Identification of Compounds That Inhibit Estrogen-Related Receptor Alpha Signaling Using High-Throughput Screening Assays

Chemistry domain of applicability evaluation against existing estrogen receptor high-throughput assay-based activity models

Introduction

The U. S. Environmental Protection Agency's Endocrine Disruptor Screening Program (EDSP) Tier 1 assays are used to screen for potential endocrine system-disrupting chemicals. A model integrating data from 16 high-throughput screening assays to predict estrogen receptor (ER) agonism has been proposed as an alternative to some low-throughput Tier 1 assays. Later work demonstrated that as few as four assays could replicate the ER agonism predictions from the full model with 98% sensitivity and 92% specificity. The current study utilized chemical clustering to illustrate the coverage of the EDSP Universe of Chemicals (UoC) tested in the existing ER pathway models and to investigate the utility of chemical clustering to evaluate the screening approach using an existing 4-assay model as a test case. Although the full original assay battery is no longer available, the demonstrated contribution of chemical clustering is broadly applicable to assay sets, chemical inventories, and models, and the data analysis used can also be applied to future evaluation of minimal assay models for consideration in screening.

Methods

Chemical structures were collected for 6,947 substances via the CompTox Chemicals Dashboard from the over 10,000 UoC and grouped based on structural similarity, generating 826 chemical clusters. Of the 1,812 substances run in the original ER model, 1,730 substances had a single, clearly defined structure. The ER model chemicals with a clearly defined structure that were not present in the EDSP UoC were assigned to chemical clusters using a k-nearest neighbors approach, resulting in 557 EDSP UoC clusters containing at least one ER model chemical.

Results and Discussion

Performance of an existing 4-assay model in comparison with the existing full ER agonist model was analyzed as related to chemical clustering. This was a case study, and a similar analysis can be performed with any subset model in which the same chemicals (or subset of chemicals) are screened. Of the 365 clusters containing >1 ER model chemical, 321 did not have any chemicals predicted to be agonists by the full ER agonist model. The best 4-assay subset ER agonist model disagreed with the full ER agonist model by predicting agonist activity for 122 chemicals from 91 of the 321 clusters. There were 44 clusters with at least two chemicals and at least one agonist based upon the full ER agonist model, which allowed accuracy predictions on a per-cluster basis. The accuracy of the best 4-assay subset ER agonist model ranged from 50% to 100% across these 44 clusters, with 32 clusters having accuracy ≥90%. Overall, the best 4-assay subset ER agonist model resulted in 122 false-positive and only 2 false-negative predictions compared with the full ER agonist model. Most false positives (89) were active in only two of the four assays, whereas all but 11 true positive chemicals were active in at least three assays. False positive chemicals also tended to have lower area under the curve (AUC) values, with 110 out of 122 false positives having an AUC value below 0.214, which is lower than 75% of the positives as predicted by the full ER agonist model. Many false positives demonstrated borderline activity. The median AUC value for the 122 false positives from the best 4-assay subset ER agonist model was 0.138, whereas the threshold for an active prediction is 0.1.

Conclusion

Our results show that the existing 4-assay model performs well across a range of structurally diverse chemicals. Although this is a descriptive analysis of previous results, several concepts can be applied to any screening model used in the future. First, the clustering of the chemicals provides a means of ensuring that future screening evaluations consider the broad chemical space represented by the EDSP UoC. The clusters can also assist in prioritizing future chemicals for screening in specific clusters based on the activity of known chemicals in those clusters. The clustering approach can be useful in providing a framework to evaluate which portions of the EDSP UoC chemical space are reliably covered by in silico and in vitro approaches and where predictions from either method alone or both methods combined are most reliable. The lessons learned from this case study can be easily applied to future evaluations of model applicability and screening to evaluate future datasets.

Senescence and autophagy relation with the expressional status of non-canonical estrogen receptors in testes and adrenals of roe deer (Capreolus capreolus) during the pre-rut period

The roe deer bucks represent a spontaneous model to study the synchronized testicular involution and recrudescence cycles. However, cellular processes and hormonal control of steroidogenic glands are scarcely known. For the present study testes and adrenal glands obtained from roe deer during the pre-rut season were used. We aimed to determine (i) senescence and autophagy involvement in testis atrophy (immunohistochemical analysis for tumor suppressor protein encoded by the cyclin-dependent kinase inhibitor 2A; p16 and microtubule-associated protein 1A/1B-light chain 3; LC3, respectively), (ii) the size of the adrenal cortex and medulla (morphometric analysis), (iii) G-protein coupled estrogen receptor (GPER) and estrogen-related receptors (ERRs; type α, β, and Y) distribution and expression (qRT-PCR and immunohistochemical analyses) and (iv) serum testosterone and estradiol levels (immunoassay ELISA). This study revealed pre-rut characteristics of testis structure with the presence of both senescence and autophagy-positive cells and higher involvement of senescence, especially in spermatogenic cells (P < 0.05). In the adrenal cortex, groups of cells exhibiting shrinkage were observed. The presence of ERRs in cells of the seminiferous epithelium and interstitial Leydig cells and GPER presence distinctly in Leydig cells was revealed. In adrenals, these receptors were localized in groups of normal-looking cells and those with shrinkage. Morphometric analysis showed differences in cortex width which was smaller (P < 0.05) than that of the medulla. A weak immunohistochemical signal was observed for ERRβ when compared to ERRα and ERRγ. The mRNA expression level of ERRα and ERRγ was lower (P < 0.001 and P < 0.05, respectively) while ERRβ was higher (P < 0.001) in adrenals when compared to testes. mRNA GPER expression was similar in both glands. In the pre-rut season, the testosterone level was 4.89 ng/ml while the estradiol level was 0.234 ng/ml. We postulate that: (i) senescence and autophagy may be involved in both reinitiation of testis function and/or induction of abnormal processes, (ii) hormonal modulation of testis inactivity may affect adrenal cortex causing cell shrinkage, (iii) ERRs and GPER localization in spermatogenic cells and interstitial cells, as well as cortex cells, may maintain and control the morpho-functional status of both glands, and (iv) androgens and estrogens (via ERRs and GPER) drive cellular processes in the testis and adrenal pre-rut physiology.

Ligand pathways in estrogen-related receptors

The three subtypes of estrogen-related receptors ERRα, ERRβ, and ERRγ are nuclear receptors mediating metabolic processes in various tissues such as the skeletal muscle, fat tissue, bone, and liver. Although the knowledge on their physiological ligands is limited, they have been implicated as drug targets for important indications including diabetes, cardiovascular diseases, and osteoporosis. As in other nuclear receptors, their ligand binding pocket is buried within the core of the receptor and connected to its surrounding by ligand pathways. Here, we investigated these pathways with conventional molecular dynamics as well as metadynamics simulations to reveal their distribution and their capability to facilitate ligand translocation. Dependent on the ERR subtype and the conformational state of the receptor, we could detect different pathways to be favored. Overall, the results suggested pathways IIIa and IIIb to be favored in the agonistic conformation, while antagonists preferred pathways I, II, and V. Along the pathways, the ligands passed different gating mechanisms of the receptor, including groups of protein residues as well as whole secondary structure elements, to leave the binding site. Even though these pathways are suggested to influence ligand specificity of the receptors and their elucidation might advance rational drug design, they have not yet been studied in ERRs.Communicated by Ramaswamy H. Sarma.

Triclocarban and triclosan exacerbate high-fat diet-induced hepatic lipid accumulation at environmental related levels: The potential roles of estrogen-related receptors pathways

Triclosan (TCS) and triclocarban (TCC) have become ubiquitous pollutants detected in human body with concentrations up to hundreds of nanomolar levels. Previous studies about the hepatic lipid accumulation induced by TCS and TCC were focused on pollutant itself, which showed weak or no effects. High-fat diet (HFD), as a known environmental factor contributing to lipid metabolism-related disorders, its synergistic action with environmental pollutants deserves concern. The present study aimed to demonstrate the combined effects and potential molecular mechanisms of TCS and TCC with HFD at cellular and animal levels. The in vitro studies showed that TCC and TCS alone had negligible impact on lipid accumulation in HepG2 cells but induced lipid deposition at nanomolar levels when co-exposure with fatty acid. TCC exhibited much higher induction effects than TCS, which was related to their differential regulatory roles in adipogenic-related genes expression. The in vivo studies showed that TCC had little influence on hepatic lipid accumulation in mice fed with normal diet (ND) but could exacerbate the lipid accumulation in mice fed with HFD. Meanwhile, TCC-induced dyslipidemia in mice fed with HFD was more significant than that fed with ND. Therefore, we speculated that TCC might increase the risk of nonalcoholic fatty liver disease (NAFLD) and atherosclerosis in HFD humans. Molecular mechanism studies showed that TCC and TCS could bind to and activate estrogen-related receptor α (ERRα) and ERRγ as well as regulate their expression. TCC had higher activity on ERRα and ERRγ than TCS, which explained partly the differential regulatory roles of two receptors in the lipid accumulation induced by TCC and TCS. This work revealed synergistic effects and molecular mechanisms of TCC and TCS with excessive fatty acid on the hepatic lipid metabolism, which provided a novel insight into the toxic mechanism of pollutants from the perspective of dietary habits.

Screening Method for the Identification of Compounds That Activate Pregnane X Receptor

The pregnane X receptor (PXR) is a nuclear receptor found mainly in the liver and intestine, whose main function is to regulate the expression of drug-metabolizing enzymes and transporters. Recently, it has been noted that PXR plays critical roles in energy homeostasis, immune response, and cancer. Therefore, identifying chemicals or compounds that can modulate PXR is of great interest, as these can result in downstream toxicity or, alternatively, may have therapeutic potential. Testing one compound at a time for PXR activity would be inefficient and take thousands of hours for large compound libraries. Here, we describe a high-throughput screening method that encompasses plating and treating HepG2-CYP3A4-hPXR cells in a 1536-well plate, as well as reading and interpreting assay (e.g., luciferase reporter gene activity) endpoints. These cells are stably transfected with a human PXR expression vector and CYP3A4-promoter-driven luciferase reporter vector, allowing the identification of compounds that activate PXR through cytochrome 450 3A4. We also describe how to analyze the data from each assay and explain follow-up steps, namely pharmacological characterization and quantitative polymerase chain reaction (qPCR) assays, which can be performed to confirm results from the original screen. These methods can be used to identify and confirm hPXR activators after completion of a compound screening. Published 2022. This article is a U.S. Government work and is in the public domain in the USA. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Establishment of a high-throughput assay to identify hPXR activators Basic Protocol 2: Quantitative high-throughput screening a compound library to classify hPXR activators Basic Protocol 3: Performing pharmacological characterization and qPCR assays to confirm hPXR activators.

PGC-1β and ERRα Promote Glutamine Metabolism and Colorectal Cancer Survival via Transcriptional Upregulation of PCK2

BACKGROUND

Previous studies have shown that Peroxisome Proliferator-Activated Receptor Gamma, Coactivator 1 Beta (PGC-1β) and Estrogen-Related Receptor Alpha (ERRα) are over-expressed in colorectal cancer and promote tumor survival.

METHODS

In this study, we use immunoprecipitation of epitope tagged endogenous PGC-1β and inducible PGC-1β mutants to show that amino acid motif LRELL on PGC-1β is responsible for the physical interaction with ERRα and promotes ERRα mRNA and protein expression. We use RNAsequencing to determine the genes regulated by both PGC-1β & ERRα and find that mitochondrial Phosphoenolpyruvate Carboxykinase 2 (PCK2) is the gene that decreased most significantly after depletion of both genes.

RESULTS

Depletion of PCK2 in colorectal cancer cells was sufficient to reduce anchorage-independent growth and inhibit glutamine utilization by the TCA cycle. Lastly, shRNA-mediated depletion of ERRα decreased anchorage-independent growth and glutamine metabolism, which could not be rescued by plasmid derived expression of PCK2.

DISCUSSION

These findings suggest that transcriptional control of PCK2 is one mechanism used by PGC-1β and ERRα to promote glutamine metabolism and colorectal cancer cell survival.

Identification of Gender- and Subtype-Specific Gene Expression Associated with Patient Survival in Low-Grade and Anaplastic Glioma in Connection with Steroid Signaling

Simple Summary

Gliomas are primary brain tumors that are initially slow growing but progress to be more aggressive and, ultimately, fatal within a few years. They are more common in men than in women, suggesting a protective role for female hormones. By analyzing patient data collected in the public TGCA-LGG database, we have demonstrated a link between the expression level of key steroid biosynthesis enzymes or hormone receptors with patient survival, in ways that are dependent on gender and molecular subtype. We also determined the genes which expression associated with these actors of steroid signaling and the functions they perform, to decipher the mechanisms underlying gender-dependent differences. Together, these results establish, for the first time, the involvement of hormones in low-grade and anaplastic gliomas and provide clues for refining their classification and, thus, facilitating more personalized management of patients.

Abstract

Low-grade gliomas are rare primary brain tumors, which fatally evolve to anaplastic gliomas. The current treatment combines surgery, chemotherapy, and radiotherapy. If gender differences in the natural history of the disease were widely described, their underlying mechanisms remain to be determined for the identification of reliable markers of disease progression. We mined the transcriptomic and clinical data from the TCGA-LGG and CGGA databases to identify male-over-female differentially expressed genes and selected those associated with patient survival using univariate analysis, depending on molecular characteristics (IDH wild-type/mutated; 1p/19q codeleted/not) and grade. Then, the link between the expression levels (low or high) of the steroid biosynthesis enzyme or receptors of interest and survival was studied using the log-rank test. Finally, a functional analysis of gender-specific correlated genes was performed. HOX-related genes appeared to be differentially expressed between males and females in both grades, suggesting that a glioma could originate in perturbation of developmental signals. Moreover, aromatase, androgen, and estrogen receptor expressions were associated with patient survival and were mainly related to angiogenesis or immune response. Therefore, consideration of the tight control of steroid hormone production and signaling seems crucial for the understanding of glioma pathogenesis and emergence of future targeted therapies.

Latent Variables Capture Pathway-Level Points of Departure in High-Throughput Toxicogenomic Data

Estimation of points of departure (PoDs) from high-throughput transcriptomic data (HTTr) represents a key step in the development of next-generation risk assessment (NGRA). Current approaches mainly rely on single key gene targets, which are constrained by the information currently available in the knowledge base and make interpretation challenging as scientists need to interpret PoDs for thousands of genes or hundreds of pathways. In this work, we aimed to address these issues by developing a computational workflow to investigate the pathway concentration–response relationships in a way that is not fully constrained by known biology and also facilitates interpretation. We employed the Pathway-Level Information ExtractoR (PLIER) to identify latent variables (LVs) describing biological activity and then investigated in vitro LVs’ concentration–response relationships using the ToxCast pipeline. We applied this methodology to a published transcriptomic concentration–response data set for 44 chemicals in MCF-7 cells and showed that our workflow can capture known biological activity and discriminate between estrogenic and antiestrogenic compounds as well as activity not aligning with the existing knowledge base, which may be relevant in a risk assessment scenario. Moreover, we were able to identify the known estrogen activity in compounds that are not well-established ER agonists/antagonists supporting the use of the workflow in read-across. Next, we transferred its application to chemical compounds tested in HepG2, HepaRG, and MCF-7 cells and showed that PoD estimates are in strong agreement with those estimated using a recently developed Bayesian approach (cor = 0.89) and in weak agreement with those estimated using a well-established approach such as BMDExpress2 (cor = 0.57). These results demonstrate the effectiveness of using PLIER in a concentration–response scenario to investigate pathway activity in a way that is not fully constrained by the knowledge base and to ease the biological interpretation and support the development of an NGRA framework with the ability to improve current risk assessment strategies for chemicals using new approach methodologies.

Cell-Based Assays to Identify ERR and ERR/PGC Modulators

The estrogen-related receptor alpha (ERRα, NR3B1) is an orphan nuclear receptor which plays a role in endocrine disruption, energy homeostasis, and cancer prognosis. One of the unique features of this transcription factor is the interplay with its cofactors. For instance, certain modulators require the presence of proliferator-activated receptor gamma coactivator 1 alpha (PGC-1α) alongside ERRα. Therefore, identification of ERRα agonists and antagonists require examination of this nuclear receptor alone and together with PGC-1α. In this book chapter, we describe the step-by-step protocol of a multiplex luciferase assay designed to identify ERRα agonists, antagonists, and toxicity in one quantitative high-throughput screening assay using two different stable cell lines.

Abundance of estrogen receptors involved in non-canonical signaling in the dog testis

With estrogen regulation of the reproductive system, G-protein-coupled membrane estrogen receptor (GPER) and estrogen-related receptors (ERRs) are implicated. Non-canonical receptors can bind estrogens such as environmental and pharmacological chemicals. These compounds induce rapid non-genomic pathways or receptor interaction including autoactivation. Testicular tumors occur in dogs more frequently than in other domestic animals. Also, in recent decades there were increased occurrences of various tumor types in dogs. Using qRT-PCR, Western blot and immunohistochemistry procedures in the present study, there was determination of abundance pattern of GPER, ERRα, β and γ in dog tests when there were intratubular germ cell tumors. There was quantitation of estradiol, cyclic GMP and calcium ions (Ca2⁺). There were changes (P < 0.01; P < 0.001) in GPER, ERRα and β in both mRNA transcript and protein abundances including less (P < 0.001) co-abundance of ERRγ mRNA transcript and protein. Receptors were mainly located in Leydig cells with there being receptor delocalization to the cell cytoplasm or occasionally detections in the seminiferous tubule epithelia, especially of testicular tumor tissues. There were also greater estradiol (P < 0.05) and lesser cGMP and Ca2⁺ concentrations in testicular tumor tissues indicating there was a disrupted sex steroid milieu and tumor cell metastasis. Results from the present study provide further evidence that ERRγ has marked actions in testicular germ cell tumor initiation and development and in further structural-functional disruptions of dog testis. Concomitantly, abundance pattern of GPER and ERRs, relative to concentrations of cGMP and Ca2⁺, may be an additional indicator of intratubular germ cell tumors in dogs.

Targeting the Mitochondrial Metabolic Network: A Promising Strategy in Cancer Treatment

Metabolic reprogramming is a hallmark of cancer, which implements a profound metabolic rewiring in order to support a high proliferation rate and to ensure cell survival in its complex microenvironment. Although initial studies considered glycolysis as a crucial metabolic pathway in tumor metabolism reprogramming (i.e., the Warburg effect), recently, the critical role of mitochondria in oncogenesis, tumor progression, and neoplastic dissemination has emerged. In this report, we examined the main mitochondrial metabolic pathways that are altered in cancer, which play key roles in the different stages of tumor progression. Furthermore, we reviewed the function of important molecules inhibiting the main mitochondrial metabolic processes, which have been proven to be promising anticancer candidates in recent years. In particular, inhibitors of oxidative phosphorylation (OXPHOS), heme flux, the tricarboxylic acid cycle (TCA), glutaminolysis, mitochondrial dynamics, and biogenesis are discussed. The examined mitochondrial metabolic network inhibitors have produced interesting results in both preclinical and clinical studies, advancing cancer research and emphasizing that mitochondrial targeting may represent an effective anticancer strategy.

Molecular Image-Based Prediction Models of Nuclear Receptor Agonists and Antagonists Using the DeepSnap-Deep Learning Approach with the Tox21 10K Library

The interaction of nuclear receptors (NRs) with chemical compounds can cause dysregulation of endocrine signaling pathways, leading to adverse health outcomes due to the disruption of natural hormones. Thus, identifying possible ligands of NRs is a crucial task for understanding the adverse outcome pathway (AOP) for human toxicity as well as the development of novel drugs. However, the experimental assessment of novel ligands remains expensive and time-consuming. Therefore, an in silico approach with a wide range of applications instead of experimental examination is highly desirable. The recently developed novel molecular image-based deep learning (DL) method, DeepSnap-DL, can produce multiple snapshots from three-dimensional (3D) chemical structures and has achieved high performance in the prediction of chemicals for toxicological evaluation. In this study, we used DeepSnap-DL to construct prediction models of 35 agonist and antagonist allosteric modulators of NRs for chemicals derived from the Tox21 10K library. We demonstrate the high performance of DeepSnap-DL in constructing prediction models. These findings may aid in interpreting the key molecular events of toxicity and support the development of new fields of machine learning to identify environmental chemicals with the potential to interact with NR signaling pathways.

Estrogen-Related Receptor Alpha: An Under-Appreciated Potential Target for the Treatment of Metabolic Diseases

The estrogen-related receptor alpha (ESRRA) is an orphan nuclear receptor (NR) that significantly influences cellular metabolism. ESRRA is predominantly expressed in metabolically-active tissues and regulates the transcription of metabolic genes, including those involved in mitochondrial turnover and autophagy. Although ESRRA activity is well-characterized in several types of cancer, recent reports suggest that it also has an important role in metabolic diseases. This minireview focuses on the regulation of cellular metabolism and function by ESRRA and its potential as a target for the treatment of metabolic disorders.