聚合酶链再循环的多样化底物和反应条件。

IF 3.9 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Yueyi Li, Arno Gundlach, Andrew Ellington and Julius B. Lucks*, 
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引用次数: 0

摘要

无细胞生物传感系统被设计为通用的可编程诊断技术。无细胞生物传感器的核心组件是可编程分子电路,通过执行分子计算(如逻辑评估和信号放大)来提高生物传感器的速度、灵敏度和特异性。在之前的工作中,我们开发了一个这样的电路系统,称为聚合酶链回收(PSR),它通过使用T7 RNA聚合酶脱靶转录来循环核酸输入来扩增无细胞分子电路。我们发现,PSR电路可以配置为检测RNA目标输入,并与基于变抗转录因子的生物传感器接口,以放大信号并提高灵敏度。在这里,我们扩展了PSR电路经验设计指南的发展,以推广检测多种microRNA输入的平台。我们证明了通过工程T7 RNAP可以增强PSR电路的功能,并提出了优化PSR电路性能的故障排除策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Diversifying Substrates and Reaction Conditions for Polymerase Strand Recycling

Diversifying Substrates and Reaction Conditions for Polymerase Strand Recycling

Cell-free biosensing systems are being engineered as versatile and programmable diagnostic technologies. A core component of cell-free biosensors is programmable molecular circuits that improve biosensor speed, sensitivity, and specificity by performing molecular computations such as logic evaluation and signal amplification. In previous work, we developed one such circuit system called Polymerase Strand Recycling (PSR), which amplifies cell-free molecular circuits by using T7 RNA polymerase off-target transcription to recycle nucleic acid inputs. We showed that PSR circuits can be configured to detect RNA target inputs as well as be interfaced with allosteric transcription factor-based biosensors to amplify signals and enhance sensitivity. Here we expand the development of PSR circuit empirical design guidelines to generalize the platform for detecting a diverse set of microRNA inputs. We show that PSR circuit function can be enhanced through engineering T7 RNAP, and we present troubleshooting strategies to optimize PSR circuit performance.

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来源期刊
CiteScore
8.00
自引率
10.60%
发文量
380
审稿时长
6-12 weeks
期刊介绍: The journal is particularly interested in studies on the design and synthesis of new genetic circuits and gene products; computational methods in the design of systems; and integrative applied approaches to understanding disease and metabolism. Topics may include, but are not limited to: Design and optimization of genetic systems Genetic circuit design and their principles for their organization into programs Computational methods to aid the design of genetic systems Experimental methods to quantify genetic parts, circuits, and metabolic fluxes Genetic parts libraries: their creation, analysis, and ontological representation Protein engineering including computational design Metabolic engineering and cellular manufacturing, including biomass conversion Natural product access, engineering, and production Creative and innovative applications of cellular programming Medical applications, tissue engineering, and the programming of therapeutic cells Minimal cell design and construction Genomics and genome replacement strategies Viral engineering Automated and robotic assembly platforms for synthetic biology DNA synthesis methodologies Metagenomics and synthetic metagenomic analysis Bioinformatics applied to gene discovery, chemoinformatics, and pathway construction Gene optimization Methods for genome-scale measurements of transcription and metabolomics Systems biology and methods to integrate multiple data sources in vitro and cell-free synthetic biology and molecular programming Nucleic acid engineering.
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