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. 2023 May 15:873:162209.
doi: 10.1016/j.scitotenv.2023.162209. Epub 2023 Feb 14.

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

Ray W Izquierdo-Lara  1 , Leo Heijnen  2 , Bas B Oude Munnink  1 , Claudia M E Schapendonk  1 , Goffe Elsinga  2 , Jeroen Langeveld  3 , Johan Post  4 , Divyae K Prasad  1 , Christian Carrizosa  5 , Frederic Been  2 , Janko van Beek  1 , Remy Schilperoort  4 , Rianne Vriend  6 , Ewout Fanoy  6 , Evelien I T de Schepper  7 , Reina S Sikkema  1 , Richard Molenkamp  1 , Frank M Aarestrup  8 , Gertjan Medema  9 , Marion P G Koopmans  10 , Miranda de Graaf  11
Affiliations

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

Ray W Izquierdo-Lara et al. Sci Total Environ. .

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.

Keywords: Next generation sequencing; RT-ddPCR; SARS-CoV-2; Single nucleotide variant; Viral diversity; Wastewater genomic surveillance.

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Conflict of interest statement

Declaration of competing interest All the authors declare no competing interests. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Unlabelled Image
Graphical abstract
Fig. 1
Fig. 1
Geographic and temporal distribution of WW and clinical samples in Rotterdam. A) Map of Rotterdam and Bergschenhoek indicating the catchment areas. B) Temporal distribution of WW samples per location. Katendrecht, Pretorialaan and Pretorialaan-Zuidplein WWs form a cascade were an upstream catchment discharges into the next larger downstream catchment. Green, magenta and cyan dots represent WW samples that were either sequenced, evaluated by RT-ddPCR or both, respectively. C) Distribution over time of the number of sequenced clinical surveillance samples (blue line, left Y-axis) and number of SARS-CoV-2 positive cases per 100,000 individuals within Rotterdam (red line, right Y-axis). Both the number of obtained sequences and the number of positive cases over time are shown as 7-day rolling averages (date ± 3 days). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 2
Fig. 2
Relative abundance of SARS-CoV-2 major lineages in a single WW catchment area (Heemraadsplein). A) Heatmap showing the frequency of the unique signature mutations per lineage. B) Median of the relative frequencies of the unique signature mutations in WW samples of Heemraadsplein (red line) vs. relative abundance of the lineage in clinical samples from Rotterdam during the same time period (gray shaded area). Plots for all locations are shown in Supplementary Fig. S1. All available data per time-point were taken into account to perform the calculations. Clinical data is shown as 7-day rolling average (date ± 3 days). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) Relative abundance of SARS-CoV-2 major lineages in a single WW catchment area (Heemraadsplein). A) Heatmap showing the frequency of the unique signature mutations per lineage. B) Median of the relative frequencies of the unique signature mutations in WW samples of Heemraadsplein (red line) vs. relative abundance of the lineage in clinical samples from Rotterdam during the same time period (gray shaded area). Plots for all locations are shown in Supplementary Fig. S1. All available data per time-point were taken into account to perform the calculations. Clinical data is shown as 7-day rolling average (date ± 3 days). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 3
Fig. 3
Comparison of the pooled relative abundance of SARS-CoV-2 variants in Rotterdam's WW (red) versus relative occurrence in Rotterdam's clinical data (blue). The pooled relative abundance of each lineage for Rotterdam's WW was calculated by weighting the median of the unique signature mutations of a lineage in each WW location by the population size of that particular catchment area. All available data per time-point were taken into account to perform the calculations. Clinical data is shown as 7-day rolling average (date ± 3 days). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 4
Fig. 4
Monitoring of SARS-CoV-2 VOCs in Pretorialaan-Zuidplein WW through RT-ddPCR. A) Comparison of the estimated relative abundance of SARS-CoV-2 VOCs in WW determined by NGS, RT-ddPCR and clinical surveillance. Error bars for WW-NGS show the interquartile range of the relative abundance of the signature mutations of a specific VOC. The relative abundance estimation of the VOCs by RT-ddPCR depended on the detection of signature mutations that generate amino-acid changes in the Spike gene: the N501Y for the Alpha variant, T19R for Delta, N856K for the Omicron BA.1 variant. Error bars for WW RT-ddPCR were calculated assuming a Poisson distribution of RNA molecules in droplets. B) Spearman correlation between the relative abundances of Alpha and Delta VOCs in sewage estimated by NGS or RT-ddPCR.
Fig. 5
Fig. 5
Relative abundance of SARS-CoV-2 Delta sub-lineages circulating in the Netherlands and Rotterdam city, detected by clinical genomic surveillance (two top left panels) and by genome sequencing of samples from 9 WW catchment areas in Rotterdam. The barplots show the fraction of the Delta sub-lineages (AY.*) in Rotterdam.
Fig. 6
Fig. 6
Circulation of cryptic Spike mutations in Rotterdam WW catchment areas. A) Heatmap of the SNV frequency in the WW of Heemraadsplein, showing the most prevalent SNVs in the Spike gene for our dataset. B) Relative frequency of selected SNVs in the Spike gene in the 9 sewage locations (red lines) and in the clinical dataset (blue lines, bottom panels). Only SNVs with a Phred score > 30, a minimum coverage of 50× and at least 3 reads containing the alternative mutation (compared to the reference) were plotted. The SNV/SNP relative frequencies for clinical samples from Rotterdam and the Netherlands are plotted on a lower scale (up to 10 % and 2 %, respectively). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Supplementary Fig. S1
Supplementary Fig. S1
Median of the relative frequency of the unique signature mutations of specific lineages in WW samples of the 9 catchment areas in Rotterdam (red line) vs. relative abundance of the lineage in clinical samples from Rotterdam during the same time period (gray shaded area).
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