Promja Sutossarat, Puenpa Jiratchaya, Achakulvisut Titipat, Poovorawan Yong, Lee Su Yin, Athamanolap Pornpat, Lertanantawong Benchaporn
Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Salaya 73170, Nakhon Pathom, Thailand.
Center of Excellence in Clinical Virology, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand.
Anal Chem. 2023 Jan 12. doi: 10.1021/acs.analchem.2c05112.
Since the declaration of COVID-19 as a pandemic in early 2020, multiple variants of the severe acute respiratory syndrome-related coronavirus (SARS-CoV-2) have been detected. The emergence of multiple variants has raised concerns due to their impact on public health. Therefore, it is crucial to distinguish between different viral variants. Here, we developed a machine learning web-based application for SARS-CoV-2 variant identification via duplex real-time polymerase chain reaction (PCR) coupled with high-resolution melt (qPCR-HRM) analysis. As a proof-of-concept, we investigated the platform's ability to identify the Alpha, Delta, and wild-type strains using two sets of primers. The duplex qPCR-HRM could identify the two variants reliably in as low as 100 copies/μL. Finally, the platform was validated with 167 nasopharyngeal swab samples, which gave a sensitivity of 95.2%. This work demonstrates the potential for use as automated, cost-effective, and large-scale viral variant surveillance.
自2020年初新型冠状病毒肺炎（COVID-19）被宣布为大流行病以来，已检测到严重急性呼吸综合征相关冠状病毒（SARS-CoV-2）的多个变种。多个变种的出现因其对公众健康的影响而引发担忧。因此，区分不同的病毒变种至关重要。在此，我们开发了一种基于机器学习的网络应用程序，用于通过双重实时聚合酶链反应（PCR）结合高分辨率熔解曲线分析（qPCR-HRM）来鉴定SARS-CoV-2变种。作为概念验证，我们使用两组引物研究了该平台鉴定阿尔法、德尔塔和野生型毒株的能力。双重qPCR-HRM能够在低至100拷贝/微升的情况下可靠地鉴定这两个变种。最后，该平台用167份鼻咽拭子样本进行了验证，灵敏度为95.2%。这项工作证明了其作为自动化、经济高效且大规模病毒变种监测手段的潜力。
