Genome Sequencing of Sewage Detects Regionally Prevalent SARS-CoV-2 Variants
- Creators
- Crits-Christoph, Alexander
- Kantor, Rose S.
- Olm, Matthew R.
- Whitney, Oscar N.
- Al-Shayeb, Basem
- Lou, Yue Clare
- Flamholz, Avi
- Kennedy, Lauren C.
- Greenwald, Hannah
- Hinkle, Adrian
- Hetzel, Jonathan
- Spitzer, Sara
- Koble, Jeffery
- Tan, Asako
- Hyde, Fred
- Schroth, Gary
- Kuersten, Scott
- Banfield, Jillian F.
- Nelson, Kara L.
Abstract
Viral genome sequencing has guided our understanding of the spread and extent of genetic diversity of SARS-CoV-2 during the COVID-19 pandemic. SARS-CoV-2 viral genomes are usually sequenced from nasopharyngeal swabs of individual patients to track viral spread. Recently, RT-qPCR of municipal wastewater has been used to quantify the abundance of SARS-CoV-2 in several regions globally. However, metatranscriptomic sequencing of wastewater can be used to profile the viral genetic diversity across infected communities. Here, we sequenced RNA directly from sewage collected by municipal utility districts in the San Francisco Bay Area to generate complete and nearly complete SARS-CoV-2 genomes. The major consensus SARS-CoV-2 genotypes detected in the sewage were identical to clinical genomes from the region. Using a pipeline for single nucleotide variant calling in a metagenomic context, we characterized minor SARS-CoV-2 alleles in the wastewater and detected viral genotypes which were also found within clinical genomes throughout California. Observed wastewater variants were more similar to local California patient-derived genotypes than they were to those from other regions within the United States or globally. Additional variants detected in wastewater have only been identified in genomes from patients sampled outside California, indicating that wastewater sequencing can provide evidence for recent introductions of viral lineages before they are detected by local clinical sequencing. These results demonstrate that epidemiological surveillance through wastewater sequencing can aid in tracking exact viral strains in an epidemic context.
Additional Information
© 2021 Crits-Christoph et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license. Received 21 September 2020; Accepted 15 December 2020; Published 19 January 2021. We gratefully acknowledge the originating and submitting laboratories of SARS-CoV-2 genomes in the GISAID EpiCoV database (www.gisaid.org) that were used for our comparisons to clinical samples and in particular the Innovative Genomics Institute SARS-CoV-2 Sequencing Group for Alameda County genomes. We also gratefully acknowledge Vinson Fan for assistance with RT-qPCR and the laboratory of Robert Tjian for sharing materials. Funding was provided to K.L.N. and J.F.B. by a Rapid Research Response grant from the Innovative Genomics Institute (IGI) and a seed grant from the Center for Information Technology Research in the Interest of Society (CITRIS) at UC Berkeley. Data availability: Sequencing data for this project has been released under NCBI BioProject ID PRJNA661613. Processed data, reproducible code, and workflows for the analyses performed are available at https://github.com/alexcritschristoph/wastewater_sarscov2.Attached Files
Published - mBio-2021-Crits-Christoph-e02703-20.full.pdf
Submitted - 2020.09.13.20193805v1.full.pdf
Supplemental Material - inline-supplementary-material-1.xlsx
Supplemental Material - inline-supplementary-material-2.xlsx
Supplemental Material - inline-supplementary-material-3.xlsx
Supplemental Material - inline-supplementary-material-4.xlsx
Files
Name | Size | Download all |
---|---|---|
md5:687554d519e5fe4ea2051e448eec9769
|
1.7 MB | Preview Download |
md5:6041fcdf02fdd36386f822d68e2635e5
|
9.6 kB | Download |
md5:77a63c2f031c0ade8c679741af57fa79
|
31.1 kB | Download |
md5:6d3fe78de1943ac07479343a82cb8444
|
23.6 kB | Download |
md5:6045df288dc329df9f68d19a877a2d8e
|
87.6 kB | Download |
md5:de1c2f9beb65fc33fb8117ec7e98b5b4
|
870.4 kB | Preview Download |
Additional details
- PMCID
- PMC7845645
- Eprint ID
- 106736
- Resolver ID
- CaltechAUTHORS:20201119-100731171
- Innovative Genomics Institute (IGI)
- University of California, Berkeley
- Created
-
2020-11-19Created from EPrint's datestamp field
- Updated
-
2023-06-01Created from EPrint's last_modified field
- Caltech groups
- COVID-19