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      Up-regulation of the kinase gene SGK1 by progesterone activates the AP-1–NDRG1 axis in both PR-positive and -negative breast cancer cells

      research-article
      , § , , § , , , § , , § , , , § , , , , § , , , , § , , , , § , , , ** , , § , 3
      The Journal of Biological Chemistry
      American Society for Biochemistry and Molecular Biology
      progesterone, breast cancer, functional genomics, cell signaling, microRNA (miRNA), N-Myc-downstream regulated gene 1 (NDRG1), post-transcriptional regulation, Serum- and glucocorticoid-regulated kinase 1 (SGK1), transcription factor AP-1

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          Abstract

          Preoperative progesterone intervention has been shown to confer a survival benefit to breast cancer patients independently of their progesterone receptor (PR) status. This observation raises the question how progesterone affects the outcome of PR-negative cancer. Here, using microarray and RNA-Seq–based gene expression profiling and ChIP-Seq analyses of breast cancer cells, we observed that the serum- and glucocorticoid-regulated kinase gene ( SGK1) and the tumor metastasis–suppressor gene N-Myc downstream regulated gene 1 ( NDRG1) are up-regulated and that the microRNAs miR-29a and miR-101-1 targeting the 3′-UTR of SGK1 are down-regulated in response to progesterone. We further demonstrate a dual-phase transcriptional and post-transcriptional regulation of SGK1 in response to progesterone, leading to an up-regulation of NDRG1 that is mediated by a set of genes regulated by the transcription factor AP-1. We found that NDRG1, in turn, inactivates a set of kinases, impeding the invasion and migration of breast cancer cells. In summary, we propose a model for the mode of action of progesterone in breast cancer. This model helps decipher the molecular basis of observations in a randomized clinical trial of the effect of progesterone on breast cancer and has therefore the potential to improve the prognosis of breast cancer patients receiving preoperative progesterone treatment.

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          Most cited references33

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          Identifying ChIP-seq enrichment using MACS.

          Model-based analysis of ChIP-seq (MACS) is a computational algorithm that identifies genome-wide locations of transcription/chromatin factor binding or histone modification from ChIP-seq data. MACS consists of four steps: removing redundant reads, adjusting read position, calculating peak enrichment and estimating the empirical false discovery rate (FDR). In this protocol, we provide a detailed demonstration of how to install MACS and how to use it to analyze three common types of ChIP-seq data sets with different characteristics: the sequence-specific transcription factor FoxA1, the histone modification mark H3K4me3 with sharp enrichment and the H3K36me3 mark with broad enrichment. We also explain how to interpret and visualize the results of MACS analyses. The algorithm requires ∼3 GB of RAM and 1.5 h of computing time to analyze a ChIP-seq data set containing 30 million reads, an estimate that increases with sequence coverage. MACS is open source and is available from http://liulab.dfci.harvard.edu/MACS/.
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            The Gene Expression Omnibus Database.

            The Gene Expression Omnibus (GEO) database is an international public repository that archives and freely distributes high-throughput gene expression and other functional genomics data sets. Created in 2000 as a worldwide resource for gene expression studies, GEO has evolved with rapidly changing technologies and now accepts high-throughput data for many other data applications, including those that examine genome methylation, chromatin structure, and genome-protein interactions. GEO supports community-derived reporting standards that specify provision of several critical study elements including raw data, processed data, and descriptive metadata. The database not only provides access to data for tens of thousands of studies, but also offers various Web-based tools and strategies that enable users to locate data relevant to their specific interests, as well as to visualize and analyze the data. This chapter includes detailed descriptions of methods to query and download GEO data and use the analysis and visualization tools. The GEO homepage is at http://www.ncbi.nlm.nih.gov/geo/.
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              MicroRNA-mediated feedback and feedforward loops are recurrent network motifs in mammals.

              MicroRNAs (miRNAs) are regulatory molecules that participate in diverse biological processes in animals and plants. While thousands of mammalian genes are potentially targeted by miRNAs, the functions of miRNAs in the context of gene networks are not well understood. Specifically, it is unknown whether miRNA-containing networks have recurrent circuit motifs, as has been observed in regulatory networks of bacteria and yeast. Here we develop a computational method that utilizes gene expression data to show that two classes of circuits-corresponding to positive and negative transcriptional coregulation of a miRNA and its targets-are prevalent in the human and mouse genomes. Additionally, we find that neuronal-enriched miRNAs tend to be coexpressed with their target genes, suggesting that these miRNAs could be involved in neuronal homeostasis. Our results strongly suggest that coordinated transcriptional and miRNA-mediated regulation is a recurrent motif to enhance the robustness of gene regulation in mammalian genomes.
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                Author and article information

                Journal
                J Biol Chem
                J. Biol. Chem
                jbc
                jbc
                JBC
                The Journal of Biological Chemistry
                American Society for Biochemistry and Molecular Biology (11200 Rockville Pike, Suite 302, Rockville, MD 20852-3110, U.S.A. )
                0021-9258
                1083-351X
                14 December 2018
                18 October 2018
                18 October 2018
                : 293
                : 50
                : 19263-19276
                Affiliations
                From the []Integrated Cancer Genomics Laboratory and
                the []Shilpee Laboratory, Advanced Centre for Treatment, Research, and Education in Cancer,
                the []Department of Medical Oncology, and
                the [** ]Department of Surgical Oncology, Tata Memorial Hospital, Tata Memorial Centre, Navi Mumbai, Maharashtra 410210, India and
                the [§ ]Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra 400094, India
                Author notes
                [3 ] To whom correspondence should be addressed: Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment, Research and Education In Cancer, Tata Memorial Centre, Maharashtra, Navi Mumbai 410210, India. Tel.: 91-22-27405056/30435056; E-mail: adutt@ 123456actrec.gov.in .
                [1]

                Supported by a research fellowship from the Homi Bhabha National Institute (HBNI), ACTREC-TMC.

                [2]

                Both authors contributed equally to this work.

                Edited by Alex Toker

                Author information
                https://orcid.org/0000-0002-6742-6378
                https://orcid.org/0000-0002-1119-4774
                Article
                RA118.002894
                10.1074/jbc.RA118.002894
                6298595
                30337371
                776a82ca-fefd-437d-b9dd-09c532e8db6d
                © 2018 Godbole et al.

                Author's Choice—Final version open access under the terms of the Creative Commons CC-BY license.

                History
                : 13 March 2018
                : 13 October 2018
                Funding
                Funded by: Wellcome Trust/Department of Biotechnology India Alliance
                Award ID: IA/I/11/2500278
                Funded by: Department of Biotechnology, Ministry of Science and Technology (DBT) , open-funder-registry 10.13039/501100001407;
                Award ID: BT/MED/30/VNCI-Hr-RCA/2015
                Funded by: Tata Memorial Centre
                Award ID: Project 2712
                Categories
                Molecular Bases of Disease

                Biochemistry
                progesterone,breast cancer,functional genomics,cell signaling,microrna (mirna),n-myc-downstream regulated gene 1 (ndrg1),post-transcriptional regulation,serum- and glucocorticoid-regulated kinase 1 (sgk1),transcription factor ap-1

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