In Silico Prediction of Distinct HMW PAH-Associated Pathways in Allergic Rhinitis: Chemokine-Related Targets of BaP and Metabolic Enzyme Targets of BaA/DBA

Yue He; Yibo He; Junfeng Xi; An  Chang; Zeyu Guo; Xianzhi Liu; Jiayu Li; Jiaxuan Li; Zhao Wang; Dong Chen

doi:10.71204/13dkj505

Authors

Yue He The First Affiliated Hospital of Jinzhou Medical University Author
Yibo He The First Affiliated Hospital of Jinzhou Medical University Author
Junfeng Xi The First Affiliated Hospital of Jinzhou Medical University Author
An Chang The First Affiliated Hospital of Jinzhou Medical University Author
Zeyu Guo The First Affiliated Hospital of Jinzhou Medical University Author
Xianzhi Liu The First Affiliated Hospital of Jinzhou Medical University Author
Jiayu Li The First Affiliated Hospital of Jinzhou Medical University Author
Jiaxuan Li The First Affiliated Hospital of Jinzhou Medical University Author
Zhao Wang The First Affiliated Hospital of Beijing University of Chinese Medicine; Beijing University of Chinese Medicine Author
Dong Chen The First Affiliated Hospital of Jinzhou Medical University; Liaoning Province Clinical Medical Research Center for Allergic Diseases Author

DOI:

https://doi.org/10.71204/13dkj505

Keywords:

Polycyclic Aromatic Hydrocarbons, Allergic Rhinitis, Network Toxicology, Molecular Docking, In Silico Analysis

Abstract

Polycyclic aromatic hydrocarbons (PAHs), major atmospheric pollutants, have been associated with allergic respiratory disease, but the molecular links between individual PAHs and allergic rhinitis (AR) remain incompletely characterized. This in silico study explored predicted associations of three high-molecular-weight PAHs—benzo[a]pyrene (BaP), benzo[a]anthracene (BaA), and dibenz[a,h]anthracene (DBA)—with AR-related molecular networks. We integrated ADMET prediction (ADMETlab 2.0), multi-source target identification, network topology analysis (STRING and Cytoscape), functional enrichment (DAVID), and molecular docking (AutoDock Vina). ADMET analysis generated multiple toxicological alerts for all three compounds. Network analysis identified 191, 138, and 159 overlapping targets for the BaP–AR, BaA–AR, and DBA–AR datasets, respectively. BaP-associated targets were preferentially enriched in chemotaxis and chemokine signaling, whereas BaA- and DBA-associated targets were more strongly enriched in xenobiotic and cytochrome P450-related metabolic pathways. Molecular docking yielded energetically favorable predicted poses between BaP and chemokine-related hub proteins (CCL2, CCL5, CXCL8, CXCL12, and CXCR4) and between BaA/DBA and metabolic hub proteins, including ABCB1, CYP1A2, and CYP3A4. Taken together, these analyses generate a hypothesis that BaP may be preferentially associated with chemokine-related inflammatory networks, whereas BaA and DBA may be associated with metabolic-enzyme networks that could influence endogenous inflammatory mediator metabolism or drug disposition. Given its computational scope, this study yields in silico-generated hypotheses and candidate targets warranting experimental validation.

Author Biography

An Chang, The First Affiliated Hospital of Jinzhou Medical University

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