Variant abundance estimation for SARS-CoV-2 in wastewater using RNA-Seq quantification.

Autor: Baaijens JA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA., Zulli A; Department of Chemical and Environmental Engineering, Yale University, New Haven, CT, USA., Ott IM; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA., Petrone ME; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA., Alpert T; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA., Fauver JR; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA., Kalinich CC; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA., Vogels CBF; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA., Breban MI; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA., Duvallet C; Biobot Analytics, Inc., Cambridge, MA, USA., McElroy K; Biobot Analytics, Inc., Cambridge, MA, USA., Ghaeli N; Biobot Analytics, Inc., Cambridge, MA, USA., Imakaev M; Biobot Analytics, Inc., Cambridge, MA, USA., Mckenzie-Bennett M; Ginkgo Bioworks, Inc., Boston, MA, USA., Robison K; Ginkgo Bioworks, Inc., Boston, MA, USA., Plocik A; Ginkgo Bioworks, Inc., Boston, MA, USA., Schilling R; Ginkgo Bioworks, Inc., Boston, MA, USA., Pierson M; Ginkgo Bioworks, Inc., Boston, MA, USA., Littlefield R; Ginkgo Bioworks, Inc., Boston, MA, USA., Spencer M; Ginkgo Bioworks, Inc., Boston, MA, USA., Simen BB; Ginkgo Bioworks, Inc., Boston, MA, USA., Hanage WP; Center for Communicable Disease Dynamics and Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA., Grubaugh ND; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA.; Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA., Peccia J; Department of Chemical and Environmental Engineering, Yale University, New Haven, CT, USA., Baym M; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Jazyk: angličtina
Zdroj: MedRxiv : the preprint server for health sciences [medRxiv] 2021 Sep 02. Date of Electronic Publication: 2021 Sep 02.
DOI: 10.1101/2021.08.31.21262938
Abstrakt: Effectively monitoring the spread of SARS-CoV-2 variants is essential to efforts to counter the ongoing pandemic. Wastewater monitoring of SARS-CoV-2 RNA has proven an effective and efficient technique to approximate COVID-19 case rates in the population. Predicting variant abundances from wastewater, however, is technically challenging. Here we show that by sequencing SARS-CoV-2 RNA in wastewater and applying computational techniques initially used for RNA-Seq quantification, we can estimate the abundance of variants in wastewater samples. We show by sequencing samples from wastewater and clinical isolates in Connecticut U.S.A. between January and April 2021 that the temporal dynamics of variant strains broadly correspond. We further show that this technique can be used with other wastewater sequencing techniques by expanding to samples taken across the United States in a similar timeframe. We find high variability in signal among individual samples, and limited ability to detect the presence of variants with clinical frequencies <10%; nevertheless, the overall trends match what we observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in variant prevalence in situations where clinical sequencing is unavailable or impractical.
Databáze: MEDLINE
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