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基于合理设计的微小RNA的基因分类器靶向大脑中的特定神经元。

Rationally Designed MicroRNA-Based Genetic Classifiers Target Specific Neurons in the Brain.

作者信息

Sayeg Marianna K, Weinberg Benjamin H, Cha Susie S, Goodloe Michael, Wong Wilson W, Han Xue

机构信息

Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, United States.

出版信息

ACS Synth Biol. 2015 Jul 17;4(7):788-795. doi: 10.1021/acssynbio.5b00040. Epub 2015 Apr 13.

Abstract

Targeting transgene expression to specific cell types in vivo has proven instrumental in characterizing the functional role of defined cell populations. Genetic classifiers, synthetic transgene constructs designed to restrict expression to particular classes of cells, commonly rely on transcriptional promoters to define cellular specificity. However, the large size of many natural promoters complicates their use in viral vectors, an important mode of transgene delivery in the brain and in human gene therapy. Here, we expanded upon an emerging classifier platform, orthogonal to promoter-based strategies, that exploits endogenous microRNA regulation to target gene expression. Such classifiers have been extensively explored in other tissues; however, their use in the nervous system has thus far been limited to targeting gene expression between neurons and supporting cells. Here, we tested the possibility of using combinatory microRNA regulation to specify gene targeting between neuronal subtypes, and successfully targeted inhibitory cells in the neocortex. These classifiers demonstrate the feasibility of designing a new generation of microRNA-based neuron-type- and brain-region-specific gene expression targeting neurotechnologies.

摘要

在体内将转基因表达靶向特定细胞类型已被证明有助于表征特定细胞群体的功能作用。基因分类器是一种合成转基因构建体,旨在将表达限制在特定类别的细胞中,通常依靠转录启动子来定义细胞特异性。然而,许多天然启动子的大尺寸使其在病毒载体中的应用变得复杂,而病毒载体是大脑和人类基因治疗中一种重要的转基因递送方式。在此,我们扩展了一个新兴的分类器平台,该平台与基于启动子的策略正交,利用内源性微小RNA调控来靶向基因表达。此类分类器已在其他组织中得到广泛探索;然而,它们在神经系统中的应用迄今为止仅限于在神经元和支持细胞之间靶向基因表达。在此,我们测试了使用组合微小RNA调控在神经元亚型之间指定基因靶向的可能性,并成功地靶向了新皮层中的抑制性细胞。这些分类器证明了设计新一代基于微小RNA的神经元类型和脑区特异性基因表达靶向神经技术的可行性。

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