Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA.
Nat Commun. 2018 Oct 17;9(1):4306. doi: 10.1038/s41467-018-06634-y.
Mycobacterium tuberculosis is a serious human pathogen threat exhibiting complex evolution of antimicrobial resistance (AMR). Accordingly, the many publicly available datasets describing its AMR characteristics demand disparate data-type analyses. Here, we develop a reference strain-agnostic computational platform that uses machine learning approaches, complemented by both genetic interaction analysis and 3D structural mutation-mapping, to identify signatures of AMR evolution to 13 antibiotics. This platform is applied to 1595 sequenced strains to yield four key results. First, a pan-genome analysis shows that M. tuberculosis is highly conserved with sequenced variation concentrated in PE/PPE/PGRS genes. Second, the platform corroborates 33 genes known to confer resistance and identifies 24 new genetic signatures of AMR. Third, 97 epistatic interactions across 10 resistance classes are revealed. Fourth, detailed structural analysis of these genes yields mechanistic bases for their selection. The platform can be used to study other human pathogens.
结核分枝杆菌是一种严重的人类病原体，表现出对抗微生物药物耐药性（AMR）的复杂进化。因此，许多公开的描述其 AMR 特征的数据集需要进行不同数据类型的分析。在这里，我们开发了一个参考菌株无关的计算平台，该平台使用机器学习方法，辅以遗传相互作用分析和 3D 结构突变映射，来识别 13 种抗生素的 AMR 进化特征。该平台应用于 1595 个测序菌株，得出了四个关键结果。首先，泛基因组分析表明结核分枝杆菌具有高度保守性，测序变异集中在 PE/PPE/PGRS 基因中。其次，该平台证实了 33 个已知赋予耐药性的基因，并确定了 24 个新的 AMR 遗传特征。第三，揭示了 10 个耐药类别中的 97 个上位性相互作用。第四，对这些基因的详细结构分析为它们的选择提供了机制基础。该平台可用于研究其他人类病原体。
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