Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany.
Department of Mathematics and Computer Science, Free University of Berlin, Berlin, Germany.
mSystems. 2024 Mar 19;9(3):e0094523. doi: 10.1128/msystems.00945-23. Epub 2024 Feb 20.
Bacterial plasmids play a major role in the spread of antibiotic resistance genes. However, their characterization via DNA sequencing suffers from the low abundance of plasmid DNA in those samples. Although sample preparation methods can enrich the proportion of plasmid DNA before sequencing, these methods are expensive and laborious, and they might introduce a bias by enriching only for specific plasmid DNA sequences. Nanopore adaptive sampling could overcome these issues by rejecting uninteresting DNA molecules during the sequencing process. In this study, we assess the application of adaptive sampling for the enrichment of low-abundant plasmids in known bacterial isolates using two different adaptive sampling tools. We show that a significant enrichment can be achieved even on expired flow cells. By applying adaptive sampling, we also improve the quality of plasmid assemblies and reduce the sequencing time. However, our experiments also highlight issues with adaptive sampling if target and non-target sequences span similar regions.
Antimicrobial resistance causes millions of deaths every year. Mobile genetic elements like bacterial plasmids are key drivers for the dissemination of antimicrobial resistance genes. This makes the characterization of plasmids via DNA sequencing an important tool for clinical microbiologists. Since plasmids are often underrepresented in bacterial samples, plasmid sequencing can be challenging and laborious. To accelerate the sequencing process, we evaluate nanopore adaptive sampling as an method for the enrichment of low-abundant plasmids. Our results show the potential of this cost-efficient method for future plasmid research but also indicate issues that arise from using reference sequences.
细菌质粒在抗生素耐药基因的传播中起着重要作用。然而，通过 DNA 测序对其进行特征描述存在问题，因为在这些样本中质粒 DNA 的丰度较低。尽管样品制备方法可以在测序前富集质粒 DNA 的比例，但这些方法昂贵且繁琐，并且可能通过仅富集特定的质粒 DNA 序列而引入偏差。纳米孔自适应采样可以通过在测序过程中拒绝不感兴趣的 DNA 分子来克服这些问题。在这项研究中，我们使用两种不同的自适应采样工具评估了自适应采样在已知细菌分离物中富集低丰度质粒的应用。我们表明，即使在过期的流动池上也可以实现显著的富集。通过应用自适应采样，我们还提高了质粒组装的质量并减少了测序时间。然而，我们的实验还突出了如果目标和非目标序列跨越相似区域，自适应采样可能会出现问题。
抗生素耐药性每年导致数百万人死亡。细菌质粒等移动遗传元件是抗生素耐药基因传播的关键驱动因素。这使得通过 DNA 测序对质粒进行特征描述成为临床微生物学家的重要工具。由于质粒在细菌样本中通常代表性不足，因此质粒测序可能具有挑战性且繁琐。为了加速测序过程，我们评估了纳米孔自适应采样作为富集低丰度质粒的方法。我们的结果表明，这种具有成本效益的方法具有用于未来质粒研究的潜力，但也指出了使用参考序列时出现的问题。
