Disentangling the Regulatory Response of Agrobacterium tumefaciens CHLDO to Glyphosate for Engineering Whole-Cell Phosphonate Biosensors

IF 3.7 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

ACS Synthetic Biology Pub Date : 2024-09-30 DOI:10.1021/acssynbio.4c0049710.1021/acssynbio.4c00497

Fiorella Masotti, Nicolas Krink, Nicolas Lencina, Natalia Gottig, Jorgelina Ottado* and Pablo I. Nikel*,

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引用次数: 0

Abstract

Phosphonates (PHTs), organic compounds with a stable C–P bond, are widely distributed in nature. Glyphosate (GP), a synthetic PHT, is extensively used in agriculture and has been linked to various human health issues and environmental damage. Given the prevalence of GP, developing cost-effective, on-site methods for GP detection is key for assessing pollution and reducing exposure risks. We adopted Agrobacterium tumefaciens CHLDO, a natural GP degrader, as a host and the source of genetic parts for constructing PHT biosensors. In this bacterial species, the phn gene cluster, encoding the C–P lyase pathway, is regulated by the PhnF transcriptional repressor. We selected the phnG promoter, which displays a dose-dependent response to GP, to build a set of whole-cell biosensors. Through stepwise genetic optimization of the transcriptional cascade, we created a whole-cell biosensor capable of detecting GP in the 0.25–50 μM range in various samples, including soil and water.

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厘清农杆菌 CHLDO 对草甘膦的调控响应以设计全细胞膦酸盐生物传感器

膦酸盐（PHTs）是一种具有稳定 C-P 键的有机化合物，在自然界中广泛分布。草甘膦（GP）是一种人工合成的 PHT，广泛用于农业，与各种人类健康问题和环境破坏有关。鉴于 GP 的普遍存在，开发具有成本效益的现场 GP 检测方法是评估污染和降低暴露风险的关键。我们采用农杆菌 CHLDO（一种天然的 GP 降解菌）作为构建 PHT 生物传感器的宿主和基因部分来源。在这种细菌中，编码 C-P 裂解酶途径的 phn 基因簇受 PhnF 转录抑制因子调控。我们选择了phnG启动子来构建全细胞生物传感器。通过对转录级联的逐步遗传优化，我们创建了一种全细胞生物传感器，能够检测各种样品（包括土壤和水）中 0.25-50 μM 范围内的 GP。

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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.