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Identifying SARS-CoV-2 Variants of Concern through Saliva-Based RT-qPCR by Targeting Recurrent Mutation Sites.

ABSTRACT: SARS-CoV-2 variants of concern (VOCs) continue to pose a public health threat which necessitates a real-time monitoring strategy to complement whole genome sequencing. Thus, we investigated the efficacy of competitive probe RT-qPCR assays for six mutation sites identified in SARS-CoV-2 VOCs and, after validating the assays with synthetic RNA, performed these assays on positive saliva samples. When compared with whole genome sequence results, the SΔ69-70 and ORF1aΔ3675-3677 assays demonstrated 93.60 and 68.00% accuracy, respectively. The SNP assays (K417T, E484K, E484Q, L452R) demonstrated 99.20, 96.40, 99.60, and 96.80% accuracies, respectively. Lastly, we screened 345 positive saliva samples from 7 to 22 December 2021 using Omicron-specific mutation assays and were able to quickly identify rapid spread of Omicron in Upstate South Carolina. Our workflow demonstrates a novel approach for low-cost, real-time population screening of VOCs. IMPORTANCE SARS-CoV-2 variants of concern and their many sublineages can be characterized by mutations present within their genetic sequences. These mutations can provide selective advantages such as increased transmissibility and antibody evasion, which influences public health recommendations such as mask mandates, quarantine requirements, and treatment regimens. Our RT-qPCR workflow allows for strain identification of SARS-CoV-2 positive saliva samples by targeting common mutation sites shared between variants of concern and detecting single nucleotides present at the targeted location. This differential diagnostic system can quickly and effectively identify a wide array of SARS-CoV-2 strains, which can provide more informed public health surveillance strategies in the future.

SUBMITTER: Ham RE

PROVIDER: S-EPMC9241879 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:SARS-CoV-2 variants of concern, demonstrating higher infection rate and lower vaccine effectiveness as compared with the original virus, are important factors propelling the ongoing COVID-19 global outbreak. Therefore, prompt identification of these variants in the environment is essential for pandemic assessment and containment efforts. One well established tool for such viral monitoring is the use of wastewater systems. Here, we describe continuous monitoring of traces of SARS-CoV-2 viruses in the municipal wastewater of a large city in Israel. By observing morbidity fluctuations (during three main COVID-19 surges) occurring in parallel with Pfizer-BioNTech COVID-19 vaccine vaccination rate, compromised immunity was revealed in the current morbidity peak. RT-qPCR assays for the Original (D614G), Alpha and Beta variants had been previously developed and are being employed for wastewater surveillance. In the present study we developed a sensitive RT-qPCR assay designed for the rapid, direct detection of Gamma and Delta variants of concern. Sensitive quantification and detection of the various variants showed the prevalence of the original variant during the first morbidity peak. The dominance of the Alpha variant over the original variant correlated with the second morbidity peak. These variants decreased concurrently with an increase in vaccinations (Feb-March 2021) and the observed decrease in morbidity. The appearance and subsequent rise of the Delta variant became evident and corresponded to the third morbidity peak (June-August 2021). These results suggest a high vaccine neutralization efficiency towards the Alpha variant compared to its neutralization efficiency towards the Delta variant. Moreover, the third vaccination dose (booster) seems to regain neutralization efficiency towards the Delta variant. The developed assays and wastewater-based epidemiology are important tools aiding in morbidity surveillance and disclosing vaccination efforts and immunity dynamics in the community.

Dataset Information

Identifying SARS-CoV-2 Variants of Concern through Saliva-Based RT-qPCR by Targeting Recurrent Mutation Sites.

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