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

Abstract

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.

Figure 1. Comparison of GFP-ERβ:PRL-HeLa vs. GFP-ERα:PRL-HeLa stable cell lines

A) Experimental workflow for BPX analysis. B) PRL-HeLa array cells (ERα in red and ERβ in blue) were treated with 10nM E2 for the indicated times and percent of cells with an array was measured. C-E) Six point dose response for the percent arrays measurement at the 30 min time point for E2 (C), 4-OHT (D) and Raloxifene (E). F) Six point dose response for the array area measurement (in pixels) after 30 min of E2 treatment. G-I) recruitment of SRC-1 (G), SRC-2 (H) and SRC-3 (I) to the array after 30 min of E2 and 4OHT treatment measured as loading (Bolt et al 2013). * p<0.05 between ERα and ERβ. J) Serine 5 phosphorylated RNA Polymerase II loading to the array after 30 minutes of E2 treatment (6 point dose-response). K) dsRED2 RNA FISH time course analysis after E2 treatment represented as intensity at the array normalized to vehicle treatment which was set as 1.

Figure 2. HTM platforms define ERα vs. ERβ selectivity of BPXs

A) log EC50 values were calculated after six point dose responses either using a protein fragment complementation assay (PCA) or the PRL array stable cell lines. In the heatmap red indicates high activity compounds while blue indicates low activity compounds. B-E) examples of dose-response curves for four BPXs comparing PCA and PRL array platforms.

Figure 3. Analysis of efficacy and chromatin remodeling potential of BPXs

Percent of cells with an array (A) or array area (B) was measured at the maximal BPX dose (10μM) after 30min of treatment of GFP-ERα:PRL-HeLa and GFP-ERβ:PRL-HeLa cells. Heat maps were generated after normalizing the data to E2, which is set as 1.

Figure 4. Transcriptional response to BPXs in PRL array cell lines

A-B) dsRED2 RNA FISH intensity at the array after 30 min of BPX 10μM (A) or BPX+E2 10nM (B) in GFP-ERα:PRL-HeLa (grey bars) or GFP-ERβ:PRL-HeLa (black bars) cells. C-D) Venn diagrams representing the different categories of responses by BPXs in ERα- (C) or ERβ-containing cells (D). See text for further description of the categories.

Figure 5. Effect of BPXs on endogenous ERα activity in MCF-7 breast cancer cells

A) MCF-7 cells were treated for 24hrs with BPXs (10μM, grey bars) or BPX+E2 10nM (black bars) and then labeled with ERα antibody. Nuclear ER intensity was then quantified. Data are represented as relative to vehicle control. B) MCF-7 cells were treated for 24hrs with BPXs (10μM, grey bars) or BPX+E2 10nM (black bars) and then hybridized with GREB1 intron mRNA FISH probes. Data indicates the average number of transcriptionally active foci per cell after setting vehicle treatment at 1.

Figure 6. Clustering Analysis of BPXs activity across all the HTM assays performed

Data in each assay row was range normalized before clustering analysis. Clustering was performed using Euclidean distance for both the compounds and the assays.