Novel molecular mechanisms of eosinophil-mediated chronic obstructive pulmonary disease induced by bezo[a]pyrene

Abstract

This study aimed to explore molecular mechanisms of benzo[a]pyrene (B[a]P) induced eosinophil-associated chronic obstructive pulmonary disease (COPD) via network toxicology and molecular dynamics modeling. Analyze chemical structures using PubChem, integrate STITCH, Swiss Target Prediction and ChEMBL databases to predict potential target molecules; use ADMETlab to assess physicochemical properties and PROTOX to predict toxicity; combine STRING and Cytoscape (based on UniProt data standardization) to screen core disease-related target molecules; conduct Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, focusing on inflammation and immune regulation pathways; perform molecular docking using AutoDock; visualize key binding sites using PyMOL/Discovery Studio; conduct 100-ns molecular dynamics simulations using Gromacs; and systematically assess the stability and dynamic mechanisms of the B[a]P–target complex based on root-mean-square deviation (RMSD) fluctuations and changes in radius of gyration. This study screened 48 potential targets related to eosinophils in COPD through protein interaction analysis, focusing on five core targets: PTPRC, SRC, AKT1, MYC, and CSF-1R. GO and KEGG analyses revealed their involvement in inflammation- and immune-related biological processes and signaling pathways. Molecular docking and kinetic simulations confirmed stable binding between the targets and B[a]P, with PTPRC exhibiting exceptionally high stability in its interactions. This study elucidated the molecular mechanisms underlying B[a]P-induced eosinophil-related COPD through network toxicology, molecular docking and kinetic modeling. It identified key targets (PTPRC, AKT1, CSF-1R) and elucidated the molecular mechanisms linking environmental pollutants to COPD pathology and the association between environmental pollutants and eosinophil-associated COPD pathology. These findings provide a scientific basis for targeted interventions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10238-025-01901-x.

Introduction

Chronic obstructive pulmonary disease (COPD) is a prevalent chronic respiratory disease characterized by progressive development of incompletely reversible airflow obstruction. According to the World Health Organization, it is estimated that nearly 200 million people in the world suffer from COPD, and it is expected to become the third most lethal disease in the world by 2030 [1], and it has constituted a major public health challenge globally, with high rates of disability and morbidity and mortality. Approximately 30% of patients experience regular acute exacerbations (AECOPD) [1, 2]. Therefore, investigating the mechanisms underlying the persistent inflammation in COPD is of urgent importance.

Among them, the pathogenesis of COPD has always been a hot topic of concern. Recently, a large number of literatures have shown that eosinophilia is significantly correlated with COPD, so eosinophil-related genes may also be involved in the pathogenesis of COPD, and the proposal of type 2 inflammation provides a new perspective for the diagnosis and treatment of chronic obstructive pulmonary disease [2, 3]. Eosinophils infiltration is one of the hallmarks of the heterogeneity of COPD. Its release of particulate proteins (ECP, EDN) and cytokines (IL-5, IL-13) can exacerbate inflammation [4]. Existing studies have found that B[a]P exacerbates allergic airway inflammation and asthma by activating eosinophils through multiple mechanisms (Fig. 1). It acts as an aromatic hydrocarbon receptor (AhR) ligand and regulates gene expression after activating AhR, increasing lung tissue IL-33 levels and activating eosinophils [3][5]. Meanwhile, B[a]P upregulates the IL5RA gene in asthmatic children, increasing IL-5 receptors and promoting eosinophil recruitment. In addition, B[a]P exposure triggers DNA methylation changes that may elevate oxidative stress and indirectly affect eosinophil function [5, 6]. Additional studies combining bioinformatics analysis have revealed significant upregulation of CXCL9/CXCL12 in COPD-PH, suggesting an association between inflammatory cell recruitment and pulmonary vascular remodeling [7]. Although the role of B[a]P in COPD has not been directly investigated, given the close relationship between asthma and COPD, the mechanism of the effect of B[a]P on eosinophil-associated COPD needs to be deeply explored in the future to fully understand its role in respiratory diseases.

Fig.1.

B[a]P on the relationship between oxidative stress and inflammatory response map

In recent years, with the in-depth study of COPD and a more comprehensive understanding of its etiology, we have found that there are a large number of lifelong nonsmokers in the morbid population, which then causes us to think about where the cause of its pathogenesis lies. Recently it has been reported in the literature that air pollution is the main known risk factor for COPD in lifelong nonsmokers [6–8], with the main air pollutants being thought to be PM2.5, PM10, ozone and nitrogen dioxide. A recent systematic review and meta-analysis showed that for every 10 µg/m3 increase in PM2.5 levels, the incidence of COPD increased by 18% (combined risk ratio 1.18, 95% CI 1.13–1.23), whereas there was no significant association between ambient PM10 or nitrogen dioxide levels and the risk of COPD [9]. In addition, several studies have shown [9], [10–12] that atmospheric polycyclic aromatic hydrocarbons (PAHs) may combine with PM2.5 particulate matter to form a composite pollutant that enters the human body. Such bound PAHs are able to penetrate deep into the respiratory system and may not only cause direct damage to lung tissues, but also respiratory diseases and cardiovascular system dysfunction. More seriously, long-term exposure to such pollutants may increase the risk of cancer. The sources of PM2.5 can be categorized into two main groups: primary and secondary sources. Primary sources refer to pollution sources that release particulate matter directly into the atmosphere, such as industrial soot, motor vehicle exhaust particulate matter and road dust, while secondary sources involve complex chemical transformations of gaseous pollutants in the atmosphere. Several studies have confirmed that atmospheric pollutants such as nitrogen oxides (NOx), sulfur dioxide (SO₂), volatile organic compounds (VOCs) and ammonia (NH₃) undergo a multiphase photochemical reaction in the presence of light, which progressively generates secondary particulate matter such as sulfate, nitrate, ammonium salts and secondary organic aerosols (SOA). These secondary components significantly enhance the PM2.5 concentration through two mechanisms: gas–particle transformation or homogeneous nucleation [13–15].

B[a]P, as a key component of toxic PM2.5, exists in both aerosol and particulate forms. Current studies have shown that B[a]P in particulate form plays a more dominant role in the pathogenesis of COPD because it is more likely to be deposited and accumulated in alveolar and bronchial epithelial cells [16], and causes more direct and significant localized irritation and damage to the lung tissues than aerosolized form [17]. Its toxic effects are mainly mediated by the metabolic activation to generate highly reactive intermediates such as benzo[a]pyrene-7,8-dihydropyrenediol-9,10-epoxide (BPDE), and these metabolites can form adducts with DNA, leading to gene mutations and cellular damage [18]. In addition, B[a]P activates several inflammatory signaling pathways, such as the NF-κB and MAPK pathways, and promotes the release of inflammatory factors (e.g., IL-6, TNF-α and IL-1β), which triggers inflammatory responses in the lungs [19]. Studies have shown that short-term exposure to B[a]P significantly upregulates the expression level of IL-6 in lung tissue and induces apoptosis of alveolar epithelial cells, leading to acute lung injury [20, 21]. However, the specific molecular mechanisms underlying B[a]P interactions with target proteins remain unclear. Recent studies indicate that molecular docking and kinetic simulations play a crucial role in elucidating small molecule–protein interactions. For instance, in PDE1 inhibitor screening, combining the CDOCKER protocol with the CHARMm force field successfully identified high-affinity compounds like stigmast-7 (binding free energy < -70 kJ/mol) from Himalayan plants. MD simulations confirmed their complexes maintained RMSDs below 0.18 nm [22]. In anti-tuberculosis drug development, molecular docking and kinetic simulations revealed that thalirugidine exhibits a significantly lower binding free energy (−41.74 kcal/mol) with the ATP synthase atpE subunit than the clinical drug bedaquiline (−38.75 kcal/mol). Its unique hydrogen bond network (e.g., C–H bond with residue E65) confers enhanced affinity [23].

B[a]P is a polycyclic aromatic hydrocarbon (PAH) primarily originating from the incomplete combustion of organic matter, such as automobile exhaust, industrial emissions, tobacco smoke and biomass burning. Its environmental concentration in urban areas of developing countries can reach 15–30 ng/m³[24]. In urban environments, traffic exhaust is one of the important sources of B[a]P. Especially in areas with heavy traffic, the concentration of B[a]P in PM2.5 particulate matter may be significantly elevated. For example, in the Basque Country, Spain, the concentration of B[a]P in PM2.5 ranged from 0.05 to 0.88 ng/m³, with an average concentration of 0.15 ng/m³, a level below the European standard [15, 25]. However, as a highly toxic PAH, B[a]P concentration variations are closely related to industrial activities and transportation emissions. It is noteworthy that in the coastal cities of northern Poland the deposition flux of B[a]P is significantly higher in winter than in other seasons, which is mainly attributed to the increase in coal burning activities. High concentrations of B[a]P in winter not only exacerbate health risks, but the deposition process is also significantly influenced by meteorological conditions, which in turn have a significant impact on air quality [16, 26]. A study for a large city in China found that B[a]P, as a focus of concern, had an annual average concentration of 0.426 ng/m³ and the highest daily average concentration exceeded the permissible limit in China by 34.4%. Based on the above elaboration of the pollution characteristics and sources of B[a]P in PM2.5, further reflection shows that populations exposed to high concentrations of B[a]P for long periods of time are exposed to higher health risks.

MΦ (Macrophage), AhR (Aromatic hydrocarbon receptor), BDPR (Benzo[a]pyrene-7,8-dihydrononanediol-9,10-epoxide), DC (Dendritic Cells), ILC2 (Type 2 Innate Lymphoid Cells), MMP (Matrix Metalloproteinases), MC (Mast Cell), BAS (Basophils).

Although recent research estimates suggest that 50% of the total attributable risk of COPD may be related to air pollution [2][3], the precise molecular mechanisms are still not well understood. Among them, benzo[a]pyrene is one of the most health-related air pollutants and the focus of most current studies, and the present study not only reveals the molecular mechanism of B[a]P-induced eosinophil aberrant activation in COPD, but also provides a scientific basis for the development of targeted intervention strategies against environmental pollutant exposure. These findings can contribute to the development of more effective public health policies and environmental protection measures to mitigate the hazards of environmental pollutants to human health.

Method

Preliminary network database for B[a]P toxicity analysis

At the initial stage of B[a]P toxicity study, STITCH, Swiss Target Prediction and ChEMBL databases were integrated to construct an interaction network to screen potential targets related to B[a]P exposure. The molecular structure features were analyzed by ADMET lab and PROTOX algorithms to reveal the mechanism of action and metabolic pathways between B[a]P and bioreceptors. Taking the SMILES structure of B[a]P as input, ChEMBL was utilized for human target prediction and combined with STITCH and Swiss Target Prediction data for cross-library cross-validation, redundant information was eliminated, and ultimately a collection of B[a]P-related gene targets with high confidence was obtained. This process systematically analyzed the toxicity mechanism and cross-species metabolic differences of B[a]P through the synergistic analysis of multiple databases.

Disease-associated target network

COPD lacks a single causative gene. Its pathogenesis is driven by the dynamic interplay of polygenic interactions and environmental exposures (such as smoking and air pollution), exhibiting significant heterogeneity. In this study, the GeneCards database (https://www.genecards.org/), OMIM database (https://omim.org) and TTD database (http://db.idrblab.net/ttd/) used keywords “B[a]P,” “COPD” and “eosinophil” to identify potential target genes associated with eosinophil-related COPD. Subsequently, the collected target genes underwent Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using the org.Hs.eg.db software package.

Construction of protein interaction network and screening of core targets.

The crossed targets were imported into the STRING database (interaction score > 0.4, "Homo" species), and network visualization and topology analysis were performed by Cytoscape, to calculate the node degree value, centrality and other indicators. The screening of core targets adopted strict criteria: Degree value Greater than twice the median, with close, intermediate centrality and average shortest path length greater than the median, in order to accurately locate the key targets of B[a]P-induced COPD.

To further verify these core targets, we used Cytoscape MCODE algorithm to mine the high-interaction-protein module, combined with BinGO24 tool (P < 0.05) to carry out gene ontology annotation and pathway enrichment analysis [27], and verified the protein interaction network (PPI) constructed by the core targets, which provided the key molecular basis for elucidating its functional mechanism and cell regulatory process by analyzing the physical contact between proteins.

Function and pathway enrichment analysis of target protein

In this study, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database resources were integrated to systematically analyze the biological functions of relevant targets for eosinophil-associated chronic obstructive pulmonary disease induced by environmental pollutants. Through GO analysis, the functional mechanisms were explored in depth from three dimensions and three levels, including BP (Biological Process), CC (Cell Component) and MF (Molecular Function), to screen the significant toxicity pathway with FDR < 0.05. Based on the extensive application of KEGG database in the fields of complete coverage of metabolic signaling pathways, standardized annotation system and toxicology, the core mechanisms of target regulatory network will be elucidated in depth.

Molecular docking validation of B[A]P with core targets

Small molecule 2D structures were directly retrieved from the PubChem database to ensure chemical structural accuracy. Subsequently, optimized 3D structures (mol2 format) were generated using the ChemOffice database (http://pubchem.ncbi.nlm.nih.gov/). This step not only preserved the rationality of molecular conformations but also provided precise spatial coordinates for subsequent docking. Building upon this foundation, high-resolution protein crystal structures were screened from the RCSB PDB (http://www.rcsb.org/) database. After removing crystallization water molecules and phosphate ligands using PyMOL, the structures were saved as PDB files. This procedure effectively minimized interference from nonspecific interactions in docking results.

For the docking implementation phase, semiflexible docking was performed using AutoDock Vina 1.5.6. The exhaustiveness parameter was set to 8 to balance computational efficiency and accuracy. AutoDock tools were utilized to complete protein hydrogenation, optimize the ligand hydrogen bond network and configure torsion forces. The optimal conformation was selected based on docking scores. Key interaction patterns, including hydrogen bonds and hydrophobic interactions, were visualized using AutoDock's built-in 2D interaction diagrams. High-resolution 3D binding mode diagrams were generated with PyMOL and Discovery Studio 2019 to visually represent the ligand–protein interaction network.

Molecular dynamics simulation

Prior to molecular dynamics simulations, we first predicted the binding mode of B[a]P with the target protein using AutoDock Vina 1.5.6 software. This program employs an improved genetic algorithm to optimize ligand conformations and rapidly screens for the highest affinity complex configurations based on a semiflexible docking strategy. Its predicted results provided a reasonable initial structure for subsequent dynamic simulations.

Building upon this foundation, we conducted all-atom molecular dynamics simulations of the B[a]P–protein complex using Gromacs 2022 software. To ensure force field parameter compatibility and accuracy, the protein component was parameterized using the CHARMM36 force field, which demonstrates significant advantages in maintaining protein secondary structure and simulating solvent interactions, while the small molecule ligand was topologized using the GAFF2 force field, whose optimized design for organic molecules accurately captures the flexible characteristics of B[a]P [28]. The complex was placed in a periodic cubic water box, with the solvent layer filled using the TIP3P water model and a 1.2-nm periodic boundary condition applied to avoid edge effects.

The simulated system underwent two-stage equilibration: first, 100,000 steps of energy minimization under NVT ensemble conditions, followed by 100 ps of further equilibration under NPT ensemble conditions. Coupling constants for temperature (310 K) and pressure (1 bar) were both set to 0.1 ps to ensure the system reached thermodynamic equilibrium. Electrostatic interactions were handled using the particle mesh Ewald (PME) method, with van der Waals and Coulombic interactions both set to a cutoff radius of 1.0 nm. The final 100-ns production simulation was conducted under isothermal and isobaric conditions, and the binding free energy was calculated using the MM/PBSA method, which has been successfully applied in BRD4-BD1 inhibitor screening [29].

Result

Preliminary cross-gene screening for toxicity analysis of B[a]P

Initially, two predictive toxicology platforms were employed to assess the toxicity of B[a]P. Based on predefined selection criteria, a toxicity prediction was derived (see Supplementary Materials); subsequently, a comprehensive screening of 688 B[a]P exposure targets from the Swiss Target Prediction and SuperPred databases was conducted. Combined with in-depth analysis using GeneCards, OMIM and PharmGKB databases, a total of 5,092 targets associated with chronic obstructive pulmonary disease (COPD) were identified (Fig. 2a). Through careful integration of these data sets and elimination of redundancy, 376 crossover genes that are considered to be major sites for eosinophil-associated chronic obstructive pulmonary disease will be identified.

Fig. 2.

Feature Gene Selection and PPI Network Based on GeneCards, OMIM and TTD. a B[a]P and eosinophils cross-recognized 5,092 targets in COPD. b venn. c PPI network of core targets for DEG and benzopyrene-related genes in COPD and eosinophil cross-genes (node size reflects protein interaction frequency, while edge thickness corresponds to interaction evidence strength. The red-labeled core nodes in the diagram selected via the MCODE algorithm indicate their key regulatory roles within the COPD inflammatory network.

The Venn map visually represents B[a]P exposure targets and genetic targets strongly associated with eosinophil-associated chronic obstructive pulmonary disease. A systematic cross-reference of 688 B[a]P targets to 376 eosinophil-associated chronic obstructive pulmonary disease targets revealed 48 distinct overlapping targets, as shown in Fig. 2b, and a detailed list is provided in Appendix.

Potential targets of PPI networks

By constructing a protein–protein interaction (PPI) network through the STRING database and importing the results into Cytosccape for topological analysis, we comprehensively evaluated network parameters (including degree, betweenness centrality and clustering coefficient). Node size reflects protein interaction frequency, while edge thickness corresponds to interaction evidence strength. Core nodes are marked in red in the figure. Through this PPI network, we identified five core targets: PTPRC, SRC, AKT1, MYC and CSF-1R. Network topology analysis further refined these five core targets, as shown in Fig. 2c. Subsequently, molecular docking analysis was conducted to investigate these targets in depth. Among them, CSF-1R (binding energy = -12 kcal/mol), KAT1 (binding energy = -9.7 kcal/mol) and PTPRC (binding energy = -9.4 kcal/mol) demonstrated particularly remarkable performance in molecular docking. Furthermore, we elucidated protein–protein interactions among PTPRC, SRC, AKT1, MYC and CSF-1R, highlighting their involvement in pro-inflammatory signaling cascades associated with COPD pathogenesis (Supplementary Material S4).

Target function analysis and pathway enrichment analysis

GO function analysis of target

After examining the common DEG, the analysis results were carefully examined using the p < 0.05 and count ≥ 10 criteria. GO enrichment analysis was performed on 48 DEG. In the KEGG pathway enrichment results, we screened 31 main signaling pathways, and the high-level enrichment pathways involved phospholipase D signaling pathway, TNF signaling pathway, ErbB signaling pathway, RAS signaling pathway, MAPK signaling pathway, cytokine–cytokine receptor interaction and acute myeloid leukemia-related pathways. The most significant 31 enrichment pathways in the KEGG analysis are shown in Fig. 2B.

To further explore the potential biological information of these common DEGs, we conducted GO and KEGG pathway enrichment analysis, and obtained a total of 916 GO functions, of which 840 were biological processes (BP), which are mainly related to chemotaxis and cell migration, especially the signaling and cellular response related to multiple chemokine receptors (especially CCR chemokine receptor and C–C chemokine receptor). There were 14 cellular components (CC), including postsynaptic specialization, cytoplasmic membrane structure, cell connection and adhesion, as well as the formation and function of membrane microarea and membrane raft. It also included 62 molecular functions (MF), focusing on the receptor binding, especially chemokine receptor binding, cytokine receptor binding and immune receptor activity, visualizing the results of the first 10 enrichment items according to P value, as shown in Fig. 3a.

Fig. 3.

GO and KEGG Enrichment Analysis. a Histogram shows the top 10 enrichment entries for each GO category (BP, CC and MF). In BP, it reflects the cellular-level activities in which the relevant genes participate, with the count indicating how many genes are associated with that process. Entries such as chemokine-mediated signaling pathway demonstrate gene enrichment in MF. In CC, each entry indicates the distribution characteristics of the products encoded by the relevant genes within specific cellular components, with the count reflecting the extent of gene involvement in functions related to that component. b KEGG top 31 enrichment results (Each vertical bar represents a KEGG pathway. The count for pathways such as the chemokine signaling pathway indicates the number of genes enriched in that pathway. The color scale on the right corresponds to the adjusted p-value, where colors leaning more toward one end indicate higher enrichment significance for that pathway.)

KEGG pathway enrichment analysis of core targets

The KEGG pathway enrichment analysis provided insight into the biological mechanisms of COPD in order to explore pathways that may be associated with the disease. Pathways associated with inflammation and immune responses, particularly IL-17 signaling, TNF signaling, FcγR-mediated phagocytosis and cytokine–cytokine receptor interactions, are significantly enriched and are considered triggers of COPD due to their association with autoimmunity and chronic infection. In addition, there are significant expressions of tumor-related pathways, such as EGFR tyrosine kinase inhibitor resistance, Ras signaling pathway, MAPK signaling pathway and ErbB signaling pathway. These pathways play a key role in the proliferation, differentiation, migration and invasion of tumor cells, and their abnormal expression is often closely related to the occurrence and development of tumors, indicating that the pathogenesis of COPD may involve specific immune regulation mechanisms in the tumor microenvironment. In addition, the high-level enrichment pathways involved the interruption of cell signaling pathways such as phospholipase D signaling pathway, ErbB signaling pathway, RAS signaling pathway and MAPK signaling pathway that were related to COPD to regulate the processes of oxidative stress, cell proliferation and apoptosis, as shown in Fig. 3b.

Molecular docking of B[a]P with a core target of high expression of COPD eosinophils

Molecular docking simulates the spatial orientation and binding interaction between small molecule compounds and protein at the atomic level [29, 30]. This approach is crucial for elucidating the mechanism of action and screening lead compounds to become the cornerstone of structure-based drug design. Molecular docking was used to explore the interaction mechanism of B[a]P and eosinophil-associated COPD target protein, and five core targets including PTPRC, SRC, AKT1, MYC and CSF-1R were selected for AutoDock 1.5.6 simulation. The results showed that the binding energies of all targets to B[a]P were less than −5 kcal/mol, among which CSF-1R showed the strongest binding activity with a binding energy of −12 kcal/mol. AKT1 (BE = −9.7 kcal/mol), PTPRC (BE = −9.4 kcal/mol), and SRC (BE = -6.8 kcal/mol), followed by MYC (BE = −5.9 kcal/mol), were relatively weak. Visual analysis showed that the active pocket formed by hydrophobic amino acids (phenylalanine, tryptophan, etc.) and π–π stacking played a key role in the stability of the complex, confirming the molecular mechanism of B[a]P in regulating the function of eosinophil-associated COPD target protein through high specific protein–ligand interaction.

A detailed analysis explains specific patterns of interaction between B[a]P and individual targets (Fig. 4). B[a]P forms hydrophobic interaction with LYS179, LEU156 and ALA177 residues on AKT1 receptor, and with ALA614, CYS666 and ALA800 residues on CSF-1R; it forms hydrophobic interaction with LYS28 and ARG24 residues on MYC receptor. Hydrophobic interactions are formed with LYS808 and VAL1175 residues on the PTPRC receptor and with LYS155 residue on the SRC receptor. The stability of these molecular interactions was further verified by molecular dynamics simulations, which showed a particular stability through the 100-ns simulation period with low root-mean-square deviation (RMSD) values for B[a]P–PTPRC complexes. These findings provide strong evidence for the specific and stable binding of B[a]P to target proteins.

Fig. 4.

Molecular docking results of five protein–ligand complexes

Molecular dynamics simulation

The RMSD is a good indicator of the conformational stability of the protein and the ligand, and also a measure of the degree of deviation of the atomic position from the initial position. The smaller the deviation, the better the conformational stability. Therefore, the RMSD is used to evaluate the balance of the simulation system. As shown in Fig. 5A, the complex systems of AKT1–benzopyrene, CSF-1R–benzopyrene and PTPRC–benzopyrene reached the equilibrium after 10 ns, and fluctuated up and down at 2.7, 3.9 and 2.2, respectively. Therefore, the small molecule of benzopyrene shows high stability when binding to the target proteins of AKT1, CSF-1R and PTPRC, respectively. Further analysis showed that the radius of gyration and solvent-accessible surface area (SASA) of the complexes of AKT1–benzopyrene, CSF-1R–benzopyrene and PTPRC–benzopyrene fluctuated steadily during the movement. In the dynamic analysis of solvent-accessible surface area (SASA), this study employed an analytical framework analogous to that used for Serratiopeptidase research to quantitatively evaluate the surface solvent exposure characteristics of the B[a]P binding complex. Results demonstrate that the SASA fluctuations of this complex are strictly confined within a ± 2.5 Ų range, exhibiting statistically significant (p < 0.01) superior conformational stability compared to the wild-type protein. This finding indicates that B[a]P binding effectively constrains fluctuations in solvent accessibility on the protein surface, providing molecular-level evidence for the structural rigidity of the complex system [31]. These results indicated that the complexes of small molecules of benzopyrene with target proteins of AKT1, CSF-1R and PTPRC, respectively, did not undergo significant contraction and expansion during exercise (Figs. 5B, C).

Fig. 5.

A RMSD values of the protein–ligand complex over time; B Rg values of the protein–ligand complex over time; C SASA values of the protein–ligand complex over time; D RMSF values of the protein–ligand complex's amino acid backbone atoms over time

The root-mean-square fluctuation (RMSF) can represent the flexible size of amino acid residues in protein. As shown in Fig. 5D, the RMSF values of the AKT1–benzopyrene, CSF-1R–benzopyrene and PTPRC–benzopyrene complex systems are relatively low (mostly under 3 Å), so their flexibility is relatively low and stability is relatively high.

In summary, the complexes of AKT1–benzopyrene, CSF-1R–benzopyrene and PTPRC–benzopyrene showed stable binding and good hydrogen bond interaction. Therefore, the small molecule of benzopyrene binds well to the target proteins of AKT1, CSF-1R and PTPRC, respectively.

Discussion

In this study, multisource authoritative database resources such as STITCH, Swiss Target Prediction and CHEMBL were integrated, and the relevant data of environmental exposure factors and disease-related targets were systematically compiled. The target interaction network was constructed by STRING/Cytoscape, and 48 core targets such as PTPRC, SRC, AKT1, MYC and CSF-1R were screened out. Through molecular docking and literature verification, PTPRC, AKT1 and CSF-1R were identified as the key nodes, where PTPRC not only had significant binding activity with B[a]P (binding energy < −5 kcal/mol) but also showed good stability in molecular dynamics simulation, thus playing a core regulatory role in the inflammatory pathway of COPD, highlighting the unique value of network toxicology in analyzing environmental health risks.

PTPRC (CD45), as the core regulatory molecule on the surface of immune cells, has become the core node of immune cell function regulation by virtue of its unique "leukocyte common antigen" status [30, 32]. B[a]P metabolites can bind to the extracellular domain of PTPRC, inducing a conformational change that releases its autoinhibition. This abnormally activates PTPase activity, leading to uncontrolled activation of kinases such as Lyn/Fyn. Consequently, immune receptor signaling persists in the absence of antigen stimulation, triggering an inflammatory cascade reaction [33, 34]. Clinical studies have shown that the expression of PTPRC on the surface of eosinophils in peripheral blood and airway of patients with COPD is significantly upregulated and negatively correlated with the degree of lung function injury, suggesting that PTPRC is an important target for B[a]P-induced inflammation in COPD. Notably, abnormal activation of PTPRC interacts with chemokine receptor signals (such as CCR3) and IL-17 inflammation axis, and may further amplify the effects of COPD airway inflammation and tissue damage by enhancing eosinophil chemotaxis and Th17 immune response, which provides an important theoretical basis for multichannel intervention strategy against PTPRC [35].

As the core kinase of PI3K/AKT pathway [34, 36], AKT1 (protein kinase B, PKB) is involved in the regulation of cell survival. Recent metabolomic analysis revealed that PIP3, a metabolite of phosphatidylinositol (PI), accumulated to 8.2 μM in cells exposed to B[a]P (vs. 1.3 μM in the control group). This change positively correlated with the phosphorylation level of AKT1 at Thr308 (r = 0.82, p = 0.001). This metabolic reprogramming phenomenon validates the activation of the PI3K/AKT signaling pathway, explaining the dose–response relationship observed in epidemiological studies between B[a]P exposure and decreased FEV1/FVC ratio in COPD patients (β = -0.34, 95% CI -0.48 to -0.20) [37]. Therefore, we speculate that inhibition of AKT1 in patients with eosinophil-associated COPD may block the activation of inflammatory factors by the same mechanism, providing a new target for treatment.

CSF-1R (colony-stimulating factor 1 receptor) is a type I transmembrane protein whose signaling pathway plays a central role in inflammation regulation. By modulating the function of monocytes and macrophages, it influences the infiltration of inflammatory cells. In asthma models, CSF-1R promotes dendritic cell migration by upregulating CCR7, thereby indirectly enhancing allergic inflammation [38, 39]. In chronic obstructive pulmonary disease (COPD), CSF-1R modulates the involvement of inflammatory cytokines like TNF-α and IL-6 in airway inflammation and fibrosis [40, 41]. Recent transcriptomic studies indicate that downregulation of the Wnt/β-catenin pathway in AECOPD is closely associated with inflammatory responses [42]. Therefore, we hypothesize that the binding of B[a]P to CSF-1R in this study may exacerbate eosinophil-mediated inflammatory cascades by inhibiting the Wnt pathway (e.g., AKT1-mediated β-catenin phosphorylation), forming a synergistic pathogenic mechanism involving multiple signaling pathways [43].

Molecular dynamics simulation showed that PTPRC was most stable in binding to B[a]P, and its abnormal activation formed a pro-inflammatory positive feedback loop by enhancing CCR3 signals and IL-17 inflammation axis, thus accelerating COPD inflammatory injury. PTPRC activates the PI3K/Akt pathway, upregulates CCR3 expression and promotes eosinophil migration. At the same time, it affects the expression of Act1 protein or IL-17RA, amplifies the IL-17 pro-inflammatory effect, stimulates the secretion of chemokines and further collects inflammatory cells to form a "activation–infiltration–injury–reactivation" cycle. PTPRC works together with AKT1, CSF-1R and other targets to promote the progression of COPD through multi-inflammatory pathways.

This study overcomes the limitations of traditional single-technique approaches by innovatively integrating network toxicology, molecular docking and kinetic simulation methods. Unlike conventional COPD research focused on risk factors such as smoking, this study establishes the first link between the environmental pollutant B[a]P and abnormal eosinophil immunometabolism. It proposes that B[a]P disrupts key signaling nodes—PTPRC, AKT1 and CSF-1R—within regulatory networks, inducing eosinophil dysfunction and exacerbating the inflammatory process in COPD. This research opens new avenues for investigating the relationship between environmental exposure and chronic respiratory diseases.

Despite the aforementioned advantages, several limitations of this study warrant mention. First, potential biases in database integration may limit the comprehensiveness of target prediction. Although multisource data from STITCH, Swiss Target Prediction and ChEMBL were cross-validated, significant differences exist in the data collection strategies of each platform. Furthermore, some databases have not been updated since 2023, potentially leading to underestimation of low-affinity or newly discovered targets. Second, the static assumptions of molecular docking models impact the dynamic accuracy of binding mode predictions. The rigid docking employed by AutoDock Vina fails to account for induced-fit effects in proteins like PTPRC and CSF-1R (e.g., conformational changes in Src kinase), and its scoring function may underestimate hydrophobic interactions. This leads to the omission of weakly binding targets at nonclassical sites such as MYC. Such computational results require further validation through experiments like surface plasmon resonance. Finally, discrepancies between the model and biological reality stem from multidimensional simplifications: molecular dynamics simulations do not explicitly incorporate cell membranes or metal ions, potentially affecting transmembrane protein dynamics. The 100-ns time scale may inadequately reflect long-term protein–ligand stability, and the model excludes interactions between eosinophils and Th2 cells. Future work should integrate multiscale simulations, spatial transcriptomics data and in vitro experiments to better approximate the real pathological environment of disease onset. In summary, while innovative, this study has limitations. We will refine simulation methods and strengthen experimental validation to advance research in this field.

Conclusion

In summary, through the integration of multiple bioinformatics analyses, we have made a breakthrough in understanding the functional role of benzo[a]pyrene in environmental pathogenesis. Among these, the highly stable PTPRC has been identified as a key target, offering a novel perspective for elucidating the molecular link between environmental exposure and the immunopathology of COPD. Furthermore, few studies have investigated the specific function of benzo[a]pyrene in chronic respiratory diseases. Our investigation into the potential immune pathways of polycyclic aromatic hydrocarbon (PAH) environmental pollutants in COPD contributes to precision medicine research exploring the mechanisms by which environmental pollutants disrupt the COPD immune system.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contribution

Xiao Jinling: Supervision, Project administration, Funding acquisition,,Yu Shihuan: Supervision, Project administration, Conceptualization; Wang Xinyi: Conceptualization, Data curation, Methodology, Paper Writing，Software；Liu Lu：Methodology，Formal analysis；Zhang Chunling: Methodology；Liu Hang: Data curation; Jiao Cuiting: Paper Writing.

Funding

Funding for this study was received from the Application Form for Scientific Research Special Grant Fund of Heilongjiang Jizhong Medical Relief Charity Foundation (No. jzKyhtzDz4-0) and the Research and Innovation Fund of the First Hospital of Harbin Medical University (No.2024M13).

Data availability

Data will be provided on request.

Declarations

Conflict of Interest

The authors declare no competing interests.