Please use this identifier to cite or link to this item: https://hdl.handle.net/10137/11567
Title: Taking the next-gen step: Comprehensive antimicrobial resistance detection from Burkholderia pseudomallei.
Authors: Madden, Danielle E
Webb, Jessica R
Steinig, Eike J
Currie, Bart J
Price, Erin P
Sarovich, Derek S
Citation: Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.
EBioMedicine. 2020 Dec 3;63:103152. doi: 10.1016/j.ebiom.2020.103152.
Abstract: BACKGROUND: Antimicrobial resistance (AMR) poses a major threat to human health. Whole-genome sequencing holds great potential for AMR identification; however, there remain major gaps in accurately and comprehensively detecting AMR across the spectrum of AMR-conferring determinants and pathogens. METHODS: Using 16 wild-type Burkholderia pseudomallei and 25 with acquired AMR, we first assessed the performance of existing AMR software (ARIBA, CARD, ResFinder, and AMRFinderPlus) for detecting clinically relevant AMR in this pathogen. B. pseudomallei was chosen due to limited treatment options, high fatality rate, and AMR caused exclusively by chromosomal mutation (i.e. single-nucleotide polymorphisms [SNPs], insertions-deletions [indels], copy-number variations [CNVs], inversions, and functional gene loss). Due to poor performance with existing tools, we developed ARDaP (Antimicrobial Resistance Detection and Prediction) to identify the spectrum of AMR-conferring determinants in B. pseudomallei. FINDINGS: CARD, ResFinder, and AMRFinderPlus failed to identify any clinically-relevant AMR in B. pseudomallei; ARIBA identified AMR encoded by SNPs and indels that were manually added to its database. However, none of these tools identified CNV, inversion, or gene loss determinants, and ARIBA could not differentiate AMR determinants from natural genetic variation. In contrast, ARDaP accurately detected all SNP, indel, CNV, inversion, and gene loss AMR determinants described in B. pseudomallei (n≈50). Additionally, ARDaP accurately predicted three previously undescribed determinants. In mixed strain data, ARDaP identified AMR to as low as ~5% allelic frequency. INTERPRETATION: Existing AMR software packages are inadequate for chromosomal AMR detection due to an inability to detect resistance conferred by CNVs, inversions, and functional gene loss. ARDaP overcomes these major shortcomings. Further, ARDaP enables AMR prediction from mixed sequence data down to 5% allelic frequency, and can differentiate natural genetic variation from AMR determinants. ARDaP databases can be constructed for any microbial species of interest for comprehensive AMR detection. FUNDING: National Health and Medical Research Council (BJC, EPP, DSS); Australian Government (DEM, ES); Advance Queensland (EPP, DSS).
Click to open Pubmed Article: https://www.ezpdhcs.nt.gov.au/login?url=https://www.ncbi.nlm.nih.gov/pubmed/33285499
Journal title: EBioMedicine
Volume: 63
Pages: 103152
Publication Date: 2020-12-03
Type: Journal Article
URI: https://hdl.handle.net/10137/11567
DOI: 10.1016/j.ebiom.2020.103152
Appears in Collections:(a) NT Health Research Collection

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