Identification of Selective Estrogen Receptor Modulators by Their Gene Expression Fingerprints

Zajchowski, Deborah A.; Kauser, Katalin; Zhu, Daguang; Webster, Lynn; Aberle, S. B. D.; White, Frank A.; Liu, Hsiao-Lai; Humm, Rhonda; MacRobbie, Jean; Ponte, P; Hegele‐Hartung, Christa; Knauthe, R.; Fritzemeier, Karl-Heinrich; Vergona, Ronald; Rubanyi, Gabor M.

doi:10.1074/jbc.m909865199

Cited by 37 publications

(19 citation statements)

References 43 publications

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“…E2 regulates ER␣-and ER␤-dependent genes on average one log more strongly than any of the four potent MERAkt activators, and ZK427 regulates ER␣-dependent genes (41). As shown in Tables 3 and 4, none of the genes commonly regulated by the four potent MERAKT activators was consistently or strongly regulated by either E2 or ZK427, indicating that the mechanism of their regulation in unlikely to be ER dependent.…”

Section: Activation Of Merakt Kinase Activitymentioning

confidence: 88%

“…Last, ER-independent regulation common to the four potent MERAKT activators is unlikely given the dissimilarity of their chemical structures. Ral is a benzothiophene, ICI is a 7␣-substituted E2, and Tam and ZK955 are triphenylethylenes (41). These compounds are unlikely to have similar effects on gene expression independent of their common ability to bind to the mutant ER-HBD of MERAKT.…”

Section: Discussionmentioning

confidence: 99%

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Identification of AKT-regulated genes in inducible MERAkt cells

Bartholdi

Salamon

Feldman³

et al. 2001

Physiological Genomics

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kinase B plays a critical role in the phosphoinositide 3-kinase (PI3-kinase) pathway regulating cell growth, differentiation, and oncogenic transformation. Akt1-regulated genes were identified by cDNA array hybridization analysis using an inducible AKT1 protein, MERAKT. Treatment of MERAkt cells with estrogen receptor ligands resulted in phosphorylative activation of MERAKT. Genes differentially expressed in MERAkt/NIH3T3 cells treated with tamoxifen, raloxifene, ICI-182780, and ZK955, were identified at 3 and 20 h. AKT activation resulted in the repression of c-myc, early growth response 1 (EGR1), transforming growth factor ␤ receptor III (TGF-␤r III), and thrombospondin-1 (THBS1). Although c-myc induction is often associated with oncogenic transformation, the c-myc repression observed here is consistent with the anti-apoptotic function of AKT. Repression of THBS1 and EGR1 is consistent with the known pro-angiogenic functions of AKT. AKT-regulated genes were found to be largely distinct from platelet-derived growth factor-␤ (PDGF␤)-regulated genes; only T-cell deathassociated gene 51 (TDAG51) was induced in both cases. In contrast to their repression by AKT, c-myc, THBS1, and EGR1 were induced by PDGF␤, indicating negative interference between elements upstream and downstream of AKT1 in the PDGF␤ signal transduction pathway.protein kinase B; cDNA array hybridization; estrogen inducible expression; oncogenesis; gene regulation; signal transduction networks AKT1 IS A SERINE/THREONINE protein kinase whose activity is positively regulated by phosphoinositide 3-kinase (PI3-kinase) and negatively regulated by the phosphatases PTEN and PP2A. AKT transduces signals from a number of extracellular stimuli (including insulin, cytokines, and mechanical stress) that affect glucose metabolism, cell cycle progression, protein synthesis, tumor neovascularization, and apoptotic susceptibility (9). 1 Overexpression, hyperactivation, or constitutive activation of AKT is oncogenic. Akt1 was first identified as the transforming gene, v-Akt, encoded by the AKT8 retrovirus. v-Akt differs from its cellular homolog, Akt1, in that it has a 5Ј fused viral gag sequence that causes constitutive activation of the AKT1 kinase. Constitutively active AKT causes cellular transformation in vitro, and cells transfected with constitutively active AKT1 form tumors in nude mice. In vivo, mice transgenic for constitutively activated AKT1 develop T cell lymphomas rapidly (33). Cotransfection with dominant-negative AKT blocks transformation, indicating that AKT activity is necessary and sufficient for oncogenic transformation. AKT activity is elevated in samples of ovarian, breast, prostate, lung, and glioblastoma tumors (4, 36).The purpose of this study was to identify Akt1-regulated genes to aid in understanding the mechanism of AKT1-mediated oncogenesis. The use of an inducible form of the AKT1 protein, MERAKT, made it possible to study the pattern and kinetics of Akt1-regulated gene expression using a single cell line. Activity of the MERAKT kinase...

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Section: Activation Of Merakt Kinase Activitymentioning

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Identification of AKT-regulated genes in inducible MERAkt cells

Bartholdi

Salamon

Feldman³

et al. 2001

Physiological Genomics

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“…It has been previously shown that it is possible to group SERMs based on differences and similarities in the pattern of gene expression changes that they produced (Zajchowski et al, 2000). The authors evaluated 24 gene per cell combinations comprising 10 different known oestrogen-responsive genes in eight cell lines representing four cell types.…”

Section: Discussionmentioning

confidence: 99%

Molecular classification of selective oestrogen receptor modulators on the basis of gene expression profiles of breast cancer cells expressing oestrogen receptor α

Levenson¹,

Kliakhandler²,

Svoboda³

et al. 2002

Br J Cancer

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The purpose of this study was to classify selective oestrogen receptor modulators based on gene expression profiles produced in breast cancer cells expressing either wtERa or mutant 351 ERa. In total, 54 microarray experiments were carried out by using a commercially available Atlas cDNA Expression Arrays (Clontech), containing 588 cancer-related genes. Nine sets of data were generated for each cell line following 24 h of treatment: expression data were obtained for cells treated with vehicle EtOH (Control); with 10 79 or 10 78 M oestradiol; with 10 76 M 4-hydroxytamoxifen; with 10 76 M raloxifene; with 10 76 M idoxifene, with 10 76 M EM 652, with 10 76 M GW 7604; with 5610 75 M resveratrol and with 10 76 M ICI 182,780. We developed a new algorithm 'Expression Signatures' to classify compounds on the basis of differential gene expression profiles. We created dendrograms for each cell line, in which branches represent relationships between compounds. Additionally, clustering analysis was performed using different subsets of genes to assess the robustness of the analysis. In general, only small differences between gene expression profiles treated with compounds were observed with correlation coefficients ranged from 0.83 to 0.98. This observation may be explained by the use of the same cell context for treatments with compounds that essentially belong to the same class of drugs with oestrogen receptors related mechanisms. The most surprising observation was that ICI 182,780 clustered together with oestrodiol and raloxifene for cells expressing wtERa and clustered together with EM 652 for cells expressing mutant 351 ERa. These data provide a rationale for a more precise and elaborate study in which custom made oligonucleotide arrays can be used with comprehensive sets of genes known to have consensus and putative oestrogen response elements in their promoter regions.

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“…An obvious drawback of estrogen-based systems in the mouse is that estrogen plays an important physiological role in the regulation of cell proliferation and differentiation. As a consequence, other endogenous estrogen-responsive genes will also be induced upon estrogen administration (56,165)…”

Section: Hormone Receptor-based Transgenicsmentioning

confidence: 99%

Of mice and models: improved animal models for biomedical research

Bockamp

Maringer

Spangenberg

et al. 2002

Physiological Genomics

147

109

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The ability to engineer the mouse genome has profoundly transformed biomedical research. During the last decade, conventional transgenic and gene knockout technologies have become invaluable experimental tools for modeling genetic disorders, assigning functions to genes, evaluating drugs and toxins, and by and large helping to answer fundamental questions in basic and applied research. In addition, the growing demand for more sophisticated murine models has also become increasingly evident. Good state-of-principle knowledge about the enormous potential of second-generation conditional mouse technology will be beneficial for any researcher interested in using these experimental tools. In this review we will focus on practice, pivotal principles, and progress in the rapidly expanding area of conditional mouse technology. The review will also present an internet compilation of available tetracycline-inducible mouse models as tools for biomedical research ( http://www.zmg.uni-mainz.de/tetmouse/ ).

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Identification of Selective Estrogen Receptor Modulators by Their Gene Expression Fingerprints

Cited by 37 publications

References 43 publications

Identification of AKT-regulated genes in inducible MERAkt cells

Identification of AKT-regulated genes in inducible MERAkt cells

Molecular classification of selective oestrogen receptor modulators on the basis of gene expression profiles of breast cancer cells expressing oestrogen receptor α

Of mice and models: improved animal models for biomedical research

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