Accuracy of Different Bioinformatics Methods in Detecting Antibiotic Resistance and Virulence Factors from Staphylococcus aureus Whole-Genome Sequences
Accuracy of Different Bioinformatics Methods in Detecting Antibiotic Resistance and Virulence Factors from Staphylococcus aureus Whole-Genome Sequences
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DOI：
10.1128/jcm.01815-17
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发表时间：
2018-09-01
影响因子：
9.4
通讯作者：
Peto, Tim
中科院分区：
文献类型：
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作者：
Mason, Amy;Foster, Dona;Peto, Tim
原则上，全基因组测序（WGS）可以直接从基因型预测表型抗性，取代基于实验室的测试。然而，迄今为止，尚未系统地探讨不同生物信息学方法对基因型-表型差异的贡献。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]），用于预测先前通过标准实验室方法（纸片扩散、肉汤和/或琼脂稀释以及 PCR）表征的 1,379 个金黄色葡萄球菌分离株中是否存在 83 种不同的耐药决定簇和毒力基因以及总体抗菌敏感性。总的来说，所有三种方法之间 99.5% (113,830/114,457) 的个体耐药决定簇/毒力基因预测是相同的，只有 627 (0.5%) 不一致的预测，表明总体一致性很高（Fleiss' kappa = 0.98，P < 0.0001）。除盒式重组酶 ccrC(b) 外，所有基因的三种方法中仅存在一种差异。 98.3% (14,224 /14,464) 病例的基因型抗菌药物敏感性预测与实验室表型相符（2,720 例[18.8%] 耐药，11,504 例[79.5%] 敏感）。实验室表型和综合基因型预测之间的分歧更大（97例[0.7%]表型易感，但所有生物信息学方法报告耐药；89[0.6%]表型耐药，但所有生物信息学方法报告易感）比三种生物信息学方法（54例[0.4%]例，16例表型耐药，38例表型易感）之间存在更大的分歧。然而，在 36/54 (67%) 的病例中，共有基因型与实验室表型相匹配。在这项研究中，选择这三种特定的生物信息学方法来鉴定金黄色葡萄球菌中的抗性决定簇或其他基因并不重要，所有方法都表现出彼此以及表型/分子方法的高度一致性。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。
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 Staphylococcus aureus 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, P < 0.0001). Discrepancies when identified were in only one of the three methods for all genes except the cassette recombinase, ccrC(b). 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 S. aureus 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.