Inflammatix Inc., CA, 94085, Sunnyvale, USA.
Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, CA, 94305, USA.
Genome Med. 2023 Aug 28;15(1):64. doi: 10.1186/s13073-023-01216-0.
Viral acute respiratory illnesses (viral ARIs) contribute significantly to human morbidity and mortality worldwide, but their successful treatment requires timely diagnosis of viral etiology, which is complicated by overlap in clinical presentation with the non-viral ARIs. Multiple pandemics in the twenty-first century to date have further highlighted the unmet need for effective monitoring of clinically relevant emerging viruses. Recent studies have identified conserved host response to viral infections in the blood.
We hypothesize that a similarly conserved host response in nasal samples can be utilized for diagnosis and to rule out viral infection in symptomatic patients when current diagnostic tests are negative. Using a multi-cohort analysis framework, we analyzed 1555 nasal samples across 10 independent cohorts dividing them into training and validation.
Using six of the datasets for training, we identified 119 genes that are consistently differentially expressed in viral ARI patients (N = 236) compared to healthy controls (N = 146) and further down-selected 33 genes for classifier development. The resulting locked logistic regression-based classifier using the 33-mRNAs had AUC of 0.94 and 0.89 in the six training and four validation datasets, respectively. Furthermore, we found that although trained on healthy controls only, in the four validation datasets, the 33-mRNA classifier distinguished viral ARI from both healthy or non-viral ARI samples with > 80% specificity and sensitivity, irrespective of age, viral type, and viral load. Single-cell RNA-sequencing data showed that the 33-mRNA signature is dominated by macrophages and neutrophils in nasal samples.
This proof-of-concept signature has potential to be adapted as a clinical point-of-care test ('RespVerity') to improve the diagnosis of viral ARIs.
病毒急性呼吸道感染（viral ARIs）在全球范围内导致了大量的发病率和死亡率，但它们的成功治疗需要及时诊断病毒病因，这因与非病毒 ARIs 的临床表现重叠而变得复杂。迄今为止，21 世纪的多次大流行进一步凸显了有效监测临床上相关新兴病毒的未满足需求。最近的研究已经确定了血液中病毒感染的保守宿主反应。
我们假设，在症状性患者的当前诊断测试为阴性时，鼻样本中类似保守的宿主反应也可用于诊断和排除病毒感染。我们使用多队列分析框架，分析了 10 个独立队列中的 1555 个鼻样本，将它们分为训练集和验证集。
我们使用其中六个数据集进行训练，确定了 119 个在病毒 ARI 患者（N=236）与健康对照组（N=146）相比差异表达的基因，进一步筛选出 33 个基因用于分类器开发。使用这 33 个 mRNA 的基于锁定逻辑回归的分类器在六个训练数据集和四个验证数据集中的 AUC 分别为 0.94 和 0.89。此外，我们发现，尽管仅基于健康对照进行训练，在四个验证数据集中，33-mRNA 分类器能够区分病毒 ARI 与健康或非病毒 ARI 样本，特异性和敏感性均超过 80%，而与年龄、病毒类型和病毒载量无关。单细胞 RNA 测序数据显示，33-mRNA 特征主要由鼻样本中的巨噬细胞和中性粒细胞主导。
该概念验证签名具有作为临床即时护理测试（'RespVerity'）的潜力，以改善病毒 ARIs 的诊断。
