摘要

基因组测序技术和全基因组关联研究（GWAS）的进展为微生物表型的分子基础提供了前所未有的见解，并能够鉴定真实人群中潜在的遗传变异。然而，由于缺乏可靠和准确的方法，基因组测序在细菌临床表型分析中的应用具有挑战性。在这里，我们报告了一种使用基因组测序数据预测微生物抗性模式的方法。我们使用GWAS从四个独立的群体中分析了1,680株肺炎链球菌分离株的全基因组序列，并鉴定了与抗生素基本类别抗性表型相关的可能的遗传变异热点。在假定抗性赋予的SNP可能与特定抗性基因结合的前提下，我们退步调查了热点位点并量化了SNP和/或基因的数量，如果累积会赋予对于否则易感应变。我们将这种方法称为“抵抗距离”。它可以用来鉴定使用基因组测序的细菌对细菌的完全抗生素抗性的蠕变。这种方法可用作未来基于测序的方法的基础，用于预测医院微生物学和公共卫生环境中细菌菌株的耐药谱。

Advances in genome sequencing technologies and genome-wide association studies (GWAS) have provided unprecedented insights into the molecular basis of microbial phenotypes and enabled the identification of the underlying genetic variants in real populations. However, utilization of genome sequencing in clinical phenotyping of bacteria is challenging due to the lack of reliable and accurate approaches. Here, we report a method for predicting microbial resistance patterns using genome sequencing data. We analyzed whole genome sequences of 1,680 Streptococcus pneumoniae isolates from four independent populations using GWAS and identified probable hotspots of genetic variation which correlate with phenotypes of resistance to essential classes of antibiotics. With the premise that accumulation of putative resistance-conferring SNPs, potentially in combination with specific resistance genes, precedes full resistance, we retrogressively surveyed the hotspot loci and quantified the number of SNPs and/or genes, which if accumulated would confer full resistance to an otherwise susceptible strain. We name this approach the ‘distance to resistance’. It can be used to identify the creep towards complete antibiotics resistance in bacteria using genome sequencing. This approach serves as a basis for the development of future sequencing-based methods for predicting resistance profiles of bacterial strains in hospital microbiology and public health settings.

https://www.nature.com/articles/srep42808