Danielle E Madden1, Jessica R Webb2, Eike J Steinig3, Bart J Currie4, Erin P Price5, Derek S Sarovich6. 1. GeneCology Research Centre, University of the Sunshine Coast, Sippy Downs, Queensland, Australia; Sunshine Coast Health Institute, Sunshine Coast University Hospital, Birtinya, Queensland, Australia. 2. Global and Tropical Health Division, Menzies School of Health Research, Charles Darwin University, Tiwi, Northern Territory, Australia. 3. Australian Institute of Tropical and Health Medicine, James Cook University, Townsville, Queensland, Australia. 4. Global and Tropical Health Division, Menzies School of Health Research, Charles Darwin University, Tiwi, Northern Territory, Australia; Department of Infectious Diseases and Northern Territory Medical Program, Royal Darwin Hospital, Tiwi, Northern Territory, Australia. 5. GeneCology Research Centre, University of the Sunshine Coast, Sippy Downs, Queensland, Australia; Sunshine Coast Health Institute, Sunshine Coast University Hospital, Birtinya, Queensland, Australia; Global and Tropical Health Division, Menzies School of Health Research, Charles Darwin University, Tiwi, Northern Territory, Australia. 6. GeneCology Research Centre, University of the Sunshine Coast, Sippy Downs, Queensland, Australia; Sunshine Coast Health Institute, Sunshine Coast University Hospital, Birtinya, Queensland, Australia; Global and Tropical Health Division, Menzies School of Health Research, Charles Darwin University, Tiwi, Northern Territory, Australia. Electronic address: dsarovich@usc.edu.au.
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).
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).
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