Effects of consumer products chemicals ingredients and their mixtures on the estrogen receptor/androgen receptor transcriptional activation

Cheminformatic Read-Across Approach Revealed Ultraviolet Filter Cinoxate as an Obesogenic Peroxisome Proliferator-Activated Receptor γ Agonist

This study introduces a novel cheminformatic read-across approach designed to identify potential environmental obesogens, substances capable of disrupting metabolism and inducing obesity by mainly influencing nuclear hormone receptors (NRs). Leveraging real-valued two-dimensional features derived from chemical fingerprints of 8435 Tox21 compounds, cluster analysis and subsequent statistical testing revealed 385 clusters enriched with compounds associated with specific NR targets. Notably, one cluster exhibited selective enrichment in peroxisome proliferator-activated receptor γ (PPARγ) agonist activity, prominently featuring methoxy cinnamate ultraviolet (UV) filters and obesogen-related compounds. Experimental validation confirmed that 2-ethoxyethyl 4-methoxycinnamate, an organic UV filter cinoxate, could selectively bind to PPARγ (Ki = 18.0 μM), eliciting an obesogenic phenotype in human bone marrow-derived mesenchymal stem cells during adipogenic differentiation. Molecular docking and further experiments identified cinoxate as a potent PPARγ full agonist, demonstrating a preference for coactivator SRC3 recruitment. Moreover, cinoxate upregulated transcription levels of genes encoding lipid metabolic enzymes in normal human epidermal keratinocytes as primary cells exposed during clinical usage. This study provides compelling evidence for the efficacy of cheminformatic read-across analysis in prioritizing potential obesogens, showcasing its utility in unveiling cinoxate as an obesogenic PPARγ agonist.

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.

Endocrine disrupting effects of parabens in zebrafish (Danio rerio): New insights from transcriptomics, metabolomics, and molecular dynamics simulation

Parabens (PBs), a group of widely used synthetic preservatives with potential endocrine disrupting activity, have been detected with increasing frequency in organisms and environmental matrices. This study assessed the hormone interference effects of four typical PBs, namely methylparaben (MeP), ethylparaben (EtP), propylparaben (PrP), and butylparaben (BuP), in zebrafish and elucidated the probable underlying mechanisms. Transcriptomic and metabolomic analyses showed that the differentially expressed genes and metabolites were associated with the tyrosine metabolism, arachidonate metabolism, and glycerophospholipid metabolism, indicating they were essential precursors of steroid hormone biosynthesis and metabolism. Histopathological analysis revealed impaired gonad development in the zebrafish exposed to PBs, as evidenced by the significantly increased vitellogenin (VTG) and estradiol (E2) levels. Furthermore, molecular dynamics simulation suggested that the four PBs could preferentially activate the zebrafish estrogen receptor, zfERβ2, to regulate the downstream pathways. Disruption of the amino acid metabolism and lipid metabolism, and activation of zfERβ2 signaling pathway were found to be the key mechanisms for the endocrine disrupting effects of PBs. The hormone interference effects of PBs were apparently dependent on the shared oxybenzene on their structures, with the degree of interference determined largely by the length of their alkyl chains. These findings provide new insights into the endocrine disrupting effects of PBs and could help better assess their risk to human health.

Computational modeling approaches for developing a synergistic effect prediction model of estrogen agonistic activity

The concerns regarding the potential health threats caused by estrogenic endocrine-disrupting chemicals (EDCs) and their mixtures manufactured by the chemical industry are increasing worldwide. Conventional experimental tests for understanding the estrogenic activity of mixtures are expensive and time-consuming. Although non-testing methods using computational modeling approaches have been developed to reduce the number of traditional tests, they are unsuitable for predicting synergistic effects because current prediction models consider only a single chemical. Thus, the development of predictive models is essential for predicting the mixture toxicity, including chemical interactions. However, selecting suitable computational modeling approaches to develop a high-performance prediction model requires considerable time and effort. In this study, we provide a suitable computational approach to develop a predictive model for the synergistic effects of estrogenic activity. We collected datasets on mixture toxicity based on the synergistic effect of estrogen agonistic activity in binary mixtures. Using the model deviation ratio approach, we classified the labels of the binary mixtures as synergistic or non-synergistic effects. We assessed five molecular descriptors, four machine learning-based algorithms, and a deep learning-based algorithm to provide a suitable computational modeling approach. Compared with other modeling approaches, the prediction model using the deep learning-based algorithm and chemical-protein network descriptors exhibited the best performance in predicting the synergistic effects. In conclusion, we developed a new high-performance binary classification model using a deep neural network and chemical-protein network-based descriptors. The developed model will be helpful for the preliminary screening of the synergistic effects of binary mixtures during the development process of chemical products.

Mixture Effects of Bisphenol A and Its Structural Analogs on Estrogen Receptor Transcriptional Activation

Bisphenol A (BPA) exposure has been widely linked to endocrine-disrupting effects. Recently, many substitutes for BPA have been developed as safe structural analogs. However, they have still been reported to have similar adverse effects. The current study evaluated the effects of bisphenol A and eight structural analogs on the transcription of estrogen receptor alpha (ERα). The effects of binary and ternary mixtures prepared from different combinations of BPA analogs were also evaluated for transcription activity. The measured data of the mixtures were compared to the predicted data obtained by the full logistic model, and the model deviation ratio (MDR) was calculated to determine whether the effects were synergistic, antagonistic, or additive. Overall, the results suggest that the effect of bisphenol compound are additive in binary and ternary mixtures.

Hormone receptor activities of complex mixtures of known and suspect chemicals in personal silicone wristband samplers worn in office buildings

Humans are exposed to increasingly complex mixtures of hormone-disrupting chemicals from a variety of sources, yet, traditional research methods only evaluate a small number of chemicals at a time. We aimed to advance novel methods to investigate exposures to complex chemical mixtures. Silicone wristbands were worn by 243 office workers in the USA, UK, China, and India during four work shifts. We analyzed extracts of the wristbands for: 1) 99 known (targeted) chemicals; 2) 1000+ unknown chemical features, tentatively identified through suspect screening; and 3) total hormonal activities towards estrogen (ER), androgen (AR), and thyroid hormone (TR) receptors in human cell assays. We evaluated associations of chemicals with hormonal activities using Bayesian kernel machine regression models, separately for targeted versus suspect chemicals (with detection ≥50%). Every wristband exhibited hormonal activity towards at least one receptor: 99% antagonized TR, 96% antagonized AR, and 58% agonized ER. Compared to men, women were exposed to mixtures that were more estrogenic (180% higher, adjusted for country, age, and skin oil abundance in wristband), anti-androgenic (110% higher), and complex (median 836 detected chemical features versus 780). Adjusted models showed strong associations of jointly increasing chemical concentrations with higher hormonal activities. Several targeted and suspect chemicals were important co-drivers of overall mixture effects, including chemicals used as plasticizers, fragrance, sunscreen, pesticides, and from other or unknown sources. This study highlights the role of personal care products and building microenvironments in hormone-disrupting exposures, and the substantial contribution of chemicals not often identifiable or well-understood to those exposures.

Bisphenol A and Di(2-Ethylhexyl) Phthalate promote pulmonary carcinoma in female rats via estrogen receptor beta: In vivo and in silico analysis

The prevalence of lung cancer in women currently merits our attentions. However, cigarette exposure alone does not tell the whole story that lung cancer is more prevalent among non-smoking women. Since female lung cancer is closely linked to estrogen levels, many of endocrine disrupting chemicals (EDCs), as the substances similar to estrogen, affect hormone levels and become a potential risk of female lung cancer. Additionally, the combined toxicity of EDCs in daily environment has only been discussed on a limited scale. Consequently, this study explored the cancer-promoting effect of two representative substances of EDCs namely Bisphenol A (BPA) and Di(2-Ethylhexyl) Phthalate (DEHP) after their exposure alone or in combination, using a rat pulmonary tumor model published previously, combining bioinformatics analysis based on The Comparative Toxicogenomics Database (CTD) and The Cancer Genome Atlas (TCGA) databases. It demonstrated that BPA and DEHP enhanced the promotion of pulmonary tumor in female rats, either alone or in combination. Mechanistically, BPA and DEHP mainly directly bound and activated ESR2 protein, phosphorylated CREB protein, activated HDAC6 transcriptionally, induced the production of the proto-oncogene c-MYC, and accelerated the formation of pulmonary tumor in female rats. Remarkably, BPA, rather than DEHP, exhibited a much more critical effect in female lung cancer. Additionally, the transcription factor ESR2 was most affected in carcinogenesis, causing genetic disruption. Furthermore, the TCGA database revealed that ESR2 could enhance the promotion and progression of non-small cell lung cancer in females via activating the WNT/β-catenin pathway. Finally, our findings demonstrated that BPA and DEHP could enhance the promotion of pulmonary carcinoma via ESR2 in female rats and provide a potential and valuable insight into the causes and prevention of lung cancer in non-smoking women due to EDCs exposure.