Wu Chin-Ting, Shropshire William C, Bhatti Micah M, Cantu Sherry, Glover Israel K, Anand Selvalakshmi Selvaraj, Liu Xiaojun, Kalia Awdhesh, Treangen Todd J, Chemaly Roy F, Spallone Amy, Shelburne Samuel
medRxiv. 2024 Aug 20:2024.08.19.24312266. doi: 10.1101/2024.08.19.24312266.
Routine use of whole genome sequencing (WGS) has been shown to help identify transmission of pathogens causing healthcare-associated infections (HAIs). However, the current gold standard of short-read, Illumina-based WGS is labor and time-intensive. In light of recent improvements in long-read Oxford Nanopore Technologies (ONT) sequencing, we sought to establish a low resource utilization approach capable of providing accurate WGS-based comparisons of HAI pathogens within a time frame allowing for infection prevention and control (IPC) interventions.
WGS was prospectively performed on antimicrobial-resistant pathogens at increased risk of potential healthcare transmission using the ONT MinION sequencer with R10.4.1 flow cells and Dorado basecalling algorithm. Potential transmission was assessed via Ridom SeqSphere+ for core genome multilocus sequence typing and MINTyper for reference-based core genome single nucleotide polymorphisms using previously published cut-off values. The accuracy of our ONT pipeline was determined relative to Illumina-based WGS data generated from the same genomic DNA sample.
Over a six-month period, 242 bacterial isolates from 216 patients were sequenced by a single operator. Compared to the Illumina gold-standard data, our ONT pipeline achieved a Q score of 60 for assembled genomes, even with a coverage rate of as low as 40X. The mean time from initiating DNA extraction to complete genetic analysis was 2 days (IQR 2-3.25 days). We identified five potential transmission clusters comprising 21 isolates (8.7% of all sequenced strains). Combining ONT WGS data with epidemiological data, >70% (15/21) of the isolates originated from patients with potential healthcare transmission links.
Via a stand-alone ONT pipeline, we detected potentially transmitted HAI pathogens rapidly and accurately, aligning closely with epidemiological data. Our low-resource method has the potential to assist in the efficient detection and deployment of preventative measures against HAI transmission.
常规使用全基因组测序（WGS）已被证明有助于识别引起医疗保健相关感染（HAIs）的病原体传播。然而，当前基于Illumina的短读长WGS的金标准既耗费人力又耗时。鉴于长读长牛津纳米孔技术（ONT）测序的最新进展，我们试图建立一种低资源利用方法，能够在允许进行感染预防和控制（IPC）干预的时间范围内，对HAIs病原体进行基于WGS的准确比较。
使用配备R10.4.1流动槽和Dorado碱基识别算法的ONT MinION测序仪，对具有潜在医疗传播风险增加的耐抗菌性病原体进行前瞻性WGS。通过Ridom SeqSphere+进行核心基因组多位点序列分型，并使用先前公布的临界值通过MINTyper进行基于参考的核心基因组单核苷酸多态性分析，以评估潜在传播。相对于从相同基因组DNA样本生成的基于Illumina的WGS数据，确定我们的ONT流程的准确性。
在六个月的时间里，由一名操作人员对来自216名患者的242株细菌分离株进行了测序。与Illumina金标准数据相比，即使覆盖率低至40X，我们的ONT流程对组装基因组的Q评分为60。从开始DNA提取到完成基因分析的平均时间为2天（四分位距2 - 3.25天）。我们确定了五个潜在传播簇，包含21株分离株（占所有测序菌株的8.7%）。将ONT WGS数据与流行病学数据相结合，超过70%（15/21）的分离株来自具有潜在医疗传播联系的患者。
通过独立的ONT流程，我们快速准确地检测到了潜在传播的HAIs病原体，与流行病学数据密切吻合。我们的低资源方法有可能有助于高效检测和部署针对HAIs传播的预防措施。
