Identification of novel steroid target genes through the combination of bioinformatics and functional analysis of hormone response elements

详细信息    查看全文

• 作者：Kuniko Horie-Inoue ; Kenichi Takayama ; Hidemasa U. Bono ; Yasuyoshi Ouchi ; Yasushi Okazaki and Satoshi Inoue
• 关键词：Androgen receptor ; Progesterone receptor ; Glucocorticoid receptor ; Steroid target gene ; Hormone response element ; Chromatin immunoprecipitation ; Quantitative PCR ; Transcriptional start site
• 刊名：Biochemical and Biophysical Research Communications
• 出版年：2006
• 出版时间：6 January 2006
• 年：2006
• 卷：339
• 期：1
• 页码：99-106
• 全文大小：189 K

文摘

Steroid hormone receptors including androgen receptor (AR), glucocorticoid receptor (GR), progesterone receptor (PR), and mineralocorticoid receptor (MR) recognize and bind to identical consensus hormone response elements (HREs), which consist of two hexameric half-sites (5′-AGAACA-3′) arranged as inverted repeats with a 3-bp spacer. Although only a few near-consensus HRE sequences have been identified in the transcriptional regulatory regions of known steroid target genes, it has been unclear whether the exact consensus sequences function as bona fide HREs in vivo. A genome-wide in silico screening of palindromic HREs identified 565 exact consensus sequences in human genome (NCBI 35 assembly). In this study, of 565 exact consensus elements, functional in vivo receptor binding was evaluated regarding 26 sequences located within 10 kb upstream to the 5′ end of annotated genes through chromatin immunoprecipitation (ChIP) assay using cells endogenously expressing steroid hormone receptors. Hormone responsiveness of proximal gene expression was examined through quantitative RT-PCR. As far as performing ChIP assay for AR, GR, and PR, 14 of 26 elements significantly recruited at least one of the receptors by hormone treatment (>2-fold enrichment versus vehicle). In terms of gene expression in the vicinity of the above 14 functional perfect HREs, four genes were upregulated by >2-fold with hormone treatment. The present data suggest that the combination of bioinformatics analysis and quantitative experimental evaluation is useful to identify novel functional HREs that may contribute to the transcriptional regulation of steroid target genes.