SUMMARYZinc-finger nucleases (ZFNs) allow targeted gene inactivation in a wide range of model organisms. However, construction of target-specific ZFNs is technically challenging. Here, we evaluate a straightforward modular assembly-based approach for ZFN construction and gene inactivation in zebrafish. From an archive of 27 different zinc-finger modules, we assembled more than 70 different zinc-finger cassettes and evaluated their specificity using a bacterial one-hybrid assay. In parallel, we constructed ZFNs from these cassettes and tested their ability to induce lesions in zebrafish embryos. We found that the majority of zinc-finger proteins assembled from these modules have favorable specificities and nearly one-third of modular ZFNs generated lesions at their targets in the zebrafish genome. To facilitate the application of ZFNs within the zebrafish community we constructed a public database of sites in the zebrafish genome that can be targeted using this archive. Importantly, we generated new germline mutations in eight different genes, confirming that this is a viable platform for heritable gene inactivation in vertebrates. Characterization of one of these mutants, gata2a, revealed an unexpected role for this transcription factor in vascular development. This work provides a resource to allow targeted germline gene inactivation in zebrafish and highlights the benefit of a definitive reverse genetic strategy to reveal gene function.

The widespread use of zinc finger nucleases (ZFNs) for genome engineering is hampered by the fact that only a subset of sequences can be efficiently recognized using published finger archives. We describe a set of validated two-finger modules that complement existing finger archives and expand the range of ZFN-accessible sequences by three-fold. Using this archive, we successfully introduce lesions at 9 of 11 target sites in the zebrafish genome.

Cys2-His2 zinc finger proteins (ZFPs) are the largest family of transcription factors in higher metazoans. They also represent the most diverse family with regards to the composition of their recognition sequences. Although there are a number of ZFPs with characterized DNA-binding preferences, the specificity of the vast majority of ZFPs is unknown and cannot be directly inferred by homology due to the diversity of recognition residues present within individual fingers. Given the large number of unique zinc fingers and assemblies present across eukaryotes, a comprehensive predictive recognition model that could accurately estimate the DNA-binding specificity of any ZFP based on its amino acid sequence would have great utility. Toward this goal, we have used the DNA-binding specificities of 678 two-finger modules from both natural and artificial sources to construct a random forest-based predictive model for ZFP recognition. We find that our recognition model outperforms previously described determinant-based recognition models for ZFPs, and can successfully estimate the specificity of naturally occurring ZFPs with previously defined specificities.