A computational "genome walk" technique to identify regulatory interactions in gene networks.
To delineate the astronomical number of possible interactions of all genes in a genome is a task for which conventional experimental techniques are ill-suited. Sorely needed are rapid and inexpensive methods that identify candidates for interacting genes, candidates that can be further investigated by experiment. The subject of this paper is the application of a novel method to the genome of the yeast Saccharomyces cerevisiae. The method applies to an important class of gene interactions, that is, transcriptional regulation via transcription factors (TFs) that bind to specific enhancer or silencer sites on DNA. The method addresses the question: which of the genes in a genome are likely to be regulated by one or more TFs with known DNA binding specificity? It takes advantage of the fact that many TFs show cooperativity in transcriptional activation which manifests itself in closely spaced TF binding sites. Such "clusters" of binding sites are very unlikely to occur by chance alone, as opposed to individual sites, which are often abundant both in the genome and in promoter regions. Statistical information about binding site clusters in the genome, can be complemented by information about (i) known biochemical functions of the TF, (ii) the structure of its binding site, and (iii) function of the genes near the cluster, to identify genes likely to be regulated by a given transcription factor. Previously, binding sites of well characterized transcription factors in Saccharomyces cerevisiae were analyzed. Here, the method is applied to a somewhat different situation: the yeast DNA binding activity yE2F, similar to the mammalian transcription factor E2F. yE2F has a DNA binding specificity identical to E2F, and its binding site shows UAS activity in a GAL1-based promoter construct. However, despite its high conservation, the in vivo function of yE2F is unknown. The analysis carried out the here suggests candidate genes for regulation by yE2F.
Santa Fe Institute, NM 87501, USA. aw@santafe.edu
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Wagner
D000818:Animals; D001483:Base Sequence; D001665:Binding Sites; D016386:Chromosome Walking / Q000379:methods*; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D016384:Consensus Sequence; D017124:Conserved Sequence; D004247:DNA / Q000737:chemistry / Q000378:metabolism; D004742:Enhancer Elements, Genetic; D005786:Gene Expression Regulation; D016678:Genome; D016681:Genome, Fungal; D008322:Mammals; D008957:Models, Genetic; D020218:Response Elements; D012441:Saccharomyces cerevisiae / Q000235:genetics; D014157:Transcription Factors / Q000378:metabolism; D014158:Transcription, Genetic
D014157:Transcription Factors; D004247:DNA