Estrogenic Activities of 517 Chemicals by Yeast Two-Hybrid Assay
01 Aug 2000-Journal of Health Science (The Pharmaceutical Society of Japan)-Vol. 46, Iss: 4, pp 282-298
TL;DR: A simple and rapid screening method using the yeast two-hybrid system based on the ligand-dependent interaction of nuclear hormone receptors with coactivators to test the estrogenic activity of chemicals.
Abstract: One of the urgent tasks in understanding endocrine disruptors (EDs) is to compile a list of suspected substances among the huge number of chemicals by using the screening test method. We developed a simple and rapid screening method using the yeast two-hybrid system based on the ligand-dependent interaction of nuclear hormone receptors with coactivators. To date, we have tested the estrogenic activity of more than 500 chemicals including natural substances, medicines, pesticides, and industrial chemicals. 64 compounds were evaluated as positive, and most of these demonstrated a common structure; phenol with a hydrophobic moiety at the para-position without bulky groups at the ortho-position. These results are expected to facilitate further risk assessment of chemicals.
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TL;DR: The ER-binding affinities of the isoflavonoids were not sufficiently high to explain their potent antagonistic activities, thus suggesting 17β-estradiol-non-competitive mechanisms.
Abstract: Phytoestrogens containing isoflavonoids are thought to exhibit preventative effects on estrogen-responsive diseases. Chemical modifications, such as prenylation, in biosynthetic processes enhance the structural variety of isoflavonoids and prompted us to carry out a structure-activity relationship study. We determined the estrogenic/anti-estrogenic activities and estrogen receptor (ER)-binding affinities of eight kinds of prenylated isoflavones isolated from Millettia pachycarpa (Leguminosae), and those of two kinds of non-prenylated compounds (genistein and daidzein). By comparing these compounds, the pharmacophores for estrogenic/anti-estrogenic activities were elucidated. None of the tested compounds (except genistein) were estrogenic on ligand-dependent yeast-two hybrid assay. On the other hand, 5 isoflavones showed distinct anti-estrogenic activity. Unexpectedly, the most potent antagonists, isoerysenegalensein E and 6,8-diprenylorobol, showed anti-estrogenic activity comparable to that of 4-hydroxytamoxifen, a typical ER antagonist. This suggests that genistein became an antagonist after prenylation and hydroxylation. The pharmacophores providing genistein with strong anti-estrogenic activity were as follows: prenyl groups of the 6- and 8-positions on the A-ring, hydroxyl group of the 6-prenyl moiety or the B-ring (catechol form), non-cyclization of the prenyl group with the A-ring, and non-hydroxylation of the 8-prenyl group on the A-ring. The ER-binding affinities of the isoflavonoids were not sufficiently high to explain their potent antagonistic activities, thus suggesting 17β-estradiol-non-competitive mechanisms.
38 citations
TL;DR: This work explored several statistical learning methods (support vector machines, k-nearest neighbor, probabilistic neural network and C4.5 decision tree) for predicting ER agonists from comprehensive set of known ER agonist and other compounds and suggests that statistical learning techniques such as SVM are potentially useful for facilitating the prediction of ER agonistic and for characterizing the molecular descriptors associated with ER agonism.
Abstract: Specific estrogen receptor (ER) agonists have been used for hormone replacement therapy, contraception, osteoporosis prevention, and prostate cancer treatment. Some ER agonists and partial-agonists induce cancer and endocrine function disruption. Methods for predicting ER agonists are useful for facilitating drug discovery and chemical safety evaluation. Structure–activity relationships and rule-based decision forest models have been derived for predicting ER binders at impressive accuracies of 87.1–97.6% for ER binders and 80.2–96.0% for ER non-binders. However, these are not designed for identifying ER agonists and they were developed from a subset of known ER binders. This work explored several statistical learning methods (support vector machines, k-nearest neighbor, probabilistic neural network and C4.5 decision tree) for predicting ER agonists from comprehensive set of known ER agonists and other compounds. The corresponding prediction systems were developed and tested by using 243 ER agonists and 463 ER non-agonists, respectively, which are significantly larger in number and structural diversity than those in previous studies. A feature selection method was used for selecting molecular descriptors responsible for distinguishing ER agonists from non-agonists, some of which are consistent with those used in other studies and the findings from X-ray crystallography data. The prediction accuracies of these methods are comparable to those of earlier studies despite the use of significantly more diverse range of compounds. SVM gives the best accuracy of 88.9% for ER agonists and 98.1% for non-agonists. Our study suggests that statistical learning methods such as SVM are potentially useful for facilitating the prediction of ER agonists and for characterizing the molecular descriptors associated with ER agonists.
38 citations
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TL;DR: It is demonstrated that the algal-bacterial system has the potential to remove BPA and its biodegradation intermediates and the amounts of oxygen demanded by the bacteria are insufficient for effective BPA degradation.
Abstract: The degradation of bisphenol A (BPA) by Chlorella sorokiniana and BPA-degrading bacteria was investigated. The results show that BPA was partially removed by a monoculture of C. sorokiniana, but the remaining BPA accounted for 50.2, 56.1, and 60.5 % of the initial BPA concentrations of 10, 20, and 50 mg L(-1), respectively. The total algal BPA adsorption and accumulation were less than 1 %. C. sorokiniana-bacterial system effectively removed BPA with photosynthetic oxygen provided by the algae irrespective of the initial BPA concentration. The growth of C. sorokiniana in the algal system was inhibited by BPA concentrations of 20 and 50 mg L(-1), but not in the algal-bacterial system. This observation indicates that bacterial growth in the algal-bacterial system reduced the BPA-inhibiting effect on algae. A total of ten BPA biodegradation intermediates were identified by GC-MS. The concentrations of the biodegradation intermediates decreased to a low level at the end of the experiment. The hypothetical carbon mass balance analysis showed that the amounts of oxygen demanded by the bacteria are insufficient for effective BPA degradation. However, adding an external carbon source could compensate for the oxygen shortage. This study demonstrates that the algal-bacterial system has the potential to remove BPA and its biodegradation intermediates.
38 citations
Cites background from "Estrogenic Activities of 517 Chemic..."
...…intermediates, such as 4,4-dihydroxy-alpha-methylstilbene, 2,2- b i s ( 4 - hyd roxypheny l ) p r opano i c a c i d ( IV ) p - hydroxyacetophenone (p-HAP), and hydroquinone (HQ), exhibit oestrogenic activity (Nishihara et al. 2000; Yoshihara et al. 2001; Ike et al. 2002; Suzuki et al. 2004)....
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...In addition, not only BPA but also its biodegradation intermediates, such as 4,4-dihydroxy-alpha-methylstilbene, 2,2b i s ( 4 - hyd roxypheny l ) p r opano i c a c i d ( IV ) p hydroxyacetophenone (p-HAP), and hydroquinone (HQ), exhibit oestrogenic activity (Nishihara et al. 2000; Yoshihara et al. 2001; Ike et al. 2002; Suzuki et al. 2004)....
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...However, according to previous studies, there are biodegradation intermediates that exhibit oestrogenic activity and non-oestrogenic activity (Ike et al. 2002; Nishihara et al. 2000; Nomiyama et al. 2007; Suzuki et al. 2004; Yoshihara et al. 2001)....
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TL;DR: A substantial body of work in the published literature related to QSAR models for NR ligands is summarized, with special emphasis on different computational approaches and specific applications.
Abstract: The nuclear receptor (NR) superfamily is ligand-dependent transcriptional factors that mediate gene expression in humans and wildlife. These receptor-mediated effects are stimulated and/or inhibited by endogenous cognate ligands for each NR but also by exogenous substances including natural products and synthetic chemicals. The NRs and their ligands have thus attracted broad scientific interest, particularly in the pharmaceutical industry for drug discovery and in toxicology and environmental science for risk assessment as, for example, pertaining to endocrine disrupting chemicals. Besides advancing our fundamental knowledge of NR biology, these scientific efforts are generating relevant biological data on NR ligands particularly with respect to their binding affinities, receptor specificities, and agonist versus antagonist activities. These data from diverse sources serve as input for construction of quantitative structure–activity relationship (QSAR) models and related approaches that employ statistical regression techniques to correlate variations between the biological activities of NR ligands and their calculated structural and physicochemical properties. In this review, we attempt to summarize the substantial body of work in the published literature related to QSAR models for NR ligands, with special emphasis on different computational approaches and specific applications. Special attention is placed on the estrogen receptor, for which the greatest amount of relevant information is known at present. We also describe efforts to create ‘benchmark’ sets of high-quality biological data on NR ligands that may serve as resources for building statistically robust and predictive QSAR models.
38 citations
TL;DR: The authors performed a reporter gene assay for estrogen receptor (ER)-alpha agonists and antagonists of 10 chemicals that showed both estrogen agonistic and reduced the estrogenic effect of ethinyl estradiol in a rat uterotrophic assay.
37 citations
References
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Book•
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01 Jan 1996
TL;DR: The cause of disruptions in animal breeding cycles, accompanied by increases in birth defects, sexual abnormalities and reproductive failure, is traced to the pervasive presence in the environment of chemicals that mimic hormones and trick the reproductive system.
Abstract: For years, scientists have noticed disruptions in animal breeding cycles, accompanied by increases in birth defects, sexual abnormalities and reproductive failure. Humans are not immune either, with sperm counts dropping by as much as 50% in recent decades and with women seeing a rise in hormone-related cancers, endometriosis and other disorders. This book traces the cause of these aberrations and diseases to the pervasive presence in the environment of chemicals that mimic hormones and trick the reproductive system. The conclusions are as obvious as they are inescapable - unless we make vital changes in the way we manufacture and employ the artefacts of our "good life", there will be no life at all.
917 citations