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. 2022 Sep 27:2022:2394398.
doi: 10.1155/2022/2394398. eCollection 2022.

Network Toxicology Guided Mechanism Study on the Association between Thyroid Function and Exposures to Polychlorinated Biphenyls Mixture

Chunxia Li  1 , Hong Xing  1 , Qiaoyu He  1 , Jing Liu  1 , Hong Liu  1 , Yue Li  1 , Xiaopeng Chen  1
Affiliations

Network Toxicology Guided Mechanism Study on the Association between Thyroid Function and Exposures to Polychlorinated Biphenyls Mixture

Chunxia Li et al. Biomed Res Int. .

Abstract

Polychlorinated biphenyls (PCBs) are persistent and highly toxic pollutants, which can accumulate in organisms and produce toxic effects, especially damaging the function of thyroid hormones. So far, the molecular mechanism of PCBs mixture and their metabolites interfering with thyroid hormones has not been studied thoroughly except for individual compounds. In this study, PubMed, Web of Science, and STITCH databases were used to search PCBs and their corresponding target proteins. The intersection of PCBs and thyroid hormone dysfunction target proteins was obtained from GeneCards. The "compounds-targets-pathways" network was constructed by Cytoscape software. And KEGG and Go analyses were performed for key targets. Finally, molecular docking was used to verify the binding effect. Four major active components, five key targets, and 10 kernel pathways were successfully screened by constructing the network. Functional enrichment analysis showed that the interference was mediated by cancer, proteoglycans, PI3K-Akt, thyroid hormone, and FoxO signaling pathways. The molecular docking results showed that the binding energies were less than -5 kcal·mol-1. PCBs and their metabolites may act on the key targets of MAPK3, MAPK1, RXRA, PIK3R1, and TP53. The toxic effect of sulfated and methyl sulfone PCBs is greater. The method of screening targets based on the simultaneous action of multiple PCBs can provide a reference for other research. The targets were not found in previous metabolite toxicity studies. It also provides a bridge for the toxic effects and experimental research of PCBs and their metabolites in the future.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
General workflow of network toxicology and molecular docking in the study.
Figure 2
Figure 2
Venn diagram of PCBs and thyroid hormone dysfunction targets. The diagram was built using Venny 2.1 online tool (http://bioinfogp.cnb.csic.es/tools/venny/) according to the data from the STRING database.
Figure 3
Figure 3
PPI network of PCBs interfering with thyroid hormone function. The PPI network was built using Cytoscape software 3.6.1 (http://cytoscape.org/) according to the data from the STRING database. The larger the node is, the more orange the color is, indicating that the target is more important in the network, the closer to the target in the inner circle, the more important the target is, the closer to the target in the inner circle, the more important the target is.
Figure 4
Figure 4
Go (BP MF CC) function enrichment analysis diagram. The diagram was built using bioinformatics online tool (http://www.bioinformatics.com.cn/) according to the data from the DAVID 6.8 database. BP: biological process; MF: molecular function; CC: cellular component.
Figure 5
Figure 5
The target distribution of PCB in the thyroid hormone signaling pathway. The diagram was built using the KEGG database (https://www.kegg.jp/) (red rectangle indicating potential targets: GSK3B, MAP2K1, THRB, THRA, PDPK1, SRC, PIK3R1, ESR1, RXRA, and TP53.)
Figure 6
Figure 6
“Components-targets-pathways” network diagram. The network diagram was built using Cytoscape software 3.6.1. (https://cytoscape.org/). (The purple box represents PCBs pollutants, the blue box represents the chemical components of PCBs pollutants related to thyroid dyscalculia, the green box represents intersection targets, and the red box represents key pathways. Grey lines represent interactions).
Figure 7
Figure 7
Transformation pathway of three metabolites of PCBs in the human body (CYPs: cytochrome P450 enzymes; SULTs: sulfotransferases; MeSO2: methyl sulfone).
Figure 8
Figure 8
Heat map of the molecular docking energy scoring. The heat map was built using MeV software (http://mev.tm4.org/) (the closer the color is to blue, the better the binding ability.)
Figure 9
Figure 9
The 3D view of the molecular docking pattern of each protein with its ligand. The 3D pattern of target interaction was marked by PyMOL software (https://pymol.org/2/), PLIP database (https://projects.biotec.tu-dresden.de/plip-web/plip/index), and AutoDock Vina software (https://vina.scripps.edu/) (yellow dotted line: hydrogen bond; red dotted line: hydrophobic interaction; green dotted line: Π-stacking; purple dotted line: halogen bonds; blue dotted line: salt bridges. (a) 6GES, (b) 6SLG, (c) 7JIS, (d) 5G4N, (e) 6JNO; 3D: 3-dimensional).
Figure 10
Figure 10
The 3D visual analysis results of molecular docking of PCBs metabolite with key target proteins. The 3D visual analysis results of molecular docking were using PyMOL software (https://http://pymol.org/2/), PLIP database (https://projects.biotec.tu-dresden.de/plip-web/plip/index), and AutoDock Vina software (http://vina.scripps.edu/) (yellow dotted line: hydrogen bond; red dotted line: hydrophobic interaction; green dotted line: Π-stacking; purple dotted line: halogen bonds; blue dotted line: salt bridges. (a) 6GES and 3'-sulfate-PCB-180, (b) 6SLG and 3-MeSO2-PCB-118, (c) 7JIS and 3-MeSO2-PCB-118, (d) 5G4N and 3-MeSO2-PCB-118, (e) 6JNO and 3-sulfate-PCB-118; 3D: three-dimensional).
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