Pentabromoethylbenzene (PBEB), as one of the novel brominated flame retardants (NFBRs), has caused increasing public concern for health risks. Till now, information regarding potential effects of PBEB on thyroid function remains unclear. Herein, we investigated thyroid disruption of PBEB in vitro and in silico and evaluated thyroid dysfunction induced by PBEB using Sprague–Dawley rats. PBEB showed thyroid receptor (TR) β antagonistic activity with IC50 of 9.82 × 10–7 M in the dual-luciferase reporter gene assay and induced relative reorientation of helix 11 (H11) and H12 of the TR ligand binding domain as revealed by molecular dynamics simulations. PBEB (0.2, 2, 20 mg/kg BW/d) markedly altered the transcriptome profile of thyroid with induction of 17, 42, and 119 differentially expressed genes (DEGs) involved in thyroid hormone signaling and synthesis pathway, of which transthyretin and albumin are common DEGs. The 28-d exposure to PBEB significantly decreased the triiodothyronine level (from 7.23 to 5.67 ng/mL) and increased the thyrotropin level (from 7.88 to 12.86 mU/L) for female rats. PBEB consequently reduced thyroid weight and altered its morphology with more depleted follicles. Overall, our study provides the first account of evidence on PBEB exerted thyroid disruption, transcriptome aberration, and morphological alteration, facilitating health risk assessment of PBEB and structurally related NBFRs.
Persistent, mobile, and toxic (PMT) substances and very persistent and very mobile (vPvM) substances can transport over long distances from various sources, increasing the public health risk. A rapid and high-throughput screening of PMT/vPvM substances is thus warranted to the risk prevention and mitigation measures. Herein, we construct a machine learning-based screening system integrated with five models for high-throughput classification of PMT/vPvM substances. The models are constructed with 44 971 substances by conventional learning, deep learning, and ensemble learning algorithms, among which, LightGBM and XGBoost outperform other algorithms with metrics exceeding 0.900. Good model interpretability is achieved through the number of free halogen atoms (fr_halogen) and the logarithm of partition coefficient (MolLogP) as the two most critical molecular descriptors representing the persistence and mobility of substances, respectively. Our screening system exhibits a great generalization capability with area under the receiver operating characteristic curve (AUROC) above 0.951 and is successfully applied to the persistent organic pollutants (POPs), prioritized PMT/vPvM substances, and pesticides. The screening system constructed in this study can serve as an efficient and reliable tool for high-throughput risk assessment and the prioritization of managing emerging contaminants.
Thousands of contaminants are used worldwide and eventually released into the environment, presenting a challenge of health risk assessment. The identification of key toxic pathways and characterization of interactions with target biomacromolecules are essential for health risk assessments. The adverse outcome pathway (AOP) incorporates toxic mechanisms into health risk assessment by emphasizing the relationship among molecular initiating events (MIEs), key events (KEs), and adverse outcome (AO). Herein, we attempted the use of AOP to decipher the toxic effects of 2,6-di-tert-butylphenol (2,6-DTBP) and its para-quinone metabolite 2,6-di-tert-butyl-1,4-benzoquinone (2,6-DTBQ) based on integrated transcriptomics, molecular modeling, and cell-based assays. Through transcriptomics and quantitative real-time PCR validation, we identified retinoic acid receptor β (RARβ) as the key target biomacromolecule. The epigenetic analysis and molecular modeling revealed RARβ interference as one MIE, including DNA methylation and conformational changes. In vitro assays extended subsequent KEs, including altered protein expression of p-Erk1/2 and COX-2, and promoted cancer cell H4IIE proliferation and metastasis. These toxic effects altogether led to carcinogenic risk as the AO of 2,6-DTBP and 2,6-DTBQ, in line with chemical carcinogenesis identified from transcriptome profiling. Overall, our simplified AOP network of 2,6-DTBP and 2,6-DTBQ facilitates relevant health risk assessment.
Thioxanthones (TXs) are photoinitiators widely used in UV curable resins and food packaging, and their residues have been frequently detected in human bodies. Our current understanding of the susceptibility of residual TXs to metabolism and their effects on human health is very limited. The in vitro metabolism of TXs and its toxic effects on cytochrome P450 (CYP) (the key xenobiotic metabolizing enzymes) were examined in this study. 2-Chlorothioxanthone (2-Cl-TX) significantly inhibited the enzymatic activities of CYP1A2 and CYP3A4 with IC 50 of 8.36 and 0.86 μM, respectively. The exposure to 2-Cl-TX at 2.5 μM up-regulated the mRNA expression of CYP1A2 and CYP3A4 in human hepatocellular carcinoma cells to 3.03-fold and 2.02-fold, respectively. 2-Cl-TX at 2.5 μM caused 2.19-fold and 1.98-fold overexpression of CYP1A2 and CYP3A4, respectively. In vitro studies revealed that 2-Cl-TX was biotransformed into two metabolites through the sulfoxidation of the sulfur atom, or via the hydroxylation of aromatic carbon. Results from this study, including the metabolic susceptibility of residual 2-Cl-TX, the proposed metabolites and the significant toxic effect on the activities, mRNA, and protein expression of CYP1A2 and CYP3A4, are vital to the human health and safety risk assessment from this ubiquitous xenobiotic.
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