Variant abundance estimation for SARS-CoV-2 in wastewater using RNA-Seq quantification.
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ABSTRACT:
SUBMITTER: Baaijens JA
PROVIDER: S-EPMC8423229 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Effectively monitoring the spread of SARS-CoV-2 mutants is essential to efforts to counter the ongoing pandemic. Predicting lineage abundance from wastewater, however, is technically challenging. We show that by sequencing SARS-CoV-2 RNA in wastewater and applying algorithms initially used for transcriptome quantification, we can estimate lineage abundance in wastewater samples. We find high variability in signal among individual samples, but the overall trends match those 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 mutant prevalence in situations where clinical sequencing is unavailable.
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Project description:BackgroundThe genomes of SARS-CoV-2 are classified into variants, some of which are monitored as variants of concern (e.g. the Delta variant B.1.617.2 or Omicron variant B.1.1.529). Proportions of these variants circulating in a human population are typically estimated by large-scale sequencing of individual patient samples. Sequencing a mixture of SARS-CoV-2 RNA molecules from wastewater provides a cost-effective alternative, but requires methods for estimating variant proportions in a mixed sample.ResultsWe propose a new method based on a probabilistic model of sequencing reads, capturing sequence diversity present within individual variants, as well as sequencing errors. The algorithm is implemented in an open source Python program called VirPool. We evaluate the accuracy of VirPool on several simulated and real sequencing data sets from both Illumina and nanopore sequencing platforms, including wastewater samples from Austria and France monitoring the onset of the Alpha variant.ConclusionsVirPool is a versatile tool for wastewater and other mixed-sample analysis that can handle both short- and long-read sequencing data. Our approach does not require pre-selection of characteristic mutations for variant profiles, it is able to use the entire length of reads instead of just the most informative positions, and can also capture haplotype dependencies within a single read.
| S-EPMC9761630 | biostudies-literature
Project description:SARS-CoV-2 RNA in wastewater settled solids is associated with COVID-19 incidence in sewersheds and therefore, there is a strong interest in using these measurements to augment traditional disease surveillance methods. A wastewater surveillance program should provide rapid turn around for sample measurements (ideally within 24 hours), but storage of samples is necessary for a variety of reasons including biobanking. Here we investigate how storage of wastewater solids at 4 °C, -20 °C, and -80 °C affects measured concentrations of SARS-CoV-2 RNA. We find that short term (7 or 8 d) storage of raw solids at 4 °C has little effect on measured concentrations of SARS-CoV-2 RNA, whereas longer term storage at 4 °C (35-122 d) or freezing reduces measurements by 60%, on average. We show that normalizing SARS-CoV-2 RNA concentrations by concentrations of pepper mild mottle virus (PMMoV) RNA, an endogenous wastewater virus, can correct for changes during storage as storage can have a similar effect on PMMoV RNA as on SARS-CoV-2 RNA. The reductions in SARS-CoV-2 RNA in solids during freeze thaws is less than those reported for the same target in liquid influent by several authors.
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Project description:Wastewater-based epidemiology has become an important tool for the surveillance of SARS-CoV-2 outbreaks. However, the detection of viruses in sewage is challenging and to date there is no standard method available which has been validated for the sensitive detection of SARS-CoV-2. In this paper, we describe a simple concentration method based on polyethylene glycol (PEG) precipitation, followed by RNA extraction and a one-step quantitative reverse transcription PCR (qRT-PCR) for viral detection in wastewater. PEG-based concentration of viruses is a simple procedure which is not limited by the availability of expensive equipment and has reduced risk of disruption to consumable supply chains. The concentration and RNA extraction steps enable 900-1500× concentration of wastewater samples and sufficiently eliminates the majority of organic matter, which could inhibit the subsequent qRT-PCR assay. Due to the high variation in the physico-chemical properties of wastewater samples, we recommend the use of process control viruses to determine the efficiency of each step. This procedure enables the concentration and the extraction the DNA/RNA of different viruses and hence can be used for the surveillance of different viral targets for the comprehensive assessment of viral diseases in a community.
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Project description:Since the start of the COVID-19 pandemic, different methods have been used to detect the presence of genetic material of SARS-CoV-2 in wastewater. The use of wastewater for SARS-CoV-2 RNA detection and quantification showed different problems, associated to the complexity of the matrix and the lack of standard methods used to analyze the presence of an enveloped virus, such as coronavirus. Different strategies for the concentration process were selected to carry out the detection and quantification of SARS-CoV-2 RNA in wastewater: (a) aluminum hydroxide adsorption-precipitation, (b) pre-treatment with glycine buffer and precipitation with polyethylene-glycol (PEG) and (c) ultrafiltration (Centricon). Our results showed that the reduction of organic matter, using the pre-treatment with glycine buffer before the concentration with Centricon or aluminum hydroxide adsorption-precipitation, improved the recovery percentage of the control virus, Mengovirus (MgV) (8.37% ± 5.88 n = 43; 6.97% ± 6.51 n = 20, respectively), and the detection of SARS-CoV-2 in comparison with the same methodology without a pre-treatment. For the concentration with Centricon, the use of 100 mL of wastewater, instead of 200 mL, increased the MgV recovery, and allowed a positive detection of SARS-CoV-2 with N1 and N2 targets. The quantity of SARS-CoV-2 RNA detected in wastewater did not show a direct correlation with the number of confirmed cases, but the study of its upwards or downwards trend over time enabled the detection of an increase of epidemiological data produced in September 2020, January 2021 and April 2021.
| S-EPMC9063616 | biostudies-literature
Project description:BackgroundThe accurate identification of SARS-CoV-2 (SC2) variants and estimation of their abundance in mixed population samples (e.g., air or wastewater) is imperative for successful surveillance of community level trends. Assessing the performance of SC2 variant composition estimators (VCEs) should improve our confidence in public health decision making. Here, we introduce a linear regression based VCE and compare its performance to four other VCEs: two re-purposed DNA sequence read classifiers (Kallisto and Kraken2), a maximum-likelihood based method (Lineage deComposition for Sars-Cov-2 pooled samples (LCS)), and a regression based method (Freyja).MethodsWe simulated DNA sequence datasets of known variant composition from both Illumina and Oxford Nanopore Technologies (ONT) platforms and assessed the performance of each VCE. We also evaluated VCEs performance using publicly available empirical wastewater samples collected for SC2 surveillance efforts. Bioinformatic analyses were performed with a custom NextFlow workflow (C-WAP, CFSAN Wastewater Analysis Pipeline). Relative root mean squared error (RRMSE) was used as a measure of performance with respect to the known abundance and concordance correlation coefficient (CCC) was used to measure agreement between pairs of estimators.ResultsBased on our results from simulated data, Kallisto was the most accurate estimator as it had the lowest RRMSE, followed by Freyja. Kallisto and Freyja had the most similar predictions, reflected by the highest CCC metrics. We also found that accuracy was platform and amplicon panel dependent. For example, the accuracy of Freyja was significantly higher with Illumina data compared to ONT data; performance of Kallisto was best with ARTICv4. However, when analyzing empirical data there was poor agreement among methods and variations in the number of variants detected (e.g., Freyja ARTICv4 had a mean of 2.2 variants while Kallisto ARTICv4 had a mean of 10.1 variants).ConclusionThis work provides an understanding of the differences in performance of a number of VCEs and how accurate they are in capturing the relative abundance of SC2 variants within a mixed sample (e.g., wastewater). Such information should help officials gauge the confidence they can have in such data for informing public health decisions.
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Project description:In the Fall of 2020, university campuses in the United States resumed on-campus instruction and implemented wastewater monitoring for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While quantitative polymerase chain reaction (qPCR) tests were deployed successfully to detect viral RNA in wastewater across campuses, the feasibility of detecting viral variants from a residential building like a dormitory was unclear. Here, we demonstrate that wastewater surveillance from a dormitory with at least three infected students could lead to the identification of viral genomes with more than 95% coverage. Our results indicate that viral variant detection from wastewater is achievable at a dormitory and that coronavirus disease 2019 (COVID-19) wastewater surveillance programs will benefit from the implementation of viral whole genome sequencing at universities.
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Project description:Quantitative viral load assays have transformed our understanding of viral diseases. They hold similar potential to advance COVID-19 control and prevention, but SARS-CoV-2 viral load tests are not yet widely available. SARS-CoV-2 molecular diagnostic tests, which typically employ real-time RT-PCR, yield semiquantitative results only. Droplet digital RT-PCR (RT-ddPCR) offers an attractive platform for SARS-CoV-2 RNA quantification. Eight primer/probe sets originally developed for real-time RT-PCR-based SARS-CoV-2 diagnostic tests were evaluated for use in RT-ddPCR; three were identified as the most efficient, precise, and sensitive for RT-ddPCR-based SARS-CoV-2 RNA quantification. For example, the analytical efficiency for the E-Sarbeco primer/probe set was approximately 83%, whereas assay precision, measured as the coefficient of variation, was approximately 2% at 1000 input copies/reaction. Lower limits of quantification and detection for this primer/probe set were 18.6 and 4.4 input SARS-CoV-2 RNA copies/reaction, respectively. SARS-CoV-2 RNA viral loads in a convenience panel of 48 COVID-19-positive diagnostic specimens spanned a 6.2log10 range, confirming substantial viral load variation in vivo. RT-ddPCR-derived SARS-CoV-2 E gene copy numbers were further calibrated against cycle threshold values from a commercial real-time RT-PCR diagnostic platform. This log-linear relationship can be used to mathematically derive SARS-CoV-2 RNA copy numbers from cycle threshold values, allowing the wealth of available diagnostic test data to be harnessed to address foundational questions in SARS-CoV-2 biology.
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Project description:High-throughput RNA sequencing (RNA-seq) promises to revolutionize our understanding of genes and their role in human disease by characterizing the RNA content of tissues and cells. The realization of this promise, however, is conditional on the development of effective computational methods for the identification and quantification of transcripts from incomplete and noisy data. In this article, we introduce iReckon, a method for simultaneous determination of the isoforms and estimation of their abundances. Our probabilistic approach incorporates multiple biological and technical phenomena, including novel isoforms, intron retention, unspliced pre-mRNA, PCR amplification biases, and multimapped reads. iReckon utilizes regularized expectation-maximization to accurately estimate the abundances of known and novel isoforms. Our results on simulated and real data demonstrate a superior ability to discover novel isoforms with a significantly reduced number of false-positive predictions, and our abundance accuracy prediction outmatches that of other state-of-the-art tools. Furthermore, we have applied iReckon to two cancer transcriptome data sets, a triple-negative breast cancer patient sample and the MCF7 breast cancer cell line, and show that iReckon is able to reconstruct the complex splicing changes that were not previously identified. QT-PCR validations of the isoforms detected in the MCF7 cell line confirmed all of iReckon's predictions and also showed strong agreement (r(2) = 0.94) with the predicted abundances.
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Project description:Wastewater surveillance for pathogens using reverse transcription-polymerase chain reaction (RT-PCR) is an effective and resource-efficient tool for gathering community-level public health information, including the incidence of coronavirus disease-19 (COVID-19). Surveillance of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in wastewater can potentially provide an early warning signal of COVID-19 infections in a community. The capacity of the world's environmental microbiology and virology laboratories for SARS-CoV-2 RNA characterization in wastewater is increasing rapidly. However, there are no standardized protocols or harmonized quality assurance and quality control (QA/QC) procedures for SARS-CoV-2 wastewater surveillance. This paper is a technical review of factors that can cause false-positive and false-negative errors in the surveillance of SARS-CoV-2 RNA in wastewater, culminating in recommended strategies that can be implemented to identify and mitigate some of these errors. Recommendations include stringent QA/QC measures, representative sampling approaches, effective virus concentration and efficient RNA extraction, PCR inhibition assessment, inclusion of sample processing controls, and considerations for RT-PCR assay selection and data interpretation. Clear data interpretation guidelines (e.g., determination of positive and negative samples) are critical, particularly when the incidence of SARS-CoV-2 in wastewater is low. Corrective and confirmatory actions must be in place for inconclusive results or results diverging from current trends (e.g., initial onset or reemergence of COVID-19 in a community). It is also prudent to perform interlaboratory comparisons to ensure results' reliability and interpretability for prospective and retrospective analyses. The strategies that are recommended in this review aim to improve SARS-CoV-2 characterization and detection for wastewater surveillance applications. A silver lining of the COVID-19 pandemic is that the efficacy of wastewater surveillance continues to be demonstrated during this global crisis. In the future, wastewater should also play an important role in the surveillance of a range of other communicable diseases.
| S-EPMC8386095 | biostudies-literature