Simultaneous Detection of Multiple Analytes at Ambient Temperature Using Eukaryotic Artificial Cells with Modular and Robust Synthetic Riboswitches.

IF 3.7 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS ACS Synthetic Biology Pub Date : 2024-12-27 DOI:10.1021/acssynbio.4c00696
Hajime Takahashi, Yuri Ikemoto, Atsushi Ogawa
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Abstract

Cell-free systems, which can express an easily detectable output (protein) with a DNA or mRNA template, are promising as foundations of biosensors devoid of cellular constraints. Moreover, by encasing them in membranes such as natural cells to create artificial cells, these systems can avoid the adverse effects of environmental inhibitory molecules. However, the bacterial systems generally used for this purpose do not function well at ambient temperatures. We here encapsulated a eukaryotic cell-free system consisting of wheat germ extract (WGE) and a DNA template encoding an analyte-responsive regulatory RNA (called a riboswitch) into giant unilamellar vesicles (GUVs) to create eukaryotic artificial cell-based sensors that function well at ambient temperature. First, we improved our previously reported eukaryotic synthetic riboswitches and WGE for use in GUVs by chimerizing two internal ribosome entry sites and optimizing magnesium concentrations, respectively, both of which increased the expression efficiency in GUVs several fold. Then, a DNA template encoding one of these riboswitches followed by a reporter protein was encapsulated with the optimized GUV-friendly WGE. Importantly, our previously established versatile method allowed for the rational design of highly efficient eukaryotic riboswitches that are responsive to a user-defined analyte. In fact, we utilized this method to successfully create three types of artificial cells, each of which responded to a specific, membrane-permeable analyte with wide-range, analyte-dose dependency and high sensitivity at ambient temperature. Finally, due to their orthogonality and robustness, we were able to mix a cocktail of these artificial cells to achieve simultaneous detection of the three analytes without significant barriers.

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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
Engineered Bacillus subtilis as Oral Probiotics To Enhance Clearance of Blood Lactate. Correction to "Toward a Circular Bioeconomy: Designing Microbes and Polymers for Biodegradation". Simultaneous Detection of Multiple Analytes at Ambient Temperature Using Eukaryotic Artificial Cells with Modular and Robust Synthetic Riboswitches. Advances in Engineering Myeloid Cells for Cell Therapy Applications. LevSeq: Rapid Generation of Sequence-Function Data for Directed Evolution and Machine Learning.
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