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Integrated omics and machine learning uncover the molecular basis of environmental toxicant 6PPD-Q-induced non-obstructive azoospermia.

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Ecotoxicology and environmental safety 📖 저널 OA 3.2% 2026 Vol.310() p. 119794
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Jiang X, Lu H, Ou G, Zhang T, Xie L, Zhou J, Hou W, Xu Q, Hu W, Zou W, Cao Y

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[BACKGROUND] N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine-quinone (6PPD-Q), a transformation product of tire rubber antioxidants, has been increasingly recognized as an emerging environmental co

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APA Jiang X, Lu H, et al. (2026). Integrated omics and machine learning uncover the molecular basis of environmental toxicant 6PPD-Q-induced non-obstructive azoospermia.. Ecotoxicology and environmental safety, 310, 119794. https://doi.org/10.1016/j.ecoenv.2026.119794
MLA Jiang X, et al.. "Integrated omics and machine learning uncover the molecular basis of environmental toxicant 6PPD-Q-induced non-obstructive azoospermia.." Ecotoxicology and environmental safety, vol. 310, 2026, pp. 119794.
PMID 41616688

Abstract

[BACKGROUND] N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine-quinone (6PPD-Q), a transformation product of tire rubber antioxidants, has been increasingly recognized as an emerging environmental contaminant with potential reproductive toxicity. Although growing evidence implicates 6PPD-Q exposure in male infertility, its role in specific pathological conditions such as sertoli cell-only syndrome (SCOS) in non-obstructive azoospermia (NOA) remains unclear.

[METHODS] Potential toxicological targets of 6PPD-Q were predicted by integrating multiple public databases and intersecting them with SCOS-related differentially expressed genes (DEGs) to identify candidate toxic genes. Enrichment analyses were performed to uncover key biological processes and pathways. Machine learning algorithms were applied to identify hub genes and evaluated model performance. Molecular docking and dynamics simulations were used to assess the binding interactions between 6PPD-Q and core proteins. In vivo mouse exposure and in vitro TM4 cell assays were subsequently conducted to support the biological relevance of the computational predictions.

[RESULTS] A total of 1705 potential 6PPD-Q targets were identified, with 316 overlapping SCOS-related DEGs. Enrichment analyses indicated involvement of estrogen signaling, germ cell apoptosis, and chromosomal segregation. Machine learning identified five hub genes-androgen receptor (AR), minichromosome maintenance complex component 4 (MCM4), caspase-8 (CASP8), B-cell lymphoma 2 (BCL-2), and aurora kinase A (AURKA)-with AR and CASP8 being consistently highlighted across multiple machine learning approaches as core targets of 6PPD-Q-induced SCOS. Molecular docking suggested strong affinity between 6PPD-Q and AR (ΔG = -8.2 kcal/mol) and MCM4 (ΔG = -8.4 kcal/mol), confirmed by stable molecular dynamics (MD) simulations. In vivo, 6PPD-Q (0.4 - 4 mg/kg) induced dose-dependent testicular injury and altered expression of core protein, while in assays showed decreased TM4 cell viability with increasing 6PPD-Q concentrations.

[CONCLUSIONS] This study provides a mechanistic hypothesis for 6PPD-Q-induced testicular dysfunction and offers candidate targets for future experimental validation in environmental reproductive toxicology.

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