Combining Short- and Long-Read Sequencing Technologies to Identify SARS-CoV-2 Variants in Wastewater

During the COVID-19 pandemic, the monitoring of SARS-CoV-2 RNA in wastewater was used to track the evolution and emergence of variant lineages and gauge infection levels in the community, informing appropriate public health responses without relying solely on clinical testing. As more sublineages were discovered, it increased the difficulty in identifying distinct variants in a mixed population sample, particularly those without a known lineage. Here, we compare the sequencing technology from Illumina and from Oxford Nanopore Technologies, in order to determine their efficacy at detecting variants of differing abundance, using 248 wastewater samples from various Quebec and Ontario cities. Our study used two analytical approaches to identify the main variants in the samples: the presence of signature and marker mutations and the co-occurrence of signature mutations within the same amplicon. We observed that each sequencing method detected certain variants at different frequencies as each method preferentially detects mutations of distinct variants. Illumina sequencing detected more mutations with a predominant lineage that is in low abundance across the population or unknown for that time period, while Nanopore sequencing had a higher detection rate of mutations that are predominantly found in the high abundance B.1.1.7 (Alpha) lineage as well as a higher sequencing rate of co-occurring mutations in the same amplicon. We present a workflow that integrates short-read and long-read sequencing to improve the detection of SARS-CoV-2 variant lineages in mixed population samples, such as wastewater.

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Publication Details

Journal
Viruses
Published
2024-09-21
DOI
https://doi.org/10.3390/v16091495
Citations
4
Primary Topic
SARS-CoV-2 detection and testing
Type
article
Field-Weighted Citation Impact
1.56

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Combining Short- and Long-Read Sequencing Technologies to Identify SARS-CoV-2 Variants in Wastewater

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4 citations
Viruses
SARS-CoV-2 detection and testing
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article

Combining Short- and Long-Read Sequencing Technologies to Identify SARS-CoV-2 Variants in Wastewater

Thomas Maere, Mounia Hachad, Sally Lee, Alexandra Tsitouras, Sarah J. Reiling, José Héctor Gálvez, Arnaud N’Guessan, Ju‐Ling Liu, Dominic Frigon, Jiannis Ragoussis, Peter A. Vanrolleghem, Sukriye Celikkol-Aydin, Stephanie K. Loeb, Eyerusalem Goitom, B. Jesse Shapiro, Terrance P. Snutch, Fernando Sánchez-Quete, Robert Delatolla, Shu‐Huang Chen, Gabrielle Jayme, Sarah Dorner, Élisabeth Mercier
article en
4 citations

Abstract

During the COVID-19 pandemic, the monitoring of SARS-CoV-2 RNA in wastewater was used to track the evolution and emergence of variant lineages and gauge infection levels in the community, informing appropriate public health responses without relying solely on clinical testing. As more sublineages were discovered, it increased the difficulty in identifying distinct variants in a mixed population sample, particularly those without a known lineage. Here, we compare the sequencing technology from Illumina and from Oxford Nanopore Technologies, in order to determine their efficacy at detecting variants of differing abundance, using 248 wastewater samples from various Quebec and Ontario cities. Our study used two analytical approaches to identify the main variants in the samples: the presence of signature and marker mutations and the co-occurrence of signature mutations within the same amplicon. We observed that each sequencing method detected certain variants at different frequencies as each method preferentially detects mutations of distinct variants. Illumina sequencing detected more mutations with a predominant lineage that is in low abundance across the population or unknown for that time period, while Nanopore sequencing had a higher detection rate of mutations that are predominantly found in the high abundance B.1.1.7 (Alpha) lineage as well as a higher sequencing rate of co-occurring mutations in the same amplicon. We present a workflow that integrates short-read and long-read sequencing to improve the detection of SARS-CoV-2 variant lineages in mixed population samples, such as wastewater.

VirusesVol. 16(9)
University of British Columbia (CA), University of Ottawa (CA), Montreal Heart Institute (CA), McGill Genome Centre (CA), Canada's Michael Smith Genome Sciences Centre (CA), Université Laval (CA), Polytechnique Montréal (CA), McGill University (CA), Toronto Metropolitan University (CA), Université de Montréal (CA)
McGill University, Canada Foundation for Innovation
Clean water and sanitation
Openalex Percentile: Top 17%
SARS-CoV-2 detection and testing
1.56
DOIOpen Access PDFOpenAlex
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