < 0.05; ** p < 0.01 (n = 6 per group). Abbreviations: CTL = control; M = model; DJC-M = Danzhi Jiangtang Capsule medium-dose group; DJC-H = Danzhi Jiangtang Capsule high-dose group; CCL2 = C-C motif chemokine ligand 2; CASP1 = cysteine-dependent aspartate-directed protease 1; CCR2 = C-C motif chemokine receptor 2; GSDMD = Gasdermin D; NLRP3 = NLR family pyrin domain containing 3; GAPDH = glyceraldehyde-3-phosphate dehydrogenase; Tubulin = β-Tubulin. Please click here to view a larger version of this figure.

Gene	Primer Type	Sequence (5'→3')
NLRP3	Forward	CCTGAGCAGCCTCATCAGAA
	Reverse	GCAAGTGCTGCAGTTTCTCC
Caspase-1	Forward	AAAGACAAGCCCAAGGTGATC
	Reverse	CCAAGTCACAAGACCAGGCATA
GSDMD	Forward	AGTGCTCCAGAACCAGAACCG
	Reverse	TCACCACAAACAGGTCATCCC
CCL2	Forward	CAGGTCCCTGTCATGCTTCT
	Reverse	GTGGGGCGTTAACTGCATCT
CCR2	Forward	ACGATGATGGTGAGCCTTGTC
	Reverse	TGCAGCATAGTGAGCCCAGA
β-actin	Forward	GTGACGTTGACATCCGTAAAGA
	Reverse	GTAACAGTCCGCCTAGAAGCAC

Table 1: Primer sequences used for RT-qPCR.

Discussion

DN remains a leading cause of ESRD, characterized by a complex and multifactorial pathogenesis that is not yet fully understood^20,21. Although DJC has shown promising clinical efficacy in ameliorating DN, the underlying molecular mechanisms have remained largely elusive²². In this study, we used a multi-omics analytical scheme integrating network pharmacology, machine learning tools, binding simulation assays, and in vivo verification to explore the pharmacological actions of DJC. This multi-pronged approach allowed us to screen for potential targets, computationally prioritize core pathways, and experimentally assess key findings, thereby strengthening the basis for the exploratory conclusions.

Primary network pharmacology analysis identified 68 potential targets associated with DJC’s effects in DN, suggesting a classic multi-target mechanism of action, a hallmark of Traditional Chinese Medicine (TCM)^23,24. Subsequent functional enrichment analysis revealed that these targets were significantly associated with inflammatory responses, cellular stress, and metabolic dysregulation pathways, such as the AGE-RAGE and MAPK signaling pathways^25,26,27,28. These findings provided the first layer of evidence that DJC's renoprotective effects may involve modulating chronic inflammation and cellular injury, which are central to the progression of DN. To move beyond a broad network analysis and pinpoint the most critical targets, we integrated three distinct machine learning algorithms. This approach filtered the initial hub genes down to two core targets: CASP1 and CCL2²⁹. CCL2, a potent chemokine, recruits monocytes and macrophages to inflammatory lesions by binding to its receptor, CCR2, thereby bridging innate and adaptive immunity^30,31. In the context of renal disease, CCL2 is secreted by stressed glomerular and tubular cells, and its signaling is known to drive renal inflammation and fibrosis^32,33. Elevated CCL2 levels are strongly correlated with the severity of renal damage in DN^34,35,36,37. Concurrently, CASP1 is the pivotal enzyme of the inflammasome complex. Activation of this molecule promotes cleavage maturation for two proinflammatory mediators: IL-1β, IL-18, and, more critically, this protease cleaves Gasdermin D to initiate pyroptosis, an inflammation-regulated cell death subtype^38,39,40,41. Both the CCL2/CCR2 axis and CASP1-mediated pyroptosis have been increasingly implicated as key drivers of renal cell death and inflammation in DN^42,43. Our molecular docking results provided additional predictive support for this hypothesis, suggesting moderate binding affinities between DJC's active compounds (e.g., ursolic acid) and both CASP1 and CCL2, suggesting potential interactions.

A strength of this study is the experimental follow-up of these computational predictions. Our in vivo experiments in db/db mice showed that DJC treatment attenuated renal histopathological damage, reduced fibrosis, and improved indicators of renal injury. At the molecular level, DJC administration markedly downregulated the expression of both CCL2 and its receptor CCR2 in the kidneys. Second, we found that DJC reduced expression of markers associated with the pyroptosis pathway, as evidenced by lower levels of NLRP3, cleaved Caspase-1, and the active pyroptosis executor GSDMD-N⁴⁴. These results are consistent with the network pharmacology and machine learning predictions, indicating that DJC may attenuate DN in part in association with the CCL2/CCR2 axis and the NLRP3/Caspase-1/GSDMD-associated pyroptosis signaling pathway.

This study possesses several strengths. By integrating "dry lab" bioinformatics with "wet lab" experiments, we identified the active ingredients and potential therapeutic targets of DJC. The multi-target nature of DJC, as revealed here, makes it particularly well-suited to treating complex diseases such as DN, potentially overcoming the limitations associated with single-target drugs.

However, we also acknowledge certain limitations. First, while we identified several active compounds, this study did not determine which specific compound or combination of compounds is primarily responsible for the observed anti-pyroptotic effects. In addition, the machine learning algorithm is prone to overfitting, and molecular docking technology has inherent limitations. Next, mechanistic assays targeted the CCL2/CCR2 axis and pyroptosis-associated pathways; other parallel pathways identified in our KEGG analysis may also contribute to DJC's efficacy. Finally, due to limited indicators of renal function and the exclusive use of male animals, the present study cannot confirm that DJC ameliorates DN specifically through the CCL2/CCR2 axis and CCL2/CCR2-mediated pyroptosis. Subsequent in vitro cellular experiments, including pathway inhibitor intervention and gene overexpression assays, will be performed to further verify whether DJC exerts its renoprotective effects specifically by targeting CCL2/CCR2-dependent pyroptosis.

In conclusion, this multidisciplinary study provides evidence that DJC may alleviate renal inflammation and injury in DN, in association with changes in the CCL2/CCR2 axis and in markers of NLRP3/Caspase-1/GSDMD-mediated pyroptosis. The data may provide theoretical support for further investigation of DJC and pyroptosis-related pathways in DN.

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Creatinine (CRE) Assay Kit	Nanjing Jiancheng Bioengineering Institute	C011-2-1
ECL	Biosharp	BL520B
Eosin Staining Kit	Servicebio	G1002
Goat anti-rabbit IgG-HRP Conjugate	Abbkine	A21020
Hematoxylin Staining Kit	Servicebio	G1004
Masson's Trichrome Stain Kit	Servicebio	G1006
Mouse IL-1β ELISA Kit	MeiMian	MM-0040M1
Mouse IL-18 ELISA Kit	MeiMian	MM-0169M1
Rabbit anti-mouse Caspase1 Antibody	Affinity	AF5418
Rabbit anti-mouse CCL2 Antibody	Abcam	ab315478
Rabbit anti-mouse cleaved-Caspase-1 Antibody	Affinity	AF4005
Rabbit anti-mouse GAPDH Antibody	Zenbio	R380626
Rabbit anti-mouse GSDMD-N Antibody	Abcam	ab219800
Rabbit anti-mouse NLRP3 Antibody	Abcam	ab263899
Rabbit anti-mouse Tubulin Antibody	Affinity	AF7011
Reverse Transcription Kit	Biosharp	BL696A
RIPA Lysis Buffer	Beyotime Biotechnology	P0013
RIPA Lysis Buffer	Leagene	PS0013
RNA isolation reagent	Servicebio	G3013-100ML
SYBR Green Master Mix	Servicebio	G3326-05
Urinary Protein Quantification Kit	Nanjing Jiancheng Bioengineering Institute	C035-2-1
software name	version
AutoDock Vina	1.2.3
Cytoscape software	3.10.0
GraphPad Prism	9.0
ImageJ	java 1.8.0_345
PyMOL	2.5
R software	4.2.0