Gordon N C, Price J R, Cole K, Everitt R, Morgan M, Finney J, Kearns A M, Pichon B, Young B, Wilson D J, Llewelyn M J, Paul J, Peto T E A, Crook D W, Walker A S, Golubchik T
NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, United Kingdom.
J Clin Microbiol. 2014 Apr;52(4):1182-91. doi: 10.1128/JCM.03117-13. Epub 2014 Feb 5.
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.
全基因组测序（WGS）有可能提供一个单一平台，用于提取预测生物体表型所需的所有信息。然而，在针对特定生物体的大型独立研究中，其提供准确预测的能力尚未得到证实。在本研究中，我们旨在开发一种用于抗菌药物敏感性的基因型预测方法。对501株不相关的金黄色葡萄球菌分离株的全基因组进行了测序，并使用BLASTn对一组已知的耐药决定因素（染色体突变和质粒携带的基因）对组装好的基因组进行查询。将结果与常规临床实验室对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、利福平和平阳霉素）进行的表型药敏试验进行比较。我们通过重复药敏试验和对序列的人工检查来调查差异，并利用这些信息优化耐药决定因素组和BLASTn算法。然后，我们在一个由491株不相关分离株组成的独立验证集中测试了优化工具的性能，通过自动肉汤稀释法（BD Phoenix）和纸片扩散法重复获得表型结果。在验证集中，与标准药敏试验方法相比，基因组预测方法的总体敏感性和特异性分别为0.97（95%置信区间[95%CI]，0.95至0.98）和0.99（95%CI，0.99至1）。极重大错误率为0.5%，重大错误率为0.7%。WGS与常规抗菌药敏试验方法一样敏感和特异。对于金黄色葡萄球菌以及最终其他主要细菌病原体的耐药性预测，WGS是一种很有前景的替代培养方法。
