<br>跟上病原体的步伐：改进铜绿假单胞菌基因组的抗菌药物耐药性检测和预测,Genome Medicine

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跟上病原体的步伐：改进铜绿假单胞菌基因组的抗菌药物耐药性检测和预测
Genome Medicine ( IF 10.4 ) Pub Date : 2024-06-07 , DOI: 10.1186/s13073-024-01346-z
Danielle E Madden _{1,

2} , Timothy Baird _{1,

2,

3} , Scott C Bell _{4,

5} , Kate L McCarthy _{6,

7} , Erin P Price _{1,

2} , Derek S Sarovich _{1,

2}

Affiliation

抗生素耐药性 (AMR) 是一种日益严重的威胁，需要紧急缓解以避免进入后抗生素时代。由于多重耐药率和泛耐药率不断增加，铜绿假单胞菌是最大的抗菌药物耐药性问题之一。由于其明确性和可转移性，鸟枪法测序在计算机 AMR 分析中越来越受欢迎；然而，准确、全面的铜绿假单胞菌基因组 AMR 预测仍然是一个尚未解决的问题。我们首先构建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，我们在全球分离数据集 (n = 1877) 中进行比较基因组学和微生物全基因组关联研究分析，并使用配对的抗菌表型和基因组数据来识别新的 AMR 变异。最后，我们将在 AMR 检测和预测工具 ARDaP 中实现的铜绿假单胞菌 AMR 数据库的性能与之前发布的三个计算机 AMR 基因检测或表型预测工具（abritAMR、AMRFinderPlus、ResFinder）在全球数据集中进行了比较以及未经分析的验证数据集 (n = 102)。我们的 AMR 数据库包含 3639 个移动 AMR 基因和 728 个染色体变异，其中包括 75 个以前未报告的染色体 AMR 变异、10 个与异常抗菌敏感性相关的变异，以及我们证明不太可能赋予 AMR 的 281 个染色体变异。当针对全局和验证数据集进行测试时，我们的管道在 10 种临床相关抗生素中分别实现了 85% 和 81% 的基因型-表型平衡准确率 (bACC)，而 abritAMR 的准确率仅为 56% 和 54%、58% 和 54%使用 AMRFinderPlus，使用 ResFinder 分别为 60% 和 53%。 ARDaP 的卓越性能主要归功于包含染色体 AMR 变异，而大多数 AMR 识别工具通常无法识别这些变异。我们的 ARDaP 软件和相关的 AMR 变异数据库提供了预测铜绿假单胞菌 AMR 表型的准确工具，远远超过了现有工具的性能。实施 ARDaP 对铜绿假单胞菌基因组和宏基因组进行常规 AMR 预测将改善 AMR 识别，解决对抗这种难治性病原体的关键问题。然而，我们对铜绿假单胞菌耐药组的理解仍然存在知识差距，特别是粘菌素 AMR 的基础。

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Keeping up with the pathogens: improved antimicrobial resistance detection and prediction from Pseudomonas aeruginosa genomes

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.

更新日期：2024-06-07

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