A 2-Gene Host Signature for Improved Accuracy of COVID-19 Diagnosis Agnostic to Viral Variants.
A 2-Gene Host Signature for Improved Accuracy of COVID-19 Diagnosis Agnostic to Viral Variants.
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DOI：
10.1128/msystems.00671-22
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发表时间：
2023-02-23
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影响因子：
6.4
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中科院分区：
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SARS-CoV-2变种的持续出现是可能导致病毒PCR检测结果假阴性的几个因素之一。由于高病毒滴度样本的微量污染，此类测试也容易出现假阳性结果。宿主免疫反应标记物提供了感染的正交指示，与直接病毒检测相结合可以减轻这些担忧。在这里，我们利用来自 COVID-19、其他病毒性急性呼吸道疾病和非病毒性疾病 (n = 318) 患者的鼻咽拭子 RNA-seq 数据来开发支持向量机分类器，该分类器依赖于简约的 2 基因宿主签名来诊断 COVID-19。我们发现，最佳分类器包括与非病毒条件相比在 COVID-19 中强烈诱导的干扰素刺激基因（例如 IFI6），以及在其他病毒感染中更强烈诱导的第二种免疫反应基因（例如 GBP5）。在独立 RNA-seq 队列上进行评估时，IFI6+GBP5 分类器的受试者工作特征曲线下面积 (AUC) 大于 0.9 (n = 553)。我们进一步提供了概念验证证明，表明该分类器可以在临床相关的 RT-qPCR 检测中实施。最后，我们证明其在常见 SARS-CoV-2 变体中的性能表现强劲，并且不受交叉污染的影响，证明了其在提高 COVID-19 诊断准确性方面的实用性。重要性在这项工作中，我们研究上呼吸道基因表达，以开发和验证基于 2 基因宿主的 COVID-19 诊断分类器，然后展示其在临床实用 qPCR 检测中的实施。我们发现宿主分类器可用于减少假阴性结果，例如由于 SARS-CoV-2 变体在引物目标位点处存在突变，以及减少由于实验室交叉污染而导致的假阳性病毒 PCR 结果。这两种类型的错误都会带来严重后果，要么是无法识别的病毒传播，要么是不必要的隔离和接触者追踪。鉴于病毒变体的不断出现以及假阳性 PCR 检测的持续挑战，这项工作与正在进行的 COVID-19 大流行直接相关。它还表明基于泛呼吸道病毒宿主的诊断的可行性，这在医院和疗养院等聚集环境中具有价值，在这些环境中，未被识别的呼吸道病毒传播尤其值得关注。
The continued emergence of SARS-CoV-2 variants is one of several factors that may cause false-negative viral PCR test results. Such tests are also susceptible to false-positive results due to trace contamination from high viral titer samples. Host immune response markers provide an orthogonal indication of infection that can mitigate these concerns when combined with direct viral detection. Here, we leverage nasopharyngeal swab RNA-seq data from patients with COVID-19, other viral acute respiratory illnesses, and nonviral conditions (n = 318) to develop support vector machine classifiers that rely on a parsimonious 2-gene host signature to diagnose COVID-19. We find that optimal classifiers include an interferon-stimulated gene that is strongly induced in COVID-19 compared with nonviral conditions, such as IFI6, and a second immune-response gene that is more strongly induced in other viral infections, such as GBP5. The IFI6+GBP5 classifier achieves an area under the receiver operating characteristic curve (AUC) greater than 0.9 when evaluated on an independent RNA-seq cohort (n = 553). We further provide proof-of-concept demonstration that the classifier can be implemented in a clinically relevant RT-qPCR assay. Finally, we show that its performance is robust across common SARS-CoV-2 variants and is unaffected by cross-contamination, demonstrating its utility for improved accuracy of COVID-19 diagnostics. IMPORTANCE In this work, we study upper respiratory tract gene expression to develop and validate a 2-gene host-based COVID-19 diagnostic classifier and then demonstrate its implementation in a clinically practical qPCR assay. We find that the host classifier has utility for mitigating false-negative results, for example due to SARS-CoV-2 variants harboring mutations at primer target sites, and for mitigating false-positive viral PCR results due to laboratory cross-contamination. Both types of error carry serious consequences of either unrecognized viral transmission or unnecessary isolation and contact tracing. This work is directly relevant to the ongoing COVID-19 pandemic given the continued emergence of viral variants and the continued challenges of false-positive PCR assays. It also suggests the feasibility of pan-respiratory virus host-based diagnostics that would have value in congregate settings, such as hospitals and nursing homes, where unrecognized respiratory viral transmission is of particular concern.