Benzo[a]pyrene and Rheumatoid Arthritis: An Integrated Computational Investigation

Summary

This study employed an integrated computational toxicology approach to systematically investigate the link between Benzo[a]pyrene exposure and rheumatoid arthritis. The analysis identified five core target genes, revealed their enrichment in key immune pathways, and validated stable BaP-protein binding, elucidating potential molecular mechanisms for environmental pollutant-induced rheumatoid arthritis (RA).

Abstract

Polycyclic aromatic hydrocarbons (PAHs), ubiquitous environmental pollutants, are considered significant environmental factors contributing to the pathogenesis of RA. Benzo[a]pyrene (BaP), a key component of PAHs, may be associated with RA onset; however, the underlying toxicological mechanism remains to be fully elucidated. In this study, we systematically addressed this knowledge gap using an integrated approach combining network toxicology, machine learning, and molecular docking. Initially, a network toxicology analysis was conducted based on the molecular structure of BaP. By integrating and screening target information from multiple databases, 15 potential RA-related target genes of BaP were ultimately identified, and their interaction network was constructed. GO and KEGG enrichment analyses revealed that these genes were significantly enriched in biological processes such as leukocyte migration and immune cell signal transduction and were associated with the NF-κB and T cell receptor signaling pathways, among others. Subsequent topological analysis using the STRING database and Cytoscape software screened out five core genes (LCK, ZAP70, ITK, GZMA, and ITGAL), whose importance was further validated through machine learning. Molecular docking and molecular dynamics simulation results indicated that BaP exhibits strong binding affinity for the protein products of these target genes, resulting in the formation of conformationally stable complexes. In summary, this study employs an integrated computational approach to elucidate the potential mechanisms by which BaP may contribute to RA development, thereby offering a theoretical foundation for future investigations into the prevention and treatment of RA associated with environmental pollutants.

Introduction

RA is a common autoimmune disease characterized by chronic synovitis and pannus formation, which drive the progressive destruction of cartilage and bone erosion. These pathological changes lead to joint dysfunction, an increased risk of pathological fractures, and ultimately disability, thereby severely impairing the quality of life of affected individuals¹. While the etiology of RA is not fully understood, its pathogenesis is generally attributable to the combined effects of genetic (e.g., HLA-DR4 and HLA-DR1 gene subtypes) and environmental factors (e.g., smoking, alcohol consumption, Epstein-Barr virus infection, exposure to air pollution)

Protocol

Ethics statement
This study did not directly involve any human participants or animal subjects.

BaP Target acquisition
BaP was characterized by integrating data from multiple databases. The PubChem database (https://pubchem.ncbi.nlm.nih.gov/) was queried using the keyword "Benzo[a]pyrene" to obtain its chemical structure and canonical 2D structure (SMILES string: C1=CC=C2C3=C4C(=CC2=C1)C=CC5=C4C(=CC=C5)C=C3)¹⁴. Potential BaP targets were retrieved from the ChEMBL (https://www.ebi.ac.uk/chembl/), SEA (https://sea.bkslab.org/), and PharmMapper (http://lilab-ecust.cn/pharmmapper) databases

Results

BaP Target acquisition
Data regarding the molecular structure of BaP were obtained from the PubChem database (Figure 2A). The potential biological targets of BaP were systematically predicted by integrating information from three complementary databases—ChEMBL, PharmMapper, and SEA—resulting in the identification of 474 potential targets (Figure 2B).

Discussion

RA is a complex autoimmune disease resulting from the interaction between genetic susceptibility and environmental factors. Among numerous environmental risk factors, PAHs, one of the most common air pollutants, are considered an important link connecting environmental exposure to RA onset. Earlier studies preliminarily revealed that PAHs can influence the balance of immune cell differentiation via the AHR pathway, as well as induce oxidative stress. BaP, a highly representative component of PAHs, has drawn significant a.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
AutoDock Vina	https://vina.scripps.edu	1.2.5 (SCR_011958)	Molecular docking software
clusterProfiler (R package)	https://bioconductor.org/packages/clusterProfiler	4.10.0 (SCR_016884)	R package for enrichment analysis
CytoHubba (Cytoscape plugin)	https://apps.cytoscape.org/apps/cytohubba	0.1	Plugin for hub gene identification (Degree algorithm)
Cytoscape	https://cytoscape.org	3.10.1 (SCR_003032)	Network visualization software
Discovery Studio	Dassault Systèmes BIOVIA	2021	Software for 2D interaction diagram generation
enrichplot (R package)	https://bioconductor.org/packages/enrichplot	1.22.0 (SCR_021165)	R package for enrichment result visualization
GROMACS	https://www.gromacs.org	2025.3 (SCR_014565)	Molecular dynamics simulation software
limma (R package)	https://bioconductor.org/packages/limma	3.58.1 (SCR_010943)	R package for differential expression analysis
org.Hs.eg.db (R package)	https://bioconductor.org/packages/org.Hs.eg.db	3.18.0 (SCR_006442)	R annotation package for human gene identifiers
PyMol	Schrödinger, Inc	2.5.7 (SCR_000305)	Molecular visualization software
QtGrace	https://sourceforge.net/projects/grace/	0.2.6	Plotting tool for trajectory analysis
R (programming environment)	https://www.r-project.org	4.3.1 (SCR_001905)	Statistical computing software
STRING database	https://string-db.org	12 (SCR_005223)	Protein-protein interaction database
venn (R package)	https://cran.r-project.org/package=venn	1.11	R package for Venn diagram generation
WGCNA (R package)	https://cran.r-project.org/package=WGCNA	1.72 (SCR_003302)	Dedicated R package for weighted co-expression network analysis