Abstract
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.
Original language | English (US) |
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Article number | 162572 |
Journal | Science of the Total Environment |
Volume | 876 |
DOIs | |
State | Published - Jun 10 2023 |
Funding
This study was funded by the Walder Foundation Chicago Coronavirus Assessment Network (Chicago CAN). We thank Dr. Ira Heimler at the Illinois Department of Public Health (IDPH) for performing timely sequencing work using the Illumina COVIDseq kit for this study. We thank Stephanie Greenwald at the Environmental Sample Preparation and Sequencing Facility, Argonne National Laboratory for assistance with the QIAseq DIRECT SARS-CoV-2 kit sequencing work and Fragment Analyzer analysis for this study.
Keywords
- Amplicon sequencing
- Illumina COVIDseq
- QIAseq DIRECT
- RNA fragmentation
- Sample processing methods
- Wastewater SARS-CoV-2
ASJC Scopus subject areas
- Environmental Engineering
- Environmental Chemistry
- Waste Management and Disposal
- Pollution