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QUIRMIA-A基于表型的算法用于推断埃舍里奇亚大肠杆菌中诺抗性机制

Frank Imkamp¹, Elias Bodendoerfer¹, Stefano Mancini¹

¹Institute of Medical Microbiology, University of Zurich, 8006 Zurich, Switzerland.

Antibiotics (Basel, Switzerland)

|July 29, 2023

概括

一个新的算法,QUIRMIA,通过分析磁盘扩散数据,准确地推断大肠杆菌中诺抗性机制. 这种方法可靠地检测低水平的耐药性,改善临床耐药性预测和预防策略.

科学领域:

微生物学微生物学
遗传学是一个遗传学.
抗微生物耐药性抗微生物耐药性

背景情况:

大肠杆菌中诺隆耐药性主要来自于 gyrA 和 parC 基因的突变.
诸如药物透性的改变,排泄机制和等离子体等因素也会导致耐药性.
低级抗性通常与单个 gyrA 突变有关,而高级抗性则涉及 gyrA 和 parC 的多个突变.

研究的目的:

使用光盘扩散数据开发决策树,以推断大肠杆菌中诺隆耐药机制.
为预测临床耐药性定义流行病学截止值.
创建一个诊断算法,用于对大肠杆菌耐药性的连贯基因型/表型分类.

主要方法:

对553个大肠杆菌分离体的表型敏感性测试,使用磁盘扩散对抗纳利迪西克酸和诺基诺.
隔离物的全基因组测序 (WGS).
基于流行病学切线来分类抗性水平的QUInolone抗性机制推断算法 (QUIRMIA) 的开发.

主要成果:

QUIRMIA与全基因组测序数据显示出高一致性 (98%),用于推断耐药性机制.
基于EUCAST的分类,其二元易感/耐药类别,未能准确识别和分类具有低水平醇耐药性的分离物.
奎尔米亚成功地区分了易感,低级耐药和高级耐药的大肠杆菌分离物.

结论:

奎尔米亚提供了一种可靠的方法,用于基因型/表型分类,对大肠杆菌中的诺隆耐药性进行分类.
将QUIRMIA集成到专家规则集中,如EUCAST,可以增强低水平耐药分离物的检测.
更好地检测低级耐药性可以帮助预测临床结果,并防止进一步出现抗菌素耐药性.

关键词:

美国大肠杆菌 (E. coli).诊断算法诊断算法现型/基因型相关性相关性奇诺隆耐药性奇诺隆耐药性整个基因组的测序.

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