Monitoring SARS-CoV-2 variants alterations in Nice neighborhoods by wastewater nanopore sequencing

Background: Wastewater surveillance was proposed as an epidemiological tool to define the prevalence and evolution of the SARS-CoV-2 epidemics. However, most implemented SARS-CoV-2 wastewater surveillance projects were based on qPCR measurement of virus titers and did not address the mutational spectrum of SARS-CoV-2 circulating in the population. Methods: We have implemented a nanopore RNA sequencing monitoring system in the city of Nice (France, 550,000 inhabitants). Between October 2020 and March 2021, we monthly analyzed the SARS-CoV-2 variants in 113 wastewater samples collected in the main wastewater treatment plant and 20 neighborhoods. Findings: We initially detected the lineages predominant in Europe at the end of 2020 (B.1.160, B.1.177, B.1.367, B.1.474, and B.1.221). In January, a localized emergence of a variant (Spike:A522S) of the B.1.1.7 line-age occurred in one neighborhood. It rapidly spread and became dominant all over the city. Other variants of concern (B.1.351, P.1) were also detected in some neighborhoods, but at low frequency. Comparison with individual clinical samples collected during the same week showed that wastewater sequencing correctly identified the same lineages as those found in COVID-19 patients. Interpretation: Wastewater sequencing allowed to document the diversity of SARS-CoV-2 sequences within the different neighborhoods of the city of Nice. Our results illustrate how sequencing of sewage samples can be used to track pathogen sequence diversity in the current pandemics and in future infectious disease outbreaks. Translation: For the French translation of the abstract see Supplementary Materials section. (C) 2021 The Author(s). Published by Elsevier Ltd.

Automated summary

This study implemented a nanopore RNA sequencing monitoring system in Nice to analyze SARS-CoV-2 variants in wastewater samples, revealing the diversity of sequences in different neighborhoods. Wastewater sequencing correctly identified lineages found in clinical samples, demonstrating its potential for tracking pathogen sequence diversity in current and future outbreaks.