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

Summary

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.

Abstract

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.

Introduction

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 assoc....

Protocol

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....

Results

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 important.......

Discussion

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.

Materials

List of materials used in this article

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