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

Authors

Hui Li, Qin Guo, Tiantian Zhang, Shufen Zhou, Chengshan Guo

Published in

BMC pharmacology & toxicology. Jul 04, 2026. Epub Jul 04, 2026.

Abstract

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.
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.
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.
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.

PMID:
42401960
Bibliographic data and abstract were imported from PubMed on 05 Jul 2026.

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