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BackgroundEstrogens play an important role in breast cancer (BC) development and progression; when the two isoforms of the estrogen receptor (ERα and ERβ) are co-expressed each of them mediate specific effects of these hormones in BC cells. ERβ has been suggested to exert an antagonist role toward the oncogenic activities of ERα, and for this reason it is considered an oncosuppressor. As clinical evidence regarding a prognostic role for this receptor subtype in hormone-responsive BC is still limited and conflicting, more knowledge is required on the biological functions of ERβ in cancer cells. We have previously described the ERβ and ERα interactomes from BC cells, identifying specific and distinct patterns of protein interactions for the two receptors. In particular, we identified factors involved in mRNA splicing and maturation as important components of both ERα and ERβ pathways. Guided by these findings, here we performed RNA sequencing to investigate in depth the differences in the early transcriptional events and RNA splicing patterns induced by estradiol in cells expressing ERα alone or ERα and ERβ.ResultsExon skipping was the most abundant splicing event in the post-transcriptional regulation by estradiol. We identified several splicing events induced by ERα alone and by ERα+ERβ, demonstrating for the first time that ERβ significantly affects estrogen-induced splicing in BC cells, as revealed by modification of a subset of ERα-dependent splicing by ERβ, as well as by the presence of splicing isoforms only in ERβ+cells. In particular, we observed that ERβ+BC cell lines exhibited around 2-fold more splicing events than the ERβ- cells. Interestingly, we identified putative direct targets of ERβ-mediated alternative splicing by correlating the genomic locations of ERβ and ERα binding sites with estradiol-induced differential splicing in the corresponding genes.ConclusionsTaken together, these results demonstrate that ERβ significantly affects estrogen-induced early transcription and mRNA splicing in hormone-responsive BC cells, providing novel information on the biological role of ERβ in these tumors.

This article discusses a study on DNA methylation changes in hormone-responsive breast cancer cells. The researchers inhibited two proteins, ERα and DOT1L, and analyzed the resulting DNA methylation patterns using bioinformatics and functional annotation analyses. They found that the differentially methylated CpGs were mainly located in gene promoters and enhancers, and these changes were associated with estrogen response gene pathways. The study provides valuable data for future research on the role of DNA methylation in breast cancer. The article also includes information about funding sources, conflicts of interest, and references to other relevant articles. [Extracted from the article]

In the complex and articulated machinery of the human genome, less than 2% of the transcriptome encodes for proteins, while at least 75% is actively transcribed into non-coding RNAs (ncRNAs). Among the non-coding transcripts, those ≥200 nucleotides long (lncRNAs) are receiving growing attention for their involvement in human diseases, particularly cancer. Genomic studies have revealed the multiplicity of processes, including neoplastic transformation and tumor progression, in which lncRNAs are involved by regulating gene expression at epigenetic, transcriptional, and post-transcriptional levels by mechanism(s) that still need to be clarified. In breast cancer, several lncRNAs were identified and demonstrated to have either oncogenic or tumor-suppressive roles. The functional understanding of the mechanisms of lncRNA action in this disease could represent a potential for translational applications, as these molecules may serve as novel biomarkers of clinical use and potential therapeutic targets. This review highlights the relationship between lncRNAs and the principal hallmark of the luminal breast cancer phenotype, estrogen receptor α (ERα), providing an overview of new potential ways to inhibit estrogenic signaling via this nuclear receptor toward escaping resistance to endocrine therapy. [ABSTRACT FROM AUTHOR]

The article discusses that breast cancer resistance to endocrine therapy results from constitutively active or aberrant estrogen receptor signaling, and ways to block pathway in these tumors are sought after. The methyltransferase is a cofactor in BC cell chromatin, in which the two proteins colocalize to regulate estrogen target gene transcription.

Estrogen responsive breast cancer cell lines have been extensively studied to characterize transcriptional patterns in hormone-responsive tumors. Nevertheless, due to current technological limitations, genome-wide studies have typically been limited to population averaged data. Here we obtain, for the first time, a characterization at the single-cell level of the states and expression signatures of a hormone-starved MCF-7 cell system responding to estrogen. To do so, we employ a recently proposed model that allows for dissecting single-cell states from time-course microarray data. We show that within 32 hours following stimulation, MCF-7 cells traverse, most likely, six states, with a faster early response followed by a progressive deceleration. We also derive the genome-wide transcriptional profiles of such single-cell states and their functional characterization. Our results support a scenario where estrogen promotes cell cycle progression by controlling multiple, sequential regulatory steps, whose single-cell events are here identified. [ABSTRACT FROM AUTHOR]

Background Qualitative and quantitative analysis of small non-coding RNAs by next generation sequencing (smallRNA-Seq) represents a novel technology increasingly used to investigate with high sensitivity and specificity RNA population comprising microRNAs and other regulatory small transcripts. Analysis of smallRNA-Seq data to gather biologically relevant information, i.e. detection and differential expression analysis of known and novel noncoding RNAs, target prediction, etc., requires implementation of multiple statistical and bioinformatics tools from different sources, each focusing on a specific step of the analysis pipeline. As a consequence, the analytical workflow is slowed down by the need for continuous interventions by the operator, a critical factor when large numbers of datasets need to be analyzed at once. Results We designed a novel modular pipeline (iMir) for comprehensive analysis of smallRNA-Seq data, comprising specific tools for adapter trimming, quality filtering, differential expression analysis, biological target prediction and other useful options by integrating multiple open source modules and resources in an automated workflow. As statistics is crucial in deepsequencing data analysis, we devised and integrated in iMir tools based on different statistical approaches to allow the operator to analyze data rigorously. The pipeline created here proved to be efficient and time-saving than currently available methods and, in addition, flexible enough to allow the user to select the preferred combination of analytical steps. We present here the results obtained by applying this pipeline to analyze simultaneously 6 smallRNASeq datasets from either exponentially growing or growth-arrested human breast cancer MCF-7 cells, that led to the rapid and accurate identification, quantitation and differential expression analysis of ~450 miRNAs, including several novel miRNAs and isomiRs, as well as identification of the putative mRNA targets of differentially expressed miRNAs. In addition, iMir allowed also the identification of ~70 piRNAs (piwi-interacting RNAs), some of which differentially expressed in proliferating vs growth arrested cells. Conclusion The integrated data analysis pipeline described here is based on a reliable, flexible and fully automated workflow, useful to rapidly and efficiently analyze high-throughput smallRNASeq data, such as those produced by the most recent high-performance next generation sequencers. iMir is available at http://www.labmedmolge.unisa.it/inglese/research/imir. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is a heterogeneous disease characterized by different biopathological features, differential response to therapy and substantial variability in long-term-survival. BC heterogeneity recapitulates genetic and epigenetic alterations affecting transformed cell behavior. The estrogen receptor alpha positive (ERα+) is the most common BC subtype, generally associated with a better prognosis and improved long-term survival, when compared to ERα-tumors. This is mainly due to the efficacy of endocrine therapy, that interfering with estrogen biosynthesis and actions blocks ER-mediated cell proliferation and tumor spread. Acquired resistance to endocrine therapy, however, represents a great challenge in the clinical management of ERα+ BC, causing tumor growth and recurrence irrespective of estrogen blockade. Improving overall survival in such cases requires new and effective anticancer drugs, allowing adjuvant treatments able to overcome resistance to first-line endocrine therapy. To date, several studies focus on the application of loss-of-function genome-wide screenings to identify key (hub) "fitness" genes essential for BC progression and representing candidate drug targets to overcome lack of response, or acquired resistance, to current therapies. Here, we review the biological significance of essential genes and relative functional pathways affected in ERα+ BC, most of which are strictly interconnected with each other and represent potential effective targets for novel molecular therapies. [ABSTRACT FROM AUTHOR]

Estrogen Receptor alpha (ERα) is a ligand-inducible transcription factor that mediates estrogen signaling in hormone-responsive cells, where it controls key cellular functions by assembling in gene-regulatory multiprotein complexes. For this reason, interaction proteomics has been shown to represent a useful tool to investigate the molecular mechanisms underlying ERα action in target cells. RNAs have emerged as bridging molecules, involved in both assembly and activity of transcription regulatory protein complexes. By applying Tandem Affinity Purification (TAP) coupled to mass spectrometry (MS) before and after RNase digestion in vitro, we generated a dataset of nuclear ERα molecular partners whose association with the receptor involves RNAs. These data provide a useful resource to elucidate the combined role of nuclear RNAs and the proteins identified here in ERα signaling to the genome in breast cancer and other cell types. Design Type(s) parallel group design • protein and RNA interaction identification objective • cell type comparison design Measurement Type(s) RNA-Protein Interaction • protein-protein interaction detection Technology Type(s) mass spectrometry Factor Type(s) experimental condition • biological replicate Sample Characteristic(s) MCF7 cell Machine-accessible metadata file describing the reported data (ISA-Tab format) [ABSTRACT FROM AUTHOR]