Department of Infectious Disease and Infection Control, Saitama Medical University, Saitama, Japan.
Center for Clinical Infectious Diseases and Research, Saitama Medical University, Saitama, Japan.
Front Cell Infect Microbiol. 2020 Jan 30;10:11. doi: 10.3389/fcimb.2020.00011. eCollection 2020.
Differentiation between mitis group streptococci (MGS) bacteria in routine laboratory tests has become important for obtaining accurate epidemiological information on the characteristics of MGS and understanding their clinical significance. The most reliable method of MGS species identification is multilocus sequence analysis (MLSA) with seven house-keeping genes; however, because this method is time-consuming, it is deemed unsuitable for use in most clinical laboratories. In this study, we established a scheme for identifying 12 species of MGS () using the MinION nanopore sequencer (Oxford Nanopore Technologies, Oxford, UK) with the taxonomic aligner "What's in My Pot?" (WIMP; Oxford Nanopore's cloud-based analysis platform) and Kraken2 pipeline with the custom database adjusted for MGS species identification. The identities of the species in reference genomes ( = 514), clinical isolates ( = 31), and reference strains ( = 4) were confirmed via MLSA. The nanopore simulation reads were generated from reference genomes, and the optimal cut-off values for MGS species identification were determined. For 31 clinical isolates ( = 8, = 17 and = 6) and 4 reference strains ( = 1, = 1, = 1, and = 1), a sequence library was constructed via a Rapid Barcoding Sequencing Kit for multiplex and real-time MinION sequencing. The optimal cut-off values for the identification of MGS species for analysis by WIMP and Kraken2 pipeline were determined. The workflow using Kraken2 pipeline with a custom database identified all 12 species of MGS, and WIMP identified 8 MGS bacteria except , and . The results obtained by MinION with WIMP and Kraken2 pipeline were consistent with the MGS species identified by MLSA analysis. The practical advantage of whole genome analysis using the MinION nanopore sequencer is that it can aid in MGS surveillance. We concluded that MinION sequencing with the taxonomic aligner enables accurate MGS species identification and could contribute to further epidemiological surveys.
在常规实验室检测中,鉴别口腔链球菌(MGS)细菌变得尤为重要,这有助于获得有关 MGS 特征的准确流行病学信息并了解其临床意义。MGS 种属鉴定最可靠的方法是使用 7 个看家基因的多位点序列分析(MLSA);然而,由于这种方法耗时,因此被认为不适合大多数临床实验室使用。在这项研究中,我们使用牛津纳米孔技术(Oxford Nanopore Technologies)的 MinION 纳米孔测序仪(英国牛津)和基于云端的分类器“WIMP”(What's in My Pot?,Oxford Nanopore 的云端分析平台)以及 Kraken2 管道(带有调整用于 MGS 种属鉴定的自定义数据库)建立了一种鉴定 12 种 MGS ( )的方案。通过 MLSA 确定了参考基因组( = 514 个)、临床分离株( = 31 个)和参考菌株( = 4 个)中各物种的身份。从参考基因组生成了纳米孔模拟读取,确定了用于 MGS 种属鉴定的最佳截止值。对于 31 个临床分离株( = 8 个 、 = 17 个 和 = 6 个)和 4 个参考菌株( = 1 个 、 = 1 个 、 = 1 个和 = 1 个),通过快速条形码测序试剂盒构建了一个用于多重实时 MinION 测序的文库。通过 WIMP 和 Kraken2 管道分析确定了最佳的 MGS 种属鉴定截止值。使用带有自定义数据库的 Kraken2 管道的工作流程鉴定了所有 12 种 MGS,而 WIMP 则鉴定了 8 种 MGS 细菌,除了 、 和 。使用 WIMP 和 Kraken2 管道的 MinION 测序结果与 MLSA 分析鉴定的 MGS 物种一致。使用 MinION 纳米孔测序仪进行全基因组分析的实际优势在于可以辅助 MGS 监测。我们得出结论,使用分类器的 MinION 测序能够准确鉴定 MGS 种属,并有助于进一步的流行病学调查。