Monitoring SARS-CoV-2 RNA in Wastewater with RT-qPCR and Chip-Based RT-dPCR: Sewershed-Level Trends and Relationships to COVID-19

Metaviromic reveals the dynamics and diversity of the virosphere in wastewater samples from Natal, Brazil

The COVID-19 pandemic underscored the significance of omics technology and Wastewater-Based Epidemiology for epidemic preparedness. This study investigates the virosphere in wastewater samples from Natal (Brazil), aiming to understand its structure, relationships, and potential. Metaviromic analysis was used on DNA and RNA from weekly samples collected over a year (June/2021 to May/2022) from three wastewater treatment plants. The virosphere showed stability, particularly in viruses infecting microorganisms and plants. However, an alternation of representatives of viruses that infect animals has been observed. Among the most abundant viruses infecting microorganisms are genera associated with the bacterial genera Escherichia, Pseudomonas, and Caulobacte. Regarding the viruses infecting plants, Sobemovirus and Tobamovirus are the most abundant genera. Odontoglossum ringspot virus was identified as a possible RNA virus biomarker. Among DNA viruses infecting animals, genera Bocaparvovirus and Mastadenovirus are the most prevalent. Intriguingly, some Poxviridae family members were observed in the samples. Co-occurrence network analysis identified potential biomarkers like Volepox virus, Anatid herpesvirus 1, and Caviid herpesvirus 2. Among RNA viruses affecting animals, Mamastrovirus, Rotavirus, and Norovirus genera were the most abundant pathogens. Furthermore, members of the Coronaviridae family exhibited a high degree of centrality values in the co-occurrence network, even connecting with unclassified viruses. The study emphasizes the importance of research in understanding the roles of unclassified viruses. In addition, we observed an association between Coronaviridae reads, rainfall, and the number of reported COVID-19 cases. Our study highlights the diversity and complexity of the viral community in wastewater and the need for research to understand better the ecological roles unclassified viruses play. Such advances will significantly contribute to our preparedness and response to future viral threats. Furthermore, our study contributes to knowledge of virosphere dynamics, offering insights that can contribute to the direction of future public health policies and interventions.

Advancing Pathogen Identification: The Role of Digital PCR in Enhancing Diagnostic Power in Different Settings

Digital polymerase chain reaction (dPCR) has emerged as a groundbreaking technology in molecular biology and diagnostics, offering exceptional precision and sensitivity in nucleic acid detection and quantification. This review highlights the core principles and transformative potential of dPCR, particularly in infectious disease diagnostics and environmental surveillance. Emphasizing its evolution from traditional PCR, dPCR provides accurate absolute quantification of target nucleic acids through advanced partitioning techniques. The review addresses the significant impact of dPCR in sepsis diagnosis and management, showcasing its superior sensitivity and specificity in early pathogen detection and identification of drug-resistant genes. Despite its advantages, challenges such as optimization of experimental conditions, standardization of data analysis workflows, and high costs are discussed. Furthermore, we compare various commercially available dPCR platforms, detailing their features and applications in clinical and research settings. Additionally, the review explores dPCR's role in water microbiology, particularly in wastewater surveillance and monitoring of waterborne pathogens, underscoring its importance in public health protection. In conclusion, future prospects of dPCR, including methodological optimization, integration with innovative technologies, and expansion into new sectors like metagenomics, are explored.

Comparison of RT-ddPCR and RT-qPCR platforms for SARS-CoV-2 detection: Implications for future outbreaks of infectious diseases

The increased frequency of human infectious disease outbreaks caused by RNA viruses worldwide in recent years calls for enhanced public health surveillance for better future preparedness. Wastewater-based epidemiology (WBE) is emerging as a valuable epidemiological tool for providing timely population-wide surveillance for disease prevention and response complementary to the current clinical surveillance system. Here, we compared the analytical performance and practical applications between predominant molecular detection methods of RT-qPCR and RT-ddPCR on SARS-CoV-2 detection in wastewater surveillance. When pure viral RNA was tested, RT-ddPCR exhibited superior quantification accuracy at higher concentration levels and achieved more sensitive detection with reduced variation at low concentration levels. Furthermore, RT-ddPCR consistently demonstrated more robust and accurate measurement either in the background of the wastewater matrix or with the presence of mismatches in the target regions of the consensus assay. Additionally, by detecting mock variant RNA samples, we found that RT-ddPCR outperformed RT-qPCR in virus genotyping by targeting specific loci with signature mutations in allele-specific (AS) assays, especially at low levels of allele frequencies and concentrations, which increased the possibility for sensitive low-prevalence variant detection in the population. Our study provides insights for detection method selection in the WBE applications for future infectious disease outbreaks.

Early warning of statewide COVID-19 Omicron wave by sentineled urbanized sewer network monitoring using digital PCR in a province capital city, of Gujarat, India

Wastewater-based epidemiology (WBE) has been implemented globally. However, there remains confusion about the number and frequency of samples to be collected, as well as which types of treatment systems can provide reliable specific details about the virus prevalence in specific areas or communities, enabling prompt management and intervention measures. More research is necessary to fully comprehend the possibility of deploying sentinel locations in sewer networks in larger geographic areas. The present study introduces the first report on wastewater-based surveillance in Gandhinagar City using digital PCR (d-PCR) as a SARS-Cov-2 quantification tool, which describes the viral load from five pumping stations in Gandhinagar from October 2021 to March 2022. Raw wastewater samples (n = 119) were received and analyzed weekly to detect SARS-CoV-2 RNA, 109 of which were positive for N1 or N2 genes. The monthly variation analysis in viral genome copies depicted the highest concentrations in January 2022 and February 2022 (p < 0.05; Wilcoxon signed rank test) coincided with the Omicron wave, which contributed mainly from Vavol and Jaspur pumping stations. Cross-correlation analysis indicated that WBE from five stations in Gandhinagar, i.e., capital city sewer networks, provided two-week lead times to the citywide and statewide active cases (time-series cross-correlation function [CCF]; 0.666 and 0.648, respectively), mainly from individual contributions of the urbanized Kudasan and Vavol stations (CCF; 0.729 and 0.647, respectively). These findings suggest that sewer pumping stations in urbanized neighborhoods can be used as sentinel sites for statewide clinical surveillance and that WBE surveillance using digital PCR can be an efficient monitoring and management tool.

Long-term study on wastewater SARS-CoV-2 surveillance across United Arab Emirates

Wastewater-based epidemiology (WBE) demonstrates an efficient tool to monitor and predict SARS-CoV-2 community distribution. Many countries across the world have adopted the technique, however, most of these studies were conducted for a short duration with a limited sampling size. In this study, long-term reliability and quantification of wastewater SARS-CoV-2 surveillance is reported via analyzing 16,858 samples collected from 453 different locations across the United Arab Emirates (UAE) from May 2020 to June 2022. The collected composite samples were first incubated at 60 °C followed by filtration, concentration, and then RNA extraction using commercially available kits. The extracted RNA was then analyzed by one-step RT-qPCR and RT-ddPCR, and the data was compared to the reported clinical cases. The average positivity rate in the wastewater samples was found to be 60.61 % (8.41-96.77 %), however, the positivity rate obtained from the RT-ddPCR was significantly higher than the RT-qPCR suggesting higher sensitivity of RT-ddPCR. Time-lagged correlation analysis indicated an increase in positive cases in the wastewater samples when the clinical positive cases declined suggesting that wastewater data are highly affected by the unreported asymptomatic, pre-symptomatic and recovering individuals. The weekly SARS-CoV-2 viral count in the wastewater samples are positively correlated with the diagnosed new clinical cases throughout the studied period and the studied locations. Viral count in wastewater peaked approximately one to two weeks prior to the peaks appearing in active clinical cases indicating that wastewater viral concentrations are effective in predicting clinical cases. Overall, this study further confirms the long-term sensitivity and robust approach of WBE to detect trends in SARS-CoV-2 spread and helps contribute to pandemic management.

Wastewater surveillance and an automated robot: effectively tracking SARS-CoV-2 transmission in the post-epidemic era

Wastewater-based epidemiology (WBE) has exhibited great utility in the early and rapid identification of SARS-CoV-2. However, the efficacy of wastewater surveillance under China's previous strict epidemic prevention policy remains to be described. We collected the WBE data of wastewater treatment plants (WWTPs) in the Third People's Hospital of Shenzhen and several communities to determine the significant effectiveness of routine wastewater surveillance in monitoring the local spread of SARS-CoV-2 under tight containment of the epidemic. The results of 1 month of continuous wastewater surveillance showed that positive signals for SARS-CoV-2 RNA were detected in the wastewater samples, and a significant positive correlation was observed between the virus concentration and the number of daily cases. In addition, the community's domestic wastewater surveillance results were confirmed even 3 days before, or simultaneously with, the infected patient being confirmed as having the virus. Meanwhile, an automated sewage virus detection robot, ShenNong No.1 robot, was developed, showing a high degree of agreement with experimental data, offering the possibility of large-scale multi-point surveillance. Overall, our results illustrated the clear indicative role of wastewater surveillance in combating COVID-19 and provided a practical basis for rapidly expanding the feasibility and value of routine wastewater surveillance for future emerging infectious diseases.

Evaluation of intra- and inter-lab variability in quantifying SARS-CoV-2 in a state-wide wastewater monitoring network

In December 2019, SARS-CoV-2, the virus that causes coronavirus disease 2019, was first reported and subsequently triggered a global pandemic. Wastewater monitoring, a strategy for quantifying viral gene concentrations from wastewater influents within a community, has served as an early warning and management tool for the spread of SARS-CoV-2 in a community. Ohio built a collaborative statewide wastewater monitoring network that is supported by eight labs (university, government, and commercial laboratories) with unique sample processing workflows. Consequently, we sought to characterize the variability in wastewater monitoring results for network labs. Across seven trials between October 2020 and November 2021, eight participating labs successfully quantified two SARS-CoV-2 RNA targets and human fecal indicator virus targets in wastewater sample aliquots with reproducible results, although recovery efficiencies of spiked surrogates ranged from 3 to 75%. When SARS-CoV-2 gene fragment concentrations were adjusted for recovery efficiency and flow, the proportion of variance between laboratories was minimized, serving as the best model to account for between-lab variance. Another adjustment factor (alone and in different combinations with the above factors) considered to account for sample and measurement variability includes fecal marker normalization. Genetic quantification variability can be attributed to many factors, including the methods, individual samples, and water quality parameters. In addition, statistically significant correlations were observed between SARS-CoV-2 RNA and COVID-19 case numbers, supporting the notion that wastewater surveillance continues to serve as an effective monitoring tool. This study serves as a real-time example of multi-laboratory collaboration for public health preparedness for infectious diseases.

Digital PCR: A Partitioning-Based Application for Detection and Surveillance of SARS-CoV-2 from Sewage Samples

The wastewater-based surveillance of SARS-CoV-2 has emerged as a potential tool for cost-effective, simple, and long-term monitoring of the pandemic. Since the COVID-19 pandemic, several developed countries have incorporated the national wastewater surveillance program into their national policies related to pandemic management. Various research groups have utilized the approach of real-time quantitative reverse transcription PCR (RT-qPCR) for the quantification of SARS-CoV-2 from environmental samples like sewage water. However, detection and quantification using RT-qPCR relies on standards and is known to have lesser tolerance to inhibitors present in the sample. Unlike RT-qPCR, digital PCR (dPCR) offers an absolute and sensitive quantification without a need reference and offers higher tolerance to inhibitors present in the wastewater samples. Additionally, the accuracy of detection increases with the presence of rare target copies in the sample. The methodology herein presented comprises the detection and quantification of SARS-CoV-2 from sewer shed samples using the dPCR approach. The main features of the process include virus concentration and absolute quantification of the virus surpassing the substantial presence of inhibitors in the sample. This chapter presents the optimized PEG and NaCl-based protocol for virus concentration followed by nucleic acid extraction and quantification using CDC-approved N1 + N2 assay. The protocol uses MS2 bacteriophage as a process recovery or internal control.The methodology herein described highlights the importance of digital PCR technologies for environmental surveillance of important emerging pathogens or pandemics.