Structures of Endocrine-Disrupting Chemicals Correlate with the Activation of 12 Classic Nuclear Receptors

PM2.5 exposure contributes to anxiety and depression-like behaviors via phenyl-containing compounds interfering with dopamine receptor

As a global problem, fine particulate matter (PM2.5) really needs local fixes. Considering the increasing epidemiological relevance to anxiety and depression but inconsistent toxicological results, the most important question is to clarify whether and how PM2.5 causally contributes to these mental disorders and which components are the most dangerous for crucial mitigation in a particular place. In the present study, we chronically subjected male mice to a real-world PM2.5 exposure system throughout the winter heating period in a coal combustion area and revealed that PM2.5 caused anxiety and depression-like behaviors in adults such as restricted activity, diminished exploratory interest, enhanced repetitive stereotypy, and elevated acquired immobility, through behavioral tests including open field, elevated plus maze, marble-burying, and forced swimming tests. Importantly, we found that dopamine signaling was perturbed using mRNA transcriptional profile and bioinformatics analysis, with Drd1 as a potential target. Subsequently, we developed the Drd1 expression-directed multifraction isolating and nontarget identifying framework and identified a total of 209 compounds in PM2.5 organic extracts capable of reducing Drd1 expression. Furthermore, by applying hierarchical characteristic fragment analysis and molecular docking and dynamics simulation, we clarified that phenyl-containing compounds competitively bound to DRD1 and interfered with dopamine signaling, thereby contributing to mental disorders. Taken together, this work provides experimental evidence for researchers and clinicians to identify hazardous factors in PM2.5 and prevent adverse health outcomes and for local governments and municipalities to control source emissions for diminishing specific disease burdens.

Recognizing high-priority disinfection byproducts based on experimental and predicted endocrine disrupting data: Virtual screening and in vitro study

So far, about 130 disinfection by-products (DBPs) and several DBPs-groups have had their potential endocrine-disrupting effects tested on some endocrine endpoints. However, it is still not clear which specific DBPs, DBPs-groups/subgroups may be the most toxic substances or groups/subgroups for any given endocrine endpoint. In this study, we attempt to address this issue. First, a list of relevant DBPs was updated, and 1187 DBPs belonging to 4 main-groups (aliphatic, aromatic, alicyclic, heterocyclic) and 84 subgroups were described. Then, the high-priority endocrine endpoints, DBPs-groups/subgroups, and specific DBPs were determined from 18 endpoints, 4 main-groups, 84 subgroups, and 1187 specific DBPs by a virtual-screening method. The results demonstrate that most of DBPs could not disturb the endocrine endpoints in question because the proportion of active compounds associated with the endocrine endpoints ranged from 0 (human thyroid receptor beta) to 32% (human transthyretin (hTTR)). All the endpoints with a proportion of active compounds greater than 10% belonged to the thyroid system, highlighting that the potential disrupting effects of DBPs on the thyroid system should be given more attention. The aromatic and alicyclic DBPs may have higher priority than that of aliphatic and heterocyclic DBPs by considering the activity rate and potential for disrupting effects. There were 2 (halophenols and estrogen DBPs), 12, and 24 subgroups that belonged to high, moderate, and low priority classes, respectively. For individual DBPs, there were 23 (2%), 193 (16%), and 971 (82%) DBPs belonging to the high, moderate, and low priority groups, respectively. Lastly, the hTTR binding affinity of 4 DBPs was determined by an in vitro assay and all the tested DBPs exhibited dose-dependent binding potency with hTTR, which was consistent with the predicted result. Thus, more efforts should be performed to reveal the potential endocrine disruption of those high research-priority main-groups, subgroups, and individual DBPs.

Exposure to UV-B filter octylmethoxycinnamate and human health effects: Focus on endocrine disruptor actions

Human skin is the first line of photoprotection against UV radiation. However, despite having its defence mechanisms, the photoprotection that the skin exerts is not enough. To protect human skin, the inclusion of UV filters in the cosmetic industry has grown significantly as a photoprotection strategy. Octylmethoxycinnamate, also designated by octinoxate, or 2-ethylhexyl-4-methoxycinnamate (CAS number: 5466-77-3) is one of the most widely used UV-B filter in the cosmetic industry. The toxic effects of OMC have alarmed the public, but there is still no consensus in the scientific community about its use. This article aims to provide an overview of the UV filters' photoprotection, emphasizing the OMC and the possible negative effects it may have on the public health. Moreover, the current legislation will be addressed. In summary, the recommendations should be rethought to assess their risk-benefit, since the existing literature warns us to endocrine-disrupting effects of OMC. Further studies should be focus on the toxicity of OMC alone, in mixture and should consider its degradation products, to improve the knowledge of its risk assessment as EDC.

CatNet: Sequence-based deep learning with cross-attention mechanism for identifying endocrine-disrupting chemicals

Endocrine-disrupting chemicals (EDCs) pose significant environmental and health risks due to their potential to interfere with nuclear receptors (NRs), key regulators of physiological processes. Despite the evident risks, the majority of existing research narrows its focus on the interaction between compounds and the individual NR target, neglecting a comprehensive assessment across the entire NR family. In response, this study assembled a comprehensive human NR dataset, capturing 49,244 interactions between 35,467 unique compounds and 42 NRs. We introduced a cross-attention network framework, "CatNet", innovatively integrating compound and protein representations through cross-attention mechanisms. The results showed that CatNet model achieved excellent performance with an area under the receiver operating characteristic curve (AUCROC) = 0.916 on the test set, and exhibited reliable generalization on unseen compound-NR pairs. A distinguishing feature of our research is its capacity to expand to novel targets. Beyond its predictive accuracy, CatNet offers a valuable mechanistic perspective on compound-NR interactions through feature visualization. Augmenting the utility of our research, we have also developed a graphical user interface, empowering researchers to predict chemical binding to diverse NRs. Our model enables the prediction of human NR-related EDCs and shows the potential to identify EDCs related to other targets.

Alkylphenols disrupt estrogen homeostasis via diradical cross-coupling reactions: A novel pathway of endocrine disruption

Estrogen, being an essential class of sex hormone, is an important target of endocrine disruption chemicals. It is well known that environmental disruptors could activate or inhibit estrogen receptors, acting as agonists or antagonists, and thus affect the circulating estrogen concentrations. Here, we report enzyme-mediated diradical cross-coupling reactions between alkylphenols (e.g., 2,4-di-tert-butylphenol [DBP], 4-nonylphenol [4-NP], and 4-tert-octylphenol [4-t-OP]) and estrogens (e.g., estradiol [E2]) that generate coupling metabolites and disrupt estrogen homeostasis. Among the phenolic xenobiotics, the screening of metabolic products revealed that alkylphenols had the highest reaction activities and generated coupling metabolites with high abundances (DBP-O-E2, 4-t-OP-O-E2, and 4-NP-O-E2). The coupling reactions were catalyzed by cytochrome P450 3A4 (CYP3A4) and verified by the detection of the coupling products in general populations. In vitro and in vivo exposures together with CYP3A4 inhibition demonstrated that cross-coupling reactions of phenols and E2 significantly reduced the normal levels of E2. We further established a unique spin-trapping-based high-throughput screening method to show the existence of diradicals in the coupling reaction. Density functional theory calculations revealed that spin aromatic delocalization was the fundamental cause of the high rebound barrier and sufficient lifetime of phenoxy radicals that enabled phenolic cross-coupling triggered by cytochrome P450. The identified mechanistic details for diradical cross-coupling reactions provide a novel pathway for phenolic chemicals to disrupt estrogen homeostasis.

EDC-Predictor: A Novel Strategy for Prediction of Endocrine-Disrupting Chemicals by Integrating Pharmacological and Toxicological Profiles

Identification of endocrine-disrupting chemicals (EDCs) is crucial in the reduction of human health risks. However, it is hard to do so because of the complex mechanisms of the EDCs. In this study, we propose a novel strategy named EDC-Predictor to integrate pharmacological and toxicological profiles for the prediction of EDCs. Different from conventional methods that only focus on a few nuclear receptors (NRs), EDC-Predictor considers more targets. It uses computational target profiles from network-based and machine learning-based methods to characterize compounds, including both EDCs and non-EDCs. The best model constructed by these target profiles outperformed those models by molecular fingerprints. In a case study to predict NR-related EDCs, EDC-Predictor showed a wider applicability domain and higher accuracy than four previous tools. Another case study further demonstrated that EDC-Predictor could predict EDCs targeting other proteins rather than NRs. Finally, a free web server was developed to make EDC prediction easier (http://lmmd.ecust.edu.cn/edcpred/). In summary, EDC-Predictor would be a powerful tool in EDC prediction and drug safety assessment.

Advances and applications of machine learning and deep learning in environmental ecology and health

Machine learning (ML) and deep learning (DL) possess excellent advantages in data analysis (e.g., feature extraction, clustering, classification, regression, image recognition and prediction) and risk assessment and management in environmental ecology and health (EEH). Considering the rapid growth and increasing complexity of data in EEH, it is of significance to summarize recent advances and applications of ML and DL in EEH. This review summarized the basic processes and fundamental algorithms of the ML and DL modeling, and indicated the urgent needs of ML and DL in EEH. Recent research hotspots such as environmental ecology and restoration, environmental fate of new pollutants, chemical exposures and risks, chemical hazard identification and control were highlighted. Various applications of ML and DL in EEH demonstrate their versatility and technological revolution, and present some challenges. The perspective of ML and DL in EEH were further outlined to promote the innovative analysis and cultivation of the ML-driven research paradigm.

The crosstalk effects of polybrominated diphenyl ethers on the retinoic acid and thyroid hormone signaling pathway

The toxicological and pathological influences of polybrominated diphenyl ethers (PBDEs) on the animal central nervous system have attracted worldwide attention. However, their mechanism of action has not been completely elucidated. Given that retinoic acid (RA) and thyroid hormone (TH) signaling pathway are closely related to neurodevelopment, the crosstalk between the two signaling pathways at the levels of metabolite conversion, gene expression and ligand-receptor interaction after exposure to two representative PBDE congeners (BDE-47 and BDE-209) using zebrafish larvae, dual reporter gene assay, and docking simulation was studied. Our results clarified that BDE-47 could disrupt the transport and metabolism of retinoids, induce changes in expression of key genes, bind with the seven nuclear receptors, and activate RA signaling pathway. BDE-47 exhibited more effects on the indicators of the two signaling pathways than BDE-209. Furthermore, BDE-47 may disrupt TH signaling pathway by disrupting RA signaling pathway, indicating that RA signal is priorly influenced than TH signal. This work offered a new perspective to elucidate TH signal disruption mechanism induced by PBDEs from RA signaling pathway, which is of great significance to elucidate the health effects of PBDEs.

Molecular Dynamics Simulation Prediction of the Partitioning Constants (KH, Kiw, Kia) of 82 Legacy and Emerging Organic Contaminants at the Water-Air Interface

The tendency of organic contaminants (OCs) to partition between different phases is a key set of properties that underlie their human and ecological health impacts and the success of remediation efforts. A significant challenge associated with these efforts is the need for accurate partitioning data for an ever-expanding list of OCs and breakdown products. All-atom molecular dynamics (MD) simulations have the potential to help generate these data, but existing studies have applied these techniques only to a limited variety of OCs. Here, we use established MD simulation approaches to examine the partitioning of 82 OCs, including many compounds of critical concern, at the water-air interface. Our predictions of the Henry's law constant (K_H) and interfacial adsorption coefficients (K_iw, K_ia) correlate strongly with experimental results, indicating that MD simulations can be used to predict K_H, K_iw, and K_ia values with mean absolute deviations of 1.1, 0.3, and 0.3 logarithmic units after correcting for systematic bias, respectively. A library of MD simulation input files for the examined OCs is provided to facilitate future investigations of the partitioning of these compounds in the presence of other phases.

In vitro and in silico assessment of endocrine disrupting effects of food contaminants through pregnane X receptor

As a promiscuous xenobiotic receptor, pregnane X receptor (PXR) has been confirmed to participate in numerous physiological process. In addition to the conventional estrogen/androgen receptor, PXR also serves as an alternative target for environmental chemical contaminants. In this work, the PXR-mediated endocrine disrupting effects of typical food contaminants were explored. Firstly, the time-resolved fluorescence resonance energy transfer assays confirmed the PXR binding affinities of 2,2',4,4',5,5'-hexachlorobiphenyl, bis(2-ethylhexyl) phthalate, dibutyl phthalate, chlorpyrifos, bisphenol A, and zearalenone, with IC50 values ranging from 1.88 to 4284.00 nM. Then their PXR agonist activities were assessed by PXR-mediated CYP3A4 reporter gene assays. Subsequently, the regulation of gene expressions of PXR and its targets CYP3A4, UGT1A1, and MDR1 by these compounds was further investigated. Intriguingly, all the tested compounds interfered with these gene expressions, confirming their endocrine disrupting effects via PXR-mediated signaling. The compound-PXR-LBD binding interactions were explored by molecular docking and molecular dynamics simulations to unravel the structural basis of their PXR binding capacities. The weak intermolecular interactions are key players in stabilizing these compound-PXR-LBD complexes. During the simulation process, 2,2',4,4',5,5'-hexachlorobiphenyl remained stable while the other 5 compounds underwent relatively severe disturbances. In conclusion, these food contaminants might exhibit endocrine disrupting effects via PXR.

Protective effects of Korean Red Ginseng against toxicity of endocrine-disrupting chemicals

Several chemicals have been developed owing to the progression of industrialization, among which endocrine-disrupting chemicals (EDCs; essential for plastic production) are used as plasticizers and flame retardants. Plastics have become an essential element in modern life because they provide convenience, thus increasing EDCs exposure to humans. EDCs cause adverse effects such as deterioration of reproductive function, cancer, and neurological abnormalities by disrupting the endocrine system and hence are classified as "dangerous substances." Additionally, they are toxic to various organs but continue to be used. Therefore, it is necessary to review the contamination status of EDCs, select potentially hazardous substances for management, and monitor the safety standards. In addition, it is necessary to discover substances that can protect against EDC toxicity and conduct active research on the protective effects of these substances. According to recent research, Korean Red Ginseng (KRG) exhibits protective effects against several toxicities caused by EDCs to humans. In this review, the effects of EDCs on the human body and the role of KRG in protection against EDC toxicity are discussed.

Occurrence and Temporal Trends of Benzotriazole UV Stabilizers in Mollusks (2010-2018) from the Chinese Bohai Sea Revealed by Target, Suspect, and Nontarget Screening Analysis

Benzotriazole UV stabilizers (BZT-UVs), including 2-(3,5-di-tert-amyl-2-hydroxyphenyl)benzotriazole (UV-328) that is currently under consideration for listing under the Stockholm Convention, are applied in many commodities and industrial products. However, limited information is available on the interannual variation of their environmental occurrence. In this study, an all-in-one strategy combining target, suspect, and nontarget screening analysis was established to comprehensively explore the temporal trends of BZT-UVs in mollusks collected from the Chinese Bohai Sea between 2010 and 2018. Significant residue levels of the target analytes were determined with a maximum total concentration of 6.4 × 10³ ng/g dry weight. 2-(2-Hydroxy-3-tert-butyl-5-methyl-phenyl)-5-chloro-benzotriazole (UV-326), 5-chloro-2-(3,5-di-tert-butyl-2-hydroxyphenyl)benzotriazole (UV-327), and 2-(2-hydroxy-5-methylphenyl) benzotriazole (UV-P) were the predominant analogues, and UV-328 was the most frequently detected BZT-UV with a detection frequency (DF) of 87%. Whereas five biotransformation products and six impurity-like BZT-UVs were tentatively identified, their low DFs and semi-quantified concentrations suggest that the targeted analytes were the predominant BZT-UVs in the investigated area. A gradual decrease in the total concentrations of BZT-UVs was observed, accompanied by downward trends of the abundant compounds (e.g., UV-326 and UV-P). Consequently, the relative abundance of UV-327 increased because of its consistent environmental presence. These results suggest that continuous monitoring and risk assessment of BZT-UVs other than UV-328 are of importance in China.

Identification of Glucocorticoid Receptor Antagonistic Activities and Responsible Compounds in House Dust: Bioaccessibility Should Not Be Ignored

More and more contaminants in dust have been found to be glucocorticoid receptor (GR) disrupting chemicals. However, little is known about the related potency and responsible toxicants, especially for the main bioaccessible ones in dust. An effect-directed analysis (EDA)-based workflow was developed, including solvent-based exhaustive extraction/tenax-assisted bioaccessible extraction (TBE), high-throughput bioassays, suspect and non-target analysis, as well as in silico candidate selection, for a more realistic identification of responsible contaminants in dust. None of the 39 dust samples from 23 cities in China exhibited GR agonistic activity, while GR antagonistic potencies were detected in 34.8% of samples, being significantly different from the high detection frequency of GR agonistic activities in other environmental media. The GR antagonistic potencies of the dust samples were all reduced after bioaccessible extraction. The mean bioaccessibility of GR antagonistic potency compared with the related exhaustive extracts was 36.8%, and the lowest value was 9%. By using in silico candidate selection, greater than 99% candidate chemical structures which were found by a non-target screening strategy were removed. Di-n-butyl phthalate (DnBP), diisobutyl phthalate (DiBP), and nicotine (NIC) were responsible for the activities of the exhaustive extracts of dust, contributing up to 91% potencies. DiBP and DnBP were also responsible for the bioaccessible activities, contributing up to 79% potencies. However, the contribution from NIC decreased significantly and can be ignored because of its low bioaccessibility. This study suggests that the improved workflow combining extraction, reporter gene bioassays, suspect and non-target analysis, as well as in silico candidate selection is useful for EDA analysis in dust samples. In addition, exhaustive extraction may overestimate the risk of contaminants, while bioaccessibility evaluation based on bioaccessible extraction is essential in both effect evaluation and toxicant identification.

Screening of Endocrine Disrupting Potential of Surface Waters via an Affinity-Based Biosensor in a Rural Community in the Yellow River Basin, China

Overcoming the limitations of traditional analytical methods and developing technologies to continuously monitor environments and produce a comprehensive picture of potential endocrine-disrupting chemicals (EDCs) has been an ongoing challenge. Herein, we developed a portable nuclear receptor (NR)-based biosensor within 90 min to perform highly sensitive analyses of a broad range of EDCs in environmental water samples. Based on the specific binding of the fluorescence-labeled NRs with their ligands, the receptors were attached to the EDC-functionalized fiber surface by competing with EDCs in the samples. The biosensor emitted fluorescence due to the evanescent wave excitation, thereby resulting in a turn-off sensing mode. The biosensor showed a detection limit of 5 ng/L E₂-binding activity equivalent (E₂-BAE) and 93 ng/L T₃-BAE. As a case study, the biosensor was used to map the estrogenic binding activities of surface waters obtained from a rural community in the Yellow River basin in China. When the results obtained were compared with those from the traditional yeast two-hybrid bioassay, a high correlation was observed. It is anticipated that the good universality and versatility exhibited by this biosensor for various EDCs, which is achieved by using different NRs, will significantly promote the continuous assessment of global EDCs.

Development, Validation, and Application of a Human Reproductive Toxicity Prediction Model Based on Adverse Outcome Pathway

A growing number of environmental contaminants have been proved to have reproductive toxicity to males and females. However, the unclear toxicological mechanism of reproductive toxicants limits the development of virtual screening methods. By consolidating androgen (AR)-/estrogen receptors (ERs)-mediated adverse outcome pathways (AOPs) with more than 8000 chemical substances, we uncovered relationships between chemical features, a series of pathway-related effects, and reproductive apical outcomes─changes in sex organ weights. An AOP-based computational model named RepTox was developed and evaluated to predict and characterize chemicals' reproductive toxicity for males and females. Results showed that RepTox has three outstanding advantages. (I) Compared with the traditional models (37 and 81% accuracy, respectively), AOP significantly improved the predictive robustness of RepTox (96.3% accuracy). (II) Compared with the application domain (AD) of models based on small in vivo datasets, AOP expanded the ADs of RepTox by 1.65-fold for male and 3.77-fold for female, respectively. (III) RepTox implied that hydrophobicity, cyclopentanol substructure, and several topological indices (e.g., hydrogen-bond acceptors) were important, unbiased features associated with reproductive toxicants. Finally, RepTox was applied to the inventory of existing chemical substances of China and identified 2100 and 7281 potential toxicants to the male and female reproductive systems, respectively.