Citation:

Rooney, J., N. Ryan, J. Liu, R. Houtman, R. van Beuningen, J. Hsieh, G. Chang, S. Chen, AND Jon Corton. A Gene Expression Biomarker Identifies Chemical Modulators of Estrogen Receptor α in an MCF-7 Microarray Compendium. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, 34(2):313-329, (2021). https://doi.org/10.1021/acs.chemrestox.0c00243

Impact/Purpose:

A complementary approach to multiple HTS assays is the use of HT transcriptomic (HTTr) screening. HTTr technologies have the potential to examine many more pathways simultaneously and in the near future, could be used in testing programs as Tier 1 assays, defined as those that are carried out prior to more targeted Tier 2 screening to validate predictions based on transcriptomic profiles (Thomas et al., 2019). A number of promising techniques are now available that have been adapted to HTS to allow measurement of expression of targeted genes from lysates of treated cells (e.g., (Yeakley et al., 2017)). Parallel computational methods need to be developed to predict modulation of molecular targets and their dose-response characteristics (Corton et al., 2019) that can be linked to the network of adverse outcome pathways (AOPs) (Edwards et al., 2016) relevant to chemical-induced toxicity. One major challenge to interpreting HTTr data is how to predict the primary targets of chemicals. Our group has implemented a computational strategy to build gene expression biomarkers, test their predictive accuracy, and use them in screening programs (e.g., (Rooney et al., 2018b, Ryan et al., 2016)). Biomarkers are built utilizing microarray comparisons from cells or tissues in which the factor is known to be perturbed in a predictable manner after chemical exposure or genetic modulation. The biomarker is compared in a pair-wise fashion to microarray profiles of interest using a rank-based method (Kupershmidt et al., 2010) that allows an assessment of the number of overlapping genes and their degree of correlation. We have used these methods to develop predictive biomarkers for a number of transcription factors that are regulated by xenobiotic chemicals in the mouse and rat liver (Oshida et al., 2015a, Oshida et al., 2015b, Oshida et al., 2015c, Oshida et al., 2016a, Oshida et al., 2016b, Rooney, 2018b). We have applied this strategy to build and validate a biomarker that can predict ERα modulation in the human breast cancer cell line MCF-7. The method is very accurate resulting in balanced accuracies of 95% or 98% for ERα agonism or antagonism, respectively. The approach can also accurately replicate the results of the ER computational model, allowing the 18 assays to be replaced by one assay that detects both agonists and antagonists (Ryan et al., 2016). To demonstrate the potential of using genomic data to screen for potential EDCs, we used here the ERα biomarker approach to screen for chemicals that modulate ERα in a large compendium of gene expression profiles derived from MCF-7 cells. The profiles include those from the Connectivity Map (CMAP) project in which a collection of genome-wide transcription expression data was collected from cultured human cells treated with ~1200 bioactive small molecules (Lamb et al., 2006, Lamb, 2007). The CMAP database and associated tools have proven useful to identify drug candidates used to treat a number of diseases (Hurle et al., 2013). Using our biomarker approach, ~170 compounds were identified in the compendium that modulate ERα. While these included known ERα agonists and antagonists/selective ERα modulators, many of these compounds had not been previously shown to have ERα modulating activity. We confirmed the ERα modulating activity of the novel compounds using global and targeted gene expression in wild-type and ERα-null cells, trans-activation assays, and cell-free ERα and co-regulator interaction assays.

Description:

Identification of chemicals that affect hormone-regulated systems will help to predict endocrine disruption. In our previous study, a 46 gene biomarker was found to be an accurate predictor of estrogen receptor (ER) α modulation in chemically treated MCF-7 cells. Here, potential ERα modulators were identified using the biomarker by screening a microarray compendium consisting of ∼1600 gene expression comparisons representing exposure to ∼1200 chemicals. A total of ∼170 chemicals were identified as potential ERα modulators. In the Connectivity Map 2.0 collection, 75 and 39 chemicals were predicted to activate or suppress ERα, and they included 12 and six known ERα agonists and antagonists/selective ERα modulators, respectively. Nineteen and eight of the total number were also identified as active in an ERα transactivation assay carried out in an MCF-7-derived cell line used to screen the Tox21 10K chemical library in agonist or antagonist modes, respectively. Chemicals predicted to modulate ERα in MCF-7 cells were examined further using global and targeted gene expression in wild-type and ERα-null cells, transactivation assays, and cell-free ERα coregulator interaction assays. Environmental chemicals classified as weak and very weak agonists were confirmed to activate ERα including apigenin, kaempferol, and oxybenzone. Novel activators included digoxin, nabumetone, ivermectin, and six progestins. Novel suppressors included emetine, mifepristone, niclosamide, and proscillaridin. Our strategy will be useful to identify environmentally relevant ERα modulators in future high-throughput transcriptomic screens.

URLs/Downloads:

Record Details: