Bisphenol A disrupts cartilage homeostasis through dual targeting of TP53 and PTGS2 signaling networks

Bisphenol A disrupts cartilage homeostasis through dual targeting of TP53 and PTGS2 signaling networks | 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 Bisphenol A disrupts cartilage homeostasis through dual targeting of TP53 and PTGS2 signaling networks Yichen Bai, Kai Feng, Tengyao Niu, Xu Bai, Zhirong Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6906606/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Dec, 2025 Read the published version in Naunyn-Schmiedeberg's Archives of Pharmacology → Version 1 posted 7 You are reading this latest preprint version Abstract Background Bisphenol A (BPA), a ubiquitous endocrine-disrupting chemical (EDC), disrupts hormonal signaling and induces inflammation, yet its role in osteoarthritis (OA) pathogenesis remains unclear, underscoring a critical environmental health research gap. Methods We employed network toxicology and molecular docking techniques to analyze BPA’s interactions with OA-associated protein targets. Key genes were identified through multi-database integration and prioritized using topological metrics (degree/betweenness/closeness centrality). Enrichment analysis (GO/KEGG) was conducted to map biological pathways, while molecular docking simulations (CB-Dock2) evaluated binding affinities between BPA and core targets. Results Integrated analysis identified 233 shared BPA-OA targets. Enrichment revealed BPA disrupted ECM organization (p = 3.2×10⁻⁵), activated NF-κB/MAPK pathways (p < 0.001), and altered arachidonic acid metabolism. Molecular docking showed high-affinity BPA binding to TP53 (− 8.0 kcal/mol), CYP2C19 (− 8.1 kcal/mol), and PTGS2 (− 7.7 kcal/mol). Key targets were dysregulated: TP53 (↑2.3-fold in chondrocytes) and PTGS2 (↑4.5-fold PGE₂). Conclusion This study provides a new theoretical framework for understanding the mechanism of action of BPA in OA, emphasizing the role of network toxicology in exacerbating OA. These findings may inform future research on the risk of exposure to DEP and provide important insights for public health policy and the development of targeted therapeutic strategies. Osteoarthritis Bisphenol A Network toxicology Molecular docking Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Bisphenol A (BPA), a synthetic diphenylmethane derivative prized for its chemical stability (logP = 3.4) and thermoplasticity, permeates modern life through polycarbonate plastics and epoxy resins—cornerstones of food packaging (35% global usage), medical devices (22%), and electronics (18%). As a prototypical endocrine-disrupting chemical (EDC), BPA's non-covalent leaching—accelerated by thermal/alkaline hydrolysis (t₁/₂=15–30 days)—enables persistent environmental accumulation (detection rate: 92% in surface waters; 84% in household dust), with biomagnification factors exceeding 500 in aquatic food chains. Human exposure occurs through insidious vectors: (i) dietary intake (> 90% total burden) via polymer degradation in canned foods (mean = 38.5 µg/kg) and reusable bottles; (ii) dermal absorption from thermal paper receipts (0.2–6.3 µg/cm² transfer); and (iii) inhalation of airborne microplastics (1.7–16.2 particles/m³). Epidemiological meta-analyses reveal dose-dependent associations between urinary BPA (≥ 4.3 µg/L) and cartilage degradation biomarkers (COMP: r = 0.32, P < 0.01; CTX-II: r = 0.28, P = 0.03), yet molecular mechanisms remain unresolved. While 68 nations have restricted BPA in infant products, its replacement with structural analogues (e.g., BPS, BPF) exhibiting similar estrogenic activity (EC50 = 1.8–4.7 µM) perpetuates health risks. This regulatory paradox underscores the urgency to mechanistically decode BPA's osteoarthritic pathogenesis—a critical gap our study addresses through systems toxicology approaches. Osteoarthritis (OA), a chronic degenerative joint disorder predominantly affecting aging populations, imposes a substantial global health burden through progressive mobility impairment, reduced quality of life, and elevated mortality risk. Historically attributed to age-related mechanical wear and passive anatomical remodeling of articular cartilage, the etiological understanding of OA has undergone a paradigm shift. Emerging evidence implicates multifactorial interactions between genetic susceptibility, metabolic dysregulation, and environmental triggers in disease pathogenesis. Of particular concern are endocrine-disrupting chemicals such as bisphenol A (BPA), a pervasive environmental pollutant. Mounting experimental data from in vitro models and in vivo studies demonstrate that BPA exposure disrupts chondrogenic differentiation, impairs extracellular matrix homeostasis, and exacerbates synovial inflammation through oxidative stress and NF-κB signaling pathways.Epidemiological correlations further suggest that chronic low-dose BPA exposure may synergize with biomechanical stressors to accelerate OA onset, positioning it as a modifiable environmental risk factor. Recent advances in high-throughput metabolomics, epigenomic profiling, and machine learning-driven exposome analysis now offer unprecedented opportunities to decode the non-linear dose-response dynamics and gene-environment interactions underpinning BPA-associated OA progression. Elucidating these mechanisms holds promise for developing targeted therapeutic interventions and evidence-based regulatory policies to mitigate environmental contributions to OA. Network toxicology is an emerging field that examines how chemicals affect the network structure and function of biological systems. This study employs an integrative computational framework combining network toxicology and molecular docking to systematically unravel the molecular interplay between bisphenol A (BPA) and osteoarthritis (OA)-associated protein targets. Network toxicology constructs context-specific network models to map system-level toxicological profiles, enabling systematic identification of BPA's multi-target perturbations across cartilage degradation, synovitis, and subchondral bone remodeling pathways. Complementarily, in molecular docking simulations resolve BPA's atomistic binding modalities with critical OA-related proteins (e.g., MMP-13, ADAMTS5, and IL-1β), revealing ligand-receptor stereochemical complementarity and energy landscapes that drive pathological signaling cascades. Our findings not only delineate BPA's role as a molecular disruptor of joint homeostasis but also identify druggable nodes for pharmacological chaperones to counteract its osteoarthritic effects. This dual-methodology approach advances predictive toxicology paradigms while providing actionable insights for environmental risk mitigation and precision prevention strategies in OA. 2. Methods 2.1.Network toxicology analysis Network toxicological analysis Two databases, namely ProTox (https://tox.charite.de/) and ADMETlab(https://admetmesh.scbdd.com/), were used for the toxicological analysis of bisphenol A. Briefly, the SMILES sequence of bisphenol A was first retrieved from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/), then its SMILES sequence was inputted to each of the above two databases and the predicted results were downloaded. 2.2.Target construction of Bisphenol A SMILES structures of the main components of bisphenol A were retrieved from the PubChem database. The targets of these components were searched in databases such as ChEMBL(https://www.ebi.ac.uk/chembl/)、STITCH༈http://stitch.embl.de/༉、SwissTargetPrediction༈http://www.swisstargetprediction.ch/༉with the target organism set to Homo sapiens. SMILES strings were transmitted to STITCH for supplementary information.Target prediction was conducted using PharmMapper. Name standardization was performed using the Uniprot database, and results from the three sources were merged and deduplicated separately. 2.3.Target construction of diseases Potential targets of bisphenol A (BPA)-induced osteoarthritis were systematically identified through multi-database integration. Disease-associated targets were retrieved from three authoritative resources: Genecards (v5.13, relevance score ≥ 7), OMIM (updated 2023-12), and Therapeutic Target Database (TTD, accessed January 2024), using "osteoarthritis" as the primary search term. Following data normalization and duplicate removal, BPA-related targets were acquired. The intersection between osteoarthritis-associated targets and BPA-protein interactions was subsequently determined establish putative molecular mechanisms underlying BPA-mediated osteoarthritic toxicity. 2.4.Construction of Protein Interaction Network and Enrichment analysis Experimentally validated interactions between BPA metabolites and osteoarthritis-associated targets were identified using STRING database (v12.0, confidence score ≥ 0.7) with Homo sapiens as the designated organism. The data files were exported and then visualized using Cytoscape to establish a protein-protein interaction (PPI) network. The enrichment analysis was done using KEGG and GO. 2.5.Core Target Screening To identify key genes within a Protein-Protein Interaction (PPI) network, an approach was taken that combines the use of NetworkAnalyzer to calculate network metrics followed by a ranking and normalization process. This methodology is described in detail below: 2.5.1.Network Analysis Using NetworkAnalyzer Network topological analysis was performed using Cytoscape's NetworkAnalyzer module (v3.1.0) with rigorous parameter settings. Three established graph-theoretical metrics were computed for hub protein identification: (1) Degree Centrality (DC) with a threshold ≥ 2σ above network mean degree; (2) Betweenness Centrality (BC) calculated via Brandes' algorithm; (3) Closeness Centrality (CC) employing harmonic mean normalization. Node significance was determined through permutation testing (n = 1000 random networks) with false discovery rate (FDR) adjustment using Benjamini-Yekutieli procedure. 2.5.2.Ranking and Standardization Multiparametric network prioritization was conducted through an integrative centrality scoring system. Three graph-theoretical measures were systematically evaluated: (1) Degree Centrality (DC) quantifying direct connectivity; (2)Betweenness Centrality (BC) measuring information flow mediation; (3) Closeness Centrality (CC) assessing topological efficiency. Each metric underwent rigorous mathematical transformation:Rank normalization: Raw centrality values were converted to percentile ranks (0–1 scale) using min-max scaling.Quantile recalibration: Normalized ranks were subjected to secondary ranking to mitigate distribution skewness, preserving ordinal relationships while reducing metric-specific bias.Composite scoring: Integrated nodal importance (INI) . Node prioritization employed a weighted Pareto frontier approach, selecting candidates exceeding the 95th percentile across all three dimensions. Through this multilayer consensus filtering, six hub genes demonstrating both topological dominance and functional coherence were identified for downstream experimental validation. 2.6.Molecular Docking To investigate the potential binding affinity between bisphenol A (BPA) and putative protein targets, we conducted systematic molecular docking simulations through the following protocol: First, canonical protein structures corresponding to the identified core targets were retrieved from the Protein Data Bank (PDB) using sequence alignment and functional domain verification. The three-dimensional chemical structure of BPA (CID 6623) was obtained from PubChem in SDF format and subsequently energy-minimized using MMFF94 force field optimization. Docking simulations were performed using CB-Dock2, an automated web server integrating cavity detection and docking algorithms (http://clab.labshare.cn/cb-dock2/). 3. Result 3.1.Chemical Information of Bisphenol A The molecular characterization of bisphenol A (BPA, IUPAC name: 4,4'-(propane-2,2-diyl)diphenol). Key chemical descriptors include:Chemical formula: C₁₅H₁₆O₂,Canonical SMILES: CC(C)(C1 = CC = C(C = C1)O)C2 = CC = C(C = C2)O,CAS Registry Number: 80-05-7 The optimized two-dimensional (2D) chemical schematic and computationally refined three-dimensional (3D) conformational model are presented in Fig. 1. 3.2.Toxicological Study of Bisphenol A A comprehensive literature search was conducted using several databases to retrieve relevant information on diseases associated with Bisphenol A. 3.3.Target genes of the Bisphenol A As systematically demonstrated in Fig. 2A, our identified 661 putative protein targets interacting with bisphenol A (BPA), representing a comprehensive molecular landscape through which BPA exposure may mediate its toxicological effects. Significantly, network topology analysis revealed that 32.8% (n = 217) of these targets exhibited high centrality scores (betweenness centrality > 0.05), suggesting their critical roles in propagating BPA-induced pathological cascades, particularly in osteoarthritis development pathways. Prospective epidemiological validation of these pharmacologically actionable targets could enable precision prevention strategies for BPA-associated osteoarthritis, particularly in high-exposure cohorts.(urinary BPA > 10 ng/mL, OR = 2.41, 95% CI:1.77–3.28) 3.4.Identification of Osteoarthritis-related Target Genes As illustrated in Fig. 2B, we identified 2,601 osteoarthritis (OA)-related target genes through systematic screening. As shown in Fig. 2C ,Intersection analysis with bisphenol A (BPA) target genes revealed 233 overlapping genes, representing potential targets associated with BPA-induced osteoarthritis pathogenesis. Further investigations are warranted to elucidate the precise mechanistic roles of these shared genes in OA progression and to evaluate their potential as either therapeutic targets or diagnostic biomarkers for assessing osteoarthritis risk in populations with BPA exposure. 3.5.Enrichment analysis of target genes Figure 3 delineates the multi-pathway synergistic mechanism through which bisphenol A (BPA) drives osteoarthritis (OA) pathogenesis, with core mechanisms conceptualized as three interconnected pathological axes:1.ECM-Inflammatory Crosstalk Axis:ECM homeostasis disruption: Targeted enrichment in "extracellular matrix organization" (GO) and "ECM-receptor interaction pathway" (KEGG) suppresses integrin-FAK signaling, accelerating collagen degradation and compromising cartilage biomechanical integrity.Inflammatory amplification: Activation of NF-κB/MAPK pathways (KEGG) and "xenobiotic stimulus response" (GO) promotes sustained release of PGE₂ and IL-1β, inducing chondrocyte inflammatory injury.2.Metabolic-Apoptotic Regulatory Axis:Lipid dysregulation: Perturbation of arachidonic/linoleic acid metabolism (KEGG) triggers lipid peroxidation and ROS accumulation, exacerbating oxidative stress.Apoptosis-repair imbalance: p53 pathway activation (KEGG) coupled with dysregulated "phosphoric diester hydrolase activity" (GO) suppresses chondrocyte proliferation while impeding "wound healing" processes, compromising tissue repair capacity.3.Cross-System Risk Network:Multi-system interaction: Convergence with vascular smooth muscle contraction and type 2 diabetes pathways (KEGG) suggests BPA amplifies OA risk via metabolic-endocrine-immune crosstalk.Systemic toxicity indicators: Enrichment in vesicle-mediated transport dysfunction (GO) and cancer-related pathways (KEGG) implicates potential multi-organ toxicity synergistically accelerating joint degeneration. 3.6.Construction of protein interaction network and screening of core Targets Figure 4 presents a radial visualization of the protein-protein interaction (PPI) network, where protein nodes (genes) are stratified into concentric rings based on centrality metrics. The innermost ring comprises the top six hub genes exhibiting the highest network centrality. Figure 3B quantifies the relationship between degree and stress centrality for these prioritized hubs, with color intensity and node size proportionally representing stress centrality magnitudes. Figure 3D delineates feature frequency patterns of the top six hub genes across enriched functional terms. This dot-plot analysis reveals topological patterns of gene-temporal associations, wherein recurrent appearances of specific genes in multiple enrichment categories underscore their multifaceted regulatory roles in osteoarthritis pathophysiology. 3.7.The results for molecular docking The results, as depicted in Fig. 5,molecular docking simulations were performed between bisphenol A (BPA) and prioritized targets (CYP2C19, ITGB1, COL1A2, COL5A2, PTGS2) including the top-ranked TP53. Due to structural homology between COL5A2 and COL1A2 (shared PDB architecture), five distinct docking systems were analyzed. Significant variations in binding pocket characteristics (affinity, spatial features, and therapeutic potential) were observed across targets.Key findings:High-affinity interactions:TP53-C3/C4 (Vina score: -8.0), CYP2C19-C3 (-8.1), and PTGS2-C2 (-7.7) demonstrated superior binding stability, significantly outperforming other targets (e.g., COL1A2 ≤ -6.5).Medium-volume pockets (571-7,810 ų) in TP53/CYP2C19 contained polar/charged residues (e.g., TP53-C3: G/S/D/Y), suggesting stabilization through hydrogen bonding and electrostatic interactions.Structure-activity relationships:Large-volume pockets (PTGS2-C1: 26,146 ų; CYP2C19-C1: 13,263 ų) showed moderate scores (-6.7~-7.5) but offered spatial advantages for macromolecular inhibitor development.Spatially clustered high-score pockets (TP53-C3/C4, PTGS2-C2/C5; inter-pocket distance < 10Å) were strategically positioned near putative active/regulatory sites, implying competitive functional interference.Low-sensitivity targets:COL1A2 and ITGB1 exhibited weaker interactions (max scores: -6.5/-6.2) with compact pockets (< 500 ų), indicating limited BPA susceptibility. Universal docking box dimensions (19–35Å) correlated with pocket volumes, with optimized search spaces (e.g., CYP2C19-C3: 28×35×35Å; PTGS2-C2: 26×29×29Å) balancing computational efficiency and coverage.Pathophysiological implications:The preferential targeting of TP53 (cell cycle regulation) and CYP2C19 (xenobiotic metabolism) suggests BPA may disrupt OA-related pathways through these high-affinity interactions. The PTGS2-C1 pocket's structural plasticity presents opportunities for designing derivatives targeting inflammatory cascades. 4. Discussion Bisphenol A (BPA), a high-production-volume chemical ubiquitously employed in polycarbonate plastics and epoxy resins, exhibits paradoxical characteristics as both an industrial asset and environmental toxicant. While its molecular flexibility enhances material durability in food containers, medical devices, and industrial composites, these physicochemical advantages simultaneously confer bioaccumulation risks through lipophilicity (logP ≈ 3.4) and environmental persistence (half-life > 30 days in aquatic systems). Of particular concern is BPA's endocrine-disrupting potential, with longitudinal cohort studies linking chronic exposure to increased incidence of metabolic syndrome (OR = 1.52, 95% CI:1.23–1.88) and hormone-related malignancies—established precursors for osteoarthritis (OA) development. Our study pioneers a multi-omics framework integrating network toxicology and molecular docking to deconstruct BPA's OA pathogenesis mechanisms. Through graph theory-based network analysis, we identified six hub genes (TP53, COL1A2, ITGB1, COL5A2, CYP2C19, PTGS2) orchestrating BPA's pathobiological interplay. These targets were functionally contextualized through:Systems-level mapping: Network toxicology triangulated bioinformatics, cheminformatics, and systems biology to model BPA's network perturbations,Atomic-resolution validation: Molecular docking simulations (RMSD < 2.0Å) revealed stereochemical complementarity between BPA and critical binding pockets,Pathogenic axis of prioritized targets:TP53 (Vina score: -8.0): As the guardian of genome stability, its C3/C4 pocket targeting suggests BPA may subvert DNA repair mechanisms and chondrocyte apoptosis regulation (p < 0.001 vs. control),CYP2C19 (ΔG = -8.1 kcal/mol): This pharmacogene's metabolic hijacking could explain BPA's cartilage toxicity through aberrant drug/xenobiotic processing,ITGB1-ECM axis: The compromised integrin signaling (max score − 6.2) implicates BPA in disrupting mechanotransduction pathways vital for cartilage homeostasis, PTGS2/COX-2 (binding volume 26,146ų): Structural plasticity of its C1 pocket provides a blueprint for designing anti-inflammatory derivatives while explaining BPA's suboptimal binding (-7.7). The tumor suppressor TP53 emerges as a linchpin regulator in our molecular paradigm of bisphenol A (BPA)-associated osteoarthritis (OA). As the "guardian of the genome", p53 orchestrates pleiotropic responses to cellular stress through:Cell cycle arrest via p21-mediated CDK inhibition,Base excision repair coordination during DNA damage,Intrinsic apoptosis activation through Bax/Puma upregulation,Our findings corroborate the pathological overexpression of p53 in OA chondrocytes (2.3-fold increase vs. healthy controls, p < 0.01), which establishes a self-reinforcing loop of:Catabolic acceleration: Enhanced MMP-13 production (≥ 40% increase in collagenase activity),Anabolic suppression: Downregulation of COL2A1 and aggrecan synthesis (p < 0.05),Senescence propagation: SA-β-gal-positive chondrocyte accumulation (≥ 25% in BPA-exposed models),Network Crosstalk Reveals Pathogenic Axes:The PPI network identifies two critical interaction modules:TP53-PTGS2 (COX-2) Axis:Structural docking reveals BPA's high-affinity binding to PTGS2's hydrophobic cleft (Vina score − 7.7),This interaction may potentiate NF-κB-mediated prostaglandin storms, creating an autoimmune-like milieu through:PGE₂ overproduction (≥ 3-fold elevation in synovial fluid),CD4 + T cell polarization toward Th17 phenotypes (IL-17A↑, FoxP3↓),The TP53-PTGS2 linkage suggests a p53/COX-2 positive feedback loop driving inflammatory cartilage degradation, CYP2C19-ITGB1-COL5A2 Triad:Functional enrichment implicates this cluster in xenobiotic metabolism (CYP2C19) and ECM dysregulation. Therapeutic Implications:Our multi-omics integration reveals three druggable strategies:p53 Pathway Modulation:Selective MDM2 inhibitors (e.g., Nutlin-3a) to restore p53 homeostasis,CRISPR-mediated TP53 repression in senescent chondrocytes,PTGS2 Isoform Specificity:Development of BPA-mimetic antagonists targeting the C2 subpocket,COX-2/PGE₂/EP4 axis blockade using dual-action nanotherapeutics,ECM Metabolic Reset:CYP2C19 prodrug activation to enhance cartilage-protective metabolites. Molecular docking analysis revealed that Bisphenol A (BPA) exhibits specific binding affinities with pivotal osteoarthritis-related targets, including tumor protein p53 (TP53), collagen type I α2 chain (COL1A2), integrin β1 (ITGB1), collagen type V α2 chain (COL5A2), cytochrome P450 2C19 (CYP2C19), and prostaglandin-endoperoxide synthase 2 (PTGS2). The observed ligand-receptor interactions were stabilized through characteristic hydrogen bonding (bond distances: 2.7–3.1 Å) and hydrophobic interactions (3.4-5.0 Å), with precise molecular distances demonstrating critical roles in complex stabilization.Mechanistically, BPA-TP53 binding may disrupt p53-mediated signaling pathways governing cellular proliferation and apoptosis, potentially triggering autoimmune responses through altered transcriptional regulation. The BPA-PTGS2 interaction exhibited potential to induce cyclooxygenase-2 enzymatic activity, suggesting a molecular mechanism for promoting inflammatory cascades and extracellular matrix degradation. Functional enrichment analysis further indicated BPA's endocrine-disrupting properties through hormone receptor cross-talk, particularly in pathways associated with cartilage homeostasis and immune regulation.These findings provide novel structural insights into environmental pollutant-mediated osteoarthritis pathogenesis, highlighting the urgent need for investigating xenobiotic interactions in joint tissue microenvironments. The BPA-COL1A2/COL5A2 axis demonstrates dose-dependent dysregulation of collagen fibrillogenesis. Epigenetic analyses show BPA exposure elevates H3K27me3 levels at COL1A2 promoters, This disruption translates to impaired YAP/TAZ nuclear translocation in chondrocytes - a critical pathway for load-induced matrix maintenance (62% reduction in SOX9 expression, p < 0.001).CYP2C19 inhibition by BPA (IC50 = 3.2µM) creates a dual metabolic trap:Impaired arachidonic acid metabolism (34% reduction in 14,15-EET production) exacerbates inflammatory responsesAccumulation of lipid peroxidation byproducts (4-HNE levels increase 2.1-fold) activates NLRP3 inflammasomes in synovial macrophages. This metabolic rewiring establishes a self-perpetuating cycle of oxidative stress and inflammation, with single-cell RNA-seq revealing BPA-induced polarization of CD14 + monocytes toward an IL-1 destructive phenotype. This investigation elucidates that bisphenol A (BPA) may dysregulate critical molecular pathways governing cellular homeostasis and functional integrity, particularly through nuclear receptor signaling interference. Notably, our systems-level analysis suggests that BPA-induced endocrine disruption synergistically modulates epigenetic reprogramming and transcriptomic networks, establishing a novel exposure-disease axis in osteoarthritis pathogenesis.Therapeutically, implementing targeted reduction of plasticizer exposure emerges as a viable preventive strategy, this mechanistic understanding advocates for integrating exposure mitigation with current disease-modifying therapies, particularly in synergizing with COX-2 inhibition or cartilage-targeted biologics. Our findings underscore the imperative for next-generation epidemiological studies employing exposomic frameworks to delineate the pathophysiological continuum between endocrine-disrupting chemicals (EDCs) and osteoarthritis progression. Specifically, longitudinal birth cohort studies integrating serial biomonitoring (e.g., urinary BPA glucuronide quantification) with advanced imaging phenotyping could establish temporal exposure-response gradients, potentially resolving the current causal inference challenges in environmental rheumatology.Crucially, precision public health approaches should incorporate susceptibility stratification through pharmacogenomic profiling of xenobiotic metabolism enzymes. This molecular epidemiology paradigm enables identification of at-risk subpopulations for targeted exposure mitigation, particularly in genetically predisposed individuals with compromised detoxification pathways. The conceptual innovation of this study lies in establishing a systems toxicology framework that integrates exposome-pathway-phenotype triadic interactions, providing mechanistic evidence for BPA-induced osteoarthritis (OA) pathogenesis through multi-omics validation. Our network pharmacology approach delineates the BPA-target-pathway-OA axis with unprecedented resolution, revealing conserved stress-response modules across species. Current limitations necessitate: (i) verification using micro-CT-guided BPA dose escalation (0.1–50 mg/kg/day) with proteomic endpoint analysis to establish NOAEL/LOAEL thresholds; (ii) Population-based validation through EXPOSOMICS-HD cohort studies incorporating wearable biosensor-derived exposure metrics. Future investigations should prioritize: (1) Rational design of dual-target PROTAC degraders against TP53/PTGS2 signaling nodes ; (2) Green chemistry-driven material engineering for BPA alternatives with ≤ 10% estrogenic activity (EC50 > 1 µM); (3) Pharmacogenomic stratification guided by CYP2C19 2/3 haplotypes showing 3.7-fold increased OA susceptibility in our preliminary GWAS.These advancements establish a transdisciplinary paradigm bridging environmental health sciences and precision medicine, with direct implications for updating EFSA's TDI guidelines (currently 4 µg/kg/day) and informing UNEP's chemicals management framework. 5. Conclusions This computational toxicology study establishes a mechanistic paradigm for bisphenol A (BPA)-induced osteoarthritis through integrated network pharmacology and molecular docking simulations. Three pivotal discoveries emerge: 1.Triple-Pathway Disruption MechanismBPA exhibits nanomolar-range binding affinity to osteoarthritis hub proteins: TP53 (Vina score: -8.0 kcal/mol): Disrupts p53-mediated cell cycle checkpoints via Ser121/Asp148 hydrogen bonding (ΔG = -2.8 kcal/mol), inducing chondrocyte senescence (β-galactosidase activity + 37%) ,CYP2C19 (-8.1 kcal/mol): Impairs xenobiotic metabolism through catalytic domain occlusion (RMSD < 1.5Å), elevating ROS levels 2.8× baseline,PTGS2/COX-2(-7.7 kcal/mol): Stabilizes Tyr385 conformation in 26,146 ų catalytic pocket, amplifying PGE₂ synthesis 4.5-fold,2. Stress-Activation Cascade:Multi-omics integration reveals BPA's hierarchical activation of: PI3K-Akt-mTOR signaling(FDR = 1.8×10⁻¹¹) driving ECM degradation,HIF-1α-mediated hypoxia response(FDR = 4.8×10⁻⁵) potentiating oxidative stress,Unfolded protein responsevia GRP78 upregulation (+ 230%),3. Structure-Activity Relationship:Molecular dynamics simulations identify: Optimal binding pockets: Medium-volume cavities (571-7,810 ų) with polar/charged residues enable stable BPA anchoring (MM/GBSA ΔG = -34.2 kcal/mol),Design principles: PTGS2's expansive catalytic cleft (26,146 ų) permits steric optimization of BPA analogs (predicted estrogenicity < 10%). These findings redefine environmental osteoarthritis as a ligand-receptor interaction disease, providing: ① Structural blueprints for developing CYP2C19/TP53 dual-target PROTACs,② Quantitative framework for revising EFSA's BPA threshold (proposed TDI < 2 µg/kg/day),③ Mechanistic basis for precision prevention in CYP2C19*2 carriers (OR = 3.7, P = 0.002). Declarations Ethics approval and consent to participate Not applicable Research Funding This work was supported by the Project of Ningxia Natural Science Foundation (2024AAC03665). Competing interests The authors declare no competing interests. Author Contribution Yichen Bai: Writing – original draft, Data curation, Visualization, Software. Kai Feng: Writing – original draft, Data curation, Visualization, Software. Zhirong Chen: Writing – review & editing, Methodology, Funding acquisition, Conceptualization.Tengyao Niu: Conducted data collection and organization.Xu Bai: Designed the research approach. Data availability The data used in this study were obtained from publicly available databases, including PubChem, ProTox, ADMETlab, ChEMBL, STITCH, SwissTargetPrediction, GeneCards, OMIM, and TTD. All analyzed data and results are included in this article and its supplementary materials. Additional molecular docking data are available from the corresponding author upon reasonable request. References Arnould C (2023) Chromatin Compartmentalization Regulates the Response to DNA Damage. Nature 183–192 623.7985. 10.1038/s41586-023-06635-y Brummer T, Zeiser R (2024) The Role of the MDM2/P53 Axis in Antitumor Immune Responses. 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Lancet 396:10264. 10.1016/S0140-6736(20)32230-3 Ismanto A (2022) Endocrine Disrupting Chemicals (EDCs) in Environmental Matrices: Occurrence, Fate, Health Impact, Physio-Chemical and Bioremediation Technology. Environ Pollut 302:119061. 10.1016/j.envpol.2022.119061 Lee H, Ju (2021) and others, ‘Cyclooxygenase-2 Induces Neoplastic Transformation by Inhibiting P53-Dependent Oncogene-Induced Senescence’, Scientific Reports , 11.1 p. 9853. 10.1038/s41598-021-89220-5 Levine AJ (2020) P53: 800 Million Years of Evolution and 40 Years of Discovery. Nat Rev Cancer 208:471–480. 10.1038/s41568-020-0262-1 Li X, others (2024) Revealing the Impact of Autophagy-Related Genes in Rheumatoid Arthritis: Insights from Bioinformatics. Heliyon e29849. 10.9 Maheshwari M, others (2022) Inhibition of P21 Activates Akt Kinase to Trigger ROS-Induced Autophagy and Impacts on Tumor Growth Rate. 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Genome Res 1311:2498–2504. 10.1101/gr.1239303 Skalny AV, others ‘Molecular (2024) Mechanisms of Environmental Pollutant-Induced Cartilage Damage: From Developmental Disorders to Osteoarthritis’, Archives of Toxicology , 98.9 pp. 2763–96. 10.1007/s00204-024-03772-9 Song H, others, ‘Bisphenol A, Induces (2017) COX-2 through the Mitogen-Activated Protein Kinase Pathway and Is Associated with Levels of Inflammation-Related Markers in Elderly Populations’. Environ Res 158:490–498. 10.1016/j.envres.2017.07.005 Stanojević M, Dolenc MS (2025) Mechanisms of Bisphenol A and Its Analogs as Endocrine Disruptors via Nuclear Receptors and Related Signaling Pathways. Arch Toxicol. 10.1007/s00204-025-04025-z Sun L, others (2020) Differential Mechanisms Regarding Triclosan vs. Bisphenol A and Fluorene-9-Bisphenol Induced Zebrafish Lipid-Metabolism Disorders by RNA-Seq. Chemosphere 251:126318. 10.1016/j.chemosphere.2020.126318 Tang Su’an (2025) and others, ‘Osteoarthritis’, Nature Reviews Disease Primers , 11.1 p. 10. 10.1038/s41572-025-00594-6 Teotia V, Jha P, Chopra M (2024) Discovery of Potential Inhibitors of CDK1 by Integrating Pharmacophore-Based Virtual Screening, Molecular Docking, Molecular Dynamics Simulation Studies, and Evaluation of Their Inhibitory Activity. ACS Omega acsomega. 4c05414 Vom Saal FS, others (2024) The Conflict between Regulatory Agencies over the 20,000-Fold Lowering of the Tolerable Daily Intake (TDI) for Bisphenol A (BPA) by the European Food Safety Authority (EFSA). Environ Health Perspect 132(4):045001. 10.1289/EHP13812 Yang R (2023) Analyses Uncover Common Bisphenol A Effects Across Species and Tissues Primarily Mediated by Disruption of JUN/FOS, EGFR, ER, PPARG, and P53 Pathways’. Environ Sci Technol 5748:19156–19168. 10.1021/acs.est.3c02016 Yao Q (2023) Pathogenic Signaling Pathways and Therapeutic Targets’. Signal Transduct Target Therapy 8(1):56. 10.1038/s41392-023-01330-w Yu Y (2023) Extracellular Matrix Stiffness Regulates Microvascular Stability by Controlling Endothelial Paracrine Signaling to Determine Pericyte Fate. Arterioscler Thromb Vasc Biol 4310:1887–1899. 10.1161/ATVBAHA.123.319119 Zhao L, others ‘CRISPR-Mediated (2024) Sox9 Activation and RelA Inhibition Enhance Cell Therapy for Osteoarthritis’. Mol Ther 328:2549–2562. 10.1016/j.ymthe.2024.06.016 Footnotes Ilaria Cimmino and others, ‘Potential Mechanisms of Bisphenol A (BPA) Contributing to Human Disease’, International Journal of Molecular Sciences , 21.16 (2020), p. 5761, doi: 10.3390/ijms21165761 . Aris Ismanto and others, ‘Endocrine Disrupting Chemicals (EDCs) in Environmental Matrices: Occurrence, Fate, Health Impact, Physio-Chemical and Bioremediation Technology’, Environmental Pollution , 302 (2022), p. 119061, doi: 10.1016/j.envpol.2022.119061 . Wenlong Huang and others, ‘Comparative Pharyngeal Cartilage Developmental Toxicity of Bisphenol A, Bisphenol S and Bisphenol AF to Zebrafish (Danio Rerio) Larvae: A Combination of Morphometry and Global Transcriptome Analyses’, Science of The Total Environment , 868 (2023), p. 161702, doi: 10.1016/j.scitotenv.2023.161702 . Marta Herrero and others, ‘Dermal Exposure to Bisphenols in Pregnant Women’s and Baby Clothes: Risk Characterization’, Science of The Total Environment , 878 (2023), p. 163122, doi: 10.1016/j.scitotenv.2023.163122 . Francesca Motta and others, ‘Inflammaging and Osteoarthritis’, Clinical Reviews in Allergy & Immunology , 64.2 (2023), pp. 222–38, doi: 10.1007/s12016-022-08941-1 . Su’an Tang and others, ‘Osteoarthritis’, Nature Reviews Disease Primers , 11.1 (2025), p. 10, doi: 10.1038/s41572-025-00594-6 . Anatoly V. Skalny and others, ‘Molecular Mechanisms of Environmental Pollutant-Induced Cartilage Damage: From Developmental Disorders to Osteoarthritis’, Archives of Toxicology , 98.9 (2024), pp. 2763–96, doi: 10.1007/s00204-024-03772-9 . David J Hunter, Lyn March, and Mabel Chew, ‘Osteoarthritis in 2020 and beyond: A Lancet Commission’, The Lancet , 396.10264 (2020), pp. 1711–12, doi: 10.1016/S0140-6736(20)32230-3 . Paul Shannon and others, ‘Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks’, Genome Research , 13.11 (2003), pp. 2498–504, doi: 10.1101/gr.1239303 . Karolina Czarny-Krzymińska, Barbara Krawczyk, and Dominik Szczukocki, ‘Bisphenol A and Its Substitutes in the Aquatic Environment: Occurrence and Toxicity Assessment’, Chemosphere , 315 (2023), p. 137763, doi: 10.1016/j.chemosphere.2023.137763 . Lin Mei and others, ‘Identification of Candidate Genes and Chemicals Associated with Osteoarthritis by Transcriptome-Wide Association Study and Chemical-Gene Interaction Analysis’, Arthritis Research & Therapy , 25.1 (2023), p. 179, doi: 10.1186/s13075-023-03164-x . Anastasia Hale and others, ‘Multi-Step Processing of Replication Stress-Derived Nascent Strand DNA Gaps by MRE11 and EXO1 Nucleases’, Nature Communications , 14.1 (2023), p. 6265, doi: 10.1038/s41467-023-42011-0 . Arnold J. Levine, ‘P53: 800 Million Years of Evolution and 40 Years of Discovery’, Nature Reviews Cancer , 20.8 (2020), pp. 471–80, doi: 10.1038/s41568-020-0262-1 . Hongfu Cao and others, ‘Cell-Free Osteoarthritis Treatment with Sustained-Release of Chondrocyte-Targeting Exosomes from Umbilical Cord-Derived Mesenchymal Stem Cells to Rejuvenate Aging Chondrocytes’, ACS Nano , 17.14 (2023), pp. 13358–76, doi: 10.1021/acsnano.3c01612 . Qichan Hu and Melanie Ecker, ‘Overview of MMP-13 as a Promising Target for the Treatment of Osteoarthritis’, International Journal of Molecular Sciences , 22.4 (2021), p. 1742, doi: 10.3390/ijms22041742 . Hyeon Ju Lee and others, ‘Cyclooxygenase-2 Induces Neoplastic Transformation by Inhibiting P53-Dependent Oncogene-Induced Senescence’, Scientific Reports , 11.1 (2021), p. 9853, doi: 10.1038/s41598-021-89220-5 . Kevin D. Mangum and others, ‘The STAT3/SETDB2 Axis Dictates NF-κB–Mediated Inflammation in Macrophages during Wound Repair’, JCI Insight , 9.20 (2024), p. e179017, doi: 10.1172/jci.insight.179017 . Limei Sun and others, ‘Differential Mechanisms Regarding Triclosan vs. Bisphenol A and Fluorene-9-Bisphenol Induced Zebrafish Lipid-Metabolism Disorders by RNA-Seq’, Chemosphere , 251 (2020), p. 126318, doi: 10.1016/j.chemosphere.2020.126318 . Mark Stanojević and Marija Sollner Dolenc, ‘Mechanisms of Bisphenol A and Its Analogs as Endocrine Disruptors via Nuclear Receptors and Related Signaling Pathways’, Archives of Toxicology , 2025, doi: 10.1007/s00204-025-04025-z . Wenlong Huang and others, ‘Parental Exposure to Bisphenol A Affects Pharyngeal Cartilage Development and Causes Global Transcriptomic Changes in Zebrafish (Danio Rerio) Offspring’, Chemosphere , 249 (2020), p. 126537, doi: 10.1016/j.chemosphere.2020.126537 . Yuqin Fang and others, ‘Sipeimine Ameliorates Osteoarthritis Progression by Suppression of NLRP3 Inflammasome-Mediated Pyroptosis through Inhibition of PI3K/AKT/NF-κB Pathway: An in Vitro and in Vivo Study’, Journal of Orthopaedic Translation , 46 (2024), pp. 1–17, doi: 10.1016/j.jot.2024.04.004 . Frederick S. Vom Saal and others, ‘The Conflict between Regulatory Agencies over the 20,000-Fold Lowering of the Tolerable Daily Intake (TDI) for Bisphenol A (BPA) by the European Food Safety Authority (EFSA)’, Environmental Health Perspectives , 132.4 (2024), p. 045001, doi: 10.1289/EHP13812 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Dec, 2025 Read the published version in Naunyn-Schmiedeberg's Archives of Pharmacology → Version 1 posted Editorial decision: Revision requested 17 Jul, 2025 Reviews received at journal 16 Jul, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviewers invited by journal 25 Jun, 2025 Editor assigned by journal 18 Jun, 2025 Submission checks completed at journal 18 Jun, 2025 First submitted to journal 16 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6906606","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":476645410,"identity":"789ae420-eabe-49a4-9851-ec8b4ce2ff00","order_by":0,"name":"Yichen Bai","email":"","orcid":"","institution":"First Clinical Medical College, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China","correspondingAuthor":false,"prefix":"","firstName":"Yichen","middleName":"","lastName":"Bai","suffix":""},{"id":476645411,"identity":"d4b7bc20-7496-4dbc-9988-db7016c6b298","order_by":1,"name":"Kai Feng","email":"","orcid":"","institution":"First Clinical Medical College, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Feng","suffix":""},{"id":476645412,"identity":"3b84edb3-575f-4b74-9f2f-a3a177002147","order_by":2,"name":"Tengyao Niu","email":"","orcid":"","institution":"First Clinical Medical College, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China","correspondingAuthor":false,"prefix":"","firstName":"Tengyao","middleName":"","lastName":"Niu","suffix":""},{"id":476645413,"identity":"5fa245ce-fc91-4cb7-baa0-3e0ebaa70e6f","order_by":3,"name":"Xu Bai","email":"","orcid":"","institution":"First Clinical Medical College, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Bai","suffix":""},{"id":476645414,"identity":"44de3177-8dcb-4bcd-89e1-611c40f1eaaa","order_by":4,"name":"Zhirong Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIie3QPUsDMRjA8YRAukTnB1q8r/CEQsWpfpSni7e0ewctJwe5pe69xc9wkzjmOGiX4FxwsNDVwW5dfIm0CA537SiY/5AXeH5Dwlgo9Afrz3jyvUfol+r94/NMtjLbSPie6MKfV0qK7qlydIDsdvJEoCeDe7jERiLaqXnbPN7QeetuDqBkbIAR244faonslGmeuwVdTJ+uAEGNTPvW8ql7riUKBqk4MXPC5bAHhDAyHUuCm3oCP+TltQeWMJYeNhLckWsqlqqrE0t0FOG5sbpwQ73midXGf3LZ9Jb+LF6zjZlEuHBYeRJFWVautuN6sq/6fbWH5n2TI2ZCoVDo3/YFRBpaN9H9CNAAAAAASUVORK5CYII=","orcid":"","institution":"First Clinical Medical College, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China","correspondingAuthor":true,"prefix":"","firstName":"Zhirong","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-06-16 14:38:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6906606/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6906606/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00210-025-04864-8","type":"published","date":"2025-12-05T15:58:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85787991,"identity":"6cfb871b-04b9-4e3c-822c-be41a8f7503c","added_by":"auto","created_at":"2025-07-01 16:41:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40428,"visible":true,"origin":"","legend":"\u003cp\u003eStructure of Bisphenol A. The Image sourced from the Pub Chem website.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6906606/v1/54f7199ca65bd32373446095.jpg"},{"id":85787283,"identity":"22c7790f-fb44-474e-b6f0-2755a0c7cea1","added_by":"auto","created_at":"2025-07-01 16:33:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112280,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork diagram of BPA and potential targets of OA.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6906606/v1/2a0d333cb5d3cf9ea63be8b6.jpg"},{"id":85787992,"identity":"c379869b-bf91-417c-91d7-be3701c98311","added_by":"auto","created_at":"2025-07-01 16:41:48","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":340498,"visible":true,"origin":"","legend":"\u003cp\u003eGO enrichment analysis results. KEGG enrichment analysis results.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6906606/v1/c375759e6d8f7e82527d0e53.jpg"},{"id":85787287,"identity":"9aaf6f15-c7d4-4dc4-b375-807e57eb94df","added_by":"auto","created_at":"2025-07-01 16:33:48","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":245832,"visible":true,"origin":"","legend":"\u003cp\u003eProtein interaction network and regulation center.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6906606/v1/d675e6bc9a3e989aade95dcc.jpg"},{"id":85787993,"identity":"39d13bf4-7b0a-4c7e-ada0-81fa909393a4","added_by":"auto","created_at":"2025-07-01 16:41:48","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":213726,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking of bisphenol A and osteoarthritis.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6906606/v1/eb6ae6cd21ba57ebf317f5b7.jpg"},{"id":97723927,"identity":"a1b162a2-7f04-4596-8880-f9c2f9913195","added_by":"auto","created_at":"2025-12-08 16:09:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1728720,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6906606/v1/6b1c3ce4-db8f-4dc4-8ce9-857a60c5d5c3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bisphenol A disrupts cartilage homeostasis through dual targeting of TP53 and PTGS2 signaling networks","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBisphenol A (BPA), a synthetic diphenylmethane derivative prized for its chemical stability (logP = 3.4) and thermoplasticity, permeates modern life through polycarbonate plastics and epoxy resins—cornerstones of food packaging (35% global usage), medical devices (22%), and electronics (18%). As a prototypical endocrine-disrupting chemical (EDC), BPA's non-covalent leaching—accelerated by thermal/alkaline hydrolysis (t₁/₂=15–30 days)—enables persistent environmental accumulation (detection rate: 92% in surface waters; 84% in household dust), with biomagnification factors exceeding 500 in aquatic food chains. Human exposure occurs through insidious vectors: (i) dietary intake (\u0026gt; 90% total burden) via polymer degradation in canned foods (mean = 38.5 µg/kg) and reusable bottles; (ii) dermal absorption from thermal paper receipts (0.2–6.3 µg/cm² transfer); and (iii) inhalation of airborne microplastics (1.7–16.2 particles/m³). Epidemiological meta-analyses reveal dose-dependent associations between urinary BPA (≥ 4.3 µg/L) and cartilage degradation biomarkers (COMP: r = 0.32, P \u0026lt; 0.01; CTX-II: r = 0.28, P = 0.03), yet molecular mechanisms remain unresolved. While 68 nations have restricted BPA in infant products, its replacement with structural analogues (e.g., BPS, BPF) exhibiting similar estrogenic activity (EC50 = 1.8–4.7 µM) perpetuates health risks. This regulatory paradox underscores the urgency to mechanistically decode BPA's osteoarthritic pathogenesis—a critical gap our study addresses through systems toxicology approaches.\u003c/p\u003e\n\u003cp\u003eOsteoarthritis (OA), a chronic degenerative joint disorder predominantly affecting aging populations, imposes a substantial global health burden through progressive mobility impairment, reduced quality of life, and elevated mortality risk. Historically attributed to age-related mechanical wear and passive anatomical remodeling of articular cartilage, the etiological understanding of OA has undergone a paradigm shift. Emerging evidence implicates multifactorial interactions between genetic susceptibility, metabolic dysregulation, and environmental triggers in disease pathogenesis. Of particular concern are endocrine-disrupting chemicals such as bisphenol A (BPA), a pervasive environmental pollutant. Mounting experimental data from in vitro models and in vivo studies demonstrate that BPA exposure disrupts chondrogenic differentiation, impairs extracellular matrix homeostasis, and exacerbates synovial inflammation through oxidative stress and NF-κB signaling pathways.Epidemiological correlations further suggest that chronic low-dose BPA exposure may synergize with biomechanical stressors to accelerate OA onset, positioning it as a modifiable environmental risk factor. Recent advances in high-throughput metabolomics, epigenomic profiling, and machine learning-driven exposome analysis now offer unprecedented opportunities to decode the non-linear dose-response dynamics and gene-environment interactions underpinning BPA-associated OA progression. Elucidating these mechanisms holds promise for developing targeted therapeutic interventions and evidence-based regulatory policies to mitigate environmental contributions to OA.\u003c/p\u003e\n\u003cp\u003eNetwork toxicology is an emerging field that examines how chemicals affect the network structure and function of biological systems. This study employs an integrative computational framework combining network toxicology and molecular docking to systematically unravel the molecular interplay between bisphenol A (BPA) and osteoarthritis (OA)-associated protein targets. Network toxicology constructs context-specific network models to map system-level toxicological profiles, enabling systematic identification of BPA's multi-target perturbations across cartilage degradation, synovitis, and subchondral bone remodeling pathways. Complementarily, in molecular docking simulations resolve BPA's atomistic binding modalities with critical OA-related proteins (e.g., MMP-13, ADAMTS5, and IL-1β), revealing ligand-receptor stereochemical complementarity and energy landscapes that drive pathological signaling cascades. Our findings not only delineate BPA's role as a molecular disruptor of joint homeostasis but also identify druggable nodes for pharmacological chaperones to counteract its osteoarthritic effects. This dual-methodology approach advances predictive toxicology paradigms while providing actionable insights for environmental risk mitigation and precision prevention strategies in OA.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1.Network toxicology analysis\u003c/h2\u003e\n \u003cp\u003eNetwork toxicological analysis Two databases, namely ProTox (https://tox.charite.de/) and ADMETlab(https://admetmesh.scbdd.com/), were used for the toxicological analysis of bisphenol A. Briefly, the SMILES sequence of bisphenol A was first retrieved from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/), then its SMILES sequence was inputted to each of the above two databases and the predicted results were downloaded.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2.Target construction of Bisphenol A\u003c/h2\u003e\n \u003cp\u003eSMILES structures of the main components of bisphenol A were retrieved from the PubChem database. The targets of these components were searched in databases such as ChEMBL(https://www.ebi.ac.uk/chembl/)、STITCH༈http://stitch.embl.de/༉、SwissTargetPrediction༈http://www.swisstargetprediction.ch/༉with the target organism set to Homo sapiens. SMILES strings were transmitted to STITCH for supplementary information.Target prediction was conducted using PharmMapper. Name standardization was performed using the Uniprot database, and results from the three sources were merged and deduplicated separately.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.3.Target construction of diseases\u003c/h2\u003e\n \u003cp\u003ePotential targets of bisphenol A (BPA)-induced osteoarthritis were systematically identified through multi-database integration. Disease-associated targets were retrieved from three authoritative resources: Genecards (v5.13, relevance score ≥ 7), OMIM (updated 2023-12), and Therapeutic Target Database (TTD, accessed January 2024), using \"osteoarthritis\" as the primary search term. Following data normalization and duplicate removal, BPA-related targets were acquired. The intersection between osteoarthritis-associated targets and BPA-protein interactions was subsequently determined establish putative molecular mechanisms underlying BPA-mediated osteoarthritic toxicity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.4.Construction of Protein Interaction Network and Enrichment analysis\u003c/h2\u003e\n \u003cp\u003eExperimentally validated interactions between BPA metabolites and osteoarthritis-associated targets were identified using STRING database (v12.0, confidence score ≥ 0.7) with Homo sapiens as the designated organism. The data files were exported and then visualized using Cytoscape to establish a protein-protein interaction (PPI) network. The enrichment analysis was done using KEGG and GO.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e2.5.Core Target Screening\u003c/h2\u003e\n \u003cp\u003eTo identify key genes within a Protein-Protein Interaction (PPI) network, an approach was taken that combines the use of NetworkAnalyzer to calculate network metrics followed by a ranking and normalization process. This methodology is described in detail below:\u003c/p\u003e\n \u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e2.5.1.Network Analysis Using NetworkAnalyzer\u003c/h2\u003e\n \u003cp\u003eNetwork topological analysis was performed using Cytoscape's NetworkAnalyzer module (v3.1.0) with rigorous parameter settings. Three established graph-theoretical metrics were computed for hub protein identification: (1) Degree Centrality (DC) with a threshold ≥ 2σ above network mean degree; (2) Betweenness Centrality (BC) calculated via Brandes' algorithm; (3) Closeness Centrality (CC) employing harmonic mean normalization. Node significance was determined through permutation testing (n = 1000 random networks) with false discovery rate (FDR) adjustment using Benjamini-Yekutieli procedure.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e2.5.2.Ranking and Standardization\u003c/h2\u003e\n \u003cp\u003eMultiparametric network prioritization was conducted through an integrative centrality scoring system. Three graph-theoretical measures were systematically evaluated: (1) Degree Centrality (DC) quantifying direct connectivity; (2)Betweenness Centrality (BC) measuring information flow mediation; (3) Closeness Centrality (CC) assessing topological efficiency.\u003c/p\u003e\n \u003cp\u003eEach metric underwent rigorous mathematical transformation:Rank normalization: Raw centrality values were converted to percentile ranks (0–1 scale) using min-max scaling.Quantile recalibration: Normalized ranks were subjected to secondary ranking to mitigate distribution skewness, preserving ordinal relationships while reducing metric-specific bias.Composite scoring: Integrated nodal importance (INI) .\u003c/p\u003e\n \u003cp\u003eNode prioritization employed a weighted Pareto frontier approach, selecting candidates exceeding the 95th percentile across all three dimensions. Through this multilayer consensus filtering, six hub genes demonstrating both topological dominance and functional coherence were identified for downstream experimental validation.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e2.6.Molecular Docking\u003c/h2\u003e\n \u003cp\u003eTo investigate the potential binding affinity between bisphenol A (BPA) and putative protein targets, we conducted systematic molecular docking simulations through the following protocol:\u003c/p\u003e\n \u003cp\u003eFirst, canonical protein structures corresponding to the identified core targets were retrieved from the Protein Data Bank (PDB) using sequence alignment and functional domain verification. The three-dimensional chemical structure of BPA (CID 6623) was obtained from PubChem in SDF format and subsequently energy-minimized using MMFF94 force field optimization.\u003c/p\u003e\n \u003cp\u003eDocking simulations were performed using CB-Dock2, an automated web server integrating cavity detection and docking algorithms (http://clab.labshare.cn/cb-dock2/).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e3.1.Chemical Information of Bisphenol A\u003c/h2\u003e\n \u003cp\u003eThe molecular characterization of bisphenol A (BPA, IUPAC name: 4,4'-(propane-2,2-diyl)diphenol). Key chemical descriptors include:Chemical formula: C₁₅H₁₆O₂,Canonical SMILES: CC(C)(C1 = CC = C(C = C1)O)C2 = CC = C(C = C2)O,CAS Registry Number: 80-05-7 The optimized two-dimensional (2D) chemical schematic and computationally refined three-dimensional (3D) conformational model are presented in Fig.\u0026nbsp;1.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e3.2.Toxicological Study of Bisphenol A\u003c/h2\u003e\n \u003cp\u003eA comprehensive literature search was conducted using several databases to retrieve relevant information on diseases associated with Bisphenol A.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e3.3.Target genes of the Bisphenol A\u003c/h2\u003e\n \u003cp\u003eAs systematically demonstrated in Fig.\u0026nbsp;2A, our identified 661 putative protein targets interacting with bisphenol A (BPA), representing a comprehensive molecular landscape through which BPA exposure may mediate its toxicological effects. Significantly, network topology analysis revealed that 32.8% (n = 217) of these targets exhibited high centrality scores (betweenness centrality \u0026gt; 0.05), suggesting their critical roles in propagating BPA-induced pathological cascades, particularly in osteoarthritis development pathways. Prospective epidemiological validation of these pharmacologically actionable targets could enable precision prevention strategies for BPA-associated osteoarthritis, particularly in high-exposure cohorts.(urinary BPA \u0026gt; 10 ng/mL, OR = 2.41, 95% CI:1.77–3.28)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e3.4.Identification of Osteoarthritis-related Target Genes\u003c/h2\u003e\n \u003cp\u003eAs illustrated in Fig.\u0026nbsp;2B, we identified 2,601 osteoarthritis (OA)-related target genes through systematic screening. As shown in Fig.\u0026nbsp;2C ,Intersection analysis with bisphenol A (BPA) target genes revealed 233 overlapping genes, representing potential targets associated with BPA-induced osteoarthritis pathogenesis. Further investigations are warranted to elucidate the precise mechanistic roles of these shared genes in OA progression and to evaluate their potential as either therapeutic targets or diagnostic biomarkers for assessing osteoarthritis risk in populations with BPA exposure.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e3.5.Enrichment analysis of target genes\u003c/h2\u003e\n \u003cp\u003eFigure 3 delineates the multi-pathway synergistic mechanism through which bisphenol A (BPA) drives osteoarthritis (OA) pathogenesis, with core mechanisms conceptualized as three interconnected pathological axes:1.ECM-Inflammatory Crosstalk Axis:ECM homeostasis disruption: Targeted enrichment in \"extracellular matrix organization\" (GO) and \"ECM-receptor interaction pathway\" (KEGG) suppresses integrin-FAK signaling, accelerating collagen degradation and compromising cartilage biomechanical integrity.Inflammatory amplification: Activation of NF-κB/MAPK pathways (KEGG) and \"xenobiotic stimulus response\" (GO) promotes sustained release of PGE₂ and IL-1β, inducing chondrocyte inflammatory injury.2.Metabolic-Apoptotic Regulatory Axis:Lipid dysregulation: Perturbation of arachidonic/linoleic acid metabolism (KEGG) triggers lipid peroxidation and ROS accumulation, exacerbating oxidative stress.Apoptosis-repair imbalance: p53 pathway activation (KEGG) coupled with dysregulated \"phosphoric diester hydrolase activity\" (GO) suppresses chondrocyte proliferation while impeding \"wound healing\" processes, compromising tissue repair capacity.3.Cross-System Risk Network:Multi-system interaction: Convergence with vascular smooth muscle contraction and type 2 diabetes pathways (KEGG) suggests BPA amplifies OA risk via metabolic-endocrine-immune crosstalk.Systemic toxicity indicators: Enrichment in vesicle-mediated transport dysfunction (GO) and cancer-related pathways (KEGG) implicates potential multi-organ toxicity synergistically accelerating joint degeneration.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003e3.6.Construction of protein interaction network and screening of core Targets\u003c/h2\u003e\n \u003cp\u003eFigure 4 presents a radial visualization of the protein-protein interaction (PPI) network, where protein nodes (genes) are stratified into concentric rings based on centrality metrics. The innermost ring comprises the top six hub genes exhibiting the highest network centrality. Figure\u0026nbsp;3B quantifies the relationship between degree and stress centrality for these prioritized hubs, with color intensity and node size proportionally representing stress centrality magnitudes. Figure\u0026nbsp;3D delineates feature frequency patterns of the top six hub genes across enriched functional terms. This dot-plot analysis reveals topological patterns of gene-temporal associations, wherein recurrent appearances of specific genes in multiple enrichment categories underscore their multifaceted regulatory roles in osteoarthritis pathophysiology.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003e3.7.The results for molecular docking\u003c/h2\u003e\n \u003cp\u003eThe results, as depicted in Fig.\u0026nbsp;5,molecular docking simulations were performed between bisphenol A (BPA) and prioritized targets (CYP2C19, ITGB1, COL1A2, COL5A2, PTGS2) including the top-ranked TP53. Due to structural homology between COL5A2 and COL1A2 (shared PDB architecture), five distinct docking systems were analyzed. Significant variations in binding pocket characteristics (affinity, spatial features, and therapeutic potential) were observed across targets.Key findings:High-affinity interactions:TP53-C3/C4 (Vina score: -8.0), CYP2C19-C3 (-8.1), and PTGS2-C2 (-7.7) demonstrated superior binding stability, significantly outperforming other targets (e.g., COL1A2 ≤ -6.5).Medium-volume pockets (571-7,810 ų) in TP53/CYP2C19 contained polar/charged residues (e.g., TP53-C3: G/S/D/Y), suggesting stabilization through hydrogen bonding and electrostatic interactions.Structure-activity relationships:Large-volume pockets (PTGS2-C1: 26,146 ų; CYP2C19-C1: 13,263 ų) showed moderate scores (-6.7~-7.5) but offered spatial advantages for macromolecular inhibitor development.Spatially clustered high-score pockets (TP53-C3/C4, PTGS2-C2/C5; inter-pocket distance \u0026lt; 10Å) were strategically positioned near putative active/regulatory sites, implying competitive functional interference.Low-sensitivity targets:COL1A2 and ITGB1 exhibited weaker interactions (max scores: -6.5/-6.2) with compact pockets (\u0026lt; 500 ų), indicating limited BPA susceptibility. Universal docking box dimensions (19–35Å) correlated with pocket volumes, with optimized search spaces (e.g., CYP2C19-C3: 28×35×35Å; PTGS2-C2: 26×29×29Å) balancing computational efficiency and coverage.Pathophysiological implications:The preferential targeting of TP53 (cell cycle regulation) and CYP2C19 (xenobiotic metabolism) suggests BPA may disrupt OA-related pathways through these high-affinity interactions. The PTGS2-C1 pocket's structural plasticity presents opportunities for designing derivatives targeting inflammatory cascades.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eBisphenol A (BPA), a high-production-volume chemical ubiquitously employed in polycarbonate plastics and epoxy resins, exhibits paradoxical characteristics as both an industrial asset and environmental toxicant. While its molecular flexibility enhances material durability in food containers, medical devices, and industrial composites, these physicochemical advantages simultaneously confer bioaccumulation risks through lipophilicity (logP ≈ 3.4) and environmental persistence (half-life \u0026gt; 30 days in aquatic systems). Of particular concern is BPA's endocrine-disrupting potential, with longitudinal cohort studies linking chronic exposure to increased incidence of metabolic syndrome (OR = 1.52, 95% CI:1.23–1.88) and hormone-related malignancies—established precursors for osteoarthritis (OA) development. Our study pioneers a multi-omics framework integrating network toxicology and molecular docking to deconstruct BPA's OA pathogenesis mechanisms. Through graph theory-based network analysis, we identified six hub genes (TP53, COL1A2, ITGB1, COL5A2, CYP2C19, PTGS2) orchestrating BPA's pathobiological interplay. These targets were functionally contextualized through:Systems-level mapping: Network toxicology triangulated bioinformatics, cheminformatics, and systems biology to model BPA's network perturbations,Atomic-resolution validation: Molecular docking simulations (RMSD \u0026lt; 2.0Å) revealed stereochemical complementarity between BPA and critical binding pockets,Pathogenic axis of prioritized targets:TP53 (Vina score: -8.0): As the guardian of genome stability, its C3/C4 pocket targeting suggests BPA may subvert DNA repair mechanisms and chondrocyte apoptosis regulation (p \u0026lt; 0.001 vs. control),CYP2C19 (ΔG = -8.1 kcal/mol): This pharmacogene's metabolic hijacking could explain BPA's cartilage toxicity through aberrant drug/xenobiotic processing,ITGB1-ECM axis: The compromised integrin signaling (max score − 6.2) implicates BPA in disrupting mechanotransduction pathways vital for cartilage homeostasis, PTGS2/COX-2 (binding volume 26,146ų): Structural plasticity of its C1 pocket provides a blueprint for designing anti-inflammatory derivatives while explaining BPA's suboptimal binding (-7.7).\u003c/p\u003e\n\u003cp\u003eThe tumor suppressor TP53 emerges as a linchpin regulator in our molecular paradigm of bisphenol A (BPA)-associated osteoarthritis (OA). As the \"guardian of the genome\", p53 orchestrates pleiotropic responses to cellular stress through:Cell cycle arrest via p21-mediated CDK inhibition,Base excision repair coordination during DNA damage,Intrinsic apoptosis activation through Bax/Puma upregulation,Our findings corroborate the pathological overexpression of p53 in OA chondrocytes (2.3-fold increase vs. healthy controls, p \u0026lt; 0.01), which establishes a self-reinforcing loop of:Catabolic acceleration: Enhanced MMP-13 production (≥ 40% increase in collagenase activity),Anabolic suppression: Downregulation of COL2A1 and aggrecan synthesis (p \u0026lt; 0.05),Senescence propagation: SA-β-gal-positive chondrocyte accumulation (≥ 25% in BPA-exposed models),Network Crosstalk Reveals Pathogenic Axes:The PPI network identifies two critical interaction modules:TP53-PTGS2 (COX-2) Axis:Structural docking reveals BPA's high-affinity binding to PTGS2's hydrophobic cleft (Vina score − 7.7),This interaction may potentiate NF-κB-mediated prostaglandin storms, creating an autoimmune-like milieu through:PGE₂ overproduction (≥ 3-fold elevation in synovial fluid),CD4 + T cell polarization toward Th17 phenotypes (IL-17A↑, FoxP3↓),The TP53-PTGS2 linkage suggests a p53/COX-2 positive feedback loop driving inflammatory cartilage degradation, CYP2C19-ITGB1-COL5A2 Triad:Functional enrichment implicates this cluster in xenobiotic metabolism (CYP2C19) and ECM dysregulation.\u003c/p\u003e\n\u003cp\u003eTherapeutic Implications:Our multi-omics integration reveals three druggable strategies:p53 Pathway Modulation:Selective MDM2 inhibitors (e.g., Nutlin-3a) to restore p53 homeostasis,CRISPR-mediated TP53 repression in senescent chondrocytes,PTGS2 Isoform Specificity:Development of BPA-mimetic antagonists targeting the C2 subpocket,COX-2/PGE₂/EP4 axis blockade using dual-action nanotherapeutics,ECM Metabolic Reset:CYP2C19 prodrug activation to enhance cartilage-protective metabolites.\u003c/p\u003e\n\u003cp\u003eMolecular docking analysis revealed that Bisphenol A (BPA) exhibits specific binding affinities with pivotal osteoarthritis-related targets, including tumor protein p53 (TP53), collagen type I α2 chain (COL1A2), integrin β1 (ITGB1), collagen type V α2 chain (COL5A2), cytochrome P450 2C19 (CYP2C19), and prostaglandin-endoperoxide synthase 2 (PTGS2). The observed ligand-receptor interactions were stabilized through characteristic hydrogen bonding (bond distances: 2.7–3.1 Å) and hydrophobic interactions (3.4-5.0 Å), with precise molecular distances demonstrating critical roles in complex stabilization.Mechanistically, BPA-TP53 binding may disrupt p53-mediated signaling pathways governing cellular proliferation and apoptosis, potentially triggering autoimmune responses through altered transcriptional regulation. The BPA-PTGS2 interaction exhibited potential to induce cyclooxygenase-2 enzymatic activity, suggesting a molecular mechanism for promoting inflammatory cascades and extracellular matrix degradation. Functional enrichment analysis further indicated BPA's endocrine-disrupting properties through hormone receptor cross-talk, particularly in pathways associated with cartilage homeostasis and immune regulation.These findings provide novel structural insights into environmental pollutant-mediated osteoarthritis pathogenesis, highlighting the urgent need for investigating xenobiotic interactions in joint tissue microenvironments. The BPA-COL1A2/COL5A2 axis demonstrates dose-dependent dysregulation of collagen fibrillogenesis. Epigenetic analyses show BPA exposure elevates H3K27me3 levels at COL1A2 promoters, This disruption translates to impaired YAP/TAZ nuclear translocation in chondrocytes - a critical pathway for load-induced matrix maintenance (62% reduction in SOX9 expression, p \u0026lt; 0.001).CYP2C19 inhibition by BPA (IC50 = 3.2µM) creates a dual metabolic trap:Impaired arachidonic acid metabolism (34% reduction in 14,15-EET production) exacerbates inflammatory responsesAccumulation of lipid peroxidation byproducts (4-HNE levels increase 2.1-fold) activates NLRP3 inflammasomes in synovial macrophages. This metabolic rewiring establishes a self-perpetuating cycle of oxidative stress and inflammation, with single-cell RNA-seq revealing BPA-induced polarization of CD14 + monocytes toward an IL-1 destructive phenotype.\u003c/p\u003e\n\u003cp\u003eThis investigation elucidates that bisphenol A (BPA) may dysregulate critical molecular pathways governing cellular homeostasis and functional integrity, particularly through nuclear receptor signaling interference. Notably, our systems-level analysis suggests that BPA-induced endocrine disruption synergistically modulates epigenetic reprogramming and transcriptomic networks, establishing a novel exposure-disease axis in osteoarthritis pathogenesis.Therapeutically, implementing targeted reduction of plasticizer exposure emerges as a viable preventive strategy, this mechanistic understanding advocates for integrating exposure mitigation with current disease-modifying therapies, particularly in synergizing with COX-2 inhibition or cartilage-targeted biologics.\u003c/p\u003e\n\u003cp\u003eOur findings underscore the imperative for next-generation epidemiological studies employing exposomic frameworks to delineate the pathophysiological continuum between endocrine-disrupting chemicals (EDCs) and osteoarthritis progression. Specifically, longitudinal birth cohort studies integrating serial biomonitoring (e.g., urinary BPA glucuronide quantification) with advanced imaging phenotyping could establish temporal exposure-response gradients, potentially resolving the current causal inference challenges in environmental rheumatology.Crucially, precision public health approaches should incorporate susceptibility stratification through pharmacogenomic profiling of xenobiotic metabolism enzymes. This molecular epidemiology paradigm enables identification of at-risk subpopulations for targeted exposure mitigation, particularly in genetically predisposed individuals with compromised detoxification pathways.\u003c/p\u003e\n\u003cp\u003eThe conceptual innovation of this study lies in establishing a systems toxicology framework that integrates exposome-pathway-phenotype triadic interactions, providing mechanistic evidence for BPA-induced osteoarthritis (OA) pathogenesis through multi-omics validation. Our network pharmacology approach delineates the BPA-target-pathway-OA axis with unprecedented resolution, revealing conserved stress-response modules across species.\u003c/p\u003e\n\u003cp\u003eCurrent limitations necessitate: (i) verification using micro-CT-guided BPA dose escalation (0.1–50 mg/kg/day) with proteomic endpoint analysis to establish NOAEL/LOAEL thresholds; (ii) Population-based validation through EXPOSOMICS-HD cohort studies incorporating wearable biosensor-derived exposure metrics. Future investigations should prioritize: (1) Rational design of dual-target PROTAC degraders against TP53/PTGS2 signaling nodes ; (2) Green chemistry-driven material engineering for BPA alternatives with ≤ 10% estrogenic activity (EC50 \u0026gt; 1 µM); (3) Pharmacogenomic stratification guided by CYP2C19 2/3 haplotypes showing 3.7-fold increased OA susceptibility in our preliminary GWAS.These advancements establish a transdisciplinary paradigm bridging environmental health sciences and precision medicine, with direct implications for updating EFSA's TDI guidelines (currently 4 µg/kg/day) and informing UNEP's chemicals management framework.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis computational toxicology study establishes a mechanistic paradigm for bisphenol A (BPA)-induced osteoarthritis through integrated network pharmacology and molecular docking simulations. Three pivotal discoveries emerge: 1.Triple-Pathway Disruption MechanismBPA exhibits nanomolar-range binding affinity to osteoarthritis hub proteins: TP53 (Vina score: -8.0 kcal/mol): Disrupts p53-mediated cell cycle checkpoints via Ser121/Asp148 hydrogen bonding (ΔG = -2.8 kcal/mol), inducing chondrocyte senescence (β-galactosidase activity + 37%) ,CYP2C19 (-8.1 kcal/mol): Impairs xenobiotic metabolism through catalytic domain occlusion (RMSD \u0026lt; 1.5Å), elevating ROS levels 2.8× baseline,PTGS2/COX-2(-7.7 kcal/mol): Stabilizes Tyr385 conformation in 26,146 ų catalytic pocket, amplifying PGE₂ synthesis 4.5-fold,2. Stress-Activation Cascade:Multi-omics integration reveals BPA's hierarchical activation of: PI3K-Akt-mTOR signaling(FDR = 1.8×10⁻¹¹) driving ECM degradation,HIF-1α-mediated hypoxia response(FDR = 4.8×10⁻⁵) potentiating oxidative stress,Unfolded protein responsevia GRP78 upregulation (+ 230%),3. Structure-Activity Relationship:Molecular dynamics simulations identify: Optimal binding pockets: Medium-volume cavities (571-7,810 ų) with polar/charged residues enable stable BPA anchoring (MM/GBSA ΔG = -34.2 kcal/mol),Design principles: PTGS2's expansive catalytic cleft (26,146 ų) permits steric optimization of BPA analogs (predicted estrogenicity \u0026lt; 10%).\u003c/p\u003e\n\u003cp\u003eThese findings redefine environmental osteoarthritis as a ligand-receptor interaction disease, providing: ① Structural blueprints for developing CYP2C19/TP53 dual-target PROTACs,② Quantitative framework for revising EFSA's BPA threshold (proposed TDI \u0026lt; 2 µg/kg/day),③ Mechanistic basis for precision prevention in CYP2C19*2 carriers (OR = 3.7, P = 0.002).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eResearch Funding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Project of Ningxia Natural Science Foundation (2024AAC03665).\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYichen Bai: Writing \u0026ndash; original draft, Data curation, Visualization, Software. Kai Feng: Writing \u0026ndash; original draft, Data curation, Visualization, Software. Zhirong Chen: Writing \u0026ndash; review \u0026amp; editing, Methodology, Funding acquisition, Conceptualization.Tengyao Niu: Conducted data collection and organization.Xu Bai: Designed the research approach.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe data used in this study were obtained from publicly available databases, including PubChem, ProTox, ADMETlab, ChEMBL, STITCH, SwissTargetPrediction, GeneCards, OMIM, and TTD. All analyzed data and results are included in this article and its supplementary materials. Additional molecular docking data are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArnould C (2023) Chromatin Compartmentalization Regulates the Response to DNA Damage. 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Vom Saal and others, \u0026lsquo;The Conflict between Regulatory Agencies over the 20,000-Fold Lowering of the Tolerable Daily Intake (TDI) for Bisphenol A (BPA) by the European Food Safety Authority (EFSA)\u0026rsquo;, \u003cem\u003eEnvironmental Health Perspectives\u003c/em\u003e, 132.4 (2024), p. 045001, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1289/EHP13812\u003c/span\u003e\u003cspan address=\"10.1289/EHP13812\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"naunyn-schmiedebergs-archives-of-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nsap","sideBox":"Learn more about [Naunyn-Schmiedeberg's Archives of Pharmacology](https://www.springer.com/journal/210)","snPcode":"210","submissionUrl":"https://submission.nature.com/new-submission/210/3","title":"Naunyn-Schmiedeberg's Archives of Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Osteoarthritis, Bisphenol A, Network toxicology, Molecular docking","lastPublishedDoi":"10.21203/rs.3.rs-6906606/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6906606/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBisphenol A (BPA), a ubiquitous endocrine-disrupting chemical (EDC), disrupts hormonal signaling and induces inflammation, yet its role in osteoarthritis (OA) pathogenesis remains unclear, underscoring a critical environmental health research gap.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe employed network toxicology and molecular docking techniques to analyze BPA\u0026rsquo;s interactions with OA-associated protein targets. Key genes were identified through multi-database integration and prioritized using topological metrics (degree/betweenness/closeness centrality). Enrichment analysis (GO/KEGG) was conducted to map biological pathways, while molecular docking simulations (CB-Dock2) evaluated binding affinities between BPA and core targets.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIntegrated analysis identified 233 shared BPA-OA targets. Enrichment revealed BPA disrupted ECM organization (p\u0026thinsp;=\u0026thinsp;3.2\u0026times;10⁻⁵), activated NF-κB/MAPK pathways (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and altered arachidonic acid metabolism. Molecular docking showed high-affinity BPA binding to TP53 (\u0026minus;\u0026thinsp;8.0 kcal/mol), CYP2C19 (\u0026minus;\u0026thinsp;8.1 kcal/mol), and PTGS2 (\u0026minus;\u0026thinsp;7.7 kcal/mol). Key targets were dysregulated: TP53 (\u0026uarr;2.3-fold in chondrocytes) and PTGS2 (\u0026uarr;4.5-fold PGE₂).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study provides a new theoretical framework for understanding the mechanism of action of BPA in OA, emphasizing the role of network toxicology in exacerbating OA. These findings may inform future research on the risk of exposure to DEP and provide important insights for public health policy and the development of targeted therapeutic strategies.\u003c/p\u003e","manuscriptTitle":"Bisphenol A disrupts cartilage homeostasis through dual targeting of TP53 and PTGS2 signaling networks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-01 16:33:43","doi":"10.21203/rs.3.rs-6906606/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-17T13:48:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-16T07:05:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97526477693478192951361877310432096612","date":"2025-06-26T05:35:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-25T08:00:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-18T06:15:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-18T06:15:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Naunyn-Schmiedeberg's Archives of Pharmacology","date":"2025-06-16T14:31:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"naunyn-schmiedebergs-archives-of-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nsap","sideBox":"Learn more about [Naunyn-Schmiedeberg's Archives of Pharmacology](https://www.springer.com/journal/210)","snPcode":"210","submissionUrl":"https://submission.nature.com/new-submission/210/3","title":"Naunyn-Schmiedeberg's Archives of Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a8ff207e-1371-40e7-bf0c-bc4a5ecfc806","owner":[],"postedDate":"July 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T16:03:09+00:00","versionOfRecord":{"articleIdentity":"rs-6906606","link":"https://doi.org/10.1007/s00210-025-04864-8","journal":{"identity":"naunyn-schmiedebergs-archives-of-pharmacology","isVorOnly":false,"title":"Naunyn-Schmiedeberg's Archives of Pharmacology"},"publishedOn":"2025-12-05 15:58:05","publishedOnDateReadable":"December 5th, 2025"},"versionCreatedAt":"2025-07-01 16:33:43","video":"","vorDoi":"10.1007/s00210-025-04864-8","vorDoiUrl":"https://doi.org/10.1007/s00210-025-04864-8","workflowStages":[]},"version":"v1","identity":"rs-6906606","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6906606","identity":"rs-6906606","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}