Inference of hierarchical regulatory network of estrogen-dependent breast cancer through ChIP-based data

Abstract

BackgroundGlobal profiling of in vivo protein-DNA interactions using ChIP-based technologies has evolved rapidly in recent years. Although many genome-wide studies have identified thousands of ERα binding sites and have revealed the associated transcription factor (TF) partners, such as AP1, FOXA1 and CEBP, little is known about ERα associated hierarchical transcriptional regulatory networks.ResultsIn this study, we applied computational approaches to analyze three public available ChIP-based datasets: ChIP-seq, ChIP-PET and ChIP-chip, and to investigate the hierarchical regulatory network for ERα and ERα partner TFs regulation in estrogen-dependent breast cancer MCF7 cells. 16 common TFs and two common new TF partners (RORA and PITX2) were found among ChIP-seq, ChIP-chip and ChIP-PET datasets. The regulatory networks were constructed by scanning the ChIP-peak region with TF specific position weight matrix (PWM). A permutation test was performed to test the reliability of each connection of the network. We then used DREM software to perform gene ontology function analysis on the common genes. We found that FOS, PITX2, RORA and FOXA1 were involved in the up-regulated genes.We also conducted the ERα and Pol-II ChIP-seq experiments in tamoxifen resistance MCF7 cells (denoted as MCF7-T in this study) and compared the difference between MCF7 and MCF7-T cells. The result showed very little overlap between these two cells in terms of targeted genes (21.2% of common genes) and targeted TFs (25% of common TFs). The significant dissimilarity may indicate totally different transcriptional regulatory mechanisms between these two cancer cells.ConclusionsOur study uncovers new estrogen-mediated regulatory networks by mining three ChIP-based data in MCF7 cells and ChIP-seq data in MCF7-T cells. We compared the different ChIP-based technologies as well as different breast cancer cells. Our computational analytical approach may guide biologists to further study the underlying mechanisms in breast cancer cells or other human diseases.

Inference of hierarchical regulatory network of TCF7L2 binding sites in MCF7 cell line

“…ChIPMotifs was applied separately on the identified binding peaks that are associated with either up- or down-regulated genes, to identify cis-regulatory modules for human TFs based on the PWMs, where stringent thresholds, core score = 1 and PWM score = 0.95, were used. The detailed equation and procedure is described in Gu et al (2010).…”

“…Given the nature of the transcriptional regulation is usually via a hierarchical architecture, it is necessary to identify other partnering factors with TCF7L2 and dissect the TCF7L2 regulated network. In this study, we performed a computational analysis using an analytical framework (Figure 1) modified from our previous approach (Gu et al, 2010), to investigate the hierarchical regulatory information for TCF7L2 in MCF7.…”

Transcriptional Network Architecture of Breast Cancer Molecular Subtypes

“…Global network metrics often provide information regarding the system as a whole; while local parameters provide information regarding the relevance of particular nodes (Barabasi and Oltvai, 2004; Newman, 2010; Barabási et al, 2011; Biane et al, 2016; Robinson and Nielsen, 2016). The transcriptional network approach has proven be useful to unveil transcriptional regulation in cancer (Carro et al, 2010; House et al, 2010; Pe'er and Hacohen, 2011; Madhamshettiwar et al, 2012) and in particular in breast cancer (Van De Vijver et al, 2002; Lim et al, 2009; Cicatiello et al, 2010; Gu et al, 2010; Tovar et al, 2015; Castro et al, 2016). …”

HE4 expression is associated with hormonal elements and mediated by importin-dependent nuclear translocation

“…If demethylated regions in the HE4 promoter are upstream of hormonal elements responsive to ER-a, then stable overexpression of these hormone response elements may downregulate ER-a gene expression. Alternatively, other hormonal responsive elements on the HE4 promoter may be more active than ER-a, such as RARrelated orphan receptor A (RORA), which has been shown to assist estradiol-mediated upregulation of gene expression 26 . Interestingly, tamoxifen and other antiestrogens have been shown to exhibit significant apoptotic effects even in ER-negative ovarian cancer cell Confocal microscopy of SKOV3 WT and OVCAR8 WT cells stained with HE4 primary antibody.…”