High content imaging-based assay to classify estrogen receptor-α ligands based on defined mechanistic outcomes

A novel ERβ high throughput microscopy platform for testing endocrine disrupting chemicals

In this study we present an inducible biosensor model for the Estrogen Receptor Beta (ERβ), GFP-ERβ:PRL-HeLa, a single-cell-based high throughput (HT) in vitro assay that allows direct visualization and measurement of GFP-tagged ERβ binding to ER-specific DNA response elements (EREs), ERβ-induced chromatin remodeling, and monitor transcriptional alterations via mRNA fluorescence in situ hybridization for a prolactin (PRL)-dsRED2 reporter gene. The model was used to accurately (Z' = 0.58-0.8) differentiate ERβ-selective ligands from ERα ligands when treated with a panel of selective agonists and antagonists. Next, we tested an Environmental Protection Agency (EPA)-provided set of 45 estrogenic reference chemicals with known ERα in vivo activity and identified several that activated ERβ as well, with varying sensitivity, including a subset that is completely novel. We then used an orthogonal ERE-containing transgenic zebrafish (ZF) model to cross validate ERβ and ERα selective activities at the organism level. Using this environmentally relevant ZF assay, some compounds were confirmed to have ERβ activity, validating the GFP-ERβ:PRL-HeLa assay as a screening tool for potential ERβ active endocrine disruptors (EDCs). These data demonstrate the value of sensitive multiplex mechanistic data gathered by the GFP-ERβ:PRL-HeLa assay coupled with an orthogonal zebrafish model to rapidly identify environmentally relevant ERβ EDCs and improve upon currently available screening tools for this understudied nuclear receptor.

Machine Learning Methods for Endocrine Disrupting Potential Identification Based on Single-Cell Data

Humans are continuously exposed to a variety of toxicants and chemicals which is exacerbated during and after environmental catastrophes such as floods, earthquakes, and hurricanes. The hazardous chemical mixtures generated during these events threaten the health and safety of humans and other living organisms. This necessitates the development of rapid decision-making tools to facilitate mitigating the adverse effects of exposure on the key modulators of the endocrine system, such as the estrogen receptor alpha (ERα), for example. The mechanistic stages of the estrogenic transcriptional activity can be measured with high content/high throughput microscopy-based biosensor assays at the single-cell level, which generates millions of object-based minable data points. By combining computational modeling and experimental analysis, we built a highly accurate data-driven classification framework to assess the endocrine disrupting potential of environmental compounds. The effects of these compounds on the ERα pathway are predicted as being receptor agonists or antagonists using the principal component analysis (PCA) projections of high throughput, high content image analysis descriptors. The framework also combines rigorous preprocessing steps and nonlinear machine learning algorithms, such as the Support Vector Machines and Random Forest classifiers, to develop highly accurate mathematical representations of the separation between ERα agonists and antagonists. The results show that Support Vector Machines classify the unseen chemicals correctly with more than 96% accuracy using the proposed framework, where the preprocessing and the PCA steps play a key role in suppressing experimental noise and unraveling hidden patterns in the dataset.

Binary Classification of the Endocrine Disrupting Chemicals by Artificial Neural Networks

We develop a machine learning framework that integrates high content/high throughput image analysis and artificial neural networks (ANNs) to model the separation between chemical compounds based on their estrogenic receptor activity. Natural and man-made chemicals have the potential to disrupt the endocrine system by interfering with hormone actions in people and wildlife. Although numerous studies have revealed new knowledge on the mechanism through which these compounds interfere with various hormone receptors, it is still a very challenging task to comprehensively evaluate the endocrine disrupting potential of all existing chemicals and their mixtures by pure in vitro or in vivo approaches. Machine learning offers a unique advantage in the rapid evaluation of chemical toxicity through learning the underlying patterns in the experimental biological activity data. Motivated by this, we train and test ANN classifiers for modeling the activity of estrogen receptor-α agonists and antagonists at the single-cell level by using high throughput/high content microscopy descriptors. Our framework preprocesses the experimental data by cleaning, scaling, and feature engineering where only the middle 50% of the values from each sample with detectable receptor-DNA binding is considered in the dataset. Principal component analysis is also used to minimize the effects of experimental noise in modeling where these projected features are used in classification model building. The results show that our ANN-based nonlinear data-driven framework classifies the benchmark agonist and antagonist chemicals with 98.41% accuracy.

Reconsidering "low-dose"-Impacts of oral estrogen exposure during preimplantation embryo development

Perturbations of estrogen signaling during developmental stages of high plasticity may lead to adverse effects later in life. Endocrine-disrupting chemicals (EDC) are compounds that interfere with the endocrine system by particularly mimicking the action of endogenous estrogens as functional agonists or antagonists. EDCs compose synthetic and naturally occurring compounds discharged into the environment, which may be taken up via skin contact, inhalation, orally due to contaminated food or water, or via the placenta during in utero development. Although estrogens are efficiently metabolized by the liver, the role of circulating glucuro- and/or sulpho-conjugated estrogen metabolites in the body has not been fully addressed to date. Particularly, the role of intracellular cleavage to free functional estrogens could explain the hitherto unknown mode of action of adverse effects of EDC at very low concentrations currently considered safe. We summarize and discuss findings on estrogenic EDC with a focus on early embryonic development to highlight the need for reconsidering low dose effects of EDC.

The potential role of environmental factors in modulating mitochondrial DNA epigenetic marks

Many studies implicate mitochondrial dysfunction in the development and progression of numerous chronic diseases. Mitochondria are responsible for most cellular energy production, and unlike other cytoplasmic organelles, mitochondria contain their own genome. Most research to date, through investigating mitochondrial DNA copy number, has focused on larger structural changes or alterations to the entire mitochondrial genome and their role in human disease. Using these methods, mitochondrial dysfunction has been linked to cancers, cardiovascular disease, and metabolic health. However, like the nuclear genome, the mitochondrial genome may experience epigenetic alterations, including DNA methylation that may partially explain some of the health effects of various exposures. Recently, there has been a movement to understand human health and disease within the context of the exposome, which aims to describe and quantify the entirety of all exposures people encounter throughout their lives. These include, among others, environmental pollutants, occupational exposures, heavy metals, and lifestyle and behavioral factors. In this chapter, we summarize the current research on mitochondria and human health, provide an overview of the current knowledge on mitochondrial epigenetics, and describe the experimental and epidemiologic studies that have investigated particular exposures and their relationships with mitochondrial epigenetic modifications. We conclude the chapter with suggestions for future directions in epidemiologic and experimental research that is needed to advance the growing field of mitochondrial epigenetics.

Quality Control for Single Cell Imaging Analytics Using Endocrine Disruptor-Induced Changes in Estrogen Receptor Expression

BACKGROUND

Diverse toxicants and mixtures that affect hormone responsive cells [endocrine disrupting chemicals (EDCs)] are highly pervasive in the environment and are directly linked to human disease. They often target the nuclear receptor family of transcription factors modulating their levels and activity. Many high-throughput assays have been developed to query such toxicants; however, single-cell analysis of EDC effects on endogenous receptors has been missing, in part due to the lack of quality control metrics to reproducibly measure cell-to-cell variability in responses.

OBJECTIVE

We began by developing single-cell imaging and informatic workflows to query whether the single cell distribution of the estrogen receptor-α (ER), used as a model system, can be used to measure effects of EDCs in a sensitive and reproducible manner.

METHODS

We used high-throughput microscopy, coupled with image analytics to measure changes in single cell ER nuclear levels on treatment with ∼100 toxicants, over a large number of biological and technical replicates.

RESULTS

We developed a two-tiered quality control pipeline for single cell analysis and tested it against a large set of biological replicates, and toxicants from the EPA and Agency for Toxic Substances and Disease Registry lists. We also identified a subset of potentially novel EDCs that were active only on the endogenous ER level and activity as measured by single molecule RNA fluorescence in situ hybridization (RNA FISH).

DISCUSSION

We demonstrated that the distribution of ER levels per cell, and the changes upon chemical challenges were remarkably stable features; and importantly, these features could be used for quality control and identification of endocrine disruptor toxicants with high sensitivity. When coupled with orthogonal assays, ER single cell distribution is a valuable resource for high-throughput screening of environmental toxicants. https://doi.org/10.1289/EHP9297.

Mitochondrial Epigenetics and Environmental Health: Making a Case for Endocrine Disrupting Chemicals

Recent studies implicate mitochondrial dysfunction in the development and progression of numerous chronic diseases, which may be partially due to modifications in mitochondrial DNA (mtDNA). There is also mounting evidence that epigenetic modifications to mtDNA may be an additional layer of regulation that controls mitochondrial biogenesis and function. Several environmental factors (eg, smoking, air pollution) have been associated with altered mtDNA methylation in a handful of mechanistic studies and in observational human studies. However, little is understood about other environmental contaminants that induce mtDNA epigenetic changes. Numerous environmental toxicants are classified as endocrine disrupting chemicals (EDCs). Beyond their actions on hormonal pathways, EDC exposure is associated with elevated oxidative stress, which may occur through or result in mitochondrial dysfunction. Although only a few studies have assessed the impacts of EDCs on mtDNA methylation, the current review provides reasons to consider mtDNA epigenetic disruption as a mechanism of action of EDCs and reviews potential limitations related to currently available evidence. First, there is sufficient evidence that EDCs (including bisphenols and phthalates) directly target mitochondrial function, and more direct evidence is needed to connect this to mtDNA methylation. Second, these and other EDCs are potent modulators of nuclear DNA epigenetics, including DNA methylation and histone modifications. Finally, EDCs have been shown to disrupt several modulators of mtDNA methylation, including DNA methyltransferases and the mitochondrial transcription factor A/nuclear respiratory factor 1 pathway. Taken together, these studies highlight the need for future research evaluating mtDNA epigenetic disruption by EDCs and to detail specific mechanisms responsible for such disruptions.

Predicting the Estrogen Receptor Activity of Environmental Chemicals by Single-Cell Image Analysis and Data-driven Modeling

A comprehensive evaluation of toxic chemicals and understanding their potential harm to human physiology is vital in mitigating their adverse effects following exposure from environmental emergencies. In this work, we develop data-driven classification models to facilitate rapid decision making in such catastrophic events and predict the estrogenic activity of environmental toxicants as estrogen receptor-α (ERα) agonists or antagonists. By combining high-content analysis, big-data analytics, and machine learning algorithms, we demonstrate that highly accurate classifiers can be constructed for evaluating the estrogenic potential of many chemicals. We follow a rigorous, high throughput microscopy-based high-content analysis pipeline to measure the single cell-level response of benchmark compounds with known in vivo effects on the ERα pathway. The resulting high-dimensional dataset is then pre-processed by fitting a non-central gamma probability distribution function to each feature, compound, and concentration. The characteristic parameters of the distribution, which represent the mean and the shape of the distribution, are used as features for the classification analysis via Random Forest (RF) and Support Vector Machine (SVM) algorithms. The results show that the SVM classifier can predict the estrogenic potential of benchmark chemicals with higher accuracy than the RF algorithm, which misclassifies two antagonist compounds.

Classification of estrogenic compounds by coupling high content analysis and machine learning algorithms

Environmental toxicants affect human health in various ways. Of the thousands of chemicals present in the environment, those with adverse effects on the endocrine system are referred to as endocrine-disrupting chemicals (EDCs). Here, we focused on a subclass of EDCs that impacts the estrogen receptor (ER), a pivotal transcriptional regulator in health and disease. Estrogenic activity of compounds can be measured by many in vitro or cell-based high throughput assays that record various endpoints from large pools of cells, and increasingly at the single-cell level. To simultaneously capture multiple mechanistic ER endpoints in individual cells that are affected by EDCs, we previously developed a sensitive high throughput/high content imaging assay that is based upon a stable cell line harboring a visible multicopy ER responsive transcription unit and expressing a green fluorescent protein (GFP) fusion of ER. High content analysis generates voluminous multiplex data comprised of minable features that describe numerous mechanistic endpoints. In this study, we present a machine learning pipeline for rapid, accurate, and sensitive assessment of the endocrine-disrupting potential of benchmark chemicals based on data generated from high content analysis. The multidimensional imaging data was used to train a classification model to ultimately predict the impact of unknown compounds on the ER, either as agonists or antagonists. To this end, both linear logistic regression and nonlinear Random Forest classifiers were benchmarked and evaluated for predicting the estrogenic activity of unknown compounds. Furthermore, through feature selection, data visualization, and model discrimination, the most informative features were identified for the classification of ER agonists/antagonists. The results of this data-driven study showed that highly accurate and generalized classification models with a minimum number of features can be constructed without loss of generality, where these machine learning models serve as a means for rapid mechanistic/phenotypic evaluation of the estrogenic potential of many chemicals.

Chemical contaminants or toxicants pose environmental and health-related risks for exposure. The ability to rapidly understand their biological impact, specifically on a key modulator of important physiological and pathological states in the human body is essential for diagnosing and avoiding undesirable health outcomes during environmental emergencies. In this study, we use advanced data analytics for creating statistical models that can accurately predict the endocrinological activity of toxic chemicals based on high throughput/high content image analysis data. We focus on a subclass of chemicals that affect the estrogen receptor (ER), which is a pivotal transcriptional regulator in health and disease. The multidimensional imaging data of these benchmark chemicals are used to train a classification model to ultimately predict the impact of unknown compounds on the ER, either as agonists or antagonists. To this end, we evaluate linear and nonlinear classifiers for predicting the estrogenic activity of unknown compounds and use feature selection, data visualization, and model discrimination methodologies to identify the most informative features for the classification of ER agonists/antagonists.

A Mechanistic High-Content Analysis Assay Using a Chimeric Androgen Receptor That Rapidly Characterizes Androgenic Chemicals

Human health is at risk from environmental exposures to a wide range of chemical toxicants and endocrine-disrupting chemicals (EDCs). As part of understanding this risk, the U.S. Environmental Protection Agency (EPA) has been pursuing new high-throughput in vitro assays and computational models to characterize EDCs. EPA models have incorporated our high-content analysis-based green fluorescent protein estrogen receptor (GFP-ER): PRL-HeLa assay, which allows direct visualization of ER binding to DNA regulatory elements. Here, we characterize a modified functional assay based on the stable expression of a chimeric androgen receptor (ARER), wherein a region containing the native AR DNA-binding domain (DBD) was replaced with the ERα DBD (amino acids 183-254). We demonstrate that the AR agonist dihydrotestosterone induces GFP-ARER nuclear translocation, PRL promoter binding, and transcriptional activity at physiologically relevant concentrations (<1 nM). In contrast, the AR antagonist bicalutamide induces only nuclear translocation of the GFP-ARER receptor (at μM concentrations). Estradiol also fails to induce visible chromatin binding, indicating androgen specificity. In a screen of reference chemicals from the EPA and the Agency for Toxic Substances and Disease Registry, the GFP-ARER cell model identified and mechanistically grouped activity by known (anti-)androgens based on the ability to induce nuclear translocation and/or chromatin binding. Finally, the cell model was used to identify potential (anti-)androgens in environmental samples in collaboration with the Houston Ship Channel/Galveston Bay Texas A&M University EPA Superfund Research Program. Based on these data, the chromatin-binding, in vitro assay-based GFP-ARER model represents a selective tool for rapidly identifying androgenic activity associated with drugs, chemicals, and environmental samples.

Evidence for Bisphenol B Endocrine Properties: Scientific and Regulatory Perspectives

BACKGROUND

The substitution of bisphenol A (BPA) by bisphenol B (BPB), a very close structural analog, stresses the need to assess its potential endocrine properties.

OBJECTIVE

This analysis aimed to investigate whether BPB has endocrine disruptive properties in humans and in wildlife as defined by the World Health Organization (WHO) definition used in the regulatory field, that is, a) adverse effects, b) endocrine activity, and c) plausible mechanistic links between the observed endocrine activity and adverse effects.

METHODS

We conducted a systematic review to identify BPB adverse effects and endocrine activities by focusing on animal models and in vitro mechanistic studies. The results were grouped by modality (estrogenic, androgenic, thyroid hormone, steroidogenesis-related, or other endocrine activities). After critical analysis of results, lines of evidence were built using a weight-of-evidence approach to establish a biologically plausible link. In addition, the ratio of BPA to BPB potency was reported from studies investigating both bisphenols.

RESULTS

Among the 36 articles included in the analysis, 3 subchronic studies consistently reported effects of BPB on reproductive function. In rats, the 28-d and 48-week studies showed alteration of spermatogenesis associated with a lower height of the seminiferous tubules, the alteration of several sperm parameters, and a weight loss for the testis, epididymis, and seminal vesicles. In zebrafish, the results of a 21-d reproductive study demonstrated that exposed fish had a lower egg production and a lower hatching rate and viability. The in vitro and in vivo mechanistic data consistently demonstrated BPB's capacity to decrease testosterone production and to exert an estrogenic-like activity similar to or greater than BPA's, both pathways being potentially responsible for spermatogenesis impairment in rats and fish.

CONCLUSION

The available in vivo, ex vivo, and in vitro data, although limited, coherently indicates that BPB meets the WHO definition of an endocrine disrupting chemical currently used in a regulatory context. https://doi.org/10.1289/EHP5200.

Leveraging Image-Derived Phenotypic Measurements for Drug-Target Interaction Predictions

In recent years, protein kinases have become some of the most significant drug targets in cancer patients. Kinases are known to regulate the activity of many human proteins, and consequently their inhibition has been used to control cancer proliferation. A significant challenge in drug discovery is the rapid and efficient identification of new small molecules. In this study, we propose a novel in silico drug discovery approach to identify kinase targets that impinge on nuclear receptor signaling with data generated using high-content analysis (HCA). A high-throughput imaging dataset was generated from an siRNA human kinome screen on engineered cells that allow direct visualization of effects on estrogen receptor-α or a chimeric progesterone receptor B binding to specific DNA. Two types of kinase descriptors are extracted from these imaging data: first, a population-median-based descriptor and second a bag-of-words (BoW) descriptor that can capture heterogeneity information in the imaging data. Using these descriptors, we provide prediction results of drug-kinase-target interactions based on single-task learning, multi-task learning, and collaborative filtering methods. The best performing model in target-based drug discovery gives an area under the receiver operating characteristic curve (AUC) of 0.86, whereas the best model in ligand-based discovery gives an AUC of 0.79. These promising results suggest that imaging-based information can be used as an additional source of information to existing virtual screening methods, thereby making the drug discovery process more time and cost efficient.

High throughput microscopy identifies bisphenol AP, a bisphenol A analog, as a novel AR down-regulator

Prostate cancer remains a deadly disease especially when patients become resistant to drugs that target the Androgen Receptor (AR) ligand binding domain. At this stage, patients develop recurring castrate-resistant prostate cancers (CRPCs). Interestingly, CRPC tumors maintain dependency on AR for growth; moreover, in CRPCs, constitutively active AR splice variants (e.g., AR-V7) begin to be expressed at higher levels. These splice variants lack the ligand binding domain and are rendered insensitive to current endocrine therapies. Thus, it is of paramount importance to understand what regulates the expression of AR and its splice variants to identify new therapeutic strategies in CRPCs. Here, we used high throughput microscopy and quantitative image analysis to evaluate effects of selected endocrine disruptors on AR levels in multiple breast and prostate cancer cell lines. Bisphenol AP (BPAP), which is used in chemical and medical industries, was identified as a down-regulator of both full length AR and the AR-V7 splice variant. We validated its activity by performing time-course, dose-response, Western blot and qPCR analyses. BPAP also reduced the percent of cells in S phase, which was accompanied by a ~60% loss in cell numbers and colony formation in anchorage-independent growth assays. Moreover, it affected mitochondria size and cell metabolism. In conclusion, our high content analysis-based screening platform was used to classify the effect of compounds on endogenous ARs, and identified BPAP as being capable of causing AR (both full-length and variants) down-regulation, cell cycle arrest and metabolic alterations in CRPC cell lines.

Characterizing properties of non-estrogenic substituted bisphenol analogs using high throughput microscopy and image analysis

Animal studies have linked the estrogenic properties of bisphenol A (BPA) to adverse effects on the endocrine system. Because of concerns for similar effects in humans, there is a desire to replace BPA in consumer products, and a search for BPA replacements that lack endocrine-disrupting bioactivity is ongoing. We used multiple cell-based models, including an established multi-parametric, high throughput microscopy-based platform that incorporates engineered HeLa cell lines with visible ERα- or ERβ-regulated transcription loci, to discriminate the estrogen-like and androgen-like properties of previously uncharacterized substituted bisphenol derivatives and hydroquinone. As expected, BPA induced 70–80% of the estrogen-like activity via ERα and ERβ compared to E2 in the HeLa prolactin array cell line. 2,2’ BPA, Bisguaiacol F, CHDM 4-hydroxybuyl acrylate, hydroquinone, and TM modified variants of BPF showed very limited estrogen-like or androgen-like activity (< 10% of that observed with the control compounds). Interestingly, TM-BFP and CHDM 4-hydroxybuyl acrylate, but not their derivatives, demonstrated evidence of anti-estrogenic and anti-androgenic activity. Our findings indicate that Bisguaiacol F, TM-BFP-ER and TM-BPF-DGE demonstrate low potential for affecting estrogenic or androgenic endocrine activity. This suggest that the tested compounds could be suitable commercially viable alternatives to BPA.

Differential Regulation of Progesterone Receptor-Mediated Transcription by CDK2 and DNA-PK

Progesterone receptor (PR) function is altered by cell signaling, but the mechanisms of kinase-specific regulation are not well defined. To examine the role of cell signaling in the regulation of PR transcriptional activity, we have utilized a previously developed mammalian-based estrogen-response element promoter array cell model and automated cell imaging and analysis platform to visualize and quantify effects of specific kinases on different mechanistic steps of PR-mediated target gene activation. For these studies, we generated stable estrogen-response element array cell lines expressing inducible chimeric PR that contains a swap of the estrogen receptor-α DNA-binding domain for the DNA-binding domain of PR. We have focused on 2 kinases important for steroid receptor activity: cyclin-dependent kinase 2 and DNA-dependent protein kinase. Treatment with either a Cdk1/2 inhibitor (NU6102) or a DNA-dependent protein kinase inhibitor (NU7441) decreased hormone-mediated chromatin decondensation and transcriptional activity. Further, we observed a quantitative reduction in the hormone-mediated recruitment of select coregulator proteins with NU6102 that is not observed with NU7441. In parallel, we determined the effect of kinase inhibition on hormone-mediated induction of primary and mature transcripts of endogenous genes in T47D breast cancer cells. Treatment with NU6102 was much more effective than NU7441, in inhibiting induction of PR target genes that exhibit a rapid increase in primary transcript expression in response to hormone. Taken together, these results indicate that the 2 kinases regulate PR transcriptional activity by distinct mechanisms.

Use of HCA in subproteome-immunization and screening of hybridoma supernatants to define distinct antibody binding patterns

Understanding the properties and functions of complex biological systems depends upon knowing the proteins present and the interactions between them. Recent advances in mass spectrometry have given us greater insights into the participating proteomes, however, monoclonal antibodies remain key to understanding the structures, functions, locations and macromolecular interactions of the involved proteins. The traditional single immunogen method to produce monoclonal antibodies using hybridoma technology are time, resource and cost intensive, limiting the number of reagents that are available. Using a high content analysis screening approach, we have developed a method in which a complex mixture of proteins (e.g., subproteome) is used to generate a panel of monoclonal antibodies specific to a subproteome located in a defined subcellular compartment such as the nucleus. The immunofluorescent images in the primary hybridoma screen are analyzed using an automated processing approach and classified using a recursive partitioning forest classification model derived from images obtained from the Human Protein Atlas. Using an ammonium sulfate purified nuclear matrix fraction as an example of reverse proteomics, we identified 866 hybridoma supernatants with a positive immunofluorescent signal. Of those, 402 produced a nuclear signal from which patterns similar to known nuclear matrix associated proteins were identified. Detailed here is our method, the analysis techniques, and a discussion of the application to further in vivo antibody production.

Systems level-based RNAi screening by high content analysis identifies UBR5 as a regulator of estrogen receptor-α protein levels and activity

Estrogen receptor-α (ERα) is a central transcription factor that regulates mammary gland physiology and a key driver in breast cancer. In the present study, we aimed to identify novel modulators of ERα-mediated transcriptional regulation via a custom-built siRNA library screen. This screen was directed against a variety of coregulators, transcription modifiers, signaling molecules and DNA damage response proteins. By utilizing a microscopy-based, multi-end point, estrogen responsive biosensor cell line platform, the primary screen identified a wide range of factors that altered ERα protein levels, chromatin remodeling and mRNA output. We then focused on UBR5, a ubiquitin ligase and known oncogene that modulates ERα protein levels and transcriptional output. Finally, we demonstrated that UBR5 also affects endogenous ERα target genes and E2-mediated cell proliferation in breast cancer cells. In conclusion, our multi-end point RNAi screen identified novel modulators of ERα levels and activity, and provided a robust systems level view of factors involved in mechanisms of nuclear receptor action and pathophysiology. Utilizing a high throughput RNAi screening approach we identified UBR5, a protein commonly amplified in breast cancer, as a novel regulator of ERα protein levels and transcriptional activity.

The myImageAnalysis project: a web-based application for high-content screening

A major challenge faced by screening centers developing image-based assays is the wide range of assays needed compared to the limited resources that are available to effectively analyze and manage them. To overcome this limitation, we have developed the web-based myImageAnalysis (mIA) application, integrated with an open database connectivity compliant database and powered by Pipeline Pilot (PLP) that incorporates dataset tracking, scheduling and archiving, image analysis, and data reporting. For system administrators, mIA provides automated methods for managing and archiving data. For the biologist, this application allows those without any programming or image analysis experience to quickly develop, validate, and share results of complex image-based assays. Further, the structure of the application within PLP allows those with experience in PLP programming to easily add additional analysis tools as required. The tools within mIA allow users to assess basic (cell count, protein per cell, protein subcellular localization) and more advanced (engineered cell lines analysis, cell toxicity) biological image-based assays that employ advanced statistics and provides key assay performance metrics.

Interaction of the glucocorticoid and androgen receptors in adipogenesis

Glucocorticoids and androgens are important regulators of adipose tissue function. A new study by Hartig et al. in this issue of Chemistry & Biology provides relevant information regarding androgen receptor activity and its link to glucocorticoid action in human adipocytes during the process of preadipocyte differentiation.

Defining estrogenic mechanisms of bisphenol A analogs through high throughput microscopy-based contextual assays

Environmental exposures to chemically heterogeneous endocrine-disrupting chemicals (EDCs) mimic or interfere with hormone actions and negatively affect human health. Despite public interest and the prevalence of EDCs in the environment, methods to mechanistically classify these diverse chemicals in a high throughput (HT) manner have not been actively explored. Here, we describe the use of multiparametric, HT microscopy-based platforms to examine how a prototypical EDC, bisphenol A (BPA), and 18 poorly studied BPA analogs (BPXs), affect estrogen receptor (ER). We show that short exposure to BPA and most BPXs induces ERα and/or ERβ loading to DNA changing target gene transcription. Many BPXs exhibit higher affinity for ERβ and act as ERβ antagonists, while they act largely as agonists or mixed agonists and antagonists on ERα. Finally, despite binding to ERs, some BPXs exhibit lower levels of activity. Our comprehensive view of BPXs activities allows their classification and the evaluation of potential harmful effects. The strategy described here used on a large-scale basis likely offers a faster, more cost-effective way to identify safer BPA alternatives.

Inhibitors of SCF-Skp2/Cks1 E3 ligase block estrogen-induced growth stimulation and degradation of nuclear p27kip1: therapeutic potential for endometrial cancer

In many human cancers, the tumor suppressor, p27(kip1) (p27), a cyclin-dependent kinase inhibitor critical to cell cycle arrest, undergoes perpetual ubiquitin-mediated proteasomal degradation by the E3 ligase complex SCF-Skp2/Cks1 and/or cytoplasmic mislocalization. Lack of nuclear p27 causes aberrant cell cycle progression, and cytoplasmic p27 mediates cell migration/metastasis. We previously showed that mitogenic 17-β-estradiol (E2) induces degradation of p27 by the E3 ligase Skp1-Cullin1-F-Box- S phase kinase-associated protein2/cyclin dependent kinase regulatory subunit 1 in primary endometrial epithelial cells and endometrial carcinoma (ECA) cell lines, suggesting a pathogenic mechanism for type I ECA, an E2-induced cancer. The current studies show that treatment of endometrial carcinoma cells-1 (ECC-1) with small molecule inhibitors of Skp2/Cks1 E3 ligase activity (Skp2E3LIs) stabilizes p27 in the nucleus, decreases p27 in the cytoplasm, and prevents E2-induced proliferation and degradation of p27 in endometrial carcinoma cells-1 and primary ECA cells. Furthermore, Skp2E3LIs increase p27 half-life by 6 hours, inhibit cell proliferation (IC50, 14.3μM), block retinoblastoma protein (pRB) phosphorylation, induce G1 phase block, and are not cytotoxic. Similarly, using super resolution fluorescence localization microscopy and quantification, Skp2E3LIs increase p27 protein in the nucleus by 1.8-fold. In vivo, injection of Skp2E3LIs significantly increases nuclear p27 and reduces proliferation of endometrial epithelial cells by 42%-62% in ovariectomized E2-primed mice. Skp2E3LIs are specific inhibitors of proteolytic degradation that pharmacologically target the binding interaction between the E3 ligase, SCF-Skp2/Cks1, and p27 to stabilize nuclear p27 and prevent cell cycle progression. These targeted inhibitors have the potential to be an important therapeutic advance over general proteasome inhibitors for cancers characterized by SCF-Skp2/Cks1-mediated destruction of nuclear p27.

Profiling 976 ToxCast chemicals across 331 enzymatic and receptor signaling assays

Understanding potential health risks is a significant challenge due to the large numbers of diverse chemicals with poorly characterized exposures and mechanisms of toxicities. The present study analyzes 976 chemicals (including failed pharmaceuticals, alternative plasticizers, food additives, and pesticides) in Phases I and II of the U.S. EPA's ToxCast project across 331 cell-free enzymatic and ligand-binding high-throughput screening (HTS) assays. Half-maximal activity concentrations (AC50) were identified for 729 chemicals in 256 assays (7,135 chemical-assay pairs). Some of the most commonly affected assays were CYPs (CYP2C9 and CYP2C19), transporters (mitochondrial TSPO, norepinephrine, and dopaminergic), and GPCRs (aminergic). Heavy metals, surfactants, and dithiocarbamate fungicides showed promiscuous but distinctly different patterns of activity, whereas many of the pharmaceutical compounds showed promiscuous activity across GPCRs. Literature analysis confirmed >50% of the activities for the most potent chemical-assay pairs (54) but also revealed 10 missed interactions. Twenty-two chemicals with known estrogenic activity were correctly identified for the majority (77%), missing only the weaker interactions. In many cases, novel findings for previously unreported chemical-target combinations clustered with known chemical-target interactions. Results from this large inventory of chemical-biological interactions can inform read-across methods as well as link potential targets to molecular initiating events in adverse outcome pathways for diverse toxicities.

Profiling 976 ToxCast chemicals across 331 enzymatic and receptor signaling assays

Understanding potential health risks is a significant challenge due to the large numbers of diverse chemicals with poorly characterized exposures and mechanisms of toxicities. The present study analyzes 976 chemicals (including failed pharmaceuticals, alternative plasticizers, food additives, and pesticides) in Phases I and II of the U.S. EPA's ToxCast project across 331 cell-free enzymatic and ligand-binding high-throughput screening (HTS) assays. Half-maximal activity concentrations (AC50) were identified for 729 chemicals in 256 assays (7,135 chemical-assay pairs). Some of the most commonly affected assays were CYPs (CYP2C9 and CYP2C19), transporters (mitochondrial TSPO, norepinephrine, and dopaminergic), and GPCRs (aminergic). Heavy metals, surfactants, and dithiocarbamate fungicides showed promiscuous but distinctly different patterns of activity, whereas many of the pharmaceutical compounds showed promiscuous activity across GPCRs. Literature analysis confirmed >50% of the activities for the most potent chemical-assay pairs (54) but also revealed 10 missed interactions. Twenty-two chemicals with known estrogenic activity were correctly identified for the majority (77%), missing only the weaker interactions. In many cases, novel findings for previously unreported chemical-target combinations clustered with known chemical-target interactions. Results from this large inventory of chemical-biological interactions can inform read-across methods as well as link potential targets to molecular initiating events in adverse outcome pathways for diverse toxicities.

Coactivators enable glucocorticoid receptor recruitment to fine-tune estrogen receptor transcriptional responses

Nuclear receptors (NRs) are central regulators of pathophysiological processes; however, how their responses intertwine is still not fully understood. The aim of this study was to determine whether and how steroid NRs can influence each other’s activity under co-agonist treatment. We used a unique system consisting of a multicopy integration of an estrogen receptor responsive unit that allows direct visualization and quantification of estrogen receptor alpha (ERα) DNA binding, co-regulator recruitment and transcriptional readout. We find that ERα DNA loading is required for other type I nuclear receptors to be co-recruited after dual agonist treatment. We focused on ERα/glucocorticoid receptor interplay and demonstrated that it requires steroid receptor coactivators (SRC-2, SRC-3) and the mediator component MED14. We then validated this cooperative interplay on endogenous target genes in breast cancer cells. Taken together, this work highlights another layer of mechanistic complexity through which NRs cross-talk with each other on chromatin under multiple hormonal stimuli.

Feed-forward inhibition of androgen receptor activity by glucocorticoid action in human adipocytes

We compared transcriptomes of terminally differentiated mouse 3T3-L1 and human adipocytes to identify cell-specific differences. Gene expression and high content analysis (HCA) data identified the androgen receptor (AR) as both expressed and functional, exclusively during early human adipocyte differentiation. The AR agonist dihydrotestosterone (DHT) inhibited human adipocyte maturation by downregulation of adipocyte marker genes, but not in 3T3-L1. It is interesting that AR induction corresponded with dexamethasone activation of the glucocorticoid receptor (GR); however, when exposed to the differentiation cocktail required for adipocyte maturation, AR adopted an antagonist conformation and was transcriptionally repressed. To further explore effectors within the cocktail, we applied an image-based support vector machine (SVM) classification scheme to show that adipocyte differentiation components inhibit AR action. The results demonstrate human adipocyte differentiation, via GR activation, upregulates AR but also inhibits AR transcriptional activity.

Peptidylarginine deiminase 2-catalyzed histone H3 arginine 26 citrullination facilitates estrogen receptor α target gene activation

Cofactors for estrogen receptor α (ERα) can modulate gene activity by posttranslationally modifying histone tails at target promoters. Here, we found that stimulation of ERα-positive cells with 17β-estradiol (E2) promotes global citrullination of histone H3 arginine 26 (H3R26) on chromatin. Additionally, we found that the H3 citrulline 26 (H3Cit26) modification colocalizes with ERα at decondensed chromatin loci surrounding the estrogen-response elements of target promoters. Surprisingly, we also found that citrullination of H3R26 is catalyzed by peptidylarginine deiminase (PAD) 2 and not by PAD4 (which citrullinates H4R3). Further, we showed that PAD2 interacts with ERα after E2 stimulation and that inhibition of either PAD2 or ERα strongly suppresses E2-induced H3R26 citrullination and ERα recruitment at target gene promoters. Collectively, our data suggest that E2 stimulation induces the recruitment of PAD2 to target promoters by ERα, whereby PAD2 then citrullinates H3R26, which leads to local chromatin decondensation and transcriptional activation.