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. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Estrogen+hormone+physiology:+reproductive+findings+from+estrogen+receptor+mutant+mice.">Estrogen hormone physiology: reproductive findings from estrogen receptor mutant mice.</a> Reprod Biol. 14 (1), 3-8 (2014).</li><li>Madak-Erdogan, Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrative+genomics+of+gene+and+metabolic+regulation+by+estrogen+receptors+alpha+and+beta,+and+their+coregulators.">Integrative genomics of gene and metabolic regulation by estrogen receptors alpha and beta, and their coregulators.</a> Mol Syst Biol. 9, 676 (2013).</li><li>Gong, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transcriptomic+analysis+identifies+gene+networks+regulated+by+estrogen+receptor+alpha+(ERalpha)+and+ERbeta+that+control+distinct+effects+of+different+botanical+estrogens.">Transcriptomic analysis identifies gene networks regulated by estrogen receptor alpha (ERalpha) and ERbeta that control distinct effects of different botanical estrogens.</a> Nucl Recept Signal. 12, e001 (2014).</li><li>Bergamaschi, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+forkhead+transcription+factor+FOXM1+promotes+endocrine+resistance+and+invasiveness+in+estrogen+receptor-positive+breast+cancer+by+expansion+of+stem-like+cancer+cells.">The forkhead transcription factor FOXM1 promotes endocrine resistance and invasiveness in estrogen receptor-positive breast cancer by expansion of stem-like cancer cells.</a> Breast Cancer Res. 16 (5), 436 (2014).</li><li>Madak-Erdogan, Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nuclear+and+extranuclear+pathway+inputs+in+the+regulation+of+global+gene+expression+by+estrogen+receptors.">Nuclear and extranuclear pathway inputs in the regulation of global gene expression by estrogen receptors.</a> Mol Endocrinol. 22 (9), 2116-2127 (2008).</li><li>Madak-Erdogan, Z., Lupien, M., Stossi, F., Brown, M., Katzenellenbogen, B. 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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=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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(62), (2012).</li><li>. <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; Assays for high-throughput quantitation of DNA, RNA, and oligos. , (2006).</li><li>Kim, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TopHat2:+accurate+alignment+of+transcriptomes+in+the+presence+of+insertions,+deletions+and+gene+fusions.">TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions.</a> Genome Biol. 14 (4), R36 (2013).</li><li>Langdon, W. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Performance+of+genetic+programming+optimised+Bowtie2+on+genome+comparison+and+analytic+testing+(GCAT)+benchmarks.">Performance of genetic programming optimised Bowtie2 on genome comparison and analytic testing (GCAT) benchmarks.</a> BioData Min. 8, (2015).</li><li>Zhang, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Model-based+analysis+of+ChIP-Seq(MACS).">Model-based analysis of ChIP-Seq(MACS).</a> Genome Biol. 9 (9), R137 (2008).</li><li>Gao, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metscape:+a+Cytoscape+plug-in+for+visualizing+and+interpreting+metabolomic+data+in+the+context+of+human+metabolic+networks.">Metscape: a Cytoscape plug-in for visualizing and interpreting metabolomic data in the context of human metabolic networks.</a> Bioinformatics. 26, 971-973 (2010).</li><li>Schroeder, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+RIN:+an+RNA+integrity+number+for+assigning+integrity+values+to+RNA+measurements.">The RIN: an RNA integrity number for assigning integrity values to RNA measurements.</a> BMC Mol Biol. 7 (3), (2006).</li><li>Mokry, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Efficient+double+fragmentation+ChIP-seq+provides+nucleotide+resolution+protein-DNA+binding+profiles.">Efficient double fragmentation ChIP-seq provides nucleotide resolution protein-DNA binding profiles.</a> PLoS One. 5 (11), e15092 (2010).</li><li>Park, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ChIP-seq:+advantages+and+challenges+of+a+maturing+technology.">ChIP-seq: advantages and challenges of a maturing technology.</a> Nat Rev Genet. 10, 669-680 (2009).</li><li>Landt, S. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ChIP-seq+guidelines+and+practices+of+the+ENCODE+and+modENCODE+consortia.">ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia.</a> Genome Res. 22 (9), 1813-1831 (2012).</li><li>Hah, N., Kraus, W. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hormone-regulated+transcriptomes:+lessons+learned+from+estrogen+signaling+pathways+in+breast+cancer+cells.">Hormone-regulated transcriptomes: lessons learned from estrogen signaling pathways in breast cancer cells.</a> Mol Cell Endocrinol. 382, 652-664 (2014).</li></ol>

University of Illinois received an Investigator Initiated grant from Pfizer Inc. ZME and YCZ received salary support from this grant.

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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			<td style="width:119px;">D5205</td>
			<td>ChIP DNA Clean &#38; Concentrator</td>
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			<td>DNA Quantitation</td>
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			<td height="20" style="height:20px;">RNase-Free DNase Set</td>
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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 ...

The overall goal of this experiment is to understand the direct metabolic regulation of ER Alpha in breast cancer, using genome-wide transcriptome, systrome, and metabolome data. This method can help answer key questions in the nucleoreceptor and cancer biology fields, such as the role of certain transcription factors in regulating metabolism to contribute to cell aggressiveness. The main advantage of this technique is that it gives a global picture of gene and resulting metabolic regulation nuance.Demonstrating the procedure will be Yiru Chen, a postdoctoral fellow from my laboratory. After culturing MCF7 cells according to the text protocol, harvest the cells by adding one milliliter of RNA isolation reagent to each well, and incubate at room temperature for five minutes. Use a pipette to collect the cell lysate and transfer it into 1.5 milliliter tubes.To isolate the RNA, add 200 microliters of chloroform to one milliliter of cell lysate, and vortex for 10 seconds. After incubating at room temperature for five minutes, centrifuge the samples at 16, 000 x g and four degrees Celsius for 15 minutes. Following centrifugation, carefully transfer the aqueous top phase to a new tube.Add 500 microliters of 100%isopropanol to the aqueous phase, and mix by inverting. Then, incubate at negative 20 degrees Celsius overnight. The following morning, centrifuge the samples at 1600 x g and four degrees Celsius for 10 minutes.Discard the supernatant, and add 750 microliters RNase-free 70%ethanol, and briefly vortex. Centrifuge the samples at 6, 000 x g and four degrees Celsius for five minutes. Then, remove the supernatant, and centrifuge the samples at 16, 000 x g and four degrees Celsius for one minute.With a gel loading tip, remove the remaining ethanol by pipetting. And set the tubes upside-down to air dry for 10 minutes. Then, add 20 to 50 microliters of DEPC water to dissolve the pellet, and purify and quantify the RNA according to the text protocol.Once MCF7 cells have been cultured according to the text protocol, add 250 microliters of 16%formaldehyde into five milliliters of medium to the cells to cross-link chromatin and incubate at room temperature for 15 minutes. Then use five milliliters of ice-cold 1X to wash the cells and stop the cross-linking reaction. Harvest the cells in 250 microliters of cell lysis buffer per plate.Then, to fragment the chromatin to the required sizes, sonicate the cells eight times for 30 seconds each at an amp of 30. Cool each sample on ice after each 30 second sonication. Next, after centrifuging the samples at 16, 000 x g and four degrees Celsius for 10 minutes, carefully pipette the supernatant without disturbing the pellet at the bottom.Apply a five microliter aliquot of the supernatant to a 1%agarose gel, and carry out electrophoresis to determine the DNA size. The ideal fragment length should be between 200 and 500 base pairs. To carry out immunoprecipitation of the chromatin, save 25 microliters of the cell lysate as input.Then, in a 50 milliliter tube, mix four milliliters of the cell lysate with 20 milliliters of IP buffer, ER Alpha antibody, and protein A and protein G-coded magnetic beads. Incubate the mixture by rotation at four degrees Celsius overnight, to allow the formation of chromatin-antibody complexes. After using wash buffers to wash the beads, according to the text protocol, add 500 microliters of elution buffer to the beads, and elute the DNA into four tubes.For the DNA input samples, elute into 125 microliters of elution buffer. Next, place the tubes in a heating block and cover them with foil, and place a weight on top to prevent them from opening. Incubate the tubes at 65 degrees Celsius overnight.To isolate the DNA from the pulldown samples using a ChIP DNA isolation kit, cool the tubes at room temperature for five minutes. In the meantime, warm up the elution buffer to 65 degrees Celsius. Add 625 microliters of binding buffer to each tube.If a precipitant forms, add another 250 microliters of binding buffer until the solution is clear. Load the sample onto the column and centrifuge at 16, 000 x g for 30 seconds. Then use 250 microliters of wash buffer to wash the column twice.With 15 microliters of elution buffer, elute the DNA. Combine the DNA from the four tubes of the same pulldown to arrive at 55 microliters of DNA. Measure DNA concentration and carry out QPCR according to the text protocol.Using MCF7 cells cultured according to the text protocol, add five milliliters of ice-cold 1X PBS to the plate. Then tilt the plate several times before removing the PBS. Repeat two more times, removing as much PBS as possible after the last wash.Add 750 microliters of pre-chilled acetone to each plate, and scrape the cells. Then, combine the cells from two plates into a two milliliter tube on ice. To count the cells for normalization, for each treatment, use two extra plates for cell quantification.Then, to detach the cells from the plates, add two milliliters of HE buffer and incubate for five minutes. Mix the cells thoroughly by pipetting in the HE buffer. Then, use 20 microliters of the cell solution in a hemocytometer to count the cell number under a microscope.Store the samples at negative 80 degrees Celsius before using gas chromatography mass spectrometry analysis to identify and quantify the metabolites. Perform integrative analysis according to the text protocol. This figure shows the integrity and overall quality of RNA samples purified for an RNA-Seq experiment.The analysis shows all samples have sharp and clean bands of 18S and 28S RRNAs, indicating the integrity and purity of the samples. An RNA Integrity Number, or RIN, of 10, indicates intact RNA. Six represents partially degraded RNA, and a three indicates a sample that is strongly degraded.In these experiments, all RNA samples received RIN values between 9.6 and 9.9. Carrying out ultra high throughput sequencing resulted in approximately 18 million high-quality sequencing reads per sample. A representative fast QC report, which analyzes the overall quality of the raw data, is shown here.For each read, the quality score was 32 to 34 for the first five base pairs, and 36 to 38 from six to 100 base pairs. Among 17, 990, 863 reads generated from this sample, most of them had a quality score higher than 34. This figure shows an overview of about 100 metabolites represented by peaks that show changes in MCF7 cells due to estradiol treatment.The top 10 metabolites with significant changes from E2 treatment are presented in this table. And this table lists the pathways that were up-and down-regulated upon E2 treatment. Once mastered, this technique can be done in a week if it is performed properly.While attempting this procedure, it's important to remember to pay attention to the health of the cells, and quality of the samples that will be analyzed. Following this procedure, other methods like exome sequencing or GRO-Seq that rely on sequencing data can be performed in order to answer additional questions like the identity of additional MRNAs, enhancer MRNAs, or long non-coding RNAs whose transcription are induced by treatments. After its development, this technique paved the way for researchers in the field of nuclear receptors to explore metabolic regulation by nuclear receptors in cell culture systems or in mice.After watching this video, you should have a good understanding of how to analyze and integrate data from multiple Omics approaches. Don't forget that working with Trizol, formaldehyde and methanol can be extremely hazardous and precautions such as wearing personal protection equipment such as gloves and lab coats should always be worn while performing this procedure.

systems-biology-metabolic-regulation-estrogen-receptor-signaling

Research

JoVE Journal

Medicine

10.3K Views.  University of Illinois, Urbana-Champaign. The overall goal of this experiment is to understand the direct metabolic regulation of ER Alpha in breast cancer, using genome-wide transcriptome, systrome, and metabolome data. This method can help answer key questions in the nucleoreceptor and cancer biology fields, such as the role of certain transcription factors in regulating metabolism to contribute to cell aggressiveness. The main advantage of this technique is that it gives a global picture of gene and resulting metabolic regulation ...