Wastewater surveillance of pathogens and antibiotic resistance using qPCR, ddPCR, and metagenomics

Abstract

Infectious diseases and antibiotic resistance pose profound threats to global health, emphasizing the critical necessity of integrated surveillance systems. While clinical monitoring remains essential, wastewater-based epidemiology (WBE) is emerging as an available approach to provide population-level health insights and has significantly advanced over the course of COVID-19. To fulfill diverse public health monitoring goals, it is essential to employ different techniques for effective detection, and their utility should be evaluated. In my thesis, I first developed a practical screening workflow built on several multiplex genotyping RT-qPCR assays to enable simultaneous discrimination of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Omicron sub-lineages in wastewater. These assays showed great analytical performance, even with the background of other genotypes. By applying the workflow to wastewater treatment plant (WWTP) influent samples, I witnessed the swift appearance and prevalence of Omicron VOC BA.4/BA.5. The derived infection trends and variant dynamics aligned with the clinical data, demonstrating that multiplex PCR is a rapid and high-throughput method to enhance wastewater surveillance. As the most commonly used molecular detection methods, I further compared the operational capabilities of RT-qPCR and RT-ddPCR for virus detection under wastewater surveillance contexts using SARS-CoV-2 as a representative. By testing with synthetic RNA and wastewater samples, RT-ddPCR was observed to show superior quantification accuracy and precision, especially for trace detection. Also, RT-ddPCR demonstrated more robust detection with the effect of wastewater matrices or assay-template mismatches. Additionally, RT-ddPCR was observed to be advantageous in virus genotyping, particularly at low allele frequencies and concentrations. Such systematic evaluations could be informative for addressing infectious disease outbreaks in the future. Moreover, to achieve a more comprehensive understanding of microorganisms in wastewater, a large-scale metagenomic surveillance was conducted based on Illumina short-read sequencing and Oxford Nanopore sequencing for 190 wastewater samples collected from sewer manholes across Hong Kong. By combining the two sequencing strategies, a large collection of genomes representing 1,498 unique microbial species was retrieved from the wastewater, including numerous unknown taxa. Some genomes were found to be associated with the human microbiome and showed relatively high abundance in the wastewater samples. The potential pathogens were subsequently identified, and their distribution characteristics were further explored, revealing certain sampling sites as hotspots of microbial risk. This work illustrated the feasibility of utilizing genome-centric metagenomics for wastewater surveillance. Finally, the antibiotic resistance in the wastewater was holistically characterized using the metagenomic sequencing data through read-based and assembly-based approaches. The resistome profiles exhibited spatial and seasonal variance, and I found a resistome shift during wastewater conveyance, with samples collected from sewer manholes showing higher similarity to human feces resistome compared to downstream WWTPs. With the integration of long-read data, the host ranges and potential mobility of the antibiotic resistance were highly resolved. By combining with homology analysis, their transmission status and risk levels were further inferred. This work establishes a baseline framework for antibiotic resistance surveillance through wastewater metagenomic sequencing. Overall, this thesis delivers deep insights into wastewater surveillance using multiple techniques to address the multifaceted needs of public health monitoring.