title: A modulator based regulatory network for ERα signaling pathway.
abstract: BACKGROUND
  Estrogens control multiple functions of hormone-responsive breast cancer cells. They regulate diverse physiological processes in various tissues through genomic and non-genomic mechanisms that result in activation or repression of gene expression. Transcription regulation upon estrogen stimulation is a critical biological process underlying the onset and progress of the majority of breast cancer. ERα requires distinct co-regulator or modulators for efficient transcriptional regulation, and they form a regulatory network. Knowing this regulatory network will enable systematic study of the effect of ERα on breast cancer.
  
  
  METHODS
  To investigate the regulatory network of ERα and discover novel modulators of ERα functions, we proposed an analytical method based on a linear regression model to identify translational modulators and their network relationships. In the network analysis, a group of specific modulator and target genes were selected according to the functionality of modulator and the ERα binding. Network formed from targets genes with ERα binding was called ERα genomic regulatory network; while network formed from targets genes without ERα binding was called ERα non-genomic regulatory network. Considering the active or repressive function of ERα, active or repressive function of a modulator, and agonist or antagonist effect of a modulator on ERα, the ERα/modulator/target relationships were categorized into 27 classes.
  
  
  RESULTS
  Using the gene expression data and ERα Chip-seq data from the MCF-7 cell line, the ERα genomic/non-genomic regulatory networks were built by merging ERα/ modulator/target triplets (TF, M, T), where TF refers to the ERα, M refers to the modulator, and T refers to the target. Comparing these two networks, ERα non-genomic network has lower FDR than the genomic network. In order to validate these two networks, the same network analysis was performed in the gene expression data from the ZR-75.1 cell. The network overlap analysis between two cancer cells showed 1% overlap for the ERα genomic regulatory network, but 4% overlap for the non-genomic regulatory network.
  
  
  CONCLUSIONS
  We proposed a novel approach to infer the ERα/modulator/target relationships, and construct the genomic/non-genomic regulatory networks in two cancer cells. We found that the non-genomic regulatory network is more reliable than the genomic regulatory network.
authors.affiliation: Center for Computational Biology and Bioinformatics, Indiana University, Indianapolis, IN, USA. hengwu@umail.iu.edu
authors.forename: Heng-Yi
authors.initials: HY
authors.lastname: Wu
authors.forename: Pengyue
authors.initials: P
authors.lastname: Zheng
authors.forename: Guanglong
authors.initials: G
authors.lastname: Jiang
authors.forename: Yunlong
authors.initials: Y
authors.lastname: Liu
authors.forename: Kenneth P
authors.initials: KP
authors.lastname: Nephew
authors.forename: Tim H M
authors.initials: TH
authors.lastname: Huang
authors.forename: Lang
authors.initials: L
authors.lastname: Li
mesh_terms: D045744:Cell Line, Tumor; D047369:Chromatin Immunoprecipitation; D047628:Estrogen Receptor alpha / Q000235:genetics* / Q000378:metabolism; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D061986:MCF-7 Cells; D015398:Signal Transduction; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism
chemical_list: D047628:Estrogen Receptor alpha; D014157:Transcription Factors