Cassens Jacob, Kipp Evan J, Frank Lexi E, Larsen Peter A, Oliver Jonathan D
Division of Environmental Health Sciences, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America.
Department of Veterinary and Biomedical Sciences, University of Minnesota, Saint Paul, Minnesota, United States of America.
bioRxiv. 2025 Aug 27:2025.08.26.672273. doi: 10.1101/2025.08.26.672273.
Ticks pose substantial threats to public health. Blacklegged ticks () are responsible for most tick-borne diseases in the US, transmitting seven human pathogens. Molecular surveillance for tick-borne pathogens has been outpaced by their emergence, revealing a critical need to develop agnostic strategies that characterize emerging and putative pathogens. Oxford Nanopore Technology's nanopore adaptive sampling (NAS), an approach that selectively enriches or depletes for target genomes or genetic loci, provides an opportunity to generate real-time genomic insights into tick-borne pathogens. In the current study, we performed PCR and NAS on pooled infected and -uninfected ticks to evaluate the diagnostic capability of NAS. We found that NAS generates extensive datasets on tick-borne pathogens from individual ticks that aid in distinguishing true and false positive samples. Using a pooled approach consisting of whole genomic DNA from 168 total ticks multiplexed over seven sequencing experiments, our results indicated that NAS is extremely specific (0.97 [95% CI: 0.93, 1.00]) with moderate sensitivity (0.48 [95% CI: 0.41, 0.55]), suggesting a strong capacity to confirm when present at the expense of an elevated false-negative rate. We found that quality-based filtering of sequence data has a profound influence on diagnostic metrics, emphasizing the need to optimize pooling strategy, wet-lab procedures, and bioinformatic pipelines to enhance the sensitivity of NAS for detecting tick-borne pathogens.
蜱虫对公众健康构成重大威胁。美国大多数蜱传疾病是由黑腿蜱()引起的,它能传播七种人类病原体。蜱传病原体的分子监测已跟不上它们的出现速度,这表明迫切需要制定能鉴定新出现和假定病原体的通用策略。牛津纳米孔技术公司的纳米孔适应性采样(NAS)是一种能选择性富集或去除目标基因组或基因座的方法,为实时获取蜱传病原体的基因组信息提供了机会。在本研究中,我们对混合的感染和未感染蜱虫进行了PCR和NAS检测,以评估NAS的诊断能力。我们发现,NAS能从单个蜱虫中生成大量关于蜱传病原体的数据集,有助于区分真阳性和假阳性样本。通过一种将来自168只蜱虫的全基因组DNA混合用于七次测序实验的混合方法,我们的结果表明,NAS具有极高的特异性(0.97 [95%置信区间:0.93, 1.00]),但灵敏度中等(0.48 [95%置信区间:0.41, 0.55]),这表明它有很强的能力在病原体存在时进行确认,但代价是假阴性率升高。我们发现基于质量的序列数据过滤对诊断指标有深远影响,强调需要优化混合策略、湿实验室程序和生物信息学流程,以提高NAS检测蜱传病原体的灵敏度。