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Comprehensive antibiotic-linked mutation assessment by resistance mutation sequencing (RM-seq)

Mutation acquisition is a major mechanism of bacterial antibiotic resistance that remains insufficiently characterised. Here we present RM-seq, a new amplicon-based deep sequencing workflow based on a molecular barcoding technique adapted from Low Error Amplicon sequencing (LEA-seq). RM-seq allows d...

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Autores principales: Guérillot, Romain, Li, Lucy, Baines, Sarah, Howden, Brian, Schultz, Mark B., Seemann, Torsten, Monk, Ian, Pidot, Sacha J., Gao, Wei, Giulieri, Stefano, Gonçalves da Silva, Anders, D’Agata, Anthony, Tomita, Takehiro, Peleg, Anton Y., Stinear, Timothy P., Howden, Benjamin P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6117896/
https://www.ncbi.nlm.nih.gov/pubmed/30165908
http://dx.doi.org/10.1186/s13073-018-0572-z
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author Guérillot, Romain
Li, Lucy
Baines, Sarah
Howden, Brian
Schultz, Mark B.
Seemann, Torsten
Monk, Ian
Pidot, Sacha J.
Gao, Wei
Giulieri, Stefano
Gonçalves da Silva, Anders
D’Agata, Anthony
Tomita, Takehiro
Peleg, Anton Y.
Stinear, Timothy P.
Howden, Benjamin P.
author_facet Guérillot, Romain
Li, Lucy
Baines, Sarah
Howden, Brian
Schultz, Mark B.
Seemann, Torsten
Monk, Ian
Pidot, Sacha J.
Gao, Wei
Giulieri, Stefano
Gonçalves da Silva, Anders
D’Agata, Anthony
Tomita, Takehiro
Peleg, Anton Y.
Stinear, Timothy P.
Howden, Benjamin P.
author_sort Guérillot, Romain
collection PubMed
description Mutation acquisition is a major mechanism of bacterial antibiotic resistance that remains insufficiently characterised. Here we present RM-seq, a new amplicon-based deep sequencing workflow based on a molecular barcoding technique adapted from Low Error Amplicon sequencing (LEA-seq). RM-seq allows detection and functional assessment of mutational resistance at high throughput from mixed bacterial populations. The sensitive detection of very low-frequency resistant sub-populations permits characterisation of antibiotic-linked mutational repertoires in vitro and detection of rare resistant populations during infections. Accurate quantification of resistance mutations enables phenotypic screening of mutations conferring pleiotropic phenotypes such as in vivo persistence, collateral sensitivity or cross-resistance. RM-seq will facilitate comprehensive detection, characterisation and surveillance of resistant bacterial populations (https://github.com/rguerillot/RM-seq). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13073-018-0572-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-61178962018-09-05 Comprehensive antibiotic-linked mutation assessment by resistance mutation sequencing (RM-seq) Guérillot, Romain Li, Lucy Baines, Sarah Howden, Brian Schultz, Mark B. Seemann, Torsten Monk, Ian Pidot, Sacha J. Gao, Wei Giulieri, Stefano Gonçalves da Silva, Anders D’Agata, Anthony Tomita, Takehiro Peleg, Anton Y. Stinear, Timothy P. Howden, Benjamin P. Genome Med Method Mutation acquisition is a major mechanism of bacterial antibiotic resistance that remains insufficiently characterised. Here we present RM-seq, a new amplicon-based deep sequencing workflow based on a molecular barcoding technique adapted from Low Error Amplicon sequencing (LEA-seq). RM-seq allows detection and functional assessment of mutational resistance at high throughput from mixed bacterial populations. The sensitive detection of very low-frequency resistant sub-populations permits characterisation of antibiotic-linked mutational repertoires in vitro and detection of rare resistant populations during infections. Accurate quantification of resistance mutations enables phenotypic screening of mutations conferring pleiotropic phenotypes such as in vivo persistence, collateral sensitivity or cross-resistance. RM-seq will facilitate comprehensive detection, characterisation and surveillance of resistant bacterial populations (https://github.com/rguerillot/RM-seq). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13073-018-0572-z) contains supplementary material, which is available to authorized users. BioMed Central 2018-08-31 /pmc/articles/PMC6117896/ /pubmed/30165908 http://dx.doi.org/10.1186/s13073-018-0572-z Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), 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 (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Method
Guérillot, Romain
Li, Lucy
Baines, Sarah
Howden, Brian
Schultz, Mark B.
Seemann, Torsten
Monk, Ian
Pidot, Sacha J.
Gao, Wei
Giulieri, Stefano
Gonçalves da Silva, Anders
D’Agata, Anthony
Tomita, Takehiro
Peleg, Anton Y.
Stinear, Timothy P.
Howden, Benjamin P.
Comprehensive antibiotic-linked mutation assessment by resistance mutation sequencing (RM-seq)
title Comprehensive antibiotic-linked mutation assessment by resistance mutation sequencing (RM-seq)
title_full Comprehensive antibiotic-linked mutation assessment by resistance mutation sequencing (RM-seq)
title_fullStr Comprehensive antibiotic-linked mutation assessment by resistance mutation sequencing (RM-seq)
title_full_unstemmed Comprehensive antibiotic-linked mutation assessment by resistance mutation sequencing (RM-seq)
title_short Comprehensive antibiotic-linked mutation assessment by resistance mutation sequencing (RM-seq)
title_sort comprehensive antibiotic-linked mutation assessment by resistance mutation sequencing (rm-seq)
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6117896/
https://www.ncbi.nlm.nih.gov/pubmed/30165908
http://dx.doi.org/10.1186/s13073-018-0572-z
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