RESUMO
Engineered zinc-finger nucleases (ZFNs) enable targeted genome modification. Here we describe context-dependent assembly (CoDA), a platform for engineering ZFNs using only standard cloning techniques or custom DNA synthesis. Using CoDA-generated ZFNs, we rapidly altered 20 genes in Danio rerio, Arabidopsis thaliana and Glycine max. The simplicity and efficacy of CoDA will enable broad adoption of ZFN technology and make possible large-scale projects focused on multigene pathways or genome-wide alterations.
Assuntos
Endonucleases/genética , Endonucleases/metabolismo , Engenharia de Proteínas , Dedos de Zinco/fisiologia , Animais , Arabidopsis/genética , Proteínas de Ligação a DNA/genética , Proteínas de Ligação a DNA/metabolismo , Genoma , Glycine max/genética , Peixe-Zebra/genética , Dedos de Zinco/genéticaRESUMO
We performed targeted mutagenesis of a transgene and nine endogenous soybean (Glycine max) genes using zinc-finger nucleases (ZFNs). A suite of ZFNs were engineered by the recently described context-dependent assembly platform--a rapid, open-source method for generating zinc-finger arrays. Specific ZFNs targeting dicer-like (DCL) genes and other genes involved in RNA silencing were cloned into a vector under an estrogen-inducible promoter. A hairy-root transformation system was employed to investigate the efficiency of ZFN mutagenesis at each target locus. Transgenic roots exhibited somatic mutations localized at the ZFN target sites for seven out of nine targeted genes. We next introduced a ZFN into soybean via whole-plant transformation and generated independent mutations in the paralogous genes DCL4a and DCL4b. The dcl4b mutation showed efficient heritable transmission of the ZFN-induced mutation in the subsequent generation. These findings indicate that ZFN-based mutagenesis provides an efficient method for making mutations in duplicate genes that are otherwise difficult to study due to redundancy. We also developed a publicly accessible Web-based tool to identify sites suitable for engineering context-dependent assembly ZFNs in the soybean genome.
Assuntos
Endonucleases/química , Endonucleases/metabolismo , Genes Duplicados/genética , Genes de Plantas/genética , Técnicas Genéticas , Glycine max/genética , Mutagênese/genética , Dedos de Zinco/genética , Sequência de Bases , Proteínas de Fluorescência Verde/metabolismo , Padrões de Herança/genética , Internet , Dados de Sequência Molecular , Mutação/genética , Raízes de Plantas/genética , Reação em Cadeia da Polimerase , Transgenes/genéticaRESUMO
BACKGROUND: Precise and efficient methods for gene targeting are critical for detailed functional analysis of genomes and regulatory networks and for potentially improving the efficacy and safety of gene therapies. Oligomerized Pool ENgineering (OPEN) is a recently developed method for engineering C2H2 zinc finger proteins (ZFPs) designed to bind specific DNA sequences with high affinity and specificity in vivo. Because generation of ZFPs using OPEN requires considerable effort, a computational method for identifying the sites in any given gene that are most likely to be successfully targeted by this method is desirable. RESULTS: Analysis of the base composition of experimentally validated ZFP target sites identified important constraints on the DNA sequence space that can be effectively targeted using OPEN. Using alternate encodings to represent ZFP target sites, we implemented Naïve Bayes and Support Vector Machine classifiers capable of distinguishing "active" targets, i.e., ZFP binding sites that can be targeted with a high rate of success, from those that are "inactive" or poor targets for ZFPs generated using current OPEN technologies. When evaluated using leave-one-out cross-validation on a dataset of 135 experimentally validated ZFP target sites, the best Naïve Bayes classifier, designated ZiFOpT, achieved overall accuracy of 87% and specificity+ of 90%, with an ROC AUC of 0.89. When challenged with a completely independent test set of 140 newly validated ZFP target sites, ZiFOpT performance was comparable in terms of overall accuracy (88%) and specificity+ (92%), but with reduced ROC AUC (0.77). Users can rank potentially active ZFP target sites using a confidence score derived from the posterior probability returned by ZiFOpT. CONCLUSION: ZiFOpT, a machine learning classifier trained to identify DNA sequences amenable for targeting by OPEN-generated zinc finger arrays, can guide users to target sites that are most likely to function successfully in vivo, substantially reducing the experimental effort required. ZiFOpT is freely available and incorporated in the Zinc Finger Targeter web server (http://bindr.gdcb.iastate.edu/ZiFiT).