Project description:Gene transcription mediated by RNA polymerase II (pol-II) is a key step in gene expression. The dynamics of pol-II moving along the transcribed region influences the rate and timing of gene expression. In this work we present a probabilistic model of transcription dynamics which is fitted to pol-II occupancy time course data measured using ChIP-Seq. The model can be used to estimate transcription speed and to infer the temporal pol-II activity profile at the gene promoter. Model parameters are determined using either maximum likelihood estimation or via Bayesian inference using Markov chain Monte Carlo sampling. The Bayesian approach provides confidence intervals for parameter estimates and allows the use of priors that capture domain knowledge, e.g. the expected range of transcription speeds, based on previous experiments. The model describes the movement of pol-II down the gene body and can be used to identify the time of induction for transcriptionally engaged genes. By clustering the inferred promoter activity time profiles, we are able to determine which genes respond quickly to stimuli and group genes that share activity profiles and may therefore be co-regulated. We apply our methodology to biological data obtained using ChIP-seq to measure pol-II occupancy genome-wide when MCF-7 human breast cancer cells are treated with estradiol (E2). The transcription speeds we obtain agree with those obtained previously for smaller numbers of genes with the advantage that our approach can be applied genome- wide. We validate the biological significance of the pol-II promoter activity clusters by investigating cluster-specific transcription factor binding patterns and determining canonical pathway enrichment. MCF-7 cells were mock treated or with 10nM 17b-E2 to seven time points (5', 10', 20', 40', 80', 160', 320') Genome-wide identification of RNA polymerase II (RNAPII) occupancy at different time points following E2 induction of MCF-7 cells

Project description:One of the greatest advances in therapy of estrogen receptor positive breast cancer has been the development of endocrine therapy. More than two thirds of primary breast tumors express estrogen receptor alpha (ERα) and their growth is mainly dependent on estrogen receptor activity. Several therapeutic approaches have thus been designed to inhibit ER activity in breast tissue. Tamoxifen, a selective ER modulator, is one of the main treatment options for premenopausal women with advanced ER positive breast cancer, and has also become well established as adjuvant therapy in the early breast cancer setting (Davies et al., 2011; Group, 1998; Jaiyesimi et al., 1995). In contrast to tamoxifen, which in other tissues such as bone and endometrium, can exert partial agonistic activity (Braithwaite et al., 2003; Fisher et al., 1994; Pietras, 2006), the selective ER downregulator, fulvestrant, is a potent ER antagonist. Binding of fulvestrant to ER inhibits receptor dimerization and nuclear uptake, prevents estrogen-response element binding and accelerates ER degradation (Dauvois et al., 1993; Dowsett et al., 2005; Fawell et al., 1990; Wakeling et al., 1991). Fulvestrant is approved for treatment of postmenopausal women with advanced breast cancer who have relapsed or progressed on first-line endocrine therapy (Howell et al., 2002; Osborne et al., 2002; Robertson et al., 2003). Estrogen deprivation by treatment with aromatase inhibitors (such as letrozole, anastrozole or exemestane) which block synthesis of estrogens is another effective therapy option in postmenopausal women (Bonneterre et al., 2000; Cuzick et al., 2010; Dowsett et al., 2010; Mouridsen et al., 2001; Nabholtz et al., 2000). The large majority of patients with ER+ breast cancer experience an initial response to endocrine therapy, however, in many of these patients, the disease subsequently progresses and eventually anti-estrogen therapy ceases to have effect. Biomarkers predicting response to estrogen deprivation and new alternative treatments targeting the pathways supporting estrogen independent growth are therefore urgently needed (Patani and Martin, 2014; Patani et al., 2013). In order to understand how tumors respond to withdrawal of their main growth signal, a comparison of molecular features of tumor before and during endocrine therapy is necessary, and the fate of individual cancer cell sub-clones should be monitored. Since studies of cellular dynamics are very difficult to perform with clinical material, we made use of a patient derived, estrogen dependent, orthotopically growing luminal-like primary breast cancer xenograft model (PDX) (Bergamaschi et al., 2009; Huuse et al., 2012). This PDX model is a representative model for luminal breast cancers as the molecular characteristics, cellular heterogeneity and estrogen dependency of the primary tumor are retained over many passages. Functionally different subpopulations of cancer cells were previously defined by expression of the markers CD24 and SSEA-4 in this model (Skrbo et al., 2014), and it is therefore a suitable model for comparison of cellular and molecular effects of estrogen deprivation and ER-signaling inhibition. In this study, a quantitative MS-based proteomic analysis using SILAC and a label-free approach were performed, aiming to map changes in protein expression induced by endocrine therapy. Inhibition of ER-signaling seemed to induce CD24 and SSEA-4 expression on residual tumor cells. Proteomic analysis revealed overexpression of enzymes involved in TCA cycle, oxidative phosphorylation and fatty acid beta-oxidation following endocrine treatment when compared to untreated tumors. These results indicated possible reprogramming of cell metabolism and utilization of aerobic respiration, i.e. oxidative phosphorylation, in response to endocrine therapy.

Project description:Estrogen receptor Î± (ERÎ±) is an important biomarker of breast cancer severity and a common therapeutic target. Recent studies have demonstrated that in addition to its role in promoting proliferation, ERÎ± also protects tumors against metastatic transformation. Current therapeutics antagonize ERÎ± and interfere with both beneficial and detrimental signaling pathways stimulated by ERÎ±. The goal of this study is to uncover the dynamics of coding and non-coding RNA (microRNA) expression in response to estrogen stimulation and identify potential therapeutic targets that more specifically inhibit ERÎ±-stimulated growth and survival pathways without interfering with its protective features. To achieve this, we exposed MCF7 cells (an estrogen receptor positive model cell line for breast cancer) to estrogen and prepared a time course of paired mRNA and miRNA sequencing libraries at ten time points throughout the first 24 hours of the response to estrogen. From these data, we identified three primary expression trendsâ??transient, induced, and repressedâ??that were each enriched for genes with distinct cellular functions. Integrative analysis of paired mRNA and microRNA temporal expression profiles identified miR-503 as the strongest candidate master regulator of the estrogen response, in part through suppression of ZNF217â??an oncogene that is frequently amplified in cancer. We confirmed experimentally that miR-503 directly targets ZNF217 and that over-expression of miR-503 suppresses breast cancer cell proliferation. Overall, these data indicate that miR-503 acts as a potent estrogen-induced tumor suppressor microRNA that opposes cellular proliferation and has promise as a therapeutic for breast cancer. More generally, our work provides a systems-level framework for identifying functional interactions that shape the temporal dynamics of gene expression. Quantification of miRNAs in MCF7 cells responding to estrogen following a period of estrogen starvation. Three independent biological replicates (30 samples: 3 replicates x 10 time points) of MCF7 cells were exposed to 10nM Estradiol for 0, 1, 2, 3, 4, 5, 6, 8, 12 , or 24 hours, and total RNA was extracted from the samples. Total RNA was used to generate paired RNA and miRNA sequencing. The miRNA libraries were prepared using the Bioo Scientific NextFLEX v2 library preparation kit.

Project description:Estrogen receptor Î± (ERÎ±) is an important biomarker of breast cancer severity and a common therapeutic target. Recent studies have demonstrated that in addition to its role in promoting proliferation, ERÎ± also protects tumors against metastatic transformation. Current therapeutics antagonize ERÎ± and interfere with both beneficial and detrimental signaling pathways stimulated by ERÎ±. The goal of this study is to uncover the dynamics of coding and non-coding RNA (microRNA) expression in response to estrogen stimulation and identify potential therapeutic targets that more specifically inhibit ERÎ±-stimulated growth and survival pathways without interfering with its protective features. To achieve this, we exposed MCF7 cells (an estrogen receptor positive model cell line for breast cancer) to estrogen and prepared a time course of paired mRNA and miRNA sequencing libraries at ten time points throughout the first 24 hours of the response to estrogen. From these data, we identified three primary expression trendsâ??transient, induced, and repressedâ??that were each enriched for genes with distinct cellular functions. Integrative analysis of paired mRNA and microRNA temporal expression profiles identified miR-503 as the strongest candidate master regulator of the estrogen response, in part through suppression of ZNF217â??an oncogene that is frequently amplified in cancer. We confirmed experimentally that miR-503 directly targets ZNF217 and that over-expression of miR-503 suppresses breast cancer cell proliferation. Overall, these data indicate that miR-503 acts as a potent estrogen-induced tumor suppressor microRNA that opposes cellular proliferation and has promise as a therapeutic for breast cancer. More generally, our work provides a systems-level framework for identifying functional interactions that shape the temporal dynamics of gene expression. Quantification of mRNAs in MCF7 cells responding to estrogen following a period of estrogen starvation. Three independent biological replicates (30 samples: 3 replicates x 10 time points) of MCF7 cells were exposed to 10nM Estradiol for 0, 1, 2, 3, 4, 5, 6, 8, 12 , or 24 hours, and total RNA was extracted from the samples. Total RNA was used to generate paired RNA and miRNA sequencing. RNA libraries were prepared using an Illumina TruSeq stranded mRNA library preparation kit.

			Action	DRS
	SRR771415.fastq.gz	Fastqsanger.gz
	SRR771416.fastq.gz	Fastqsanger.gz
	SRR771417.fastq.gz	Fastqsanger.gz
	SRR771418.fastq.gz	Fastqsanger.gz
	SRR771419.fastq.gz	Fastqsanger.gz

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