KBERG: KnowledgeBase for Estrogen Responsive Genes

The Estrogen Receptor α-Cistrome Beyond Breast Cancer

Although many tissues express estrogen receptor (ER)α, most studies focus on breast cancer where ERα occupies just a small fraction of its total repertoire of potential DNA-binding sites, based on sequence. This raises the question: Can ERα occupy these other potential binding sites in a different context? Ligands, splice variants, posttranslational modifications, and acquired mutations of ERα affect its conformation, which may alter chromatin interactions. To date, literature describes the DNA-binding sites of ERα (the ERα cistrome) in breast, endometrium, liver, and bone, in which the receptor mainly binds to enhancers. Chromosomal boundaries provide distinct areas for dynamic gene regulation between tissues, where the usage of enhancers deviates. Interactions of ERα with enhancers and its transcriptional complex depend on the proteome, which differs per cell type. This review discusses the biological variables that influence ERα cistromics, using reports from human specimens, cell lines, and mouse tissues, to assess whether ERα genomics in breast cancer can be translated to other tissue types.

Expression profile of oestrogen receptors and oestrogen-related receptors is organ specific and sex dependent: the Japanese medaka Oryzias latipes model

Gene expression of all known subtypes of oestrogen receptor (ER) and oestrogen-related receptor (ERR) in multiple organs and both sexes of the Japanese medaka Oryzias latipes was profiled and systematically analysed. As revealed by statistical analyses and low-dimensional projections, the expressions of ERRs proved to be organ and sex dependent, which is in contrast with the ubiquitous nature of ERs. Moreover, expressions of specific ERR isoforms (ERRγ1, ERRγ2) were strongly correlated with that of all ERs (ERα, ERβ1 and ERβ2), suggesting the existence of potential interactions. Findings of this study shed light on the co-regulatory role of particular ERRs in oestrogen-ERs signalling and highlight the potential importance of ERRs in determining organ and sex-specific oestrogen responses. Using O. latipes as an alternative vertebrate model, this study provides new directions that call for collective efforts from the scientific community to unravel the mechanistic action of ER-ERR cross-talks, and their intertwining functions, in a cell and sex-specific manner in vivo.

Identification of estrogen responsive genes using esophageal squamous cell carcinoma (ESCC) as a model

Background

Estrogen therapy has positively impact the treatment of several cancers, such as prostate, lung and breast cancers. Moreover, several groups have reported the importance of estrogen induced gene regulation in esophageal cancer (EC). This suggests that there could be a potential for estrogen therapy for EC. The efficient design of estrogen therapies requires as complete as possible list of genes responsive to estrogen. Our study develops a systems biology methodology using esophageal squamous cell carcinoma (ESCC) as a model to identify estrogen responsive genes. These genes, on the other hand, could be affected by estrogen therapy in ESCC.

Results

Based on different sources of information we identified 418 genes implicated in ESCC. Putative estrogen responsive elements (EREs) mapped to the promoter region of the ESCC genes were used to initially identify candidate estrogen responsive genes. EREs mapped to the promoter sequence of 30.62% (128/418) of ESCC genes of which 43.75% (56/128) are known to be estrogen responsive, while 56.25% (72/128) are new candidate estrogen responsive genes. EREs did not map to 290 ESCC genes. Of these 290 genes, 50.34% (146/290) are known to be estrogen responsive. By analyzing transcription factor binding sites (TFBSs) in the promoters of the 202 (56+146) known estrogen responsive ESCC genes under study, we found that their regulatory potential may be characterized by 44 significantly over-represented co-localized TFBSs (cTFBSs). We were able to map these cTFBSs to promoters of 32 of the 72 new candidate estrogen responsive ESCC genes, thereby increasing confidence that these 32 ESCC genes are responsive to estrogen since their promoters contain both: a/mapped EREs, and b/at least four cTFBSs characteristic of ESCC genes that are responsive to estrogen. Recent publications confirm that 47% (15/32) of these 32 predicted genes are indeed responsive to estrogen.

Conclusion

To the best of our knowledge our study is the first to use a cancer disease model as the framework to identify hormone responsive genes. Although we used ESCC as the disease model and estrogen as the hormone, the methodology can be extended analogously to other diseases as the model and other hormones. We believe that our results provide useful information for those interested in genes responsive to hormones and in the design of hormone-based therapies.

In Silico discovery of transcription factors as potential diagnostic biomarkers of ovarian cancer

BACKGROUND

Our study focuses on identifying potential biomarkers for diagnosis and early detection of ovarian cancer (OC) through the study of transcription regulation of genes affected by estrogen hormone.

RESULTS

The results are based on a set of 323 experimentally validated OC-associated genes compiled from several databases, and their subset controlled by estrogen. For these two gene sets we computationally determined transcription factors (TFs) that putatively regulate transcription initiation. We ranked these TFs based on the number of genes they are likely to control. In this way, we selected 17 top-ranked TFs as potential key regulators and thus possible biomarkers for a set of 323 OC-associated genes. For 77 estrogen controlled genes from this set we identified three unique TFs as potential biomarkers.

CONCLUSIONS

We introduced a new methodology to identify potential diagnostic biomarkers for OC. This report is the first bioinformatics study that explores multiple transcriptional regulators of OC-associated genes as potential diagnostic biomarkers in connection with estrogen responsiveness. We show that 64% of TF biomarkers identified in our study are validated based on real-time data from microarray expression studies. As an illustration, our method could identify CP2 that in combination with CA125 has been reported to be sensitive in diagnosing ovarian tumors.

Estrogen-responsive genes in macrophages of the bony fish gilthead seabream: a transcriptomic approach

The role of sex steroids in the modulation of fish immune responses has received little attention. Previous studies have demonstrated that 17β-estradiol (E(2)) is able to alter the response of gilthead seabream leukocytes to infectious agents. We have used suppression subtractive hybridization to identify genes upregulated by E(2) (50 ng/ml) in macrophage cultures from gilthead seabream. We isolated 393 up-regulated cDNA fragments that led to the identification of 162 candidate estrogen-responsive genes. Functional analyses revealed the presence of several enriched immune processes and molecular pathways. The E(2) up-regulation of some immune-relevant genes was further confirmed by real time RT-PCR. Bioinformatics analysis revealed the ability of E(2) to orchestrate profound alterations in the macrophage expression profile, especially immune-related processes and pathways. This is the first report on E(2)-dependent modifications of fish macrophage transcriptome and lends weight to a suggested role for estrogen in the immune system, the possible significance of which is discussed.

Discovery-driven research and bioinformatics in nuclear receptor and coregulator signaling

Nuclear receptors (NRs) are a superfamily of ligand-regulated transcription factors that interact with coregulators and other transcription factors to direct tissue-specific programs of gene expression. Recent years have witnessed a rapid acceleration of the output of high-content data platforms in this field, generating discovery-driven datasets that have collectively described: the organization of the NR superfamily (phylogenomics); the expression patterns of NRs, coregulators and their target genes (transcriptomics); ligand- and tissue-specific functional NR and coregulator sites in DNA (cistromics); the organization of nuclear receptors and coregulators into higher order complexes (proteomics); and their downstream effects on homeostasis and metabolism (metabolomics). Significant bioinformatics challenges lie ahead both in the integration of this information into meaningful models of NR and coregulator biology, as well as in the archiving and communication of datasets to the global nuclear receptor signaling community. While holding great promise for the field, the ascendancy of discovery-driven research in this field brings with it a collective responsibility for researchers, publishers and funding agencies alike to ensure the effective archiving and management of these data. This review will discuss factors lying behind the increasing impact of discovery-driven research, examples of high-content datasets and their bioinformatic analysis, as well as a summary of currently curated web resources in this field. This article is part of a Special Issue entitled: Translating nuclear receptors from health to disease.

Digital candidate gene approach (DigiCGA) for identification of cancer genes

The candidate gene approach is one of the most commonly used methods for identifying genes underlying disease traits. Advances in genomics have greatly contributed to the development of this approach in the past decade. More recently, with the explosion of genomic resources accessible via the public Web, digital candidate gene approach (DigiCGA) has emerged as a new development in this field. DigiCGA, an approach still in its infancy, has already achieved some primary success in cancer gene discovery. However, a detailed discussion concerning the applications of DigiCGA in cancer gene identification has not been addressed. This chapter will focus on discussing DigiCGA in a generalized sense and its applications to the identification of cancer genes, including the cancer gene resources, application status, platform and tools, challenges, and prospects.

HRTBLDb: an informative data resource for hormone receptors target binding loci

Three hormone receptors, the estrogen receptor (ER), the androgen receptor (AR) and glucocorticoid receptor (GR) play an important role in regulating the cellular differentiation tissue development of skin, bone, the brain and the endocrine system; therefore, there is a strong scientific need to identify and characterize hormone receptor transcriptional regulation. Given that the vast amount of regulatory data for hormone being produced by ChIP-based high-throughput experiments is widely scattered in disparate, poorly cross-indexed data stores, a flexible platform for organizing and relating these data would provide significant value. We created a data management system called the Hormone Receptor Target Binding Loci, HRTBLDb (http://motif.bmi.ohio-state.edu/hrtbldb), to address this problem. This database contains hormone receptor binding regions (binding loci) from in vivo ChIP-based high-throughput experiments as well as in silico, computationally predicted, binding motifs and cis-regulatory modules for the co-occurring transcription factor binding motifs, which are within a binding locus. It also contains individual binding sites whose regulatory action has been verified by in vitro experiments. The current version contains 44,673 binding elements with 114 hormone response elements which are verified by in vitro experiments; 75 binding motifs which occur with a hormone response element and whose co-regulatory action is verified by in vitro experiments; 18,472 binding loci from in vivo experiments; and 26,012 computationally predicted binding motifs.

Estrogen-mediated suppression of the gene encoding protein tyrosine phosphatase PTPRO in human breast cancer: mechanism and role in tamoxifen sensitivity

We have previously demonstrated the tumor suppressor characteristics of protein tyrosine phosphatase receptor-type O (PTPRO) in leukemia and lung cancer, including its suppression by promoter methylation. Here, we show tumor-specific methylation of the PTPRO CpG island in primary human breast cancer. PTPRO expression was significantly reduced in established breast cancer cell lines MCF-7 and MDA-MB-231 due to promoter methylation compared with its expression in normal human mammary epithelial cells (48R and 184). Further, the silenced gene could be demethylated and reactivated in MCF-7 and MDA-MB-231 cells upon treatment with 5-Azacytidine, a DNA hypomethylating agent. Because PTPRO promoter harbors estrogen-responsive elements and 17beta-estradiol (E2) plays a role in breast carcinogenesis, we examined the effect of E2 and its antagonist tamoxifen on PTPRO expression in human mammary epithelial cells and PTPRO-expressing breast cancer cell line Hs578t. Treatment with E2 significantly curtailed PTPRO expression in 48R and Hs578t cells, which was facilitated by ectopic expression of estrogen receptor (ER)beta but not ERalpha. On the contrary, treatment with tamoxifen increased PTPRO expression. Further, knockdown of ERbeta by small interfering RNA abolished these effects of E2 and tamoxifen. Chromatin immunoprecipitation assay showed association of c-Fos and c-Jun with PTPRO promoter in untreated cells, which was augmented by tamoxifen-mediated recruitment of ERbeta to the promoter. Estradiol treatment resulted in dissociation of c-Fos and c-Jun from the promoter. Ectopic expression of PTPRO in the nonexpressing MCF-7 cells sensitized them to growth-suppressive effects of tamoxifen. These data suggest that estrogen-mediated suppression of PTPRO is probably one of the early events in estrogen-induced tumorigenesis and that expression of PTPRO could facilitate endocrine therapy of breast cancer.

Prioritizing genes of potential relevance to diseases affected by sex hormones: an example of myasthenia gravis

Background

About 5% of western populations are afflicted by autoimmune diseases many of which are affected by sex hormones. Autoimmune diseases are complex and involve many genes. Identifying these disease-associated genes contributes to development of more effective therapies. Also, association studies frequently imply genomic regions that contain disease-associated genes but fall short of pinpointing these genes. The identification of disease-associated genes has always been challenging and to date there is no universal and effective method developed.

Results

We have developed a method to prioritize disease-associated genes for diseases affected strongly by sex hormones. Our method uses various types of information available for the genes, but no information that directly links genes with the disease. It generates a score for each of the considered genes and ranks genes based on that score. We illustrate our method on early-onset myasthenia gravis (MG) using genes potentially controlled by estrogen and localized in a genomic segment (which contains the MHC and surrounding region) strongly associated with MG. Based on the considered genomic segment 283 genes are ranked for their relevance to MG and responsiveness to estrogen. The top three ranked genes, HLA-G, TAP2 and HLA-DRB1, are implicated in autoimmune diseases, while TAP2 is associated with SNPs characteristic for MG. Within the top 35 prioritized genes our method identifies 90% of the 10 already known MG-associated genes from the considered region without using any information that directly links genes to MG. Among the top eight genes we identified HLA-G and TUBB as new candidates. We show that our ab-initio approach outperforms the other methods for prioritizing disease-associated genes.

Conclusion

We have developed a method to prioritize disease-associated genes under the potential control of sex hormones. We demonstrate the success of this method by prioritizing the genes localized in the MHC and surrounding region and evaluating the role of these genes as potential candidates for estrogen control as well as MG. We show that our method outperforms the other methods. The method has a potential to be adapted to prioritize genes relevant to other diseases.