Centre for Bioinnovation, University of the Sunshine Coast, Sippy Downs, QLD, Australia.
Sunshine Coast Health Institute, Birtinya, Queensland, Australia.
Genome Med. 2024 Jun 7;16(1):78. doi: 10.1186/s13073-024-01346-z.
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.
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).
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.
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.
抗菌药物耐药性（AMR）是一个日益严重的威胁，需要紧急加以缓解，以避免进入后抗生素时代。铜绿假单胞菌由于其多重和泛耐药率不断增加，是对 AMR 最关注的细菌之一。由于其明确性和可转移性， shotgun 测序在 AMR 计算分析方面得到了广泛关注；然而，从铜绿假单胞菌基因组中准确和全面地预测 AMR 仍然是一个尚未解决的问题。
我们首先整理了迄今为止最全面的已知铜绿假单胞菌 AMR 变体数据库。接下来，我们对来自全球分离株数据集（n = 1877）的具有配对抗菌表型和基因组数据的微生物进行了比较基因组学和全基因组关联研究分析，以确定新的 AMR 变体。最后，我们将我们的铜绿假单胞菌 AMR 数据库（在我们的 AMR 检测和预测工具 ARDaP 中实现）的性能与三个先前发表的计算 AMR 基因检测或表型预测工具——abritAMR、AMRFinderPlus 和 ResFinder——进行了比较，这些工具分别在全球数据集和分析前验证数据集（n = 102）上进行了比较。
我们的 AMR 数据库包含 3639 个移动 AMR 基因和 728 个染色体变体，包括 75 个以前未报告的染色体 AMR 变体、10 个与不寻常的抗菌药物敏感性相关的变体和 281 个我们认为不太可能导致 AMR 的染色体变体。当我们的工具针对全球数据集和验证数据集进行测试时，我们的方法在 10 种临床相关抗生素的基因型-表型平衡准确率（bACC）分别达到了 85%和 81%，而 abritAMR 的准确率仅为 56%和 54%，AMRFinderPlus 的准确率为 58%和 54%，ResFinder 的准确率为 60%和 53%。ARDaP 的优越性能主要归因于包含染色体 AMR 变体，而这些变体通常无法用大多数 AMR 识别工具识别。
我们的 ARDaP 软件和相关的 AMR 变体数据库为预测铜绿假单胞菌的 AMR 表型提供了一种准确的工具，远远超过了当前工具的性能。在常规的铜绿假单胞菌基因组和宏基因组中使用 ARDaP 进行 AMR 预测将提高 AMR 的识别能力，解决对抗这种治疗难治性病原体的一个关键方面。然而，我们对铜绿假单胞菌耐药组的了解仍存在知识差距，特别是粘菌素 AMR 的基础。
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