Abstract

Public health efforts to control the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic rely on accurate information on the spread of the disease in the community. Acute and surveillance testing has been primarily used to characterize the extent of the disease. However, obtaining a representative sample of the human population is challenging because of limited testing capacity and incomplete testing compliance. Wastewater-based epidemiology is an agnostic alternative to surveillance testing that provides an average sample from the population served by the treatment facility. We compare the performance of reverse transcription quantitative PCR (RT-qPCR) and reverse transcription digital droplet PCR (RT-dPCR) for analysis of SARS-CoV-2 RNA in a regional wastewater treatment facility in northern Indiana, USA from the earliest stages of the pandemic. 1-L grab samples of wastewater were clarified and concentrated. Nucleic acids were extracted from aliquots and analyzed in parallel using the two methods. Synthetic viral nucleic acids were used for method development and generation of add-in standard-curves. Both methods were highly sensitive in detecting SARS-CoV-2 in wastewater, with detection limits as low as 1 copy per 500 mL wastewater. RT-qPCR and RT-dPCR provided essentially identical coefficients of variation (s/overline{mathrm{x} } = 0.15) for triplicate measurements made on wastewater samples taken on 16 days. We also observed a sevenfold decrease in viral load from a grab sample that was frozen at – 80 °C for 92 days compared to results obtained without freezing. Freezing samples before analysis should be discouraged. Finally, we found that treatment with a glycine release buffer resulted in a fourfold inhibition in RT-qPCR signal; treatment with a glycine release buffer also should be discouraged. Despite their prevalence and convenience in wastewater analysis, glycine release and freezing samples severely and additively (~ tenfold) degraded recovery and detection of SARS-CoV-2.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.