转录因子工程的无细胞自动工作流程

IF 3.7 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS ACS Synthetic Biology Pub Date : 2024-10-07 DOI:10.1021/acssynbio.4c0047110.1021/acssynbio.4c00471
Holly M. Ekas, Brenda Wang, Adam D. Silverman, Julius B. Lucks, Ashty S. Karim and Michael C. Jewett*, 
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引用次数: 0

摘要

代谢途径、基因系统和工程蛋白质的设计和优化依赖于高通量检测,以简化 "设计-构建-测试-学习 "的周期。然而,检测开发是一个费时费力的过程。在这里,我们为基于无细胞基因表达(CFE)的自动化工作流程的定制优化创建了一种可推广的方法,该方法在反应灵活性、控制和数据时间方面比体内检测具有明显优势。我们以在回声声学液体处理器上设计高精度、高准确度的转移为中心,介绍了试验检测和方案开发各阶段的验证策略。然后,我们通过设计基于转录因子的生物传感器来展示我们平台的功效。作为一个模型,我们在不到 48 小时的时间内,在 3682 个独特的 CFE 反应中快速生成并检测了 127 个 MerR 和 134 个 CadR 转录因子变体库,从而提高了汞和镉检测的检出限、选择性和动态范围。为此,我们评估了一系列配体条件的灵敏度(对 MerR 和 CadR 而言,分别为 0.1、1、10 μM 汞和 0、1、10、100 μM 镉)和选择性(针对银、砷、镉、钴、铜、汞、镍、铅和锌)。我们预计,我们基于回声的无细胞方法可用于加速合成生物学中的多种设计工作流程。
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An Automated Cell-Free Workflow for Transcription Factor Engineering

The design and optimization of metabolic pathways, genetic systems, and engineered proteins rely on high-throughput assays to streamline design-build-test-learn cycles. However, assay development is a time-consuming and laborious process. Here, we create a generalizable approach for the tailored optimization of automated cell-free gene expression (CFE)-based workflows, which offers distinct advantages over in vivo assays in reaction flexibility, control, and time to data. Centered around designing highly accurate and precise transfers on the Echo Acoustic Liquid Handler, we introduce pilot assays and validation strategies for each stage of protocol development. We then demonstrate the efficacy of our platform by engineering transcription factor-based biosensors. As a model, we rapidly generate and assay libraries of 127 MerR and 134 CadR transcription factor variants in 3682 unique CFE reactions in less than 48 h to improve limit of detection, selectivity, and dynamic range for mercury and cadmium detection. This was achieved by assessing a panel of ligand conditions for sensitivity (to 0.1, 1, 10 μM Hg and 0, 1, 10, 100 μM Cd for MerR and CadR, respectively) and selectivity (against Ag, As, Cd, Co, Cu, Hg, Ni, Pb, and Zn). We anticipate that our Echo-based, cell-free approach can be used to accelerate multiple design workflows in synthetic biology.

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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.
期刊最新文献
Issue Publication Information Issue Editorial Masthead Ribozyme-Mediated Gene-Fragment Complementation for Nondestructive Reporting of DNA Transfer within Soil. Macroscopic Assembly of Materials with Engineered Bacterial Spores via Coiled-Coil Interaction. A High-Throughput Screen for Antiproliferative Peptides in Mammalian Cells Identifies Key Transcription Factor Families.
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