Matrix and Steiner-triple-system smart pooling assays for high-performance transcription regulatory network mapping.
ABSTRACT Yeast one-hybrid (Y1H) assays provide a gene-centered method for the identification of interactions between gene promoters and regulatory transcription factors (TFs). To date, Y1H assays have involved library screens that are relatively expensive and laborious. We present two Y1H strategies that allow immediate prey identification: matrix assays that use an array of 755 individual Caenorhabditis elegans TFs, and smart-pool assays that use TF multiplexing. Both strategies simplify the Y1H pipeline and reduce the cost of protein-DNA interaction identification. We used a Steiner triple system (STS) to create smart pools of 4-25 TFs. Notably, we uniplexed a small number of highly connected TFs to allow efficient assay deconvolution. Both strategies outperform library screens in terms of coverage, confidence and throughput. These versatile strategies can be adapted both to TFs in other systems and, likely, to other biomolecules and assays as well.
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ABSTRACT: As a result of numerous genome sequencing projects, large numbers of candidate open reading frames are being identified, many of which have no known function. Analysis of these genes typically involves the transfer of DNA segments into a variety of vector backgrounds for protein expression and functional analysis. We describe a method called recombinational cloning that uses in vitro site-specific recombination to accomplish the directional cloning of PCR products and the subsequent automatic subcloning of the DNA segment into new vector backbones at high efficiency. Numerous DNA segments can be transferred in parallel into many different vector backgrounds, providing an approach to high-throughput, in-depth functional analysis of genes and rapid optimization of protein expression. The resulting subclones maintain orientation and reading frame register, allowing amino- and carboxy-terminal translation fusions to be generated. In this paper, we outline the concepts of this approach and provide several examples that highlight some of its potential.Genome Research 12/2000; 10(11):1788-95. · 14.40 Impact Factor
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ABSTRACT: Protein interaction mapping using large-scale two-hybrid analysis has been proposed as a way to functionally annotate large numbers of uncharacterized proteins predicted by complete genome sequences. This approach was examined in Caenorhabditis elegans, starting with 27 proteins involved in vulval development. The resulting map reveals both known and new potential interactions and provides a functional annotation for approximately 100 uncharacterized gene products. A protein interaction mapping project is now feasible for C. elegans on a genome-wide scale and should contribute to the understanding of molecular mechanisms in this organism and in human diseases.Science 01/2000; 287(5450):116-22. · 31.03 Impact Factor
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ABSTRACT: Transcription regulatory networks are composed of interactions between transcription factors and their target genes. Whereas unicellular networks have been studied extensively, metazoan transcription regulatory networks remain largely unexplored. Caenorhabditis elegans provides a powerful model to study such metazoan networks because its genome is completely sequenced and many functional genomic tools are available. While C. elegans gene predictions have undergone continuous refinement, this is not true for the annotation of functional transcription factors. The comprehensive identification of transcription factors is essential for the systematic mapping of transcription regulatory networks because it enables the creation of physical transcription factor resources that can be used in assays to map interactions between transcription factors and their target genes. By computational searches and extensive manual curation, we have identified a compendium of 934 transcription factor genes (referred to as wTF2.0). We find that manual curation drastically reduces the number of both false positive and false negative transcription factor predictions. We discuss how transcription factor splice variants and dimer formation may affect the total number of functional transcription factors. In contrast to mouse transcription factor genes, we find that C. elegans transcription factor genes do not undergo significantly more splicing than other genes. This difference may contribute to differences in organism complexity. We identify candidate redundant worm transcription factor genes and orthologous worm and human transcription factor pairs. Finally, we discuss how wTF2.0 can be used together with physical transcription factor clone resources to facilitate the systematic mapping of C. elegans transcription regulatory networks. wTF2.0 provides a starting point to decipher the transcription regulatory networks that control metazoan development and function.Genome biology 02/2005; 6(13):R110. · 10.30 Impact Factor