Introduction
Kidney stones are one of the most common urinary system diseases worldwide, with an incidence rate ranging from 114 to 720 cases per 100,000 people. 1 Existing studies have shown a sharp increase in the incidence of kidney stones over the past three decades, which may be associated with changes in lifestyle, climate and environmental variations, and geographical factors. 2 , 3 Kidney stones also carry a high recurrence risk; 4 statistics indicate that the annual recurrence rate is estimated at 10–23%, 50% within 5–10 years, and as high as 75% within 20 years. 5 Recurrence of kidney stones often leads to various complications, including but not limited to renal colic, pyelonephritis, hydronephrosis, urinary tract infections, 6 hematuria, and even urothelial carcinoma. 7 , 8 Despite the continuous advancement of medical technology and the availability of multiple treatment options for kidney stones, frequent recurrence severely impairs patients’ quality of life and imposes a heavy economic burden on the global healthcare system. However, the potential impact of chemical pollutants such as phthalates on the exacerbation of urolithiasis remains poorly understood.
With the rapid development of global industrialization, di-(2-ethylhexyl) phthalate (DEHP), a key component of plasticizers and softeners, has been widely used in the plastics industry. 9 It is applied in the production of building materials, synthetic leather, cables, wires, fast-food packaging, children’s toys, cosmetics, and skin care products, 10 , 11 permeating all aspects of daily life including clothing, food, housing, and transportation. 12 Studies have shown that long-term exposure to DEHP exerts adverse effects on animal health, 13 and DEHP mediates diverse toxic responses through multiple signaling pathways after entering the body. 14 As it is mainly metabolized by the kidneys, concerns regarding its nephrotoxicity have attracted widespread attention from researchers. 15 A large case-control study published in 2024 reported that phthalate exposure was associated with oxidative stress, early renal injury, and an increased risk of calcium urolithiasis. 16 However, the potential molecular mechanisms driving this synergistic effect remain largely elusive.
Network toxicology is an emerging bioinformatics-based field that integrates toxicogenomics, systems biology, and network pharmacology. 17–19 It provides a systems-level framework for predicting the multi-target and multi-pathway effects of chemical exposures. Using network toxicology together with experimental validation, this study aimed to clarify the biological mechanisms by which DEHP may promote kidney stone formation and progression, thereby providing a theoretical basis for future mechanistic and preventive research.
Materials and Methods
Network Toxicology Analysis
Toxicity Analysis and Prediction of DEHP
The keyword “Di(2-ethylhexyl) phthalate” was entered into the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ) to obtain the 2D structure and SMILES string of the compound (February 2025). Subsequently, the SMILES string was input into the ProTox 3.0 database ( https://tox.charite.de/protox3/ ) and ADMETlab 3.0 database ( https://admetlab3.scbdd.com/ ) to acquire the analytical results of its toxicity prediction (February 2025), including the predicted median lethal dose (LD50) of DEHP, toxicity class, and targeted toxicity to various human organs.
Identification of Potential Human Targets of DEHP
Potential human targets of DEHP were retrieved from ChEMBL using “DEHP” as the search term, with the Target module restricted to Homo sapiens. Target identifiers were converted to gene symbols using UniProt annotations. The DEHP SMILES string was then submitted to STITCH, with the species set to Homo sapiens and the confidence score threshold set at ≥0.4. SwissTargetPrediction was also queried using the SMILES string, with Homo sapiens selected and a probability threshold of >0.11. Targets from the three databases were combined and deduplicated to generate the final set of potential DEHP targets.
Identification of Kidney Stone-Related Targets
Kidney stone-related genes were retrieved from GeneCards, OMIM, and TTD using the term “kidney stones”. The results from the three databases were combined and deduplicated to obtain the final disease-related target set.
Construction of the Compound–Gene–Disease Network
A compound-gene-disease network linking DEHP, the overlapping target genes, and kidney stones was constructed using Cytoscape (version 3.9.1).
Construction of the Protein–Protein Interaction Network
The overlapping targets were uploaded to the STRING database ( https://string-db.org ). The Organism parameter was set to “Homo sapiens” with a confidence threshold of 0.995 to construct the Protein-Protein Interaction (PPI) Network. Subsequently, the obtained data were imported into Cytoscape software (Version 3.9.1) for processing, and some obviously isolated nodes were removed. A reasonable evaluation was conducted by adopting six centrality indices, namely Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Eigenvector Centrality (EC), Network Centrality (NC), and Local Average Connectivity (LAC). Hub targets were subsequently ranked using the Maximum Clique Centrality (MCC) algorithm implemented in the cytoHubba plugin of Cytoscape, and the six highest-ranked targets were selected as hub proteins.
Molecular Docking
Molecular docking is a computer simulation-based method for predicting the binding modes between protein receptors and small-molecule ligands. 20 The previously downloaded 2D structure of DEHP was converted into a 3D structure using Chem3D software, adjusted to an energy-minimized state, and saved in.mol2 format. The structures of key protein receptors in the PPI network were retrieved from the PDB database ( https://www.rcsb.org/ ). First, the structures were imported into PyMOL software (Version 2.4.0) to remove water molecules and endogenous ligands. Subsequently, preprocessing steps including hydrogenation and dehydration were performed using AutoDockTools software (Version 1.5.6), and the structures were saved in.pdbqt format. Next, the active pocket was identified, and molecular docking was conducted using AutoDock Vina software. The binding energy between the receptor and ligand was calculated to evaluate their binding activity. Finally, the receptor-ligand complex was visualized using PyMOL software (Version 2.4.0).
Molecular Dynamics Simulation
Molecular dynamics (MD) simulation can be used to observe the motion trajectories and behaviors of the receptor-ligand complex within a certain period of time, thereby evaluating the stability of the system. 21 In this study, GROMACS 2022 software was used to perform MD simulation of the small molecule-protein complex under the conditions of constant temperature (300 K) and constant pressure (1 bar). The topological structure of the small molecule ligand was constructed using the GAFF2 force field, while the protein receptor adopted the CHARMM36 force field and TIP3P water model. The simulation time was set to 100 ns, and sodium and chloride ions were added to neutralize the net charge of the system. First, energy minimization was achieved through the steepest descent method and conjugate gradient method, followed by a 100,000-step equilibration process under the canonical ensemble (NVT) and isothermal-isobaric ensemble (NPT). Finally, each of the six DEHP–protein complexes underwent a 50,000,000-step MD simulation, corresponding to a simulation time of 100 ns, under periodic boundary conditions. After the simulation, PyMOL software was used for visual analysis of the simulation trajectories.
GO and KEGG Enrichment Analysis
To investigate the biological functions and pathways associated with the overlapping targets, GO and KEGG enrichment analyses were performed using the clusterProfiler package in R (version 4.4.2). Terms with P < 0.05 were considered statistically significant. GO analysis covered the biological process (BP), cellular component (CC), and molecular function (MF) categories, and the top 10 terms in each category were displayed. For KEGG analysis, the top 30 enriched pathways were visualized.
Experimental Verification
Animal Models and Reagents
All animal experimental procedures were approved by the Animal Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (Approval No. TJH-202509015) and were conducted in accordance with the Chinese national standard Laboratory Animal—Guideline for Ethical Review of Animal Welfare (GB/T 35892-2018). Twenty 8-week-old male C57BL/6J mice (body weight: 23–25 g) with specific pathogen-free (SPF) grade were purchased from Beijing Weitong Lihua Experimental Animal Technology Co., Ltd., Hubei Branch (License No.: SCXK (Hubei) 2022-0030). The mice were housed in a suitable animal room (temperature: 18–25°C, humidity: 50–70%, well-ventilated and regularly disinfected) and cared for by special personnel. After a 1-week acclimation period, the mice were randomly divided into four groups according to the random number table method: normal control group, glyoxylic acid-induced model group (100 mg/kg), glyoxylic acid + low-dose DEHP (250 mg/kg) group, and glyoxylic acid + high-dose DEHP (500 mg/kg) group, with 5 mice in each group. Glyoxylic acid was administered via intraperitoneal injection (i.p.), and DEHP was given by gavage for one consecutive week. DEHP was dissolved in corn oil and administered by oral gavage at a volume of 8 mL/kg once daily. Mice in the normal control and model groups received an equivalent volume of vehicle. At the end of the experiment, after being fully anesthetized with a 50 mg/kg intraperitoneal injection of pentobarbital sodium, the depth of anesthesia was strictly monitored by observing the loss of corneal reflex. Mouse serum and kidney tissue samples were collected for analysis. Following sample collection, the animals were euthanized by exsanguination under deep anesthesia in accordance with the AVMA Guidelines for the Euthanasia of Animals. Death was confirmed by the cessation of vital signs (absence of heartbeat, breathing and corneal reflexes) for more than 3 minutes. The study was reported in accordance with the ARRIVE 2.0 guidelines. 22
DEHP was purchased from MCE (CAS No.: 117-81-7) with a purity of ≥ 99.5%. Glyoxylic acid was obtained from Sigma-Aldrich (USA); Von Kossa staining kit was purchased from Servicebio (Wuhan, China); hematoxylin-eosin (HE) staining kit was supplied by Ruchuang Biotechnology Co., Ltd. (Shanghai, China).
HE Pathological Section Staining and Renal Function Assessment
Serum samples from the four groups of mice were assayed for blood urea nitrogen (BUN) using a BUN assay kit (Pribolab, Ningbo, China) and creatinine (Cr) with a Cr assay kit (Pribolab, Ningbo, China). Kidney sections were prepared following standard procedures: paraffin sections were dewaxed to water, stained with hematoxylin for cell nuclei and eosin for cytoplasm, dehydrated, and mounted. Finally, the sections were examined under a microscope (Nikon, Japan). Four senior pathologists were randomly assigned and independently evaluated the sections in a blinded manner, based on the degree of renal tubular epithelial cell swelling, brush border loss, tubular dilation, atrophy, necrosis, and rupture. The scoring criteria were as follows: 0 (no injury), 1 (<25% injury), 2 (25–50% injury), 3 (50–75% injury), and 4 (>75% injury), for semi-quantitative analysis of renal injury severity.
Immunohistochemical Staining
After dewaxing and rehydration of paraffin-embedded tissue sections, antigen retrieval was performed using heat-induced epitope retrieval (HIER). Endogenous peroxidase activity was blocked with 3% hydrogen peroxide solution, followed by blocking with 5% goat serum at room temperature for 30 minutes. The primary antibody against LC3B (CY5992) was added and incubated at 4°C overnight. On the next day, HRP-conjugated secondary antibody (Wuhan Servicebio Technology Co., Ltd.) was added and incubated at room temperature for 30 minutes, followed by color development with DAB chromogenic substrate. Cell nuclei were counterstained with hematoxylin. After dehydration and clearing, the sections were mounted with neutral balsam and observed under a light microscope. Similarly, immunohistochemical (IHC) staining was conducted on kidney tissues to detect the expression of kidney injury molecule-1 (KIM-1) protein (Cat. No.: A24493).
Von Kossa Staining
Kidney sections were stained with the Von Kossa staining kit (Servicebio, Wuhan, China). Von Kossa staining solution was added to the sections, which were continuously irradiated with ultraviolet light for 4 hours, followed by thorough washing with distilled water. Subsequently, the sections were stained with hematoxylin and eosin, dehydrated, mounted, and examined under a microscope (Nikon, Japan). Semi-quantitative analysis was performed to assess the degree of renal crystal deposition in each group.
TUNEL Staining
Paraffin sections were subjected to dewaxing and rehydration through xylene (15 min each) and absolute ethanol (5 min each), air-dried, and rinsed with distilled water. The sections were placed in EDTA repair buffer (pH 9.0) for antigen retrieval at 95°Cfor 25 min. After cooling, they were rinsed with distilled water and washed 3 times (5 min each) on a decolorizing shaker. Following partial drying, permeabilization buffer was added for incubation at room temperature for 20 min, and the sections were washed 3 times (5 min each) with PBS (pH 7.4). TdT enzyme and TUNEL reaction solution were mixed at a ratio of 1:50, added to the sections, and incubated in a humidified chamber at 37°C for 30 min protected from light. The sections were then washed 3 times (5 min each) with PBS, followed by incubation with DAPI staining solution for 10 min protected from light. After 3 additional washes with PBS (5 min each), the sections were partially dried, mounted with anti-fade mounting medium, and observed and imaged under a fluorescence microscope.
Cell Modeling and Drug Intervention
First, COM (calcium oxalate monohydrate) crystals were purchased from Shanghai Sinopharm Group (Shanghai, China). A stock suspension was prepared in autoclaved double-distilled water (ddH 2 O), sonicated briefly to disperse aggregates, and diluted to a final working concentration of 2 mM (expressed as oxalate) immediately before use in cell experiments. Human renal tubular epithelial cells (HK‑2) were purchased from Wuhan Servicebio Technology Co., Ltd. and cultured in DMEM/F12 medium supplemented with 10% fetal bovine serum (FBS) (Wuhan Servicebio Technology Co., Ltd.). DEHP was dissolved in DMSO, and the final solvent concentration in the culture medium did not exceed 0.1%. An equivalent amount of vehicle was added to the control and COM groups. Cells were maintained in a humidified incubator (Thermo Fisher Scientific, USA) at 37°C with 5% CO 2 .
CCK-8 Cell Viability Assay
HK-2 cells were seeded into 96-well plates according to the experimental protocol and incubated for 24 hours. Subsequently, the cells were randomly divided into four groups: control group, COM group (2 mM), COM (2 mM) + low-dose DEHP group (100 μM), and COM (2 mM) + high-dose DEHP group (300 μM). When the cell confluence reached approximately 80%, the cells were washed with phosphate-buffered saline (PBS), and corresponding drugs were added to each group for intervention. After 24 hours of intervention, the CCK-8 kit (Wuhan Boster Biological Technology Co., Ltd.) was used. Following a 1:10 dilution of the reagent, 100 μL of the diluted CCK-8 detection solution was added to each well, and the plates were incubated at 37°C for 3 hours. The absorbance was measured at 450 nm using a multimode microplate reader (BD, USA), and the cell viability rate was calculated.
Cell Crystal Deposition Assay
HK-2 cells were seeded into 6-well plates and cultured. When the cell confluence reached approximately 80%, the cells were randomly divided into groups (as described previously) and administered drug intervention. After 24 hours of incubation, images were captured under a bright-field inverted microscope, and the percentage of crystal deposition was calculated in at least 6 random fields of view per well.
Statistical Analysis
Statistical analysis of all data was performed using GraphPad Prism software (Version 9.5.0). Quantitative data with a normal distribution were expressed as mean ± standard deviation ( \documentclass[12pt]{minimal} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \DeclareFontFamily{T1}{linotext}{} \DeclareFontShape{T1}{linotext}{m}{n} {linotext }{} \DeclareSymbolFont{linotext}{T1}{linotext}{m}{n} \DeclareSymbolFontAlphabet{\mathLINOTEXT}{linotext} \begin{document}${\mathrm{\overline x}}\; \pm \;{\mathrm{s}}$\end{document} ). Unpaired t -test was used for comparisons between two groups, while one-way analysis of variance (ANOVA) was applied for comparisons among multiple groups, followed by Tukey HSD post-hoc test for multiple comparisons. Statistical significance was considered at P < 0.05 (*p < 0.05, **p < 0.01, ***p < 0.001).
Results
Toxicological Prediction and Assessment of DEHP Based on the Database
Basic chemical information of DEHP was obtained from the PubChem database: PubChem CID: 8343, molecular formula: C 2 4 H 3 8 O 4 , IUPAC Name: bis(2-ethylhexyl) benzene-1,2-dicarboxylate, InChIKey: BJQHLKABXJIVAM-UHFFFAOYSA-N, SMILES: CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC. Detailed toxicity predictions of DEHP were performed using the ProTox 3.0 database, with a predicted median lethal dose (LD 5 0 ) of 1340 mg/kg and a toxicity class of 4. This compound exerts significant potential effects on humans, including nephrotoxicity (predicted probability: 0.57), carcinogenicity (predicted probability: 0.86), and predicted blood–brain barrier permeability (probability: 0.87) ( Tables 1, 2 and Figure 1A ). The prediction results from ADMETlab 3.0 showed that DEHP has a skin sensitization probability of 0.943, eye irritation probability of 0.968, carcinogenicity probability of 0.557, and cardiac toxicity probability of 0.204. In the endocrine system, it exhibits strong interference with estrogen receptors, with a binding affinity probability of 0.306, indicating potential endocrine-disrupting hazards. Table 1 Physicochemical Properties of Bis(2-Ethylhexyl) Phthalate (DEHP) Parameter Value Compound Bis(2-ethylhexyl) phthalate (DEHP) Canonical SMILES CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC Molecular weight (g mol −1 ) 390.56 Hydrogen-bond acceptors 4 Hydrogen-bond donors 0 Heavy atom count 28 Number of bonds 28 Rotatable bonds 16 Molecular refractivity 116.3 Topological polar surface area (Å 2 ) 52.6 Octanol/water partition coefficient (logP) 6.43 Note : The physicochemical data presented in this table were obtained from the ProTox 3.0 web server. Table 2 In Silico Toxicity Prediction Profile of Bis(2-Ethylhexyl) Phthalate (DEHP) Classification Toxicity Endpoint Shorthand Prediction Probability Organ toxicity Nephrotoxicity Nephro Active 0.57 Toxicity endpoints Carcinogenicity Carcino Active 0.86 Toxicity endpoints Immunotoxicity Immuno Inactive 0.97 Toxicity endpoints Mutagenicity Mutagen Inactive 0.99 Toxicity endpoints Cytotoxicity Cyto Inactive 0.87 Toxicity endpoints Blood–brain barrier Bbb Active 0.87 Toxicity endpoints Ecotoxicity Eco Inactive 0.55 Toxicity endpoints Clinical toxicity Clinical Inactive 0.76 Toxicity endpoints Nutritional toxicity Nutri Inactive 0.92 Note : The toxicity prediction data presented in this table were obtained from the ProTox 3.0 web server. Figure 1 Network toxicology-based identification of potential targets linking di(2-ethylhexyl) phthalate (DEHP) exposure to kidney stones. ( A ) Acute toxicity of DEHP predicted using the ProTox-3.0 platform. The predicted median lethal dose (LD 5 0 ) was 1340 mg/kg, corresponding to toxicity class 4. ( B ) DEHP-related targets were retrieved from the ChEMBL, STITCH, and SwissTargetPrediction databases, whereas nephrolithiasis-associated genes were collected from GeneCards, OMIM, and the Therapeutic Target Database (TTD). After integration, removal of duplicates, and intersection analysis, 457 shared candidate targets were identified. ( C ) Compound–target–disease network illustrating the potential associations among DEHP, the shared candidate targets, and nephrolithiasis. ( D ) Protein–protein interaction (PPI) network constructed using the 457 shared candidate targets. Nodes represent proteins, and edges indicate known or predicted functional interactions between proteins. ( E ) Network of hub targets identified through topological analysis of the PPI network. Node size and color gradient indicate relative topological importance, with larger nodes and warmer colors representing targets with greater potential importance within the network. A. DEHP toxicity prediction with LD50 of 1340 mg/kg, toxicity class 4, 100 percent average similarity and prediction accuracy. B. Venn diagrams show DEHP-related targets from ChEMBL, STITCH, SwissTargetPrediction and kidney stone genes from GeneCards, OMIM, TTD. Intersection shows 457 shared targets. C. Compound to target to disease network with DEHP, shared targets and nephrolithiasis. D. Protein to protein interaction network with nodes as proteins and edges as interactions. E. Hub target network with node size and gradient indicating topological importance. Five diagrams showing DEHP toxicity, target identification and network interactions.
Table 1
Physicochemical Properties of Bis(2-Ethylhexyl) Phthalate (DEHP)
Note : The physicochemical data presented in this table were obtained from the ProTox 3.0 web server.
| Parameter | Value |
| Compound | Bis(2-ethylhexyl) phthalate (DEHP) |
| Canonical SMILES | CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC |
| Molecular weight (g mol −1 ) | 390.56 |
| Hydrogen-bond acceptors | 4 |
| Hydrogen-bond donors | 0 |
| Heavy atom count | 28 |
| Number of bonds | 28 |
| Rotatable bonds | 16 |
| Molecular refractivity | 116.3 |
| Topological polar surface area (Å 2 ) | 52.6 |
| Octanol/water partition coefficient (logP) | 6.43 |
Table 2
In Silico Toxicity Prediction Profile of Bis(2-Ethylhexyl) Phthalate (DEHP)
Note : The toxicity prediction data presented in this table were obtained from the ProTox 3.0 web server.
| Classification | Toxicity Endpoint | Shorthand | Prediction | Probability |
| Organ toxicity | Nephrotoxicity | Nephro | Active | 0.57 |
| Toxicity endpoints | Carcinogenicity | Carcino | Active | 0.86 |
| Toxicity endpoints | Immunotoxicity | Immuno | Inactive | 0.97 |
| Toxicity endpoints | Mutagenicity | Mutagen | Inactive | 0.99 |
| Toxicity endpoints | Cytotoxicity | Cyto | Inactive | 0.87 |
| Toxicity endpoints | Blood–brain barrier | Bbb | Active | 0.87 |
| Toxicity endpoints | Ecotoxicity | Eco | Inactive | 0.55 |
| Toxicity endpoints | Clinical toxicity | Clinical | Inactive | 0.76 |
| Toxicity endpoints | Nutritional toxicity | Nutri | Inactive | 0.92 |
The Potential Target Sites of DEHP in the Human Body
A total of 1965 DEHP targets were obtained after deduplication and integration of the targets retrieved from the ChEMBL database, STITCH database, and SwissTargetPrediction database, including 1912 from the ChEMBL database, 10 from the STITCH database, and 108 from the SwissTargetPrediction database.
Disease-Related Targets of Kidney Stones
GeneCards, OMIM, and TTD yielded 2183, 32, and 105 kidney stone-related genes, respectively. After deduplication, 2243 unique disease-related genes were obtained. Intersecting these genes with the potential DEHP targets identified 457 overlapping genes, which were used to construct the Venn diagram and compound-gene-disease network ( Figure 1B ).
PPI Network Analysis and Hub Target Identification
The PPI network generated from STRING was imported into Cytoscape for topological analysis. The network contained 134 nodes and 280 edges, with an average degree of 4.179, a diameter of 10, a radius of 5, a characteristic path length of 4.090, a clustering coefficient of 0.307, a density of 0.031, a heterogeneity of 0.898, and a centralization of 0.105. Hub screening using the Maximum Clique Centrality (MCC) algorithm identified six key proteins: CTNNB1, SRC, HSP90AA1, EP300, EGFR, and TP53 ( Figure 1C–E ).
Molecular Docking
Molecular docking can reflect the binding affinity between DEHP and core targets. When the binding energy is < −5.0 kcal/mol, it indicates high binding activity; 23 when the binding energy is < −7.0 kcal/mol, it demonstrates a relatively stable interaction between the ligand and receptor with strong binding activity ( Figure 2A–F ). Among them, DEHP exhibited the most stable binding with TP53, with a binding energy of −7.5 kcal/mol, followed by EGFR, EP300, CTNNB1, HSP90AA1, and SRC in sequence. Their respective binding energies from molecular docking were −6.2 kcal/mol, −6.0 kcal/mol, −6.0 kcal/mol, −5.1 kcal/mol, and −4.4 kcal/mol ( Table 3 ). Table 3 Molecular Docking Results for DEHP with Selected Protein Targets Compound Target Binding Energy (kcal mol −1 ) Cavity Volume (Å 3 ) DEHP TP53 −7.5 3691 DEHP EGFR −6.2 6053 DEHP EP300 −6.0 176 DEHP CTNNB1 −6.0 243 DEHP HSP90AA1 −5.1 292 DEHP SRC −4.4 83 Figure 2 Molecular docking analysis of DEHP with hub proteins. ( A ) Predicted binding mode of DEHP with TP53. ( B ) Predicted binding mode of DEHP with CTNNB1. ( C ) Predicted binding mode of DEHP with EGFR. ( D ) Predicted binding mode of DEHP with HSP90AA1. ( E ) Predicted binding mode of DEHP with EP300. ( F ) Predicted binding mode of DEHP with SRC. Proteins are represented as molecular surfaces, DEHP as yellow sticks, and neighboring amino acid residues as cyan sticks. Image A displays TP53′s molecular surface with a highlighted pocket, connected to an inset showing DEHP as stick bonds interacting with amino acid side chains. Image B shows CTNNB1′s surface with a highlighted region, linked to an inset where DEHP interacts with residues like ASN 387, TRP 383 and ARG 386. Image C presents EGFR′s surface with a pocket and an inset magnifying DEHP surrounded by labeled residues. Image D illustrates HSP90AA1′s surface with a pocket and an inset showing DEHP with labeled residues. Image E depicts EP300′s surface with a pocket and an inset showing DEHP among labeled residues. Image F shows SRC′s surface with a pocket and an inset displaying DEHP interacting with labeled residues. A diagram showing molecular docking of DEHP with TP53, CTNNB1, EGFR, HSP90AA1, EP300 and SRC.
Table 3
Molecular Docking Results for DEHP with Selected Protein Targets
| Compound | Target | Binding Energy (kcal mol −1 ) | Cavity Volume (Å 3 ) |
| DEHP | TP53 | −7.5 | 3691 |
| DEHP | EGFR | −6.2 | 6053 |
| DEHP | EP300 | −6.0 | 176 |
| DEHP | CTNNB1 | −6.0 | 243 |
| DEHP | HSP90AA1 | −5.1 | 292 |
| DEHP | SRC | −4.4 | 83 |
Molecular Dynamics Simulation
To more accurately evaluate the binding affinity and stability between the aforementioned receptors and ligands, 100 ns Molecular Dynamics (MD) Simulations were performed for each docking result individually. Validation was conducted using four parameters—root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), and number of hydrogen bonds—and free energy landscapes (FELs) were plotted. 24 The RMSD profiles showed that most complexes reached relatively stable conformational states during the later stages of the simulations, although the magnitude and pattern of fluctuations varied among complexes. DEHP could bind firmly to the proteins with minimal fluctuations during the process, and no structural disruption or dissociation was observed. RMSF values describe the fluctuation status of amino acid residues in proteins and atoms in the ligand; higher values indicate more significant fluctuations, while lower values reflect relative stability. Most residues exhibited relatively low RMSF values, whereas higher fluctuations were observed in several terminal or flexible regions. Rg values typically reflect the compactness of the structure: higher Rg indicates significant expansion of the structure during dynamic simulation, whereas lower Rg suggests a more stable and compact system. The results demonstrated that all six complexes remained stable throughout the simulation. Approximately two hydrogen bonds were maintained on average between DEHP and each target protein during the simulations, supporting persistent ligand–protein interactions. ( Figure 3A–F ). Figure 3 Molecular dynamics simulations and conformational stability analyses of DEHP in complex with six hub proteins. ( A ) Molecular dynamics simulation results for the DEHP–CTNNB1 complex. ( B ) Molecular dynamics simulation results for the DEHP–SRC complex. ( C ) Molecular dynamics simulation results for the DEHP–HSP90AA1 complex. ( D ) Molecular dynamics simulation results for the DEHP–EP300 complex. ( E ) Molecular dynamics simulation results for the DEHP–EGFR complex. ( F ) Molecular dynamics simulation results for the DEHP–TP53 complex. For each complex, the panels from left to right show the RMSD profile, RMSF profile, radius of gyration, number of hydrogen bonds, covariance matrix, and Gibbs free-energy landscape. In the covariance matrices, positive and negative values indicate correlated and anticorrelated atomic motions, respectively. Lower-energy regions in the free-energy landscapes represent more favorable conformational states. The image showing DEHP CTNNB1, DEHP SRC, DEHP HSP90AA1, DEHP EP300, DEHP EGFR and DEHP TP53 molecular dynamics simulation summaries. Each row repeats six plot types: RMSD line plot, RMS fluctuation line plot, radius of gyration multi line plot, hydrogen bonds bar timeline, covariance heatmap and Gibbs energy landscape contour heatmap. Metric meanings as labeled: RMSD is root mean square deviation over time, RMS fluctuation is per residue flexibility, radius of gyration is compactness over time, hydrogen bonds count ligand protein hydrogen bonds over time, covariance shows correlated and anticorrelated motions on a relative scale and Gibbs energy landscape shows relative free energy basins in principal component space. Shared axes and units by plot type as printed: RMSD plots use x axis label, Time in picosecond and y axis label, RMSD in nanometer. RMS fluctuation plots use x axis label, Residue and y axis label, RMS fluctuation in nanometer. Radius of gyration plots use x axis label, Time in picosecond and y axis label, Radius of gyration in nanometer, with multiple traces labeled as radius of gyration components, with exact component names not readable. Hydrogen bonds plots use x axis label, Time in picosecond and y axis label, Number of hydrogen bonds. Covariance heatmaps use x axis label, Atom index and y axis label, Atom index, with a side color scale showing negative to positive values; numeric range and units are not specified. Gibbs energy landscape plots use x axis label, PC1 and y axis label, PC2, with a side color scale labeled Gibbs energy landscape; numeric range and units are not specified. DEHP CTNNB1: RMSD rises early then fluctuates around a higher level to the end. RMS fluctuation shows repeated peaks across residues. Radius of gyration traces cluster while one trace trends downward. Hydrogen bond bars appear intermittently with denser bars later. Covariance shows a diagonal band plus off diagonal patches. Gibbs energy landscape shows one main basin with a compact low region. DEHP SRC: RMSD stays lower then trends upward near the end. RMS fluctuation increases toward higher residue numbers with a pronounced rise at the end. Radius of gyration traces cluster with small oscillations. Hydrogen bond bars appear throughout with alternating dense and sparse segments. Covariance shows block like regions. Gibbs energy landscape shows two main basins. DEHP HSP90AA1: RMSD rises quickly then plateaus with small oscillations. RMS fluctuation is moderate with a sharp rise at the far right. Radius of gyration shows mild drift. Hydrogen bond bars occur in clusters. Covariance is dominated by a strong diagonal. Gibbs energy landscape shows a compact basin near one side. DEHP EP300: RMSD is mostly flat with two tall spikes mid to late time. RMS fluctuation has repeated peaks and a rising tail at the end. Radius of gyration traces are separated but roughly parallel. Hydrogen bond bars are sparse early and denser later. Covariance is mostly uniform with a faint diagonal. Gibbs energy landscape shows a long curved low energy band. DEHP EGFR: RMSD rises early then remains high with small fluctuations. RMS fluctuation is low to moderate with occasional peaks. Radius of gyration fluctuates together with slight upward drift near the end. Hydrogen bond bars form a dense band across much of the timeline. Covariance is patchy. Gibbs energy landscape shows a broad central basin. DEHP TP53: RMSD rises then levels with moderate oscillations. RMS fluctuation has several peaks and a higher region toward the right. Radius of gyration traces cluster with small fluctuations. Hydrogen bond bars are intermittent with dense clusters. Covariance shows diagonal structure with several off diagonal patches. Gibbs energy landscape shows a U shaped low region with localized minima. Six row molecular dynamics plots for DEHP complexes show RMSD, RMSF, radius of gyration, bonds, covariance, energy.
Potential Molecular Mechanisms by Which DEHP Promotes Kidney Stone Progression
A total of 3180 GO terms were enriched among the overlapping targets ( Figure 4A and B ), including 2756 BP, 140 CC, and 284 MF terms. The leading BP terms included response to xenobiotic stimulus, positive regulation of the MAPK cascade, response to hypoxia, regulation of autophagy, and execution phase of apoptosis. CC terms were mainly associated with the apical part of the cell, plasma membrane microdomains, and voltage-gated sodium channel complexes. MF terms included DNA-binding transcription factor binding, metal ion transmembrane transporter activity, and growth factor binding. KEGG analysis identified 211 enriched pathways ( Figure 4C and D ), with PI3K-Akt and MAPK signaling among the most prominent. Pathways related to HIF-1 signaling, EGFR signaling, lipid metabolism, diabetic complications, and endocrine resistance were also enriched. Collectively, these results suggest that DEHP-associated targets may influence cell survival, stress responses, ion transport, and metabolic adaptation during kidney stone progression. Figure 4 GO and KEGG enrichment analyses of the shared candidate targets linking DEHP to kidney stones. ( A ) Bubble plot of Gene Ontology enrichment analysis. ( B ) Bar plot of Gene Ontology enrichment analysis. ( C ) Bubble plot of Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. ( D ) Bar plot of Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Red boxes highlight Gene Ontology terms related to the regulation of autophagy and the execution phase of apoptosis. The images illustrate Gene Ontology (GO) and KEGG enrichment analyses. Image A is a GO bubble plot with GeneRatio (0.00-0.10) on the x-axis and GO terms (Biological Process, Cellular Component, Molecular Function) on the y-axis. Bubble size shows Count and color indicates p.adjust values (0.01-0.05). Key BP terms include xenobiotic stimulus response and MAPK cascade regulation. CC terms highlight apical cell parts and MF terms involve DNA-binding transcription factor binding. Larger bubbles appear in BP terms, smaller in MF. Image B is a GO bar chart focusing on Count. Image C is a KEGG bubble plot with GeneRatio (0.06-0.18) on the x-axis and pathways like PI3K-Akt signaling on the y-axis. Bubble size reflects Count, with p.adjust values (1.0e-03, 1.0e-02, 0.05). Higher GeneRatio values are at the top. Image D is a KEGG bar chart emphasizing Count. Both analyses highlight GeneRatio and Count to showcase enrichment results. A 4-plot figure of Gene Ontology and pathway enrichment for shared candidate targets.
DEHP Exacerbates Calcium Oxalate Crystal-Induced Renal Injury in Mice
Calcium oxalate crystal deposition and adhesion can injure renal tubular epithelial cells, while injured cells may further promote crystal nucleation and retention, creating a self-reinforcing cycle. Compared with the normal control group, the model group exhibited greater tubular morphological injury. Tubular atrophy, necrosis, and brush-border loss were more pronounced in the DEHP intervention groups than in the model group. KIM-1 immunohistochemistry also showed significantly stronger staining in the DEHP-treated groups, confirming aggravated renal tubular injury ( Figure 5 ). Figure 5 In vivo validation of DEHP-induced nephrotoxicity in a glyoxylic acid-treated mouse model. ( A ) Chemical structure of DEHP. ( B ) Experimental design. Mice were assigned to the normal control, glyoxylic acid, glyoxylic acid plus low-dose DEHP, or glyoxylic acid plus high-dose DEHP groups, treated for 7 consecutive days, and sacrificed on day 8. ( C ) Representative hematoxylin and eosin (H&E)-stained images of the renal cortex and medulla and immunohistochemical staining of kidney injury molecule-1 (KIM-1) in each group (×400). ( D ) Quantification of renal injury scores. ( E ) Quantification of KIM-1 expression levels. P.O.: per os; i.p.: intraperitoneal. Statistical significance is indicated as follows: * P < 0.05, ** P < 0.01, and *** P < 0.001. A multi-panel figure illustrates DEHP-induced nephrotoxicity in a glyoxylic acid-treated mouse model. Image A displays DEHP′s chemical structure. Image B outlines the experimental timeline with four groups: Normal Control, Glyoxylic Acid 100 mg/kg, Gly 100 mg/kg + DEHP 250 mg/kg and Gly 100 mg/kg + DEHP 500 mg/kg, treated over 7 days and sacrificed on day 8. DEHP was administered orally, glyoxylic acid intraperitoneally. Image C shows 400x magnified micrographs: renal cortex and medulla stained with hematoxylin and eosin and kidney injury molecule-1 immunohistochemical staining. Tubular injury and kidney injury molecule-1 staining increase with DEHP dose. Image D is a bar chart showing kidney damage scores across the four groups, indicating a dose-related increase. Image E is another bar chart showing kidney injury molecule-1 protein expression, also dose-related. Asterisks denote statistical significance between groups. Figure: DEHP nephrotoxicity via structure, study design, histology, bar charts.
DEHP Promotes the Deposition of Calcium Oxalate Crystals
In vitro, DEHP increased calcium oxalate crystal deposition and reduced HK-2 cell viability ( Figure 6A–C ). Consistent with these findings, DEHP further increased renal crystal deposition in mice. At the end of the experiment, DEHP-treated mice had lower body weights than the model group and showed significantly increased BUN and serum creatinine levels, indicating worsened renal function ( Figure 6D–H ). Figure 6 DEHP promotes calcium oxalate crystal deposition in vitro and aggravates renal crystal deposition and dysfunction in mice. ( A ) Representative images of HK-2 cells and calcium oxalate monohydrate (COM) crystals following the indicated treatments (×400; scale bar, 50 μm). ( B ) Quantification of COM crystal deposition. ( C ) Quantification of HK-2 cell viability. ( D ) Representative Von Kossa-stained kidney sections from the normal control, glyoxylic acid, glyoxylic acid plus 250 mg/kg DEHP, and glyoxylic acid plus 500 mg/kg DEHP groups at low and high magnifications (×15 and ×400, respectively). ( E ) Changes in mouse body weight. ( F ) Blood urea nitrogen levels. ( G ) Serum creatinine levels. ( H ) Quantification of Von Kossa-positive areas. Statistical significance is indicated as follows: * P < 0.05, ** P < 0.01, and *** P < 0.001. Image A: HK-2 cell micrographs at 400x magnification show increased COM crystal deposits from Normal Control to COM, further rising with DEHP (100 microM, 300 microM). Image B: Bar chart shows COM crystal deposition increasing from Normal Control to COM, further with DEHP. Image C: Bar chart shows HK-2 cell viability decreasing from Normal Control to COM, further with DEHP. Image D: Von kossa staining at 15x and 400x magnifications shows dark deposits increasing from Normal Control to Glyoxylic Acid, further with DEHP (250mg/kg, 500mg/kg). Image E: Bar chart shows body weight fold change lower in Gly and DEHP groups than Normal Control. Image F: Bar chart shows BUN levels increasing from Normal Control to Gly, further with DEHP. Image G: Bar chart shows Serum creatinine levels rising across Gly and DEHP groups. Image H: Bar chart shows Von kossa positive area increasing from Normal Control to Gly, further with DEHP. Comparisons marked with asterisks. Multi-panel scientific figure on DEHP effects on COM crystals, HK-2 viability and kidney Von kossa staining.
DEHP Alters Autophagy- and Apoptosis-Related Markers in Renal Tubular Epithelial Cells
The overlapping targets identified above were enriched in biological processes related to cellular homeostasis, including apoptosis and autophagy. To examine whether DEHP affects programmed cell death in renal tubular epithelial cells, TUNEL staining was performed. DEHP exposure increased the number of TUNEL-positive cells, with a more pronounced effect than in the model group. LC3B, a commonly used marker of autophagosome-associated activity, was assessed by immunohistochemistry. LC3B staining intensity was highest in the high-dose DEHP group and lowest in the control group ( Figure 7A–C ). Figure 7 DEHP exacerbates renal tubular epithelial cell apoptosis and increases LC3B expression in glyoxylic acid-treated mice. ( A ) Representative images of TUNEL fluorescence staining and LC3B immunohistochemical staining in kidney tissues from each group. Nuclei were counterstained with DAPI (blue), TUNEL-positive cells are shown in red, and LC3B-positive staining appears brown (scale bar, 50 μm). ( B ) Quantification of the TUNEL-positive cell rate. ( C ) Quantification of LC3B expression levels. Statistical significance is indicated as follows: ** P < 0.01, and *** P < 0.001. The figure consists of three sections. Section A displays kidney tissue images across four groups: Normal Control, Glyoxylic Acid, Gly plus DEHP 250 mg/kg and Gly plus DEHP 500 mg/kg. Images are labeled for DAPI (nuclei), TUNEL (apoptotic cells), Merge and LC3B staining. TUNEL-positive cells and LC3B intensity increase from Normal Control to higher DEHP doses. Section B is a bar chart showing TUNEL positive rates: near zero for Normal Control, ~2% for Glyoxylic Acid, ~9% for Gly plus DEHP 250 mg/kg and ~13% for Gly plus DEHP 500 mg/kg. Significance is marked with two asterisks (<0.01) and three asterisks (<0.001) between groups. Section C is another bar chart showing LC3B protein expression: ~1.8 for Normal Control, ~2.3 for Glyoxylic Acid, ~2.7 for Gly plus DEHP 250 mg/kg and ~3.2 for Gly plus DEHP 500 mg/kg, with three asterisks (<0.001) between all groups. Figure: TUNEL, LC3B staining, bar charts for 4 experimental groups.
Discussion
Di(2-ethylhexyl) phthalate (DEHP) is a widely used plasticizer and an environmental endocrine-disrupting chemical capable of inducing renal injury. Although kidney stone formation is closely associated with environmental factors, previous studies have largely focused on water quality, dietary exposures, and related determinants, 25 , 26 whereas the contribution of environmental pollutants remains insufficiently understood.
By integrating network toxicology, molecular simulation, and experimental validation, the present study provides evidence that DEHP exposure aggravates calcium oxalate crystal-induced renal injury and promotes kidney stone progression. The principal findings were as follows: (1) a total of 457 overlapping targets associated with both DEHP exposure and kidney stones were identified, among which CTNNB1, SRC, HSP90AA1, EP300, EGFR, and TP53 were prioritized as hub proteins; (2) molecular docking and molecular dynamics simulations suggested that DEHP may form relatively stable interactions with these proteins; and (3) in experimental models, DEHP increased tubular injury, crystal deposition, apoptosis, and LC3B expression. Collectively, these findings link computational predictions to concordant renal injury phenotypes and point to disruption of renal tubular epithelial cell homeostasis as a plausible biological basis for the observed effects.
Enrichment analyses indicated that the identified targets were predominantly involved in the PI3K-Akt, MAPK, HIF-1, and EGFR-related signaling pathways, as well as biological processes associated with apoptosis and autophagy. These pathways collectively regulate cell survival, stress adaptation, and programmed cell death. During kidney stone formation, calcium oxalate crystals injure renal tubular epithelial cells, while the damaged epithelial surface in turn facilitates further crystal adhesion and retention. DEHP may therefore amplify this self-reinforcing pathogenic cycle by impairing cellular stress adaptation and disturbing the balance among cell survival, apoptosis, and autophagy. In vivo, DEHP exacerbated glyoxylic acid-induced tubular injury, increased kidney injury molecule-1 (KIM-1) expression and crystal deposition, and further elevated blood urea nitrogen and serum creatinine levels. In HK-2 cells, DEHP enhanced crystal adhesion and deposition while reducing cell viability. TUNEL staining further indicated increased apoptosis, whereas the elevated LC3B signal suggested altered autophagy-related activity. Taken together, these results suggest that DEHP disrupts key homeostatic processes in renal tubular epithelial cells, particularly the balance between autophagy and apoptosis, thereby promoting crystal deposition and accelerating kidney stone progression.
DEHP exposure is ubiquitous in modern populations, and its potential multi-organ toxicity warrants substantial attention. Previous studies have demonstrated that DEHP impairs the reproductive system, 27 for example by disrupting the hypothalamic-pituitary-gonadal (HPG) axis and consequently compromising male reproductive health. 28 In females, excessive DEHP exposure during childhood has been associated with an increased risk of central precocious puberty. 29 Exposure during the reproductive years has also been linked to higher risks of reproductive tract tumors, 30 polycystic ovary syndrome, 31 and endometriosis, 32 whereas perinatal exposure may contribute to implantation failure and placental dysfunction. 33 DEHP can additionally induce metabolic disturbances and increase susceptibility to obesity and related metabolic disorders. 34 , 35 With respect to immunotoxicity, DEHP exposure has been associated with impaired immune responses, a higher prevalence of autoimmune diseases, 36 and increased respiratory allergic symptoms, 37 and may further aggravate sepsis-induced immunosuppression and organ injury. 38 Regarding neurotoxicity, DEHP may participate in the onset and progression of neurodegenerative disorders, including Alzheimer’s disease, 39 persistently alter the properties of neurons in the hypothalamic paraventricular nucleus, 40 and disrupt the neural homeostasis required for normal emotional and cognitive function. 41 Moreover, epidemiological and experimental evidence suggests that DEHP exposure may increase cancer risk, including by elevating breast cancer susceptibility, 42 promoting multiple myeloma progression, and inducing therapeutic resistance. 43
In summary, building on previous epidemiological evidence linking phthalate exposure to early renal injury and calcium-containing urinary stones, this study provides further experimental support for the ability of DEHP to aggravate crystal-associated renal tubular injury.
Although these findings help bridge the knowledge gap between plasticizer exposure and urinary system diseases and may offer new perspectives for kidney stone-related public health policy and clinical prevention, several limitations should be acknowledged. First, this study was confined to cell-based and animal experiments and did not include clinical samples from patients with kidney stones; therefore, the direct translational relevance of the present findings to clinical practice remains to be established. Second, human exposure to DEHP is generally chronic, low-dose, and cumulative, whereas the in vivo and in vitro models used here involved short-term interventions at predefined concentrations and thus may not fully recapitulate real-world physiological exposure patterns. Third, the core targets and signaling pathways were derived primarily from computational predictions, and their causal roles have not yet been validated using pathway-specific assays, genetic manipulation, or pharmacological inhibition. Future studies should establish long-term cumulative DEHP exposure models, incorporate clinical specimens and rigorously designed population-based investigations, and combine multi-omics approaches with targeted functional experiments to clarify the long-term toxic effects and underlying mechanisms of sustained DEHP exposure on renal crystal deposition and kidney stone progression.
Conclusion
Our findings indicate that DEHP aggravates calcium oxalate crystal-associated renal tubular injury and kidney stone progression, potentially by disrupting apoptosis–autophagy homeostasis in renal tubular epithelial cells. Network toxicology and in silico analyses further suggest the involvement of several candidate molecular targets and signaling pathways.
Abbreviations
DEHP, Di-(2-ethylhexyl) phthalate; BP, Biological Process; CC, Cellular Component; MF, Molecular Function; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MD, Molecular Dynamics; PPI, Protein–Protein Interaction; RMSD, Root Mean Square Deviation; RMSF, Root Mean Square Fluctuation; Rg, Radius of Gyration; HE, Hematoxylin-eosin Staining; BUN, Blood Urea Nitrogen; Cr, Creatinine; IHC, Immunohistochemical; PDB, Protein Data Bank.
Ethics Approval and Consent to Participate
The animal study was approved by the Animal Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (Approval No. TJH-202509015). All procedures involving animals were conducted in accordance with the Chinese national standard Laboratory Animal—Guideline for Ethical Review of Animal Welfare (GB/T 35892-2018). The database-based bioinformatics component of this study used only legally obtained, publicly available data and did not involve direct interaction with human participants, collection of human biological samples, or access to identifiable or sensitive personal information. Therefore, this component was exempt from ethics review in accordance with Item (1) of Article 32 of the Measures for Ethical Review of Life Science and Medical Research Involving Human Subjects, issued in China on February 18, 2023 (Guo Wei Ke Jiao Fa [2023] No. 4). Informed consent was therefore not required.
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Disclosure
The authors declare no competing interests in this work.
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