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A modulator based regulatory network for ER? signaling pathway.

ABSTRACT: 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. ER? 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 ER? on breast cancer.To investigate the regulatory network of ER? and discover novel modulators of ER? 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 ER? binding. Network formed from targets genes with ER? binding was called ER? genomic regulatory network; while network formed from targets genes without ER? binding was called ER? non-genomic regulatory network. Considering the active or repressive function of ER?, active or repressive function of a modulator, and agonist or antagonist effect of a modulator on ER?, the ER?/modulator/target relationships were categorized into 27 classes.Using the gene expression data and ER? Chip-seq data from the MCF-7 cell line, the ER? genomic/non-genomic regulatory networks were built by merging ER?/ modulator/target triplets (TF, M, T), where TF refers to the ER?, M refers to the modulator, and T refers to the target. Comparing these two networks, ER? 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 ER? genomic regulatory network, but 4% overlap for the non-genomic regulatory network.We proposed a novel approach to infer the ER?/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.

SUBMITTER: Wu HY

PROVIDER: S-EPMC3481450 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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A modulator based regulatory network for ERα signaling pathway.

Wu Heng-Yi HY Zheng Pengyue P Jiang Guanglong G Liu Yunlong Y Nephew Kenneth P KP Huang Tim H M TH Li Lang L

BMC genomics 20121026

<h4>Background</h4>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 re ...[more]

PMID: 23134758

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Project description:Non-functional pituitary adenoma (NFPA) seriously affects hypothanamus-pituitary-target organ axis system, with a series of molecule alterations in the multiple levels of genome, transcriptome, proteome, and post-translational modifications, and those molecules mutually interact in a molecular-network system. Meta analysis coupled with IPA pathway-network program was used to comprehensively analyze nine sets of documented NFPA omics data, including NFPA quantitative transcriptomics data [280 differentially expressed genes (DEGs)], NFPA quantitative proteomics data [50 differentially expressed proteins (DEPs)], NFPA mapping protein data (218 proteins), NFPA mapping protein nitration data (9 nitroproteins and 3 non-nitrated proteins), invasive NFPA quantitative transriptomics data (346 DEGs), invasive NFPA quantitative proteomics data (57 DEPs), control mapping protein data (1469 proteins), control mapping protein nitration data (8 nitroproteins), and control mapping phosphorylation data (28 phosphoproteins). A total of 62 molecular-networks with 861 hub-molecules and 519 canonical-pathways including 54 cancer-related canonical pathways were revealed. A total of 42 hub-molecule panels and 9 canonical-pathway panels were identified to significantly associate with tumorigenesis. Four important molecular-network systems, including PI3K/AKT, mTOR, Wnt, and ERK/MAPK pathway-systems, were confirmed in NFPAs by PTMScan experiments with altered expression-patterns and phosphorylations. Nineteen high-frequency hub-molecules were also validated in NFPAs with PTMScan experiment with at least 2.5-fold changes in expression or phosphorylation, including ERK, ERK1/2, Jnk, MAPK, Mek, p38 MAPK, AKT, PI3K complex, p85, PKC, FAK, Rac, Shc, HSP90, NF?B Complex, histone H3, AP1, calmodulin, and PLC. Furthermore, mTOR and Wnt pathway-systems were confirmed in NFPAs by immunoaffinity Western blot analysis, with significantly decreased expression of PRAS40 and increased phosphorylation levels of p-PRAS40 (Thr246) in mTOR pathway in NFPAs compared to controls, and with the decreased protein expressions of GSK-3? and GSK-3?, significantly increased phosphorylation levels of p-GSK3? (Ser21) and p-GSK3? (Ser9), and increased expression level of ?-catenin in Wnt pathway in NFPAs compared to controls. Those findings provided a comphrensive and large-scale pathway network data for NFPAs, and offer the scientific evidence for insights into the accurate molecular mechanisms of NFPA and discovery of the effective biomarkers for diagnosis, prognosis, and determination of therapeutic targets.

Dataset Information

A modulator based regulatory network for ER? signaling pathway.

Publications

A modulator based regulatory network for ERα signaling pathway.

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