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.
EBioMedicine. 2021 Jan;63:103152. doi: 10.1016/j.ebiom.2020.103152. Epub 2020 Dec 4.
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.
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.
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.
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.
National Health and Medical Research Council (BJC, EPP, DSS); Australian Government (DEM, ES); Advance Queensland (EPP, DSS).
抗生素耐药性（AMR）对人类健康构成重大威胁。全基因组测序在 AMR 鉴定方面具有巨大潜力；然而，在准确和全面地检测 AMR 方面，仍然存在很大的差距，无法全面检测到赋予 AMR 的决定因素和病原体。
我们使用 16 株野生型伯克霍尔德氏菌和 25 株获得性 AMR 对现有的 AMR 软件（ARIBA、CARD、ResFinder 和 AMRFinderPlus）进行了评估，以检测该病原体中临床相关的 AMR。选择伯克霍尔德氏菌是因为其治疗选择有限、死亡率高，以及 AMR 仅由染色体突变引起（即单核苷酸多态性（SNP）、插入缺失（indels）、拷贝数变异（CNVs）、倒位和功能基因缺失）。由于现有工具的性能不佳，我们开发了 ARDaP（抗生素耐药性检测和预测）来识别伯克霍尔德氏菌中 AMR 赋予决定因素的范围。
CARD、ResFinder 和 AMRFinderPlus 未能在伯克霍尔德氏菌中识别任何临床相关的 AMR；ARIBA 识别了 SNP 和 indels 编码的 AMR，这些 SNP 和 indels 是手动添加到其数据库中的。然而，这些工具都没有识别 CNV、倒位或基因缺失决定因素，ARIBA 也无法将 AMR 决定因素与自然遗传变异区分开来。相比之下，ARDaP 准确地检测到了在伯克霍尔德氏菌中描述的所有 SNP、indel、CNV、倒位和基因缺失 AMR 决定因素（n≈50）。此外，ARDaP 还准确地预测了三个以前未描述的决定因素。在混合菌株数据中，ARDaP 能够以低至约 5%的等位基因频率检测到 AMR。
由于无法检测到由 CNV、倒位和功能基因缺失引起的耐药性，现有的 AMR 软件包无法用于染色体 AMR 检测。ARDaP 克服了这些主要缺点。此外，ARDaP 能够从混合序列数据中以低至 5%的等位基因频率预测 AMR，并能够将自然遗传变异与 AMR 决定因素区分开来。可以为任何感兴趣的微生物物种构建 ARDaP 数据库，以进行全面的 AMR 检测。
澳大利亚健康与医疗研究委员会（BJC、EPP、DSS）；澳大利亚政府（DEM、ES）；昆士兰先进技术（EPP、DSS）。
