Regulation of DNA replication fork genes by 17beta-estradiol

CAPER, a novel regulator of human breast cancer progression

CAPER is an estrogen receptor (ER) co-activator that was recently shown to be involved in human breast cancer pathogenesis. Indeed, we reported increased expression of CAPER in human breast cancer specimens. We demonstrated that CAPER was undetectable or expressed at relatively low levels in normal breast tissue and assumed a cytoplasmic distribution. In contrast, CAPER was expressed at higher levels in ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) specimens, where it assumed a predominantly nuclear distribution. However, the functional role of CAPER in human breast cancer initiation and progression remained unknown. Here, we used a lentiviral-mediated gene silencing approach to reduce the expression of CAPER in the ER-positive human breast cancer cell line MCF-7. The proliferation and tumorigenicity of MCF-7 cells stably expressing control or human CAPER shRNAs was then determined via both in vitro and in vivo experiments. Knockdown of CAPER expression significantly reduced the proliferation of MCF-7 cells in vitro. Importantly, nude mice injected with MCF-7 cells harboring CAPER shRNAs developed smaller tumors than mice injected with MCF-7 cells harboring control shRNAs. Mechanistically, tumors derived from mice injected with MCF-7 cells harboring CAPER shRNAs displayed reduced expression of the cell cycle regulators PCNA, MCM7, and cyclin D1, and the protein synthesis marker 4EBP1. In conclusion, knockdown of CAPER expression markedly reduced human breast cancer cell proliferation in both in vitro and in vivo settings. Mechanistically, knockdown of CAPER abrogated the activity of proliferative and protein synthesis pathways.

On Inference for Kendall's τ within a Longitudinal Data Setting

Kendall's τ is a non-parametric measure of correlation based on ranks and is used in a wide range of research disciplines. Although methods are available for making inference about Kendall's τ, none has been extended to modeling multiple Kendall's τs arising in longitudinal data analysis. Compounding this problem is the pervasive issue of missing data in such study designs. In this paper, we develop a novel approach to provide inference about Kendall's τ within a longitudinal study setting under both complete and missing data. The proposed approach is illustrated with simulated data and applied to an HIV prevention study.

A response to information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments

Background

For gene expression data obtained from a time-course microarray experiment, Liu et al. [1] developed a new algorithm for clustering genes with similar expression profiles over time. Performance of their proposal was compared with three other methods including the order-restricted inference based methodology of Peddada et al. [2,3]. In this note we point out several inaccuracies in Liu et al. [1] and conclude that the order-restricted inference based methodology of Peddada et al. (programmed in the software ORIOGEN) indeed operates at the desired nominal Type 1 error level, an important feature of a statistical decision rule, while being computationally substantially faster than indicated by Liu et al. [1].

Results

Application of ORIOGEN to the well-known breast cancer cell line data of Lobenhofer et al. [4] revealed that ORIOGEN software took only 21 minutes to run (using 100,000 bootstraps with p = 0.0025), substantially faster than the 72 hours found by Liu et al. [1] using Matlab. Also, based on a data simulated according to the model and parameters of simulation 1 (σ² = 1, M = 5) in [1] we found that ORIOGEN took less than 30 seconds to run in stark contrast to Liu et al. who reported that their implementation of the same algorithm in R took 2979.29 seconds. Furthermore, for the simulation studies reported in [1], unlike the claims made by Liu et al. [1], ORIOGEN always maintained the desired false positive rate. According to Figure three in Liu et al. [1] their algorithm had a false positive rate ranging approximately from 0.20 to 0.70 for the scenarios that they simulated.

Conclusions

Our comparisons of run times indicate that the implementations of ORIOGEN's algorithm in Matlab and R by Liu et al. [1] is inefficient compared to the publicly available JAVA implementation. Our results on the false positive rate of ORIOGEN suggest some error in Figure three of Liu et al. [1], perhaps due to a programming error.

Controlling false discoveries in multidimensional directional decisions, with applications to gene expression data on ordered categories

Microarray gene expression studies over ordered categories are routinely conducted to gain insights into biological functions of genes and the underlying biological processes. Some common experiments are time-course/dose-response experiments where a tissue or cell line is exposed to different doses and/or durations of time to a chemical. A goal of such studies is to identify gene expression patterns/profiles over the ordered categories. This problem can be formulated as a multiple testing problem where for each gene the null hypothesis of no difference between the successive mean gene expressions is tested and further directional decisions are made if it is rejected. Much of the existing multiple testing procedures are devised for controlling the usual false discovery rate (FDR) rather than the mixed directional FDR (mdFDR), the expected proportion of Type I and directional errors among all rejections. Benjamini and Yekutieli (2005, Journal of the American Statistical Association 100, 71-93) proved that an augmentation of the usual Benjamini-Hochberg (BH) procedure can control the mdFDR while testing simple null hypotheses against two-sided alternatives in terms of one-dimensional parameters. In this article, we consider the problem of controlling the mdFDR involving multidimensional parameters. To deal with this problem, we develop a procedure extending that of Benjamini and Yekutieli based on the Bonferroni test for each gene. A proof is given for its mdFDR control when the underlying test statistics are independent across the genes. The results of a simulation study evaluating its performance under independence as well as under dependence of the underlying test statistics across the genes relative to other relevant procedures are reported. Finally, the proposed methodology is applied to a time-course microarray data obtained by Lobenhofer et al. (2002, Molecular Endocrinology 16, 1215-1229). We identified several important cell-cycle genes, such as DNA replication/repair gene MCM4 and replication factor subunit C2, which were not identified by the previous analyses of the same data by Lobenhofer et al. (2002) and Peddada et al. (2003, Bioinformatics 19, 834-841). Although some of our findings overlap with previous findings, we identify several other genes that complement the results of Lobenhofer et al. (2002).

Information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments

Background

Time-course microarray experiments produce vector gene expression profiles across a series of time points. Clustering genes based on these profiles is important in discovering functional related and co-regulated genes. Early developed clustering algorithms do not take advantage of the ordering in a time-course study, explicit use of which should allow more sensitive detection of genes that display a consistent pattern over time. Peddada et al. [1] proposed a clustering algorithm that can incorporate the temporal ordering using order-restricted statistical inference. This algorithm is, however, very time-consuming and hence inapplicable to most microarray experiments that contain a large number of genes. Its computational burden also imposes difficulty to assess the clustering reliability, which is a very important measure when clustering noisy microarray data.

Results

We propose a computationally efficient information criterion-based clustering algorithm, called ORICC, that also takes account of the ordering in time-course microarray experiments by embedding the order-restricted inference into a model selection framework. Genes are assigned to the profile which they best match determined by a newly proposed information criterion for order-restricted inference. In addition, we also developed a bootstrap procedure to assess ORICC's clustering reliability for every gene. Simulation studies show that the ORICC method is robust, always gives better clustering accuracy than Peddada's method and saves hundreds of times computational time. Under some scenarios, its accuracy is also better than some other existing clustering methods for short time-course microarray data, such as STEM [2] and Wang et al. [3]. It is also computationally much faster than Wang et al. [3].

Conclusion

Our ORICC algorithm, which takes advantage of the temporal ordering in time-course microarray experiments, provides good clustering accuracy and is meanwhile much faster than Peddada's method. Moreover, the clustering reliability for each gene can also be assessed, which is unavailable in Peddada's method. In a real data example, the ORICC algorithm identifies new and interesting genes that previous analyses failed to reveal.

Role of SP transcription factors in hormone-dependent modulation of genes in MCF-7 breast cancer cells: microarray and RNA interference studies

17beta-estradiol (E(2)) binds estrogen receptor alpha (ESR1) in MCF-7 cells and increases cell proliferation and survival through induction or repression of multiple genes. ESR1 interactions with DNA-bound specificity protein (SP) transcription factors is a nonclassical genomic estrogenic pathway and the role of SP transcription factors in mediating hormone-dependent activation or repression of genes in MCF-7 cells was investigated by microarrays and RNA interference. MCF-7 cells were transfected with a nonspecific oligonucleotide or a cocktail of small inhibitory RNAs (iSP), which knockdown SP1, SP3, and SP4 proteins, and treated with dimethylsulfoxide or 10 nM E(2) for 6 h. E(2) induced 62 and repressed 134 genes and the induction or repression was reversed in approximately 62% of the genes in cells transfected with iSP (ESR1/SP dependent), whereas hormonal activation or repression of the remaining genes was unaffected by iSP (SP independent). Analysis of the ESR1/SP-dependent and SP-independent genes showed minimal overlap with respect to the GO terms (functional processes) in genes induced or repressed, suggesting that the different genomic pathways may contribute independently to the hormone-induced phenotype in MCF-7 cells.

Genome wide transcriptional profiling in breast cancer cells reveals distinct changes in hormone receptor target genes and chromatin modifying enzymes after proteasome inhibition

Steroid hormone receptors, like glucocorticoid (GR) and estrogen receptors (ER), are master regulators of genes that control many biological processes implicated in health and disease. Gene expression is dependent on receptor levels which are tightly regulated by the ubiquitin-proteasome system. Previous studies have shown that proteasome inhibition increases GR, but decreases ER-mediated gene expression. At the gene expression level this divergent role of the proteasome in receptor-dependent transcriptional regulation is not well understood. We have used a genomic approach to examine the impact of proteasome activity on GR- and ER-mediated gene expression in MCF-7 breast cancer cells treated with dexamethasone (DEX) or 17beta-estradiol (E2), the proteasome inhibitor MG132 (MG) or MG132 and either hormone (MD or ME2) for 24 h. Transcript profiling reveals that inhibiting proteasome activity modulates gene expression by GR and ER in a similar manner in that several GR and ER target genes are upregulated and downregulated after proteasome inhibition. In addition, proteasome inhibition modulates receptor-dependent genes involved in the etiology of a number of human pathological states, including multiple myeloma, leukemia, breast/prostate cancer, HIV/AIDS, and neurodegenerative disorders. Importantly, our analysis reveals that a number of transcripts encoding histone and DNA modifying enzymes, prominently histone/DNA methyltransferases and demethylases, are altered after proteasome inhibition. As proteasome inhibitors are currently in clinical trials as therapy for multiple myeloma, HIV/AIDS and leukemia, the possibility that some of the target molecules are hormone regulated and chromatin modifying enzymes is intriguing in this era of epigenetic therapy.

Identification of functional networks of estrogen- and c-Myc-responsive genes and their relationship to response to tamoxifen therapy in breast cancer

Background

Estrogen is a pivotal regulator of cell proliferation in the normal breast and breast cancer. Endocrine therapies targeting the estrogen receptor are effective in breast cancer, but their success is limited by intrinsic and acquired resistance.

Methodology/Principal Findings

With the goal of gaining mechanistic insights into estrogen action and endocrine resistance, we classified estrogen-regulated genes by function, and determined the relationship between functionally-related genesets and the response to tamoxifen in breast cancer patients. Estrogen-responsive genes were identified by transcript profiling of MCF-7 breast cancer cells. Pathway analysis based on functional annotation of these estrogen-regulated genes identified gene signatures with known or predicted roles in cell cycle control, cell growth (i.e. ribosome biogenesis and protein synthesis), cell death/survival signaling and transcriptional regulation. Since inducible expression of c-Myc in antiestrogen-arrested cells can recapitulate many of the effects of estrogen on molecular endpoints related to cell cycle progression, the estrogen-regulated genes that were also targets of c-Myc were identified using cells inducibly expressing c-Myc. Selected genes classified as estrogen and c-Myc targets displayed similar levels of regulation by estrogen and c-Myc and were not estrogen-regulated in the presence of siMyc. Genes regulated by c-Myc accounted for 50% of all acutely estrogen-regulated genes but comprised 85% (110/129 genes) in the cell growth signature. siRNA-mediated inhibition of c-Myc induction impaired estrogen regulation of ribosome biogenesis and protein synthesis, consistent with the prediction that estrogen regulates cell growth principally via c-Myc. The ‘cell cycle’, ‘cell growth’ and ‘cell death’ gene signatures each identified patients with an attenuated response in a cohort of 246 tamoxifen-treated patients. In multivariate analysis the cell death signature was predictive independent of the cell cycle and cell growth signatures.

Conclusions/Significance

These functionally-based gene signatures can stratify patients treated with tamoxifen into groups with differing outcome, and potentially identify distinct mechanisms of tamoxifen resistance.

Adaptive choice of the number of bootstrap samples in large scale multiple testing

It is a common practice to use resampling methods such as the bootstrap for calculating the p-value for each test when performing large scale multiple testing. The precision of the bootstrap p-values and that of the false discovery rate (FDR) relies on the number of bootstraps used for testing each hypothesis. Clearly, the larger the number of bootstraps the better the precision. However, the required number of bootstraps can be computationally burdensome, and it multiplies the number of tests to be performed. Further adding to the computational challenge is that in some applications the calculation of the test statistic itself may require considerable computation time. As technology improves one can expect the dimension of the problem to increase as well. For instance, during the early days of microarray technology, the number of probes on a cDNA chip was less than 10,000. Now the Affymetrix chips come with over 50,000 probes per chip. Motivated by this important need, we developed a simple adaptive bootstrap methodology for large scale multiple testing, which reduces the total number of bootstrap calculations while ensuring the control of the FDR. The proposed algorithm results in a substantial reduction in the number of bootstrap samples. Based on a simulation study we found that, relative to the number of bootstraps required for the Benjamini-Hochberg (BH) procedure, the standard FDR methodology which was the proposed methodology achieved a very substantial reduction in the number of bootstraps. In some cases the new algorithm required as little as 1/6th the number of bootstraps as the conventional BH procedure. Thus, if the conventional BH procedure used 1,000 bootstraps, then the proposed method required only 160 bootstraps. This methodology has been implemented for time-course/dose-response data in our software, ORIOGEN, which is available from the authors upon request.

Vascular endothelial growth factor receptor-2 expression is down-regulated by 17beta-estradiol in MCF-7 breast cancer cells by estrogen receptor alpha/Sp proteins

17beta-Estradiol (E2) induces and represses gene expression in breast cancer cells; however, the mechanisms of gene repression are not well understood. In this study, we show that E2 decreases vascular endothelial growth factor receptor 2 (VEGFR2) mRNA levels in MCF-7 cells, and this gene was used as a model for investigating pathways associated with E2-dependent gene repression. Deletion analysis of the VEGFR2 promoter indicates that the proximal GC-rich motifs at -58 and -44 are critical for the E2-dependent decreased response in MCF-7 cells. Mutation or deletion of these GC-rich elements results in loss of hormone responsiveness and shows that the -60 to -37 region of the VEGFR2 promoter is critical for both basal and hormone-dependent decreased VEGFR2 expression in MCF-7 cells. Western blot, immunofluorescent staining, RNA interference, and EMSAs support a role for Sp proteins in hormone-dependent down-regulation of VEGFR2 in MCF-7 cells, primarily through estrogen receptor (ER)alpha/Sp1 and ERalpha/Sp3 interactions with the VEGFR2 promoter. Using chromatin immuno-precipitation and transient transfection/RNA interference assays we show that the ERalpha/Sp protein-promoter interactions are accompanied by recruitment of the co-repressors SMRT (silencing mediator of retinoid and thyroid hormone receptor) and NCoR (nuclear receptor corepressor) to the promoter and that SMRT and NCoR knockdown reverse E2-mediated down-regulation of VEGFR2 expression in MCF-7 cells. This study illustrates that both SMRT and NCoR are involved in E2-dependent repression of VEGFR2 in MCF-7 cells.

Interplay of genes regulated by estrogen and diindolylmethane in breast cancer cell lines

Diindolylmethane (DIM), a biologically active congener of indole-3-carbinol (I3C) derived from cruciferous vegetables, is a promising agent for the prevention of estrogen-sensitive cancers. Both DIM and estrogen affect transcription of genes by binding receptors, such as aryl hydrocarbon receptor (AhR) or estrogen receptors (ER). Gene regulation by DIM and estradiol (E2) can be very complex. While DIM typically binds the AhR, this complex can directly associate with the ER, recruit co-activators that bind to estrogen-responsive promoters, and activate transcription. Alternately, DIM can bind the ER directly. In this study, we have analyzed gene expression using microarray profiling and quantitative real time-polymerase chain reaction in MCF7 breast cancer cells treated with E2 (1 nM) or DIM (25 microM) alone or in combination for 16 h. The interplay of E2 and DIM was reflected in the expression of a subset of genes (<90) in which the combination of E2 and DIM acted either additively or antagonistically to alter gene expression.

Inhibitory effects of estrogen receptor beta on specific hormone-responsive gene expression and association with disease outcome in primary breast cancer

Introduction

The impact of interactions between the two estrogen receptor (ER) subtypes, ERα and ERβ, on gene expression in breast cancer biology is not clear. The goal of this study was to examine transcriptomic alterations in cancer cells co-expressing both receptors and the association of gene expression signatures with disease outcome.

Methods

Transcriptional effects of ERβ overexpression were determined in a stably transfected cell line derived from ERα-positive T-47D cells. Microarray analysis was carried out to identify differential gene expression in the cell line, and expression of key genes was validated by quantitative polymerase chain reaction. Microarray and clinical data from patient samples were then assessed to determine the in vivo relevance of the expression profiles observed in the cell line.

Results

A subset of 14 DNA replication and cell cycle-related genes was found to be specifically downregulated by ERβ. Expression profiles of four genes, CDC2, CDC6, CKS2, and DNA2L, were significantly inversely correlated with ERβ transcript levels in patient samples, consistent with in vitro observations. Kaplan-Meier analysis revealed better disease outcome for the patient group with an expression signature linked to higher ERβ expression as compared to the lower ERβ-expressing group for both disease-free survival (p = 0.00165) and disease-specific survival (p = 0.0268). These findings were further validated in an independent cohort.

Conclusion

Our findings revealed a transcriptionally regulated mechanism for the previously described growth inhibitory effects of ERβ in ERα-positive breast tumor cells and provide evidence for a functional and beneficial impact of ERβ in primary breast tumors.

Order-restricted inference for ordered gene expression (ORIOGEN) data under heteroscedastic variances

This article extends the order restricted inference approach for time-course or dose-response gene expression microarray data, introduced by Peddada and colleagues (2003) for the case when gene expression is heteroscedastic over time or dose. The new methodology uses an iterative algorithm to estimate mean expression at various times/doses when mean expression is subject to pre-defined patterns or profiles, known as order-restrictions. Simulation studies reveal that the resulting bootstrap-based methodology for gene selection maintains the false positive rate at the nominal level while competing well with ORIOGEN in terms of power. The proposed methodology is illustrated using a breast cancer cell-line data analyzed by Peddada and colleagues (2003).

Importance of dosage standardization for interpreting transcriptomal signature profiles: evidence from studies of xenoestrogens

To obtain insights into similarities and differences in the biological actions of related drugs or toxic agents, their transcriptomal signature profiles (TSPs) have been examined in a large number of studies. However, many such reports did not provide proper justification for the dosage criteria of each agent. Using a well characterized cell culture model of estrogen-dependent proliferation of MCF7 human breast cancer cells, we demonstrate how different approaches to dosage standardization exert critical influences on TSPs, leading to different and even conflicting conclusions. Using quantitative cellular response (QCR)-based dosage criteria, TSPs were determined by Affymetrix microarray when cells were proliferating at comparable rates in the presence of various estrogens. We observed that TSPs of the xenoestrogens (e.g., genistein or bisphenol A) were clearly different from the TSP of 17beta-estradiol; namely, the former strongly enhanced expression of genes involved in mitochondrial oxidative phosphorylation, whereas the latter showed minimal effects. In contrast, TSPs for genistein and 17beta-estradiol were indistinguishable by using the marker gene expression-based dosage criteria, conditions in which there was comparable expression of the mRNA transcripts for the estrogen-inducible WISP2 gene. Our findings indicate that determination and interpretation of TSPs in pharmacogenomic and toxicogenomic studies that examine the transcriptomal actions of related agents by microarray require a clear rationale for the dosage standardization method to be used. We suggest that future studies involving TSP analyses use quantitative and objective dosage standardization methods, such as those with quantitative cellular response or marker gene expression-based dosage criteria.

Estrogen down-regulation of the corepressor N-CoR: mechanism and implications for estrogen derepression of N-CoR-regulated genes

The nuclear receptor corepressor N-CoR plays a crucial role in the repressive activity of diverse transcription factors, yet little is known about what regulates its cellular level. We have found that estrogen markedly down-regulates N-CoR protein levels in estrogen receptor (ER)-positive breast cancer cells without affecting N-CoR mRNA levels, whereas levels of the related corepressor SMRT are unaffected. This effect is attributable to estrogen up-regulation of the ubiquitin ligase Siah2, which is a rapid and primary transcriptional response mediated by the ER, and precedes the loss of N-CoR. Treatment with proteasomal inhibitor or with small interfering RNA against Siah2 prevented the down-regulation of N-CoR by estrogen. Furthermore, the expression of 24-hydroxylase, a gene repressed by unliganded vitamin D receptor through its interaction with N-CoR, was up-regulated by estrogen and required Siah2. Our results illustrate a mechanism by which the estrogen-ER complex markedly reduces the level of N-CoR through a process involving the up-regulation of Siah2 and the subsequent targeting of N-CoR for proteasomal degradation. These findings reveal that, although estrogen directly regulates the transcription of many genes, by regulating a gene such as Siah2 it can exert profound "secondary" effects on cellular activity through mechanisms such as targeting regulatory proteins for degradation. This estrogen-evoked down-regulation of N-CoR could have a global derepressive effect on genes whose repression depends on N-CoR and thereby have broad impact on the activity of transcription factors and nuclear receptors whose actions involve N-CoR.

From the Cover: Location analysis of estrogen receptor alpha target promoters reveals that FOXA1 defines a domain of the estrogen response

Nuclear receptors can activate diverse biological pathways within a target cell in response to their cognate ligands, but how this compartmentalization is achieved at the level of gene regulation is poorly understood. We used a genome-wide analysis of promoter occupancy by the estrogen receptor alpha (ERalpha) in MCF-7 cells to investigate the molecular mechanisms underlying the action of 17beta-estradiol (E2) in controlling the growth of breast cancer cells. We identified 153 promoters bound by ERalpha in the presence of E2. Motif-finding algorithms demonstrated that the estrogen response element (ERE) is the most common motif present in these promoters whereas conventional chromatin immunoprecipitation assays showed E2-modulated recruitment of coactivator AIB1 and RNA polymerase II at these loci. The promoters were linked to known ERalpha targets but also to many genes not directly associated with the estrogenic response, including the transcriptional factor FOXA1, whose expression correlates with the presence of ERalpha in breast tumors. We found that ablation of FOXA1 expression in MCF-7 cells suppressed ERalpha binding to the prototypic TFF1 promoter (which contains a FOXA1 binding site), hindered the induction of TFF1 expression by E2, and prevented hormone-induced reentry into the cell cycle. Taken together, these results define a paradigm for estrogen action in breast cancer cells and suggest that regulation of gene expression by nuclear receptors can be compartmentalized into unique transcriptional domains by means of licensing of their activity to cofactors such as FOXA1.

Estrogen-induced changes in IGF-I, Myb family and MAP kinase pathway genes in human uterine leiomyoma and normal uterine smooth muscle cell lines

Many studies have implicated numerous hormones, growth factors, cytokines and other signal transduction molecules in the pathogenesis of uterine leiomyoma. Estrogen and estrogen-related genes are thought to play a key role in the growth of uterine leiomyomas, but the molecular mechanisms are unclear. In an attempt to investigate various pathways that might be involved in estrogen-regulated uterine leiomyoma growth as well as to identify any novel effector genes, microarray studies comparing estrogen-treated uterine leiomyoma cells (UtLM) and normal myometrial cells to untreated cells were performed. Several genes were differentially expressed in estrogen treated UtLM cells, including insulin-like growth factor-I (IGF-I) and others potentially involved in the IGF-I signalling pathway, specifically genes for A-myb, a transcription factor which promotes cell cycle progression and for MKP-1, a dual specificity phosphatase that dephosphorylates mitogen-activated protein kinase. IGF-I and A-myb were up-regulated in estrogen-treated cells while MKP-1 was down-regulated. Two other cell cycle promoting genes, c-fos and myc, were also down-regulated in estrogen treated UtLM cells. These genes are typically up-regulated in response to estrogen in some cells, notably breast epithelial cells, yet consistently have lower expression levels in uterine leiomyoma tissue when compared to autologous myometrium. Our results demonstrate some novel genes that may play a role in the growth of uterine leiomyoma, strengthen the case for involvement of the IGF-I pathway in the response of UtLM to estrogen and corroborate evidence that uterine smooth muscle cells respond to estrogen with a different gene expression pattern than that seen in epithelial cells.

Identifying estrogen receptor alpha target genes using integrated computational genomics and chromatin immunoprecipitation microarray

The estrogen receptor alpha (ERalpha) regulates gene expression by either direct binding to estrogen response elements or indirect tethering to other transcription factors on promoter targets. To identify these promoter sequences, we conducted a genome-wide screening with a novel microarray technique called ChIP-on-chip. A set of 70 candidate ERalpha loci were identified and the corresponding promoter sequences were analyzed by statistical pattern recognition and comparative genomics approaches. We found mouse counterparts for 63 of these loci and classified 42 (67%) as direct ERalpha targets using classification and regression tree (CART) statistical model, which involves position weight matrix and human-mouse sequence similarity scores as model parameters. The remaining genes were considered to be indirect targets. To validate this computational prediction, we conducted an additional ChIP-on-chip assay that identified acetylated chromatin components in active ERalpha promoters. Of the 27 loci upregulated in an ERalpha-positive breast cancer cell line, 20 having mouse counterparts were correctly predicted by CART. This integrated approach, therefore, sets a paradigm in which the iterative process of model refinement and experimental verification will continue until an accurate prediction of promoter target sequences is derived.

Computational method for discovery of estrogen responsive genes

Estrogen has a profound impact on human physiology and affects numerous genes. The classical estrogen reaction is mediated by its receptors (ERs), which bind to the estrogen response elements (EREs) in target gene's promoter region. Due to tedious and expensive experiments, a limited number of human genes are functionally well characterized. It is still unclear how many and which human genes respond to estrogen treatment. We propose a simple, economic, yet effective computational method to predict a subclass of estrogen responsive genes. Our method relies on the similarity of ERE frames across different promoters in the human genome. Matching ERE frames of a test set of 60 known estrogen responsive genes to the collection of over 18,000 human promoters, we obtained 604 candidate genes. Evaluating our result by comparison with the published microarray data and literature, we found that more than half (53.6%, 324/604) of predicted candidate genes are responsive to estrogen. We believe this method can significantly reduce the number of testing potential estrogen target genes and provide functional clues for annotating part of genes that lack functional information.

Breast cancer oestrogen independence mediated by BCAR1 or BCAR3 genes is transmitted through mechanisms distinct from the oestrogen receptor signalling pathway or the epidermal growth factor receptor signalling pathway

Introduction

Tamoxifen is effective for endocrine treatment of oestrogen receptor-positive breast cancers but ultimately fails due to the development of resistance. A functional screen in human breast cancer cells identified two BCAR genes causing oestrogen-independent proliferation. The BCAR1 and BCAR3 genes both encode components of intracellular signal transduction, but their direct effect on breast cancer cell proliferation is not known. The aim of this study was to investigate the growth control mediated by these BCAR genes by gene expression profiling.

Methods

We have measured the expression changes induced by overexpression of the BCAR1 or BCAR3 gene in ZR-75-1 cells and have made direct comparisons with the expression changes after cell stimulation with oestrogen or epidermal growth factor (EGF). A comparison with published gene expression data of cell models and breast tumours is made.

Results

Relatively few changes in gene expression were detected in the BCAR-transfected cells, in comparison with the extensive and distinct differences in gene expression induced by oestrogen or EGF. Both BCAR1 and BCAR3 regulate discrete sets of genes in these ZR-75-1-derived cells, indicating that the proliferation signalling proceeds along distinct pathways. Oestrogen-regulated genes in our cell model showed general concordance with reported data of cell models and gene expression association with oestrogen receptor status of breast tumours.

Conclusions

The direct comparison of the expression profiles of BCAR transfectants and oestrogen or EGF-stimulated cells strongly suggests that anti-oestrogen-resistant cell proliferation is not caused by alternative activation of the oestrogen receptor or by the epidermal growth factor receptor signalling pathway.

Discovery of estrogen receptor alpha target genes and response elements in breast tumor cells

Microarray analysis has identified 89 estrogen target genes. The cis-regulatory elements found upstream of those genes are not well conserved in mouse and human.

Background

Estrogens and their receptors are important in human development, physiology and disease. In this study, we utilized an integrated genome-wide molecular and computational approach to characterize the interaction between the activated estrogen receptor (ER) and the regulatory elements of candidate target genes.

Results

Of around 19,000 genes surveyed in this study, we observed 137 ER-regulated genes in T-47D cells, of which only 89 were direct target genes. Meta-analysis of heterogeneous in vitro and in vivo datasets showed that the expression profiles in T-47D and MCF-7 cells are remarkably similar and overlap with genes differentially expressed between ER-positive and ER-negative tumors. Computational analysis revealed a significant enrichment of putative estrogen response elements (EREs) in the cis-regulatory regions of direct target genes. Chromatin immunoprecipitation confirmed ligand-dependent ER binding at the computationally predicted EREs in our highest ranked ER direct target genes, NRIP1, GREB1 and ABCA3. Wider examination of the cis-regulatory regions flanking the transcriptional start sites showed species conservation in mouse-human comparisons in only 6% of predicted EREs.

Conclusions

Only a small core set of human genes, validated across experimental systems and closely associated with ER status in breast tumors, appear to be sufficient to induce ER effects in breast cancer cells. That cis-regulatory regions of these core ER target genes are poorly conserved suggests that different evolutionary mechanisms are operative at transcriptional control elements than at coding regions. These results predict that certain biological effects of estrogen signaling will differ between mouse and human to a larger extent than previously thought.

Identification of human and mouse CatSper3 and CatSper4 genes: characterisation of a common interaction domain and evidence for expression in testis

BACKGROUND

CatSper1 and CatSper2 are two recently identified channel-like proteins, which show sperm specific expression patterns. Through targeted mutagenesis in the mouse, CatSper1 has been shown to be required for fertility, sperm motility and for cAMP induced Ca2+ current in sperm. Both channels resemble a single pore forming repeat from a four repeat voltage dependent Ca2+ /Na+ channel. However, neither CatSper1 or CatSper2 have been shown to function as cation channels when transfected into cells, singly or in conjunction. As the pore forming units of voltage gated cation channels form a tetramer it has been suggested that the known CatSper proteins require additional subunits and/or interaction partners to function.

RESULTS

Using in silico gene identification and prediction techniques, we have identified two further members of the CatSper family, CatSper3 and Catsper4. Each carries a single channel-forming domain with the predicted pore-loop containing the consensus sequence TxDxW. Each of the new CatSper genes has evidence for expression in the testis. Furthermore we identified coiled-coil protein-protein interaction domains in the C-terminal tails of each of the CatSper channels, implying that CatSper channels 1,2,3 and 4 may interact directly or indirectly to form a functional tetramer.

CONCLUSIONS

The topological and sequence relationship of CatSper1 and CatSper2 to the four repeat Ca2+ /Na+ channels suggested other members of this family may exist. We have identified a further two novel CatSper genes, conserved in both the human and mouse genomes. Furthermore, all four of the CatSper proteins are predicted to contain a common coiled-coil protein-protein interaction domain in their C-terminal tail. Coupled with expression data this leads to the hypothesis that the CatSper proteins form a functional hetero-tetrameric channel in sperm.

Estrogen receptor-dependent proteasomal degradation of the glucocorticoid receptor is coupled to an increase in mdm2 protein expression

Glucocorticoids and estrogens regulate a number of vital physiological processes. We developed a model breast cancer cell line, MCF-7 M, to examine potential mechanisms by which the ligand-bound estrogen receptor (ER) regulates glucocorticoid receptor (GR)-mediated transcription. MCF-7 cells, which endogenously express ERalpha, were stably transfected with mouse mammary tumor virus promoter-luciferase (MMTV-LUC) reporter and GR expression constructs. Our results demonstrate that treatment with estrogen agonists (17beta-estradiol [E2], diethylstilbestrol, genistein), but not antagonists (tamoxifen or raloxifene), for 48 h inhibits GR-mediated MMTV-LUC transcription and chromatin remodeling. Furthermore, estrogen agonists inhibit glucocorticoid induction of p21 mRNA and protein levels, suggesting that the repressive effect applies to other GR-regulated genes and proteins in MCF-7 cells. Importantly, GR transcriptional activity is compromised because treatment with estrogen agonists down regulates GR protein levels. The protein synthesis inhibitor cycloheximide and the proteasome inhibitor MG132 block E2-mediated decrease in GR protein levels, suggesting that estrogen agonists down regulate the GR via the proteasomal degradation pathway. In support of this, we demonstrate that E2-mediated GR degradation is coupled to an increase in p53 and its key regulator protein Mdm2 (murine double minute 2), an E3 ubiquitin ligase shown to target the GR for degradation. Using the chromatin immunoprecipitation assay, we demonstrate an E2-dependent recruitment of ERalpha to the Mdm2 promoter, suggesting a role of ER in the regulation of Mdm2 protein expression and hence the enhanced GR degradation in the presence of estrogen agonists. Our study shows that cross talk between the GR and ER involves multiple signaling pathways, indicative of the mechanistic diversity within steroid receptor-regulated transcription.