Danielle E Madden, Timothy Baird, Scott C Bell, Kate L McCarthy, Erin P Price, Derek S Sarovich
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引用次数: 0
Abstract
Background: Antimicrobial resistance (AMR) is an intensifying threat that requires urgent mitigation to avoid a post-antibiotic era. Pseudomonas aeruginosa represents one of the greatest AMR concerns due to increasing multi- and pan-drug resistance rates. Shotgun sequencing is gaining traction for in silico AMR profiling due to its unambiguity and transferability; however, accurate and comprehensive AMR prediction from P. aeruginosa genomes remains an unsolved problem.
Methods: We first curated the most comprehensive database yet of known P. aeruginosa AMR variants. Next, we performed comparative genomics and microbial genome-wide association study analysis across a Global isolate Dataset (n = 1877) with paired antimicrobial phenotype and genomic data to identify novel AMR variants. Finally, the performance of our P. aeruginosa AMR database, implemented in our AMR detection and prediction tool, ARDaP, was compared with three previously published in silico AMR gene detection or phenotype prediction tools-abritAMR, AMRFinderPlus, ResFinder-across both the Global Dataset and an analysis-naïve Validation Dataset (n = 102).
Results: Our AMR database comprises 3639 mobile AMR genes and 728 chromosomal variants, including 75 previously unreported chromosomal AMR variants, 10 variants associated with unusual antimicrobial susceptibility, and 281 chromosomal variants that we show are unlikely to confer AMR. Our pipeline achieved a genotype-phenotype balanced accuracy (bACC) of 85% and 81% across 10 clinically relevant antibiotics when tested against the Global and Validation Datasets, respectively, vs. just 56% and 54% with abritAMR, 58% and 54% with AMRFinderPlus, and 60% and 53% with ResFinder. ARDaP's superior performance was predominantly due to the inclusion of chromosomal AMR variants, which are generally not identified with most AMR identification tools.
Conclusions: Our ARDaP software and associated AMR variant database provides an accurate tool for predicting AMR phenotypes in P. aeruginosa, far surpassing the performance of current tools. Implementation of ARDaP for routine AMR prediction from P. aeruginosa genomes and metagenomes will improve AMR identification, addressing a critical facet in combatting this treatment-refractory pathogen. However, knowledge gaps remain in our understanding of the P. aeruginosa resistome, particularly the basis of colistin AMR.
背景:抗生素耐药性(AMR)是一个日益严重的威胁,亟需加以缓解,以避免后抗生素时代的到来。由于铜绿假单胞菌对多种药物和泛药物的耐药率不断上升,铜绿假单胞菌已成为最令人担忧的 AMR 之一。霰弹枪测序因其明确性和可转移性,在默观AMR分析中越来越受到重视;然而,从铜绿假单胞菌基因组中准确、全面地预测AMR仍是一个尚未解决的问题:方法:我们首先建立了迄今为止最全面的铜绿假单胞菌 AMR 变异数据库。接下来,我们对具有成对抗菌表型和基因组数据的全球分离数据集(n = 1877)进行了比较基因组学和微生物全基因组关联研究分析,以确定新型 AMR 变异。最后,我们在AMR检测和预测工具ARDaP中实施了铜绿假单胞菌AMR数据库,并在全球数据集和未经分析的验证数据集(n = 102)上,将该数据库的性能与之前发布的三种硅学AMR基因检测或表型预测工具--abritAMR、AMRFinderPlus和ResFinder--进行了比较:我们的AMR数据库包括3639个移动AMR基因和728个染色体变异体,其中包括75个以前未报道过的染色体AMR变异体、10个与异常抗菌素敏感性相关的变异体以及281个染色体变异体。与 abritAMR 的 56% 和 54%、AMRFinderPlus 的 58% 和 54% 以及 ResFinder 的 60% 和 53% 相比,在全球数据集和验证数据集的测试中,我们的管道在 10 种临床相关抗生素上的基因型-表型平衡准确率 (bACC) 分别达到了 85% 和 81%。ARDaP 性能优越的主要原因是包含了染色体 AMR 变体,而大多数 AMR 鉴定工具通常无法鉴定出染色体 AMR 变体:结论:我们的 ARDaP 软件和相关 AMR 变异数据库为预测铜绿假单胞菌的 AMR 表型提供了准确的工具,远远超过了现有工具的性能。将 ARDaP 用于铜绿微囊桿菌基因组和元基因组的常规 AMR 预测将改善 AMR 鉴定,从而解决抗击这种难治性病原体的关键问题。然而,我们对铜绿假单胞菌抗药性组的了解,尤其是对可乐定 AMR 基础的了解仍然存在差距。
期刊介绍:
Genome Medicine is an open access journal that publishes outstanding research applying genetics, genomics, and multi-omics to understand, diagnose, and treat disease. Bridging basic science and clinical research, it covers areas such as cancer genomics, immuno-oncology, immunogenomics, infectious disease, microbiome, neurogenomics, systems medicine, clinical genomics, gene therapies, precision medicine, and clinical trials. The journal publishes original research, methods, software, and reviews to serve authors and promote broad interest and importance in the field.