Name: systems biology of metabolic regulation by estrogen receptor signaling in breast cancer
Uploaded: 2016-03-17T14:00:00
Description: Watch this Scientific Journal Video about Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer at JoVE.com

This work was supported by grants from the National Institute of Food and Agriculture, U.S. Department of Agriculture, award ILLU-698-909 (ZME) and Pfizer, Inc (ZME). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the U.S. Department of Agriculture.

1. Preparation of RNA-Seq Samples
<ol>
	<li>Cell Culture and Treatment
	<ol>
		<li>Culture the MCF7 cells in Improved MEM (IMEM) plus phenol-red medium with 10% Heat inactive FBS, 1% penicillin/streptomycin at 37 &#176;C in humidified 5% CO2 atmosphere. 
		Note: Same cell line and cell culture condition is used throughout the study.</li>
		<li>Seed 100,000 MCF7 cells per well in 6-well plates. Have triplicates for each treatment. On day 3, remove the medium and add 2 ml fresh medium with or without 10<s...

<ol>
	<li>Hamilton, K. J., Arao, Y., Korach, K. 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S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genomic+collaboration+of+estrogen+receptor+alpha+and+extracellular+signal-regulated+kinase+2+in+regulating+gene+and+proliferation+programs.">Genomic collaboration of estrogen receptor alpha and extracellular signal-regulated kinase 2 in regulating gene and proliferation programs.</a> Mol Cell Biol. 31, 226-236 (2011).</li><li>Madak-Erdogan, Z., Ventrella, R., Petry, L., Katzenellenbogen, B. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Novel+roles+for+ERK5+and+cofilin+as+critical+mediators+linking+ERalpha-driven+transcription,+actin+reorganization,+and+invasiveness+in+breast+cancer.">Novel roles for ERK5 and cofilin as critical mediators linking ERalpha-driven transcription, actin reorganization, and invasiveness in breast cancer.</a> Mol Cancer Res. 12 (5), 714-727 (2014).</li><li>Wang, Z., Gerstein, M., Snyder, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA-Seq:+a+revolutionary+tool+for+transcriptomics.">RNA-Seq: a revolutionary tool for transcriptomics.</a> Nat Rev Genet. 10 (1), 57-63 (2009).</li><li>Invitrogen. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Quant-iT&#8482; PicoGreen &#174; dsDNA Reagent and Kits. , (2008).</li><li>Lee, P. 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In this paper, we described generation and integrative analysis of RNA-Seq, ChIP-Seq and metabolomics data from MCF-7 cells that are treated with E2. We developed a set of protocols strategizing to utilize the most efficient methods and user friendly soft wares which produce biologically relevant discovery. To our knowledge, ours is the first study to integrate -omics data from three different analysis and identified new metabolic pathways that ER&#945; directly regulated in breast cancer cells.
<p class="jove_conten...

Estrogens are important regulators of many physiological processes in both females and males including reproductive tissues, metabolic tissues, brain and bone1. In addition to beneficial effects in these tissues, estrogens also drive cancers that arise from mammary and reproductive tissues. Estrogens mainly work through ERs to induce cell type specific effects. Deep sequencing of transcripts regulated by ER&#945; using RNA-Seq and genome-wide ER&#945; DNA binding sites analysis using ChIP-Seq proved to be useful to understand how ER&#945; works in different tissues and cancers that arise from them. We and others have published gene expression profiles associated with different receptors (ER&#945; v.s. ER&#946;)2,3, different ligands3-5 and different coregulators2,4,6,7.
RNA-Seq is the main method to examine the transcriptome, offering higher precision and efficiency compared to microarray based gene expression analysis8. RNA obtained from cell lines2-4,7, tissues or tumor samples are sequenced, mapped to available genomic assemblies and differentially regulated genes are identified. Chromatin Immunoprecipitation (ChIP) is employed to dissect the transcription factor and coregulator chromatin binding to known regulatory binding sites. ChIP-Seq (ChIP followed by high throughput sequencing) provides unbiased detection of global binding sites. Metabolomics is another increasingly used system biology approach, which quantitatively measures, dynamic multiparametric response of living systems to various stimuli including chemicals and genetic perturbations.
By performing global metabolic profiling, a functional readout can be obtained from cells, tissues, and blood. In addition, information from transcriptome experiments do not always reflect actual changes in the level of enzymes that contribute to biochemical pathways. Combined analysis of transcriptome and metabolome data enables us to identify and correlate changes in gene expression with actual metabolite changes. Harnessing the information from all these large scale datasets provide the mechanistic details to understand the role of transcription factors regulating complex biological systems, especially ones that pertain to human development and diseases like cancer and diabetes.
The complex nature of the mammalian genome makes it challenging to integrate and fully interpret the data obtained from the transcriptome, cistrome and metabolome experiments. Identifying functional binding events that would lead to changes in expression of target genes is important because once functional binding sites are identified; ensuing analysis, including transcription binding (TF) motif analysis, could be performed with higher accuracy. This leads to the identification of biologically meaningful TF cascades and mechanisms. Also direct comparison of RNA-Seq and ChIP-Seq experiments are not always possible since the data from each experiment have differing scales and noises and in some cases meaningful signals are obscured by population noise. We are not aware of any study that integrated information from these three independent but related approaches to understand the direct metabolic regulation by ER&#945; in breast cancer. Therefore, our overall goal in this paper is to relate productive binding events to gene expression and metabolite changes. In order to achieve this goal, we integrated data from RNA-Seq, ChIP-Seq and metabolomics experiments and identified those estrogen induced ER&#945; binding events that would lead to gene expression changes in metabolic pathways. For the first time, we provide a complete set of protocols (Figure 1) for generating ChIP-Seq, RNA-Seq and metabolomics profiling and performing integrative analysis of the data to uncover novel gene circuits regulating metabolism of breast cancer cell lines.

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systems biology of metabolic regulation by estrogen receptor signaling in breast cancer

With the advent of the -omics approaches our understanding of the chronic diseases like cancer and metabolic syndrome has improved. However, effective mining of the information in the large-scale datasets that are obtained from gene expression microarrays, deep sequencing experiments or metabolic profiling is essential to uncover and then effectively target the critical regulators of diseased cell phenotypes. Estrogen Receptor &#945; (ER&#945;) is one of the master transcription factors regulating the gene programs that are important for estrogen responsive breast cancers. In order to understand to role of ER&#945; signaling in breast cancer metabolism we utilized transcriptomic, cistromic and metabolomic data from MCF-7 cells treated with estradiol. In this report we described generation of samples for RNA-Seq, ChIP-Seq and metabolomics experiments and the integrative computational analysis of the obtained data. This approach is useful in delineating novel molecular mechanisms and gene regulatory circuits that are regulated by a particular transcription factor which impacts metabolism of normal or diseased cells.

Transcriptomics 
To analyze differentially expressed genes by E2 treatment, we chose to perform an RNA-Seq experiment. In addition to providing information about mRNA levels, RNA-Seq data can also be used to monitor changes in non-coding RNA (long non-coding RNAs, microRNAs) and alternative splicing events. We did not provide information on the analysis of non-coding RNAs or alternatively transcribed genes, since scope of our study is to identify protein coding genes that are importa...

Watch this Scientific Journal Video about Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer at JoVE.com

Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer

Integration of data from genome-wide sequencing experiments and metabolomics experiments is a challenge. In this paper we report, for the first time, generation, analysis and integration of transcriptome, cistrome and metabolome data from breast cancer cells treated with estradiol.

With the advent of the -omics approaches our understanding of the chronic diseases like cancer and metabolic syndrome has improved. However, effective ...

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