alexa Androgen receptor binding sites identified by a GREF_GATA model.
Diabetes & Endocrinology

Diabetes & Endocrinology

Journal of Steroids & Hormonal Science

Author(s): Masuda K, Werner T, Maheshwari S, Frisch M, Oh S,

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Abstract Changes in transcriptional regulation can be permissive for tumor progression by allowing for selective growth advantage of tumor cells. Tumor suppressors can effectively inhibit this process. The PMEPA1 gene, a potent inhibitor of prostate cancer cell growth is an androgen-regulated gene. We addressed the question of whether or not androgen receptor can directly bind to specific PMEPA1 promoter upstream sequences. To test this hypothesis we extended in silico prediction of androgen receptor binding sites by a modeling approach and verified the actual binding by in vivo chromatin immunoprecipitation assay. Promoter upstream sequences of highly androgen-inducible genes were examined from microarray data of prostate cancer cells for transcription factor binding sites (TFBSs). Results were analyzed to formulate a model for the description of specific androgen receptor binding site context in these sequences. In silico analysis and subsequent experimental verification of the selected sequences suggested that a model that combined a GREF and a GATA TFBS was sufficient for predicting a class of functional androgen receptor binding sites. The GREF matrix family represents androgen receptor, glucocorticoid receptor and progesterone receptor binding sites and the GATA matrix family represents GATA binding protein 1-6 binding sites. We assessed the regulatory sequences of the PMEPA1 gene by comparing our model-based GREF_GATA predictions to weight matrix-based predictions. Androgen receptor binding to predicted promoter upstream sequences of the PMEPA1 gene was confirmed by chromatin immunoprecipitation assay. Our results suggested that androgen receptor binding to cognate elements was consistent with the GREF_GATA model. In contrast, using only single GREF weight matrices resulted in additional matches, apparently false positives. Our findings indicate that complex models based on datasets selected by biological function can be superior predictors as they recognize TFBSs in their functional context. This article was published in J Mol Biol and referenced in Journal of Steroids & Hormonal Science

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