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Title: Development and application of a transcriptional sensor for detection of heterologous acrylic acid production in E. coli
Authors: Raghavan, Sarada S.
Chee, Sharon
Li, Juntao
Poschmann, Jeremie
Nagarajan, Niranjan
Wei, Siau Jia
Verma, Chandra Shekhar
Ghadessy, Farid John
Keywords: Science::Biological sciences
Issue Date: 2019
Source: Raghavan, S. S., Chee, S., Li, J., Poschmann, J., Nagarajan, N., Wei, S. J., . . . Ghadessy, F. J. (2019). Development and application of a transcriptional sensor for detection of heterologous acrylic acid production in E. coli. Microbial Cell Factories, 18(1), 139-. doi:10.1186/s12934-019-1185-y
Journal: Microbial Cell Factories
Abstract: Background: Acrylic acid (AA) is a widely used commodity chemical derived from non-renewable fossil fuel sources. Alternative microbial-based production methodologies are being developed with the aim of providing “green” acrylic acid. These initiatives will benefit from component sensing tools that facilitate rapid and easy detection of in vivo AA production. Results: We developed a novel transcriptional sensor facilitating in vivo detection of acrylic acid (AA). RNAseq analysis of Escherichia coli exposed to sub-lethal doses of acrylic acid identified a selectively responsive promoter (PyhcN) that was cloned upstream of the eGFP gene. In the presence of AA, eGFP expression in E. coli cells harbouring the sensing construct was readily observable by fluorescence read-out. Low concentrations of AA (500 μM) could be detected whilst the closely related lactic and 3-hydroxy propionic acids failed to activate the sensor. We further used the developed AA-biosensor for in vivo FACS-based screening and identification of amidase mutants with improved catalytic properties for deamination of acrylamide to acrylic acid. Conclusions: The transcriptional AA sensor developed in this study will benefit strain, enzyme and pathway engineering initiatives targeting the efficient formation of bio-acrylic acid.
ISSN: 1475-2859
DOI: 10.1186/s12934-019-1185-y
Schools: School of Biological Sciences 
Organisations: Bioinformatics Institute, A*STAR
Rights: © 2019 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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