Targeted editing of goat genome with modular-assembly zinc finger nucleases based on activity prediction by computational molecular modeling |
| |
Authors: | Kai Xiong Shanshan Li Hongxiao Zhang Ye Cui Debing Yu Yan Li Wenxing Sun Yingying Fu Yun Teng Zhi Liu Xiaolong Zhou Peng Xiao Juan Li Honglin Liu Jie Chen |
| |
Affiliation: | 1. College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, 210095, People’s Republic of China 2. Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, People’s Republic of China 3. Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, People’s Republic of China 4. Center for Drug Discovery, China Pharmaceutical University, Nanjing, 210009, People’s Republic of China
|
| |
Abstract: | Zinc finger nuclease (ZFN) technology can mediate targeted genome modification to produce transgenic animals in a high-efficient and biological-safe way. Modular assembly is a rapid, convenient and open-source method for the synthesis of ZFNs. However, this biotechnology is hampered by multistep construction, low-efficiency editing and off-target cleavage. Here we synthesized and tested six pairs of three- or four-finger ZFNs to target one site in goat beta-lactoglobulin (BLG, a dominant allergen in goat milk) gene. Homology modeling was applied to build the structure model of ZFNs to predict their editing activities targeting at goat BLG gene. Goat fibroblast cells were transfected with plasmids that encoded ZFN pairs, and genomic DNA was isolated 72 h later for genome editing efficiency assay. The results of editing efficiency assay demonstrated that ZFNs with optimal interaction modes can edit goat BLG gene more efficiently, whereas ZFNs with unexpected interaction modes showed lower activities in editing BLG gene. We concluded that modular-assembly ZFNs can provide a rapid, public-available, and easy-to-practice platform for transgenic animal research and molecular modeling would help as a useful tool for ZFNs activity prediction. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|