Deep Whole-Genome Sequencing to Detect Mixed Infection of Mycobacterium tuberculosis.
Deep Whole-Genome Sequencing to Detect Mixed Infection of Mycobacterium tuberculosis.
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深度全基因组测序检测结核分枝杆菌混合感染。
DOI：
10.1371/journal.pone.0159029
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Luo T
中科院分区：
文献类型：
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作者：
Gan M;Liu Q;Yang C;Gao Q;Luo T
多株结核分枝杆菌（MTB）混合感染与结核病（TB）治疗效果差相关。传统的基因分型方法已被用于检测结核分枝杆菌混合感染，但其灵敏度和分辨率有限。深度全基因组测序（WGS）已被证明是研究MTB群体异质性的高度敏感和区分性。在这里，我们开发了一种基于遗传学的方法来检测MTB混合感染使用WGS数据。我们从公共数据库中收集了全球782株MTB的WGS数据。通过将短读段映射到祖先MTB参考基因组，我们将个体菌株的同质和异质单核苷酸变异（SNV）称为。我们基于652个MTB菌株的68，639个同源SNV构建了MTB基因组数据库。如果通过将单个样品的SNV映射到PCR基因组数据库中鉴定出多个进化路径，则确定混合感染。模拟结果表明，当次要菌株的测序深度低至1× coverage时，以及当两个混合菌株的基因组距离低至16个SNV时，该方法可以特异性地检测混合感染。通过将我们的方法应用于所有782个样本，我们检测到47个混合感染，其中45个是由当地流行菌株引起的。结果表明，我们的方法是高度敏感和区分的MTB分离株的深层WGS数据识别混合感染。
Mixed infection by multiple Mycobacterium tuberculosis (MTB) strains is associated with poor treatment outcome of tuberculosis (TB). Traditional genotyping methods have been used to detect mixed infections of MTB, however, their sensitivity and resolution are limited. Deep whole-genome sequencing (WGS) has been proved highly sensitive and discriminative for studying population heterogeneity of MTB. Here, we developed a phylogenetic-based method to detect MTB mixed infections using WGS data. We collected published WGS data of 782 global MTB strains from public database. We called homogeneous and heterogeneous single nucleotide variations (SNVs) of individual strains by mapping short reads to the ancestral MTB reference genome. We constructed a phylogenomic database based on 68,639 homogeneous SNVs of 652 MTB strains. Mixed infections were determined if multiple evolutionary paths were identified by mapping the SNVs of individual samples to the phylogenomic database. By simulation, our method could specifically detect mixed infections when the sequencing depth of minor strains was as low as 1× coverage, and when the genomic distance of two mixed strains was as small as 16 SNVs. By applying our methods to all 782 samples, we detected 47 mixed infections and 45 of them were caused by locally endemic strains. The results indicate that our method is highly sensitive and discriminative for identifying mixed infections from deep WGS data of MTB isolates.