Rapid and effective detection of SARS-CoV-2 virus and new mutants to enhance wastewater surveillance

Abstract

Wastewater-based epidemiology (WBE) has been widely used as a complementary approach to the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) clinical surveillance. Importantly, the methods for rapid, effective, and robust detection, discrimination, and quantification of the SARS-CoV-2 virus and its variants for prompt public health responses were continually needed.

In my thesis, I first evaluated the performances of seven commonly used primerprobe sets in RT-qPCR detection. The results demonstrated that all selected primerprobe sets reliably detected SARS-CoV-2 in standard solutions. Wastewater matrix did not influence the detection value of SARS-CoV-2 concentrated from 30mL of wastewater but introduced inhibitory impacts on 920 mL of wastewater. Besides, the SARS-CoV-2 signals could be detected even after 69 days by most primer-probe sets. Evaluation of diagnostic effectiveness in finding COVID-19 cases showed that the N1 set stood out, and the practical utility was proven by applying N1 set to monitor the COVID-19 outbreak in Hong Kong.

To effectively control the ongoing outbreaks of fast-spreading SARS-CoV-2 variants, I further designed eight assays based on allele-specific RT-qPCR for realtime discrimination of eight SARS-CoV-2 variants in wastewater. In silico analysis of the designed assays for identifying SARS-CoV-2 variants yielded satisfied specificity and sensitivity. All assays could sensitively discriminate and quantify target variants at levels as low as 10 viral copies per µL with minimal cross-reactivity to the corresponding nontarget genotypes, even for wastewater samples containing mixtures of SARS-CoV-2 variants with variable abundances. Employing this method successfully identified and quantified Beta, Delta and Omicron variants in wastewater collected from community and quarantine hotels, which highly alighed with clinical data and validated the practical effectiveness.

Additionally, with the emergence of new variants of SARS-CoV-2, accumulated mutations that occurred in the SARS-CoV-2 genome raise new challenges for RTqPCR diagnosis used in wastewater surveillance. Hence, I further re-evaluated the performances and modified the mismatches of routine detection assays according to in silico analysis results. The results showed that five of seven original assays had better sensitivity for detecting Omicron variants, with the limits of detection (LODs) ranging from 1.53 to 2.76 copies per μL. The modified assays exhibited higher sensitivity and specificity, along with better reproducibility in detecting 81 wastewater samples. Moreover, the sequencing results of six wastewater samples by Illumina also validated the presence of mismatches in the primer/probe binding sites.

Finally, I employed wastewater genomic sequencing to provide valuable information for the genomic diversity of SARS-CoV-2 in the surveyed population. I first used mock wastewater samples to evaluate the impacts of sequencing throughput on genomic analysis. And I further evaluated the effectiveness of the adopted variant deconvolution method. Furthermore, employing the optimized pipeline, I deciphered the prevalence of SARS-CoV-2 variants in two pandemic periods in Hong Kong. Importantly, the wastewater data can reveal the variant trends 16 days before the clinical data did.

Overall, my study provides deep insights into rapid, effective, and cost-efficient surveillance of SARS-CoV-2 variants in wastewater samples for prompt public health responses.