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

Podsumowanie

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.

Streszczenie

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 α (ERα) 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 ERα 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.

Wprowadzenie

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 ERα using RNA-Seq and genome-wide ERα DNA binding sites analysis using ChIP-Seq proved to be useful to understand how ERα works in different tissues and cancers that arise from them. We and others have published gene expression profiles associated with different receptors (ERα v.s. ERβ)^2,3, different ligands^3-5 and different coregulators^2,4,6,7.

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 lines^2-4,7, 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 ERα 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 ERα 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.

Protokół

1. Preparation of RNA-Seq Samples

1. Cell Culture and Treatment
   1. Culture the MCF7 cells in Improved MEM (IMEM) plus phenol-red medium with 10% Heat inactive FBS, 1% penicillin/streptomycin at 37 °C in humidified 5% CO₂ atmosphere.
      Note: Same cell line and cell culture condition is used throughout the study.
   2. 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 °C in humidified 5% CO₂ atmosphere.
   3. 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
2. RNA Isolation
   1. Add 200 µl 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 °C. Following centrifugation, transfer the aqueous phase (top) to a new tube without disturbing the other phases.
   2. Add 500 µl 100% isopropanol to a new tube with the transferred-aqueous phase, and mix by inverting. Incubate at -20 °C overnight. Centrifuge the samples at 16,000 x g for 10 min at 4 °C. Observe a pellet at the bottom of the tube. Discard the supernatant and wash the pellet with 750 µl RNase-free 70% ethanol by brief vortex.
   3. Centrifuge samples at 6,000 x g for 5 min at 4 °C. Remove the supernatant, and centrifuge the sample at 16,000 x g for 1 min at 4 °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 µl DEPC treated water depending on the size of the pellets.
3. 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.
4. Take about 0.5 to 1 μg purified RNA for sequencing.
   NOTE: Biotech center prepares the sequencing libraries and performs paired-end read sequencing using methods detailed previously².

2. Preparation of ChIP-Seq Sample

1. Cell Culture and Treatment
   1. 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 °C in humidified 5% CO₂ atmosphere).
2. Crosslink and Cell Harvest
   1. Add 250 µl 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.
   2. Harvest the cells in 250 µl Cell Lysis buffer (10 mM EDTA, 50 mM Tri HCl, 1% SDS, 0.5% N,N-dimethyldodecan-1-amine oxide) per plate.
3. Fragmentation of Chromatin
   1. 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.
   2. Centrifuge at 16, 000 x g for 10 min at 4 °C, and carefully pipette the supernatant without disturbing the pellet at the bottom.
   3. Take a 5 µl 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.
4. Chromatin Immunoprecipitation
   1. Save 25 µl 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), ERα antibody (500 µl F10 and 500 µl HC-20), and 500 µl ProtA and ProtG protein coating magnetic beads. Incubate the mixture by rotation at 4 °C overnight to allow the formation of chromatin- antibody complex.
5. Washes and Reverse-crosslinking
   1. Use a magnetic stand to collect the magnetic beads on the magnetized side to remove the wash buffer.
   2. 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.
   3. 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).
   4. 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).
   5. Wash the magnetic beads in 25 ml 1x TE buffer (1 mM EDTA, and 10 mM Tris HCl) twice.
   6. Elute DNA in 500 µl Elution buffer (1% SDS, and 10 mM NaHCO₃₎ and then allocate the 500 µl DNA elution into 4 tubes. For the DNA from inputs, elute DNA in 125 µl Elution buffer.
   7. Incubate the tubes at 65 °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.
6. DNA Isolation
   1. 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.
      1. Warm up the Elution buffer at 65 °C. Add 625 µl binding buffer to each tube. If the precipitate forms, add another 250 µl binding buffer until the solution is clear.
      2. Load the sample onto the column and centrifuge at 16,000 x g for 30 sec. Wash the sample with 250 µl wash buffer. Remember to add ethanol to the wash buffer when first time to use it. Repeat the wash.
      3. Elute the DNA in 15 µl Elution buffer. Combine the DNA of same pull down from 4 tubes to achieve around 55 µl DNA.
   2. Isolate the Input DNA Using a DNA Purification Kit.
      1. Warm up the Elution buffer at 65 °C. Add 625 µl binding buffer in each tube and incubate for 10 min. Load the sample onto the column in a 2 ml collection tube.
      2. Wash the sample with 750 µl wash buffer, centrifuge at 16,000 x g for 1 min. Repeat centrifuging to remove any remaining buffer. Elute the DNA into 30 µl of EB.
   3. Measure the DNA Concentration Using a Commercial Fluorochrome Assay with Modification¹¹.
      1. Dilute the 20x TE buffer into 1x TE as the assay buffer for diluting the reagent and DNA samples. Take 5 µl isolated DNA and dilute it into 50 µl in 1x TE.
      2. From 2 µg/ml DNA stock solution make a DNA standard ranging from blank to 1, 10, 100, 1000 µg/ml. In a 96 well plate, allocate 25 µl of each standard as well as DNA dilution in duplicate. (Triplicate for standard is recommended.)
      3. 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 µl working reagent into each sample. Incubate at room temperature for 5 min. Cover the plate with aluminum foil to avoid light.
      4. 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.
7. Verification of ChIP Experiment Using qPCR
   1. Take 2 µl isolated DNA and diluted into 20 µl in water. Set up the qPCR reaction in triplicate by mixing 2.5 µl DNA dilution, 5 µl Green I PCR master mix, 1 µl primer, and 1.5 µl nuclease free water.
   2. Run the qPCR with the Fast Real-Time PCR system using the following program: Step 1: 95 °C for 60 sec, Step 2: 95 °C for 10 sec, 60 °C for 60 sec, repeat Step 2 39 times.
8. 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.

3. Metabolomics Assay Sample Preparation

1. Cell Culture and Treatment
   1. 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 °C in humidified 5% CO₂ atmosphere).
2. Sample Collection
   1. 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.
   2. Add 750 µl 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.
   3. Cell count for normalization
      1. 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% NaHCO_3, and 1 mM EDTA) into each plate and incubate for 5 min.
      2. Collect and mix well the cells by pipetting cells in the HE buffer. Use total of 20 µl cell solution to count the cell number using a hemocytometer.
3. Store the samples at -80 °C before sending to the Metabolomics center to identify and quantify the metabolites using Gas chromatography mass spectrometry (GC-MS) analysis.

4. Integrative Analysis

1. RNA-Seq Data Analysis:
   1. Perform Quality check of FASTQ files using FastQC:Read QC (http://galaxy.illinois.edu)
      1. 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.
   2. 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.
   3. Align reads to hg19 using Tophat2 with RefSeq genome reference annotation¹² (default values).
      1. Select Tophat2 under tool of NGS:RNA analysis. Indicate the library paired type (single-end vs paired-end).
      2. Upload the input FASTQ file from the current history. Select the reference genome. Choose defaults settings. Or use the full parameter list to modify.
      3. 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.
   4. Upload BAM files to sequencing data analysis tool. Calculate expression values using Quantification Tool.
   5. Identify differentially up-regulated genes using Expression Analysis Tool using default values and fold change >2 and p-value <0.05. Likewise, identify differentially down-regulated genes using Expression Analysis Tool using default values and fold change <0.5 and p-value <0.05.
2. ChIP-Seq Data Analysis:
   1. Align FASTQ Reads to hg19 using Bowtie2¹³ (Default Values) in Galaxy.
      1. 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.
      2. 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.
   2. Identify peaks using MACS¹⁴ a p-value cutoff of 6.0e-7 and FDR of 0.01, as previously described².
3. Metabolomics Data Analysis:
   1. Convert chemical names of metabolites to KEGG_IDs.
   2. Identify differentially regulated metabolites by comparing levels detected in Vehicle vs. E2 treated samples (a 2 fold cut-off was used in this study).
4. Integrative Analysis:
   1. Identify genes that have ERα binding sites using sequencing data analysis tool. Translate Regions to genes function using 20 kb as distance cut-off.
   2. Identify differentially up- and down-regulated genes from RNA-Seq data analysis using fold change >2 and fold change <0.5, respectively with the p-value <0.05 as cut off. Compare to the list of ERα binding site containing genes using Venn diagram comparison.
   3. Use this list to identify E2 modulated metabolic pathways using Metscape¹⁵ module of Cytoscape.
      1. By selecting Metscape module from apps, choose Build network and then pathway-based option.
      2. Select the Organism (Human). Select data file of E2 unregulated compound list (KEGG ID) as well E2 up-regulated gene list.
      3. Choose Compound -Reaction -Enzyme-Gene as the Network type. Choose compound/genes as the query.
      4. Build network. Repeat using the E2 down-regulated compound and genes.

Wyniki

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...

Dyskusje

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 ERα directly regulated in breast cancer cells.

Materiały

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