Your browser doesn't support javascript.
Detection of SARS-CoV-2 B.1.1.529 (Omicron) variant by SYBR Green-based RT-qPCR (preprint)
medrxiv; 2023.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2023.05.16.23289717
ABSTRACT

Background:

The COVID-19 pandemic is unceasingly spreading across the globe, and recently a highly transmissible Omicron SARS-CoV-2 variant (B.1.1.529) has been discovered in South Africa and Botswana. Rapid identification of this variant is essential for pandemic assessment and containment. However, variant identification is mainly being performed using expensive and time-consuming genomic sequencing. Methods and

results:

In this study we propose an alternative RT-qPCR approach for the detection of the Omicron BA.1 variant using a low-cost and rapid SYBR Green method. We have designed specific primers to confirm the deletion mutations in the spike (S {triangleup}143-145) and the nucleocapsid (N {triangleup}31-33) which are characteristics of this variant. For the evaluation, we used 120 clinical samples from patients with PCR-con[fi]rmed SARS-CoV-2 infections, and displaying an S-gene target failure (SGTF) when using TaqPath COVID-19 kit (Thermo Fisher Scientific, Waltham, USA) that included the ORF1ab, S, and N gene targets. Our results showed that all the 120 samples harbored S {triangleup}143-145 and N {triangleup}31-33, which was further confirmed by Whole genome sequencing (WGS) of 4 samples thereby validating our SYBR Green-based protocol.

Conclusions:

This protocol can be easily implemented to rapidly confirm the diagnosis of the Omicron BA.1 variant in COVID-19 patients and prevent its spread among populations, especially in countries with high prevalence of SGTF profile.
Subject(s)

Full text: Available Collection: Preprints Database: medRxiv Main subject: Severe Acute Respiratory Syndrome / COVID-19 Language: English Year: 2023 Document Type: Preprint

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: Preprints Database: medRxiv Main subject: Severe Acute Respiratory Syndrome / COVID-19 Language: English Year: 2023 Document Type: Preprint