Engineering a PbrR-Based Biosensor for Cell-Free Detection of Lead at the Legal Limit

IF 3.7 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS ACS Synthetic Biology Pub Date : 2024-09-10 DOI:10.1021/acssynbio.4c00456

Holly M. Ekas, Brenda Wang, Adam D. Silverman, Julius B. Lucks, Ashty S. Karim, Michael C. Jewett

引用次数: 0

Abstract

Industrialization and failing infrastructure have led to a growing number of irreversible health conditions resulting from chronic lead exposure. While state-of-the-art analytical chemistry methods provide accurate and sensitive detection of lead, they are too slow, expensive, and centralized to be accessible to many. Cell-free biosensors based on allosteric transcription factors (aTFs) can address the need for accessible, on-demand lead detection at the point of use. However, known aTFs, such as PbrR, are unable to detect lead at concentrations regulated by the Environmental Protection Agency (24–72 nM). Here, we develop a rapid cell-free platform for engineering aTF biosensors with improved sensitivity, selectivity, and dynamic range characteristics. We apply this platform to engineer PbrR mutants for a shift in limit of detection from 10 μM to 50 nM lead and demonstrate use of PbrR as a cell-free biosensor. We envision that our workflow could be applied to engineer any aTF.

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设计一种基于 PbrR 的生物传感器，用于无细胞检测法定限值的铅含量

工业化和基础设施的衰退导致越来越多的人因长期接触铅而出现不可逆转的健康问题。虽然最先进的化学分析方法可以准确、灵敏地检测铅，但这些方法过于缓慢、昂贵且集中，很多人都无法使用。基于异位转录因子（aTFs）的无细胞生物传感器可以满足在使用点按需检测铅的需要。然而，已知的 aTF（如 PbrR）无法检测环境保护局规定浓度（24-72 nM）的铅。在此，我们开发了一种快速无细胞平台，用于设计具有更高灵敏度、选择性和动态范围特性的 aTF 生物传感器。我们应用该平台设计了 PbrR 突变体，将检测限从 10 μM 提高到 50 nM，并演示了如何将 PbrR 用作无细胞生物传感器。我们设想我们的工作流程可以应用于设计任何 aTF。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACS Synthetic Biology 生物-

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.