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Droplet digital RT-PCR to detect SARS-CoV-2 signature mutations of variants of concern in wastewater.

ABSTRACT: Wastewater surveillance has shown to be a valuable and efficient tool to obtain information about the trends of COVID-19 in the community. Since the recent emergence of new variants, associated with increased transmissibility and/or antibody escape (variants of concern), there is an urgent need for methods that enable specific and timely detection and quantification of the occurrence of these variants in the community. In this study, we demonstrate the use of RT-ddPCR on wastewater samples for specific detection of mutation N501Y. This assay enabled simultaneous enumeration of lineage B.1.351 (containing the 501Y mutation) and Wild Type (WT, containing 501N) SARS-CoV-2 RNA. Detection of N501Y was possible in samples with mixtures of WT with low proportions of B.1.351 (0.5%) and could accurately determine the proportion of N501Y and WT in mixtures of SARS-CoV-2 RNA. The application to raw sewage samples from the cities of Amsterdam and Utrecht demonstrated that this method can be applied to wastewater samples. The emergence of N501Y in Amsterdam and Utrecht wastewater aligned with the emergence of B.1.1.7 as causative agent of COVID-19 in the Netherlands, indicating that RT-ddPCR of wastewater samples can be used to monitor the emergence of the N501Y mutation in the community. It also indicates that RT-ddPCR could be used for sensitive and accurate monitoring of current (like K417N, K417T, E484K, L452R) or future mutations present in SARS-CoV-2 variants of concern. Monitoring these mutations can be used to obtain insight in the introduction and spread of VOC and support public health decision-making regarding measures to limit viral spread or allocation of testing or vaccination.

SUBMITTER: Heijnen L

PROVIDER: S-EPMC8332926 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The ongoing pandemic caused by the emergence of SARS-CoV-2 has resulted in millions of deaths worldwide despite the various measures announced by the authorities. Wastewater-based epidemiology has the ability to provide a day-to-day estimation of the number of infected people in a fast and cost-effective manner. However, owing to the complex nature of wastewater, wastewater monitoring for viral genome copies is affected by the extensive viral fragmentation that takes place all the way to the sewage and the analytical lab. The aim of this study was to evaluate different methodologies for the concentration and extraction of viruses in wastewaters and to select and improve an option that maximizes the recovery of SARS-CoV-2. We compare 5 different concentration methods and 4 commercially available kits for the RNA extraction. To evaluate the performance and the recovery of these, SARS-CoV-2 isolated from patients was used as a spike control. Additionally, the presence of SARS-CoV-2 in all wastewater samples was determined using reverse transcription quantitative PCR (RT-qPCR) and reverse transcription droplet digital PCR (RT-ddPCR), targeting three genetic markers (N1, N2 and N3). Using spiked samples, recoveries were estimated 2.1–37.6% using different extraction kits and 0.1–2.1% using different concentration kits. It was found that a direct capture-based method, evaluated against a variety of concentration methods, is the best in terms of recovery, time and cost. Interestingly, we noticed a good agreement between the results provided by RT-qPCR and RT-ddPCR in terms of recovery. This evaluation can serve as a guide for laboratories establishing a protocol to perform wastewater monitoring of SARS-CoV-2. Overall, data presented here reinforces the validity of WBE for SARS-CoV-2 surveillance, uncovers potential caveats in the selection of concentration and extraction protocols and points towards optimal solutions to maximize its potential. Graphical abstract Image 1

Project description:Prevention of canine heartworm disease, caused by Dirofilaria immitis, relies on macrocyclic lactones for which drug resistance is now a concern. Although genetic polymorphisms have been associated with resistance in D. immitis populations, the mechanism is still not well understood. The lack of reliable in vitro assays to detect resistance is a limitation for confirming resistance. Ten single nucleotide polymorphisms (SNPs) were previously clinically validated in D. immitis resistant isolates, using the MiSeq platform. This technique although useful for research studies is expensive and does not facilitate rapid detection of these markers in small numbers of clinical samples. We developed a droplet digital PCR protocol for detecting SNPs correlating with ML resistance. Specific primers and hydrolysis probes encompassing the wildtype and mutant alleles were designed to amplify the SNP targets from genomic DNA of different D. immitis isolates. Allele frequencies were determined and the suitability of the ddPCR assay was assessed and compared with MiSeq data. The ddPCR assay accurately detected and quantified alternate nucleotides in two isolates of reference, the ML-susceptible Missouri (MO) and ML-resistant JYD-34, at the previously identified SNP positions. The presence of the SNPs was also determined in additional isolates with known or putative susceptible or resistant phenotypes. We observed SNP1 and SNP2 are more predictive markers and appear suitable for rapid detection and monitoring of drug resistance. Our results suggested that ddPCR could be employed to distinguish infection due to actual genetic resistance from infection with susceptible parasites and also for rapid detection of isolates not only with ML susceptible and resistant genotypes but also mixed genotypes that correspond to heterogeneous isolates containing a mixed population of ML susceptible and resistant parasites. DdPCR may be a useful tool for conducting surveys, or assessments of individual isolates, for genetic evidence of resistance or developing resistance.

Project description:Most individuals acutely infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exhibit mild symptoms. However, 10 to 20% of those infected develop long-term symptoms, referred to as post-coronavirus disease 2019 (COVID-19) condition (PCC). One hypothesis is that PCC might be exacerbated by viral persistence in tissue sanctuaries. Therefore, the accurate detection and quantification of SARS-CoV-2 are not only necessary for viral load monitoring but also crucial for detecting long-term viral persistence and determining whether viral replication is occurring in tissue reservoirs. In this study, the sensitivity and robustness of reverse transcription (RT)-droplet digital PCR (ddPCR) and RT-quantitative PCR (qPCR) techniques have been compared for the detection and quantification of SARS-CoV-2 genomic and subgenomic RNAs from oropharyngeal swabs taken from confirmed SARS-CoV-2-positive, SARS-CoV-2-exposed, and nonexposed individuals as well as from samples from mice infected with SARS-CoV-2. Our data demonstrated that both techniques presented equivalent results in the mid- and high-viral-load ranges. Additionally, RT-ddPCR was more sensitive than RT-qPCR in the low-viral-load range, allowing the accurate detection of positive results in individuals exposed to the virus. Overall, these data suggest that RT-ddPCR might be an alternative to RT-qPCR for detecting low viral loads in samples and for assessing viral persistence in samples from individuals with PCC. IMPORTANCE We developed one-step reverse transcription (RT)-droplet digital PCR (ddPCR) protocols to detect SARS-CoV-2 RNA and compared them to the gold-standard RT-quantitative PCR (RT-qPCR) method. RT-ddPCR was more sensitive than RT-qPCR in the low-viral-load range, while both techniques were equivalent in the mid- and high-viral-load ranges. Overall, these results suggest that RT-ddPCR might be a viable alternative to RT-qPCR when it comes to detecting low viral loads in samples, which is a highly relevant issue for determining viral persistence in as-yet-unknown tissue reservoirs in individuals suffering from post-COVID conditions or long COVID.

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Droplet digital RT-PCR to detect SARS-CoV-2 signature mutations of variants of concern in wastewater.

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