Advancement of Sampling Methods and Genomic Analyses for SARS-CoV-2 Wastewater and Environmental Monitoring

Abstract

Wastewater and environmental monitoring (WEM) have emerged as a pivotal tool for monitoring Coronavirus disease 2019 (COVID-19) within population, offering a non-invasive and cost-effective early-warning system to track infection dynamics. WEM for COVID-19 involves collection of wastewater samples from in-premise plumbing within buildings, within community wastewater infrastructure such as sewer systems, influent and primary sludge from wastewater resource recovery facilities (WWRFs). Various sampling methods are employed for wastewater collection, including 24-hour composite, grab, and passive sampling whereas primary sludge is collected using only 24-hour composite sampling method. Following wastewater samples collection, samples are analyzed to quantify the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) genetic material (RNA) using molecular technique such as reverse transcription polymerase chain reaction (RT-PCR) for understanding the disease prevalence at the population level. Although WEM showed promises, SARS-CoV-2 genome continues to evolve with the progression of the pandemic, leading to the emergence of multiple variants of concern (VOCs). To monitor these VOCs, wastewater genomic surveillance (WWGS) has emerged as another public health tool using composite influent wastewater samples. However, genomic analysis using influent wastewater presents significant challenges, including low concentrations of viral RNA, the presence of PCR inhibitors, RNA degradation in wastewater matrices, and high operational costs associated with sequencing. It is critical to address these challenges to improve the reliability and scalability of WWGS for monitoring emerging SARS-CoV-2 variants. The objective of this dissertation is therefore to evaluate different sampling methods, sample processing strategies such as concentration and RNA extraction methods and genomic analyses for comprehensive SARS-CoV-2 WEM. In particular, the first specific objective is to assess the SARS-CoV-2 RNA concentrations in wastewater solids collected using an autosampler, passive samplers and primary sludge samples. Results show SARS-CoV-2 RNA concentrations in wastewater solids from passive samplers can be effectively compared (p > 0.05) to conventional autosampler and primary sludge samples. The second specific objective is to evaluate the feasibility of passive sampling for WWGS of SARS-CoV-2 in high-flow WWRFs. The findings indicate that single nucleotide variants (SNVs) profile and SARS-CoV-2 lineage prevalence is similar (p > 0.05) across auto, COSCa-ball, and Torpedo passive samplers, showing concordance with clinical surveillance data. Notably, the genomic recovery of SARS-CoV-2 from passive samplers is shown to be significantly influenced by sequencing read length, where shorter reads (300 bp) results in lower genomic recovery compared to longer reads (600 bp). The third specific objective centers on optimizing a primary sludge concentration and RNA extraction method for SARS-CoV-2 genome sequencing that yields comparable or improved results compared to conventional WWGS. The findings demonstrate that our optimize sludge processing method consistently recovers near-complete (≥ 90%) SARS-CoV-2 genomes from influent wastewater and primary sludge. Genomic analyses reveal that lineages and SNV profiles are comparable between influent wastewater and primary sludge. However, primary sludge exhibits a higher likelihood of rare (low prevalence) and Canadian cryptic SNVs detection compared to influent wastewater, emphasizing its potential to enhance variant monitoring and genomic resolution in WWGS. The fourth and final specific objective of this dissertation is to compare the diagnostic performance of allele-specific (AS)-RT-qPCR and sequencing-based methods to determine their accuracy in WWGS. The research found that the frequency estimation of single allele using AS-RT-qPCR, amplicon sequencing as well as haplotype frequency estimations are similar and contain sufficient information to describe the trajectory of variant prevalence in wastewater across time. Youden's index further confirms that the diagnostic performance nearly identical across the methods. Overall, this PhD dissertation advances sampling method, sample processing strategy and genomic analyses of SARS-CoV-2 by demonstrating its potential to deliver real-time, scalable, cost-efficient, and comprehensive data, contributing to improved preparedness for future pandemics.