摘要
      近年来，宏基因组测序分析（mNGS）已被用作病原体诊断的一种替代方法，它独立于培养，能够识别所有潜在的抗生素耐药性基因（ARGs）。然而，目前的mNGS方法必须处理临床样本中少量的原核脱氧核糖核酸（DNA）和大量的宿主DNA，这显著降低了整体微生物检测分辨率。最近发布的纳米孔自适应采样（NAS）技术有助于在对每个分子进行测序时，将单个核苷酸立即映射到给定的参考。用户定义的阈值允许特定分子在物理上通过给定的测序纳米孔时，根据实时参考映射结果进行保留或排斥。我们开发了一种宏基因组学工作流程，用于对临床样本中的细菌病原体和ARGs进行超灵敏诊断，该工作流程基于高效的选择性“人类宿主耗竭”NAS测序、实时物种鉴定和物种特异性抗性基因预测。我们的方法在所有21个测序的临床支气管肺泡灌洗液（BALF）样本中（从样本到结果4.5小时）将微生物序列产量提高了至少8倍，并在物种水平上准确检测到ARG。在我们的方法中，宏基因组测序和实验室培养之间的物种水平阳性一致性百分比为100%（16/16），阴性一致性百分比是100%（5/5）。需要进一步的工作，以更有力地验证我们的方法，使其能够应用于其他感染类型。
Abstract
Metagenomic sequencing analysis (mNGS) has been implemented as an alternative approach for pathogen diagnosis in recent years, which is independent of cultivation and is able to identify all potential antibiotic resistance genes (ARGs). However, current mNGS methods have to deal with low amounts of prokaryotic deoxyribonucleic acid (DNA) and high amounts of host DNA in clinical samples, which significantly decrease the overall microbial detection resolution. The recently released nanopore adaptive sampling (NAS) technology facilitates immediate mapping of individual nucleotides to a given reference as each molecule is sequenced. User-defined thresholds allow for the retention or rejection of specific molecules, informed by the real-time reference mapping results, as they are physically passing through a given sequencing nanopore. We developed a metagenomics workflow for ultra-sensitive diagnosis of bacterial pathogens and ARGs from clinical samples, which is based on the efficient selective ‘human host depletion’ NAS sequencing, real-time species identification and species-specific resistance gene prediction. Our method increased the microbial sequence yield at least 8-fold in all 21 sequenced clinical Bronchoalveolar Lavage Fluid (BALF) samples (4.5 h from sample to result) and accurately detected the ARGs at species level. The species-level positive percent agreement between metagenomic sequencing and laboratory culturing was 100% (16/16) and negative percent agreement was 100% (5/5) in our approach. Further work is required for a more robust validation of our approach with large sample size to allow its application to other infection types.

https://academic.oup.com/bib/article-abstract/23/6/bbac453/6762743?login=false