Evidence map›Paper›PMID 30165908›Full record

ArticleGenome medicine2018

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 and 6 more

Open access · goldAbstract read
In one paragraph

Article in Genome medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
2.0field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 38 citations in OpenAlex.

  1. Mapping 25 years of research on gut microbiota and antibiotic resistance: bibliometric insights and future directions.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026
    Review
  2. Article
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  9. The future of antibiotics begins with discovering new combinations.Annals of the New York Academy of Sciences · 2021
    Review
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  11. Review
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  13. The Journal of biological chemistry · 2020
    Article
  14. Review
  15. Review
  16. Unstable chromosome rearrangements inProceedings of the National Academy of Sciences of the United States of America · 2019
    Article
  17. Article
  18. Genome-Based Prediction of Bacterial Antibiotic Resistance.Journal of clinical microbiology · 2019
    Review
  19. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors at 2 institutions in 1 country.

Romain GuérillotDepartment of Microbiology and Immunology, The University of Melbourne at the Doherty Institute for Infection & Immunity, Melbourne, Victoria, Australia.
Lucy LiDepartment of Microbiology and Immunology, The University of Melbourne at the Doherty Institute for Infection & Immunity, Melbourne, Victoria, Australia.
Sarah BainesDepartment of Microbiology and Immunology, The University of Melbourne at the Doherty Institute for Infection & Immunity, Melbourne, Victoria, Australia.
Brian HowdenDepartment of Microbiology and Immunology, The University of Melbourne at the Doherty Institute for Infection & Immunity, Melbourne, Victoria, Australia.ORCID 0000-0003-0237-1473
Mark B SchultzDepartment of Microbiology and Immunology, The University of Melbourne at the Doherty Institute for Infection & Immunity, Melbourne, Victoria, Australia.
Torsten SeemannDoherty Applied Microbial Genomics, The University of Melbourne at the Peter Doherty Institute for Infection & Immunity, Melbourne, Victoria, Australia.
Ian MonkDepartment of Microbiology and Immunology, The University of Melbourne at the Doherty Institute for Infection & Immunity, Melbourne, Victoria, Australia.
Sacha J PidotDepartment of Microbiology and Immunology, The University of Melbourne at the Doherty Institute for Infection & Immunity, Melbourne, Victoria, Australia.
Wei GaoDepartment of Microbiology and Immunology, The University of Melbourne at the Doherty Institute for Infection & Immunity, Melbourne, Victoria, Australia.
Stefano GiulieriDepartment of Microbiology and Immunology, The University of Melbourne at the Doherty Institute for Infection & Immunity, Melbourne, Victoria, Australia.
Anders Gonçalves da SilvaDepartment of Microbiology and Immunology, The University of Melbourne at the Doherty Institute for Infection & Immunity, Melbourne, Victoria, Australia.
Anthony D'AgataMicrobiological Diagnostic Unit Public Health Laboratory, The University of Melbourne at the Peter Doherty Institute for Infection & Immunity, Melbourne, Victoria, Australia.
Takehiro TomitaMicrobiological Diagnostic Unit Public Health Laboratory, The University of Melbourne at the Peter Doherty Institute for Infection & Immunity, Melbourne, Victoria, Australia.
Anton Y PelegDepartment of Infectious Diseases, The Alfred Hospital and Central Clinical School, Monash University, Melbourne, Victoria, Australia.
Timothy P StinearDepartment of Microbiology and Immunology, The University of Melbourne at the Doherty Institute for Infection & Immunity, Melbourne, Victoria, Australia.
Benjamin P HowdenDepartment of Microbiology and Immunology, The University of Melbourne at the Doherty Institute for Infection & Immunity, Melbourne, Victoria, Australia. bhowden@unimelb.edu.au.
The University of Melbourne · AUAustralian Regenerative Medicine Institute · AU

Funding

National Health and Medical Research Council GNT1008549National Health and Medical Research Council GNT1066791National Health and Medical Research Council GNT1105905
6 · The paper itself

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 ).

Indexed as

MutationAnti-Bacterial AgentsDaptomycinDrug Resistance, BacterialGenetic PleiotropyMycobacterium tuberculosisRifampinStaphylococcus aureusAnti-Bacterial AgentsDaptomycinRifampinAntibiotic resistanceDaptomycinDeep sequencingMycobacterium tuberculosisResistance mutationsRifampicinStaphylococcus aureus

Identifiers

PMID30165908
PMCPMC6117896
OpenAlexW2790760775

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.