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一种追踪蛋白质聚集体并控制朊病毒遗传的基因工具。

A Genetic Tool to Track Protein Aggregates and Control Prion Inheritance.

作者信息

Newby Gregory A, Kiriakov Szilvia, Hallacli Erinc, Kayatekin Can, Tsvetkov Peter, Mancuso Christopher P, Bonner J Maeve, Hesse William R, Chakrabortee Sohini, Manogaran Anita L, Liebman Susan W, Lindquist Susan, Khalil Ahmad S

机构信息

Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA.

Program in Molecular Biology, Cell Biology, and Biochemistry, Boston University, Boston, MA 02215, USA; Biological Design Center, Boston University, Boston, MA 02215, USA.

出版信息

Cell. 2017 Nov 2;171(4):966-979.e18. doi: 10.1016/j.cell.2017.09.041. Epub 2017 Oct 19.

Abstract

Protein aggregation is a hallmark of many diseases but also underlies a wide range of positive cellular functions. This phenomenon has been difficult to study because of a lack of quantitative and high-throughput cellular tools. Here, we develop a synthetic genetic tool to sense and control protein aggregation. We apply the technology to yeast prions, developing sensors to track their aggregation states and employing prion fusions to encode synthetic memories in yeast cells. Utilizing high-throughput screens, we identify prion-curing mutants and engineer "anti-prion drives" that reverse the non-Mendelian inheritance pattern of prions and eliminate them from yeast populations. We extend our technology to yeast RNA-binding proteins (RBPs) by tracking their propensity to aggregate, searching for co-occurring aggregates, and uncovering a group of coalescing RBPs through screens enabled by our platform. Our work establishes a quantitative, high-throughput, and generalizable technology to study and control diverse protein aggregation processes in cells.

摘要

蛋白质聚集是许多疾病的一个标志,但也是多种积极细胞功能的基础。由于缺乏定量和高通量的细胞工具,这一现象一直难以研究。在这里,我们开发了一种合成遗传工具来感知和控制蛋白质聚集。我们将该技术应用于酵母朊病毒,开发传感器来追踪它们的聚集状态,并利用朊病毒融合在酵母细胞中编码合成记忆。通过高通量筛选,我们鉴定出朊病毒治愈突变体,并设计出“抗朊病毒驱动器”,它能逆转朊病毒的非孟德尔遗传模式,并将它们从酵母群体中消除。我们通过追踪酵母RNA结合蛋白(RBPs)的聚集倾向、寻找同时出现的聚集体,并通过我们的平台进行筛选,发现了一组聚集的RBPs,从而将我们的技术扩展到酵母RNA结合蛋白。我们的工作建立了一种定量、高通量且可推广的技术,用于研究和控制细胞中各种蛋白质聚集过程。

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