Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, 412 96 Gothenburg, Sweden.
Department of Mathematical Sciences, Chalmers University of Technology and the University of Gothenburg, 412 96 Gothenburg, Sweden.
ACS Infect Dis. 2020 May 8;6(5):1076-1084. doi: 10.1021/acsinfecdis.9b00464. Epub 2020 Apr 28.
A variety of pathogenic bacteria can infect humans, and rapid species identification is crucial for the correct treatment. However, the identification process can often be time-consuming and depend on the cultivation of the bacterial pathogen(s). Here, we present a stand-alone, enzyme-free, optical DNA mapping assay capable of species identification by matching the intensity profiles of large DNA molecules to a database of fully assembled bacterial genomes (>10 000). The assay includes a new data analysis strategy as well as a general DNA extraction protocol for both Gram-negative and Gram-positive bacteria. We demonstrate that the assay is capable of identifying bacteria directly from uncultured clinical urine samples, as well as in mixtures, with the potential to be discriminative even at the subspecies level. We foresee that the assay has applications both within research laboratories and in clinical settings, where the time-consuming step of cultivation can be minimized or even completely avoided.
多种致病菌可感染人类,因此快速的物种鉴定对于正确治疗至关重要。然而,鉴定过程通常耗时且依赖于细菌病原体的培养。在这里,我们提出了一种独立的、无需酶的光学 DNA 图谱分析检测方法,通过将大 DNA 分子的强度图谱与包含已完全组装的细菌基因组(>10000 个)的数据库进行匹配,从而实现物种鉴定。该检测方法包括一种新的数据分析策略以及适用于革兰氏阴性菌和革兰氏阳性菌的通用 DNA 提取方案。我们证明,该检测方法能够直接从未经培养的临床尿液样本中识别细菌,并且即使在亚种水平也具有区分能力。我们预计该检测方法将在研究实验室和临床环境中具有应用价值,在这些环境中,培养这一耗时步骤可以最小化甚至完全避免。