Identification of Estrogen-Related Receptor α Agonists in the Tox21 Compound Library

In Silico Prediction of ERRα Agonists Based on Combined Features and Stacking Ensemble Method

Estrogen-related receptor α (ERRα) is considered a very promising target for treating metabolic diseases such as type 2 diabetes. Development of a prediction model to quickly identify potential ERRα agonists can significantly reduce the time spent on virtual screening. In this study, 298 ERRα agonists and numerous nonagonists were collected from various sources to build a new dataset of ERRα agonists. Then a total of 90 models were built using a combination of different algorithms, molecular characterization methods, and data sampling techniques. The consensus model with optimal performance was also validated on the test set (AUC=0.876, BA=0.816) and external validation set (AUC=0.867, BA=0.777) based on five selected baseline models. Furthermore, the model's applicability domain and privileged substructures were examined, and the feature importance was analyzed using the SHAP method to help interpret the model. Based on the above, it's hoped that our publicly accessible data, models, codes, and analytical techniques will prove valuable in quick screening and rational designing more novel and potent ERRα agonists.

Novel Pan-ERR Agonists Ameliorate Heart Failure Through Enhancing Cardiac Fatty Acid Metabolism and Mitochondrial Function

BACKGROUND

Cardiac metabolic dysfunction is a hallmark of heart failure (HF). Estrogen-related receptors ERRα and ERRγ are essential regulators of cardiac metabolism. Therefore, activation of ERR could be a potential therapeutic intervention for HF. However, in vivo studies demonstrating the potential usefulness of ERR agonist for HF treatment are lacking, because compounds with pharmacokinetics appropriate for in vivo use have not been available.

METHODS

Using a structure-based design approach, we designed and synthesized 2 structurally distinct pan-ERR agonists, SLU-PP-332 and SLU-PP-915. We investigated the effect of ERR agonist on cardiac function in a pressure overload-induced HF model in vivo. We conducted comprehensive functional, multi-omics (RNA sequencing and metabolomics studies), and genetic dependency studies both in vivo and in vitro to dissect the molecular mechanism, ERR isoform dependency, and target specificity.

RESULTS

Both SLU-PP-332 and SLU-PP-915 significantly improved ejection fraction, ameliorated fibrosis, and increased survival associated with pressure overload-induced HF without affecting cardiac hypertrophy. A broad spectrum of metabolic genes was transcriptionally activated by ERR agonists, particularly genes involved in fatty acid metabolism and mitochondrial function. Metabolomics analysis showed substantial normalization of metabolic profiles in fatty acid/lipid and tricarboxylic acid/oxidative phosphorylation metabolites in the mouse heart with 6-week pressure overload. ERR agonists increase mitochondria oxidative capacity and fatty acid use in vitro and in vivo. Using both in vitro and in vivo genetic dependency experiments, we show that ERRγ is the main mediator of ERR agonism-induced transcriptional regulation and cardioprotection and definitively demonstrated target specificity. ERR agonism also led to downregulation of cell cycle and development pathways, which was partially mediated by E2F1 in cardiomyocytes.

CONCLUSIONS

ERR agonists maintain oxidative metabolism, which confers cardiac protection against pressure overload-induced HF in vivo. Our results provide direct pharmacologic evidence supporting the further development of ERR agonists as novel HF therapeutics.

Estrogen receptor-related receptor (Esrra) induces ribosomal protein Rplp1-mediated adaptive hepatic translation during prolonged starvation

Protein translation is an energy-intensive ribosome-driven process that is reduced during nutrient scarcity to conserve cellular resources. During prolonged starvation, cells selectively translate specific proteins to enhance their survival (adaptive translation); however, this process is poorly understood. Accordingly, we analyzed protein translation and mRNA transcription by multiple methods in vitro and in vivo to investigate adaptive hepatic translation during starvation. While acute starvation suppressed protein translation in general, proteomic analysis showed that prolonged starvation selectively induced translation of lysosome and autolysosome proteins. Significantly, the expression of the orphan nuclear receptor, estrogen-related receptor alpha (Esrra) increased during prolonged starvation and served as a master regulator of this adaptive translation by transcriptionally stimulating 60S acidic ribosomal protein P1 (Rplp1) gene expression. Overexpression or siRNA knockdown of Esrra expression in vitro or in vivo led to parallel changes in Rplp1 gene expression, lysosome/autophagy protein translation, and autophagy. Remarkably, we have found that Esrra had dual functions by not only regulating transcription but also controling adaptive translation via the Esrra/Rplp1/lysosome/autophagy pathway during prolonged starvation.

Comparison between endocrine activity assessed using ToxCast/Tox21 database and human plasma concentration of sunscreen active ingredients/UV filters

Sunscreen products are composed of ultraviolet (UV) filters and formulated to reduce exposure to sunlight thereby lessening skin damage. Concerns have been raised regarding the toxicity and potential endocrine disrupting (ED) effects of UV filters. The ToxCast/Tox21 program, that is, CompTox, is a high-throughput in vitro screening database of chemicals that identify adverse outcome pathways, key events, and ED potential of chemicals. Using the ToxCast/Tox21 database, octisalate, homosalate, octocrylene, oxybenzone, octinoxate, and avobenzone, 6 commonly used organic UV filters, were found to have been evaluated. These UV filters showed low potency in these bioassays with most activity detected above the range of the cytotoxic burst. The pathways that were most affected were the cell cycle and the nuclear receptor pathways. Most activity was observed in liver and kidney-based bioassays. These organic filters and their metabolites showed relatively weak ED activity when tested in bioassays measuring estrogen receptor (ER), androgen receptor (AR), thyroid receptor, and steroidogenesis activity. Except for oxybenzone, all activity in the endocrine assays occurred at concentrations greater than the cytotoxic burst. Moreover, except for oxybenzone, plasma concentrations (Cmax) measured in humans were at least 100× lower than bioactive (AC50/ACC) concentrations that produced a response in ToxCast/Tox21 assays. These data are consistent with in vivo animal/human studies showing weak or negligible endocrine activity. In sum, when considered as part of a weight-of-evidence assessment and compared with measured plasma concentrations, the results show these organic UV filters have low intrinsic biological activity and risk of toxicity including endocrine disruption in humans.

Integrated multi-omics analysis of adverse cardiac remodeling and metabolic inflexibility upon ErbB2 and ERRα deficiency

Functional oncogenic links between ErbB2 and ERRα in HER2+ breast cancer patients support a therapeutic benefit of co-targeted therapies. However, ErbB2 and ERRα also play key roles in heart physiology, and this approach could pose a potential liability to cardiovascular health. Herein, using integrated phosphoproteomic, transcriptomic and metabolic profiling, we uncovered molecular mechanisms associated with the adverse remodeling of cardiac functions in mice with combined attenuation of ErbB2 and ERRα activity. Genetic disruption of both effectors results in profound effects on cardiomyocyte architecture, inflammatory response and metabolism, the latter leading to a decrease in fatty acyl-carnitine species further increasing the reliance on glucose as a metabolic fuel, a hallmark of failing hearts. Furthermore, integrated omics signatures of ERRα loss-of-function and doxorubicin treatment exhibit common features of chemotherapeutic cardiotoxicity. These findings thus reveal potential cardiovascular risks in discrete combination therapies in the treatment of breast and other cancers.

Murine hearts deficient in ErbB2 and/or ERRα are used to profile the adverse cardiac remodeling associated with potential targeted breast cancer treatments by phosphoproteomic, transcriptomic and metabolomic profiling.

Statins and cognition: Modifying factors and possible underlying mechanisms

Statins are a class of widely prescribed drugs used to reduce low-density lipoprotein cholesterol (LDL-C) and important to prevent cardiovascular diseases (CVD). Most statin users are older adults with CVD, who are also at high risk of cognitive decline. It has been suggested that statins can alter cognitive performance, although their positive or negative effects are still debated. With more than 200 million people on statin therapy worldwide, it is crucial to understand the reasons behind discrepancies in the results of these studies. Here, we review the effects of statins on cognitive function and their association with different etiologies of dementia, and particularly, Alzheimer's disease (AD). First, we summarized the main individual and statin-related factors that could modify the cognitive effects of statins. Second, we proposed the underlying mechanisms for the protective and adverse effects of statins on cognitive performance. Finally, we discussed potential causes of discrepancies between studies and suggested approaches to improve future studies assessing the impact of statins on dementia risk and cognitive function.

Use of Tox21 Screening Data to Evaluate the COVID-19 Drug Candidates for Their Potential Toxic Effects and Related Pathways

Currently, various potential therapeutic agents for coronavirus disease-2019 (COVID-19), a global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), are being investigated worldwide mainly through the drug repurposing approach. Several anti-viral, anti-bacterial, anti-malarial, and anti-inflammatory drugs were employed in randomized trials and observational studies for developing new therapeutics for COVID-19. Although an increasing number of repurposed drugs have shown anti-SARS-CoV-2 activities in vitro, so far only remdesivir has been approved by the US FDA to treat COVID-19, and several other drugs approved for Emergency Use Authorization, including sotrovimab, tocilizumab, baricitinib, paxlovid, molnupiravir, and other potential strategies to develop safe and effective therapeutics for SARS-CoV-2 infection are still underway. Many drugs employed as anti-viral may exert unwanted side effects (i.e., toxicity) via unknown mechanisms. To quickly assess these drugs for their potential toxicological effects and mechanisms, we used the Tox21 in vitro assay datasets generated from screening ∼10,000 compounds consisting of approved drugs and environmental chemicals against multiple cellular targets and pathways. Here we summarize the toxicological profiles of small molecule drugs that are currently under clinical trials for the treatment of COVID-19 based on their in vitro activities against various targets and cellular signaling pathways.

Discovery of New Estrogen-Related Receptor α Agonists via a Combination Strategy Based on Shape Screening and Ensemble Docking

Estrogen-related receptor α (ERRα), a member of nuclear receptors (NRs), plays a role in the regulation of cellular energy metabolism and is reported to be a novel potential target for type 2 diabetes therapy. To date, only a few agonists of ERRα have been identified to improve insulin sensitivity and decrease blood glucose levels. Herein, the discovery of novel potent agonists of ERRα determined using a combined virtual screening approach is described. Molecular dynamics (MD) simulations were used to obtain structural ensembles that can consider receptor flexibility. Then, an efficient virtual screening strategy with a combination of similarity search and ensemble docking was performed against the Enamine, SPECS, and Drugbank databases to identify potent ERRα agonists. Finally, a total of 66 compounds were purchased for experimental testing. Biological investigation of promising candidates identified seven compounds that have activity against ERRα with EC50 values ranging from 1.11 to 21.70 μM, with novel scaffolds different from known ERRα agonists until now. Additionally, the molecule GX66 showed micromolar inverse activity against ERRα with an IC50 of 0.82 μM. The predicted binding modes showed that these compounds were anchored in ERRα-LBP via interactions with several residues of ERRα. Overall, this study not only identified the novel potent ERRα agonists or an inverse agonist that would be the promising starting point for further exploration but also demonstrated a successful molecular dynamics-guided approach applicable in virtual screening for ERRα agonists.

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.

Immunohistochemical Analysis of PGC-1α and ERRα Expression Reveals Their Clinical Significance in Human Ovarian Cancer

Purpose

Peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) and estrogen-related receptor alpha (ERRα) play a vital role in various human cancers. The purpose of this study was to investigate whether the PGC-1α/ERRα axis could serve as an effective prognostic marker in ovarian cancer (OC).

Patients and Methods

We investigated the expression of both PGC-1α and ERRα in 42 ovarian cancer and 31 noncancerous ovarian samples by immunohistochemistry (IHC). The relationship between the expression of PGC-1α and ERRα in OC and the clinical characteristics of patients was evaluated. In addition, data from the Human Protein Atlas (HPA) database were collected to validate the prognostic significance of PGC-1α and ERRα mRNA expression in OC.

Results

PGC-1α and ERRα showed notably higher expression in OC tissues than in noncancerous tissues (P=0.0059, P=0.002). Moreover, in patients with OC, high ERRα and PGC-1α/ERRα expression significantly correlated with tumor differentiation (P=0.027; P=0.04), lymph node status (P=0.023; P=0.021), CA125 (P=0.036; P=0.021), and HE4 (P=0.021; P=0.05), while high PGC-1α expression was only significantly associated with tumor differentiation (P=0.029). The combined analysis of high PGC-1α and ERRα expression revealed a tendency towards poor cancer-specific survival (P=0.1276).

Conclusion

PGC-1α and ERRα are overexpressed in OC and might be significant prognostic factors for this cancer.

Insights into the interaction mechanisms of estrogen-related receptor alpha (ERRα) with ligands via molecular dynamics simulations

Estrogen-related receptor alpha (ERRα), a member of nuclear receptors (NRs), participates in energy metabolism. Recent experiments identified that several agonists to increase the activity of ERRα, which have a therapeutic effect in improving insulin sensitivity and lowering blood glucose levels. However, the detailed molecular mechanism about how the ligands affect the structure of ERRα remains elusive. To better understand the conformational change of ERRα complexed with agonists and inverse agonists, unbiased molecular dynamics (MD) simulations were performed on the ligand binding domain of ERRα (ERRα-LBD) bound with different ligands. According to the results, the ERRα-agonist interactions were more stable in the presence of the peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α). More importantly, we observed that the binding of inverse agonists would decrease the stability of helix 12 (H12) of ERRα. Moreover, we suggested that Phe232 and Phe414 should be key residues in the interaction pathway from ligands to H12, which provided a possible explanation about how ligands impact the structure of ERRα. These results would provide insights into the design of novel and efficient agonists of ERRα to treat metabolic diabetes.Communicated by Ramaswamy H. Sarma.

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.

Molecular Mechanisms of Polybrominated Diphenyl Ethers (BDE-47, BDE-100, and BDE-153) in Human Breast Cancer Cells and Patient-Derived Xenografts

Polybrominated diphenyl ethers (PBDEs) have been used as flame retardants in household materials. Their environmental persistence has led to continuous human exposure and significant tissue levels. Three PBDE congeners (BDE-47, BDE-100, and BDE-153) have been frequently detected in human serum. Although these compounds appear to possess endocrine disrupting activity, studies are largely missing to determine the biological mechanisms of PBDEs in breast cancer cells. Here, we assessed PBDE bioactivities with three complementary strategies: receptor binding/activity assays; nonbiased RNA-sequencing analysis using an estrogen-dependent breast cancer cell line MCF-7aroERE; and in vivo assessments using patient-derived xenograft (PDX) models of human breast cancer. According to the results from in vitro experiments, the PBDE congeners regulate distinct nuclear receptor signaling pathways. BDE-47 acts as a weak agonist of both estrogen receptor α (ERα) and estrogen-related receptor α (ERRα); it could stimulate proliferation of MCF-7aroERE and induced expression of ER-regulated genes (including cell cycle genes). BDE-153 was found to act as a weak antagonist of ERα. BDE-100 could act as (1) an agonist of aryl hydrocarbon receptor (AhR), inducing expression of CYP1A1 and CYP1B1 and (2) as a very weak agonist/antagonist of ERα. In vivo, a mixture of the three congeners with ratios detected in human serum was tested in an ER+ PDX model. The mixture exhibited estrogenic activity through apoptosis/cell cycle regulation and increased the expression of a proliferation marker, Ki-67. These results advance our understanding of the mechanisms of PBDE exposure in breast cancer cells.

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.

DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance

The progesterone receptor (PR) is important therapeutic target for many malignancies and endocrine disorders due to its role in controlling ovulation and pregnancy via the reproductive cycle. Therefore, the modulation of PR activity using its agonists and antagonists is receiving increasing interest as novel treatment strategy. However, clinical trials using the PR modulators have not yet been found conclusive evidences. Recently, increasing evidence from several fields shows that the classification of chemical compounds, including agonists and antagonists, can be done with recent improvements in deep learning (DL) using deep neural network. Therefore, we recently proposed a novel DL-based quantitative structure-activity relationship (QSAR) strategy using transfer learning to build prediction models for agonists and antagonists. By employing this novel approach, referred as DeepSnap-DL method, which uses images captured from 3-dimension (3D) chemical structure with multiple angles as input data into the DL classification, we constructed prediction models of the PR antagonists in this study. Here, the DeepSnap-DL method showed a high performance prediction of the PR antagonists by optimization of some parameters and image adjustment from 3D-structures. Furthermore, comparison of the prediction models from this approach with conventional machine learnings (MLs) indicated the DeepSnap-DL method outperformed these MLs. Therefore, the models predicted by DeepSnap-DL would be powerful tool for not only QSAR field in predicting physiological and agonist/antagonist activities, toxicity, and molecular bindings; but also for identifying biological or pathological phenomena.

Exploring the binding mode and thermodynamics of inverse agonists against estrogen-related receptor alpha

Since estrogen-related receptor alpha (ERRα), one of three estrogen-related receptors, displays constitutively active transcriptional activities and important implications in both physiological and pathological processes of breast cancers, ERRα was recently recognized as a new target to fight breast cancers, and regulating the activity of ERRα with inverse agonists has thus become a promising new therapeutic strategy. A few inverse agonists cyclohexylmethyl-(1-p-tolyl-1H-indol-3-ylmethyl)-amine (compound 1), thiadiazoacrylamide (XCT790), and 1-(2,5-diethoxy-benzyl)-3-phenyl-area analogues (compounds 2 and 3) were reported to be capable of targeting ERRα. However, the detailed mechanism by which the inverse agonists deactivate ERRα remains unclear, especially in the aspects of quantitative binding and hot spot residues. Therefore, to gain insights into the interaction modes between inverse agonists and ERRα ligand binding domain, all-atom molecular dynamics (MD) simulations were firstly carried out for the complexes of inverse agonists and ERRα. The binding free energies were then calculated with MM-PBSA method to quantitatively discuss the binding of the inverse agonists with ERRα. The binding affinities were finally decomposed to per-residue contributions to identify the hot spot residues as well as assess their role in the binding mechanism. MD simulations show that the inverse agonists stretch downwards into the ERRα ligand binding pocket (LBP) formed by H3 and H11 helices, and upon the binding H12 adopts a well-defined position in the coactivator groove, where PGC-1α binds to ERRα. Binding energy analysis indicates that compound 3 and XCT790 bind more tightly to ERRα than compounds 1 and 2, and the energy difference mainly results from the contribution of van der Waals interaction. Both binding mode analysis and affinity decomposition per-residue indicate that compound 1, XCT790, and compound 3 have similar binding spectra to ERRα, primarily interacting with the residues of H3, H5, H6/H7 loop, and H11 helix, while compound 2 lacks a significant interaction with the H5 region. The hot spot residues significantly binding to the three inverse agonists in common include Leu324, Phe328, Phe382, Leu398, Phe495, and Leu500. It is essential for an effective inverse agonist to strongly bind with the aromatic ring cluster consisting of Phe328(H3), Phe495(H11), and Phe382(H5/H6 loop) as well as Leu500.

All-atom MD simulations were for the first time carried out for the complexes of inverse agonists and ERRα, and their binding free energies were also calculated with MM-PBSA to quantitatively discuss the binding of the inverse agonists with ERRα.

Validation of Human Sterol 14α-Demethylase (CYP51) Druggability: Structure-Guided Design, Synthesis, and Evaluation of Stoichiometric, Functionally Irreversible Inhibitors

Sterol 14α-demethylases (CYP51) are the cytochrome P450 enzymes required for biosynthesis of sterols in eukaryotes, the major targets for antifungal agents and prospective targets for treatment of protozoan infections. Human CYP51 could be and, for a while, was considered as a potential target for cholesterol-lowering drugs (the role that is now played by statins, which are also in clinical trials for cancer) but revealed high intrinsic resistance to inhibition. While microbial CYP51 enzymes are often inhibited stoichiometrically and functionally irreversibly, no strong inhibitors have been identified for human CYP51. In this study, we used comparative structure/functional analysis of CYP51 orthologs from different biological kingdoms and employed site-directed mutagenesis to elucidate the molecular basis for the resistance of the human enzyme to inhibition and also designed, synthesized, and characterized new compounds. Two of them inhibit human CYP51 functionally irreversibly with their potency approaching the potencies of azole drugs currently used to inhibit microbial CYP51.

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

The nuclear receptor, estrogen-related receptor alpha (ERRα; NR3B1), plays a pivotal role in energy homeostasis. Its expression fluctuates with the demands of energy production in various tissues. When paired with the peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α), the PGC/ERR pathway regulates a host of genes that participate in metabolic signaling networks and in mitochondrial oxidative respiration. Unregulated overexpression of ERRα is found in many cancer cells, implicating a role in cancer progression and other metabolism-related diseases. Using high throughput screening assays, we screened the Tox21 10K compound library in stably transfected HEK293 cells containing either the ERRα-reporter or the reporter plus PGC-1α expression plasmid. We identified two groups of antagonists that were potent inhibitors of ERRα activity and/or the PGC/ERR pathway: nine antineoplastic agents and thirteen pesticides. Results were confirmed using gene expression studies. These findings suggest a novel mechanism of action on bioenergetics for five of the nine antineoplastic drugs. Nine of the thirteen pesticides, which have not been investigated previously for ERRα disrupting activity, were classified as such. In conclusion, we demonstrated that high-throughput screening assays can be used to reveal new biological properties of therapeutic and environmental chemicals, broadening our understanding of their modes of action.

Identification of Modulators That Activate the Constitutive Androstane Receptor From the Tox21 10K Compound Library

The constitutive androstane receptor (CAR; NR1I3) is a nuclear receptor involved in all phases of drug metabolism and disposition. However, recently it's been implicated in energy metabolism, tumor progression, and cancer therapy as well. It is, therefore, important to identify compounds that induce human CAR (hCAR) activation to predict drug-drug interactions and potential therapeutic usage. In this study, we screen the Tox21 10,000 compound collection to characterize hCAR activators. A potential novel structural cluster of compounds was identified, which included nitazoxanide and tenonitrozole, whereas known structural clusters, such as flavones and prazoles, were also detected. Four compounds, neticonazole, diphenamid, phenothrin, and rimcazole, have been identified as novel hCAR activators, one of which, rimcazole, shows potential selectivity toward hCAR over its sister receptor, the pregnane X receptor (PXR). All 4 compounds translocated hCAR from the cytoplasm into the nucleus demonstrating the first step to CAR activation. Profiling these compounds as hCAR activators would enable an estimation of drug-drug interactions, as well as identify prospective therapeutically beneficial drugs.

Thyroid hormone receptor and ERRα coordinately regulate mitochondrial fission, mitophagy, biogenesis, and function

Thyroid hormone receptor β1 (THRB1) and estrogen-related receptor α (ESRRA; also known as ERRα) both play important roles in mitochondrial activity. To understand their potential interactions, we performed transcriptome and ChIP-seq analyses and found that many genes that were co-regulated by both THRB1 and ESRRA were involved in mitochondrial metabolic pathways. These included oxidative phosphorylation (OXPHOS), the tricarboxylic acid (TCA) cycle, and β-oxidation of fatty acids. TH increased ESRRA expression and activity in a THRB1-dependent manner through the induction of the transcriptional coactivator PPARGC1A (also known as PGC1α). Moreover, TH induced mitochondrial biogenesis, fission, and mitophagy in an ESRRA-dependent manner. TH also induced the expression of the autophagy-regulating kinase ULK1 through ESRRA, which then promoted DRP1-mediated mitochondrial fission. In addition, ULK1 activated the docking receptor protein FUNDC1 and its interaction with the autophagosomal protein MAP1LC3B-II to induce mitophagy. siRNA knockdown of ESRRA, ULK1, DRP1, or FUNDC1 inhibited TH-induced autophagic clearance of mitochondria through mitophagy and decreased OXPHOS. These findings show that many of the mitochondrial actions of TH are mediated through stimulation of ESRRA expression and activity, and co-regulation of mitochondrial turnover through the PPARGC1A-ESRRA-ULK1 pathway is mediated by their regulation of mitochondrial fission and mitophagy. Hormonal or pharmacologic induction of ESRRA expression or activity could improve mitochondrial quality in metabolic disorders.

Emerging Roles of Estrogen-Related Receptors in the Brain: Potential Interactions with Estrogen Signaling

In addition to their well-known role in the female reproductive system, estrogens can act in the brain to regulate a wide range of behaviors and physiological functions in both sexes. Over the past few decades, genetically modified animal models have greatly increased our knowledge about the roles of estrogen receptor (ER) signaling in the brain in behavioral and physiological regulations. However, less attention has been paid to the estrogen-related receptors (ERRs), the members of orphan nuclear receptors whose sequences are homologous to ERs but lack estrogen-binding ability. While endogenous ligands of ERRs remain to be determined, they seemingly share transcriptional targets with ERs and their expression can be directly regulated by ERs through the estrogen-response element embedded within the regulatory region of the genes encoding ERRs. Despite the broad expression of ERRs in the brain, we have just begun to understand the fundamental roles they play at molecular, cellular, and circuit levels. Here, we review recent research advancement in understanding the roles of ERs and ERRs in the brain, with particular emphasis on ERRs, and discuss possible cross-talk between ERs and ERRs in behavioral and physiological regulations.