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本文引用的文献

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Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis.2019 年全球细菌对抗菌药物耐药性的负担:系统分析。
Lancet. 2022 Feb 12;399(10325):629-655. doi: 10.1016/S0140-6736(21)02724-0. Epub 2022 Jan 19.
2
Extended-spectrum β-lactamases: an update on their characteristics, epidemiology and detection.超广谱β-内酰胺酶:其特性、流行病学及检测的最新进展
JAC Antimicrob Resist. 2021 Jul 16;3(3):dlab092. doi: 10.1093/jacamr/dlab092. eCollection 2021 Sep.
3
Epidemiology of β-Lactamase-Producing Pathogens.产β-内酰胺酶病原体的流行病学。
Clin Microbiol Rev. 2020 Feb 26;33(2). doi: 10.1128/CMR.00047-19. Print 2020 Mar 18.
4
Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: an inter-laboratory study.从细菌分离物的全基因组测序数据中得出的抗菌药物耐药性的生物信息学预测结果不一致:一项实验室间研究。
Microb Genom. 2020 Feb;6(2). doi: 10.1099/mgen.0.000335. Epub 2020 Feb 12.
5
The EUCAST rapid disc diffusion method for antimicrobial susceptibility testing directly from positive blood culture bottles.EUCAST 快速纸片扩散法直接从阳性血培养瓶中进行抗菌药物敏感性试验。
J Antimicrob Chemother. 2020 Apr 1;75(4):968-978. doi: 10.1093/jac/dkz548.
6
Transient Silencing of Antibiotic Resistance by Mutation Represents a Significant Potential Source of Unanticipated Therapeutic Failure.突变导致抗生素耐药性短暂沉默,这代表了一个重大的潜在治疗失败的原因。
mBio. 2019 Oct 29;10(5):e01755-19. doi: 10.1128/mBio.01755-19.
7
Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data.评估基于机器学习的全基因组测序数据抗生素药敏试验性能和可靠性的影响因素。
PLoS Comput Biol. 2019 Sep 3;15(9):e1007349. doi: 10.1371/journal.pcbi.1007349. eCollection 2019 Sep.
8
Sequencing-based methods and resources to study antimicrobial resistance.基于测序的方法和资源研究抗菌药物耐药性。
Nat Rev Genet. 2019 Jun;20(6):356-370. doi: 10.1038/s41576-019-0108-4.
9
Applying Rapid Whole-Genome Sequencing To Predict Phenotypic Antimicrobial Susceptibility Testing Results among Carbapenem-Resistant Klebsiella pneumoniae Clinical Isolates.应用快速全基因组测序预测碳青霉烯类耐药肺炎克雷伯菌临床分离株的表型抗菌药物敏感性试验结果。
Antimicrob Agents Chemother. 2018 Dec 21;63(1). doi: 10.1128/AAC.01923-18. Print 2019 Jan.
10
Heavy Metal Susceptibility of Escherichia coli Isolated from Urine Samples from Sweden, Germany, and Spain.瑞典、德国和西班牙尿液样本中分离出的大肠杆菌的重金属易感性。
Antimicrob Agents Chemother. 2018 Apr 26;62(5). doi: 10.1128/AAC.00209-18. Print 2018 May.

基于全基因组序列数据预测大肠埃希菌对第三代头孢菌素的耐药性降低的局限性。

Limitations in predicting reduced susceptibility to third generation cephalosporins in Escherichia coli based on whole genome sequence data.

机构信息

Center for Research and Development Gävleborg, Uppsala University, Gävle, Sweden.

Department of Clinical Microbiology, Falun Hospital, Falun, Sweden.

出版信息

PLoS One. 2023 Nov 30;18(11):e0295233. doi: 10.1371/journal.pone.0295233. eCollection 2023.

DOI:10.1371/journal.pone.0295233
PMID:38033151
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10688838/
Abstract

Prediction of antibiotic resistance from whole genome sequence (WGS) data has been proposed. However, the performance of WGS data analysis for this matter may be influenced by the resistance mechanism's biology. This study compared traditional antimicrobial susceptibility testing with whole genome sequencing for identification of extended-spectrum beta-lactamases (ESBL) in a collection of 419 Escherichia coli isolates. BLASTn-based prediction and read mapping with srst2 gave matching results, and in 381/419 (91%) isolates WGS was congruent with phenotypic testing. Incongruent results were grouped by potential explanations into biological-related and sequence analysis-related results. Biological-related explanations included weak ESBL-enzyme activity (n = 4), inconclusive phenotypic ESBL-testing (n = 4), potential loss of plasmid during subculturing (n = 7), and other resistance mechanisms than ESBL-enzymes (n = 2). Sequence analysis-related explanations were cut-off dependency for read depth (n = 5), too stringent (n = 3) and too loose cut-off for nucleotide identity and coverage (n = 13), respectively. The results reveal limitations of both traditional antibiotic susceptibility testing and sequence-based resistance prediction and highlight the need for evidence-based standards in sequence analysis.

摘要

已有研究提出了基于全基因组序列(WGS)数据预测抗生素耐药性的方法。然而,WGS 数据分析在这方面的性能可能会受到耐药机制生物学的影响。本研究比较了传统的抗菌药物敏感性试验与全基因组测序,以鉴定 419 株大肠埃希菌分离株中的扩展谱β-内酰胺酶(ESBL)。基于 BLASTn 的预测和 srst2 的读映射得出了匹配的结果,在 419 株中的 381 株(91%)WGS 与表型检测结果一致。不一致的结果根据潜在的解释分为生物学相关和序列分析相关的结果。生物学相关的解释包括 ESBL 酶活性较弱(n = 4)、表型 ESBL 检测不明确(n = 4)、在传代过程中质粒可能丢失(n = 7)以及除 ESBL 酶以外的其他耐药机制(n = 2)。序列分析相关的解释分别是读深度的依赖于临界值(n = 5)、临界值太严格(n = 3)和核苷酸同一性和覆盖率的临界值太宽松(n = 13)。这些结果揭示了传统抗生素敏感性试验和基于序列的耐药性预测的局限性,并强调了在序列分析中需要基于证据的标准。