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 10-8 M E2 to each well. Incubate the plates for 24 hr in 37 &#176;C in humidified 5% CO2 atmosphere.</li>
		<li>To harvest the cells, add 1 ml RNA isolation reagent (acid guanidinium thiocyanate-phenol-chloroform) into each well and incubate for 5 min at room temperature (RT). Then collect the cell lysate reagent by pipetting and deposit into 1.5 ml tubes</li>
	</ol>
	</li>
	<li>RNA Isolation
	<ol>
		<li>Add 200 &#181;l chloroform to 1 ml cell lysate reagent (1.1.3) and vortex for 10 sec. Incubate at room temperature for 5 min. Centrifuge samples at 16,000 x g for 15 min at 4 &#176;C. Following centrifugation, transfer the aqueous phase (top) to a new tube without disturbing the other phases.</li>
		<li>Add 500 &#181;l 100% isopropanol to a new tube with the transferred-aqueous phase, and mix by inverting. Incubate at -20 &#176;C overnight. Centrifuge the samples at 16,000 x g for 10 min at 4 &#176;C. Observe a pellet at the bottom of the tube. Discard the supernatant and wash the pellet with 750 &#181;l RNase-free 70% ethanol by brief vortex.</li>
		<li>Centrifuge samples at 6,000 x g for 5 min at 4 &#176;C. Remove the supernatant, and centrifuge the sample at 16,000 x g for 1 min at 4 &#176;C to collect all the remaining ethanol. Remove the remaining ethanol by pipetting with a gel-loading tip and set the tubes up-side down to air-dry for 10 min. Dissolve the pellet in 20-50 &#181;l DEPC treated water depending on the size of the pellets.</li>
	</ol>
	</li>
	<li>Purify the RNA using RNA clean-up kit following the protocol4. Measure the RNA concentration using a fluorometry based assay kit following the protocol9. 
	NOTE: OD at 260 nm is taken to determine the RNA concentration and ratio of OD at 260 nm and 280 nm are used to determine the purity of sample. It is recommended checking the RNA quality using Bio-analyzer assay.</li>
	<li>Take about 0.5 to 1 &#956;g purified RNA for sequencing. 
	NOTE: Biotech center prepares the sequencing libraries and performs paired-end read sequencing using methods detailed previously2.</li>
</ol>
2. Preparation of ChIP-Seq Sample
<ol>
	<li>Cell Culture and Treatment
	<ol>
		<li>Seed 500,000 MCF7 cells per 10 cm plate in growth medium. 
		NOTE: For each pull down 16 plates were used. On day 3, remove the medium and add 5 ml fresh medium with or without 10-8 M E2 to each plate. Treat the cells for 45 min (37 &#176;C in humidified 5% CO2 atmosphere).</li>
	</ol>
	</li>
	<li>Crosslink and Cell Harvest
	<ol>
		<li>Add 250 &#181;l 37% Formaldehyde into 5 ml medium to crosslink chromatin, incubate at room temperature for 15 min. Stop crosslinking by washing cells with 5 ml ice cold 1x MEM.</li>
		<li>Harvest the cells in 250 &#181;l Cell Lysis buffer (10 mM EDTA, 50 mM Tri HCl, 1% SDS, 0.5% N,N-dimethyldodecan-1-amine oxide) per plate.</li>
	</ol>
	</li>
	<li>Fragmentation of Chromatin
	<ol>
		<li>Sonicate the cells for 30 sec x 8 at amp of 30 to reach required chromatin sizes. Cool each sample on ice after a sonication of 30 sec.</li>
		<li>Centrifuge at 16, 000 x g for 10 min at 4 &#176;C, and carefully pipette the supernatant without disturbing the pellet at the bottom.</li>
		<li>Take a 5 &#181;l aliquot of the supernatant, and check the DNA size by electrophoresis through 1.5% agarose gel as described previously10. The ideal size of DNA should be between 200-500 bp.</li>
	</ol>
	</li>
	<li>Chromatin Immunoprecipitation
	<ol>
		<li>Save 25 &#181;l of cell lysate as input. In a 50 ml tube, mix 4 ml of cell lysate with 20 ml IP buffer (2 mM EDTA, 100 mM NaCl, 20 mM Tris-HCl, and 0.5% Triton X), ER&#945; antibody (500 &#181;l F10 and 500 &#181;l HC-20), and 500 &#181;l ProtA and ProtG protein coating magnetic beads. Incubate the mixture by rotation at 4 &#176;C overnight to allow the formation of chromatin- antibody complex.</li>
	</ol>
	</li>
	<li>Washes and Reverse-crosslinking
	<ol>
		<li>Use a magnetic stand to collect the magnetic beads on the magnetized side to remove the wash buffer.</li>
		<li>Wash the magnetic beads in 25 ml Wash buffer I (2 mM EDTA, 20 mM Tris HCl, 01% SDS, 1% Triton X, and 150 mM NaCl). To thoroughly wash the magnetic beads without disturbing DNA-Protein complexes, add 25 ml wash buffer slowly to the tube along the side then invert the tubes until all the magnetic beads are mixed well with the wash buffer without appearing as a pellet. As an alternative, use the rotator to invert the tube. Do not vortex the samples. Wash the magnetic beads using the same method in the following steps.</li>
		<li>Wash the magnetic beads in 25 ml Wash-buffer II (2 mM EDTA, 20 mM Tris HCl, 01% SDS, 1% Triton X, and 500 mM NaCl).</li>
		<li>Wash the magnetic beads in 25 ml Wash-buffer III (1.6 mM EDTA, 10 mM Tris HCl, 1% NP-40, 1% Dexycholate and 250 mM LiCl).</li>
		<li>Wash the magnetic beads in 25 ml 1x TE buffer (1 mM EDTA, and 10 mM Tris HCl) twice.</li>
		<li>Elute DNA in 500 &#181;l Elution buffer (1% SDS, and 10 mM NaHCO3) and then allocate the 500 &#181;l DNA elution into 4 tubes. For the DNA from inputs, elute DNA in 125 &#181;l Elution buffer.</li>
		<li>Incubate the tubes at 65 &#176;C overnight on a heating block. Cover the tubes on the heating block with aluminum foil to keep the heat even over all the tubes. Place a weight on the tubes to keep them from opening.</li>
	</ol>
	</li>
	<li>DNA Isolation
	<ol>
		<li>Cool the tubes at RT for 5 min. Isolate DNA from the pull down samples using ChIP DNA isolation kit. Follow the directions from the manufacturer.
		<ol>
			<li>Warm up the Elution buffer at 65 &#176;C. Add 625 &#181;l binding buffer to each tube. If the precipitate forms, add another 250 &#181;l binding buffer until the solution is clear.</li>
			<li>Load the sample onto the column and centrifuge at 16,000 x g for 30 sec. Wash the sample with 250 &#181;l wash buffer. Remember to add ethanol to the wash buffer when first time to use it. Repeat the wash.</li>
			<li>Elute the DNA in 15 &#181;l Elution buffer. Combine the DNA of same pull down from 4 tubes to achieve around 55 &#181;l DNA.</li>
		</ol>
		</li>
		<li>Isolate the Input DNA Using a DNA Purification Kit.
		<ol>
			<li>Warm up the Elution buffer at 65 &#176;C. Add 625 &#181;l binding buffer in each tube and incubate for 10 min. Load the sample onto the column in a 2 ml collection tube.</li>
			<li>Wash the sample with 750 &#181;l wash buffer, centrifuge at 16,000 x g for 1 min. Repeat centrifuging to remove any remaining buffer. Elute the DNA into 30 &#181;l of EB.</li>
		</ol>
		</li>
		<li>Measure the DNA Concentration Using a Commercial Fluorochrome Assay with Modification11.
		<ol>
			<li>Dilute the 20x TE buffer into 1x TE as the assay buffer for diluting the reagent and DNA samples. Take 5 &#181;l isolated DNA and dilute it into 50 &#181;l in 1x TE.</li>
			<li>From 2 &#181;g/ml DNA stock solution make a DNA standard ranging from blank to 1, 10, 100, 1000 &#181;g/ml. In a 96 well plate, allocate 25 &#181;l of each standard as well as DNA dilution in duplicate. (Triplicate for standard is recommended.)</li>
			<li>Prepare the working reagent by making a 200-fold dilution from dsDNA reagent in 1x TE buffer in plastic tube. Cover the working reagent solution with aluminum foil to avoid light. Add 200 &#181;l working reagent into each sample. Incubate at room temperature for 5 min. Cover the plate with aluminum foil to avoid light.</li>
			<li>Measure the florescence of the samples using a fluorescence plate reader (excitation at 480 nm, emission 520 nm). Generate the DNA standard curve of florescence versus DNA concentration. Determine the concentrations of the samples based on the generated DNA standard curve.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Verification of ChIP Experiment Using qPCR
	<ol>
		<li>Take 2 &#181;l isolated DNA and diluted into 20 &#181;l in water. Set up the qPCR reaction in triplicate by mixing 2.5 &#181;l DNA dilution, 5 &#181;l Green I PCR master mix, 1 &#181;l primer, and 1.5 &#181;l nuclease free water.</li>
		<li>Run the qPCR with the Fast Real-Time PCR system using the following program: Step 1: 95 &#176;C for 60 sec, Step 2: 95 &#176;C for 10 sec, 60 &#176;C for 60 sec, repeat Step 2 39 times.</li>
	</ol>
	</li>
	<li>Send 10 ng DNA sample to Biotech center for sequencing. 
	NOTE: The biotech center prepares the ChIP DNA into libraries according to instruction, and performs single-read sequencing using methods detailed previously2.</li>
</ol>
3. Metabolomics Assay Sample Preparation
<ol>
	<li>Cell Culture and Treatment
	<ol>
		<li>Seed 400,000 MCF7 cells per 10 cm plate in growth medium. For each treatment prepare three samples. Use two plates in each sample to receive enough cells for the detection. On day 3, remove the medium and add 5 ml fresh medium with or without 10-8 M E2 to the cells. Treat the cells for 24 hr (37 &#176;C in humidified 5% CO2 atmosphere).</li>
	</ol>
	</li>
	<li>Sample Collection
	<ol>
		<li>Add 5 ml ice cold 1x PBS to the plate, aspirate the PBS after briefly tilting the plate several times. Repeat twice, and remove as much PBS as possible after the last wash.</li>
		<li>Add 750 &#181;l of pre-cold acetone to each plate and scrape the cells. Combine the cells in acetone from two plates into a 2 ml tube on ice.</li>
		<li>Cell count for normalization
		<ol>
			<li>For each treatment, use two extra plates for cell quantification. To de-attach the cells from plate, add 2 ml of HE buffer (1x HBSS, 10 mM HEPES, 0.075% NaHCO3, and 1 mM EDTA) into each plate and incubate for 5 min.</li>
			<li>Collect and mix well the cells by pipetting cells in the HE buffer. Use total of 20 &#181;l cell solution to count the cell number using a hemocytometer.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Store the samples at -80 &#176;C before sending to the Metabolomics center to identify and quantify the metabolites using Gas chromatography mass spectrometry (GC-MS) analysis.</li>
</ol>
4. Integrative Analysis
<ol>
	<li>RNA-Seq Data Analysis:
	<ol>
		<li>Perform Quality check of FASTQ files using FastQC:Read QC (http://galaxy.illinois.edu)
		<ol>
			<li>Select FastQC:Read under the tool of NGS:QC and manipulation. Upload the raw data file from current history. 
			NOTE: It is recommended to give a title for the output file to remind you what the job was for since there might be multiple outputs.</li>
		</ol>
		</li>
		<li>Execute and the output file will appear under the History. Repeat the same procedure for each sequence file. Trim adapters using Clip under NGS:FASTX Toolkit.</li>
		<li>Align reads to hg19 using Tophat2 with RefSeq genome reference annotation12 (default values).
		<ol>
			<li>Select Tophat2 under tool of NGS:RNA analysis. Indicate the library paired type (single-end vs paired-end).</li>
			<li>Upload the input FASTQ file from the current history. Select the reference genome. Choose defaults settings. Or use the full parameter list to modify.</li>
			<li>Execute and it will produce two output files: one is UCSC BED track of junctions; another one is a list of read alignments in BAM format.</li>
		</ol>
		</li>
		<li>Upload BAM files to sequencing data analysis tool. Calculate expression values using Quantification Tool.</li>
		<li>Identify differentially up-regulated genes using Expression Analysis Tool using default values and fold change &#62;2 and p-value &#60;0.05. Likewise, identify differentially down-regulated genes using Expression Analysis Tool using default values and fold change &#60;0.5 and p-value &#60;0.05.</li>
	</ol>
	</li>
	<li>ChIP-Seq Data Analysis:
	<ol>
		<li>Align FASTQ Reads to hg19 using Bowtie213 (Default Values) in Galaxy.
		<ol>
			<li>Select Bowtie2 under the tool of NSC:Mapping. Verify if the library is mate-paired (single-end vs paired-end). Upload the FASTQ file from current history.</li>
			<li>Select the reference genome. Use the default parameter settings. Execute and it will produce output file with mapped sequence reads as BAM file. Repeat the procedure for every sequence file.</li>
		</ol>
		</li>
		<li>Identify peaks using MACS14 a p-value cutoff of 6.0e-7 and FDR of 0.01, as previously described2.</li>
	</ol>
	</li>
	<li>Metabolomics Data Analysis:
	<ol>
		<li>Convert chemical names of metabolites to KEGG_IDs.</li>
		<li>Identify differentially regulated metabolites by comparing levels detected in Vehicle vs. E2 treated samples (a 2 fold cut-off was used in this study).</li>
	</ol>
	</li>
	<li>Integrative Analysis:
	<ol>
		<li>Identify genes that have ER&#945; binding sites using sequencing data analysis tool. Translate Regions to genes function using 20 kb as distance cut-off.</li>
		<li>Identify differentially up- and down-regulated genes from RNA-Seq data analysis using fold change &#62;2 and fold change &#60;0.5, respectively with the p-value &#60;0.05 as cut off. Compare to the list of ER&#945; binding site containing genes using Venn diagram comparison.</li>
		<li>Use this list to identify E2 modulated metabolic pathways using Metscape15 module of Cytoscape.
		<ol>
			<li>By selecting Metscape module from apps, choose Build network and then pathway-based option.</li>
			<li>Select the Organism (Human). Select data file of E2 unregulated compound list (KEGG ID) as well E2 up-regulated gene list.</li>
			<li>Choose Compound -Reaction -Enzyme-Gene as the Network type. Choose compound/genes as the query.</li>
			<li>Build network. Repeat using the E2 down-regulated compound and genes.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>

<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. 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. Y., Costumbrado, J., Hsu, C. Y., Kim, Y. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Agarose+gel+electrophoresis+for+the+separation+of+DNA+fragments.">Agarose gel electrophoresis for the separation of DNA fragments.</a> J Vis Exp. (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.

<table><tbody><tr><td>Triglyceride Mix</td><td>Sigma</td><td>17810-1AMP-S</td><td></td></tr><tr><td>Oleic acid&amp;nbsp;</td><td>Sigma</td><td>O1257-10MG</td><td>Oleic Acid-Water Soluble powder&amp;nbsp;</td></tr><tr><td>TRIzol Reagent</td><td>Life technologies&amp;nbsp;</td><td>15596-026</td><td></td></tr><tr><td>Dynabeads Protein A</td><td>Life technologies&amp;nbsp;</td><td>10006D</td><td></td></tr><tr><td>Dynabeads Protein G</td><td>Life technologies&amp;nbsp;</td><td>10007D</td><td></td></tr><tr><td>QIAquick PCR Purification Kit</td><td>QIAGEN</td><td>28106</td><td>DNA isolation of input sample&amp;nbsp;</td></tr><tr><td>ZYMO ChIP-DNA Isolation kit&amp;nbsp;</td><td>Zymo Research&amp;nbsp;</td><td>D5205</td><td>ChIP DNA Clean &amp;amp; Concentrator</td></tr><tr><td>Quant-iT&amp;ordf; PicoGreen&amp;uml; dsDNA Assay Kit</td><td>Invitrogen&amp;nbsp;</td><td>P7589</td><td>DNA Quantitation</td></tr><tr><td>RNase-Free DNase Set</td><td>QIAGEN</td><td>79254</td><td>RNA purification</td></tr><tr><td>DEPC Treated Water&amp;nbsp;</td><td>LIFE TECH</td><td>462224</td><td>Dnase/Rnase Free</td></tr><tr><td>Model 120 Sonic Dismembrator</td><td>Fisher Scientific&amp;nbsp;</td><td>FB120110</td><td>With 1/8 probe&amp;nbsp;</td></tr><tr><td>&amp;nbsp;Fisher Scientific Sound Enclosure</td><td>Fisher Scientific&amp;nbsp;</td><td>FB-432-A</td><td>For sound reduction</td></tr><tr><td>&amp;nbsp;Eppendorf Tubes 5.0 ml</td><td>Eppendorf&amp;nbsp;</td><td>0030 119.401</td><td>200 tubes (2 bags of 100 ea.)</td></tr><tr><td>Random Primer Mix&amp;nbsp;</td><td>NEB</td><td>S1330S</td><td></td></tr><tr><td>DNTP MIX</td><td>NEB</td><td>N0447S</td><td></td></tr><tr><td>M-MuLV Reverse Transcriptase</td><td>NEB</td><td>M0253S</td><td></td></tr><tr><td>FastStart SYBR Green Master</td><td>ROCHE</td><td>4673484001</td><td></td></tr><tr><td>Agarose</td><td>Fisher&amp;nbsp;</td><td>BP160100</td><td>1.5% agarose gel&amp;nbsp;</td></tr><tr><td>Qubit RNA HS Assay Kit</td><td>Life technologies&amp;nbsp;</td><td>Q32852</td><td>RNA quantification (100 assays)&amp;nbsp;</td></tr><tr><td>Formaldehyde</td><td>Sigma</td><td>F8775</td><td></td></tr><tr><td>Protease Inhibitor tablets&amp;nbsp;</td><td>Roche&amp;nbsp;</td><td>4693116001</td><td>Each tablet is sufficient for 10 ml lysis buffer</td></tr><tr><td>PhosSTOP Phosphatase Inhibitor Cocktail Tablets</td><td>Roche&amp;nbsp;</td><td>4906845001</td><td>Each tablet is sufficient for 10 ml lysis buffer</td></tr><tr><td>Qubit Assay Kits</td><td>Life technologies&amp;nbsp;</td><td>Q32850</td><td>For 100 assays</td></tr><tr><td>Automated cell counter</td><td>ORFLO</td><td>MXZ000</td><td></td></tr><tr><td>tube Rotator&amp;nbsp;</td><td>VWR</td><td>10136-084</td><td></td></tr><tr><td>Victor X5 Multilple plate reader&amp;nbsp;</td><td>PerkinElmer</td><td>2030-0050</td><td></td></tr><tr><td>Microcentrifuge 5417R</td><td>Eppendorf</td><td>22621807</td><td></td></tr><tr><td>Magnetic Stand</td><td>Diagenode</td><td>B04000001&amp;nbsp;</td><td></td></tr><tr><td>Fast Real-time PCR system</td><td>Applied Science&amp;nbsp;</td><td>4351405</td><td></td></tr></tbody></table>

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 ...

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

Research

JoVE Journal

Medicine

10.4K Views.  University of Illinois, Urbana-Champaign. 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. 

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

An In Vivo Estrogen Deficiency Mouse Model for Screening Exogenous Estrogen Treatments of Cardiovascular Dysfunction After Menopause

Postmenopausal women are at greater risk of developing cardiovascular diseases than premenopausal women. Female mice ovariectomized (OVX) at weaning display increased atherosclerotic lesions in the aorta compared with female mice with intact ovarian function. However, laboratory models involving estrogen-deficient mice with atherosclerosis-prone status are lacking. This deficit is crucial because clinical estrogen deficiency in menopausal women may aggravate the incidence of pre-existing or ongoing lipid disruption and atherosclerosis. In this study, we establish an in vivo estrogen-deficient mouse model by bilateral ovariectomy via a double dorsal-lateral incision in apolipoprotein E (apoE)-/- mice. We then compare the effects of 17&#946;-estradiol and pseudoprotodioscin (PPD) (a phytoestrogen) perorally administered via hazelnut spread. We find that although PPD exerts some effect on reducing final body weight and plasma TG in OVX apoE-/- mice, it has anti-atherosclerotic and cardiac-protective capacities comparable with its 17&#946;-estradiol counterpart. PPD is a phytoestrogen that has been reported to exert anti-tumor properties. Thus, the proposed method is applicable for screening phytoestrogens via peroral administration to substitute for traditional hormone replacement therapy in postmenopausal women, which has been reported to have potentially deleterious tumorigenetic capacity. Peroral administration via hazelnut spread is noninvasive, rendering it widely applicable to many patients. This article contains step-by-step demonstrations of bilateral ovariectomy via the double dorsal-lateral incision in apoE-/- mice and peroral 17&#946;-estradiol or phytoestrogen hormone replacement via hazelnut spread. Plasma lipid and cardiovascular function analyses using echocardiography follow.

Clinically, estrogen deficiency in menopausal women may aggravate the incidence of lipid disruption and atherosclerosis. We established an in vivo estrogen deficiency model by bilateral ovariectomy via a double dorsal-lateral incision in apoE-/- mice. The mouse model is applicable for screening exogenous estrogen treatments of cardiovascular dysfunction after menopause.

Postmenopausal women are at greater risk of developing cardiovascular diseases than premenopausal women. Female mice ovariectomized (OVX) at weaning ...

Biochemistry

Modeling Breast Cancer in Human Breast Tissue using a Microphysiological System

Breast cancer (BC) remains a leading cause of death for women. Despite more than $700 million invested in BC research annually, 97% of candidate BC drugs fail clinical trials. Therefore, new models are needed to improve our understanding of the disease. The NIH Microphysiological Systems (MPS) program was developed to improve the clinical translation of basic science discoveries and promising new therapeutic strategies. Here we present a method for generating MPS for breast cancers (BC-MPS). This model adapts a previously described approach of culturing primary human white adipose tissue (WAT) by sandwiching WAT between adipose-derived stem cell sheets (ASC)s. Novel aspects of our BC-MPS include seeding BC cells into non-diseased human breast tissue (HBT) containing native extracellular matrix, mature adipocytes, resident fibroblasts, and immune cells; and sandwiching the BC-HBT admixture between HBT-derived ASC sheets. The resulting BC-MPS is stable in culture ex vivo for at least 14 days. This model system contains multiple elements of the microenvironment that influence BC including adipocytes, stromal cells, immune cells, and the extracellular matrix. Thus BC-MPS can be used to study the interactions between BC and its microenvironment. 
We demonstrate the advantages of our BC-MPS by studying two BC behaviors known to influence cancer progression and metastasis: 1) BC motility and 2) BC-HBT metabolic crosstalk. While BC motility has previously been demonstrated using intravital imaging, BC-MPS allows for high-resolution time-lapse imaging using fluorescence microscopy over several days. Furthermore, while metabolic crosstalk was previously demonstrated using BC cells and murine pre-adipocytes differentiated into immature adipocytes, our BC-MPS model is the first system to demonstrate this crosstalk between primary human mammary adipocytes and BC cells in vitro.

This protocol describes the construction of an in vitro microphysiological system for studying breast cancer using primary human breast tissue with off the shelf materials.

Breast cancer (BC) remains a leading cause of death for women. Despite more than $700 million invested in BC research annually, 97% of candidate BC drugs ...

Cancer Research

Screening for Phytoestrogens using a Cell-based Estrogen Receptor  &#946; Reporter Assay

Plants are a source of food for many animals, and&#160;they can produce thousands of chemicals. Some of these compounds affect physiological processes in the vertebrates that consume them, such as endocrine function. Phytoestrogens, the most well studied endocrine-active phytochemicals, directly interact with the hypothalamo-pituitary gonadal axis of the vertebrate endocrine system. Here we present the novel use of a cell-based assay to screen plant extracts for the presence of compounds that have estrogenic biological activity. This assay uses mammalian cells engineered to highly express estrogen receptor beta (ER&#946;) and that have been transfected with a luciferase gene. Exposure to compounds with estrogenic activity results in the cells producing light. This assay is a reliable and simple way to test for biological estrogenic activity. It has several improvements over transient transfection assays, most notably, ease of use, the stability of the cells, and the sensitivity of the assay.

We have optimized a commercially available estrogen receptor &#946; reporter assay for screening human and nonhuman primate foods for estrogenic activity. We validated this assay by showing that the known estrogenic human food soy registers high, while other foods show no activity.

Plants are a source of food for many animals, and&#160;they can produce thousands of chemicals. Some of these compounds affect physiological processes in ...

Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells

Estrogen plays vital roles in mammary gland development and breast cancer progression. It mediates its function by binding to and activating the estrogen receptors (ERs), ER&#945;, and ER&#946;. ER&#945; is frequently upregulated in breast cancer and drives the proliferation of breast cancer cells. The ERs function as transcription factors and regulate gene expression. Whereas ER&#945;&#39;s regulation of protein-coding genes is well established, its regulation of noncoding microRNA (miRNA) is less explored. miRNAs play a major role in the post-transcriptional regulation of genes, inhibiting their translation or degrading their mRNA. miRNAs can function as oncogenes or tumor suppressors&#160;and are also promising biomarkers. Among the miRNA assays available, microarray and quantitative real-time polymerase chain reaction (qPCR) have been extensively used to detect and quantify miRNA levels. To identify miRNAs regulated by estrogen signaling in breast cancer, their expression in ER&#945;-positive breast cancer cell lines were compared before and after estrogen-activation using both the &#181;Paraflo-microfluidic microarrays and Dual Labeled Probes-low density arrays. Results were validated using specific qPCR assays, applying both Cyanine dye-based and Dual Labeled Probes-based chemistry. Furthermore, a time-point assay was used to identify regulations over time. Advantages of the miRNA assay approach used in this study is that it enables a fast screening of mature miRNA regulations in numerous samples, even with limited sample amounts. The layout, including the specific conditions for cell culture and estrogen treatment, biological and technical replicates, and large-scale screening followed by in-depth confirmations using separate techniques, ensures a robust detection of miRNA regulations,&#160;and eliminates false positives and other artifacts. However, mutated or unknown miRNAs, or regulations at the primary and precursor transcript level, will not be detected. The method presented here represents a thorough investigation of estrogen-mediated miRNA regulation.

Molecular signaling through both estrogen and microRNAs are critical in breast cancer development and growth. Estrogen activates the estrogen receptors, which are transcription factors. Many transcription factors can regulate the expression of microRNAs, and estrogen-regulated microRNAs can be profiled using different large-scale techniques.

Estrogen plays vital roles in mammary gland development and breast cancer progression. It mediates its function by binding to and activating the estrogen ...

A Modified In vitro Invasion Assay to Determine the Potential Role of Hormones, Cytokines and/or Growth Factors in Mediating Cancer Cell Invasion

Blood serum serves as a chemoattractant towards which cancer cells migrate and invade, facilitating their intravasation into microvessels. However, the actual molecules towards which the cells migrate remain elusive. This modified invasion assay has been developed to identify targets which drive cell migration and invasion. This technique compares the invasion index under three conditions to determine whether a specific hormone, growth factor, or cytokine plays a role in mediating the invasive potential of a cancer cell. These conditions include i) normal fetal bovine serum (FBS), ii) charcoal-stripped FBS (CS-FBS), which removes hormones, growth factors, and cytokines and iii) CS-FBS + molecule (denoted &#8220;X&#8221;). A significant change in cell invasion with CS-FBS as compared to FBS, indicates the involvement of hormones, cytokines or growth factors in mediating the change. Individual molecules can then be added back to CS-FBS to assay their ability to reverse or rescue the invasion phenotype. Furthermore, two or more factors can be combined to evaluate the additive or synergistic effects of multiple molecules in driving or inhibiting invasion. Overall, this method enables the investigator to determine whether hormones, cytokines, and/or growth factors play a role in cell invasion by serving as chemoattractants or inhibitors of invasion for a particular type of cancer cell or a specific mutant. By identifying specific chemoattractants and inhibitors, this modified invasion assay may help to elucidate signaling pathways that direct cancer cell invasion.

A Modified In vitro Invasion Assay to Determine the Potential Role of Hormones, Cytokines and/or Growth Factors in Mediating Cancer Cell Invasion

This video article describes a modified in vitro method to examine the role of hormones, cytokines and/or growth factors in driving cancer cell invasion.

Blood serum serves as a chemoattractant towards which cancer cells migrate and invade, facilitating their intravasation into microvessels. However, the ...

An Ex vivo Model to Study Hormone Action in the Human Breast

The study of hormone action in the human breast has been hampered by lack of adequate model systems. Upon in vitro culture, primary mammary epithelial cells tend to lose hormone receptor expression. Widely used hormone receptor positive breast cancer cell lines are of limited relevance to the in vivo situation. Here, we describe an ex vivo model to study hormone action in the human breast. Fresh human breast tissue specimens from surgical discard material such as reduction mammoplasties or mammectomies are mechanically and enzymatically digested to obtain tissue fragments containing ducts and lobules and multiple stromal cell types. These tissue microstructures kept in basal medium without growth factors preserve their intercellular contacts, the tissue architecture, and remain hormone responsive for several days. They are readily processed for RNA and protein extraction, histological analysis or stored in freezing medium. Fluorescence activated cell sorting (FACS) can be used to enrich for specific cell populations. This protocol provides a straightforward, standard approach for translational studies with highly complex, varied human specimens.

An Ex vivo Model to Study Hormone Action in the Human Breast

We have developed a novel ex vivo model to study hormone action in the human breast. It is based on tissue microstructures isolated from surgical breast tissue specimens which preserve tissue architecture, intercellular interactions, and paracrine signaling.

The study of hormone action in the human breast has been hampered by lack of adequate model systems. Upon in vitro culture, primary mammary epithelial ...

Using 16&#945;-[18F]-Fluoro-17&#946;-Estradiol PET to Visualize Estrogen Receptor &#945; Expression in Human Breast Cancer Xenografts in Female Ovariectomized Mice

To demonstrate how estrogen receptor alpha (ER&#945;) positive breast cancer xenografts may be visualized in BALB/c nude mice using 16&#945;-[18F]-fluoro-17&#946;-estradiol (18F-FES) positron emission tomography (PET), ovariectomized BALB/c nude mice were injected with ER&#945;-positive breast cancer cells (MCF-7, 3 &#215; 106 cells; shoulder [n = 10] or 4th inguinal mammary fat pad [n = 10]) or ER&#945;-negative breast cancer cells (MDA-MB-231, 1 &#215; 106 cells; mammary fat pad [n = 5]). Mice harboring MCF-7 cells received subcutaneous injections of 20 &#181;g of 17&#946;-estradiol (20 &#181;g/20 &#181;L; corn oil:ethanol, 9:1) in the nape of their necks 2 days prior to cell injection, followed by daily injections five times per week for 5 weeks. Tumor volumes were measured according to the formula: (L*W2)/2 (L; length, W; width). Once tumor volumes reached approximately 100 mm3, 17&#946;-estradiol injections were halted 2 days prior to mice receiving 18F-FES for PET imaging to avoid competitive binding with ER&#945;. Upon 18F-FES administration via the lateral tail vein, PET/MRI was performed for 15 min at 1 h to 1.5 h post-injection. 18F-FES uptake was not observed in ER&#945;-negative, MDA-MB-231 tumor-bearing mice. 18F-FES uptake was most pronounced in mice harboring MCF-7 tumors in the shoulder. In MCF-7 tumors grown in the inguinal mammary fat pad, 18F-FES uptake was less visible, as the intestinal excretion pattern of 18F-FES obscured the radioactivity detectable in these tumors. To use 18F-FES PET as a tool to visualize ER&#945; expression in ER&#945;-positive breast xenografts, we demonstrate that the visibility of 18F-FES uptake is clear in tumors located away from the abdominal region of mice, such as in the shoulder.

Here, we demonstrate a protocol to use 16&#945;-[18F]-fluoro-17&#946;-estradiol (18F-FES) positron emission tomography (PET) as a tool to visualize ER&#945; expression in ER&#945;-positive breast xenografts.

To demonstrate how estrogen receptor alpha (ER&#945;) positive breast cancer xenografts may be visualized in BALB/c nude mice using ...

Salidroside's Mechanism in Suppressing MCF-7 Cell Migration and Proliferation

Exploring the Pharmacological Action and Molecular Mechanism of Salidroside in Inhibiting MCF-7 Cell Proliferation and Migration

Salidroside (Sal) contains anti-carcinogenic, anti-hypoxic, and anti-inflammatory pharmacological activities. However, its underlying anti-breast cancer mechanisms have been only incompletely elucidated. Hence, this protocol intended to decode the potential of Sal in regulating the PI3K-AKT-HIF-1&#945;-FoxO1 pathway in the malignant proliferation of human breast cancer MCF-7 cells. First, the pharmacological activity of Sal against MCF-7 was evaluated by CCK-8 and cell scratch assays. Moreover, the resistance of MCF-7 cells was measured by migration and Matrigel invasion assays. For cell apoptosis and cycle assays, MCF-7 cells were processed in steps with annexin V-FITC/PI and cell cycle-staining detection kits for flow cytometry analyses, respectively. The levels of reactive oxygen species (ROS) and Ca2+ were examined by DCFH-DA and Fluo-4 AM immunofluorescence staining. The activities of Na+-K+-ATPase and Ca2+-ATPase were determined using the corresponding commercial kits. The protein and gene expression levels in apoptosis and the PI3K-AKT-HIF-1&#945;-FoxO1 pathway were further determined using western blot and qRT-PCR analyses, respectively. We found that Sal treatment significantly restricted the proliferation, migration, and invasion of MCF-7 cells with dose-dependent effects. Meanwhile, Sal administration also dramatically forced MCF-7 cells to undergo apoptosis and cell cycle arrest. The immunofluorescence tests showed that Sal observably stimulated ROS and Ca2+ production in MCF-7 cells. Further data confirmed that Sal promoted the expression levels of pro-apoptotic proteins, Bax, Bim, cleaved caspase-9/7/3, and their corresponding genes. Consistently, Sal intervention prominently reduced the expression of the Bcl-2, p-PI3K/PI3K, p-AKT/AKT, mTOR, HIF-1&#945;, and FoxO1 proteins and their corresponding genes. In conclusion, Sal can be used as a potential herb-derived compound for treating breast cancer, as it may reduce the malignant proliferation, migration, and invasion of MCF-7 cells by inhibiting the PI3K-AKT-HIF-1&#945;-FoxO1 pathway.

Author Spotlight: Exploring Salidroside's Molecular Mechanisms in Breast Cancer Treatment 

The present protocol describesa comprehensive strategy for evaluating the pharmacological action and mechanism of salidroside in inhibiting MCF-7 cell proliferation and migration.

Salidroside (Sal) contains anti-carcinogenic, anti-hypoxic, and anti-inflammatory pharmacological activities. However, its underlying anti-breast cancer ...

Sample Preparation for Metabolomics: A Method to Prepare Cell Samples for Metabolite Profiling

Source: Zhao, Y. C. et. al., <a href="https://www.jove.com/v/53832/systems-biology-metabolic-regulation-estrogen-receptor-signaling">Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer.</a> J. Vis. Exp. (2016).
This video describes a technique of preparing breast cancer cell samples for metabolite profiling. Breast cancer cells are treated with estradiol, which binds to the estrogen receptor alpha and causes change in the metabolite composition.&#160;&#160;

Source: Zhao, Y. C. et. al., Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer. J. Vis. Exp. (2016).
This video ...

JoVE Encyclopedia of Experiments

Breast Cancer

Procedures and techniques

Estrogen Receptor-Reporter Activity Assay to Screen Phytoestrogen Estrogenic Activity

Source: Fender, E. et al., <a href="https://app.jove.com/v/61005">Screening for Phytoestrogens using a Cell-based Estrogen Receptor &#946; Reporter Assay.</a> J. Vis. Exp. (2021).
This video describes a cell-based reporter assay to determine the estrogenic activity of plant-derived compounds. A compound with estrogen-like activity mimics its binding to estrogen receptors and drives downstream reporter enzyme gene transcription. The reporter enzyme generates bioluminescence in a reaction, indicating the estrogenic activity of the compound.

Source: Fender, E. et al., Screening for Phytoestrogens using a Cell-based Estrogen Receptor &#946; Reporter Assay. J. Vis. Exp. (2021).
This video ...

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

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