Spatial and temporal distribution of SARS-CoV-2 diversity circulating in wastewater

Wastewater-based epidemiology (WBE) has proven to be an effective tool for epidemiological surveillance of SARS-CoV-2 during the current COVID-19 pandemic. Furthermore, combining WBE together with high-throughput sequencing techniques can be useful for the analysis of SARS-CoV-2 viral diversity present in a given sample. The present study focuses on the genomic analysis of SARS-CoV-2 in 76 sewage samples collected during the three epidemiological waves that occurred in Spain from 14 wastewater treatment plants distributed throughout the country. The results obtained demonstrate that the metagenomic analysis of SARS-CoV-2 in wastewater allows the detection of mutations that define the B.1.1.7 lineage and the ability of the technique to anticipate the detection of certain mutations before they are detected in clinical samples. The study proves the usefulness of sewage sequencing to track Variants of Concern that can complement clinical testing to help in decision-making and in the analysis of the evolution of the pandemic.

This study was supported by projects “VIRIDIANA” (AGL2017–82909/ AEI/FEDER, UE) and MCEC-WATER (PID2020–116789RB-C42/ AEI/FEDER, UE) funded by Spanish Ministry of Science, Innovation and Universities; CSIC (202070E101), Generalitat Valenciana (Covid_19-SCI), the European Union – NextGenerationEU (SGL2103034), the COVID-19 wastewater surveillance project (VATar COVID19), funded by the Spanish Ministry for the Ecological Transition and the Demographic Challenge and the Spanish Ministry of Health, the Instituto de Salud Carlos III (Ref COV20/00140), ERC TB-RECONNECT 101001038 funded by The European Union – NextGenerationEU and the Lagoon project (PROMETEO/2021/044). EC-F is recipient of a predoctoral contract from the MICINN, Call 2018 and AP-C was supported by a postdoctoral fellowship (APOSTD/21/292). We acknowledge NILSA, FACSA, and MITECO for authorizing the sampling. The authors thank Agustin Garrido Fernández, and Andrea Lopez de Mota for their technical support.

Peer reviewed