Toxic Mechanisms of PM2.5 Constituents (LPS and BPDE) in Cardiometabolic Disease: Insights from Integrated Machine Learning and Molecular Dynamic Simulations

Toxic Mechanisms of PM2.5 Constituents (LPS and BPDE) in Cardiometabolic Disease: Insights from Integrated Machine Learning and Molecular Dynamic Simulations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Toxic Mechanisms of PM2.5 Constituents (LPS and BPDE) in Cardiometabolic Disease: Insights from Integrated Machine Learning and Molecular Dynamic Simulations Haoyue Jia, Hao Zhang, Chengyan Guan, Qiang Wan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7937700/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Exposure to specific ambient pollutants, including certain PM2.5-bound constituents like benzo[a]pyrene-7,8-dihydrodiol-9,10-epoxide (BPDE) and lipopolysaccharide (LPS), has been increasingly implicated as a significant risk factor in the global burden of cardiometabolic diseases (CMD). However, the precise toxicological mechanisms through which these pollutants adversely affect cardiometabolic health remain poorly understood. Accordingly, this study aimed to delineate the effects of BPDE and LPS, both individually and in combination, on CMD, and to investigate the underlying molecular mechanisms driving its pathogenesis. We identified 366 and 287 potential targets for BPDE and LPS, respectively, in CMD through a multi-database screening (SwissTargetPrediction, ChEMBL, PharmMapper, CTD, GEO). Rigorous bioinformatic screening—integrating the STRING platform, Cytoscape (v3.10.0), and three machine learning (ML) methods—identified nine core targets: EGFR, ESR1, JAK2, PIK3R1, HDAC3, SORD, ADK, HDAC2, and SOD2. GO and KEGG analyses demonstrated their significant enrichment in key signaling (e.g., FoxO) and metabolic pathways (e.g., lipid metabolism and atherosclerosis), suggesting a mechanistic link to pollutant-induced CMD. Molecular docking and dynamics simulations demonstrated robust binding of BPDE and LPS to EGFR and MMP9, respectively, identifying these complexes as promising therapeutic targets for pollutant-associated CMD. Cardiometabolic BPDE LPS molecular docking molecular dynamic simulations machine learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Air pollution poses a major global environmental threat to human health, particularly in low- and middle-income countries. (Campolim et al., 2024; Wan et al., 2019) Long-term exposure to air pollutants, particularly PM₂.₅, has been positively linked to elevated rates of cardiovascular mortality and morbidity (Wan et al., 2021a, 2021b) and has further been implicated in the pathogenesis of various metabolic disorders, including obesity, type 2 diabetes (T2DM), hypertension (HTN), and dyslipidemia (DLD). (Khound et al., 2025; Yang et al., 2019) These adverse outcomes are primarily mediated by mechanisms including systemic inflammation and oxidative stress. (Campolim et al., 2024; Long et al., 2025) Polycyclic aromatic hydrocarbons (PAHs) are a prominent risk factor for the harmful effects of airborne PM. Notably, research has identified benzo[a]pyrene-7,8-dihydrodiol-9,10-epoxide (BPDE), the ultimate toxic metabolite of PAHs (Y. Zhang et al., 2023), and lipopolysaccharide (LPS) as significant mediators of these pathological outcomes. (Bandowe et al., 2014; He et al., 2018) PAHs are a class of widespread organic pollutants with significant carcinogenic and ecotoxicological potential. (Freire et al., 2020; Moubarz et al., 2023) Among them, benzo[a]pyrene (BaP), a prominent and potent congener, is recognized as a Group 1 carcinogen by the International Agency for Research on Cancer (IARC). (Bauer et al., 2022) BaP is ubiquitous in the environment, present in sources such as cigarette smoke, petroleum derivatives, charbroiled food, and contaminated water. (Ling et al., 2004) Human exposure occurs primarily via inhalation, ingestion, and dermal contact. (Ye et al., 2020) Following uptake, BaP undergoes metabolic activation via the aryl hydrocarbon receptor (AhR) pathway. which upregulates enzymes such as cytochrome P450 1A1 (CYP1A1) and epoxide hydrolase, leading to its conversion into the ultimate carcinogenic metabolite, BPDE. (Bukowska et al., 2022; Ye et al., 2020) The activated BPDE forms bulky, mutagenic DNA adducts, a key event in carcinogenesis. This DNA damage induces cellular dysfunction and inflammation. (Ling et al., 2004; Piberger et al., 2018) This pathological process not only sustains carcinogenesis but also directly contributes to an increased risk of cardiovascular diseases. (Hadrup et al., 2019) LPS, an endotoxin and key virulence factor of gram-negative bacteria, activates innate and adaptive immunity, triggering both local and systemic inflammation. (Mohammad & Thiemermann, 2020; Wei et al., 2024) Chronic endotoxemia resulting from elevated LPS levels is a recognized risk factor for numerous inflammation-driven conditions, including cardiometabolic disorders. (Cai et al., 2020; Pussinen et al., 2022) Furthermore, TLR4 activation by LPS can promote cancer progression; it exacerbates the downregulation of epithelial markers such as claudin-1 and E-cadherin—a hallmark of epithelial-to-mesenchymal transition that is associated with enhanced cell migration, invasiveness, and metastasis. (Wu et al., 2020; Yang et al., 2025) Owing to their multi-target nature, environmental pollutants dysregulate the homeostatic equilibrium of endogenous biomolecules, resulting in highly complex toxicological networks. Consequently, conventional toxicology, which often relies on a single-target framework, appears inadequate for systematically evaluating the combined effects of pollutant exposures.(Antczak et al., 2010) This study leveraged a computational framework comprised of network toxicology, machine learning (ML), docking, and molecular dynamics simulations(MDS) to delineate the combined cardiometabolic toxic mechanisms of BPDE and LPS. By comparing their predicted targets and possible mechanistic pathways in cardiometabolic disease (CMD), this work aims to provide a theoretical basis for the diagnosis of toxicity-related diseases and may contribute to future chemical hazard evaluation. 2. Methods 2.1. Collection of Toxicologically Relevant Compound Targets Molecular structures (SMILES strings and 3D conformers) were obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov) and subsequently processed using ProTox-3.0 (DOI: 10.1093/nar/gkad929) and ADMETlab 3.0 (DOI: 10.1093/nar/gkae236) for comprehensive toxicity profiling. For target prediction, Swiss Target Prediction was primarily employed, retaining all targets with probability scores > 0. Three additional databases SEA (https://sea.bkslab.org/), CHEMBL (https://www.ebi.ac.uk/chembl/), and PharmMapper (https://www.lilab-ecust.cn/pharmmapper/) were leveraged to expand coverage and validate initial predictions via consensus analysis, following duplicate removal. Rigorous intersection analysis of the unified dataset identified high-confidence consensus targets, demonstrating robust prediction reliability through this multi-database cross-validation strategy. 2.2. Screening of Disease-Associated Targets Common CMD encompass several major disorders, including those of glucose metabolism (e.g., T2DM and prediabetes), DLD (e.g., hypercholesterolemia and low HDL-C), and HTN. The disease terms were searched against the GeneCards (https://www.genecards.org/), CTD (https://ctdbase.org/), and OMIM (https://www.omim.org/) databases to identify relevant disease-associated genes/targets. Predicted targets were consolidated into a standardized database through amalgamation and deduplication. To uncover the underlying mechanisms, we identified the intersecting genes between the molecular targets of the toxicants (BPDE and LPS) and the gene set associated with CMD. This analysis revealed candidate genes that may mediate the effects of toxicant exposure on CMD pathogenesis. 2.3. Construction of protein-protein interaction (PPI) networ k The intersecting genes implicated in BPDE/LPS-induced CMD were analyzed using the STRING (https://cn.string-db.org/) database. PPI network was constructed with the organism limited to Homo sapiens, using a minimum interaction score of 0.7 for high confidence and hiding disconnected nodes. The resulting network was imported into Cytoscape (v3.10.0) for further analysis. Key hub genes were subsequently identified, and network parameters were calculated using the cytoHubba plugin. The central targets of LPS and BPDE were identified based on top-ranked nodes from four topological analysis methods: betweenness centrality (BC), degree, closeness centrality (CC), and Local Average Connectivity (LAC). Specifically, the top ten nodes from each in the ranking list of each algorithm were selected for further intersection analysis. 2.4. GO and KEGG enrichment analysi s To elucidate the mechanisms underlying the cardiometabolic toxicity induced by LPS and BPDE, we performed GO and KEGG pathway enrichment analyses on the potential targets. Functional annotation was carried out using the DAVID (https://davidbioinformatics.nih.gov/) and Metascape (https://metascape.org/) databases, with Homo sapiens set as the background species. The GO analysis encompassed three categories: biological process (BP), cellular component (CC), and molecular function (MF). The KEGG analysis was employed to identify significantly enriched pathways. The top ten statistically significant (p < 0.05) GO terms and KEGG pathways were then selected and visualized using a bioinformatics platform to aid interpretation. 2.5. Identification of Critical Toxicity Targets To identify core toxicity targets of BPDE and LPS, we retrieved transcriptomic datasets related to cardiometabolic diseases from the Gene Expression Omnibus (GEO) database. Datasets GSE83872 and GSE39117 were selected based on the keyword "cardiometabolic disease". We analyzed the expression profiles of potential BPDE and LPS targets within these datasets to identify differentially expressed genes (DEGs), with the thresholds set at a p-value < 0.05 and |log₂FC| ≥ 1. From the resulting DEGs, we identified the core toxicity targets. Finally, a dendrogram and heatmap were generated using the LIMMA package in R (version 4.5.1) to visualize their expression patterns. 2.6. Co-Expression Module Construction and ML Subsequently, we applied weighted gene co-expression network analysis (WGCNA) to construct co-expression modules and identify groups of highly correlated genes. To rigorously refine the candidate gene list and mitigate the inherent bias of any single algorithm, we employed a multi-model ,ML framework for robust feature selection. We applied three distinct algorithms—Generalized Linear Model (GLM), Random Forest (RF), and Support Vector Machine (SVM)—to the core genes and identified the consensus genes that were consistently ranked as important across all models as a high-confidence signature for subsequent analysis. 2.7. Docking Analysis of LPS and BPDE with Putative Toxicological Targets Docking simulations were conducted to assess the binding potential of LPS and BPDE to putative protein targets. The three-dimensional structures of the targets, obtained from the Protein Data Bank (PDB), were preprocessed by removing all water molecules and native ligands using PyMOL prior to the docking studies. The proteins were then hydrogenated and prepared for docking using AutoDock Vina. The binding affinities between the ligands (LPS and BPDE) and the receptor proteins were evaluated based on the calculated binding energies. Finally, the two-dimensional interaction diagrams were visualized and analyzed using Ligplot. 2.8. MDS Analysis MDS were performed with GROMACS 2025. The system was described by the AMBER force field and the TIP3P water model. Simulations were performed employing periodic boundary conditions under conditions of constant temperature and pressure. The system was maintained at 310 K and 1 bar throughout the simulation. Following a two-step equilibration protocol comprising 100 ps NVT and NPT phases, a 200 ns production MDS was conducted. The MD trajectories were visualized using VMD and analyzed using PyMOL. The binding affinities of the protein-ligand complexes were then determined via the MMPBSA method. 3. Results 3.1. Toxicology analysis of BPDE and LPS Using ProTox-3.0 and ADMETlab 3.0, we evaluated the toxicological profiles of BPDE and LPS. Our predictions indicated that both compounds exhibit significant potential for cardiotoxicity, respiratory toxicity, carcinogenicity, and neurotoxicity (Table 1). Table 1. Results of BPDE and LPS toxicity Property Database results BPDE LPS ADMETlab ProTox ADMETlab ProTox Respiratory toxicity 0.97 0.68 0.631 0.77 Carcinogenicity 0.195 0.62 0.005 0.55 Mutagenicity 0.163 0.95 0.001 0.74 Neurotoxicity 0.386 0.60 0.865 0.60 Cardiotoxicity 0.512 0.78 0.585 0.59 DILI 0.6 0.66 0.952 0.96 0.3–0.5(+),0.5–0.7(++),0.7–0.9 (+++). Higher predicted toxicity probabilities correspond to greater compound risks. 3.2. Network and Enrichment Analysis of Hub Targets in BPDE-induced CMD We obtained the standard structure of BPDE from PubChem. Our subsequent integrated analysis of the Swiss Target Prediction, ChEMBL, PharmMapper, and SEA databases identified 611 non-redundant potential targets for BPDE. In parallel, we compiled 3,976 distinct CMD-associated targets from the GeneCards, CTD, and OMIM databases. A Venn analysis of these merged and deduplicated target sets revealed 366 overlapping targets at the BPDE-CMD intersection. (Fig.1A) Based on these findings, a "compound-target-disease-pathway" network was established. Construction of the PPI network using the STRING database yielded a topology comprising 343 nodes and 1,879 edges. The network displayed significant functional connectivity, evidenced by a PPI enrichment p-value of < 1.0 × 10⁻¹⁶ and an average node degree of 10.3. Subsequent visualization and topological analysis were performed in Cytoscape (v3.10.0). (Fig.1B) After calculating key topological parameters—BC, CC, Degree, and LAC—we applied a median-based threshold to identify core targets: AKT1, IL6, INS, EGFR, ALB, TNF, and ESR1. To elucidate the interactions among these core elements, a compound-disease-pathway network was established. (Fig.1E) To this end, the 366 overlapping targets were subjected to enrichment analysis. The GO results demonstrated significant enrichment in the top ten entries across BP, CC, and MF categories. (Fig.1G) The results highlighted key processes and pathways, including insulin and IGF-1 receptor signaling, activation of the PI3K/AKT pathway, and the regulation of apoptosis. Furthermore, the analysis implicated several critical elements such as growth factor signaling (e.g., EGFR, PDGFR, HGFR), extracellular communication via exosomes, and tyrosine kinase activity. These enriched terms are fundamentally involved in pathways governing cardiac metabolism, vascular dysfunction, and insulin resistance, underscoring their potential roles in the pathogenesis of cardiometabolic diseases. KEGG pathway analysis revealed significant enrichment in several key pathways, (Fig.1F) including the AGE-RAGE signaling pathway in diabetic complications, Lipid and atherosclerosis, and Pathways in cancer. Key genes within these pathways, such as EGR1 and HSP90AA1, are known to be involved in critical processes like insulin resistance, inflammation, and tumorigenesis. BPDE-induced toxicity may operate through a cross-regulatory network of the enriched genes and pathways, which pivotally links metabolic disorders to cardiovascular disease and cancer. This is supported by both GO and KEGG analyses, which implicate these mechanisms in the pathogenesis of diabetic cardiomyopathy, metabolic syndrome, and related cardiometabolic impairments. Ultimately, these findings connect metabolic dysregulation directly to cardiovascular damage. The screened hub targets were then integrated with the DEGs from datasets GSE83872 and GSE39117 using a ML-based approach to identify overlapping genes. These overlapping genes were considered core targets. Finally, a subset of core targets was selected based on significant differential expression in CMD. Representative examples include EGFR. The entire screening process was visualized using a bar chart (Fig.1D) and a Venn diagram. (Fig.1C) 3.3. Docking of BPDE targets in cardiometabolic toxicity Docking simulations using AutoDock Vina revealed a robust binding affinity between BPDE and the key target EGFR, with a computed binding energy of -11.4 kcal/mol. (Fig.2) This finding suggests a significant affinity between BPDE and EGFR, highlighting its potential critical role in the molecular mechanisms underlying BPDE-induced cardiometabolic toxicity. 3.4. Network and Enrichment Analysis of Hub Targets in LPS-induced CMD The standard two-dimensional structure of LPS was obtained from the PubChem database. Subsequently, a total of 476 potential protein targets of LPS were identified by integrating prediction results from four independent databases. To elucidate the mechanistic relevance to CMD, the overlapping targets between LPS and CMD were screened using a Venn diagram, which revealed 287 shared targets. (Fig.3A) A comprehensive "compound-target-disease-pathway" network was then built to visualize the interactions. The resulting PPI network comprised 265 nodes and 1,300 edges. Statistical assessment confirmed its high quality, with a PPI enrichment p-value < 1.0 × 10⁻¹⁶ and an average node degree of 9.09, indicating a robustly interconnected module. Using Cytoscape (v3.10.0), we computed four centrality measures (e.g., BC, CC, Degree, LAC) to topologically analyze the network and identify hub targets. (Fig.3B) The top seven nodes—EGFR, SRC, IL1B, ESR1, MMP9, STAT1, and CCND1—were identified as core targets using a median-based screening criterion. Next, we constructed a compound-disease-pathway network graph. (Fig.3E) Enrichment analysis of the 287 intersecting targets was subsequently conducted. GO analysis (Fig.3G) indicated that these targets are primarily implicated in growth factor receptor signaling pathways, proteolysis, and the regulation of cell migration and invasion. Additionally, they were associated with extracellular matrix remodeling and functions related to kinase activity and phosphorylation. KEGG pathway enrichment analysis revealed that the overlapping targets were primarily implicated in: (1) inflammation and immune responses (e.g., TNF signaling pathway); (2) regulation of cell proliferation, apoptosis, and survival (e.g., PI3K-Akt signaling pathway, Apoptosis); and (3) metabolic and vascular dysfunction (e.g., Lipid and atherosclerosis, Endocrine resistance). (Fig.3F) The GO and KEGG enrichment analyses collectively implicate the LPS-CMD intersecting targets in inflammatory and metabolic dyshomeostasis. Following the identification of hub targets, we cross-analyzed their presence within the DEGs lists from two datasets (GSE83872 and GSE39117). This analysis identified MMP9 as the sole core target that showed significant differential expression under LPS stimulation. The results of this screening process are presented in a bar chart (showing expression levels) (Fig.3D) and a Venn diagram (showing the overlap between datasets). (Fig.3C) 3.5. Docking of LPS targets in cardiometabolic toxicity Docking analysis using AutoDock Vina was conducted to evaluate the binding interactions. The calculated binding energies revealed that the toxicants form stable complexes with multiple targets. Notably, MMP9—the sole core protein target—exhibited a particularly high binding affinity (-8.6 kcal/mol), suggesting very stable binding. (Fig.4) This high affinity implies that MMP9 may be a critical target mediating the toxic effects of these compounds. 3.6. WGCNA network and ML analysis Following sample clustering analysis, which retained all samples from GSE83872 and GSE39117, we used WGCNA to construct separate co-expression networks for the BPDE and LPS control treatment groups. (Fig.5B) For each network, the optimal soft-thresholding power was selected based on scale-free topology criteria, resulting in the selection of powers of 6 and 7 for the BPDE and LPS networks, respectively. (Fig.5A) Using a minimum module size of 50 and a cut height of 0.10, we identified 5 and 16 modules in the BPDE and LPS networks, respectively (Fig.5C) . We then selected modules with module-trait correlation coefficients ≥ 0.6 for further analysis. This included the blue module (|r| = 0.6, P < 0.001) in the BPDE network and the red module (|r| = 0.62, P 0.8) were extracted from these key modules. To evaluate their diagnostic performance, we generated both boxplots (showing expression distribution) (Fig.6A) and ROC curves (Fig.6B) based on their expression levels in the respective datasets. Using the expression profiles of these hub genes as input, three ML models (GLM, RF, SVM) were built. The models achieved AUC values of 0.75, 0.833, and 0.944 in the BPDE validation set. However, their performance in the LPS set was more variable, with AUC values of 0.55, 0.75, and 0.75. These results suggest that the hub genes identified from the BPDE network exhibit strong diagnostic potential, while their applicability in the LPS context may be limited and requires further investigation. 3.7. Integrative Analysis Reveals Convergent Targets of BPDE and LPS GO enrichment analysis of core targets for BPDE- and LPS-induced cardiometabolic toxicity revealed both common and distinct pathways. (Fig7A) Both agents converged on the disruption of nitric oxide biosynthesis, highlighting a shared inflammatory-mediated toxic pathway. However, their primary mechanisms diverged significantly. BPDE preferentially dysregulated processes related to apoptosis, peptidyl-serine phosphorylation, and protein complex assembly, while enhancing proliferation and kinase activity. This suggests its toxicity stems from a disruption of cellular homeostasis. Consequently, BPDE's immunomodulatory effects are primarily indirect, potentially impairing immune resolution and promoting the survival of autoreactive lymphocytes. This may culminate in pathologies such as fibrosis, autoimmunity, or oncogenesis. In contrast, LPS predominantly targeted pathways involved in transcriptional regulation via RNA polymerase II, responses to external stimuli, and proteolytic degradation. This indicates its toxicity is mediated through the direct activation of the innate immune system (e.g., via TLR4), triggering massive transcriptional reprogramming. This leads to a hyperinflammatory state, characterized by a cytokine storm and enhanced proteolytic activity. LPS primarily targets innate immune cells, such as macrophages and dendritic cells, triggering their hyperactivation and directly causing severe tissue damage, septic shock, and multi-organ failure. Comparative KEGG pathway analysis further corroborated these distinctions (Fig7B) , with BPDE showing predominant enrichment in the FoxO, Insulin Resistance, and HIF-1 signaling pathways. Existing literature indicates that BPDE-induced dysregulation of the FoxO pathway promotes oxidative stress, a critical signal for NLRP3 inflammasome activation. (Mikse et al., 2010; N. Zhang et al., 2023) It is thus plausible that this cascade may impair cardiac insulin sensitivity and disrupt metabolic homeostasis, key drivers of cardiometabolic dysfunction. (Crossland et al., 2019; Guillet et al., 2012) This network of pathways coherently links BPDE exposure to potential cardiometabolic injury mechanisms. Its strong interaction with EGFR (binding energy: -11.4 kcal/mol) likely enhances abnormal cellular proliferation through these pathways. In contrast, LPS showed significant enrichment in pathways related to cancer (e.g., Proteoglycans in cancer, EGFR tyrosine kinase inhibitor resistance), infectious diseases (Coronavirus disease - COVID-19), and endocrine resistance. Notably, both compounds were enriched in several common pathways, including the JAK-STAT, Thyroid hormone, Prolactin, Hepatitis B, and AGE-RAGE signaling pathways. These findings highlight the fundamental differences in their toxicological mechanisms: BPDE primarily disrupts cellular homeostasis and growth, whereas LPS triggers dysregulated immune and inflammatory responses. A Venn diagram analysis revealed nine overlapping targets common to both BPDE- and LPS-induced cardiometabolic toxicity, out of a total of 26 targets identified for each toxin (Fig7D) . These targets—EGFR, ESR1, JAK2, PIK3R1, HDAC3, SORD, ADK, HDAC2, and SOD2—constitute a common regulatory hub for toxicity. 3.8. Docking of key targets The docking of BPDE with EGFR was previously established. Consequently, the current investigation shifted its focus to BPDE's binding interactions with the eight other key targets associated with toxicity (ESR1, JAK2, PIK3R1, HDAC3, SORD, ADK, HDAC2, and SOD2) (Fig. 8A) . We performed docking of LPS against the full set of nine overlapping core targets (EGFR, ESR1, JAK2, PIK3R1, HDAC3, SORD, ADK, HDAC2, and SOD2) (Fig. 8B) . According to AutoDock Vina docking results (Table 2) , all compounds exhibited binding energies below −5 kcal/mol. The EGFR-BPDE complex showed the lowest energy (-11.4 kcal/mol), and the LPS-MMP9 interaction was among the strongest with an energy of -8.6 kcal/mol. Table 2 . The binding energy of Docking. Compounds Structure ID Target BingingEnergy(kcal/mol) BPDE 8A27 EGFR -11.4 8DUB ESR1 -10.6 1PLB SORD -10.3 3UGC JAK2 -10.2 1BX4 ADK -9.8 6WBZ HDAC2 -9.2 4A69 HDAC3 -8.5 9BWQ SOD2 -8.4 2IUG PK3R1 -7.7 LSP 6ESM MMP9 -8.6 6WBZ HDAC2 -8.4 4A69 HDAC3 -8.4 1BX4 ADK -8.2 1PLB SORD -8.1 8A27 EGFR -7.6 8DUB ESR1 -7.5 3UGC JAK2 -6.5 2IUG PK3R1 -6.3 9BWQ SOD2 -6.3 Binding energy between −3 kcal/mol and −5 kcal/mol : Weak binding；Binding energy between −5 kcal/mol and −8 kcal/mol: Moderate binding；Binding energy < –8 kcal/mol: Strong binding. 3.9. Molecular dynamics simulations (MDS) verification To further investigate the binding affinity of BPDE and LPS for their respective target complexes (EGFR and MMP9), we performed MDS on the BPDE-EGFR and LPS-MMP9 complexes. To evaluate conformational stability, the root mean square deviation (RMSD) was calculated. This metric serves to quantify the average atomic displacement from a reference structure, where lower values are typically indicative of a more stable conformational state. As illustrated in Fig. 9A, the RMSD analysis revealed that the BPDE-EGFR complex achieved equilibrium after 45-100 ns, indicating stable binding. It subsequently underwent a transient fluctuation between 100-150 ns before gradually re-stabilizing after 150 ns. In contrast, the LPS-MMP9 complex reached a stable equilibrium earlier, at approximately 70 ns. Notably, the BPDE-EGFR complex exhibited the lowest final RMSD value (approximately 0.35 Å), suggesting a highly stable interaction. The root mean square fluctuation (RMSF) analysis, which evaluates residue-specific flexibility, revealed low values (0.1–0.35 Å) throughout the BPDE-EGFR complex excepting the dynamic terminal regions (Fig. 9B). Conversely, the LPS-MMP9 complex exhibited markedly higher fluctuations, consistent with greater overall flexibility. The structural compactness was further assessed by the radius of gyration (Rg). A decrease in Rg was observed for the BPDE-EGFR complex upon binding, suggesting its stabilization into a more compact conformation (Fig. 9C). In contrast, the LPS-MMP9 complex exhibited minimal Rg fluctuation throughout the 200 ns simulation, demonstrating that no significant structural expansion or compaction occurred during this period. The solvent-accessible surface area (SASA) was analyzed to evaluate changes in protein surface exposure following ligand binding. As shown in Fig. 9E, both complexes exhibited steady SASA fluctuations, suggesting that neither underwent significant expansion or contraction. To quantitatively compare the binding affinity, MM/PBSA calculations were performed. The results revealed a substantially more favorable binding energy for the LPS-MMP9 complex (ΔG = -71.57 kcal/mol, Table 3) than for the BPDE-EGFR complex (ΔG = -24.45 kcal/mol, Table 3). Consistent with this energetic trend, analysis of hydrogen bond dynamics also showed that the LPS-MMP9 complex sustained a greater number of hydrogen bonds than the BPDE-EGFR complex. Table 3. Energe Component (Kcal/mol) BPDE LPS △VDWAALS -33.46 -105.77 △EEL -5.8 -15.29 △EPB 18.1 59.03 △ENPOLAR -3.28 -9.53 △GGAS -39.27 -121.07 △GSOLV 14.82 49.5 △dG -24.45 -71.57 Tip, △VDWAALS, Change in van der Waals interaction energy. △EEL, Change in electrostatic energy. △EPB, Change in polar solvation energy. △ENPOLAR, Change in non-polar solvation energy. △GGAS, Total change in free energy in the gas phase (△EEL + △VDWAALS). △GSOLV, Total change in solvation free energy (△EPB + △ENPOLAR). △dG, Final total binding free energy (△GGAS + △GSOLV). Our integrated analysis establishes distinct contributions of the BPDE-EGFR and LPS-MMP9 complexes to system stability: the former provides superior structural integrity, while the latter exhibits stronger binding affinity. This concerted action—structural stabilization by one and potent binding driven by the other—likely represents a critical mechanism in CMD pathogenesis. 4. Discussion Air pollution is a leading global risk factor for morbidity and mortality. It is a major cause of cardiovascular diseases, which account for the largest proportion of air pollution-related deaths, and it also promotes the development of chronic cardiometabolic disorders. (Brook et al., 2017; Lu et al., 2025) Through mechanisms such as increased peripheral vasoconstriction, short-term PM2.5 exposure can trigger acute elevations in systemic blood pressure (BP) over hours to days. In contrast, chronic exposure promotes the development of HTN itself. Additionally, these pollutants exacerbate insulin resistance and facilitate the development of diabetes mellitus. (Andersen et al., 2012; Brook et al., 2017) A key mechanistic pathway involves the component LPS, a classic pathogen-associated molecular pattern (PAMP) found on PM2.5. The activation of innate immunity by LPS through Toll-like receptor 4 (TLR4) initiates critical inflammatory signaling pathways (NF-κB, MAPK) and stimulates pro-inflammatory cytokine release (e.g., TNF-α, IL-6). This resulting chronic, low-grade inflammatory milieu thereby fosters insulin resistance (IR) and underlies the pathogenesis of T2DM. (Rajagopalan & Brook, 2012) In PM2.5-exposed mouse models, the LPS component has been shown to drive IR and vascular injury via the TLR4/NADPH oxidase axis. TLR4 activation also drives vascular dysfunction; for instance, in adipose tissue, it stimulates the release of chemokines such as CCL-2, which recruits pro-inflammatory monocytes/macrophages. (Kampfrath et al., 2011) Moreover, TLR4 activation reduces endothelial nitric oxide synthase (eNOS) activity, impairing vasodilation, and elevates the expression of plasminogen activator inhibitor-1 (PAI-1) and adhesion molecules like ICAM-1, collectively promoting vascular disease. (Shi et al., 2006; Xu et al., 2011) BPDE, the main biologically active metabolite of BaP, is generated through metabolic activation by cytochrome P450 1A1/1B1 (CYP1A1/1B1) and epoxide hydrolase. It exerts its carcinogenic effect primarily by covalently binding to the N2 position of DNA guanine, forming mutagenic BPDE-DNA adducts (predominantly BPDE-N2-dG). These adducts serve as key biomarkers of DNA damage, disrupting replication and transcription and frequently inducing G→T transversion mutations.(Chen et al., 2022; Savela et al., 1996; Weng et al., 2018) Furthermore, in vitro studies using human bronchial epithelial cells (BEAS-2B) have shown that BPDE exposure induces malignant transformation, associated with downregulation of EGR1, upregulation of miR-377-3p, and activation of the Wnt/β-catenin signaling pathway. (Ke et al., 2021) We evaluated the toxicological profiles of both compounds using ADMETlab 3.0 and ProTox 3.0, which revealed significant cardiometabolic toxicity. (Banerjee et al., 2024; Fu et al., 2024) By integrating computational toxicology, Docking, ML, and MDS, we elucidated their molecular targets and mechanisms. Computational profiling predicted overlapping immunotoxic and respiratory effects for both BPDE and LPS (Table 1) . Their distinct toxicities included mutagenicity, carcinogenicity, and blood-brain barrier penetration for BPDE, contrasted with neurotoxicity, nephrotoxicity, and cardiotoxicity for LPS. Combining bioinformatics from GEO datasets, PPI network analysis, and ML algorithms, we pinpointed nine critical toxicity-related targets, among which EGFR was targeted by BPDE and MMP9 by LPS. EGFR activation drives fibroblast proliferation by upregulating the expression of genes related to DNA replication and cell cycle progression. This, in turn, leads to the excessive deposition of extracellular matrix (ECM) components, such as type I collagen, and increased tissue stiffness. (Ewoldt et al., 2024) Concomitantly, it activates inflammatory signaling pathways, such as ERK1/2, which results in increased levels of circulating and brain TNF-α. (Wei et al., 2021) EGFR transactivation, primarily induced by the angiotensin II type 1 receptor (AT1R) in blood vessels, plays a central role in vascular pathophysiology. This AT1R-mediated EGFR activation initiates a critical signaling crosstalk that amplifies cellular responses to angiotensin II. Upon transactivation, EGFR signaling drives pathological changes including increased vessel wall thickness and reduced vascular compliance. Moreover, EGFR activation potentiates the actions of angiotensin II, driving endothelial dysfunction and culminating in a cascade of vascular inflammation, functional impairment, and structural remodeling. Pathological alterations in this EGFR-mediated signaling axis are increasingly recognized as a key mechanism in the development of diverse cardiovascular disorders. (Gekle et al., 2023; Hong et al., 2025) MMP-9 degrades type IV collagen and elastin, disrupting vascular wall structure and compromising extracellular matrix integrity, thereby increasing vascular stiffness. This process is directly regulated by NF-κB, whose activation in vascular smooth muscle cells and cardiomyocytes upregulates MMP-9 transcription, further promoting the development of HTN and myocardial infarction. (Ye, 2006) Additionally, MMP-9 contributes to inflammation by stimulating the release of cytokines such as TNF-α and IL-1β, and drives pathological angiogenesis through basement membrane degradation and VEGF release. MMP-9 also modulates immune cell infiltration by activating CXCL5 and CXCL8 while inactivating CXCL12, thereby regulating neutrophil migration. Collectively, MMP-9 influences the progression of cardiovascular disease through multiple pathways, including the NF-κB/AP-1 inflammatory axis, TGF-β-mediated fibrosis, Ang II-related hemodynamic mechanisms, and immune regulatory networks. (Yabluchanskiy et al., 2013) Energy decomposition analysis within molecular dynamics simulations identified ASP800 (–5.35 kcal/mol) and ARG841 (–6.34 kcal/mol) as the most critical residues in the BPDE–EGFR complex. Analysis of the simulation trajectories suggests their substantial energy contributions are mediated by strong interactions with the ligand, such as hydrogen bonds and π–π stacking, which help stabilize the binding pocket. Similarly, in the LPS–MMP9 complex, PHE723 (–3.28 kcal/mol) and MET766 (–2.69 kcal/mol) emerged as the dominant contributors to the binding energy. Functional enrichment analyses revealed that both BPDE and LPS exposures dysregulated both common and unique pathological modules, focused on cancer pathogenesis and metabolic-vascular dysfunction. BPDE significantly promoted processes related to cell survival and proliferation—such as negative regulation of apoptosis and smooth muscle cell proliferation—along with oncogenic pathways including EGFR tyrosine kinase inhibitor resistance, PI3K-Akt, and JAK-STAT signaling. Similarly, LPS-responsive genes were enriched in immune and inflammatory processes, regulation of cell proliferation and apoptosis, and critical pathways including Toll-like receptor, TNF, NOD-like receptor, and AGE-RAGE signaling, which are central to atherosclerosis pathogenesis. Both exposures converged on strong disease associations with Diabetes Mellitus and Vascular Diseases, (Fig.7C) with BPDE additionally linked to Endothelial Dysfunction and LPS to Endotoxemia. Collectively, these results indicate that while BPDE and LPS engage partly divergent signaling cascades, they both disrupt core homeostatic processes and drive shared metabolic-vascular disorders, thereby providing a robust molecular basis their roles in complex human diseases. This preliminary investigation suggests a potential role of BPDE and LPS in CMD development; however, their precise molecular mechanisms require further elucidation. Our integrated framework, which synergizes network toxicology, ML, MDS, systematically reveals compound toxicity mechanisms at the molecular level. This strategy offers a powerful approach for generating novel hypotheses in environmental health research. These in silico findings provide a rationale for subsequent experimental studies, which could enhance research efficiency. Furthermore, the identified core targets (e.g., SORD (Schlicker et al., 2019) , ADK (Wölkart et al., 2022) ) are implicated in specific pathogenic pathways and, based on prior evidence linking them to metabolic dysregulation, these targets thus offer a rational basis for devising early diagnostic and targeted therapeutic strategies for environmentally associated CMD. This study has several limitations. A primary constraint is that our computational models did not account for potential synergistic effects from other PM₂.₅ constituents (e.g., heavy metals) or for demographic confounders such as age and sex. Consequently, a key future priority involves experimental verification of these predictions. Specifically, combined exposure assays complemented by clinical sample analyses will be essential to establish the physiological relevance and reproducibility of the proposed mechanisms. 5. Conclusion To elucidate how specific PM₂.₅ components (BPDE and LPS) may drive CMD, we applied an integrated computational strategy that converged insights from network toxicology, ML, Docking, and MDS. The multi-method approach helped to delineate complex biological interactions and identified several key molecular targets and pathways potentially involved in PM₂.₅-induced cardiometabolic impairment. These computational findings provide preliminary mechanistic insights into the combined toxicological effects of BPDE and LPS as PM₂.₅ components, supporting further evaluation of their health risks. Subsequent studies should expand on this work by investigating associations between chronic PM₂.₅ exposure and a broader range of cardiovascular and metabolic disorders. Such research would contribute to a more robust evidence base for risk assessment of airborne particulate matter and inform public health strategies aimed at reducing pollution-related cardiometabolic morbidity. Declarations Conflicts of interest : The authors declare no conflicts of interest. Funding This work was supported by the National Natural Science Foundation of China (82374367), Jiangxi Provincial Natural Science Foundation (20242BAB26163, 20232BAB206144), Jiangxi Province Key Laboratory of Traditional Chinese Medicine for Cardiovascular Diseases (20242BCC32096), NATCM's Project of High-level Construction of Key TCM Disciplines (zyyzdxk-2023113), Project of Key Discipline Construction Fund of Jiangxi University of Chinese Medicine (2023jzzdxk032), Science and Technology Innovation Team Development Program of Jiangxi University of Chinese Medicine (CXTD22011), National Traditional Chinese Medicine Inheritance and Innovation Center Construction Project. Author contributions: Haoyue Jia: Writing – original draft, Methodology, Conceptualization, Hao Zhang: Resources, Data curation. 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1","display":"","copyAsset":false,"role":"figure","size":2937977,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and Analysis of Common Targets Between BPDE and CMD. (A) . Venn diagram of potential targets. (B) Potential targets in the PPI network. (C) DEG sets from public datasets were crossed with PPI. (D) Merged and deduplicated overlapping targets from (B) and (C) (TOP20), followed by cross-analysis with DEG sets. (E) Cytoscape visualization of the BPDE-target-CMD-pathway interaction network. (F) Sankey diagram of KEGG pathway enrichment and gene association in BPDE-induced CMD. (G) Results of GO analysis for potential targets.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7937700/v1/8322370ddd379e3a286c250f.png"},{"id":95163053,"identity":"18202486-e9c8-4f1e-9790-737b2556b378","added_by":"auto","created_at":"2025-11-05 04:06:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1593446,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking of BPDE with EGFR.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7937700/v1/77c02aee15f4a4b84fc33d2a.png"},{"id":95163048,"identity":"9516f8f9-0ff4-4c03-88b9-0e4d687f2dc5","added_by":"auto","created_at":"2025-11-05 04:06:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2802198,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and Analysis of Common Targets Between LPS and CMD. (A) . Venn diagram of potential targets. (B) Potential targets in the PPI network. (C) DEG sets from public datasets were crossed with PPI. (D) Merged and deduplicated overlapping targets from (B) and (C) (TOP20), followed by cross-analysis with DEG sets. (E) Cytoscape visualization of the LPS-target-CMD-pathway interaction network. (F) Sankey diagram of KEGG pathway enrichment and gene association in LPS-induced CMD. (G) Results of GO analysis for potential targets.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7937700/v1/1e2c500c60302b465dc4a8b1.png"},{"id":95226644,"identity":"6616eeb2-604a-4715-9b5b-dcb8c5176d7b","added_by":"auto","created_at":"2025-11-05 16:31:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2091809,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking of LPS with MMP9.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7937700/v1/f749c7ee31726999ff8a09a8.png"},{"id":95163050,"identity":"59532f1b-4493-41c2-9efb-318c4a332eb3","added_by":"auto","created_at":"2025-11-05 04:06:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1232795,"visible":true,"origin":"","legend":"\u003cp\u003e(A-B) Scatter plot of Power values for BPDE (A) and LPS (B). 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(E) Venn diagram of nine shared key toxicity targets between BPDE and LPS.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7937700/v1/fb8b46cce3bab58f5ad5d568.png"},{"id":95226066,"identity":"308a504e-c094-4432-b382-f8fdf2c396d6","added_by":"auto","created_at":"2025-11-05 16:26:08","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":9183973,"visible":true,"origin":"","legend":"\u003cp\u003eDocking between BPDE/LPS and key toxicity targets. (A) LPS–ADK complex. (B) LPS–EGFR complex. (C) LPS–ERS1 complex. (D) LPS–HIDAC2 complex. (E) LPS–HIDAC3 complex. (F) LPS–JAK2 complex. (G) LPS–PIK3R1 complex. (H) LPS–SORD2 complex. (I) LPS–SORD complex. (J) BPDE–ADK complex. (K) BPDE–ERS1 complex. (L) BPDE–HIDAC2 complex. (M) BPDE–HIDAC3 complex. (N) BPDE–JAK2 complex. (O) BPDE–PIK3R1 complex. (P) BPDE–SORD2 complex. (Q) BPDE–SORD complex.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7937700/v1/6c4959a77e35a3a57d0686be.png"},{"id":95163054,"identity":"5ecd0619-c5b3-49f0-ab3f-09580aaa359e","added_by":"auto","created_at":"2025-11-05 04:06:42","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":941774,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular dynamics simulation analysis. (A) RMSD. (B) RMSF. (C) Rg. (D) H-bond. (E) SASA. (F) MM/PBSA Binding Energy. (G) Energy contribution of each amino acid residue to BPDE. (H) Energy contribution of each amino acid residue to LPS.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7937700/v1/2182757f7d717f78d07b495a.png"},{"id":95527266,"identity":"74870f9f-136a-4294-84b9-f0873c3b591c","added_by":"auto","created_at":"2025-11-10 10:12:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":21241935,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7937700/v1/61ab4026-78ea-4b0a-9a95-f5c41485b442.pdf"}],"financialInterests":"","formattedTitle":"Toxic Mechanisms of PM2.5 Constituents (LPS and BPDE) in Cardiometabolic Disease: Insights from Integrated Machine Learning and Molecular Dynamic Simulations","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAir pollution poses a major global environmental threat to human health, particularly in low- and middle-income countries. (Campolim et al., 2024; Wan et al., 2019) Long-term exposure to air pollutants, particularly PM₂.₅, has been positively linked to elevated rates of cardiovascular mortality and morbidity (Wan et al., 2021a, 2021b) and has further been implicated in the pathogenesis of various metabolic disorders, including obesity, type 2 diabetes (T2DM), hypertension (HTN), and dyslipidemia (DLD). (Khound et al., 2025; Yang et al., 2019) These adverse outcomes are primarily mediated by mechanisms including systemic inflammation and oxidative stress. (Campolim et al., 2024; Long et al., 2025) Polycyclic aromatic hydrocarbons (PAHs) are a prominent risk factor for the harmful effects of airborne PM. Notably, research has identified benzo[a]pyrene-7,8-dihydrodiol-9,10-epoxide (BPDE), the ultimate toxic metabolite of PAHs (Y. Zhang et al., 2023), and lipopolysaccharide (LPS) as significant mediators of these pathological outcomes. (Bandowe et al., 2014; He et al., 2018)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePAHs are a class of widespread organic pollutants with significant carcinogenic and ecotoxicological potential. (Freire et al., 2020; Moubarz et al., 2023) Among them, benzo[a]pyrene (BaP), a prominent and potent congener, is recognized as a Group 1 carcinogen by the International Agency for Research on Cancer (IARC). (Bauer et al., 2022) BaP is ubiquitous in the environment, present in sources such as cigarette smoke, petroleum derivatives, charbroiled food, and contaminated water. (Ling et al., 2004) Human exposure occurs primarily via inhalation, ingestion, and dermal contact. (Ye et al., 2020) Following uptake, BaP undergoes metabolic activation via the aryl hydrocarbon receptor (AhR) pathway. which upregulates enzymes such as cytochrome P450 1A1 (CYP1A1) and epoxide hydrolase, leading to its conversion into the ultimate carcinogenic metabolite, BPDE. (Bukowska et al., 2022; Ye et al., 2020) The activated BPDE forms bulky, mutagenic DNA adducts, a key event in carcinogenesis. This DNA damage induces cellular dysfunction and inflammation. (Ling et al., 2004; Piberger et al., 2018) This pathological process not only sustains carcinogenesis but also directly contributes to an increased risk of cardiovascular diseases. (Hadrup et al., 2019)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLPS, an endotoxin and key virulence factor of gram-negative bacteria, activates innate and adaptive immunity, triggering both local and systemic inflammation. (Mohammad \u0026amp; Thiemermann, 2020; Wei et al., 2024) Chronic endotoxemia resulting from elevated LPS levels is a recognized risk factor for numerous inflammation-driven conditions, including cardiometabolic disorders. (Cai et al., 2020; Pussinen et al., 2022) Furthermore, TLR4 activation by LPS can promote cancer progression; it exacerbates the downregulation of epithelial markers such as claudin-1 and E-cadherin—a hallmark of epithelial-to-mesenchymal transition that is associated with enhanced cell migration, invasiveness, and metastasis.\u0026nbsp;(Wu et al., 2020; Yang et al., 2025)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOwing to their multi-target nature, environmental pollutants dysregulate the homeostatic equilibrium of endogenous biomolecules, resulting in highly complex toxicological networks. Consequently, conventional toxicology, which often relies on a single-target framework, appears inadequate for systematically evaluating the combined effects of pollutant exposures.(Antczak et al., 2010) This study leveraged a computational framework comprised of network toxicology, machine learning (ML), docking, and molecular dynamics simulations(MDS) to delineate the combined cardiometabolic toxic mechanisms of BPDE and LPS. By comparing their predicted targets and possible mechanistic pathways in cardiometabolic disease (CMD), this work aims to provide a theoretical basis for the diagnosis of toxicity-related diseases and may contribute to future chemical hazard evaluation.\u0026nbsp;\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cem\u003e2.1. Collection of Toxicologically Relevant Compound Targets\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMolecular structures (SMILES strings and 3D conformers) were obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov) and subsequently processed using ProTox-3.0 (DOI: 10.1093/nar/gkad929) and ADMETlab 3.0 (DOI: 10.1093/nar/gkae236) for comprehensive toxicity profiling. For target prediction, Swiss Target Prediction was primarily employed, retaining all targets with probability scores \u0026gt; 0. Three additional databases SEA (https://sea.bkslab.org/), CHEMBL (https://www.ebi.ac.uk/chembl/), and PharmMapper (https://www.lilab-ecust.cn/pharmmapper/) were leveraged to expand coverage and validate initial predictions via consensus analysis, following duplicate removal. Rigorous intersection analysis of the unified dataset identified high-confidence consensus targets, demonstrating robust prediction reliability through this multi-database cross-validation strategy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Screening of Disease-Associated Targets\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCommon CMD encompass several major disorders, including those of glucose metabolism (e.g., T2DM and prediabetes), DLD (e.g., hypercholesterolemia and low HDL-C), and HTN. The disease terms were searched against the GeneCards (https://www.genecards.org/), CTD (https://ctdbase.org/), and OMIM (https://www.omim.org/) databases to identify relevant disease-associated genes/targets. Predicted targets were consolidated into a standardized database through amalgamation and deduplication. To uncover the underlying mechanisms, we identified the intersecting genes between the molecular targets of the toxicants (BPDE and LPS) and the gene set associated with CMD. This analysis revealed candidate genes that may mediate the effects of toxicant exposure on CMD pathogenesis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Construction of protein-protein interaction (PPI) networ\u003c/em\u003e\u003cem\u003ek\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe intersecting genes implicated in BPDE/LPS-induced CMD were analyzed using the STRING (https://cn.string-db.org/) database. PPI network was constructed with the organism limited to Homo sapiens, using a minimum interaction score of 0.7 for high confidence and hiding disconnected nodes. The resulting network was imported into Cytoscape (v3.10.0) for further analysis. Key hub genes were subsequently identified, and network parameters were calculated using the cytoHubba plugin. The central targets of LPS and BPDE were identified based on top-ranked nodes from four topological analysis methods: betweenness centrality (BC), degree, closeness centrality (CC), and Local Average Connectivity (LAC). Specifically, the top ten nodes from each in the ranking list of each algorithm were selected for further intersection analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; GO and KEGG enrichment analysi\u003c/em\u003e\u003cem\u003es\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate the mechanisms underlying the cardiometabolic toxicity induced by LPS and BPDE, we performed GO and KEGG pathway enrichment analyses on the potential targets. Functional annotation was carried out using the DAVID (https://davidbioinformatics.nih.gov/) and Metascape (https://metascape.org/) databases, with Homo sapiens set as the background species. The GO analysis encompassed three categories: biological process (BP), cellular component (CC), and molecular function (MF). The KEGG analysis was employed to identify significantly enriched pathways. The top ten statistically significant (p \u0026lt; 0.05) GO terms and KEGG pathways were then selected and visualized using a bioinformatics platform to aid interpretation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.5.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e \u003cem\u003eIdentification of Critical Toxicity Targets\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo identify core toxicity targets of BPDE and LPS, we retrieved transcriptomic datasets related to cardiometabolic diseases from the Gene Expression Omnibus (GEO) database. Datasets GSE83872 and GSE39117 were selected based on the keyword \"cardiometabolic disease\". We analyzed the expression profiles of potential BPDE and LPS targets within these datasets to identify differentially expressed genes (DEGs), with the thresholds set at a p-value \u0026lt; 0.05 and |log₂FC| ≥ 1. From the resulting DEGs, we identified the core toxicity targets. Finally, a dendrogram and heatmap were generated using the LIMMA package in R (version 4.5.1) to visualize their expression patterns.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.6. Co-Expression Module Construction and ML\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSubsequently, we applied weighted gene co-expression network analysis (WGCNA) to construct co-expression modules and identify groups of highly correlated genes. To rigorously refine the candidate gene list and mitigate the inherent bias of any single algorithm, we employed a multi-model ,ML framework for robust feature selection. We applied three distinct algorithms—Generalized Linear Model (GLM), Random Forest (RF), and Support Vector Machine (SVM)—to the core genes and identified the consensus genes that were consistently ranked as important across all models as a high-confidence signature for subsequent analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.7.\u003c/em\u003e\u003cem\u003eDocking Analysis of LPS and BPDE with Putative Toxicological Targets\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDocking simulations were conducted to assess the binding potential of LPS and BPDE to putative protein targets. The three-dimensional structures of the targets, obtained from the Protein Data Bank (PDB), were preprocessed by removing all water molecules and native ligands using PyMOL prior to the docking studies. The proteins were then hydrogenated and prepared for docking using AutoDock Vina. The binding affinities between the ligands (LPS and BPDE) and the receptor proteins were evaluated based on the calculated binding energies. Finally, the two-dimensional interaction diagrams were visualized and analyzed using Ligplot.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.8. MDS Analysis\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMDS were performed with GROMACS 2025. The system was described by the AMBER force field and the TIP3P water model. Simulations were performed employing periodic boundary conditions under conditions of constant temperature and pressure. The system was maintained at 310 K and 1 bar throughout the simulation. Following a two-step equilibration protocol comprising 100 ps NVT and NPT phases, a 200 ns production MDS was conducted. The MD trajectories were visualized using VMD and analyzed using PyMOL. The binding affinities of the protein-ligand complexes were then determined via the MMPBSA method.\u0026nbsp;\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cem\u003e3.1. Toxicology analysis of BPDE and LPS\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUsing ProTox-3.0 and ADMETlab 3.0, we evaluated the toxicological profiles of BPDE and LPS. Our predictions indicated that both compounds exhibit significant potential for cardiotoxicity, respiratory toxicity, carcinogenicity, and neurotoxicity (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Results of BPDE and LPS toxicity\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eProperty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eDatabase results\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eBPDE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLPS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eADMETlab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProTox\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eADMETlab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProTox\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRespiratory toxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCarcinogenicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMutagenicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNeurotoxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCardiotoxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDILI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e0.3\u0026ndash;0.5(+),0.5\u0026ndash;0.7(++),0.7\u0026ndash;0.9 (+++). Higher predicted toxicity probabilities correspond to greater compound risks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2.\u003c/em\u003e\u003cem\u003eNetwork and Enrichment Analysis of Hub Targets in BPDE-induced CMD\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe obtained the standard structure of BPDE from PubChem. Our subsequent integrated analysis of the Swiss Target Prediction, ChEMBL, PharmMapper, and SEA databases identified 611 non-redundant potential targets for BPDE. In parallel, we compiled 3,976 distinct CMD-associated targets from the GeneCards, CTD, and OMIM databases. A Venn analysis of these merged and deduplicated target sets revealed 366 overlapping targets at the BPDE-CMD intersection. \u003cstrong\u003e(Fig.1A)\u003c/strong\u003e Based on these findings, a \u0026quot;compound-target-disease-pathway\u0026quot; network was established. Construction of the PPI network using the STRING database yielded a topology comprising 343 nodes and 1,879 edges. The network displayed significant functional connectivity, evidenced by a PPI enrichment p-value of \u0026lt; 1.0 \u0026times; 10⁻\u0026sup1;⁶ and an average node degree of 10.3. Subsequent visualization and topological analysis were performed in Cytoscape (v3.10.0). \u003cstrong\u003e(Fig.1B)\u003c/strong\u003e After calculating key topological parameters\u0026mdash;BC, CC, Degree, and LAC\u0026mdash;we applied a median-based threshold to identify core targets: AKT1, IL6, INS, EGFR, ALB, TNF, and ESR1. To elucidate the interactions among these core elements, a compound-disease-pathway network was established.\u003cstrong\u003e\u0026nbsp;(Fig.1E)\u0026nbsp;\u003c/strong\u003eTo this end, the 366 overlapping targets were subjected to enrichment analysis. The GO results demonstrated significant enrichment in the top ten entries across BP, CC, and MF categories. \u003cstrong\u003e(Fig.1G)\u003c/strong\u003e The results highlighted key processes and pathways, including insulin and IGF-1 receptor signaling, activation of the PI3K/AKT pathway, and the regulation of apoptosis. Furthermore, the analysis implicated several critical elements such as growth factor signaling (e.g., EGFR, PDGFR, HGFR), extracellular communication via exosomes, and tyrosine kinase activity. These enriched terms are fundamentally involved in pathways governing cardiac metabolism, vascular dysfunction, and insulin resistance, underscoring their potential roles in the pathogenesis of cardiometabolic diseases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKEGG pathway analysis revealed significant enrichment in several key pathways, \u003cstrong\u003e(Fig.1F)\u0026nbsp;\u003c/strong\u003eincluding the AGE-RAGE signaling pathway in diabetic complications, Lipid and atherosclerosis, and Pathways in cancer. Key genes within these pathways, such as EGR1 and HSP90AA1, are known to be involved in critical processes like insulin resistance, inflammation, and tumorigenesis. BPDE-induced toxicity may operate through a cross-regulatory network of the enriched genes and pathways, which pivotally links metabolic disorders to cardiovascular disease and cancer. This is supported by both GO and KEGG analyses, which implicate these mechanisms in the pathogenesis of diabetic cardiomyopathy, metabolic syndrome, and related cardiometabolic impairments. Ultimately, these findings connect metabolic dysregulation directly to cardiovascular damage.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe screened hub targets were then integrated with the DEGs from datasets GSE83872 and GSE39117 using a ML-based approach to identify overlapping genes. These overlapping genes were considered core targets. Finally, a subset of core targets was selected based on significant differential expression in CMD. Representative examples include EGFR. The entire screening process was visualized using a bar chart \u003cstrong\u003e(Fig.1D)\u003c/strong\u003e and a Venn diagram. \u003cstrong\u003e(Fig.1C)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.3. Docking of BPDE targets in cardiometabolic toxicity\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDocking simulations using AutoDock Vina revealed a robust binding affinity between BPDE and the key target EGFR, with a computed binding energy of -11.4 kcal/mol. \u003cstrong\u003e(Fig.2)\u0026nbsp;\u003c/strong\u003eThis finding suggests a significant affinity between BPDE and EGFR, highlighting its potential critical role in the molecular mechanisms underlying BPDE-induced cardiometabolic toxicity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.4.\u003c/em\u003e\u003cem\u003eNetwork and Enrichment Analysis of Hub Targets in LPS-induced CMD\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe standard two-dimensional structure of LPS was obtained from the PubChem database. Subsequently, a total of 476 potential protein targets of LPS were identified by integrating prediction results from four independent databases. To elucidate the mechanistic relevance to CMD, the overlapping targets between LPS and CMD were screened using a Venn diagram, which revealed 287 shared targets. \u003cstrong\u003e(Fig.3A)\u003c/strong\u003e A comprehensive \u0026quot;compound-target-disease-pathway\u0026quot; network was then built to visualize the interactions. The resulting PPI network comprised 265 nodes and 1,300 edges. Statistical assessment confirmed its high quality, with a PPI enrichment p-value \u0026lt; 1.0 \u0026times; 10⁻\u0026sup1;⁶ and an average node degree of 9.09, indicating a robustly interconnected module. Using Cytoscape (v3.10.0), we computed four centrality measures (e.g., BC, CC, Degree, LAC) to topologically analyze the network and identify hub targets. \u003cstrong\u003e(Fig.3B)\u0026nbsp;\u003c/strong\u003eThe top seven nodes\u0026mdash;EGFR, SRC, IL1B, ESR1, MMP9, STAT1, and CCND1\u0026mdash;were identified as core targets using a median-based screening criterion. Next, we constructed a compound-disease-pathway network graph. \u003cstrong\u003e(Fig.3E)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEnrichment analysis of the 287 intersecting targets was subsequently conducted. GO analysis\u003cstrong\u003e\u0026nbsp;(Fig.3G)\u0026nbsp;\u003c/strong\u003eindicated that these targets are primarily implicated in growth factor receptor signaling pathways, proteolysis, and the regulation of cell migration and invasion. Additionally, they were associated with extracellular matrix remodeling and functions related to kinase activity and phosphorylation. KEGG pathway enrichment analysis revealed that the overlapping targets were primarily implicated in: (1) inflammation and immune responses (e.g., TNF signaling pathway); (2) regulation of cell proliferation, apoptosis, and survival (e.g., PI3K-Akt signaling pathway, Apoptosis); and (3) metabolic and vascular dysfunction (e.g., Lipid and atherosclerosis, Endocrine resistance).\u003cstrong\u003e\u0026nbsp;(Fig.3F)\u0026nbsp;\u003c/strong\u003eThe GO and KEGG enrichment analyses collectively implicate the LPS-CMD intersecting targets in inflammatory and metabolic dyshomeostasis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFollowing the identification of hub targets, we cross-analyzed their presence within the DEGs lists from two datasets (GSE83872 and GSE39117). This analysis identified MMP9 as the sole core target that showed significant differential expression under LPS stimulation. The results of this screening process are presented in a bar chart (showing expression levels) \u003cstrong\u003e(Fig.3D)\u003c/strong\u003e and a Venn diagram (showing the overlap between datasets). \u003cstrong\u003e(Fig.3C)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.5. Docking of LPS targets in cardiometabolic toxicity\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDocking analysis using AutoDock Vina was conducted to evaluate the binding interactions. The calculated binding energies revealed that the toxicants form stable complexes with multiple targets. Notably, MMP9\u0026mdash;the sole core protein target\u0026mdash;exhibited a particularly high binding affinity (-8.6 kcal/mol), suggesting very stable binding. \u003cstrong\u003e(Fig.4)\u003c/strong\u003e This high affinity implies that MMP9 may be a critical target mediating the toxic effects of these compounds.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.6. WGCNA network and ML analysis\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFollowing sample clustering analysis, which retained all samples from GSE83872 and GSE39117, we used WGCNA to construct separate co-expression networks for the BPDE and LPS control treatment groups. \u003cstrong\u003e(Fig.5B)\u003c/strong\u003e For each network, the optimal soft-thresholding power was selected based on scale-free topology criteria, resulting in the selection of powers of 6 and 7 for the BPDE and LPS networks, respectively. \u003cstrong\u003e(Fig.5A)\u003c/strong\u003e Using a minimum module size of 50 and a cut height of 0.10, we identified 5 and 16 modules in the BPDE and LPS networks, respectively\u003cstrong\u003e\u0026nbsp;(Fig.5C)\u003c/strong\u003e. We then selected modules with module-trait correlation coefficients \u0026ge; 0.6 for further analysis. This included the blue module (|r| = 0.6, P \u0026lt; 0.001) in the BPDE network and the red module (|r| = 0.62, P \u0026lt; 0.001) in the LPS network.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHub genes (defined as genes with module membership MM \u0026gt; 0.8) were extracted from these key modules. To evaluate their diagnostic performance, we generated both boxplots (showing expression distribution) \u003cstrong\u003e(Fig.6A)\u003c/strong\u003e and ROC curves\u003cstrong\u003e\u0026nbsp;(Fig.6B)\u0026nbsp;\u003c/strong\u003ebased on their expression levels in the respective datasets. Using the expression profiles of these hub genes as input, three ML models (GLM, RF, SVM) were built. The models achieved AUC values of 0.75, 0.833, and 0.944 in the BPDE validation set. However, their performance in the LPS set was more variable, with AUC values of 0.55, 0.75, and 0.75. These results suggest that the hub genes identified from the BPDE network exhibit strong diagnostic potential, while their applicability in the LPS context may be limited and requires further investigation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.7. Integrative Analysis Reveals Convergent Targets of BPDE and LPS\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGO enrichment analysis of core targets for BPDE- and LPS-induced cardiometabolic toxicity revealed both common and distinct pathways. \u003cstrong\u003e(Fig7A)\u0026nbsp;\u003c/strong\u003eBoth agents converged on the disruption of nitric oxide biosynthesis, highlighting a shared inflammatory-mediated toxic pathway. However, their primary mechanisms diverged significantly. BPDE preferentially dysregulated processes related to apoptosis, peptidyl-serine phosphorylation, and protein complex assembly, while enhancing proliferation and kinase activity. This suggests its toxicity stems from a disruption of cellular homeostasis. Consequently, BPDE\u0026apos;s immunomodulatory effects are primarily indirect, potentially impairing immune resolution and promoting the survival of autoreactive lymphocytes. This may culminate in pathologies such as fibrosis, autoimmunity, or oncogenesis. In contrast, LPS predominantly targeted pathways involved in transcriptional regulation via RNA polymerase II, responses to external stimuli, and proteolytic degradation. This indicates its toxicity is mediated through the direct activation of the innate immune system (e.g., via TLR4), triggering massive transcriptional reprogramming. This leads to a hyperinflammatory state, characterized by a cytokine storm and enhanced proteolytic activity. LPS primarily targets innate immune cells, such as macrophages and dendritic cells, triggering their hyperactivation and directly causing severe tissue damage, septic shock, and multi-organ failure.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eComparative KEGG pathway analysis further corroborated these distinctions\u003cstrong\u003e\u0026nbsp;(Fig7B)\u003c/strong\u003e, with BPDE showing predominant enrichment in the FoxO, Insulin Resistance, and HIF-1 signaling pathways. Existing literature indicates that BPDE-induced dysregulation of the FoxO pathway promotes oxidative stress, a critical signal for NLRP3 inflammasome activation. (Mikse et al., 2010; N. Zhang et al., 2023) It is thus plausible that this cascade may impair cardiac insulin sensitivity and disrupt metabolic homeostasis, key drivers of cardiometabolic dysfunction. (Crossland et al., 2019; Guillet et al., 2012) This network of pathways coherently links BPDE exposure to potential cardiometabolic injury mechanisms. Its strong interaction with EGFR (binding energy: -11.4 kcal/mol) likely enhances abnormal cellular proliferation through these pathways. In contrast, LPS showed significant enrichment in pathways related to cancer (e.g., Proteoglycans in cancer, EGFR tyrosine kinase inhibitor resistance), infectious diseases (Coronavirus disease - COVID-19), and endocrine resistance. Notably, both compounds were enriched in several common pathways, including the JAK-STAT, Thyroid hormone, Prolactin, Hepatitis B, and AGE-RAGE signaling pathways. These findings highlight the fundamental differences in their toxicological mechanisms: BPDE primarily disrupts cellular homeostasis and growth, whereas LPS triggers dysregulated immune and inflammatory responses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA Venn diagram analysis revealed nine overlapping targets common to both BPDE- and LPS-induced cardiometabolic toxicity, out of a total of 26 targets identified for each toxin\u003cstrong\u003e\u0026nbsp;(Fig7D)\u003c/strong\u003e. These targets\u0026mdash;EGFR, ESR1, JAK2, PIK3R1, HDAC3, SORD, ADK, HDAC2, and SOD2\u0026mdash;constitute a common regulatory hub for toxicity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.8. Docking of key targets\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe docking of BPDE with EGFR was previously established. Consequently, the current investigation shifted its focus to BPDE\u0026apos;s binding interactions with the eight other key targets associated with toxicity (ESR1, JAK2, PIK3R1, HDAC3, SORD, ADK, HDAC2, and SOD2) \u003cstrong\u003e(Fig. 8A)\u003c/strong\u003e. We performed docking of LPS against the full set of nine overlapping core targets (EGFR, ESR1, JAK2, PIK3R1, HDAC3, SORD, ADK, HDAC2, and SOD2) \u003cstrong\u003e(Fig. 8B)\u003c/strong\u003e. According to AutoDock Vina docking results \u003cstrong\u003e(Table 2)\u003c/strong\u003e, all compounds exhibited binding energies below \u0026minus;5 kcal/mol. The EGFR-BPDE complex showed the lowest energy (-11.4 kcal/mol), and the LPS-MMP9 interaction was among the strongest with an energy of -8.6 kcal/mol.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e. The binding energy of Docking.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCompounds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStructure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTarget\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBingingEnergy(kcal/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eBPDE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003e\u003cimg width=\"121\" height=\"98\" 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alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6ESM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMMP9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6WBZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHDAC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4A69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHDAC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1BX4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eADK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1PLB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSORD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8A27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8DUB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eESR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3UGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eJAK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2IUG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePK3R1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9BWQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSOD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBinding energy between \u0026minus;3\u0026thinsp;kcal/mol and \u0026minus;5\u0026thinsp;kcal/mol : Weak binding；Binding energy between \u0026minus;5\u0026thinsp;kcal/mol and \u0026minus;8\u0026thinsp;kcal/mol: Moderate binding；Binding energy \u0026lt;\u0026thinsp;\u0026ndash;8 kcal/mol: Strong binding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.9. Molecular dynamics simulations (MDS) verification\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo further investigate the binding affinity of BPDE and LPS for their respective target complexes (EGFR and MMP9), we performed MDS on the BPDE-EGFR and LPS-MMP9 complexes. To evaluate conformational stability, the root mean square deviation (RMSD) was calculated. This metric serves to quantify the average atomic displacement from a reference structure, where lower values are typically indicative of a more stable conformational state. As illustrated in Fig. 9A, the RMSD analysis revealed that the BPDE-EGFR complex achieved equilibrium after 45-100 ns, indicating stable binding. It subsequently underwent a transient fluctuation between 100-150 ns before gradually re-stabilizing after 150 ns. In contrast, the LPS-MMP9 complex reached a stable equilibrium earlier, at approximately 70 ns. Notably, the BPDE-EGFR complex exhibited the lowest final RMSD value (approximately 0.35 \u0026Aring;), suggesting a highly stable interaction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe root mean square fluctuation (RMSF) analysis, which evaluates residue-specific flexibility, revealed low values (0.1\u0026ndash;0.35 \u0026Aring;) throughout the BPDE-EGFR complex excepting the dynamic terminal regions (Fig. 9B). Conversely, the LPS-MMP9 complex exhibited markedly higher fluctuations, consistent with greater overall flexibility.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe structural compactness was further assessed by the radius of gyration (Rg). A decrease in Rg was observed for the BPDE-EGFR complex upon binding, suggesting its stabilization into a more compact conformation (Fig. 9C). In contrast, the LPS-MMP9 complex exhibited minimal Rg fluctuation throughout the 200 ns simulation, demonstrating that no significant structural expansion or compaction occurred during this period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe solvent-accessible surface area (SASA) was analyzed to evaluate changes in protein surface exposure following ligand binding. As shown in Fig. 9E, both complexes exhibited steady SASA fluctuations, suggesting that neither underwent significant expansion or contraction. To quantitatively compare the binding affinity, MM/PBSA calculations were performed. The results revealed a substantially more favorable binding energy for the LPS-MMP9 complex (\u0026Delta;G = -71.57 kcal/mol, Table 3) than for the BPDE-EGFR complex (\u0026Delta;G = -24.45 kcal/mol, Table 3). Consistent with this energetic trend, analysis of hydrogen bond dynamics also showed that the LPS-MMP9 complex sustained a greater number of hydrogen bonds than the BPDE-EGFR complex.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEnerge Component (Kcal/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBPDE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLPS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e△VDWAALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-33.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-105.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e△EEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-15.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e△EPB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e△ENPOLAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-9.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e△GGAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-39.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-121.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e△GSOLV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e△dG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-24.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-71.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTip, △VDWAALS, Change in van der Waals interaction energy. △EEL, Change in electrostatic energy. △EPB, Change in polar solvation energy. △ENPOLAR, Change in non-polar solvation energy. △GGAS, Total change in free energy in the gas phase (△EEL + △VDWAALS). △GSOLV, Total change in solvation free energy (△EPB + △ENPOLAR). △dG, Final total binding free energy (△GGAS + △GSOLV).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur integrated analysis establishes distinct contributions of the BPDE-EGFR and LPS-MMP9 complexes to system stability: the former provides superior structural integrity, while the latter exhibits stronger binding affinity. This concerted action\u0026mdash;structural stabilization by one and potent binding driven by the other\u0026mdash;likely represents a critical mechanism in CMD pathogenesis.\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAir pollution is a leading global risk factor for morbidity and mortality. It is a major cause of cardiovascular diseases, which account for the largest proportion of air pollution-related deaths, and it also promotes the development of chronic cardiometabolic disorders. (Brook et al., 2017; Lu et al., 2025) Through mechanisms such as increased peripheral vasoconstriction, short-term PM2.5 exposure can trigger acute elevations in systemic blood pressure (BP) over hours to days. In contrast, chronic exposure promotes the development of HTN itself. Additionally, these pollutants exacerbate insulin resistance and facilitate the development of diabetes mellitus. (Andersen et al., 2012; Brook et al., 2017)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA key mechanistic pathway involves the component LPS, a classic pathogen-associated molecular pattern (PAMP) found on PM2.5. The activation of innate immunity by LPS through Toll-like receptor 4 (TLR4) initiates critical inflammatory signaling pathways (NF-κB, MAPK) and stimulates pro-inflammatory cytokine release (e.g., TNF-α, IL-6). This resulting chronic, low-grade inflammatory milieu thereby fosters insulin resistance (IR) and underlies the pathogenesis of T2DM. (Rajagopalan \u0026amp; Brook, 2012) In PM2.5-exposed mouse models, the LPS component has been shown to drive IR and vascular injury via the TLR4/NADPH oxidase axis. TLR4 activation also drives vascular dysfunction; for instance, in adipose tissue, it stimulates the release of chemokines such as CCL-2, which recruits pro-inflammatory monocytes/macrophages. (Kampfrath et al., 2011) Moreover, TLR4 activation reduces endothelial nitric oxide synthase (eNOS) activity, impairing vasodilation, and elevates the expression of plasminogen activator inhibitor-1 (PAI-1) and adhesion molecules like ICAM-1, collectively promoting vascular disease. (Shi et al., 2006; Xu et al., 2011)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBPDE, the main biologically active metabolite of BaP, is generated through metabolic activation by cytochrome P450 1A1/1B1 (CYP1A1/1B1) and epoxide hydrolase. It exerts its carcinogenic effect primarily by covalently binding to the N2 position of DNA guanine, forming mutagenic BPDE-DNA adducts (predominantly BPDE-N2-dG). These adducts serve as key biomarkers of DNA damage, disrupting replication and transcription and frequently inducing G→T transversion mutations.(Chen et al., 2022; Savela et al., 1996; Weng et al., 2018) Furthermore, in vitro studies using human bronchial epithelial cells (BEAS-2B) have shown that BPDE exposure induces malignant transformation, associated with downregulation of EGR1, upregulation of miR-377-3p, and activation of the Wnt/β-catenin signaling pathway. (Ke et al., 2021)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe evaluated the toxicological profiles of both compounds using ADMETlab 3.0 and ProTox 3.0, which revealed significant cardiometabolic toxicity. (Banerjee et al., 2024; Fu et al., 2024) By integrating computational toxicology, Docking, ML, and MDS, we elucidated their molecular targets and mechanisms. Computational profiling predicted overlapping immunotoxic and respiratory effects for both BPDE and LPS \u003cstrong\u003e(Table 1)\u003c/strong\u003e. Their distinct toxicities included mutagenicity, carcinogenicity, and blood-brain barrier penetration for BPDE, contrasted with neurotoxicity, nephrotoxicity, and cardiotoxicity for LPS. Combining bioinformatics from GEO datasets, PPI network analysis, and ML algorithms, we pinpointed nine critical toxicity-related targets, among which EGFR was targeted by BPDE and MMP9 by LPS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEGFR activation drives fibroblast proliferation by upregulating the expression of genes related to DNA replication and cell cycle progression. This, in turn, leads to the excessive deposition of extracellular matrix (ECM) components, such as type I collagen, and increased tissue stiffness. (Ewoldt et al., 2024) Concomitantly, it activates inflammatory signaling pathways, such as ERK1/2, which results in increased levels of circulating and brain TNF-α. (Wei et al., 2021) EGFR transactivation, primarily induced by the angiotensin II type 1 receptor (AT1R) in blood vessels, plays a central role in vascular pathophysiology. This AT1R-mediated EGFR activation initiates a critical signaling crosstalk that amplifies cellular responses to angiotensin II. Upon transactivation, EGFR signaling drives pathological changes including increased vessel wall thickness and reduced vascular compliance. Moreover, EGFR activation potentiates the actions of angiotensin II, driving endothelial dysfunction and culminating in a cascade of vascular inflammation, functional impairment, and structural remodeling. Pathological alterations in this EGFR-mediated signaling axis are increasingly recognized as a key mechanism in the development of diverse cardiovascular disorders. (Gekle et al., 2023; Hong et al., 2025)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMMP-9 degrades type IV collagen and elastin, disrupting vascular wall structure and compromising extracellular matrix integrity, thereby increasing vascular stiffness. This process is directly regulated by NF-κB, whose activation in vascular smooth muscle cells and cardiomyocytes upregulates MMP-9 transcription, further promoting the development of HTN and myocardial infarction. (Ye, 2006) Additionally, MMP-9 contributes to inflammation by stimulating the release of cytokines such as TNF-α\u0026nbsp;and IL-1β, and drives pathological angiogenesis through basement membrane degradation and VEGF release. MMP-9 also modulates immune cell infiltration by activating CXCL5 and CXCL8 while inactivating CXCL12, thereby regulating neutrophil migration. Collectively, MMP-9 influences the progression of cardiovascular disease through multiple pathways, including the NF-κB/AP-1 inflammatory axis, TGF-β-mediated fibrosis, Ang II-related hemodynamic mechanisms, and immune regulatory networks.\u0026nbsp;(Yabluchanskiy et al., 2013)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEnergy decomposition analysis within molecular dynamics simulations identified ASP800 (–5.35 kcal/mol) and ARG841 (–6.34 kcal/mol) as the most critical residues in the BPDE–EGFR complex. Analysis of the simulation trajectories suggests their substantial energy contributions are mediated by strong interactions with the ligand, such as hydrogen bonds and\u0026nbsp;π–π\u0026nbsp;stacking, which help stabilize the binding pocket. Similarly, in the LPS–MMP9 complex, PHE723 (–3.28 kcal/mol) and MET766 (–2.69 kcal/mol) emerged as the dominant contributors to the binding energy. Functional enrichment analyses revealed that both BPDE and LPS exposures dysregulated both common and unique pathological modules, focused on cancer pathogenesis and metabolic-vascular dysfunction. BPDE significantly promoted processes related to cell survival and proliferation—such as negative regulation of apoptosis and smooth muscle cell proliferation—along with oncogenic pathways including EGFR tyrosine kinase inhibitor resistance, PI3K-Akt, and JAK-STAT signaling. Similarly, LPS-responsive genes were enriched in immune and inflammatory processes, regulation of cell proliferation and apoptosis, and critical pathways including Toll-like receptor, TNF, NOD-like receptor, and AGE-RAGE signaling, which are central to atherosclerosis pathogenesis. Both exposures converged on strong disease associations with Diabetes Mellitus and Vascular Diseases, \u003cstrong\u003e(Fig.7C)\u003c/strong\u003e with BPDE additionally linked to Endothelial Dysfunction and LPS to Endotoxemia. Collectively, these results indicate that while BPDE and LPS engage partly divergent signaling cascades, they both disrupt core homeostatic processes and drive shared metabolic-vascular disorders, thereby providing a robust molecular basis their roles in complex human diseases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis preliminary investigation suggests a potential role of BPDE and LPS in CMD development; however, their precise molecular mechanisms require further elucidation. Our integrated framework, which synergizes network toxicology, ML, MDS, systematically reveals compound toxicity mechanisms at the molecular level. This strategy offers a powerful approach for generating novel hypotheses in environmental health research. These \u003cem\u003ein silico\u003c/em\u003e findings provide a rationale for subsequent experimental studies, which could enhance research efficiency. Furthermore, the identified core targets (e.g., SORD (Schlicker et al., 2019) , ADK (Wölkart et al., 2022) ) are implicated in specific pathogenic pathways and, based on prior evidence linking them to metabolic dysregulation, these targets thus offer a rational basis for devising early diagnostic and targeted therapeutic strategies for environmentally associated CMD.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. A primary constraint is that our computational models did not account for potential synergistic effects from other PM₂.₅ constituents (e.g., heavy metals) or for demographic confounders such as age and sex. Consequently, a key future priority involves experimental verification of these predictions. Specifically, combined exposure assays complemented by clinical sample analyses will be essential to establish the physiological relevance and reproducibility of the proposed mechanisms.\u0026nbsp;\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eTo elucidate how specific PM₂.₅ components (BPDE and LPS) may drive CMD, we applied an integrated computational strategy that converged insights from network toxicology, ML, Docking, and MDS. The multi-method approach helped to delineate complex biological interactions and identified several key molecular targets and pathways potentially involved in PM₂.₅-induced cardiometabolic impairment. These computational findings provide preliminary mechanistic insights into the combined toxicological effects of BPDE and LPS as PM₂.₅ components, supporting further evaluation of their health risks. Subsequent studies should expand on this work by investigating associations between chronic PM₂.₅ exposure and a broader range of cardiovascular and metabolic disorders. Such research would contribute to a more robust evidence base for risk assessment of airborne particulate matter and inform public health strategies aimed at reducing pollution-related cardiometabolic morbidity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (82374367), Jiangxi Provincial Natural Science Foundation (20242BAB26163, 20232BAB206144), Jiangxi Province Key Laboratory of Traditional Chinese Medicine for Cardiovascular Diseases (20242BCC32096), NATCM's Project of High-level Construction of Key TCM Disciplines (zyyzdxk-2023113), Project of Key Discipline Construction Fund of Jiangxi University of Chinese Medicine (2023jzzdxk032), Science and Technology Innovation Team Development Program of Jiangxi University of Chinese Medicine (CXTD22011), National Traditional Chinese Medicine Inheritance and Innovation Center Construction Project.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHaoyue Jia:\u0026nbsp;\u003c/strong\u003eWriting – original draft, Methodology, Conceptualization, \u003cstrong\u003eHao Zhang:\u0026nbsp;\u003c/strong\u003eResources, Data curation. \u003cstrong\u003eChengyan Guan:\u0026nbsp;\u003c/strong\u003eData curation, Formal analysis,\u003cstrong\u003e\u0026nbsp;Qiang Wan:\u003c/strong\u003e Writing –review \u0026amp; editing Supervision, Resources, Funding acquisition.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAndersen, Z. 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BaP/BPDE suppressed endothelial cell angiogenesis to induce miscarriage by promoting MARCHF1/GPX4-mediated ferroptosis. \u003cem\u003eEnviron Int\u003c/em\u003e,\u003cem\u003e 180\u003c/em\u003e, 108237. https://doi.org/10.1016/j.envint.2023.108237\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cardiometabolic, BPDE, LPS, molecular docking, molecular dynamic simulations, machine learning ","lastPublishedDoi":"10.21203/rs.3.rs-7937700/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7937700/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Exposure to specific ambient pollutants, including certain PM2.5-bound constituents like benzo[a]pyrene-7,8-dihydrodiol-9,10-epoxide (BPDE) and lipopolysaccharide (LPS), has been increasingly implicated as a significant risk factor in the global burden of cardiometabolic diseases (CMD). However, the precise toxicological mechanisms through which these pollutants adversely affect cardiometabolic health remain poorly understood. Accordingly, this study aimed to delineate the effects of BPDE and LPS, both individually and in combination, on CMD, and to investigate the underlying molecular mechanisms driving its pathogenesis. We identified 366 and 287 potential targets for BPDE and LPS, respectively, in CMD through a multi-database screening (SwissTargetPrediction, ChEMBL, PharmMapper, CTD, GEO). Rigorous bioinformatic screening—integrating the STRING platform, Cytoscape (v3.10.0), and three machine learning (ML) methods—identified nine core targets: EGFR, ESR1, JAK2, PIK3R1, HDAC3, SORD, ADK, HDAC2, and SOD2. GO and KEGG analyses demonstrated their significant enrichment in key signaling (e.g., FoxO) and metabolic pathways (e.g., lipid metabolism and atherosclerosis), suggesting a mechanistic link to pollutant-induced CMD. Molecular docking and dynamics simulations demonstrated robust binding of BPDE and LPS to EGFR and MMP9, respectively, identifying these complexes as promising therapeutic targets for pollutant-associated CMD.","manuscriptTitle":"Toxic Mechanisms of PM2.5 Constituents (LPS and BPDE) in Cardiometabolic Disease: Insights from Integrated Machine Learning and Molecular Dynamic Simulations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-05 04:06:37","doi":"10.21203/rs.3.rs-7937700/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"744b83d3-ad44-4a20-8459-80056744c88b","owner":[],"postedDate":"November 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-08T15:49:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-05 04:06:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7937700","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7937700","identity":"rs-7937700","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}