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Intensity of sample processing methods impacts wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes

Wastewater SARS-CoV-2 surveillance has been deployed since the beginning of the COVID-19 pandemic to monitor the dynamics in virus burden in local communities. Genomic surveillance of SARS-CoV-2 in wastewater, particularly efforts aimed at whole genome sequencing for variant tracking and identificat...

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Autores principales: Feng, Shuchen, Owens, Sarah M., Shrestha, Abhilasha, Poretsky, Rachel, Hartmann, Erica M., Wells, George
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Published by Elsevier B.V. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9984232/
https://www.ncbi.nlm.nih.gov/pubmed/36871720
http://dx.doi.org/10.1016/j.scitotenv.2023.162572
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author Feng, Shuchen
Owens, Sarah M.
Shrestha, Abhilasha
Poretsky, Rachel
Hartmann, Erica M.
Wells, George
author_facet Feng, Shuchen
Owens, Sarah M.
Shrestha, Abhilasha
Poretsky, Rachel
Hartmann, Erica M.
Wells, George
author_sort Feng, Shuchen
collection PubMed
description Wastewater SARS-CoV-2 surveillance has been deployed since the beginning of the COVID-19 pandemic to monitor the dynamics in virus burden in local communities. Genomic surveillance of SARS-CoV-2 in wastewater, particularly efforts aimed at whole genome sequencing for variant tracking and identification, are still challenging due to low target concentration, complex microbial and chemical background, and lack of robust nucleic acid recovery experimental procedures. The intrinsic sample limitations are inherent to wastewater and are thus unavoidable. Here, we use a statistical approach that couples correlation analyses to a random forest-based machine learning algorithm to evaluate potentially important factors associated with wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes, with a specific focus on the breadth of genome coverage. We collected 182 composite and grab wastewater samples from the Chicago area between November 2020 to October 2021. Samples were processed using a mixture of processing methods reflecting different homogenization intensities (HA + Zymo beads, HA + glass beads, and Nanotrap), and were sequenced using one of the two library preparation kits (the Illumina COVIDseq kit and the QIAseq DIRECT kit). Technical factors evaluated using statistical and machine learning approaches include sample types, certain sample intrinsic features, and processing and sequencing methods. The results suggested that sample processing methods could be a predominant factor affecting sequencing outcomes, and library preparation kits was considered a minor factor. A synthetic SARS-CoV-2 RNA spike-in experiment was performed to validate the impact from processing methods and suggested that the intensity of the processing methods could lead to different RNA fragmentation patterns, which could also explain the observed inconsistency between qPCR quantification and sequencing outcomes. Overall, extra attention should be paid to wastewater sample processing (i.e., concentration and homogenization) for sufficient and good quality SARS-CoV-2 RNA for downstream sequencing.
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spelling pubmed-99842322023-03-06 Intensity of sample processing methods impacts wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes Feng, Shuchen Owens, Sarah M. Shrestha, Abhilasha Poretsky, Rachel Hartmann, Erica M. Wells, George Sci Total Environ Article Wastewater SARS-CoV-2 surveillance has been deployed since the beginning of the COVID-19 pandemic to monitor the dynamics in virus burden in local communities. Genomic surveillance of SARS-CoV-2 in wastewater, particularly efforts aimed at whole genome sequencing for variant tracking and identification, are still challenging due to low target concentration, complex microbial and chemical background, and lack of robust nucleic acid recovery experimental procedures. The intrinsic sample limitations are inherent to wastewater and are thus unavoidable. Here, we use a statistical approach that couples correlation analyses to a random forest-based machine learning algorithm to evaluate potentially important factors associated with wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes, with a specific focus on the breadth of genome coverage. We collected 182 composite and grab wastewater samples from the Chicago area between November 2020 to October 2021. Samples were processed using a mixture of processing methods reflecting different homogenization intensities (HA + Zymo beads, HA + glass beads, and Nanotrap), and were sequenced using one of the two library preparation kits (the Illumina COVIDseq kit and the QIAseq DIRECT kit). Technical factors evaluated using statistical and machine learning approaches include sample types, certain sample intrinsic features, and processing and sequencing methods. The results suggested that sample processing methods could be a predominant factor affecting sequencing outcomes, and library preparation kits was considered a minor factor. A synthetic SARS-CoV-2 RNA spike-in experiment was performed to validate the impact from processing methods and suggested that the intensity of the processing methods could lead to different RNA fragmentation patterns, which could also explain the observed inconsistency between qPCR quantification and sequencing outcomes. Overall, extra attention should be paid to wastewater sample processing (i.e., concentration and homogenization) for sufficient and good quality SARS-CoV-2 RNA for downstream sequencing. Published by Elsevier B.V. 2023-06-10 2023-03-04 /pmc/articles/PMC9984232/ /pubmed/36871720 http://dx.doi.org/10.1016/j.scitotenv.2023.162572 Text en © 2023 Published by Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Feng, Shuchen
Owens, Sarah M.
Shrestha, Abhilasha
Poretsky, Rachel
Hartmann, Erica M.
Wells, George
Intensity of sample processing methods impacts wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes
title Intensity of sample processing methods impacts wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes
title_full Intensity of sample processing methods impacts wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes
title_fullStr Intensity of sample processing methods impacts wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes
title_full_unstemmed Intensity of sample processing methods impacts wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes
title_short Intensity of sample processing methods impacts wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes
title_sort intensity of sample processing methods impacts wastewater sars-cov-2 whole genome amplicon sequencing outcomes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9984232/
https://www.ncbi.nlm.nih.gov/pubmed/36871720
http://dx.doi.org/10.1016/j.scitotenv.2023.162572
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