Graf Erin H, Simmon Keith E, Tardif Keith D, Hymas Weston, Flygare Steven, Eilbeck Karen, Yandell Mark, Schlaberg Robert
University of Utah School of Medicine, Department of Pathology, Salt Lake City, Utah, USA.
University of Utah School of Medicine, Department of Biomedical Informatics, Salt Lake City, Utah, USA ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, Utah, USA.
J Clin Microbiol. 2016 Apr;54(4):1000-7. doi: 10.1128/JCM.03060-15. Epub 2016 Jan 27.
Current infectious disease molecular tests are largely pathogen specific, requiring test selection based on the patient's symptoms. For many syndromes caused by a large number of viral, bacterial, or fungal pathogens, such as respiratory tract infections, this necessitates large panels of tests and has limited yield. In contrast, next-generation sequencing-based metagenomics can be used for unbiased detection of any expected or unexpected pathogen. However, barriers for its diagnostic implementation include incomplete understanding of analytical performance and complexity of sequence data analysis. We compared detection of known respiratory virus-positive (n= 42) and unselected (n= 67) pediatric nasopharyngeal swabs using an RNA sequencing (RNA-seq)-based metagenomics approach and Taxonomer, an ultrarapid, interactive, web-based metagenomics data analysis tool, with an FDA-cleared respiratory virus panel (RVP; GenMark eSensor). Untargeted metagenomics detected 86% of known respiratory virus infections, and additional PCR testing confirmed RVP results for only 2 (33%) of the discordant samples. In unselected samples, untargeted metagenomics had excellent agreement with the RVP (93%). In addition, untargeted metagenomics detected an additional 12 viruses that were either not targeted by the RVP or missed due to highly divergent genome sequences. Normalized viral read counts for untargeted metagenomics correlated with viral burden determined by quantitative PCR and showed high intrarun and interrun reproducibility. Partial or full-length viral genome sequences were generated in 86% of RNA-seq-positive samples, allowing assessment of antiviral resistance, strain-level typing, and phylogenetic relatedness. Overall, untargeted metagenomics had high agreement with a sensitive RVP, detected viruses not targeted by the RVP, and yielded epidemiologically and clinically valuable sequence information.
目前的传染病分子检测在很大程度上是针对病原体的,需要根据患者症状选择检测项目。对于许多由大量病毒、细菌或真菌病原体引起的综合征,如呼吸道感染,这就需要进行大量检测项目,且检出率有限。相比之下,基于下一代测序的宏基因组学可用于无偏向地检测任何预期或意外的病原体。然而,其诊断应用的障碍包括对分析性能的不完全理解以及序列数据分析的复杂性。我们使用基于RNA测序(RNA-seq)的宏基因组学方法和Taxonomer(一种超快速、交互式的基于网络的宏基因组学数据分析工具),将已知呼吸道病毒阳性(n = 42)和未筛选(n = 67)的儿科鼻咽拭子检测结果与美国食品药品监督管理局(FDA)批准的呼吸道病毒检测试剂盒(RVP;GenMark eSensor)进行了比较。非靶向宏基因组学检测出了86%的已知呼吸道病毒感染,额外的PCR检测仅确认了2份(33%)不一致样本的RVP结果。在未筛选样本中,非靶向宏基因组学与RVP的一致性良好(93%)。此外,非靶向宏基因组学还检测出了另外12种病毒,这些病毒要么未被RVP靶向,要么由于基因组序列高度不同而被遗漏。非靶向宏基因组学的标准化病毒读数计数与定量PCR确定的病毒载量相关,并显示出高批内和批间重复性。在86%的RNA-seq阳性样本中生成了部分或全长病毒基因组序列,从而能够评估抗病毒耐药性、菌株水平分型以及系统发育相关性。总体而言,非靶向宏基因组学与敏感的RVP具有高度一致性,检测出了RVP未靶向的病毒,并产生了具有流行病学和临床价值的序列信息。