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. 2026 Jul 4;27:136. doi: 10.1186/s40360-026-01180-6

Parabens exposure in systemic lupus erythematosus: insights from network toxicology and molecular docking

Hui Li 1,✉, Qin Guo 1, Tiantian Zhang 1, Shufen Zhou 1, Chengshan Guo 1,✉
PMCID: PMC13625335  PMID: 42401960

Abstract

Background

Systemic lupus erythematosus (SLE) is an autoimmune disease driven by immune dysregulation. Parabens, commonly used preservatives, are potential environmental factors that may influence immune function and contribute to autoimmune diseases like SLE, although the underlying mechanisms remain unclear.

Methods

A network toxicology approach was used to investigate potential associations between parabens and SLE-related genes. Paraben target genes were identified from ChEMBL, STITCH, and SwissTargetPrediction, while SLE-related genes were obtained from GeneCards, OMIM, and TTD. Shared paraben-SLE genes were subjected using gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses. Hub genes were identified via network analysis with Cytoscape and further evaluated using three gene expression omnibus datasets. Molecular docking simulations were performed to predict potential interactions between parabens and proteins encoded by the identified hub genes.

Results

56 shared paraben-SLE genes were identified and were enriched in immune-related processes, including leukocyte proliferation, T cell activation, and cytokine receptor binding. KEGG analysis highlighted enrichment in immune response pathways, including Toll-like receptor (TLR) signaling. Candidate hub genes, including TLR4, cytotoxic T-lymphocyte associated protein 4 (CTLA4), and CD86, were differentially expressed across three independent SLE gene expression datasets. Molecular docking simulations predicted potential binding interactions between parabens and these hub gene proteins.

Conclusions

This study identified candidate genes and pathways potentially associated with both paraben exposure and immune dysregulation in SLE, particularly TLR4, CTLA4, and CD86. These findings are based on computational predictions and require further experimental and clinical studies to determine their biological relevance and potential implications for autoimmune diseases.

Keywords: Systemic lupus erythematosus, Parabens, Network toxicology, TLR4, CTLA4, Molecular docking

Background

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by dysregulated immune responses, leading to widespread inflammation and tissue damage [1]. The pathogenesis of SLE is complex, involving both genetic predisposition and environmental triggers [2]. While the genetic basis of SLE has been extensively investigated, the contribution of environmental exposures, such as chemicals commonly encountered in daily life, remains incompletely understood [3, 4]. Among these exposures, parabens have attracted increasing attention because of their widespread used as preservatives in cosmetics, pharmaceuticals, and food products [5]. These compounds possess endocrine-disrupting properties and have been associated with a variety of adverse health outcomes [6].

Emerging evidence suggests that parabens may interact with components of the immune system and potentially influence immune regulation [7, 8]. In patients with SLE, higher paraben levels have been associated with altered immune-related markers, including reduced aryl hydrocarbon receptor expression, increased monocyte apoptosis, decreased serum glutathione levels, and changes in apoptotic and activation markers on CD8 T cells and leukocytes [9]. However, the molecular pathways potentially linking paraben exposure to immune dysregulation in SLE remain poorly understood.

To address this knowledge gap, the present study employed a network toxicology approach to explore potential molecular associations between parabens and SLE. By integrating chemical target prediction databases, disease-related gene repositories, pathway enrichment analysis, and molecular docking simulations, the study aims to identify candidate genes and biological pathways potentially associated with both paraben exposure and SLE. These findings may provide a framework for future mechanistic, experimental, and clinical studies investigating the role of environmental exposures in autoimmune diseases.

Methods

Study design

Figure 1 illustrates this network toxicology analysis, which was designed to investigate the potential molecular associations between parabens and SLE. The process involved identifying paraben target genes and SLE-related genes, followed by the identification of shared genes. These shared genes were subjected to enrichment analysis. Hub genes were then identified through network analysis using Cytoscape, and their expression was validated using gene expression omnibus (GEO) datasets. Finally, molecular docking simulations were conducted to assess the interactions between parabens and the proteins encoded by the hub genes. This study used only publicly available data from previously published studies and databases. Ethical approval and informed consent were obtained in the original studies, and no additional ethical approval was required for the present analysis.

Fig. 1.

Fig. 1

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Workflow of the network toxicology analysis. The workflow used to investigate potential associations between parabens and systemic lupus erythematosus (SLE). Target genes for methylparaben, ethylparaben, propylparaben, and butylparaben, as well as SLE-related genes, were collected from multiple public databases. Shared parabens-SLE genes were identified through gene set intersection and subsequently subjected to gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses. A protein–protein interaction network was constructed to identify candidate hub genes using Cytoscape. The expression patterns of candidate hub genes were further evaluated using gene expression omnibus (GEO) datasets. Finally, molecular docking simulations were performed to predict potential binding interactions between parabens and proteins encoded by the identified hub genes

Identification of shared parabens-SLE genes

The chemical structures of methylparaben, ethylparaben, propylparaben, and butylparaben were retrieved from the PubChem database, which provides detailed information on small molecules and their chemical structures [10]. Target genes for each paraben were identified by querying three well-established databases. ChEMBL provides bioactivity data for bioactive compounds, focusing on drugs and their molecular targets [11]. For ChEMBL, experimentally supported human protein targets associated with each paraben were included. STITCH integrates known and predicted interactions between chemicals and proteins, facilitating the exploration of chemical-protein associations [12]. SwissTargetPrediction predicts protein targets for small molecules based on chemical similarity [13]. For SwissTargetPrediction, only targets with a non-zero prediction probability were retained. All retrieved targets were standardized using official gene symbols prior to downstream analyses. Targets obtained from the three databases were merged to generate a unified target set for each paraben. The intersection across methylparaben, ethylparaben, propylparaben, and butylparaben target sets was subsequently calculated to obtain the final paraben target gene set.

Genes related to SLE were identified by retrieving relevant gene data from three comprehensive databases. GeneCards provides information on all annotated and predicted human genes, including details on their functions and biological roles [14]. Genes with a relevance score greater than 10 were selected. Online Mendelian inheritance in man (OMIM) catalogs human genes and genetic disorders, providing data on genetic variations and their inheritance patterns [15]. Therapeutic target database (TTD) is a resource for therapeutic targets, including genes and proteins involved in various biological processes [16]. The SLE-related genes from these databases were merged into a single list by taking the union of genes, to maximize coverage of currently reported SLE-related genes.

Shared parabens-SLE genes were identified by calculating the intersection between the parabens target gene set and SLE-related gene set. This method enabled the identification of genes potentially associated with both paraben exposure and SLE, providing a focused set of candidate genes for subsequent analyses.

Enrichment analysis

Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis were performed to explore the potential biological functions of the shared parabens-SLE genes [17]. GO analysis classified the genes into three categories: biological processes (BP), cellular components (CC), and molecular functions (MF), providing insight into the biological processes and molecular activities associated with these genes. KEGG pathway analysis was used to identify enriched pathways and molecular networks potentially associated with the shared parabens-SLE genes, particularly those related to immune response and inflammation.

Identification of hub genes

Hub genes are generally considered highly connected nodes within gene interaction networks and may represent biologically important candidates for further investigation. After retrieving the protein-protein interaction network for the 56 shared parabens-SLE genes from STRING, a database of known and predicted protein-protein interactions [18], hub gene candidates were identified using Cytoscape, a network analysis software platform [19]. The CytoHubba plugin was employed to rank genes by their centrality in the network using several scoring methods, including degree, MCC, MNC, and EPC, which measure the centrality of genes based on their direct interactions and connections. Additional metrics, including closeness, radiality, betweenness, and stress, were used to assess the topological importance of genes within the broader network. The MCODE plugin was used to identify highly interconnected gene clusters and provide additional support for the selected hub gene candidates [20].

Further validation was performed by examining the expression patterns of the identified hub gene candidates in three independent GEO datasets (GSE138458, GSE154851, and GSE185047) to assess whether these genes exhibited differential expression in SLE.

Molecular docking

Molecular docking simulations were conducted to explore potential binding interactions between parabens and proteins encoded by the identified hub genes. The 3D structures of hub gene-related proteins were retrieved from the research collaboratory for structural bioinformatics protein data bank (RCSB PDB), which provides experimentally determined 3D structures of biomolecules [21]. Docking simulations of methylparaben, ethylparaben, propylparaben, and butylparaben with these proteins were performed using CB-Dock2, a tool for protein-ligand docking [22], to predict potential binding modes between these small molecules and proteins implicated in immune regulation and SLE.

Results

Shared parabens-SLE genes

Target genes identified for methylparaben (Fig. 2A), ethylparaben (Fig. 2B), propylparaben (Fig. 2C), and butylparaben (Fig. 2D) were retrieved from ChEMBL, STITCH, and SwissTargetPrediction. A total of 1045 paraben target genes were identified through the intersection of genes across these four parabens (Fig. 2E). For SLE-related genes, a total of 561 genes were identified by taking the union of data from GeneCards, OMIM, and TTD (Fig. 2F). The intersection of paraben target genes and SLE-related genes resulted in the identification of 56 shared parabens-SLE genes (Fig. 2G), representing candidate genes potentially associated with both paraben exposure and SLE. The interactions among these shared genes are illustrated in the gene interaction network (Fig. 3).

Fig. 2.

Fig. 2

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Identification of paraben target genes and shared parabens-SLE genes. Venn diagrams showing the identification of target genes for methylparaben (A), ethylparaben (B), propylparaben (C), and butylparaben (D) from ChEMBL, STITCH, and SwissTargetPrediction. The intersection of target genes across the four parabens identified 1045 shared paraben target genes (E). SLE-related genes were collected from GeneCards, OMIM, and TTD, yielding 561 genes after integration (F). The intersection between paraben target genes and SLE-related genes identified 56 shared parabens-SLE genes (G)

Fig. 3.

Fig. 3

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Protein–protein interaction network of shared parabens-SLE genes. Protein-protein interaction network of the 56 shared parabens-SLE genes generated using STRING. Nodes represent genes, and edges represent interactions between the corresponding proteins

Enrichment analysis

The shared parabens-SLE genes were subjected to GO and KEGG pathway enrichment analyses to explore their potential biological potential. GO analysis indicated that the most significant BP involved immune response activation, leukocyte differentiation, and T cell activation (Fig. 4A). Enriched CC included the plasma membrane, MHC class II protein complex, and endocytic vesicles (Fig. 4B). For MF, the most significantly enriched terms included cytokine receptor binding, peptide antigen binding, and immune receptor activity (Fig. 4C). KEGG pathway analysis identified significant enrichment in several immune-related pathways, including Toll-like receptor (TLR) signaling, autoimmune thyroid disease, and herpes simplex virus infection (Fig. 4D), all of which are essential for immune regulation and inflammation in SLE pathogenesis. Figure 4E provides an integrated overview of the enrichment results, displaying the top five GO terms, the number of genes involved, selected genes, and the rich factors across BP, CC, and MF categories.

Fig. 4.

Fig. 4

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Enrichment analysis of shared parabens-SLE genes. GO and KEGG enrichment analyses of the shared parabens-SLE genes. (A) Biological processes enriched in immune-related functions. (B) Cellular components enriched among the shared genes. (C) Molecular functions enriched among the shared genes. (D) Significantly enriched KEGG pathways. (E) Integrated summary of the top five GO terms, including the number of genes involved, representative genes, and enrichment factors across the biological process, cellular component, and molecular function categories

Hub genes identification

The protein-protein interaction network of the 56 shared parabens-SLE genes is shown in Fig. 5A. Cytoscape’s method Candidate hub gene were initially identified using Cytoscape’s degree-based ranking method, which prioritizes genes according to the number of their direct interactions (Fig. 5B). Further analysis using the CytoHubba plugin identified TLR4, interleukin 4 (IL4), cytotoxic T-lymphocyte associated protein 4 (CTLA4), CD86, and C-C motif chemokine ligand 5 (CCL5) as candidate hub genes based on the overlap among eight ranking algorithms (Fig. 5C). MCODE analysis provided complementary support for these genes by identifying them within highly interconnected network modules (Fig. 5D). Expression analysis in three independent GEO datasets, GSE138458 (Fig. 6A), GSE154851 (Fig. 6B), and GSE185047 (Fig. 6C), revealed that TLR4, CTLA4, and CD86 were significantly differentially expressed in SLE samples, supporting their prioritization as candidate hub genes.

Fig. 5.

Fig. 5

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Identification of candidate hub genes in the shared parabens-SLE gene network. Protein–protein interaction network of the 56 shared parabens-SLE genes generated using STRING (A) and visualized in Cytoscape using degree-based ranking (B). Candidate hub genes, including TLR4, IL4, CTLA4, CD86, and CCL5, were identified based on the overlap among eight CytoHubba scoring methods (C), with additional support provided by MCODE analysis (D)

Fig. 6.

Fig. 6

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Expression validation of candidate hub genes in SLE datasets. Differential expression of TLR4, CTLA4, and CD86 in three independent GEO datasets: GSE138458 (A), GSE154851 (B), and GSE185047 (C). The differential expression patterns across datasets supported their selection as candidate hub genes

Molecular docking

Molecular docking simulations were performed to investigate the interactions between parabens and hub genes. CTLA4 and CD86 were docked as receptor-ligand complexes, while TLR4 was docked individually. Figure 7A and B display the CTLA4/CD86 protein complex (PDB ID 1I85) and TLR4 protein (PDB ID 2Z63), respectively. Molecular docking analyses predicted potential binding interactions between all four parabens and the validated hub proteins, TLR4 and the CTLA4/CD86 complex. The docking conformations shown in Fig. 7C–N support the possibility that parabens may interact with proteins involved in immune regulation and SLE-related pathways.

Fig. 7.

Fig. 7

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Molecular docking simulations of hub gene proteins with parabens. Molecular docking simulations were performed to predict potential binding interactions between hub gene proteins (CTLA4/CD86 (A) and TLR4 (B)) and parabens, including methylparaben (D), ethylparaben (G), propylparaben (J), and butylparaben (M). Predicted docking conformations are shown for methylparaben (C, E), ethylparaben (F, H), propylparaben (I, K), and butylparaben (L, N) with CTLA4/CD86 and TLR4

Discussion

This study provides insights into the molecular interactions between parabens and SLE through network toxicology analysis. A total of 56 overlapping genes between parabens and SLE were identified through the integration of chemical target prediction databases and SLE-related gene repositories. GO and KEGG pathway enrichment analyses suggested that the identified genes were enriched in immune response-related pathways, including TLR signaling and antigen presentation, both of which have been implicated in SLE pathogenesis. Network analysis further identified TLR4, CTLA4, and CD86 as key hub genes, and their relevance was supported by differential expression analyses in independent GEO datasets. In addition, molecular docking predicted potential interactions between parabens and these hub proteins, providing preliminary evidence for potential interactions that may warrant further investigation into how paraben exposure could influence immune pathways relevant to SLE.

Parabens are synthetic preservatives commonly used in cosmetics, pharmaceuticals, and food to prevent microbial growth. These chemicals can be absorbed through dermal contact or ingestion, resulting in widespread human exposure and potential interactions with biological targets [5]. Despite being regarded as safe at low concentrations, concerns have arisen due to their weak estrogenic activity, which has been associated with endocrine disruption and potential links to conditions like breast cancer, cardiovascular diseases and diabetes mellitus [23–25]. Growing evidence suggests that parabens may modulate the development and function of immune cells like monocytes, lymphocytes, and dendritic cells, affecting processes such as apoptosis, immune receptor expression, and intracellular signaling [26]. In Sjögren’s syndrome, paraben exposure has been linked to altered immune cell activation, cytokine production, and elevated serum levels, suggesting a potential role in disease-related immune dysregulation [27]. Similarly, in SLE, higher paraben levels have been associated with changes in immune markers such as anti-β2 glycoprotein antibodies and increased monocyte apoptosis [9].

The 56 shared parabens-SLE genes were enriched in immune-related processes, including leukocyte proliferation, T cell activation, and B cell activation, suggesting a potential role in immune regulation. These findings raise the possibility that paraben-associated genes may be linked to pathways involved in T-cell activation, a hallmark of autoimmune diseases like SLE [28]. Cellular components enriched among these genes, including MHC class II protein complexes and the plasma membrane, are important for antigen presentation and immune cell activation, processes known to be dysregulated in SLE [29]. Similarly, genes associated with cytokine receptor binding and immune receptor activity, such as CD86 and CD80, participate in signaling pathways involved in immune responses [30]. KEGG pathway analysis further identified involvement in Toll-like receptor signaling, which plays an important role in innate immune responses [31], as well as autoimmune thyroid disease and other immune-related pathways [32]. Collectively, these findings suggest that genes potentially associated with paraben exposure overlap with molecular pathways implicated in immune regulation and SLE pathogenesis, although the biological significance of these associations requires further investigation.

Network analysis identified TLR4, CTLA4, and CD86 as key hub genes with the shared parabens-SLE gene network. Given their established roles in immune regulation and previous associations with SLE, these genes may represent biologically relevant candidates for further investigation. TLR4 is a key pattern recognition receptor involved in detecting pathogens and cellular damage, initiating immune responses through the activation of pro-inflammatory signaling pathways. It plays a critical role in innate immunity, inflammation, and immune homeostasis [33]. Studies have shown that the overactivation of TLR4 signaling contributes to the production of type I interferons and other cytokines, which play crucial roles in SLE pathogenesis [34]. TLR4 activation also leads to the upregulation of autoantibodies and immune complexes, which accumulate in tissues and cause damage [35]. Moreover, dysregulation of TLR4 signaling has been associated with the development of SLE-associated symptoms, including skin rashes, nephritis, and other systemic manifestations [36, 37]. The predicted interaction between parabens and TLR4 suggests a potential molecular link through which paraben exposure may influence immune pathways relevant to SLE. Whether such effects occur under physiological conditions requires further experimental validation. CTLA4 is an immune checkpoint receptor that negatively regulates T cell activation. It is expressed on regulatory T cells and activated T cells, where it binds to CD80 and CD86 on antigen-presenting cells, competing with CD28 and inhibiting T cell activation to maintain immune homeostasis [38]. CD86 is a co-stimulatory molecule that enhances T cell activation by binding to CD28 but also suppresses T cell activity by binding to CTLA4. The balance between CD86 and CTLA4 is crucial for regulating immune responses and preventing autoimmunity [39]. The dysregulation of these molecules is a well-known feature of autoimmune diseases, including SLE. Altered expression and dysfunction of CTLA4 have been implicated in SLE pathogenesis. Elevated levels of soluble CTLA4 and genetic polymorphisms in CTLA4 have been associated with an increased susceptibility to SLE, highlighting its role in the autoimmune response [40]. Dysregulated CD86 expression has been associated with enhanced immune activation and the expansion of autoreactive lymphocytes, processes implicated in autoantibody production and SLE pathogenesis. Modulation of the CTLA4/CD86 pathway may provide a strategy for managing SLE and other autoimmune diseases [30]. Experimental studies have reported that parabens can alter macrophage CD86 expression at biologically relevant concentrations, suggesting a potential role in immune dysregulation and immunotoxicity [41]. Molecular docking predicted potential binding between parabens and CTLA4/CD86. However, whether these interactions could trigger uncontrolled T-cell activation, impair immune tolerance, or promote autoreactive antibody formation and SLE development remains unclear and requires further experimental investigation.

Beyond direct receptor-mediated effects, environmental exposures may influence immune responses through epigenetic regulation. Recent studies have demonstrated altered methylation and expression patterns of immune-related genes, including CCL5, in patients with SLE [42]. Epigenetic modifications are increasingly recognized as an important interface between environmental factors and autoimmune susceptibility. Given the endocrine-disrupting and immunomodulatory properties of parabens, it is conceivable that epigenetic mechanisms may represent one pathway through which environmental exposures influence immune regulation. However, whether parabens directly affect the epigenetic regulation of genes involved in innate immune activation, T-cell co-stimulation, or cytokine signaling remains unknown. Although these mechanisms were not examined in the present study, they provide a potential framework for future investigations into how environmental exposures may interact with immune regulatory pathways in SLE.

This study’s strength lies in its application of network toxicology, which integrates chemical databases and genomic resources to identify potential links between environmental toxins, such as parabens and SLE. By identifying key immune genes like TLR4, CTLA4, and CD86, and supporting these findings with gene expression validation and molecular docking analyses, the study provides a systematic framework for exploring how environmental exposures may be associated with immune dysregulation. These findings generate testable hypotheses regarding the potential involvement of paraben-related pathways in SLE and may help guide future mechanistic, experimental, and clinical investigations. Further studies integrating exposure assessment, functional validation, and clinical data are needed to determine the biological and clinical relevance of these predicted interactions.

However, this study has several limitations. First, the reliance on computational methods, such as gene prediction databases and molecular docking simulations, means the findings depend on the accuracy, completeness, and underlying algorithms of the available datasets. Different databases employ distinct prediction methods and curation strategies, which may introduce selection bias and affect reproducibility. Although these approaches are valuable for hypothesis generation, in vitro and in vivo experiments, together with validation in independent clinical cohorts, are required to confirm the biological relevance of the predicted interactions. In addition, molecular docking provides only static binding predictions and does not account for protein dynamics, bioavailability, metabolism, or complex physiological environments. Second, although shared genes between parabens and SLE were identified, the precise molecular mechanisms linking paraben exposure to immune dysregulation remain unclear. Genetic susceptibility and other environmental factors may also influence these associations, highlighting the need for future studies integrating genetic, environmental, and clinical data. Third, this study did not evaluate dose-response relationships or exposure thresholds for individual parabens, making it difficult to determine whether the predicted interactions occur under environmentally relevant exposure conditions. Fourth, chronic low-dose exposure to parabens is common due to their widespread use in cosmetics, pharmaceuticals, and food products and may exert subtle but biologically relevant effects on immune and inflammatory pathways [9, 43]. However, the present analysis did not address cumulative exposure over time. Furthermore, actual human exposure levels vary substantially according to product use patterns and population characteristics, factors that were not considered in this study. Finally, humans are typically exposed to multiple environmental pollutants simultaneously. Potential additive or synergistic effects between parabens and other environmental chemicals were not evaluated and warrant further investigation.

Conclusions

In conclusion, this study identifies potential genes and pathways that may link paraben exposure with immune dysregulation in SLE, particularly TLR4, CTLA4, and CD86. While molecular docking analyses suggest possible interactions between parabens and these targets, the findings are based on computational predictions and require experimental validation. Further in vitro, in vivo, and clinical studies are needed to clarify the biological relevance of these interactions and their potential implications in autoimmune diseases such as SLE.

Acknowledgements

Not applicable.

Author contributions

HL: Conceptualization, Data curation, Formal analysis, Methodology, Software, Writing – original draft, Funding acquisition. QG: Data curation, Formal analysis, Software, Writing – original draft, Funding acquisition. TZ: Conceptualization, Methodology, Writing – original draft, Funding acquisition. SZ: Data curation, Software, Writing – original draft. CG: Funding acquisition, Supervision, Writing – review & editing. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Medical and Health Research Project of Baoan District (No. 2023JD071, No. 2023JD079, and No. 2023JD250), the Key Specialties in Clinical Medicine of the People’s Hospital of Baoan Shenzhen (No. 8), and the Internal Doctoral Research Start-up Fund of the People’s Hospital of Baoan Shenzhen (20250122405). The funders have no roles in the conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript.

Data availability

All data analyzed during this study were obtained from publicly available repositories. Chemical information was retrieved from PubChem (https://pubchem.ncbi.nlm.nih.gov). Paraben target genes were obtained from ChEMBL (https://www.ebi.ac.uk/chembl), STITCH (http://stitch.embl.de), and SwissTargetPrediction (http://www.swisstargetprediction.ch). SLE-related genes were obtained from GeneCards (https://www.genecards.org), Online Mendelian Inheritance in Man (OMIM) (https://omim.org), and the Therapeutic Target Database (TTD) (https://ttd.idrblab.cn). Gene expression data were obtained from the Gene Expression Omnibus (GEO) repository (https://www.ncbi.nlm.nih.gov/geo) under accession numbers GSE138458, GSE154851, and GSE185047. Protein structures were retrieved from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) (https://www.rcsb.org), including PDB IDs 1I85 and 2Z63. All datasets analyzed during this study are publicly available from the corresponding repositories. Additional information supporting the findings of this study is available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

This study used only publicly available data from previously published studies and databases. Ethical approval and informed consent were obtained in the original studies, and no additional ethical approval was required for the present analysis.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Hui Li, Email: chipperli@163.com.

Chengshan Guo, Email: Guochengshan1@163.com.

References

  • 1.Hoi A, Igel T, Mok CC, Arnaud L. Systemic lupus erythematosus. Lancet. 2024;403(10441):2326–38. [DOI] [PubMed] [Google Scholar]
  • 2.Crow MK. Pathogenesis of systemic lupus erythematosus: risks, mechanisms and therapeutic targets. Ann Rheum Dis. 2023;82(8):999–1014. [DOI] [PubMed] [Google Scholar]
  • 3.Lanata CM, Taylor KE, Hurst-Hopf J, Nititham J, Blazer A, Trupin L, Katz P, Dall’Era M, Yazdany J, Chung SA, et al. Screening of environmental chemicals to characterize exposures in participants with systemic lupus erythematosus. Arthritis Rheumatol. 2024;76(6):905–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Xing M, Ma Y, Cui F, Li D, Wang J, Tang L, Zheng L, Yang J, Tian Y. Air pollution, genetic susceptibility, and risk of incident systemic lupus erythematosus: a prospective cohort study. Arthritis Rheumatol. 2024;76(10):1530–7. [DOI] [PubMed] [Google Scholar]
  • 5.Fransway AF, Fransway PJ, Belsito DV, Warshaw EM, Sasseville D, Fowler JF Jr., DeKoven JG, Pratt MD, Maibach HI, Taylor JS, et al. Parabens #N/A. 2019;30(1):3–31. [DOI] [PubMed] [Google Scholar]
  • 6.Bledzka D, Gromadzinska J, Wasowicz W. Parabens. From environmental studies to human health. Environ Int. 2014;67:27–42. [DOI] [PubMed] [Google Scholar]
  • 7.Ahn H, Lee H, Lee G, Park J, Sung HW, Lee E, Lee GS. Parabens disrupt non-canonical inflammasome activation. Int Immunopharmacol. 2021;101(Pt A):108196. [DOI] [PubMed] [Google Scholar]
  • 8.Casas M, Gascon M. Prenatal exposure to endocrine-disrupting chemicals and asthma and allergic diseases. J Investig Allergol Clin Immunol. 2020;30(4):215–28. [DOI] [PubMed] [Google Scholar]
  • 9.Huang KS, Chen CY, Sun CY, Su YJ. Serum parabens and its correlations with immunologic and cellular markers in Southern Taiwan industrialized city systemic lupus erythematosus patients. J Chin Med Assoc. 2022;85(10):993–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker BA, Thiessen PA, Yu B, et al. PubChem 2025 update. Nucleic Acids Res. 2025;53(D1):D1516–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Zdrazil B, Felix E, Hunter F, Manners EJ, Blackshaw J, Corbett S, de Veij M, Ioannidis H, Lopez DM, Mosquera JF, et al. The ChEMBL database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic Acids Res. 2024;52(D1):D1180–92. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Szklarczyk D, Santos A, von Mering C, Jensen LJ, Bork P, Kuhn M. STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res. 2016;44(D1):D380–384. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Daina A, Zoete V. Testing the predictive power of reverse screening to infer drug targets, with the help of machine learning. Commun Chem. 2024;7(1):105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S, Stein TI, Nudel R, Lieder I, Mazor Y, et al. The GeneCards suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinf 2016;54:1 30 31–31 30 33. [DOI] [PubMed]
  • 15.Hamosh A, Amberger JS, Bocchini C, Scott AF, Rasmussen SA. Online Mendelian Inheritance in Man (OMIM(R)): victor McKusick’s magnum opus. Am J Med Genet A. 2021;185(11):3259–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Zhou Y, Zhang Y, Zhao D, Yu X, Shen X, Zhou Y, Wang S, Qiu Y, Chen Y, Zhu F. TTD: Therapeutic target database describing target druggability information. Nucleic Acids Res. 2024;52(D1):D1465–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Sun HW, Zhang X, Shen CC. The shared circulating diagnostic biomarkers and molecular mechanisms of systemic lupus erythematosus and inflammatory bowel disease. Front Immunol. 2024;15:1354348. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Szklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R, Gable AL, Fang T, Doncheva NT, Pyysalo S, et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023;51(D1):D638–46. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Otasek D, Morris JH, Boucas J, Pico AR, Demchak B. Cytoscape automation: empowering workflow-based network analysis. Genome Biol. 2019;20(1):185. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Shi S, Tian X, Gong Y, Sun M, Liu J, Zhang J, Liu Y, Li L, Jiang S. Pivotal role of JNK protein in the therapeutic efficacy of parthenolide against breast cancer: novel and comprehensive evidences from network pharmacology, single-cell RNA sequencing and metabolomics. Int J Biol Macromol. 2024;279(Pt 3):135209. [DOI] [PubMed] [Google Scholar]
  • 21.Burley SK, Bhikadiya C, Bi C, Bittrich S, Chao H, Chen L, Craig PA, Crichlow GV, Dalenberg K, Duarte JM, et al. RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning. Nucleic Acids Res. 2023;51(D1):D488–508. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Liu Y, Yang X, Gan J, Chen S, Xiao ZX, Cao Y. CB-Dock2: improved protein-ligand blind docking by integrating cavity detection, docking and homologous template fitting. Nucleic Acids Res. 2022;50(W1):W159–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Chatterjee S, Adhikary S, Bhattacharya S, Chakraborty A, Dutta S, Roy D, Ganguly A, Nanda S, Rajak P. Parabens as the double-edged sword: understanding the benefits and potential health risks. Sci Total Environ. 2024;954:176547. [DOI] [PubMed] [Google Scholar]
  • 24.Lemini C, Silveyra P, Segovia-Mendoza M. Cardiovascular disrupting effects of bisphenols, phthalates, and parabens related to endothelial dysfunction: review of toxicological and pharmacological mechanisms. Environ Toxicol Pharmacol. 2024;107:104407. [DOI] [PubMed] [Google Scholar]
  • 25.Lee I, Park YJ, Kim MJ, Kim S, Choi S, Park J, Cho YH, Hong S, Yoo J, Park H, et al. Associations of urinary concentrations of phthalate metabolites, bisphenol A, and parabens with obesity and diabetes mellitus in a Korean adult population: Korean National Environmental Health Survey (KoNEHS) 2015–2017. Environ Int. 2021;146:106227. [DOI] [PubMed] [Google Scholar]
  • 26.Nowak K, Jablonska E, Ratajczak-Wrona W. Immunomodulatory effects of synthetic endocrine disrupting chemicals on the development and functions of human immune cells. Environ Int. 2019;125:350–64. [DOI] [PubMed] [Google Scholar]
  • 27.Liao K, Zhao Y, Qu J, Yu W, Hu S, Fang S, Zhao M, Jin H. Association of serum bisphenols, parabens, and triclosan concentrations with Sjogren syndrome in the Hangzhou, China population. Sci Total Environ. 2024;915:170031. [DOI] [PubMed] [Google Scholar]
  • 28.Li H, Boulougoura A, Endo Y, Tsokos GC. Abnormalities of T cells in systemic lupus erythematosus: new insights in pathogenesis and therapeutic strategies. J Autoimmun. 2022;132:102870. [DOI] [PubMed] [Google Scholar]
  • 29.Arnaud L, Chasset F, Martin T. Immunopathogenesis of systemic lupus erythematosus: an update. Autoimmun Rev. 2024;23(10):103648. [DOI] [PubMed] [Google Scholar]
  • 30.Darvish Z, Kheder RK, Faraj TA, Najmaldin SK, Mollazadeh S, Nosratabadi R, Esmaeili SA. A better understanding of the role of the CTLA-CD80/86 axis in the treatment of autoimmune diseases. Cell Biochem Funct. 2024;42(1):e3895. [DOI] [PubMed] [Google Scholar]
  • 31.Fisch D, Zhang T, Sun H, Ma W, Tan Y, Gygi SP, Higgins DE, Kagan JC. Molecular definition of the endogenous Toll-like receptor signalling pathways. Nature. 2024;631(8021):635–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Wang N, Jin Y, Cheng G, Zhang K, Huang B, Li Y, He J. Retrospective analysis of low-dose interleukin-2 therapy on chronic autoimmune thyroid disease with concurrent systemic lupus erythematosus. Ann Med. 2023;55(2):2238600. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Kim HJ, Kim H, Lee JH, Hwangbo C. Toll-like receptor 4 (TLR4): new insight immune and aging. Immun Ageing. 2023;20(1):67. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Wu J, Singh K, Lin A, Meadows AM, Wu K, Shing V, Bley M, Hassanzadeh S, Huffstutler RD, Schmidt MS, et al. Boosting NAD+ blunts TLR4-induced type I IFN in control and systemic lupus erythematosus monocytes. J Clin Invest 2022;132(5). [DOI] [PMC free article] [PubMed]
  • 35.Kalliolias GD, Basdra EK, Papavassiliou AG. Targeting TLR Signaling cascades in systemic lupus erythematosus and rheumatoid arthritis: an update. Biomedicines 2024;12(1). [DOI] [PMC free article] [PubMed]
  • 36.Li SJ, Ruan DD, Wu WZ, Wu M, Wu QY, Wang HL, Ji YY, Zhang YP, Lin XF, Fang ZT, et al. Potential regulatory role of the Nrf2/HMGB1/TLR4/NF-kappaB signaling pathway in lupus nephritis. Pediatr Rheumatol Online J. 2023;21(1):130. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Hsieh YT, Chen YC, Chou YC, Kuo PY, Yen YT, Tsai HW, Wang CR. Long noncoding RNA SNHG16 regulates TLR4-mediated autophagy and NETosis formation in alveolar hemorrhage associated with systemic lupus erythematosus. J Biomed Sci. 2023;30(1):78. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Liu M, Yu Y, Hu S. A review on applications of abatacept in systemic rheumatic diseases. Int Immunopharmacol. 2021;96:107612. [DOI] [PubMed] [Google Scholar]
  • 39.Kennedy A, Waters E, Rowshanravan B, Hinze C, Williams C, Janman D, Fox TA, Booth C, Pesenacker AM, Halliday N, et al. Differences in CD80 and CD86 transendocytosis reveal CD86 as a key target for CTLA-4 immune regulation. Nat Immunol. 2022;23(9):1365–78. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Suvankar S, Padhi S, Bagabir HA, Pati A, Wahid M, Mandal RK, Haque S, Panda AK. Cytotoxic T-lymphocyte associated protein 4 (CTLA4) polymorphisms are linked to systemic lupus erythematosus: an updated meta-analysis. Biotechnol Genet Eng Rev. 2023;39(2):841–58. [DOI] [PubMed] [Google Scholar]
  • 41.Tachachartvanich P, Sangsuwan R, Navasumrit P, Ruchirawat M. Assessment of immunomodulatory effects of five commonly used parabens on human THP-1 derived macrophages: implications for ecological and human health impacts. Sci Total Environ. 2024;944:173823. [DOI] [PubMed] [Google Scholar]
  • 42.Çaldıran F. In silico approach to genetics and epigenetic mechanisms in systemic lupus erythematosus: focus on immune response genes. Sağlık Bilimlerinde İleri Araştırmalar Dergisi / J Adv Res Health Sci. 2024;7(3):150–9. [Google Scholar]
  • 43.Peinado FM, Iribarne-Duran LM, Artacho-Cordon F. Human exposure to bisphenols, parabens, and benzophenones, and its relationship with the inflammatory response: a systematic review. Int J Mol Sci. 2023;24(8). [DOI] [PMC free article] [PubMed]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

All data analyzed during this study were obtained from publicly available repositories. Chemical information was retrieved from PubChem (https://pubchem.ncbi.nlm.nih.gov). Paraben target genes were obtained from ChEMBL (https://www.ebi.ac.uk/chembl), STITCH (http://stitch.embl.de), and SwissTargetPrediction (http://www.swisstargetprediction.ch). SLE-related genes were obtained from GeneCards (https://www.genecards.org), Online Mendelian Inheritance in Man (OMIM) (https://omim.org), and the Therapeutic Target Database (TTD) (https://ttd.idrblab.cn). Gene expression data were obtained from the Gene Expression Omnibus (GEO) repository (https://www.ncbi.nlm.nih.gov/geo) under accession numbers GSE138458, GSE154851, and GSE185047. Protein structures were retrieved from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) (https://www.rcsb.org), including PDB IDs 1I85 and 2Z63. All datasets analyzed during this study are publicly available from the corresponding repositories. Additional information supporting the findings of this study is available from the corresponding author upon reasonable request.

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