Development of an image analysis screen for estrogen receptor alpha (ERα) ligands through measurement of nuclear translocation dynamics

Rigorous 3-dimensional spectral data activity relationship approach modeling strategy for ToxCast estrogen receptor data classification, validation, and feature extraction

The estrogenic potential (expressed as a score composite of 18 high throughput screening bioassays) of 1528 compounds from the ToxCast database was modeled by a 3-dimensional spectral data activity relationship approach (3D-SDAR). Due to a lack of ¹⁷ O nuclear magnetic resonance (NMR) simulation software, the most informative carbon-carbon 3D-SDAR fingerprints were augmented with indicator variables representing oxygen atoms from carbonyl and carboxamide, ester, sulfonyl, nitro, aliphatic hydroxyl, and phenolic hydroxyl groups. To evaluate the true predictive performance of the authors' model the United States Environmental Protection Agency provided them with a blind test set consisting of 2008 compounds. Of these, 543 had available literature data-their binding affinity served to estimate the external classification accuracy of the developed model: predictive accuracy of 0.62, sensitivity of 0.71, and specificity of 0.53 were obtained. Compared with alternative modeling techniques, the authors' model displayed very little reduction in performance between the modeling and the prediction set. A 3D-SDAR mapping technique allowed identification of structural features essential for estrogenicity: 1) the presence of a phenolic OH group or cyclohexenone, 2) a second aromatic or phenolic ring at a distance of 6 Å to 8 Å from the oxygen of the first phenol ring, 3) the presence of a methyl group approximately 6 Å away from the centroid of a phenol ring, and 4) a carbonyl group in close proximity (∼4 Å measured to the centroid) to 1 of the phenol rings. Environ Toxicol Chem 2017;36:823-830. Published 2016 Wiley Periodicals Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.

Visual and quantitative screening of α1-adrenoceptor antagonists in living cells using quantum dots

The performance of α1-adrenoceptor antagonists in living cells was assessed using quantum dots conjugated to a derivative of the α1-adrenoceptor antagonist prazosin. The optimum receptor binding condition and apparent Kd of prazosin-conjugated quantum dots was first determined, followed by application of these structures to drug screening. Total internal reflection fluorescence microscopy and flow cytometry were used to visually and quantitatively measure the affinity of five candidate drugs. The observed affinity order and the affinity coefficient Ki were consistent with previously reported values. These results suggest that this method is suitable for specific drug screening in living cells and is able to realize the displacement assay over the large ranges of dissociation constants.

Matching the power of high throughput screening to the chemical diversity of natural products

Covering up to 2013. Application of high throughput screening technologies to natural product samples demands alterations in assay design as well as sample preparation in order to yield meaningful hit structures at the end of the campaign.

Identification of compounds by high-content screening that induce cytoplasmic to nuclear localization of a fluorescent estrogen receptor α chimera and exhibit agonist or antagonist activity in vitro

We have completed a robust high-content imaging screen for novel estrogen receptor α (ERα) agonists and antagonists by quantitation of cytoplasmic to nuclear translocation of an estrogen receptor chimera in 384-well plates. The screen was very robust, with Z' values >0.7 and coefficients of variation (CV) <5%. The screen utilized a stably transfected green fluorescent protein-tagged glucocorticoid/estrogen receptor (GFP-GRER) chimera, which consisted of the N-terminus of the glucocorticoid receptor fused to the human ERα ligand binding domain. The GFP-GRER exhibited cytoplasmic localization in the absence of ERα ligands and translocated to the nucleus in response to stimulation with ERα agonists and antagonists. The BD Pathway 435 imaging system was used for image acquisition, analysis of translocation dynamics, and cytotoxicity measurements. We screened 224,891 samples from our synthetic, pure natural product libraries, prefractionated natural product extracts library, and crude natural product extracts library, which produced a 0.003% hit rate. In addition to identifying several known ER ligands, five compounds were discovered that elicited significant activity in the screen. Transactivation potential studies demonstrated that two hit compounds behave as agonists, while three compounds elicited antagonist activity in MCF-7 cells.

Moving to the core: spatiotemporal analysis of Forkhead box O (FOXO) and nuclear factor-κB (NF-κB) nuclear translocation

Nuclear translocation of proteins is an essential aspect of normal cell function, and defects in this process have been detected in many disease-associated conditions. The detection and quantification of nuclear translocation was significantly boosted by the association of robotized microscopy with automated image analysis, a technology designated as high-content screening. Image-based high-content screening and analysis provides the means to systematically observe cellular translocation events in time and space in response to chemical or genetic perturbation at large scale. This approach yields powerful insights into the regulation of complex signaling networks independently of preconceived notions of mechanistic relationships. In this review, we briefly overview the different mechanisms involved in nucleocytoplasmic protein trafficking. In addition, we discuss high-content approaches used to interrogate the mechanistic and spatiotemporal dynamics of cellular signaling events using Forkhead box O (FOXO) proteins and the nuclear factor-κB (NF-κB) as important and clinically relevant examples.

Functional genomic and high-content screening for target discovery and deconvolution

INTRODUCTION

Functional genomic screens apply knowledge gained from the sequencing of the human genome toward rapid methods of identifying genes involved in cellular function based on a specific phenotype. This approach has been made possible through advances in both molecular biology and automation. The utility of this approach has been further enhanced through the application of image-based high-content screening: an automated microscopy and quantitative image analysis platform. These approaches can significantly enhance the acquisition of novel targets for drug discovery.

AREAS COVERED

Both the utility and potential issues associated with functional genomic screening approaches are discussed in this review, along with examples that illustrate both. The considerations for high-content screening applied to functional genomics are also presented.

EXPERT OPINION

Functional genomic screening and high-content screening are extremely useful in the identification of new drug targets. However, the technical, experimental, and computational parameters have an enormous influence on the results. Thus, although new targets are identified, caution should be applied to the interpretation of screening data in isolation. Genomic screens should be viewed as an integral component of a target identification campaign that requires both the acquisition of orthogonal data, as well as a rigorous validation strategy.

Getting the whole picture: combining throughput with content in microscopy

The increasing availability and performance of automated scientific equipment in the past decades have brought about a revolution in the biological sciences. The ease with which data can now be generated has led to a new culture of high-throughput science, in which new types of biological questions can be asked and tackled in a systematic and unbiased manner. High-throughput microscopy, also often referred to as high-content screening (HCS), allows acquisition of systematic data at the single-cell level. Moreover, it allows the visualization of an enormous array of cellular features and provides tools to quantify a large number of parameters for each cell. These features make HCS a powerful method to create data that is rich and biologically meaningful without compromising systematic capabilities. In this Commentary, we will discuss recent work, which has used HCS, to demonstrate the diversity of applications and technological solutions that are evolving in this field. Such advances are placing HCS methodologies at the frontier of high-throughput science and enable scientists to combine throughput with content to address a variety of cell biological questions.

Protein localization in disease and therapy

The eukaryotic cell is organized into membrane-covered compartments that are characterized by specific sets of proteins and biochemically distinct cellular processes. The appropriate subcellular localization of proteins is crucial because it provides the physiological context for their function. In this Commentary, we give a brief overview of the different mechanisms that are involved in protein trafficking and describe how aberrant localization of proteins contributes to the pathogenesis of many human diseases, such as metabolic, cardiovascular and neurodegenerative diseases, as well as cancer. Accordingly, modifying the disease-related subcellular mislocalization of proteins might be an attractive means of therapeutic intervention. In particular, cellular processes that link protein folding and cell signaling, as well as nuclear import and export, to the subcellular localization of proteins have been proposed as targets for therapeutic intervention. We discuss the concepts involved in the therapeutic restoration of disrupted physiological protein localization and therapeutic mislocalization as a strategy to inactivate disease-causing proteins.