摘要

由于基因组学革命，现在可以获得数千种特定菌株的全基因组序列，用于广泛的致病细菌。这种可用性使大数据信息学方法可用于研究抗菌药物耐药性（AMR）的传播和获得。在本期“临床微生物学杂志”中，M。Nguyen等人。 （J Clin Microbiol 57：e01260-18,2018，https：//doi.org/10.1128/JCM.01260-18）报告了基于全基因组测序数据的机器学习模型的结果，以预测最小抑制浓度（MIC） ）在美国收集了超过15年的5,728个非伤寒沙门氏菌基因组的抗生素。他们的主要发现表明MIC可以在+/- 1两倍稀释步骤（95-95％的置信区间）内平均准确度为95％，平均非常大的错误率为2.7％和平均主要误差率为0.1％。重要的是，这些模型预测MIC没有关于潜在基因内容的先验信息或菌株的抗性表型，使得有可能鉴定AMR决定簇并快速诊断并优先使用抗体直接从序列中获得。利用这些工具来诊断和限制抗药性赋予机制的传播可以帮助改善迫在眉睫的抗生素耐药性危机。

Thanks to the genomics revolution, thousands of strain-specific whole-genome sequences are now accessible for a wide range of pathogenic bacteria. This availability enables big-data informatics approaches to be used to study the spread and acquisition of antimicrobial resistance (AMR). In this issue of the Journal of Clinical Microbiology, M. Nguyen et al. (J Clin Microbiol 57:e01260-18, 2018, https://doi.org/10.1128/JCM.01260-18) report the results of their machine learning models based on whole-genome sequencing data to predict minimum inhibitory concentrations (MIC) of antibiotics for 5,728 nontyphoidal Salmonella genomes collected over 15 years in the USA. Their major finding demonstrates that MIC can be predicted with an average accuracy of 95% within +/-1 two-fold dilution step (confidence interval of 95-95%), an average very major error rate of 2.7% and an average major error rate of 0.1%. Importantly, these models predict MICs with no a priori information about the underlying gene content or resistance phenotypes of the strains enabling the possibility to identify AMR determinants and rapidly diagnose and prioritize antibiotic use directly from sequence. Employing such tools to diagnose and limit the spread of resistance conferring mechanism could help ameliorate the looming antibiotic resistance crisis.

https://www.ncbi.nlm.nih.gov/pubmed/30463894