Cargando…

A genome-wide approach for identification and characterisation of metabolite-inducible systems

Inducible gene expression systems are vital tools for the advancement of synthetic biology. Their application as genetically encoded biosensors has the potential to contribute to diagnostics and to revolutionise the field of microbial cell factory development. Currently, the number of compounds of b...

Descripción completa

Detalles Bibliográficos
Autores principales: Hanko, Erik K. R., Paiva, Ana C., Jonczyk, Magdalena, Abbott, Matthew, Minton, Nigel P., Malys, Naglis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7057948/
https://www.ncbi.nlm.nih.gov/pubmed/32139676
http://dx.doi.org/10.1038/s41467-020-14941-6
_version_ 1783503768872026112
author Hanko, Erik K. R.
Paiva, Ana C.
Jonczyk, Magdalena
Abbott, Matthew
Minton, Nigel P.
Malys, Naglis
author_facet Hanko, Erik K. R.
Paiva, Ana C.
Jonczyk, Magdalena
Abbott, Matthew
Minton, Nigel P.
Malys, Naglis
author_sort Hanko, Erik K. R.
collection PubMed
description Inducible gene expression systems are vital tools for the advancement of synthetic biology. Their application as genetically encoded biosensors has the potential to contribute to diagnostics and to revolutionise the field of microbial cell factory development. Currently, the number of compounds of biological interest by far exceeds the number of available biosensors. Here, we address this limitation by developing a generic genome-wide approach to identify transcription factor-based inducible gene expression systems. We construct and validate 15 functional biosensors, provide a characterisation workflow to facilitate forward engineering efforts, exemplify their broad-host-range applicability, and demonstrate their utility in enzyme screening. Previously uncharacterised interactions between sensors and compounds of biological relevance are identified by employing the largest reported library of metabolite-responsive biosensors in an automated high-throughput screen. With the rapidly growing genomic data these innovative capabilities offer a platform to vastly increase the number of biologically detectable molecules.
format Online
Article
Text
id pubmed-7057948
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-70579482020-03-06 A genome-wide approach for identification and characterisation of metabolite-inducible systems Hanko, Erik K. R. Paiva, Ana C. Jonczyk, Magdalena Abbott, Matthew Minton, Nigel P. Malys, Naglis Nat Commun Article Inducible gene expression systems are vital tools for the advancement of synthetic biology. Their application as genetically encoded biosensors has the potential to contribute to diagnostics and to revolutionise the field of microbial cell factory development. Currently, the number of compounds of biological interest by far exceeds the number of available biosensors. Here, we address this limitation by developing a generic genome-wide approach to identify transcription factor-based inducible gene expression systems. We construct and validate 15 functional biosensors, provide a characterisation workflow to facilitate forward engineering efforts, exemplify their broad-host-range applicability, and demonstrate their utility in enzyme screening. Previously uncharacterised interactions between sensors and compounds of biological relevance are identified by employing the largest reported library of metabolite-responsive biosensors in an automated high-throughput screen. With the rapidly growing genomic data these innovative capabilities offer a platform to vastly increase the number of biologically detectable molecules. Nature Publishing Group UK 2020-03-05 /pmc/articles/PMC7057948/ /pubmed/32139676 http://dx.doi.org/10.1038/s41467-020-14941-6 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Hanko, Erik K. R.
Paiva, Ana C.
Jonczyk, Magdalena
Abbott, Matthew
Minton, Nigel P.
Malys, Naglis
A genome-wide approach for identification and characterisation of metabolite-inducible systems
title A genome-wide approach for identification and characterisation of metabolite-inducible systems
title_full A genome-wide approach for identification and characterisation of metabolite-inducible systems
title_fullStr A genome-wide approach for identification and characterisation of metabolite-inducible systems
title_full_unstemmed A genome-wide approach for identification and characterisation of metabolite-inducible systems
title_short A genome-wide approach for identification and characterisation of metabolite-inducible systems
title_sort genome-wide approach for identification and characterisation of metabolite-inducible systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7057948/
https://www.ncbi.nlm.nih.gov/pubmed/32139676
http://dx.doi.org/10.1038/s41467-020-14941-6
work_keys_str_mv AT hankoerikkr agenomewideapproachforidentificationandcharacterisationofmetaboliteinduciblesystems
AT paivaanac agenomewideapproachforidentificationandcharacterisationofmetaboliteinduciblesystems
AT jonczykmagdalena agenomewideapproachforidentificationandcharacterisationofmetaboliteinduciblesystems
AT abbottmatthew agenomewideapproachforidentificationandcharacterisationofmetaboliteinduciblesystems
AT mintonnigelp agenomewideapproachforidentificationandcharacterisationofmetaboliteinduciblesystems
AT malysnaglis agenomewideapproachforidentificationandcharacterisationofmetaboliteinduciblesystems
AT hankoerikkr genomewideapproachforidentificationandcharacterisationofmetaboliteinduciblesystems
AT paivaanac genomewideapproachforidentificationandcharacterisationofmetaboliteinduciblesystems
AT jonczykmagdalena genomewideapproachforidentificationandcharacterisationofmetaboliteinduciblesystems
AT abbottmatthew genomewideapproachforidentificationandcharacterisationofmetaboliteinduciblesystems
AT mintonnigelp genomewideapproachforidentificationandcharacterisationofmetaboliteinduciblesystems
AT malysnaglis genomewideapproachforidentificationandcharacterisationofmetaboliteinduciblesystems