Estrogen receptor α target genes: The role of integrated computational genomics and chromation immunoprecipitation microarray

Victor X. Jin, Yu Wei Leu, Sandya Liyanarachchi, Hao Sun, Meiyun Fan, Kenneth P. Nephew, Tim H M Huang, Ramana V. Davuluri*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Objective - To identify these promoter sequences involve in regulating gene expression by direct or indirect methods. Design - A combination of a systematic computational approach and microaray-based ChIP-on-chip for the genome-wide identification of ERα target genes. Method - We conducted a genome-wide screening with a novel microarray technique called ChIP-on-chip. A set of 70 candidate ERα loci was identified and the corresponding promoter sequences were analysed by statistical pattern recognition and comparative genomics approaches. Results - We found mouse counterparts for 63 of these loci and classified 42 (67%) as direct ERα targets using classification and regression tree (CART) statistical model which involves position weight matrix and human-mouse sequence similarity scores as model parameters. The remaining genes were considered to be indirect targets. To validate this computational prediction, we conducted an additional ChIP-on-chip assay that identified acetylated chromatin components in active ERα promoters. Of the 27 loci upregulated in and ERα-positive breast cancer cell line, 20 having mouse counterparts were correctly predicted by CART. Discussion and Conclusion - This integrated approach, therefore, sets a paradigm in which the iterative process of model refinement and experimental verification will continue until an accurate production of promoter target sequences is derived.

Original languageEnglish (US)
Pages (from-to)64-74
Number of pages11
JournalInternational Journal of Medicine
Volume8
Issue number1
StatePublished - 2006

Keywords

  • ChIP-on-chip
  • Estrogen receptor α
  • Gene
  • Transcription factors

ASJC Scopus subject areas

  • General Medicine

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