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Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: an inter-laboratory study.

Doyle, RM; O'Sullivan, DM; Aller, SD; Bruchmann, S; Clark, T; Coello Pelegrin, A; Cormican, M; Diez Benavente, E; Ellington, MJ; McGrath, E; et al. Doyle, RM; O'Sullivan, DM; Aller, SD; Bruchmann, S; Clark, T; Coello Pelegrin, A; Cormican, M; Diez Benavente, E; Ellington, MJ; McGrath, E; Motro, Y; Phuong Thuy Nguyen, T; Phelan, J; Shaw, LP; Stabler, RA; van Belkum, A; van Dorp, L; Woodford, N; Moran-Gilad, J; Huggett, JF; Harris, KA (2020) Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: an inter-laboratory study. Microb Genom, 6 (2). ISSN 2057-5858 https://doi.org/10.1099/mgen.0.000335
SGUL Authors: Doyle, Ronan Matthew

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Abstract

Antimicrobial resistance (AMR) poses a threat to public health. Clinical microbiology laboratories typically rely on culturing bacteria for antimicrobial-susceptibility testing (AST). As the implementation costs and technical barriers fall, whole-genome sequencing (WGS) has emerged as a 'one-stop' test for epidemiological and predictive AST results. Few published comparisons exist for the myriad analytical pipelines used for predicting AMR. To address this, we performed an inter-laboratory study providing sets of participating researchers with identical short-read WGS data from clinical isolates, allowing us to assess the reproducibility of the bioinformatic prediction of AMR between participants, and identify problem cases and factors that lead to discordant results. We produced ten WGS datasets of varying quality from cultured carbapenem-resistant organisms obtained from clinical samples sequenced on either an Illumina NextSeq or HiSeq instrument. Nine participating teams ('participants') were provided these sequence data without any other contextual information. Each participant used their choice of pipeline to determine the species, the presence of resistance-associated genes, and to predict susceptibility or resistance to amikacin, gentamicin, ciprofloxacin and cefotaxime. We found participants predicted different numbers of AMR-associated genes and different gene variants from the same clinical samples. The quality of the sequence data, choice of bioinformatic pipeline and interpretation of the results all contributed to discordance between participants. Although much of the inaccurate gene variant annotation did not affect genotypic resistance predictions, we observed low specificity when compared to phenotypic AST results, but this improved in samples with higher read depths. Had the results been used to predict AST and guide treatment, a different antibiotic would have been recommended for each isolate by at least one participant. These challenges, at the final analytical stage of using WGS to predict AMR, suggest the need for refinements when using this technology in clinical settings. Comprehensive public resistance sequence databases, full recommendations on sequence data quality and standardization in the comparisons between genotype and resistance phenotypes will all play a fundamental role in the successful implementation of AST prediction using WGS in clinical microbiology laboratories.

Item Type: Article
Additional Information: © 2020 The Authors This is an Open Access article published by the Microbiology Society under the Creative Commons Attribution License
Keywords: antimicrobial resistance, antimicrobial-susceptibility testing, bioinformatics, carbapenem resistance, whole-genome sequencing
SGUL Research Institute / Research Centre: Academic Structure > Infection and Immunity Research Institute (INII)
Journal or Publication Title: Microb Genom
ISSN: 2057-5858
Language: eng
Dates:
DateEvent
12 February 2020Published
17 January 2020Accepted
Publisher License: Creative Commons: Attribution 4.0
Projects:
Project IDFunderFunder ID
UNSPECIFIEDWellcome Trusthttp://dx.doi.org/10.13039/100004440
HLT07UK National Measurement SystemUNSPECIFIED
HLT07European Metrology Programme for Innovation and ResearchUNSPECIFIED
675412Marie Skłodowska-CurieUNSPECIFIED
PubMed ID: 32048983
Go to PubMed abstract
URI: http://openaccess.sgul.ac.uk/id/eprint/111812
Publisher's version: https://doi.org/10.1099/mgen.0.000335

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