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VarLOCK - sequencing independent, rapid detection of SARS-CoV-2 variants of concern for point-of-care testing, qPCR pipelines and national wastewater surveillance (preprint)
medrxiv; 2022.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2022.01.06.21268555
ABSTRACT
The COVID-19 pandemic continues to pose a threat to the general population. The ongoing vaccination programs provide protection to individuals and facilitate the opening of society and a return to normality. However, emergent and existing SARS-CoV-2 variants capable of evading the immune system endanger the efficacy of the vaccination strategy. To preserve the efficacy of SARS-CoV-2 vaccination globally, aggressive and effective surveillance for known and emerging SARS-CoV-2 Variants of Concern (VOC) is required. Rapid and specific molecular diagnostics can provide speed and coverage advantages compared to genomic sequencing alone, benefitting the public health response and facilitating VOC containment. In this work, we expand the recently developed SARS-CoV-2 CRISPR-Cas detection technology (SHERLOCK) to allow rapid and sensitive discrimination of VOCs, that can be used at point of care and/or implemented in the pipelines of small or large testing facilities, and even determine proportion of VOCs in pooled population-level wastewater samples. This technology aims to complement the ongoing sequencing efforts to allow facile and, crucially, rapid identification of individuals infected with VOCs to help break infection chains. Here, we show the optimisation of our VarLOCK assays (Variant-specific SHERLOCK) for multiple specific mutations in the S gene of SARS-CoV-2 and validation with samples from the Cardiff University Testing Service. We also show the applicability of VarLOCK to national wastewater surveillance of SARS-CoV-2 variants. In addition, we show the rapid adaptability of the technique for new and emerging VOCs such as Omicron.

Full text: Available Collection: Preprints Database: medRxiv Language: English Year: 2022 Document Type: Preprint

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Full text: Available Collection: Preprints Database: medRxiv Language: English Year: 2022 Document Type: Preprint