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不同生物信息学方法从金黄色葡萄球菌全基因组序列中检测抗生素耐药性和毒力因子的准确性。

Accuracy of Different Bioinformatics Methods in Detecting Antibiotic Resistance and Virulence Factors from Staphylococcus aureus Whole-Genome Sequences.

机构信息

Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.

Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom

出版信息

J Clin Microbiol. 2018 Aug 27;56(9). doi: 10.1128/JCM.01815-17. Print 2018 Sep.

Abstract

In principle, whole-genome sequencing (WGS) can predict phenotypic resistance directly from a genotype, replacing laboratory-based tests. However, the contribution of different bioinformatics methods to genotype-phenotype discrepancies has not been systematically explored to date. We compared three WGS-based bioinformatics methods (Genefinder [read based], Mykrobe [de Bruijn graph based], and Typewriter [BLAST based]) for predicting the presence/absence of 83 different resistance determinants and virulence genes and overall antimicrobial susceptibility in 1,379 isolates previously characterized by standard laboratory methods (disc diffusion, broth and/or agar dilution, and PCR). In total, 99.5% (113,830/114,457) of individual resistance-determinant/virulence gene predictions were identical between all three methods, with only 627 (0.5%) discordant predictions, demonstrating high overall agreement (Fleiss' kappa = 0.98, < 0.0001). Discrepancies when identified were in only one of the three methods for all genes except the cassette recombinase, (). The genotypic antimicrobial susceptibility prediction matched the laboratory phenotype in 98.3% (14,224/14,464) of cases (2,720 [18.8%] resistant, 11,504 [79.5%] susceptible). There was greater disagreement between the laboratory phenotypes and the combined genotypic predictions (97 [0.7%] phenotypically susceptible, but all bioinformatic methods reported resistance; 89 [0.6%] phenotypically resistant, but all bioinformatics methods reported susceptible) than within the three bioinformatics methods (54 [0.4%] cases, 16 phenotypically resistant, 38 phenotypically susceptible). However, in 36/54 (67%) cases, the consensus genotype matched the laboratory phenotype. In this study, the choice between these three specific bioinformatic methods to identify resistance determinants or other genes in did not prove critical, with all demonstrating high concordance with each other and phenotypic/molecular methods. However, each has some limitations; therefore, consensus methods provide some assurance.

摘要

原则上,全基因组测序(WGS)可以直接从基因型预测表型耐药性,从而替代基于实验室的检测。然而,迄今为止,不同的生物信息学方法对基因型-表型差异的贡献尚未得到系统的探索。我们比较了三种基于 WGS 的生物信息学方法(Genefinder[基于读取]、Mykrobe[基于 de Bruijn 图]和 Typewriter[基于 BLAST]),用于预测 1379 个先前通过标准实验室方法(纸片扩散、肉汤和/或琼脂稀释和 PCR)进行特征分析的分离株中 83 种不同的耐药决定因素和毒力基因的存在/缺失以及整体抗菌药物敏感性。总的来说,三种方法之间的单个耐药决定因素/毒力基因预测的一致性为 99.5%(113830/114457),只有 627 个(0.5%)存在不一致的预测,表明整体一致性非常高(Fleiss'kappa=0.98,<0.0001)。除了盒式重组酶外,所有基因的不一致预测都仅出现在三种方法中的一种方法中()。基因型抗菌药物敏感性预测与实验室表型的匹配率为 98.3%(14224/14464)(2720 个[18.8%]耐药,11504 个[79.5%]敏感)。与实验室表型相比,实验室表型和综合基因型预测之间存在更大的差异(97 个[0.7%]表型敏感,但所有生物信息学方法均报告耐药;89 个[0.6%]表型耐药,但所有生物信息学方法均报告敏感),而在三种生物信息学方法之间(54 个[0.4%]病例,16 个表型耐药,38 个表型敏感)则更少。然而,在 54 个病例中的 36 个(67%)中,共识基因型与实验室表型匹配。在这项研究中,在这三种特定的生物信息学方法之间进行选择以识别耐药决定因素或其他基因在[研究中]并未被证明是关键的,所有方法彼此之间都具有高度的一致性,并且与表型/分子方法也具有高度的一致性。然而,每种方法都有一些局限性;因此,共识方法提供了一些保证。

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