Dataset Information

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

ABSTRACT: The TCF7L2 transcription factor (TF) is a member of Wnt signalling pathway, and may influence transcription of several genes by binding to distinct regulatory regions. Genome-wide studies have identified thousands of TCF7L2 binding sites and have revealed some associated TF partners. However, there is still a large uncharted region in the hierarchical regulatory network for TCF7L2 and the partner TFs in MCF7 cells. We analysed ChIP-seq data by searching for motifs in the enriched peak region based on TF-specific position weight matrix (PWM). We found association of FOXO1 and CAD with up-regulated genes, AP2?, PBF and AP1 with down-regulated genes. TCF7L2 and GATA3 were found to be associated with both up and down-regulated genes. Our study uncovers new TCF7L2 associated regulatory networks by mining ChIP-seq data in MCF7 cell, which may contribute to further study of the mechanisms related to Wnt pathway in breast cancer or other diseases.

SUBMITTER: Wang Y

PROVIDER: S-EPMC5218816 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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

Wang Yao Y Wang Rui R Jin Victor X VX

International journal of computational biology and drug design 20160101 1-2

The TCF7L2 transcription factor (TF) is a member of Wnt signalling pathway, and may influence transcription of several genes by binding to distinct regulatory regions. Genome-wide studies have identified thousands of TCF7L2 binding sites and have revealed some associated TF partners. However, there is still a large uncharted region in the hierarchical regulatory network for TCF7L2 and the partner TFs in MCF7 cells. We analysed ChIP-seq data by searching for motifs in the enriched peak region bas ...[more]

PMID: 28066512

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Project description:Global 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 ER? binding sites and have revealed the associated transcription factor (TF) partners, such as AP1, FOXA1 and CEBP, little is known about ER? associated hierarchical transcriptional regulatory networks.In 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 ER? and ER? 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 ER? 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.Our 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.

Project description:BackgroundCell survival and development are orchestrated by complex interlocking programs of gene activation and repression. Understanding how this gene regulatory network (GRN) functions in normal states, and is altered in cancers subtypes, offers fundamental insight into oncogenesis and disease progression, and holds great promise for guiding clinical decisions. Inferring a GRN from empirical microarray gene expression data is a challenging task in cancer systems biology. In recent years, module-based approaches for GRN inference have been proposed to address this challenge. Despite the demonstrated success of module-based approaches in uncovering biologically meaningful regulatory interactions, their application remains limited a single condition, without supporting the comparison of multiple disease subtypes/conditions. Also, their use remains unnecessarily restricted to computational biologists, as accurate inference of modules and their regulators requires integration of diverse tools and heterogeneous data sources, which in turn requires scripting skills, data infrastructure and powerful computational facilities. New analytical frameworks are required to make module-based GRN inference approach more generally useful to the research community.ResultsWe present the RMaNI (Regulatory Module Network Inference) framework, which supports cancer subtype-specific or condition specific GRN inference and differential network analysis. It combines both transcriptomic as well as genomic data sources, and integrates heterogeneous knowledge resources and a set of complementary bioinformatic methods for automated inference of modules, their condition specific regulators and facilitates downstream network analyses and data visualization. To demonstrate its utility, we applied RMaNI to a hepatocellular microarray data containing normal and three disease conditions. We demonstrate that how RMaNI can be employed to understand the genetic architecture underlying three disease conditions. RMaNI is freely available at http://inspect.braembl.org.au/bi/inspect/rmaniConclusionRMaNI makes available a workflow with comprehensive set of tools that would otherwise be challenging for non-expert users to install and apply. The framework presented in this paper is flexible and can be easily extended to analyse any dataset with multiple disease conditions.

Dataset Information

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

Publications

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

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