Researchers develop a rapid, specific, and accurate RT-qPCR-based method to detect and distinguish between Omicron sublineages

In a recent study posted to the medRxiv* preprint server, researchers developed a novel, rapid and accurate quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR)-based method to detect Omicron sublineages BA.1/BA.1.1 and BA.2 in patient samples.

Study: Efficient Tracing of the SARS-CoV-2 Omicron Variants in Santa Barbara County Using a Rapid Quantitative Reverse Transcription PCR Assay. Image Credit: Naeblys/Shutterstock
Study: Efficient Tracing of the SARS-CoV-2 Omicron Variants in Santa Barbara County Using a Rapid Quantitative Reverse Transcription PCR Assay. Image Credit: Naeblys/Shutterstock

This news article was a review of a preliminary scientific report that had not undergone peer-review at the time of publication. Since its initial publication, the scientific report has now been peer reviewed and accepted for publication in a Scientific Journal. Links to the preliminary and peer-reviewed reports are available in the Sources section at the bottom of this article. View Sources

Background

First detected in December 2022, SARS-CoV-2 new variant of concern (VOC) Omicron was highly contagious. Due to Omicron's exceptionally high transmission rates, researchers anticipated the emergence of its several sublineages. As expected, despite the lower global prevalence, BA.2 caused over 50% of the global cases sequenced by the first week of March 2022.

Mutations in BA.2 spike (S) protein facilitated its immune evasion properties and improved transmissibility. It also escaped neutralization by efficacious monoclonal antibody therapies, such as Sotrovimab, which combated Omicron BA.1/BA.1.1 successfully. Since it is impossible to tailor prevention and treatment approaches for each patient, early detection and understanding of the prevalence of specific variants in the community is crucial.

Furthermore, similar diagnostic tests, including whole-genome sequencing (WGS), typically take weeks to months to be developed and implemented. Since they take time to identify and characterize severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, they are deemed inefficient in informing clinical and public health decisions. Also, as new SARS-CoV-2 variants continue to emerge, it is crucial to have tools ready to mitigate coronavirus disease 2019 (COVID-19) cases worldwide.

About the study

In the present study, researchers developed and validated a diagnostic assay that provided retrospective and current Omicron prevalence data in less than a week. The assay required minimal optimization, basic equipment, and reagents found in all molecular biology laboratories and minimal time (only four hours) to yield results. Moreover, it was inexpensive, scalable, and inherently adaptable for future use against yet to emerge SARS-CoV-2 variants.

The assay primers targeted two unique mutations in BA.1/BA.1.1, the deletion in position 211 (N211del) and the insertion in position 214 (L214 EPE) in the SARS-CoV-2 spike (S) glycoprotein to ensure PCR amplification only when the primer binds the complementary deoxyribonucleic acid (DNA) derived from the BA.1/BA.1.1 genome. Likewise, for the BA.2 subvariant, the assay primers targeted specific mutations T19I and L24/P25/P26 deletion in the S gene. Lastly, the team designed the SARS-CoV-2 Wuhan-Hu1 primer targeting the S gene region encoding amino acids 210 to 217 that recognized all SARS-CoV-2 variants of concern (VOCs), except Omicron BA.1/BA.1.1.

Study findings

The assay detected 169 cases of Omicron infections in 270 residual SARS-CoV-2 positive samples collected from Santa Barbara County (SBC) between December 2021 to February 2022. The qRT-PCR results showed that 164 and five cases were due to infections by Omicron BA.1/BA1.1 and BA.2 subvariants, respectively, in agreement with the WGS results. Furthermore, genetic and phylogenetic analyses confirmed the presence of three distinct clusters of Omicron sublineages BA.1, BA.1.1, and BA.2 and mutations corresponding to them in the sequenced genomes.

The qRT-PCR detected Omicron BA.2 in the sixth week of 2022 when other diagnostic tests had indicated their continued circulating in the SBC population. The phylogenetic analyses of the current study also supported the local transmission of these variants.

The 29 retrospective residual SARS-CoV-2 positive nasopharyngeal swab samples served as the study control group. The WGS results have shown that these patient samples belonged to other VOCs, including Gamma, Alpha (B.1.1.7), and Delta (B.1.617.2 and AY), and variants, 20B, 20C, and Epsilon. The 12.6% of total samples belonged to these variants. Due to low viral load or improper sample preservation, 67 samples failed to amplify and could not be detected by RT-qPCR.

Conclusion

The study developed a qRT-PCR assay robustly and rapidly detected the prevalence of Omicron BA.1/BA.1.1 in the SBC community from December 2021 to January 2022. Parallelly performed WGS of patient samples confirmed the accuracy and specificity of the assay for BA.1/BA.1.1 detection.

The study data helped clinicians stop using monoclonal antibodies as therapeutics to manage active cases as soon as Omicron replaced Delta as the predominant SARS-CoV-2 variant. Additionally, the study findings triggered public health departments to enhance testing and contact tracing to mitigate community transmission. In other words, the study highlighted the significance of collaboration and open communication between researchers, clinicians at public hospitals, and public health officers.

As the COVID-19 pandemic continues, rapid deployment of such  SARS-CoV-2 variant detection tools will remain crucial for public safety.

This news article was a review of a preliminary scientific report that had not undergone peer-review at the time of publication. Since its initial publication, the scientific report has now been peer reviewed and accepted for publication in a Scientific Journal. Links to the preliminary and peer-reviewed reports are available in the Sources section at the bottom of this article. View Sources

Journal references:

Article Revisions

  • May 13 2023 - The preprint preliminary research paper that this article was based upon was accepted for publication in a peer-reviewed Scientific Journal. This article was edited accordingly to include a link to the final peer-reviewed paper, now shown in the sources section.
Neha Mathur

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Neha Mathur

Neha is a digital marketing professional based in Gurugram, India. She has a Master’s degree from the University of Rajasthan with a specialization in Biotechnology in 2008. She has experience in pre-clinical research as part of her research project in The Department of Toxicology at the prestigious Central Drug Research Institute (CDRI), Lucknow, India. She also holds a certification in C++ programming.

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