Prediction of Staphylococcus aureus antimicrobial resistance by whole-genome sequencing.
Prediction of Staphylococcus aureus antimicrobial resistance by whole-genome sequencing.
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DOI：
10.1128/jcm.03117-13
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发表时间：
2014-04
影响因子：
9.4
通讯作者：
Golubchik T
中科院分区：
文献类型：
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作者：
Gordon NC;Price JR;Cole K;Everitt R;Morgan M;Finney J;Kearns AM;Pichon B;Young B;Wilson DJ;Llewelyn MJ;Paul J;Peto TE;Crook DW;Walker AS;Golubchik T
全基因组测序（WGS）可能提供一个单一平台来提取预测生物体表型所需的所有信息。然而，它提供准确预测的能力尚未在特定生物的大型独立研究中得到证实。在这项研究中，我们的目的是开发一种基因型预测方法的抗菌药物的敏感性。对501株无关金黄色葡萄球菌分离株的全基因组进行测序，并使用BLASTn对组装的基因组进行查询，以获得一组已知的耐药决定簇（染色体突变和质粒上携带的基因）。结果与常规临床实验室进行的12种常用抗菌药物（青霉素、甲氧西林、红霉素、克林霉素、四环素、环丙沙星、万古霉素、甲氧苄啶、庆大霉素、夫西地酸、利福平和莫匹罗星）的表型药敏试验进行比较。我们通过重复药敏试验和手动检查序列来研究差异，并使用此信息优化耐药决定簇组和BLASTn算法。然后，我们在491个无关分离株的独立验证组中测试了优化工具的性能，通过自动肉汤稀释（BD Phoenix）和圆盘扩散一式两份获得表型结果。在验证集中，与标准敏感性测试方法相比，基因组预测方法的总体灵敏度和特异性分别为0.97（95%置信区间[95% CI]，0.95至0.98）和0.99（95% CI，0.99至1）。非常重大错误率为0.5%，重大错误率为0.7%。WGS的敏感性和特异性与常规药敏试验方法相同。WGS是一种很有前途的替代培养方法的抗性预测S。金黄色葡萄球菌和最终的其他主要细菌病原体。
Whole-genome sequencing (WGS) could potentially provide a single platform for extracting all the information required to predict an organism's phenotype. However, its ability to provide accurate predictions has not yet been demonstrated in large independent studies of specific organisms. In this study, we aimed to develop a genotypic prediction method for antimicrobial susceptibilities. The whole genomes of 501 unrelated Staphylococcus aureus isolates were sequenced, and the assembled genomes were interrogated using BLASTn for a panel of known resistance determinants (chromosomal mutations and genes carried on plasmids). Results were compared with phenotypic susceptibility testing for 12 commonly used antimicrobial agents (penicillin, methicillin, erythromycin, clindamycin, tetracycline, ciprofloxacin, vancomycin, trimethoprim, gentamicin, fusidic acid, rifampin, and mupirocin) performed by the routine clinical laboratory. We investigated discrepancies by repeat susceptibility testing and manual inspection of the sequences and used this information to optimize the resistance determinant panel and BLASTn algorithm. We then tested performance of the optimized tool in an independent validation set of 491 unrelated isolates, with phenotypic results obtained in duplicate by automated broth dilution (BD Phoenix) and disc diffusion. In the validation set, the overall sensitivity and specificity of the genomic prediction method were 0.97 (95% confidence interval [95% CI], 0.95 to 0.98) and 0.99 (95% CI, 0.99 to 1), respectively, compared to standard susceptibility testing methods. The very major error rate was 0.5%, and the major error rate was 0.7%. WGS was as sensitive and specific as routine antimicrobial susceptibility testing methods. WGS is a promising alternative to culture methods for resistance prediction in S. aureus and ultimately other major bacterial pathogens.