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Although current antiretroviral therapies (ART) are successful in controlling HIV-1 infection, a stable viral reservoir reactivates when ART is discontinued. Consequently, there is a major research effort to develop approaches to disrupt the latent viral reservoir and enhance the immune system's ability to clear HIV-1. A number of small molecules, termed latency reversal agents (LRAs), have been identified which can reactivate latent HIV-1 in cell lines and patients' cells ex vivo. However, clinical trials have suggested that combinations of LRAs will be required to efficiently reactivate HIV-1 in vivo, especially LRAs that act synergistically by functioning through distinct pathways. To identify novel LRAs, we used an image-based assay to screen a natural compound library for the ability to induce a low level of aggregation of resting primary CD4+ T cells from healthy donors. We identified celastrol as a novel LRA. Celastrol functions synergistically with other classes of LRA to reactivate latent HIV-1 in a Jurkat cell line, suggesting a novel mechanism in its LRA activity. Additionally, celastrol does not appear to activate resting CD4+ T cells at levels at which it can reactivate latent HIV-1. Celastrol appears to represent a novel class of LRAs and it therefore can serve as a lead compound for LRA development. [ABSTRACT FROM AUTHOR]

Environmental toxicants affect human health in various ways. Of the thousands of chemicals present in the environment, those with adverse effects on the endocrine system are referred to as endocrine-disrupting chemicals (EDCs). Here, we focused on a subclass of EDCs that impacts the estrogen receptor (ER), a pivotal transcriptional regulator in health and disease. Estrogenic activity of compounds can be measured by many in vitro or cell-based high throughput assays that record various endpoints from large pools of cells, and increasingly at the single-cell level. To simultaneously capture multiple mechanistic ER endpoints in individual cells that are affected by EDCs, we previously developed a sensitive high throughput/high content imaging assay that is based upon a stable cell line harboring a visible multicopy ER responsive transcription unit and expressing a green fluorescent protein (GFP) fusion of ER. High content analysis generates voluminous multiplex data comprised of minable features that describe numerous mechanistic endpoints. In this study, we present a machine learning pipeline for rapid, accurate, and sensitive assessment of the endocrine-disrupting potential of benchmark chemicals based on data generated from high content analysis. The multidimensional imaging data was used to train a classification model to ultimately predict the impact of unknown compounds on the ER, either as agonists or antagonists. To this end, both linear logistic regression and nonlinear Random Forest classifiers were benchmarked and evaluated for predicting the estrogenic activity of unknown compounds. Furthermore, through feature selection, data visualization, and model discrimination, the most informative features were identified for the classification of ER agonists/antagonists. The results of this data-driven study showed that highly accurate and generalized classification models with a minimum number of features can be constructed without loss of generality, where these machine learning models serve as a means for rapid mechanistic/phenotypic evaluation of the estrogenic potential of many chemicals. Author summary: Chemical contaminants or toxicants pose environmental and health-related risks for exposure. The ability to rapidly understand their biological impact, specifically on a key modulator of important physiological and pathological states in the human body is essential for diagnosing and avoiding undesirable health outcomes during environmental emergencies. In this study, we use advanced data analytics for creating statistical models that can accurately predict the endocrinological activity of toxic chemicals based on high throughput/high content image analysis data. We focus on a subclass of chemicals that affect the estrogen receptor (ER), which is a pivotal transcriptional regulator in health and disease. The multidimensional imaging data of these benchmark chemicals are used to train a classification model to ultimately predict the impact of unknown compounds on the ER, either as agonists or antagonists. To this end, we evaluate linear and nonlinear classifiers for predicting the estrogenic activity of unknown compounds and use feature selection, data visualization, and model discrimination methodologies to identify the most informative features for the classification of ER agonists/antagonists. [ABSTRACT FROM AUTHOR]

A subset of environmental chemicals acts as "obesogens" as they increase adipose mass and lipid content in livers of treated rodents. One of the most studied class of obesogens are the tin-containing chemicals that have as a central moiety tributyltin (TBT), which bind and activate two nuclear hormone receptors, Peroxisome Proliferator Activated Receptor Gamma (PPARG) and Retinoid X Receptor Alpha (RXRA), at nanomolar concentrations. Here, we have tested whether TBT chloride at such concentrations may affect the neutral lipid level in two cell line models of human liver. Indeed, using high content image analysis (HCA), TBT significantly increased neutral lipid content in a time- and concentration-dependent manner. Consistent with the observed increased lipid accumulation, RNA fluorescence in situ hybridization (RNA FISH) and RT-qPCR experiments revealed that TBT enhanced the steady-state mRNA levels of two key genes for de novo lipogenesis, the transcription factor SREBF1 and its downstream enzymatic target, FASN. Importantly, pre-treatment of cells with 2-deoxy-D-glucose reduced TBT-mediated lipid accumulation, thereby suggesting a role for active glycolysis during the process of lipid accumulation. As other RXRA binding ligands can promote RXRA protein turnover via the 26S proteasome, TBT was tested for such an effect in the two liver cell lines. We found that TBT, in a time- and dose-dependent manner, significantly reduced steady-state RXRA levels in a proteasome-dependent manner. While TBT promotes both RXRA protein turnover and lipid accumulation, we found no correlation between these two events at the single cell level, thereby suggesting an additional mechanism may be involved in TBT promotion of lipid accumulation, such as glycolysis. [ABSTRACT FROM AUTHOR]

Animal studies have linked the estrogenic properties of bisphenol A (BPA) to adverse effects on the endocrine system. Because of concerns for similar effects in humans, there is a desire to replace BPA in consumer products, and a search for BPA replacements that lack endocrine-disrupting bioactivity is ongoing. We used multiple cell-based models, including an established multi-parametric, high throughput microscopy-based platform that incorporates engineered HeLa cell lines with visible ERα- or ERβ-regulated transcription loci, to discriminate the estrogen-like and androgen-like properties of previously uncharacterized substituted bisphenol derivatives and hydroquinone. As expected, BPA induced 70–80% of the estrogen-like activity via ERα and ERβ compared to E2 in the HeLa prolactin array cell line. 2,2’ BPA, Bisguaiacol F, CHDM 4-hydroxybuyl acrylate, hydroquinone, and TM modified variants of BPF showed very limited estrogen-like or androgen-like activity (< 10% of that observed with the control compounds). Interestingly, TM-BFP and CHDM 4-hydroxybuyl acrylate, but not their derivatives, demonstrated evidence of anti-estrogenic and anti-androgenic activity. Our findings indicate that Bisguaiacol F, TM-BFP-ER and TM-BPF-DGE demonstrate low potential for affecting estrogenic or androgenic endocrine activity. This suggest that the tested compounds could be suitable commercially viable alternatives to BPA. [ABSTRACT FROM AUTHOR]

Using a chromatin immunoprecipitation-paired end diTag cloning and sequencing strategy, we mapped estrogen receptor α (ERα) binding sites in MCF-7 breast cancer cells. We identified 1,234 high confidence binding clusters of which 94% are projected to be bona fide ERα binding regions. Only 5% of the mapped estrogen receptor binding sites are located within 5 kb upstream of the transcriptional start sites of adjacent genes, regions containing the proximal promoters, whereas vast majority of the sites are mapped to intronic or distal locations (>5 kb from 5′ and 3′ ends of adjacent transcript), suggesting transcriptional regulatory mechanisms over significant physical distances. Of all the identified sites, 71% harbored putative full estrogen response elements (EREs), 25% bore ERE half sites, and only 4% had no recognizable ERE sequences. Genes in the vicinity of ERα binding sites were enriched for regulation by estradiol in MCF-7 cells, and their expression profiles in patient samples segregate ERα-positive from ERα-negative breast tumors. The expression dynamics of the genes adjacent to ERα binding sites suggest a direct induction of gene expression through binding to ERE-like sequences, whereas transcriptional repression by ERα appears to be through indirect mechanisms. Our analysis also indicates a number of candidate transcription factor binding sites adjacent to occupied EREs at frequencies much greater than by chance, including the previously reported FOXA1 sites, and demonstrate the potential involvement of one such putative adjacent factor, Sp1, in the global regulation of ERα target genes. Unexpectedly, we found that only 22%-24% of the bona fide human ERα binding sites were overlapping conserved regions in whole genome vertebrate alignments, which suggest limited conservation of functional binding sites. Taken together, this genome-scale analysis suggests complex but definable rules governing ERα binding and gene regulation. [ABSTRACT FROM AUTHOR]

Using a chromatin immunoprecipitation-paired end diTag cloning and sequencing strategy, we mapped estrogen receptor alpha (ERalpha) binding sites in MCF-7 breast cancer cells. We identified 1,234 high confidence binding clusters of which 94% are projected to be bona fide ERalpha binding regions. Only 5% of the mapped estrogen receptor binding sites are located within 5 kb upstream of the transcriptional start sites of adjacent genes, regions containing the proximal promoters, whereas vast majority of the sites are mapped to intronic or distal locations (>5 kb from 5' and 3' ends of adjacent transcript), suggesting transcriptional regulatory mechanisms over significant physical distances. Of all the identified sites, 71% harbored putative full estrogen response elements (EREs), 25% bore ERE half sites, and only 4% had no recognizable ERE sequences. Genes in the vicinity of ERalpha binding sites were enriched for regulation by estradiol in MCF-7 cells, and their expression profiles in patient samples segregate ERalpha-positive from ERalpha-negative breast tumors. The expression dynamics of the genes adjacent to ERalpha binding sites suggest a direct induction of gene expression through binding to ERE-like sequences, whereas transcriptional repression by ERalpha appears to be through indirect mechanisms. Our analysis also indicates a number of candidate transcription factor binding sites adjacent to occupied EREs at frequencies much greater than by chance, including the previously reported FOXA1 sites, and demonstrate the potential involvement of one such putative adjacent factor, Sp1, in the global regulation of ERalpha target genes. Unexpectedly, we found that only 22%-24% of the bona fide human ERalpha binding sites were overlapping conserved regions in whole genome vertebrate alignments, which suggest limited conservation of functional binding sites. Taken together, this genome-scale analysis suggests complex but definable rules governing ERalpha binding and gene regulation., Competing Interests: Competing interests. The authors have declared that no competing interests exist.