Toward controlling gene expression at will: Specific regulation of the erbB-2/HER-2 promoter by using polydactyl zinc finger proteins constructed from modular building blocks

To create a universal system for the control of gene expression, we have studied methods for the construction of novel polydactyl zinc finger proteins that recognize extended DNA sequences. Elsewhere we have described the generation of zinc finger domains recognizing sequences of the 5′-GNN-3′ subset of a 64-member zinc finger alphabet. Here we report on the use of these domains as modular building blocks for the construction of polydactyl proteins specifically recognizing 9- or 18-bp sequences. A rapid PCR assembly method was developed that, together with this predefined set of zinc finger domains, provides ready access to 17 million novel proteins that bind the 5′-(GNN)6-3′ family of 18-bp DNA sites. To examine the efficacy of this strategy in gene control, the human erbB-2 gene was chosen as a model. A polydactyl protein specifically recognizing an 18-bp sequence in the 5′-untranslated region of this gene was converted into a transcriptional repressor by fusion with Krüppel-associated box (KRAB), ERD, or SID repressor domains. Transcriptional activators were generated by fusion with the herpes simplex VP16 activation domain or with a tetrameric repeat of VP16’s minimal activation domain, termed VP64. We demonstrate that both gene repression and activation can be achieved by targeting designed proteins to a single site within the transcribed region of a gene. We anticipate that gene-specific transcriptional regulators of the type described here will find diverse applications in gene therapy, functional genomics, and the generation of transgenic organisms.

Building a dictionary for genomes: Identification of presumptive regulatory sites by statistical analysis

The availability of complete genome sequences and mRNA expression data for all genes creates new opportunities and challenges for identifying DNA sequence motifs that control gene expression. An algorithm, “MobyDick,” is presented that decomposes a set of DNA sequences into the most probable dictionary of motifs or words. This method is applicable to any set of DNA sequences: for example, all upstream regions in a genome or all genes expressed under certain conditions. Identification of words is based on a probabilistic segmentation model in which the significance of longer words is deduced from the frequency of shorter ones of various lengths, eliminating the need for a separate set of reference data to define probabilities. We have built a dictionary with 1,200 words for the 6,000 upstream regulatory regions in the yeast genome; the 500 most significant words (some with as few as 10 copies in all of the upstream regions) match 114 of 443 experimentally determined sites (a significance level of 18 standard deviations). When analyzing all of the genes up-regulated during sporulation as a group, we find many motifs in addition to the few previously identified by analyzing the subclusters individually to the expression subclusters. Applying MobyDick to the genes derepressed when the general repressor Tup1 is deleted, we find known as well as putative binding sites for its regulatory partners.