Novel Multiplexed Amplicon-Based Sequencing to Quantify SARS-CoV-2 RNA from Wastewater
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ABSTRACT:
SUBMITTER: Ni G
PROVIDER: S-EPMC8276671 | biostudies-literature | 2021 Jul
REPOSITORIES: biostudies-literature
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Project description:Sequencing Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) from wastewater has become a useful tool in monitoring the spread of viral variants. Approaches to this task have been varied, relying on differing sequencing methods and computational analyses. We used a novel computation workflow based on amplicon sequencing of SARS-CoV-2 spike domains in order to track viral populations in wastewater. As part of this workflow, we developed a program, SAM Refiner, that has a variety of outputs, including novel variant reporting as well as functions designed to remove polymerase chain reaction (PCR) generated chimeric sequences. With these methods, we were able to track viral population dynamics over time. We report here on the emergence of two variants of concern, B.1.1.7 (Alpha) and P.1 (Gamma), and their displacement of the D614G B.1 variant in a Missouri sewershed.
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Project description:MotivationTracking SARS-CoV-2 variants through genomic sequencing has been an important part of the global response to the pandemic and remains a useful tool for surveillance of the virus. As well as whole-genome sequencing of clinical samples, this surveillance effort has been aided by amplicon sequencing of wastewater samples, which proved effective in real case studies. Because of its relevance to public healthcare decisions, testing and benchmarking wastewater sequencing analysis methods is also crucial, which necessitates a simulator. Although metagenomic simulators exist, none is fit for the purpose of simulating the metagenomes produced through amplicon sequencing of wastewater.ResultsOur new simulation tool, SWAMPy (Simulating SARS-CoV-2 Wastewater Amplicon Metagenomes with Python), is intended to provide realistic simulated SARS-CoV-2 wastewater sequencing datasets with which other programs that rely on this type of data can be evaluated and improved. Our tool is suitable for simulating Illumina short-read RT-PCR amplified metagenomes.Availability and implementationThe code for this project is available at https://github.com/goldman-gp-ebi/SWAMPy. It can be installed on any Unix-based operating system and is available under the GPL-v3 license.
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Project 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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Project description:As SARS-CoV-2 variants of concern emerged, the genome sequencing of SARS-CoV-2 strains became more important. In this study, SARS-CoV-2 was sequenced using amplicon-based genome sequencing with MinION. The primer panel used in this study consisted of only 11 primer panels and the size of the amplicons was approximately 3 kb. Full genome sequences were obtained with a hundred copies of the SARS-CoV-2 genome, and 92.33% and 75.39% of the genome sequences were obtained with 10 copies of the SARS-CoV-2 genome. The few differences in nucleotide sequences originated from mutations in laboratory cultures and/or mixed nucleotide sequences. The quantification of the SARS-CoV-2 genomic RNA was done using RT-ddPCR methods, and the level of LoD indicated that this sequencing method can be used for any RT-qPCR positive clinical sample. The sequencing results of the SARS-CoV-2 variants and clinical samples showed that our methods were very reliable. The genome sequences of five individual clinical samples were almost identical, and the analysis of the sequence variance showed that most of these nucleotide substitutions were observed in the genome sequences of the other clinical samples, indicating this amplicon-based whole-genome sequencing method can be used in various clinical fields.
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Project description:Wastewater-based epidemiology (WBE) is a promising approach for monitoring the spread of SARS-CoV-2 within communities. Although qPCR-based WBE is powerful in that it allows quick and highly sensitive detection of this virus, it can provide limited information about which variants are responsible for the overall increase or decrease of this virus in sewage, and this hinders accurate risk assessments. To resolve this problem, we developed a next generation sequencing (NGS)-based method to determine the identity and composition of individual SARS-CoV-2 variants in wastewater samples. Combination and optimization of targeted amplicon-sequencing and nested PCR allowed detection of each variant with sensitivity comparable to that of qPCR. In addition, by targeting the receptor binding domain (RBD) of the S protein, which has mutations informative for variant classification, we could discriminate most variants of concern (VOC) and even sublineages of Omicron (BA.1, BA.2, BA.4/5, BA.2.75, BQ.1.1 and XBB.1). Focusing on a limited domain has a benefit of decreasing the sequencing reads. We applied this method to wastewater samples collected from a wastewater treatment plant in Kyoto city throughout 13 months (from January 2021 to February 2022) and successfully identified lineages of wild-type, alpha, delta, omicron BA.1 and BA.2 as well as their compositions in the samples. The transition of these variants was in good agreement with the epidemic situation reported in Kyoto city during that period based on clinical testing. These data indicate that our NGS-based method is useful for detecting and tracking emerging variants of SARS-CoV-2 in sewage samples. Coupled with the advantages of WBE, this method has the potential to serve as an efficient and low cost means for the community risk assessment of SARS-CoV-2 infection.
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Project description:Over the course of the COVID-19 pandemic, variants of SARS-CoV-2 have emerged that are more contagious and more likely to cause breakthrough infections. Targeted amplicon sequencing approach is a gold standard for identification and analysis of variants. However, when applied to environmental samples such as wastewater, it remains unclear how sensitive this method is for detecting variant-associated mutations in environmental samples. Here we directly compare a targeted amplicon sequencing approach (using ARTIC v3; hereafter referred to as sequencing) with RT-ddPCR quantification for the detection of five mutations that are characteristic of variants of concern (VoCs) in wastewater samples. In total, 547 wastewater samples were analyzed using both methods in parallel. When we observed positive mutation detections by RT-ddPCR, 42.6% of the detection events were missed by sequencing, due to negative detection or the limited read coverage at the mutation position. Further, when sequencing reported negative or depth-limited mutation detections, 26.7% of those events were instead positive detections by RT-ddPCR, highlighting the relatively poor sensitivity of sequencing. No or weak associations were observed between quantitative measurements of target mutations determined by RT-ddPCR and sequencing. These findings caution the use of quantitative measurements of SARS-CoV-2 variants in wastewater samples determined solely based on sequencing.
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Project description:The novel coronavirus disease 2019 (COVID-19) pandemic poses a serious public health risk. In this report, we present a modified sequencing workflow using short tiling (280bp) amplicons library preparation method paired with Illumina's iSeq100 desktop sequencer. We demonstrated the utility of our workflow in identifying gapped reads that capture characteristics of subgenomic RNA junctions within our patient cohort. These analytical and library preparation approaches allow a versatile, small footprint and decentralized deployment that can facilitate comprehensive genetics characterizations during outbreaks. Based on the sequencing data, Taqman assays were designed to accurately capture the quantity of subgenomic ORF5 and ORF7a RNA from patient samples and demonstrated utility in tracking subgenomic titres in patient samples when combined with a standard COVID-19 qRT-PCR assay.
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Project description:Wastewater surveillance allows severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection levels to be tracked in a community. Here, we present a protocol to longitudinally quantify SARS-CoV-2 RNA in wastewater using quantitative reverse-transcription PCR (RT-qPCR) and pepper mild mottle virus (PMMoV) normalization. We describe steps for the pasteurization of wastewater samples, solids separation, supernatant filtration, viral precipitation and concentration, and RNA extraction. We then detail procedures for RT-qPCR, viral concentration extrapolation, PMMoV normalization, and longitudinal analysis. This protocol has the potential to be used for surveillance of other microorganisms. For complete details on the use and execution of this protocol, please refer to Sanchez Jimenez et al.1.
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Project description:Clinical and surveillance testing for the SARS-CoV-2 virus relies overwhelmingly on RT-qPCR-based diagnostics, yet several popular assays require 2-3 separate reactions or rely on detection of a single viral target, which adds significant time, cost, and risk of false-negative results. Furthermore, multiplexed RT-qPCR tests that detect at least two SARS-CoV-2 genes in a single reaction are typically not affordable for large scale clinical surveillance or adaptable to multiple PCR machines and plate layouts. We developed a RT-qPCR assay using the Luna Probe Universal One-Step RT-qPCR master mix with publicly available primers and probes to detect SARS-CoV-2 N gene, E gene, and human RNase P (LuNER) to address these shortcomings and meet the testing demands of a university campus and the local community. This cost-effective test is compatible with BioRad or Applied Biosystems qPCR machines, in 96 and 384-well formats, with or without sample pooling, and has a detection sensitivity suitable for both clinical reporting and wastewater surveillance efforts.
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Project description:Nationwide, wastewater-based monitoring was newly established in Scotland to track the levels of SARS-CoV-2 viral RNA shed into the sewage network, during the COVID-19 pandemic. We present a curated, reference dataset produced by this national programme, from May 2020 to February 2022. Viral levels were analysed by RT-qPCR assays of the N1 gene, on RNA extracted from wastewater sampled at 162 locations. Locations were sampled up to four times per week, typically once or twice per week, and in response to local needs. We report sampling site locations with geographical coordinates, the total population in the catchment for each site, and the information necessary for data normalisation, such as the incoming wastewater flow values and ammonia concentration, when these were available. The methodology for viral quantification and data analysis is briefly described, with links to detailed protocols online. These wastewater data are contributing to estimates of disease prevalence and the viral reproduction number (R) in Scotland and in the UK.
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