Publications (7) View all
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Article: Synthetic gene expression perturbation systems with rapid, tunable, single-gene specificity in yeast.
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ABSTRACT: A general method for the dynamic control of single gene expression in eukaryotes, with no off-target effects, is a long-sought tool for molecular and systems biologists. We engineered two artificial transcription factors (ATFs) that contain Cys(2)His(2) zinc-finger DNA-binding domains of either the mouse transcription factor Zif268 (9 bp of specificity) or a rationally designed array of four zinc fingers (12 bp of specificity). These domains were expressed as fusions to the human estrogen receptor and VP16 activation domain. The ATFs can rapidly induce a single gene driven by a synthetic promoter in response to introduction of an otherwise inert hormone with no detectable off-target effects. In the absence of inducer, the synthetic promoter is inactive and the regulated gene product is not detected. Following addition of inducer, transcripts are induced >50-fold within 15 min. We present a quantitative characterization of these ATFs and provide constructs for making their implementation straightforward. These new tools allow for the elucidation of regulatory network elements dynamically, which we demonstrate with a major metabolic regulator, Gcn4p.Nucleic Acids Research 12/2012; · 8.03 Impact Factor -
Article: Recognition models to predict DNA-binding specificities of homeodomain proteins.
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ABSTRACT: MOTIVATION: Recognition models for protein-DNA interactions, which allow the prediction of specificity for a DNA-binding domain based only on its sequence or the alteration of specificity through rational design, have long been a goal of computational biology. There has been some progress in constructing useful models, especially for C(2)H(2) zinc finger proteins, but it remains a challenging problem with ample room for improvement. For most families of transcription factors the best available methods utilize k-nearest neighbor (KNN) algorithms to make specificity predictions based on the average of the specificities of the k most similar proteins with defined specificities. Homeodomain (HD) proteins are the second most abundant family of transcription factors, after zinc fingers, in most metazoan genomes, and as a consequence an effective recognition model for this family would facilitate predictive models of many transcriptional regulatory networks within these genomes. RESULTS: Using extensive experimental data, we have tested several machine learning approaches and find that both support vector machines and random forests (RFs) can produce recognition models for HD proteins that are significant improvements over KNN-based methods. Cross-validation analyses show that the resulting models are capable of predicting specificities with high accuracy. We have produced a web-based prediction tool, PreMoTF (Predicted Motifs for Transcription Factors) (http://stormo.wustl.edu/PreMoTF), for predicting position frequency matrices from protein sequence using a RF-based model.Bioinformatics 06/2012; 28(12):i84-9. · 5.47 Impact Factor -
Article: Exploring the DNA-recognition potential of homeodomains.
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ABSTRACT: The recognition potential of most families of DNA-binding domains (DBDs) remains relatively unexplored. Homeodomains (HDs), like many other families of DBDs, display limited diversity in their preferred recognition sequences. To explore the recognition potential of HDs, we utilized a bacterial selection system to isolate HD variants, from a randomized library, that are compatible with each of the 64 possible 3' triplet sites (i.e., TAANNN). The majority of these selections yielded sets of HDs with overrepresented residues at specific recognition positions, implying the selection of specific binders. The DNA-binding specificity of 151 representative HD variants was subsequently characterized, identifying HDs that preferentially recognize 44 of these target sites. Many of these variants contain novel combinations of specificity determinants that are uncommon or absent in extant HDs. These novel determinants, when grafted into different HD backbones, produce a corresponding alteration in specificity. This information was used to create more explicit HD recognition models, which can inform the prediction of transcriptional regulatory networks for extant HDs or the engineering of HDs with novel DNA-recognition potential. The diversity of recovered HD recognition sequences raises important questions about the fitness barrier that restricts the evolution of alternate recognition modalities in natural systems.Genome Research 04/2012; 22(10):1889-98. · 13.61 Impact Factor -
Article: Analysis of specific protein-DNA interactions by bacterial one-hybrid assay.
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ABSTRACT: The DNA-binding specificity of transcription factors allows the prediction of regulatory targets in a genome. However, very few factor specificities have been characterized and still too little is known about how these proteins interact with their targets to make predictions a priori. To provide a greater understanding of how proteins and DNA interact, we have developed a bacterial one-hybrid system that allows the sensitive, high-throughput, and cost-effective assay of the interaction at the protein-DNA interface. This system makes survival of the bacteria dependent on activation of the reporter gene and therefore dependent on the protein-DNA interaction that recruits the polymerase. We have used this system to characterize DNA-binding specificities for representative members of the most common DNA-binding domain (DBD) families. We have also been able to engineer DBDs with novel specificity to be used as artificial transcription factors and zinc finger nucleases. The B1H assay provides a simple and inexpensive method to investigate protein-DNA interactions that is accessible to essentially any laboratory.Methods in molecular biology (Clifton, N.J.) 01/2012; 786:79-95. -
Article: Targeted gene inactivation in zebrafish using engineered zinc-finger nucleases.
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ABSTRACT: Direct genomic manipulation at a specific locus is still not feasible in most vertebrate model organisms. In vertebrate cell lines, genomic lesions at a specific site have been introduced using zinc-finger nucleases (ZFNs). Here we adapt this technology to create targeted mutations in the zebrafish germ line. ZFNs were engineered that recognize sequences in the zebrafish ortholog of the vascular endothelial growth factor-2 receptor, kdr (also known as kdra). Co-injection of mRNAs encoding these ZFNs into one-cell-stage zebrafish embryos led to mutagenic lesions at the target site that were transmitted through the germ line with high frequency. The use of engineered ZFNs to introduce heritable mutations into a genome obviates the need for embryonic stem cell lines and should be applicable to most animal species for which early-stage embryos are easily accessible.Nature Biotechnology 07/2008; 26(6):695-701. · 29.50 Impact Factor
