As the world continues to battle and contain COVID-19, the recent identification of SARS-CoV-2 variants with higher transmissibility and increased severity has made the development of convenient variant tracking methods essential. Currently, identified variants include the B.1.17 (Alpha) variant first identified in the United Kingdom and the B.1.617.2 (Delta) variant first detected in India.
Wastewater surveillance has emerged as a critical public health tool to safely and efficiently track the SARS-CoV-2 epidemic in a non-intrusive manner, providing complementary information that enables health authorities to acquire actionable community-level information. Most recently, viral fragments of SARS-CoV-2 were detected in housing estates in Singapore through a proactive wastewater surveillance program. This information, alongside surveillance testing, allowed Singapore's Ministry of Health (MOH) to swiftly respond, isolate and conduct swab tests as part of precautionary measures.
However, detecting variants through wastewater surveillance is less commonplace due to challenges in existing technology. Next-generation sequencing (NGS) for wastewater surveillance is time-consuming and expensive. They also lack the sensitivity required to detect low variant abundances in dilute and mixed wastewater samples due to inconsistent and/or low sequencing coverage.
The method developed by the researchers is uniquely tailored to address these challenges and expands the utility of wastewater surveillance beyond testing for SARS-CoV-2, towards tracking the spread of SARS-CoV-2 variants of concern. Dr Wei Lin Lee, Research Scientist at SMART AMR and first author on the paper added, "This is especially important in countries battling SARS-CoV-2 variants. Wastewater surveillance will help find out the true proportion and spread of the variants in the local communities. Our method is sensitive enough to detect variants in highly diluted SARS-CoV-2 concentrations typically seen in wastewater samples, and produces reliable results even for samples which contain multiple SARS-CoV-2 lineages."
Led by Associate Professor Janelle Thompson of NTU, and MIT Professor and SMART AMR Principal Investigator Eric Alm, the team's research "Quantitative SARS-CoV-2 Alpha variant B.1.1.7 Tracking in Wastewater by Allele-Specific RT-qPCR" has been published in Environmental Science & Technology Letters. The research explains the innovative, open-source molecular detection method based on allele-specific RT-qPCR that detects and quantifies the B.1.1.7 (Alpha) variant. The developed assay, tested and validated in wastewater samples across 19 communities in the US, is able to reliably detect and quantify low levels of the B.1.1.7 (Alpha) variant with low cross-reactivity, and at variant proportions down to 1% in a background of mixed SARS-CoV-2 viruses.
Targeting spike protein mutations that are highly predictive of the B.1.1.7 (Alpha) variant, the method can be implemented using commercially available RT-qPCR protocols. Unlike commercially available products that use proprietary primers and probes for wastewater surveillance, the paper details the open-source method and its development that can be freely used by other organizations and research institutes for their work on wastewater surveillance of SARS-CoV-2 and its variants.
MEDICA-tradefair.com; Source: Singapore-MIT Alliance for Research and Technology (SMART)
