Li Xuefei, Lin Jingxia, Hu Yongfei, Zhou Jiajian
Dermatology Hospital, Southern Medical University, Guangzhou, China.
Front Microbiol. 2020 Oct 22;11:578795. doi: 10.3389/fmicb.2020.578795. eCollection 2020.
Antimicrobial resistance (AMR) has emerged as one of the most urgent global threats to public health. Accurate detection of AMR phenotypes is critical for reducing the spread of AMR strains. Here, we developed PARMAP (Prediction of Antimicrobial Resistance by MAPping genetic alterations in pan-genome) to predict AMR phenotypes and to identify AMR-associated genetic alterations based on the pan-genome of bacteria by utilizing machine learning algorithms. When we applied PARMAP to 1,597 strains, it successfully predicted their AMR phenotypes based on a pan-genome analysis. Furthermore, it identified 328 genetic alterations in 23 known AMR genes and discovered many new AMR-associated genetic alterations in ciprofloxacin-resistant , and it clearly indicated the genetic heterogeneity of AMR genes in different subtypes of resistant . Additionally, PARMAP performed well in predicting the AMR phenotypes of and , indicating the robustness of the PARMAP framework. In conclusion, PARMAP not only precisely predicts the AMR of a population of strains of a given species but also uses whole-genome sequencing data to prioritize candidate AMR-associated genetic alterations based on their likelihood of contributing to AMR. Thus, we believe that PARMAP will accelerate investigations into AMR mechanisms in other human pathogens.
抗菌药物耐药性（AMR）已成为对公众健康最紧迫的全球威胁之一。准确检测AMR表型对于减少AMR菌株的传播至关重要。在此，我们开发了PARMAP（通过映射泛基因组中的基因改变预测抗菌药物耐药性），以利用机器学习算法基于细菌的泛基因组预测AMR表型并识别与AMR相关的基因改变。当我们将PARMAP应用于1597株菌株时，它基于泛基因组分析成功预测了它们的AMR表型。此外，它在23个已知的AMR基因中鉴定出328个基因改变，并在耐环丙沙星菌株中发现了许多新的与AMR相关的基因改变，并且清楚地表明了不同耐药亚型中AMR基因的遗传异质性。此外，PARMAP在预测[具体物种1]和[具体物种2]的AMR表型方面表现良好，表明PARMAP框架的稳健性。总之，PARMAP不仅能精确预测给定物种的一群菌株的AMR，还能利用全基因组测序数据根据候选AMR相关基因改变对AMR的贡献可能性对其进行优先级排序。因此，我们相信PARMAP将加速对其他人类病原体中AMR机制的研究。
