Abstract

   Diabetic kidney disease (DKD) is a severe complication of diabetes,
   characterized by chronic inflammation and fibrosis. Tang Shen Ping
   Decoction (TSPD), a traditional Chinese medicine formulation, has shown
   therapeutic efficacy in DKD, yet its molecular mechanisms remain to be
   fully elucidated. To explore the multitarget mechanisms of TSPD, this
   study integrated network pharmacology, transcriptomic analysis,
   molecular docking, and molecular dynamics simulations, followed by in
   vivo and in vitro validation. A total of 248 active compounds and 649
   potential targets of TSPD were identified, among which network
   pharmacology and transcriptomic integration highlighted 21 key genes
   involved in DKD pathogenesis. Protein–protein interaction network
   analysis further identified ALB, CCL2, EGF, FN1, and PTGS2 as central
   targets. Molecular docking confirmed strong binding affinities between
   core TSPD compounds, including quercetin and kaempferol, and these
   targets, particularly CCL2. Molecular dynamics simulations validated
   the stability of these interactions, identifying CCL2 as a crucial
   therapeutic target. In vivo experiments demonstrated that TSPD
   significantly improved renal function, attenuated fibrosis, and
   down-regulated CCL2, NF-κB, and TGF-β1 expression in DKD rats. In
   vitro, TSPD effectively suppressed CCL2/NF-κB activation and reduced
   the secretion of inflammatory cytokines (TNF-α, IL-6, and IL-1β) in
   high-glucose-treated HK-2 cells. Functional analysis confirmed that
   CCL2 overexpression exacerbated inflammation, while its silencing
   enhanced the anti-inflammatory effects of TSPD. These findings reveal
   that TSPD exerts renoprotective effects by targeting the CCL2/NF-κB
   axis, offering mechanistic insights into its anti-inflammatory and
   antifibrotic actions and providing a theoretical foundation for its
   clinical application in DKD treatment.
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1. Introduction

   Diabetic kidney disease (DKD) is one of the most common and serious
   microvascular complications in patients with diabetes. According to a
   recent report by the International Diabetes Federation (IDF),
   approximately 463 million adults, or 9.0% of the world’s population,
   have diabetes, and 30–40% of these individuals are likely to develop
   DKD. DKD begins with increased microalbumin in the urine, progresses to
   proteinuria, and a gradual decline in kidney function, which can
   eventually lead to chronic kidney failure or even death. − Research has
   confirmed that inflammation plays a crucial role in the pathogenesis of
   DKD. In the diabetic state, factors such as hyperglycemia, altered
   renal hemodynamics, and impaired lipid metabolism stimulate the
   production of inflammatory mediators. These mediators trigger a series
   of inflammatory responses through pathways involving advanced glycation
   end products (AGEs) and protein kinase C (PKC), leading to pathological
   changes such as thickening of the glomerular basement membrane,
   extracellular matrix deposition in the glomerular mesangial area, and
   glomerulosclerosis. Therefore, DKD is considered to be an inflammatory
   disease. Given the critical role of inflammation in the pathogenesis of
   DKD, exploring traditional Chinese medicine strategies with
   anti-inflammatory effects is particularly important.

   Studies have shown that Traditional Chinese Medicine (TCM) can be
   effectively used in the treatment of DKD and has significant potential
   as a primary or adjunctive therapy. Tang Shen Ping Decoction (TSPD)
   consists of Pseudostellariae Radix (Taizishen, TZS), Astragalus
   membranaceus (Huangqi, HQ), Fructus corni (Shanzhuyu, SZY), Rehmanniae
   Radix Praeparata (Shudihuang, SDH), Dioscoreae Rhizoma (Shanyao, SY),
   Maydis Stigma (Yumixu, YMX), Smilacis Glabrae Rhizoma (Tufuling, TFL),
   Chuanxiong Rhizoma (Chuanxiong, CX), Salviae Miltiorrhizae Radix
   (Danshen, DS), Euonymi Alati Ramulus (Guijianyu, GJY), Rosae Laevigatae
   Fructus (Jinyingzi, JYZ), Hirudo medicinalis (Shuizhi, SZ), Bombyx
   Batryticatus (Jiangcan, JC), and Mantidis Ootheca (Sangpiaoxiao, SPX).
   Recent pharmacological studies have shown that HQ, SZY, SDY, SY, CX,
   DS, SZ, and their components exert therapeutic effects by inhibiting
   epithelial–mesenchymal transition, modulating inflammatory responses
   and oxidative stress, regulating glucose and lipid metabolism, and
   improving renal function. − In addition, TSPD has been shown to
   significantly reduce blood glucose, serum creatinine (Scr), blood urea
   nitrogen (BUN), and urinary albumin excretion rate (UAER) in patients
   with DKD, thereby alleviating disease symptoms. − Our previous research
   further confirmed that TSPD treatment significantly reduces renal
   function indicators such as BUN, Scr, and UAER, along with inflammatory
   cytokines TNF-α and IL-6 in DKD patients. However, the exact molecular
   mechanisms underlying the therapeutic effects of TSPD on DKD remain
   unclear. Further in-depth studies are needed to elucidate the
   mechanistic pathways by which TSPD exerts its nephroprotective effects.

   Network pharmacology is an emerging and popular research field that
   uncovers the complex mechanisms of TCMs by integrating bioinformatics,
   multiomics, and basic biological approaches. It employs multiple drug
   mechanisms that collectively target the same pathogenic signal
   transduction modules, thereby acting synergistically on key network
   proteins. This provides a novel avenue for investigating the
   multitarget, multicomponent effects of TCMs and has been extensively
   employed in the mechanistic studies of a multitude of diseases,
   including DKD. Network pharmacological analysis of TCMs for the
   treatment of DKD has identified a number of individual herbs and
   formulations that influence the progression of DKD, including Cordyceps
   sinensis, Salvia miltiorrhiza, Rhubarb, Shenyan Kangfu Tablets,
   PuRenDan, and Shenxiao Decoction. Nevertheless, to date, there have
   been no reports of network pharmacological research on TSPD for DKD.

   The objective of this study was to elucidate the intricate roles of the
   multifaceted components and targets of TSPD via network pharmacology.
   Furthermore, transcriptomics was employed to identify differential
   genes associated with DKD, with the findings integrated with network
   pharmacology to determine the core targets of TSPD linked to DKD with
   greater precision. Multiple enrichment analyses were subsequently
   conducted to explore the underlying molecular mechanisms, and the main
   compounds and targets were validated through molecular docking
   techniques. Finally, the specific mechanism by which TSPD improves
   fibrosis and inflammation was evaluated and confirmed in DKD rat and
   cell models. This study provides a theoretical basis for the clinical
   application of TSPD in DKD.

2. Materials and Methods

2.1. Screening and Target Prediction of Active Ingredients in TSPD

   The Traditional Chinese Medicine Systems Pharmacology Database and
   Analysis Platform (TCMSP, [41]https://tcmsp-e.com/) and the HERB
   database ([42]http://herb.ac.cn) were used to identify the chemical
   constituents of the 14 TCMs in TSPD. Among them, 11 TCMsTZS, HQ, SZY,
   SDH, SY, YMX, TFL, CX, DS, GJY, and JYZwere identified via the TCMSP
   database. The screening criteria applied in the TCMSP were oral
   bioavailability (OB) ≥30% and drug-likeness (DL) ≥0.18. Since JC, SPX,
   and SZ were not included in the TCMSP database, the HERB database was
   additionally used to retrieve and identify their chemical constituents.
   HERB integrates multiple traditional Chinese medicine databases and
   provides the most comprehensive list of herbs and their components to
   date. For these three ingredients, all the compounds related to JC,
   SPX, and SZ listed in the HERB database were included. This approach
   allowed us to identify the chemical constituents of all 14 ingredients
   in TSPD. The BATMAN-TCM database was subsequently used for target
   prediction, applying a score cutoff of 0.86 (LR = 112.67) and an
   adjusted P value of ≤0.05. The compound-related targets were obtained
   by integrating and deduplicating data from the TCMSP, HERB, and
   BATMAN-TCM databases. All obtained targets were standardized via the
   UniProt database ([43]https://www.uniprot.org/), with species
   restricted to Homo sapiens. Finally, the predicted targets were
   deduplicated and visualized in an ingredient–compound–target network
   via Cytoscape 3.10.2. The degree and radiality metrics were employed to
   identify the key active ingredients of TSPD.

2.2. Differential Expression Analysis of the [44]GSE96804 and [45]GSE99339
Data Sets

   The GEO data sets [46]GSE96804 and [47]GSE99339 were screened to select
   samples from healthy individuals and those with DKD. A total of 75
   samples were included in the study, comprising 20 from healthy
   individuals and 55 from patients with DKD. The “sva” R package was
   employed for standardization and batch effect correction, and the
   “normalizeBetweenArrays” function was utilized for data normalization.
   Following these aforementioned normalization procedures, a
   comprehensive data set was constructed by extracting and combining the
   common genes. Differential expression analysis was conducted via the
   “limma” R package, with filtering conditions of |logFC| > 1 and an
   adjusted p value <0.05. The significant genes were then visualized via
   volcano plots and heatmaps.

2.3. Construction of Protein–Protein Interaction Network of Hub Genes

   Hub genes were identified on the basis of protein–protein interactions
   by intersecting the core target genes of TSPD with the differentially
   expressed genes (DEGs) from the transcriptome data set. The hub genes
   were imported into the STRING database ([48]https://string-db.org/),
   with the species set to “H. sapiens” for analysis. The resulting
   interaction networks were analyzed and visualized via Cytoscape 3.10.2,
   with degree values calculated.

2.4. Gene Function and Pathway Enrichment Analysis

   Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and
   Genomes (KEGG) pathway enrichment analyses were conducted via R
   packages such as “clusterProfiler”, “org.Hs.eg.db”, “Enrichplot”,
   “stringi”, “GOplot”, and “ggplot2”. The enrichGO function was employed
   for GO enrichment analysis, utilizing the org.Hs.eg.db database.
   Similarly, the enrichKEGG function was employed for KEGG enrichment
   analysis, with the KEGG database ([49]https://www.kegg.jp/) serving as
   the reference. A P value of less than 0.05 was employed as the
   screening criterion. If the q value (enrichment score) was less than
   0.05, the corresponding functional terms and pathways were deemed
   statistically significant and retained.

2.5. GSEA and GSVA Analyses

   Gene set enrichment analysis (GSEA) is a comprehensive method for
   evaluating changes in gene sets by considering the enrichment of
   predefined sets across gene expression data. GSEA was conducted via
   data sets from the MSigDB database ([50]www.gsea-msigdb.org), the KEGG
   database, and the Reactome database ([51]https://reactome.org), with
   visualization facilitated by the OmicShare Analysis Platform
   ([52]http://www.omicshare.com/tools). Gene set variation analysis
   (GSVA) offers significant advantages in the context of individualized
   analysis, unsupervised data processing, and the capture of gene
   expression variations. In this study, the GSVA algorithm was employed
   to calculate a comprehensive score for each gene set, thereby enabling
   the assessment of potential biological function changes across
   different samples. For the “c2.cp.kegg_legacy.v2023.2.Hs.symbols.gmt”
   gene set, enrichment was deemed significant when the P value was less
   than 0.05. The GSVA algorithm was used to analyze data sets from
   healthy individuals and those with DKD via the R package “GSVA”.
   Pathways with a P value of less than 0.05 were identified as
   differentially expressed, and heatmaps were generated.

2.6. Molecular Docking Analysis

   The 2D structure files (Mol2 format) of the core active compounds were
   obtained from the TCMSP, and the 3D structure files (SDF format) of the
   core target proteins were retrieved from the Protein Data Bank (PDB)
   ([53]www.rcsb.org). The highest-resolution structure files were
   selected when possible. Molecular docking, dehydration, the removal of
   other heteroatoms, and automated docking visualization were performed
   via CB-Dock2. The binding affinity was evaluated via the AutoDock Vina
   score, which indicates the change in free energy associated with
   ligand–receptor binding. A negative value indicates a stable binding.
   The optimal docking conformation is indicated by the lowest AutoDock
   Vina score (kcal/mol).

2.7. Molecular Dynamics Simulation

   The receptor–ligand complexes obtained by molecular docking were
   further subjected to molecular dynamics (MD) simulations. Prior to the
   simulation, the complexes were preprocessed and separated using PyMOL.
   The General Amber Force Field (GAFF) was applied, and Sobtop was used
   to generate the initial configuration files (GRO files) for all small
   molecules, with ITP files defining the molecular force field
   parameters. The topological files for the proteins (ALB, CCL2, EGF,
   FN1, and PTGS2) were generated by using GROMACS (2020.3-MODIFIED).
   Prior to running the MD simulations, a comprehensive system preparation
   process was performed, including ionic, solvent, receptor, and ligand
   components. This preparation was performed in three main steps. First,
   energy minimization was performed using the steepest descent algorithm
   for 5000 steps to stabilize the system. Next, NVT (constant number of
   atoms, volume, and temperature) equilibration was performed by
   gradually heating the system to 300 K, while positional restraints were
   applied to the receptor and ligand. This phase lasted for 100 ps using
   the leapfrog integrator with a time step of 2 fs to maintain structural
   stability. In the third step, NPT (constant number of atoms, pressure,
   and temperature) equilibration was performed for 100 ps at a constant
   pressure of 1 bar and a temperature of 300 K to ensure system
   stabilization. After these preparatory steps, a 50 ns unconstrained MD
   production run was performed. Postsimulation analysis was performed
   using built-in GROMACS scripts to evaluate root-mean-square deviation
   (rmsd), root-mean-square fluctuation (RMSF), radius of gyration (R
   [g]), and hydrogen-bond interactions, providing insights into the
   dynamic stability and binding interactions of the receptor–ligand
   complexes.

2.8. Medicinal Preparation and Reagents

   TSPD was developed by Professor Yingming Qiu, a nationally recognized
   TCM expert at the Xiamen Hospital of Traditional Chinese Medicine. The
   TSPD preparation consisted of the following drug components and
   dosages: TZS (15 g), HQ (20 g), SZY (10 g), SDH (15 g), SY (15 g), SPX
   (10 g), YMX (30 g), TFL (30 g), CX (15 g), JC (10 g), DS (20 g), GJY
   (15 g), SZ (4 g), and JYZ (15 g). TSPD extracts were prepared according
   to a previously established method. All herbs were provided by
   Pientzehuang Honest (Xiamen, China). All of the herbs met the
   requirements of the Chinese Pharmacopoeia or the relevant herbal
   quality standards. The final concentration of raw TSPD at 100% yield
   was 1 g/mL. Irbesartan (Batch No. 2A386; 150 mg tablets; Sanofi) has
   been shown to delay the progression of DKD and was used as a positive
   control. Primary antibodies, including CCL2 (C–C motif chemokine ligand
   2, Cat. No. ab283927), NF-κB (nuclear factor kappa-B, Cat. No.
   ab76302), TGF-β1 (transforming growth factor beta 1, Cat. No. ab21571),
   and GAPDH (glyceraldehyde-3-phosphate dehydrogenase, Cat. No. ab8245)
   used in animal experiments and secondary antibodies were purchased from
   Abcam. The primary antibodies Phospho-NF-κB p65 (cat. no. 3033) and
   NF-κB (Cat. No. 8242) used in cell experiments were purchased from Cell
   Signaling Technology, and CCL2 (catalog no. ab9669) was purchased from
   Abcam. Secondary antibodies were all purchased from Abcam. The ELISA
   kits were purchased from Beijing Baiaolaibo Technology Co., Ltd.,
   including kits for the detection of rat tumor necrosis factor α (Cat.
   No. ZN2824), rat interleukin-6 (Cat. No. ZN2880), rat interleukin-1β
   (Cat. No. ZN2877), human tumor necrosis factor α (Cat. No. ZN2460),
   human interleukin-6 (Cat. No. ZN2272), and human interleukin-1β (Cat.
   No. ZN2236). UP (Urine Protein, Cat. No. C035-2-1), ALB (Albumin, Cat.
   No. A028-2-1), Cr (Creatinine, Cat. No. C011-2-1), BUN (Cat. No.
   C013-2-1), HbA1c (Glycated hemoglobin, Cat. No. H464-1-2), and INS
   (Insulin, Cat. No. H203-1-2) assay kits were purchased from the Nanjing
   Jiancheng Bioengineering Institute.

2.9. DKD Model Preparation and Experimental Grouping

   These experiments were conducted in accordance with the National
   Institutes of Health (NIH) Guide for the Care and Use of Laboratory
   Animals (National Academies Press, Washington, DC, USA) and Animal
   Research: Reporting of In Vivo Experiments (ARRIVE) Guidelines and were
   approved by the Experimental Animal Ethics Committee of Fujian Medical
   University (Approval No. IACUC FJMU 2024-Y-1267). The experiment
   adhered to the principle of reducing the number of animals used and
   alleviating their discomfort. Sixty healthy male Wistar rats (8–10
   weeks old, weighing 210–230 g) were housed in separate cages with an
   environmental temperature of 20 ± 2 °C, relative humidity of 60 ± 5%,
   noise lower than 85 dB, moderate light, and good ventilation. All rats
   were given 1 week of acclimatization with free access to food and
   water.

   A total of 50 rats were randomly selected for construction of the DKD
   model. To establish the model, 50 rats were randomly selected, and a
   diabetic kidney disease (DKD) model was constructed based on the method
   reported by Zhang et al., with slight modifications. Rats in the model
   group were fed a high-sugar and high-fat diet (HFD) for four
   consecutive weeks, consisting of 78.8% standard chow, 1% cholesterol,
   0.2% bovine bile salt, 10% egg yolk powder, and 10% lard. At the
   beginning of week 4, a single intraperitoneal injection of
   streptozotocin (STZ, 30 mg/kg, dissolved in 0.01 mol/L citrate buffer,
   pH 4.5) was administered. Simultaneously, 5 mL of 50% glucose solution
   was given orally to prevent acute hypoglycemia. Rats in the control
   group received an equivalent volume of citrate buffer by oral gavage.
   After 72 h, blood glucose levels were measured by tail vein blood
   sampling. Rats with fasting blood glucose ≥16.7 mmol/L were considered
   to have successfully established diabetes. At the end of week 4,
   morning urine samples were collected to measure the urinary
   albumin-to-creatinine ratio (UACR). A UACR value of ≥30 mg/g was used
   as the diagnostic threshold for successful DKD model establishment.
   Control rats were maintained on standard chow throughout the
   experiment.

   In this study, all 50 rats initially enrolled for DKD model
   construction were successfully established based on the dual criteria
   of fasting blood glucose ≥16.7 mmol/L and UACR ≥30 mg/g. Subsequently,
   the rats were randomly assigned into six groups (n = 10 per group):
   normal control group (normal), model group (HFD/STZ), positive control
   group (Irbesartan, 25 mg/kg/day), and TSPD-treated groups at low,
   medium, and high doses. The dosages for each group of rats were
   calculated via a body surface area ratio conversion. The medium dose
   was calculated by converting the clinically recommended human daily
   dose of 224 g (crude herbs) for a 60 kg adult to an equivalent rat dose
   (based on 200 g of rat weight), yielding a medium dose of 23.06
   g/kg/day. The low and high doses were defined as 50% and 200% of the
   medium dose, i.e., 11.53 and 46.12 g/kg/day, respectively. All doses
   were administered once daily by oral gavage for 12 weeks. The dosing
   period for all groups is 12 weeks.

2.10. Specimen Collection

   24 h urine samples were collected at the end of the fourth and twelfth
   weeks of the experiment. At the end of the second, fourth, eighth, and
   twelfth weeks, blood glucose levels were quantified via blood
   collection from the tail vein in fasted rats for at least 12 h. At the
   end of the twelfth week, the rats were anesthetized intraperitoneally
   with 5% pentobarbital sodium (40 mg/kg). The abdominal cavity was then
   rapidly opened, and 2–3 mL of blood was extracted from the abdominal
   aorta. The serum was separated by centrifugation at 5000g for 10 min at
   room temperature. The samples were then placed at −80 °C for subsequent
   experiments.

2.11. Testing for Albumin, Creatinine, Urine Protein, Urea Nitrogen, Glycated
Hemoglobin, and Insulin Levels

   The concentrations of urinary albumin (UAlb), urinary creatinine (UCr),
   urine protein (UP), ALB, serum creatinine (Scr), blood urea nitrogen
   (BUN), glycosylated hemoglobin (HbA1c), and insulin (INS) in the urine
   and serum of each group of rats were measured according to the
   instructions in the Nanjing Jiancheng Bioengineering Institute kit.

2.12. HE Staining

   The kidney tissues were fixed in 4% paraformaldehyde solution for 24 h,
   followed by gradient ethanol dehydration and clearing in xylene. The
   samples were then embedded in paraffin and sectioned to a thickness of
   4 μm. After dewaxing and rehydration, the sections were stained with
   hematoxylin for 5–10 min, differentiated with ethanol hydrochloride,
   and subsequently stained with eosin for 1–3 min. The sections were then
   dehydrated in gradient ethanol, cleared in xylene, sealed, dried, and
   observed under a microscope. The glomerular area was quantitatively
   analyzed using ImageJ software based on HE-stained images to evaluate
   glomerular hypertrophy.

2.13. PAS Staining

   The procedure for PAS staining was similar to that for HE staining,
   with the following modifications: after fixation, dehydration,
   clearing, embedding, and sectioning, the sections were oxidized in 0.5%
   periodate solution for 5 min, stained with the Schiff reagent for 15
   min, and then counterstained with hematoxylin for 1 min. The sections
   were subsequently dehydrated in gradient ethanol, cleared in xylene,
   mounted, dried, and observed under a microscope. The mesangial matrix
   expansion was quantified as a PAS-positive area fraction using ImageJ
   software.

2.14. Immunohistochemical Staining

   The kidney tissues were processed in the same manner as those used for
   the HE staining. The sections were then subjected to high-pressure
   antigen retrieval in citrate buffer for 10 min, treated with 3%
   hydrogen peroxide for 10 min, and blocked with 5% sheep serum for 30
   min. The sections were incubated with primary antibody overnight,
   washed with phosphate-buffered saline (PBS), and incubated with a
   secondary antibody for 30 min. The color was developed via DAB, and
   hematoxylin was used for counterstaining. Gradient ethanol was used for
   dehydration, and xylene was used for clearing. The sections were then
   sealed, dried, and observed under a microscope. The positive area
   fractions of TGF-β1 and CCL2 were calculated using ImageJ software to
   evaluate the intensity and extent of the immunohistochemical staining.

2.15. Western Blot Analysis

   The total protein content of the kidney tissue and cell samples was
   extracted and quantified by the BCA method. Equal quantities of protein
   were separated by SDS-PAGE and transferred to a PVDF membrane. The
   membrane was then incubated with 5% skim milk powder for 1 h. The
   membranes were incubated with the primary antibody overnight, washed
   with TBST, and then incubated with the secondary antibody for 1 h. The
   color was developed and detected by imaging with an enhanced
   chemiluminescence (ECL) reagent. Band intensities were quantified using
   ImageJ software, and the relative expression levels of target proteins
   were normalized to GAPDH. Each experiment was performed in triplicate,
   and detailed original images are provided in [54]Figure S1A,B.

2.16. Cell Culture and Treatment

   The HK-2 cell line was purchased from ATCC. The medium was DMEM/F12
   (Gibco, Cat. No. 11320033) supplemented with 10% FBS (Gibco, Cat. No.
   11320033 and No. A5670701) and 1% penicillin/streptomycin (Gibco, Cat.
   No. 15140148). The cells were stimulated for 24 h in a high-glucose
   environment of 30 mmol/L to simulate the stress response induced by
   high glucose, with mannitol (Beyotime, Cat. No. ST2362) used as an
   osmotic control to prevent hyperosmolarity. To study the effects of
   CCL2 on HK-2 cells, Lipofectamine 3000 (Invitrogen, Cat. No. L3000015)
   dissolved in a serum-free medium was used for transfection. The cells
   were either silenced or overexpressed with si-CCL2 (MedChemExpress,
   cat. no. HY-RS02089) or pCMV-CCL2 (MiaoLing Biology, Cat. No.
   [55]P39235). TSPD was prepared at concentrations of 0.001, 0.01, 0.05,
   0.1, and 1 g/mL, and the intervention lasted for 12 h. The results
   revealed that the optimal concentration was 0.1 g/mL.

2.17. RNA Extraction and Quantitative Real-Time PCR

   The total RNA was extracted from HK-2 cells via the TRIzol reagent
   (Invitrogen, Cat. No. 15596018), and the A260/A280 ratio was between
   1.8 and 2.0. One microgram of total RNA was reverse transcribed into
   cDNA via a cDNA synthesis kit (Thermo Fisher Scientific, Cat. No.
   4368814). qPCR was performed via SYBR Green qPCR Master Mix (Thermo
   Fisher Scientific, Cat. No. 4309155). The CCL2 sequences of primers
   used were as follows: forward primer (5′–3′): ACCAGCAGCAAGTGTCCCAAAG;
   reverse primer (5′–3′): TTTGCTTGTCCAGGTGGTCCATG. The GAPDH sequences of
   primers used were as follows: forward primer (5′–3′):
   ATGCCTCCTGCACCACCAACT; reverse primer (5′–3′):
   ATGGCATGGACTGTGGTCATGAGT. Data analysis was performed via the 2^–ΔΔCt
   method with GAPDH as the internal reference gene. Each sample was run
   in three biological replicates and three technical replicates.

2.18. ELISA Analysis

   The supernatant of the treated HK-2 cell culture was collected and
   centrifuged (1000g, 10 min) to remove the cell debris. The supernatant
   was stored at −80 °C until assayed. The manufacturer’s instructions
   were followed to assay and calculate the concentration of the target
   protein in the sample.

2.19. Statistical Analysis

   Bioinformatics analyses and data visualization were performed using R
   (version 4.3.2), while experimental data visualization was conducted
   with Prism (version 10.0, GraphPad Software). Statistical analyses were
   carried out using Prism, with results presented as mean ± standard
   deviation (SD). For comparisons between multiple groups, one-way
   analysis of variance (ANOVA) was performed to assess overall
   differences across the groups. Post hoc multiple comparisons were
   applied to identify specific group differences. A significance
   threshold of P < 0.05 was applied to determine statistical
   significance.

3. Results

3.1. Candidate Active Compounds in TSPD and Their Corresponding Targets

   A combination of the TCMSP and HERB databases was used to identify the
   compounds related to the 14 TCMs in the TSPD. A total of 1187 compounds
   were obtained in the TSPD, including 25 in TZS, 87 in HQ, 226 in SZY,
   76 in SDH, and 71 in SY. Additionally, 29 compounds were found in SPX,
   97 in YMX, 74 in TFL, 189 in CX, 4 in JC, 202 in DS, 24 in GJY, 35 in
   SZ, and 48 in JYZ. Since there were overlapping compounds among the 14
   TCMs, a total of 248 unique active compounds were selected for further
   analysis after removing duplicates ([56]Figure A). Further details on
   the active chemical components of all of the herbs can be found in
   [57]Table S1. Additionally, 649 targets of TSPD were identified based
   on the target predictions from three databases, with duplicates removed
   for each herb, as shown in [58]Table S2. The ingredient–compound–target
   network was constructed via Cytoscape ([59]Figure B). Among the
   identified compounds, those with the most interactions across multiple
   targets were quercetin (MOL000098), luteolin (MOL000006), kaempferol
   (MOL000422), baicalin (MOL002776), and tanshinone IIA (MOL007154).
   These compounds were identified as the key active constituents of TSPD
   with potential therapeutic effects on DKD.

1.

   [60]1
   [61]Open in a new tab

   TCMs, ingredients, and targets of TSPD. (A) Sankey diagram illustrating
   the relationship between the herbs and compounds of TSPD. (B) Network
   pharmacological analysis depicting the herb-compound-target network.
   The yellow nodes represent the 14 TCMs in TSPD, the orange nodes
   indicate the core compounds associated with TSPD, and the green nodes
   indicate the targets linked to TSPD.

3.2. Validation of Genes Associated with TSPD via the GeneCards and GEO DKD
Data Sets

   To identify key genes related to TSPD and DKD, two transcriptome data
   sets, [62]GSE96804 and [63]GSE99339, were obtained from the GEO
   database to analyze differentially expressed genes (DEGs) between DKD
   patients and healthy individuals ([64]Table S3). First, the data sets
   were normalized to ensure comparability, and the results are shown in
   [65]Figure A,B. Differential gene analysis identified 188 DEGs,
   including 76 upregulated and 112 downregulated genes. These DEGs were
   then visualized via volcano plots ([66]Figure C) and heatmaps
   ([67]Figure D). To further refine the analysis, we identified 15,174
   DKD-related targets by searching the GeneCards database. By
   intersecting the drug targets, DKD DEGs, and GeneCards DKD-related
   targets, 21 central genes were identified ([68]Figure E). A
   protein–protein interaction (PPI) network analysis of these 21 genes
   was then performed via the STRING database, and the results were
   visualized by degree via Cytoscape ([69]Figure F). This analysis
   identified ALB, CCL2, EGF, FN1, and PTGS2 as core targets associated
   with DKD.

2.

   [70]2
   [71]Open in a new tab

   Differential gene expression, hub gene screening, and PPI analysis.
   (A,B) Box plots of gene expression normalization in control and DKD
   groups. (C) Volcano plot of DEGs (|log[2]FC| > 1, p < 0.05). (D)
   Heatmap of the top 40 DEGs, with colors indicating expression levels.
   (E) Venn diagram of TSPD targets (orange), DKD_GeneCards DEGs (green),
   and DKD DEGs (blue). (F) PPI network of 21 hub genes, with nodes
   representing genes and edges indicating interactions.

3.3. GO Biological Process and KEGG Pathway Analyses of Hub Genes

   To gain a deeper understanding of the molecular mechanisms involved in
   DKD, GO and KEGG enrichment analyses were performed on the hub genes
   ([72]Table S4). The results of the GO analysis, as illustrated in
   [73]Figure A, indicated significant enrichment in biological processes
   (BPs), cellular components (CCs), and molecular functions (MFs). The BP
   category was associated primarily with metabolic processes and stress
   responses, whereas the CC category was linked to the extracellular
   space, vesicles, and membrane-related structures. The MF category
   included activities such as fatty acid binding, antioxidant activity,
   and catalytic activity. KEGG analysis ([74]Figure B) revealed that the
   DEGs were enriched in several key metabolic and signaling pathways,
   including amino acid metabolism, drug metabolism, cell signaling, and
   inflammatory response.

3.

   [75]3
   [76]Open in a new tab

   GO and KEGG analyses of hub genes. (A,B) Chord diagrams of GO (A) and
   KEGG (B) analyses, showing gene counts, enrichment, and pathway
   categories. (C,E) Bubble plots of GO analysis for the top 15
   downregulated (C) and upregulated (E) hub genes. (D,F) Sankey diagrams
   of KEGG pathways for the top 15 downregulated (D) and upregulated (F)
   hub genes.

   For the downregulated genes ([77]Figure C,D), the GO analysis revealed
   that BPs were involved mainly in amino acid metabolism, cellular ketone
   metabolism, and oxidative detoxification. The CC category included
   structures such as peroxisomes and secretory granules, whereas the MF
   category included fatty acid binding, antioxidant activity, and vitamin
   B6 binding. KEGG pathway analysis revealed enrichment of pathways, such
   as amino acid metabolism, drug metabolism, the PPAR signaling pathway,
   cytochrome P450-mediated metabolism of exogenous substances, the IL-17
   signaling pathway, and the TNF signaling pathway.

   The analysis of the upregulated genes ([78]Figure E,F) revealed that
   BPs were primarily involved in processes such as leukocyte chemotaxis,
   cytotaxis, and apoptotic cell clearance. The CC category focused on
   structures such as the collagen-containing extracellular matrix and
   lateral plasma membranes, whereas the MF category included chemokine
   activity and cell-to-cell adhesion mediator activity. KEGG analysis
   revealed enrichment of pathways, such as the NF-κB signaling pathway,
   the AGE–RAGE signaling pathway, the IL-17 signaling pathway, and the
   TNF signaling pathway. This study highlights the complex molecular
   processes underlying DKD, particularly the roles of metabolic
   regulation and inflammatory response.

3.4. GSEA and GSVA Analyses of Hub Genes

   To gain a comprehensive understanding of the molecular mechanisms of
   DKD, GSEA and GSVA were performed on whole transcriptome expression
   matrices. GSEA results, as shown in [79]Figure A–F, revealed a
   significant enrichment of several pathways and BP. Specifically, the
   GSEA-REACTOME analysis ([80]Figure A) revealed enrichment in
   lipoprotein assembly, mitochondrial translation, and metabolic
   transport. GSEA-KEGG analysis ([81]Figure B) revealed significant
   enrichment of the IL-1 pathway, MAPK pathway, ERK pathway, and JNK
   pathway in DKD. In addition, GSEA-GOBP, GOMF, and GOCC analyses
   ([82]Figure C–E) revealed significant alterations in BP, MFs, and CC,
   including amino acid metabolism, redox processes, and cellular membrane
   components. GSEA hallmark analysis ([83]Figure F) revealed a
   significant enrichment of pathways related to inflammatory response,
   immune regulation, and metabolic processes in DKD, and GSVA analysis
   ([84]Figure G) revealed distinct pathway activity patterns between DKD
   and control samples, further confirming the involvement of metabolic
   regulation and inflammatory signaling in the pathogenesis of DKD.
   Additional details are provided in [85]Table S5. These findings are
   consistent with the results of GO and KEGG analyses, reinforcing the
   critical role of metabolic dysregulation and inflammatory activation in
   DKD progression and the potential therapeutic targets.

4.

   [86]4
   [87]Open in a new tab

   GSEA and GSVA analyses of all genes. (A–F) GSEA of REACTOME (A), KEGG
   (B), GOBP (C), GOCC (D), GOMF (E), and Hallmark (F) data sets, showing
   the top 10 enriched pathways (p < 0.05). (G) Heatmap of GSVA results
   for significantly enriched genes (p < 0.05).

3.5. Molecular Docking of Key Compounds and Targets

   To explore the molecular mechanisms by which TCM exerts therapeutic
   effects on DKD, molecular docking was conducted between five core
   targets (ALB, CCL2, EGF, FN1, and PTGS2) and five key TCM active
   ingredients (luteolin, quercetin, kaempferol, baicalin, and tanshinone
   IIA). The complete set of docking data is provided in [88]Table S6. The
   heatmap results revealed that all of the active ingredients exhibited
   stable binding interactions with their respective core targets, with
   Vina scores below −7 kcal/mol ([89]Figure A). Notably, luteolin had
   binding energies of −9.9 kcal/mol with ALB and −10.1 kcal/mol with
   CCL2. The highest binding energy was observed for quercetin with CCL2,
   with a value of −11.0 kcal/mol. Kaempferol exhibited a binding energy
   of −10.6 kcal/mol with CCL2, whereas baicalin had a binding energy of
   −11.0 kcal/mol with ALB. Tanshinone IIA had the highest binding energy
   of −12.2 kcal/mol with ALB. The specific molecular docking results are
   illustrated in [90]Figure , including those for ALB with tanshinone IIA
   ([91]Figure B), CCL2 with quercetin ([92]Figure C), EGF with luteolin
   ([93]Figure D), FN1 with baicalin ([94]Figure E), and PTGS2 with
   kaempferol ([95]Figure F). These results demonstrated that the core
   active components of TSPD have strong binding affinities with target
   proteins, validating the network pharmacology approach and suggesting
   that TSPD may ameliorate DKD by modulating the inflammatory response.

5.

   [96]5
   [97]Open in a new tab

   Molecular docking of core compounds and targets. (A) Heatmap of docking
   results for five core compounds and five core targets, represented by
   the AutoDock Vina scores. (B) Molecular docking visualization of ALB
   with tanshinone IIA. (C) Visualization of the docking of CCL2 with
   quercetin. (D) Docking visualization of EGF with luteolin. (E) Docking
   visualization of FN1 with baicalin. (F) Docking visualization of PTGS2
   with kaempferol.

3.6. Molecular Dynamics Simulation Analysis of Key Compounds and Targets

   Molecular dynamics simulations were conducted to further validate the
   binding stability of key compounds with the core targets. RMSD analysis
   ([98]Figure A) showed that ALB-Baicalin remained stable throughout
   10–50 ns, while ALB-tanshinone IIA exhibited fluctuations near 40 ns.
   CCL2-quercetin remained stable across the full simulation, whereas
   CCL2-kaempferol was stable within 10–30 ns with minor fluctuations
   afterward. FN1-tanshinone IIA reached stability after 20 ns but showed
   slight variations. RMSF analysis ([99]Figure B) revealed that ALB and
   CCL2 maintained minimal structural fluctuations, whereas FN1 exhibited
   notable variations, indicating lower stability. Radius of gyration
   ([100]Figure C) indicated that ALB-baicalin and ALB-tanshinone IIA
   maintained stable compactness, while CCL2-quercetin and CCL2-kaempferol
   remained stable throughout, with FN1-tanshinone IIA showing minor
   fluctuations. Hydrogen-bond analysis ([101]Figure D) confirmed that
   ALB-baicalin and CCL2-quercetin maintained 1–5 hydrogen bonds
   throughout the simulation, while CCL2-kaempferol exhibited more dynamic
   hydrogen-bonding interactions. However, the EGF-tanshinone IIa and
   PTGS2-tanshinone IIa complexes exhibited poor stability, as detailed in
   [102]Figure S2A–D.

6.

   [103]6
   [104]Open in a new tab

   Molecular dynamics simulation analysis of key compounds and targets.
   (A) RMSD plots of backbone atoms over simulation time for each key
   compound–target complex. (B) RMSF plots showing residue-level
   flexibility of the target proteins. (C) Radius of gyration (R [g])
   plots depicting the overall compactness of the target–compound
   complexes along different axes. (D) Hydrogen-bond analysis displaying
   the number of hydrogen bonds formed between key compounds and targets
   during the simulation. The pink areas indicate the proportion of
   hydrogen bonds with a donor–acceptor distance ≤3.5 Å.

3.7. Comparison of the General Conditions of DKD Rats

   The rats in the experimental group exhibited a normal appetite, regular
   water intake, frequent activity, normal mental status, strong muscle
   tone, glossy fur, and normal urination and defecation. In contrast, the
   model group of rats presented symptoms such as emaciation, polyuria,
   polydipsia, polyphagia, reduced activity, depressed spirit, loose
   stools, and dull fur. In the treatment groups, including the irbesartan
   group and the low-, medium-, and high-dose traditional Chinese medicine
   groups, symptoms improved, although the mental state was still somewhat
   impaired, compared with that of the normal group.

3.8. Construction of the DKD Rat Model and the Effect of TSPD on Urinary
Albumin and Creatinine Levels

   To evaluate the therapeutic effects of TSPD on DKD, a DKD rat model
   (HFD/STZ) was successfully established, as evidenced by significant
   changes in UAlb, UCr, and UACR compared to the normal group at both
   weeks 4 and week 12. UAlb levels were significantly elevated in the
   HFD/STZ group, indicating glomerular dysfunction. Treatment with
   irbesartan or TSPD at low, medium, and high doses significantly reduced
   UAlb levels in a dose-dependent manner, with substantial improvements
   observed by week 12 ([105]Figure A,B). UCr levels, which were reduced
   in the HFD/STZ group, reflecting renal tubular dysfunction, showed a
   partial recovery after treatment. By week 12, UCr levels in the TSPD
   and irbesartan groups approached those of the normal group, indicating
   improvement ([106]Figure C,D). Similarly, UACR, a critical indicator of
   renal function, was significantly elevated in the HFD/STZ group but was
   significantly reduced after TSPD treatment, with high-dose TSPD
   achieving effects comparable to those of irbesartan ([107]Figure E,F).
   These results demonstrate that TSPD ameliorates DKD-related renal
   dysfunction by improving UAlb, UCr, and UACR levels.

7.

   [108]7
   [109]Open in a new tab

   Urinary levels of Alb, Cr, and UACR in different groups of rats. (A)
   UAlb levels at week 4. (B) UAlb levels at week 12. (C) UCr levels at
   week 4. (D) UCr levels at week 12. (E) UACR values at week 4. (F) UACR
   values at week 12. Statistical significance is indicated as follows: *p
   < 0.05, **p < 0.01, and ***p < 0.001 represent significant differences
   between the HFD/STZ and Normal groups. #p < 0.05, ##p < 0.01, and ###p
   < 0.001 indicate significant differences between all treatment groups
   and the HFD/STZ group. †p < 0.05 signifies significant differences
   between the medium-dose and high-dose groups compared to the low-dose
   group.

3.9. Effects of TSPD on the Renal Hypertrophy Index and Blood Functional
Indices in the DKD Rat Model

   To evaluate the effects of TSPD on the renal function and metabolic
   status in DKD rats, the renal hypertrophy index, UP levels, and blood
   functional indices were measured. The results revealed a significant
   reduction in the renal hypertrophy index in the irbesartan group and
   the medium- and high-dose TSPD groups compared with the model group (P
   < 0.05), indicating alleviation of renal hypertrophy ([110]Figure A).
   The levels of UP, an indicator of renal damage, were significantly
   elevated in the DKD model group. Treatment with irbesartan and TSPD at
   all doses markedly reduced UP levels, particularly in the high-dose
   group (P < 0.05), suggesting improved renal function ([111]Figure B).
   Blood glucose levels were notably reduced in the irbesartan- and
   TSPD-treated groups, with the high-dose TSPD group showing the most
   significant improvement ([112]Figure C). Elevated INS levels in the DKD
   model group were significantly decreased by TSPD treatment, especially
   at medium and high doses (P < 0.05, [113]Figure D). Similarly, HbA1c
   levels decreased in the high-dose TSPD group, although this reduction
   was not statistically significant in the low- and medium-dose groups
   ([114]Figure E). Scr and BUN, key indicators of renal function, were
   significantly elevated in the DKD model group. Treatment with
   irbesartan and TSPD led to a significant reduction in the Scr level (P
   < 0.05), particularly in the high-dose group ([115]Figure F). Although
   BUN levels were not significantly reduced in the low- and medium-dose
   TSPD groups, a notable decrease was observed in the high-dose group (P
   < 0.05), indicating an improvement in the renal function ([116]Figure
   G). Therefore, TSPD, especially at higher doses, has a significant
   nephroprotective effect by reducing renal hypertrophy, lowering UP
   levels, and improving blood glucose and INS, Scr, and BUN levels.

8.

   [117]8
   [118]Open in a new tab

   Renal hypertrophy indices, blood glucose changes, and blood biochemical
   indices in different groups of rats. (A) Histogram of the renal
   hypertrophy index among the different groups. (B) Histogram of UP among
   the different groups. (C) Line graph of blood glucose changes up to
   week 12 in different groups. (D) Histogram of insulin levels among the
   different groups. (E) Histogram of HbA1c among the groups. (F)
   Histogram of Scr among the different groups. (G) Histogram of BUN among
   the different groups. Statistical significance is indicated as follows:
   *p < 0.05 and **p < 0.01 represent significant differences between the
   HFD/STZ and Normal groups. #p < 0.05 and ##p < 0.01 indicate
   significant differences between all treatment groups and the HFD/STZ
   group. †p < 0.05 signifies significant differences between the
   medium-dose and high-dose groups compared to the low-dose group.

3.10. TSPD Exerts a Renoprotective Effect by Inhibiting Renal Fibrosis and
the Inflammatory Response

   To explore the therapeutic effects of TSPD on renal fibrosis and
   inflammation, morphological changes in renal tissues were examined via
   HE and PAS staining. HE staining revealed intact glomeruli and renal
   tubules in the normal group, whereas the model group presented
   pronounced glomerular hypertrophy, an increased extracellular matrix,
   and mesangial cell proliferation. Treatment with irbesartan and various
   doses of TSPD significantly alleviated these pathological changes, with
   the high-dose TSPD group showing the most marked reduction in
   glomerular hypertrophy and extracellular matrix proliferation
   ([119]Figure A). PAS staining further revealed a marked increase in
   glycogen deposition, mesangial expansion, and basement membrane
   thickening in the model group. All of the treatment groups, with the
   exception of the low-dose TSPD group, improved these pathological
   changes, indicating that TSPD inhibits glycogen deposition and renal
   tubular dilation ([120]Figure A).

9.

   [121]9
   [122]Open in a new tab

   Histopathological staining, immunohistochemistry, and Western blot
   analysis in different rat groups. (A) HE and PAS staining of the
   different groups (400× magnification). (B) IHC of TGF-β1 and CCL2 in
   different groups (400× magnification). (C) Quantification of the
   glomerular area (HE). (D) Quantification of the PAS-positive area
   fraction (PAS). (E) Quantification of the TGF-β1-positive area fraction
   (IHC). (F) Quantification of the CCL2-positive area fraction (IHC). (G)
   Western blot analysis of NF-κB, TGF-β1, and CCL2. (H) ELISA for TNF-α
   in the rat serum. (I) ELISA for IL-6 in the rat serum. (J) ELISA for
   IL-1β in the rat serum. Statistical significance is indicated as
   follows: *p < 0.05 and **p < 0.01 represent significant differences
   between the HFD/STZ and Normal groups. #p < 0.05 and ##p < 0.01
   indicate significant differences between all treatment groups and the
   HFD/STZ group. †p < 0.05 signifies significant differences between the
   medium-dose and high-dose groups compared to the low-dose group.

   On the basis of network pharmacology, enrichment analysis, and a
   literature review, the CCL2 and NF-κB signaling pathways were selected
   to elucidate the mechanisms underlying the therapeutic effects of TSPD
   on DKD. Immunohistochemical analysis revealed a significant reduction
   in the expression of CCL2 and TGF-β1 in the renal tissues of the
   treatment groups compared with the model group, with the high-dose TSPD
   group exhibiting the most pronounced inhibitory effect ([123]Figure B).
   The quantitative results of HE, PAS, and IHC staining are shown in
   [124]Figure C–F. Western blot analysis further confirmed a substantial
   decrease in the protein expression of CCL2, NF-κB, and TGF-β1 in the
   treatment groups, with the high-dose TSPD group showing the most
   prominent effect ([125]Figure G). Additionally, ELISA revealed that
   TSPD treatment significantly reduced the levels of the inflammatory
   factors TNF-α, IL-6, and IL-1β in the rat serum, particularly in the
   high-dose group ([126]Figure H–J). These findings indicate that TSPD
   effectively inhibits the expression of CCL2 and NF-κB, reduces renal
   fibrosis, and decreases inflammation, thereby exerting a therapeutic
   effect on DKD.

3.11. TSPD Attenuates the Inflammatory Response via Modulation of the
CCL2/NF-κB Signaling Pathway

   To investigate the molecular mechanism of TSPD in DKD, it was
   hypothesized that TSPD ameliorates DKD by regulating the CCL2/NF-κB
   signaling pathway, thereby alleviating the inflammatory response. HK-2
   cells were subjected to high-glucose conditions, and CCL2
   overexpression and knockdown were implemented, followed by TSPD
   intervention. The experimental groups were as follows: Control
   (Control), High Glucose (HG), High Glucose + CCL2 Overexpression (HG +
   oe-CCL2), High Glucose + CCL2 Silencing (HG+si-CCL2), High Glucose +
   TSPD (HG+TSPD), High Glucose + CCL2 Overexpression + TSPD (HG + oe-CCL2
   + TSPD), and High Glucose + CCL2 Silencing + TSPD (HG + si-CCL2 +
   TSPD). First, the CCL2 mRNA expression was evaluated via qPCR
   ([127]Figure A,B). The results revealed a significant increase in CCL2
   expression in the HG group compared with the control group, suggesting
   that high glucose induces CCL2 upregulation. TSPD treatment
   significantly suppressed this upregulation. In the HG + oe-CCL2 group,
   CCL2 mRNA levels were further increased; however, TSPD intervention (HG
   + oe-CCL2 + TSPD) effectively reversed this increase. In contrast, CCL2
   mRNA expression was decreased in the HG + si-CCL2 group, and TSPD
   treatment further increased this decrease (HG + si-CCL2 + TSPD). These
   findings suggest that TSPD inhibits CCL2 expression in a high-glucose
   environment.

10.

   [128]10
   [129]Open in a new tab

   TSPD modulates the CCL2/NF-κB signaling pathway and reduces
   inflammatory cytokine expression in different treatment groups. (A)
   Relative expression of CCL2 mRNA in the control, high glucose (HG), HG
   + TSPD, HG + oe-CCL2, and HG + oe-CCL2 + TSPD groups, as determined by
   quantitative PCR. (B) Relative expression of CCL2 mRNA in the control,
   HG, HG + TSPD, HG + si-CCL2, and HG + si-CCL2 + TSPD groups, as
   assessed by qPCR. The data are presented as the means ± SDs, with *p <
   0.05 indicating statistically significant differences between groups.
   (C) Western blot analysis of p-NF-κB p65, NF-κB p65, and CCL2 protein
   levels across different treatment groups, with GAPDH used as a loading
   control. The adjacent bar graph presents the quantitative analysis of
   protein expression. The data are expressed as the means ± SDs. (D–F)
   ELISA results showing the concentrations of TNF-α (D), IL-6 (E), and
   IL-1β (F) in the supernatants of different treatment groups. The data
   are expressed as the means ± SDs. Statistical significance is indicated
   as follows: *p < 0.05, **p < 0.01, and ***p < 0.001 denote significant
   differences between the HG group and the control group. ^p < 0.05 and
   ^^p < 0.01 represent differences before and after TSPD treatment. #p <
   0.05 and ##p < 0.01 indicate differences between HG + oe-CCL2 or HG +
   si-CCL2 groups and the HG group before and after treatment. †p < 0.05
   denotes significant differences in the HG + oe-CCL2 and HG + si-CCL2
   groups before versus after treatment.

   The activation of the NF-κB signaling pathway was subsequently assessed
   via Western blot analysis ([130]Figure C). Compared with the control
   group, the HG group presented significant activation of NF-κB
   phosphorylation and increased p-NF-κB p65 and CCL2 protein levels. TSPD
   treatment markedly reduced these protein levels. The overexpression of
   CCL2 in the HG + oe-CCL2 group led to further upregulation of p-NF-κB
   and CCL2, which was significantly attenuated by TSPD intervention (HG +
   oe-CCL2 + TSPD). Conversely, CCL2 silencing in the HG + si-CCL2 group
   substantially reduced p-NF-κB and CCL2 expression, and TSPD treatment
   (HG + si-CCL2 + TSPD) further potentiated this inhibitory effect. These
   results indicate that TSPD downregulates CCL2 expression and inhibits
   NF-κB activation, thereby modulating the CCL2/NF-κB signaling pathway.

   Finally, the levels of inflammatory cytokines in the cell supernatant
   were measured via ELISA ([131]Figure D–F). In the HG group, the levels
   of TNF-α, IL-6, and IL-1β were significantly increased, whereas TSPD
   treatment significantly reduced their expression. In the HG + oe-CCL2
   group, the secretion of TNF-α, IL-6, and IL-1β was further increased
   but this increase was significantly mitigated by TSPD intervention (HG
   + oe-CCL2 + TSPD). Similarly, in the HG + si-CCL2 group, the levels of
   these cytokines were significantly decreased and TSPD treatment (HG +
   si-CCL2 + TSPD) further enhanced this inhibitory effect. These findings
   demonstrate that TSPD significantly reduces the level of secretion of
   inflammatory factors by modulating the CCL2/NF-κB signaling pathway.

4. Discussion

   DKD is a major complication of diabetes and a leading cause of
   end-stage renal disease (ESRD). Its progression can lead to a loss of
   kidney function, necessitating long-term dialysis or kidney
   transplantation. Improving therapeutic strategies for DKD is,
   therefore, crucial to prevent disease progression and alleviate patient
   burden. TCM has demonstrated unique efficacy in treating various
   diseases, and TSPD has shown significant clinical efficacy in managing
   DKD, particularly in its early stages. However, the precise therapeutic
   mechanisms of TSPD have been inadequately explored. This study
   integrated network pharmacology, transcriptomics, and molecular docking
   to elucidate the multitarget mechanisms of TSPD in treating DKD.
   Various enrichment analyses have indicated that TSPD can significantly
   ameliorate the pathological processes of DKD by modulating oxidative
   stress and inflammatory responses. In vivo and in vitro experiments
   further demonstrated that TSPD exerts its therapeutic effects by
   inhibiting the activation of the CCL2/NF-κB signaling pathway, thereby
   reducing inflammation and improving DKD pathology. These findings
   provide an initial understanding of the mechanisms underlying the
   efficacy of TSPD in DKD.

   TSPD therapy for DKD has been used in clinical practice for more than
   two decades. According to the TCM theory, the fundamental pathogenesis
   of DKD involves Qi and Yin deficiencies and internal stasis of dampness
   and turbidity. The treatment principle involves invigorating Qi,
   activating blood circulation, and dispelling dampness. TSPD is
   formulated on the basis of these principles, with the aim of
   simultaneously addressing the root cause and symptoms of the disease
   simultaneously. The formula includes Huangqi and Shudihuang to tonify
   the spleen and kidneys and Danshen and Chuanxiong to promote blood
   circulation and remove stasis. This approach aligns with the
   pathological characteristics of DKD, providing a theoretical foundation
   for the clinical application of the TSPD.

   Based on modern pharmacological investigations, this study further
   elucidated the potential therapeutic roles of the core active compounds
   in TSPD and their herbal sources in the treatment of DKD. In this
   study, 248 compounds in TSPD that interact with 649 targets were
   obtained through the TCMSP, HERB, and BATMAN-TCM databases, revealing
   the complex pharmacological network of TSPD. Core compounds such as
   quercetin, luteolin, kaempferol, baicalin, and tanshinone IIA exhibited
   significant interactions with multiple targets. These key compounds are
   primarily derived from the herbs GJY, HQ, JYZ, TFL, DS, TZS, and YMX
   included in the TSPD. Among them, HQ, a widely used Qi-tonifying herb
   in traditional Chinese medicine, has been reported to significantly
   improve albuminuria, proteinuria, and serum creatinine levels in DKD
   patients. Its bioactive constituents, such as quercetin, kaempferol,
   and baicalin, have demonstrated anti-inflammatory, antioxidant, and
   antifibrotic properties in multiple studies. In addition, preclinical
   studies have shown that GJY can ameliorate renal injury in DKD models
   by downregulating TGF-β1 expression. DS, which contains a high
   concentration of tanshinone IIA, has been shown to exert renoprotective
   effects by regulating the TGF-β/Smad and PI3K/Akt/FoxO signaling
   pathways, thereby inhibiting glomerulosclerosis and interstitial
   fibrosis. Furthermore, JYZ and TFL may act synergistically to support
   the therapeutic efficacy of TSPD. In addition, previous studies have
   also revealed the critical roles of the core active components of TSPD
   in the treatment of DKD. For instance, quercetin exerts
   nephroprotective effects by binding to PPARα to alleviate kidney
   injury. Luteolin reduces inflammation and oxidative stress by
   inhibiting the STAT3 pathway. Kaempferol mitigates apoptosis and
   promotes autophagy via the AMPK/mTOR pathway. Baicalin attenuates
   oxidative stress and inflammatory responses in DKD through the Nrf2 and
   MAPK pathways. Tanshinone IIA ameliorates diabetes-induced renal
   fibrosis by modulating the miR-34-5p/Notch1 axis. These findings
   highlight oxidative stress and inflammation as critical pathological
   mechanisms in chronic kidney disease (CKD), with these compounds
   exerting therapeutic effects through the modulation of these pathways.
   Molecular docking analysis further revealed that these compounds have
   binding energies below −7 kcal/mol with key targets, such as ALB, CCL2,
   EGF, FN1, and PTGS2, indicating stable interactions with these
   proteins. Evidence from previous studies, together with the findings of
   network pharmacology and molecular docking in this study, confirmed the
   stable binding interactions between the core active compounds of TSPD
   and key DKD-related targets, thereby providing further support for the
   therapeutic mechanism of TSPD through a multicomponent, multitarget
   synergistic regulatory network.

   To explore the mechanisms underlying the therapeutic effects of TSPD on
   DKD, the GeneCards database and transcriptomic differential gene
   analysis were utilized to identify 21 hub genes associated with TSPD
   and DKD. KEGG, GSEA, and GSVA pathway analyses revealed significant
   enrichment of these genes in the AGE–RAGE, NF-κB, IL-17, and TNF
   pathways, suggesting that these pathways play crucial roles in DKD
   pathogenesis. These signaling pathways have been extensively studied in
   the context of DKD pathophysiology and treatment. − It is hypothesized
   that TSPD exerts its therapeutic effects by modulating these pathways,
   thereby reducing inflammation and fibrosis. GO analysis further
   revealed significant enrichment of TSPD in metabolic processes,
   oxidative stress, and inflammatory responses, suggesting that TSPD may
   intervene in the key pathological processes of DKD through multitarget
   and multipathway regulation.

   Through molecular docking analyses and molecular dynamics simulations,
   CCL2 was identified as a key target of TSPD. CCL2 is shown to bind well
   to all of the core components by molecular docking. Notably, CCL2
   maintained overall structural stability throughout the simulation, with
   minimal fluctuations upon ligand binding, supporting its role as a key
   and stable target in DKD. The strong and sustained interactions of
   CCL2-quercetin and CCL2-kaempferol, along with the preservation of
   CCL2’s structural integrity, further emphasize its therapeutic
   relevance in TSPD-mediated renal protection. These findings suggest
   that CCL2 plays a crucial role in DKD pathogenesis, and its targeted
   regulation via active compounds such as quercetin and kaempferol may
   underlie the anti-inflammatory effects of TSPD. Renal inflammation
   plays a crucial role in the onset and progression of DKD, and
   anti-inflammatory therapy holds promise for renoprotection in DKD.
   Studies have shown that CCL2 expression is significantly increased in
   DKD, leading to the accumulation of inflammatory cells in renal tissues
   and further aggravating kidney damage. Other studies have demonstrated
   that the downregulation of HMGN1 in diabetic kidneys attenuates tubular
   cell injury and protects against renal inflammation by suppressing CCL2
   and KIM-1 expression through the TLR4 pathway. Moreover, enrichment
   analysis highlighted the importance of the NF-κB signaling pathway.
   Activation of the NF-κB and TGF-β1 pathways in DKD leads to increased
   expression of proinflammatory genes, including cytokines, chemokines,
   and adhesion molecules, promoting extracellular matrix accumulation in
   glomerular and tubular tissues and contributing to renal fibrosis. ,
   Therefore, the focus was placed on the CCL2 and NF-κB signaling
   pathways to elucidate the molecular mechanisms by which TSPD
   ameliorates DKD. The DKD rat model experiments demonstrated that TSPD
   treatment reduced the levels of UAlb, UCr, UACR, UP, INS, and Scr, with
   significant effects observed particularly in the high-dose group. The
   HbA1c levels decreased significantly in the high-dose group, whereas no
   significant changes were observed in the other intervention groups,
   possibly due to HbA1c reflecting the average blood glucose level over a
   longer period. The 12 week treatment period may have been sufficient to
   exert effects only in the high-dose group. BUN levels can be influenced
   by factors such as renal function, diet, high fat intake, and
   dehydration, which explains the lack of significant differences in the
   other intervention groups except the high-dose group. HE and PAS
   staining revealed that TSPD ameliorated renal fibrosis in DKD rats.
   Western blot analysis revealed that TSPD inhibited the expression of
   CCL2 and NF-κB. The levels of inflammatory cytokines TNF-α, IL-6, and
   IL-1β also decreased significantly. These findings suggest that TSPD
   mitigates renal inflammation and fibrosis and improves renal function
   by inhibiting the CCL2/NF-κB pathway.

   The in vitro experiments further validated the mechanism of TSPD in
   treating DKD. Based on the preliminary findings from network
   pharmacology, transcriptomics, and molecular docking, it was
   hypothesized that TSPD may exert anti-inflammatory and renoprotective
   effects by modulating the CCL2/NF-κB signaling axis. In
   high-glucose-induced HK-2 cell models, CCL2 expression was
   significantly upregulated, accompanied by NF-κB activation, leading to
   elevated levels of inflammatory cytokines. TSPD intervention
   significantly inhibited the expression of CCL2 and the activation of
   NF-κB, reducing the secretion of inflammatory cytokines. Notably, in
   CCL2 overexpressing cells, although the inflammatory response was
   exacerbated, TSPD was able to partially reverse this effect,
   demonstrating its anti-inflammatory properties. Further, si-CCL2
   intervention experiments showed that TSPD more effectively reduced
   NF-κB activity and inflammatory cytokine expression, indicating that
   CCL2 plays a critical role in the anti-inflammatory mechanism of TSPD.
   These results clearly demonstrate that TSPD exerts anti-inflammatory
   effects and ameliorates the inflammatory state in DKD by directly
   regulating the CCL2/NF-κB axis.

   In summary, this study revealed that TSPD can modulate the CCL2/NF-κB
   signaling pathway through multitarget and multipathway regulation,
   thereby inhibiting cellular inflammatory responses and improving DKD
   pathology. Despite the use of various modern scientific techniques to
   elucidate the potential mechanisms of TSPD in treating DKD, several
   limitations remain. First, the core components and targets of TSPD were
   screened from multiple databases, which may affect the generalizability
   and reliability of the results. Second, although some clinical research
   data on TSPD exist, the lack of large-scale clinical trial data limits
   a comprehensive evaluation of the clinical efficacy of TSPD.
   Furthermore, the detailed mechanisms of the core components of TSPD in
   treatment require further investigation. Future work will include
   analyzing the components of TSPD through experimental studies,
   utilizing chemical profiling techniques such as HPLC and MS, collecting
   clinical samples, and conducting metabolomic and proteomic research to
   fully elucidate the mechanisms of TSPD, providing robust scientific
   evidence for its application in modernized and precision medicine.

5. Conclusions

   This study systematically elucidated the multitarget mechanism of TSPD
   in the treatment of DKD by integrating network pharmacology,
   transcriptomics, molecular docking techniques, and molecular dynamics
   simulation. The core compounds of TSPDquercetin, luteolin, kaempferol,
   baicalin, and tanshinone IIAinteract with key targets such as ALB,
   CCL2, EGF, FN1, and PTGS2 to regulate oxidative stress, inflammatory
   response, and metabolic processes. In vitro and in vivo experiments
   further confirmed that TSPD exerts its therapeutic effect through the
   CCL2/NF-κB signaling pathway, effectively reducing the production of
   inflammatory factors and alleviating renal fibrosis. These findings
   provide a comprehensive understanding of the mechanism of TSPD in the
   treatment of DKD and offer a strong scientific basis for its clinical
   application.

Supplementary Material

   [132]ao5c01492_si_001.pdf^ (1.6MB, pdf)
   [133]ao5c01492_si_002.xlsx^ (6.8MB, xlsx)

Acknowledgments