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基于计算分子建模的活性预测,利用模块化组装锌指核酸酶靶向编辑山羊基因组。

Targeted editing of goat genome with modular-assembly zinc finger nucleases based on activity prediction by computational molecular modeling.

机构信息

College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, 210095, People's Republic of China.

出版信息

Mol Biol Rep. 2013 Jul;40(7):4251-6. doi: 10.1007/s11033-013-2507-5. Epub 2013 May 5.

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.

摘要

锌指核酸酶 (ZFN) 技术可通过靶向基因组修饰,以高效且生物安全的方式生产转基因动物。模块化组装是合成 ZFN 的快速、方便和开源方法。然而,这项生物技术受到多步构建、低效率编辑和脱靶切割的阻碍。在这里,我们合成并测试了六对三或四指 ZFN,以靶向羊奶中主要过敏原β-乳球蛋白 (BLG) 基因的一个位点。同源建模被应用于构建 ZFN 的结构模型,以预测它们针对山羊 BLG 基因的编辑活性。用编码 ZFN 对的质粒转染山羊成纤维细胞,72 小时后分离基因组 DNA 进行基因组编辑效率检测。编辑效率检测的结果表明,具有最佳相互作用模式的 ZFN 可以更有效地编辑山羊 BLG 基因,而具有意外相互作用模式的 ZFN 在编辑 BLG 基因时活性较低。我们得出结论,模块化组装 ZFN 可为转基因动物研究提供快速、公开、易于实践的平台,分子建模将作为 ZFN 活性预测的有用工具。

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