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

Romain Guérillot, Lucy Li, Sarah Baines, Brian Howden, Mark B. Schultz, Torsten Seemann, Ian Monk, Sacha J. Pidot, Wei Gao, Stefano Giulieri, Anders Gonçalves da Silva, Anthony D'Agata, Takehiro Tomita, Anton Y. Peleg, Timothy P. Stinear, Benjamin P. Howden

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11 Citations (Scopus)

Abstract

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).

Original languageEnglish
Article number63
Number of pages15
JournalGenome Medicine
Volume10
Issue number1
DOIs
Publication statusPublished - 31 Aug 2018

Keywords

  • Antibiotic resistance
  • Daptomycin
  • Deep sequencing
  • Mycobacterium tuberculosis
  • Resistance mutations
  • Rifampicin
  • Staphylococcus aureus

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