(searched for: doi:10.1101/2021.08.31.21262938)
ACS ES&T Water; https://doi.org/10.1021/acsestwater.1c00434
Nature Methods, Volume 19, pp 374-380; https://doi.org/10.1038/s41592-022-01444-z
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Water, Volume 14; https://doi.org/10.3390/w14081187
Wastewater reclamation and reuse have the potential to supplement water supplies, offering resiliency in times of drought and helping to meet increased water demands associated with population growth. Non-potable water reuse represents the largest potential reuse market. Yet, economic constraints for new water reuse infrastructure and safety concerns due to microbial water quality, especially viral pathogen exposure, limit the widespread implementation of water reuse. Cost-effective, real-time methods to measure or indicate the viral quality of recycled water would do much to instill greater confidence in the practice. This manuscript discusses advancements in monitoring and modeling viral health risks in the context of water reuse. First, we describe current wastewater reclamation processes and treatment technologies with an emphasis on virus removal. Second, we review technologies for the measurement of viruses, both culture- and molecular-based, along with their advantages and disadvantages. We outline promising viral surrogates and specific pathogenic viruses that can serve as indicators of viral risk for water reuse. We suggest metagenomic analyses for viral screening and flow cytometry for quantification of virus-like particles as new approaches to complement more traditional methods. Third, we describe modeling to assess health risks through quantitative microbial risk assessments (QMRAs), the most common strategy to couple data on virus concentrations with human exposure scenarios. We then explore the potential of artificial neural networks (ANNs) to incorporate suites of data from wastewater treatment processes, water quality parameters, and viral surrogates. We recommend ANNs as a means to utilize existing water quality data, alongside new complementary measures of viral quality, to achieve cost-effective strategies to assess risks associated with infectious human viruses in recycled water. Given the review, we conclude that technologies will be ready to identify and implement viral surrogates for health risk reduction in the next decade. Incorporating modeling with monitoring data would likely result in a more robust assessment of water reuse risk.
Viruses, Volume 14; https://doi.org/10.3390/v14030610
Since the beginning of the COVID-19 pandemic, the wastewater-based epidemiology (WBE) of SARS-CoV-2 has been used as a complementary indicator to follow up on the trends in the COVID-19 spread in Belgium and in many other countries. To further develop the use of WBE, a multiplex digital polymerase chain reaction (dPCR) assay was optimized, validated and applied for the measurement of emerging SARS-CoV-2 variants of concern (VOC) in influent wastewater (IWW) samples. Key mutations were targeted in the different VOC strains, including SΔ69/70 deletion, N501Y, SΔ241 and SΔ157. The presented bioanalytical method was able to distinguish between SARS-CoV-2 RNA originating from the wild-type and B.1.1.7, B.1.351 and B.1.617.2 variants. The dPCR assay proved to be sensitive enough to detect low concentrations of SARS-CoV-2 RNA in IWW since the limit of detection of the different targets ranged between 0.3 and 2.9 copies/µL. This developed WBE approach was applied to IWW samples originating from different Belgian locations and was able to monitor spatio-temporal changes in the presence of targeted VOC strains in the investigated communities. The present dPCR assay developments were realized to bring added-value to the current national WBE of COVID-19 by also having the spatio-temporal proportions of the VoC in presence in the wastewaters.
Published: 1 February 2022
Wastewater-based epidemiology has emerged as a promising tool to monitor pathogens in a population, particularly when clinical diagnostic capacities become overwhelmed. During the ongoing COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), several jurisdictions have tracked viral concentrations in wastewater to inform public health authorities. While some studies have also sequenced SARS-CoV-2 genomes from wastewater, there have been relatively few direct comparisons between viral genetic diversity in wastewater and matched clinical samples from the same region and time period. Here we report sequencing and inference of SARS-CoV-2 mutations and variant lineages (including variants of concern) in 936 wastewater samples and thousands of matched clinical sequences collected between March 2020 and July 2021 in the cities of Montreal, Quebec City, and Laval, representing almost half the population of the Canadian province of Quebec. We benchmarked our sequencing and variant-calling methods on known viral genome sequences to establish thresholds for inferring variants in wastewater with confidence. We found that variant frequency estimates in wastewater and clinical samples are correlated over time in each city, with similar dates of first detection. Across all variant lineages, wastewater detection is more concordant with targeted outbreak sequencing than with semi-random clinical swab sampling. Most variants were first observed in clinical and outbreak data due to higher sequencing rate. However, wastewater sequencing is highly efficient, detecting more variants for a given sampling effort. This shows the potential for wastewater sequencing to provide useful public health data, especially at places or times when sufficient clinical sampling is infrequent or infeasible.
Published: 27 December 2021
Summary: As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing/sequencing capacity, which can also introduce biases. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here, we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We develop and deploy improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detect emerging variants of concern up to 14 days earlier in wastewater samples, and identify multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.