Rise and fall of SARS-CoV-2 variants in Rotterdam: Comparison of wastewater and clinical surveillance

Highlights

• SARS-CoV-2 major VOCs/lineages can be tracked in wastewater (WW) over-time.

• Estimations of the relative abundance of VOCs in WW based on NGS and RT-ddPCR correlate.

• WW NGS can be used to detect and track mutations that were not detected by clinical surveillance.

• WW genomic surveillance can detect differences in viral diversity at a spatio-temporal level, even within the same city.

Abstract

Monitoring of SARS-CoV-2 in wastewater (WW) is a promising tool for epidemiological surveillance, correlating not only viral RNA levels with the infection dynamics within the population, but also to viral diversity. However, the complex mixture of viral lineages in WW samples makes tracking of specific variants or lineages circulating in the population a challenging task. We sequenced sewage samples of 9 WW-catchment areas within the city of Rotterdam, used specific signature mutations from individual SARS-CoV-2 lineages to estimate their relative abundances in WW and compared them against those observed in clinical genomic surveillance of infected individuals between September 2020 and December 2021. We showed that especially for dominant lineages, the median of the frequencies of signature mutations coincides with the occurrence of those lineages in Rotterdam's clinical genomic surveillance. This, along with digital droplet RT-PCR targeting signature mutations of specific variants of concern (VOCs), showed that several VOCs emerged, became dominant and were replaced by the next VOC in Rotterdam at different time points during the study. In addition, single nucleotide variant (SNV) analysis provided evidence that spatio-temporal clusters can also be discerned from WW samples. We were able to detect specific SNVs in sewage, including one resulting in the Q183H amino acid change in the Spike gene, that was not captured by clinical genomic surveillance. Our results highlight the potential use of WW samples for genomic surveillance, increasing the set of epidemiological tools to monitor SARS-CoV-2 diversity.