ZFIN ID: ZDB-PUB-071227-1
Genome wide screens in yeast to identify potential binding sites and target genes of DNA-binding proteins
Zeng, J., Yan, J., Wang, T., Mosbrook-Davis, D., Dolan, K.T., Christensen, R., Stormo, G.D., Haussler, D., Lathrop, R.H., Brachmann, R.K., and Burgess, S.M.
Date: 2008
Source: Nucleic acids research   36(1): e8 (Journal)
Registered Authors: Burgess, Shawn, Yan, Jizhou
Keywords: none
MeSH Terms:
  • Animals
  • Base Sequence
  • Binding Sites
  • Computational Biology
  • Consensus Sequence
  • DNA/chemistry
  • DNA-Binding Proteins/metabolism*
  • Forkhead Transcription Factors/metabolism
  • Genomic Library
  • Genomics/methods*
  • Mice
  • Plasmids/genetics
  • Regulatory Elements, Transcriptional*
  • Saccharomyces cerevisiae/genetics*
  • Transcription Factors/metabolism*
  • Tumor Suppressor Protein p53/metabolism
  • Zebrafish/genetics
  • Zebrafish Proteins/metabolism
PubMed: 18086703 Full text @ Nucleic Acids Res.
Knowledge of all binding sites for transcriptional activators and repressors is essential for computationally aided identification of transcriptional networks. The techniques developed for defining the binding sites of transcription factors tend to be cumbersome and not adaptable to high throughput. We refined a versatile yeast strategy to rapidly and efficiently identify genomic targets of DNA-binding proteins. Yeast expressing a transcription factor is mated to yeast containing a library of genomic fragments cloned upstream of the reporter gene URA3. DNA fragments with target-binding sites are identified by growth of yeast clones in media lacking uracil. The experimental approach was validated with the tumor suppressor protein p53 and the forkhead protein FoxI1 using genomic libraries for zebrafish and mouse generated by shotgun cloning of short genomic fragments. Computational analysis of the genomic fragments recapitulated the published consensus-binding site for each protein. Identified fragments were mapped to identify the genomic context of each binding site. Our yeast screening strategy, combined with bioinformatics approaches, will allow both detailed and high-throughput characterization of transcription factors, scalable to the analysis of all putative DNA-binding proteins.