The utility of a model organism for studying biological processes is closely tied to its amenability to genome manipulation. Although tools for targeted genome engineering in mice have been available since 1987, most organisms including zebrafish have lacked efficient reverse genetic tools, which has stymied their broad implementation as a model system to study biological processes. The development of zinc finger nucleases (ZFNs) that can create double-strand breaks at desired sites in a genome has provided a universal platform for targeted genome modification. ZFNs are artificial restriction endonucleases that comprise of an array of 3- to 6-C2H2-zinc finger DNA-binding domains fused with the dimeric cleavage domain of the type IIs endonuclease FokI. C2H2-zinc fingers are the most common, naturally occurring DNA-binding domain, and their specificity can be engineered to recognize a variety of DNA sequences providing a strategy for targeting the appended nuclease domain to desired sites in a genome. The utility of ZFNs for gene editing relies on their activity and precision in vivo both of which depend on the generation of ZFPs that bind desired target sites high specificity and affinity.
Although various methods are available that allow construction of ZFPs with novel specificities, ZFNs assembled using existing approaches often display negligible in vivo activity, presumably resulting from ZFPs with either low affinity or suboptimal specificity. A root cause of this deficiency is the presence of interfering interactions at the finger-finger interface upon assembly of multiple fingers. In this study we have employed bacterial-one-hybrid (B1H)-based selections to identify two-finger zinc finger units (2F-modules) containing optimized interface residues that can be combined with published finger archives to rapidly yield ZFNs that can target more than 95% of the zebrafish and human protein-coding genes while maintaining a success rate higher than that of ZFNs constructed using available methods. In addition to genome engineering in model organisms, this advancement in ZFN design will aid in the development of ZFN-based therapeutics.
In the process of creating this archive, we have undertaken a broader study of zinc finger specificity to better understand fundamental aspects of DNA recognition. In the process we have created the largest protein-DNA interaction dataset for zinc fingers to be described that will facilitate the development of better predictive models of recognition. Ultimately, these predictive models would enable the rational design of synthetic zinc finger proteins for targeted gene regulation or genomic modification, and the prediction of genomic binding sites for naturally occurring zinc finger proteins for the construction of more accurate gene regulatory networks.
| Identifer | oai:union.ndltd.org:umassmed.edu/oai:escholarship.umassmed.edu:gsbs_diss-1640 |
| Date | 27 April 2012 |
| Creators | Gupta, Ankit |
| Publisher | eScholarship@UMassChan |
| Source Sets | University of Massachusetts Medical School |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Morningside Graduate School of Biomedical Sciences Dissertations and Theses |
| Rights | Copyright is held by the author, with all rights reserved., select |
Page generated in 0.0021 seconds