Center for Applied Genomics and Precision Medicine, Duke University Medical Center, Durham, NC, USA; Division of Infectious Diseases, Duke University Medical Center, Durham, NC, USA; Durham VA Medical Center, Durham, NC, USA.
Center for Applied Genomics and Precision Medicine, Duke University Medical Center, Durham, NC, USA.
Lancet Infect Dis. 2021 Mar;21(3):396-404. doi: 10.1016/S1473-3099(20)30486-2. Epub 2020 Sep 24.
Early and accurate identification of individuals with viral infections is crucial for clinical management and public health interventions. We aimed to assess the ability of transcriptomic biomarkers to identify naturally acquired respiratory viral infection before typical symptoms are present.
In this index-cluster study, we prospectively recruited a cohort of undergraduate students (aged 18-25 years) at Duke University (Durham, NC, USA) over a period of 5 academic years. To identify index cases, we monitored students for the entire academic year, for the presence and severity of eight symptoms of respiratory tract infection using a daily web-based survey, with symptoms rated on a scale of 0-4. Index cases were defined as individuals who reported a 6-point increase in cumulative daily symptom score. Suspected index cases were visited by study staff to confirm the presence of reported symptoms of illness and to collect biospecimen samples. We then identified clusters of close contacts of index cases (ie, individuals who lived in close proximity to index cases, close friends, and partners) who were presumed to be at increased risk of developing symptomatic respiratory tract infection while under observation. We monitored each close contact for 5 days for symptoms and viral shedding and measured transcriptomic responses at each timepoint each day using a blood-based 36-gene RT-PCR assay.
Between Sept 1, 2009, and April 10, 2015, we enrolled 1465 participants. Of 264 index cases with respiratory tract infection symptoms, 150 (57%) had a viral cause confirmed by RT-PCR. Of their 555 close contacts, 106 (19%) developed symptomatic respiratory tract infection with a proven viral cause during the observation window, of whom 60 (57%) had the same virus as their associated index case. Nine viruses were detected in total. The transcriptomic assay accurately predicted viral infection at the time of maximum symptom severity (mean area under the receiver operating characteristic curve [AUROC] 0·94 [95% CI 0·92-0·96]), as well as at 1 day (0·87 [95% CI 0·84-0·90]), 2 days (0·85 [0·82-0·88]), and 3 days (0·74 [0·71-0·77]) before peak illness, when symptoms were minimal or absent and 22 (62%) of 35 individuals, 25 (69%) of 36 individuals, and 24 (82%) of 29 individuals, respectively, had no detectable viral shedding.
Transcriptional biomarkers accurately predict and diagnose infection across diverse viral causes and stages of disease and thus might prove useful for guiding the administration of early effective therapy, quarantine decisions, and other clinical and public health interventions in the setting of endemic and pandemic infectious diseases.
US Defense Advanced Research Projects Agency.
早期准确识别病毒感染个体对于临床管理和公共卫生干预至关重要。我们旨在评估转录组生物标志物在出现典型症状之前识别自然获得性呼吸道病毒感染的能力。
在这项指数-聚类研究中，我们前瞻性地招募了 5 个学年期间在杜克大学（美国北卡罗来纳州达勒姆）的大学生（18-25 岁）队列。为了识别指数病例，我们使用基于网络的每日调查监测学生整个学年中呼吸道感染的 8 种症状的出现和严重程度，症状评分范围为 0-4 分。指数病例定义为报告累积每日症状评分增加 6 分的个体。疑似指数病例由研究人员进行访视，以确认报告的疾病症状的存在并收集生物样本。然后，我们确定了指数病例的密切接触者（即居住在指数病例附近的个体、密友和伴侣）的集群，这些人在观察期间被认为有发生有症状的呼吸道感染的风险增加。我们监测每个密切接触者 5 天的症状和病毒脱落情况，并使用基于血液的 36 个基因 RT-PCR 检测法在每天的每个时间点测量转录组反应。
在 2009 年 9 月 1 日至 2015 年 4 月 10 日期间，我们招募了 1465 名参与者。在 264 名有呼吸道感染症状的指数病例中，150 名（57%）通过 RT-PCR 证实存在病毒病因。在他们的 555 名密切接触者中，有 106 名（19%）在观察窗口期间出现有症状的呼吸道感染，并经病毒检测证实，其中 60 名（57%）与相关指数病例感染相同的病毒。总共检测到 9 种病毒。该转录组检测法在症状最严重时（平均受试者工作特征曲线下面积[AUROC]0.94 [95%CI 0.92-0.96]），以及在 1 天（0.87 [95%CI 0.84-0.90]）、2 天（0.85 [0.82-0.88]）和 3 天（0.74 [0.71-0.77]）前准确预测病毒感染，此时症状轻微或不存在，分别有 35 名个体中的 22 名（62%）、36 名个体中的 25 名（69%）和 29 名个体中的 24 名（82%）未检测到病毒脱落。
转录组生物标志物可准确预测和诊断各种病毒病因和疾病阶段的感染，因此在地方性和大流行传染病流行期间，可能有助于指导早期有效治疗、检疫决策和其他临床和公共卫生干预措施的实施。
美国国防高级研究计划局。
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