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. Author manuscript; available in PMC: 2022 Sep 1.
Published in final edited form as: Methods Mol Biol. 2022;2474:3–9. doi: 10.1007/978-1-0716-2213-1_1

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

Caitlin Lynch 1, Jinghua Zhao 1, Menghang Xia 1,*
PMCID: PMC9434653  NIHMSID: NIHMS1831751  PMID: 35294750

Abstract

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 co-activator 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.

Keywords: Estrogen-related receptor (ERR), Luciferase Report Gene, Proliferator-activated receptor gamma co-activator 1 alpha (PGC-1α)

1. Introduction

The estrogen-related receptor alpha (ERRα; NR3B1) is an orphan nuclear receptor found in various tissues, such as the heart, kidney, brain, liver, intestine, skeletal muscle, and brown adipose. Many of these are metabolically active tissues which utilize fatty acids as fuel for energy production [1,2]. Initially, a structural similarity was identified between this nuclear receptor and the estrogen receptor alpha (ERα) [3]. However, ERRα was found to not bind to natural estrogens and to directly interfere with ERα due to sharing a similar hormone response element [4,5]. Therefore, crosstalk may occur between these two differing transcription factors. Recently, it has been implicated that ERRα generates a poor prognosis on patients when found in their breast and colorectal tumors [6–8]. Therefore, identification of compounds which modulate ERRα should be a critical aspect within the toxicology of patient care.

A unique feature of ERRα is its partnership with the co-activator peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α). When paired together, the PGC/ERR axis activity is increased and influences many oncogenic signals as well as regulates the transcription of certain metabolic genes [9–12]. In ERR−/− mice on a high-fat diet, there is a resistance to obesity implying an important role [13]. When there is a deficiency of PGC-1α, metabolic failure can occur causing hepatic steatosis, a defect in muscle function, and abnormal weight control [14]. However, not all ERRα modulators require PGC-1α to be present; other co-factors may also be involved. In order to identify these different types of modulators, two separate cell lines were generated and validated to measure ERRα and PGC/ERRα signaling [15,16]. Targeting ERRα or the PGC/ERR axis with these modulators could be a valuable tool in therapeutically treating certain metabolic disorders or cancer.

Quantitative high-throughput screening is an approach used when a large compound library exists; the Tox21 10,000 (10K) compound collection is one such library. Using the previously mentioned ERRα and PGC/ERRα cell lines and the methods mentioned below, this compilation of chemicals was screened to identify agonists [17] and antagonists [18] of ERRα. A group of statins was identified as ERRα agonists when PGC-1α was not present, while a class of anthracyclines was found to be active using the PGC/ERRα cell line only, indicating their dependence on PGC-1α for activation of ERRα [17]. Antineoplastic agents and pesticides were found to be ERRα antagonists when screening the Tox21 10K compound library, and were further confirmed as such through gene expression studies [18]. These studies suggest that the protocol below can be utilized when determining a compound’s modulation of ERRα. One of the unique features of this assay is the ability to identify agonists, antagonists, and cell viability in one assay well. This greatly reduces the time and supplies necessary to complete a large screen.

2. Materials

2.1. Equipment

  1. CO2 incubator MCO-17AIC for all cell culture.

  2. 1536-well white solid bottom plates for the high-throughput screen.

  3. Multidrop Combi (Thermo Fisher Scientific Inc) for plating cells in the large screen.

  4. Pintool station (Wako Automation) to transfer compounds into the 1536-well assay plates.

  5. Bioraptr Flying Reagent Dispenser workstation (Beckman Coulter) to add activator/deactivator into the assay plates.

  6. ViewLux plate reader (PerkinElmer).

2.2. Solutions

  1. Assay and culture medium for HEK293-ERR-luc and assay media for HEK293 PGC/ERR-luc cells: High-Glucose DMEM supplemented with 10% defined fetal bovine serum (FBS), 4 mM L-Glutamine, 1 mM sodium pyruvate, and 100 U/ml penicillin/100 μg/ml streptomycin.

  2. Culture media for HEK293-PGC/ERR-luc cells: High-Glucose DMEM supplemented with 10% defined FBS, 4 mM L-glutamine, 1 mM sodium pyruvate, 100 U/ml penicillin/100 μg/ml streptomycin, and 1 μg/mL puromycin.

  3. 20 mM genistein: genistein dissolved in dimethyl sulfoxide (DMSO)

  4. 10 mM XCT790: XCT790 dissolved DMSO

  5. 20 mM tetraoctylammonium bromide: tetraoctylammonium bromide dissolved DMSO

3. Methods

3.1. Optimization of Cell-based ERR and PGC/ERR Assays

  1. Using culture medium without puromycin, plate the ERR or PGC/ERR reporter HEK293 cells into a 1536-well plate at varying densities (1000, 2000, or 3000 cells/well) in 5 μL using the Multidrop Combi. The difference in cell density should always be optimized due to the assay performance being affected by cell number.

  2. Allow plates to incubate at 37°C/5% CO2 for 6 hours.

  3. Create a positive control plate. The first column contains a concentration-response titration of XCT790, an ERR antagonist, from 10 mM (final assay concentration of 23 μM) to 23 μM (final assay concentration of 53 nM) in 16 duplicate concentrations. The second column contains a concentration-response titration of genistein, an ERR agonist. The concentration range for this control is 20 mM (final assay concentration of 92 μM) to 34 μM (final assay concentration of 39 nM) in 16 duplicate concentrations. The third and fourth columns are comprised of 12 wells of 5 mM (final assay concentration of 18 μM) XCT790, 12 wells of XCT790 at a concentration of 4 mM (final assay concentration of 14 μM), 12 wells of genistein at 10 mM (final assay concentration of 46 μM), 12 wells of 5 mM (final assay concentration of 23 μM) genistein, 12 wells of DMSO, and 4 wells of 20 mM (final assay concentration of 92 μM) tetraoctylammonium bromide.

  4. Create a plate with only DMSO in columns 5–48.

  5. Once the cells have attached, use the Pintool station to transfer 23 nL of the positive control or DMSO into the assay plates.

  6. Incubate the assay plates at 37°C/5% CO2 for 17.5 hours (See Note 1).

  7. Add 1 μL of CellTiter-Fluor reagent (Promega) and incubate the plates at 37°C/5% CO2 for 30 minutes (See Note 2).

  8. Read the fluorescence of each plate using a ViewLux plate reader.

  9. Add 5 μL of ONE-Glo reagent (Promega) and allow the plates to incubate at room temperature for 30 minutes in the dark.

  10. Read the luminescence of each plate by using a ViewLux plate reader.

  11. Determine the optimal concentration to use by comparing the signal/background (S/B) ratio, coefficient of variance (CV), Z-factor (Table 1, Note 3), and EC50/IC50 values (Table 2) for each agonist and antagonist curve as well as the graphical representation of the concentration curves, as shown in Figure 1.

Table 1.

Assay Statistics in ERR Assaya

Agonist Modeb Antagonist Modeb
Signal/Background 3.28 1.79
CV (%) 8.48 8.04
Z-factor 0.691 0.280
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a.

These assays used 2500 cells/well.

b.

These values gather information from 12 separate wells.

Table 2.

EC50/IC50 Values for genistein and XCT790

Genistein, EC50 (μM) XCT790, IC50 (μM)
Assay types 1000* 2000* 3000* 1000* 2000* 3000*
ERR 5.l9 3.34 3.24 13.3 5.10 4.16
PGC/ERR 6.56 5.28 5.79 12.2 3.44 3.09
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*

Cells/well

Fig 1.

Fig 1.

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Concentration curves of ERR and PGC/ERR agonist and antagonist optimization. The HEK293-ERR-luc cell line was used in a luciferase assay to determine positive control curves for agonists (a) and antagonists (b). A luciferase assay was performed using the HEK293-PGC/ERR-luc cell line to determine positive control curves for agonists (c) and antagonists (d).

3.2. Data Analysis

  1. Enter raw triplicate values into GraphPad Prism Software into XY table format (See Note 4).

  2. Push the analyze button.

  3. Choose Nonlinear regression (curve fit) under XY analyses.

  4. From here, choose the equation that best fits the mode used (See Note 5).

4. Notes

  1. A time course should always be performed to make sure the time used gives the optimal assay performance.

  2. Cytotoxicity should always be performed when experimenting with an antagonist assay. This will confirm the activity was due to the compound being an antagonist and not because of cytotoxicity.

  3. For good assay performance, the optimal values are above 3 for S/B, between 0.5 and 1 for Z-factor, and less than 10% CV.

  4. Before analyzing the data into nonlinear regression, make sure the data has been transformed into log of each dose.

  5. When in agonist mode, choose an equation from the Dose-response – Stimulation section.

    For the antagonist mode, choose an equation from the Dose-response – Inhibition section.

Acknowledgement

This work was supported in part by the Intramural research program of the NCATS, NIH.

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