Developing and Evaluating Molecular Detection Tools for Water Quality Monitoring

Doctoral Dissertation
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Abstract

Water quality is critical for developing urban areas, industry, and agriculture worldwide. Despite recent advances in water quality monitoring and management, inadequate water quality in many contexts remains a global challenge. For example, it is estimated that consuming wastewater contaminated water leads to 9.1% of the global burden of disease and 6.3% of all deaths per year globally. In addition, inadequate water supply has increased the focus on water quality due to greater reliance on impacted water sources, demonstrating the need for tools to accurately assess pathogen removal and wastewater treatment efficacy. Developing monitoring tools for disease-causing microbes in water is critical to addressing these global challenges.

An initial effort in this work was made to monitor water quality by evaluating the performance of viral fecal pollution indicators during wastewater treatment. Fecal indicator bacteria, E. coli and enterococci, and fecal indicator virus, somatic coliphage, were quantified via culture to evaluate their removal through an activated sludge wastewater treatment plant. At the same time, microbial source tracking (MST) methods were used to quantify fecal indicator viruses, namely the recently discovered human gut bacteriophage crAssphage (cross-assembly phage), and common human viral pathogens human adenovirus (HAdV), and human polyomavirus (HPyV) and the bacterial assay HF183/BacR287 in the same wastewater treatment process. Additional follow-on efforts were made to investigate the fate of bacterial 16S rRNA genes, mobile integron (intl1) and antibiotic resistance genes (ARGs; sul1, sul2, tetO, tetW, and ermF) through the wastewater treatment processes. In addition to this work, the fate of all these indicators were evaluated during anaerobic digestion of sewage sludge resulting from the treatment process. CrAssphage has the potential to serve as a surrogate as human-specific pathogens removal through wastewater treatment process including anaerobic digestion.

Traditional pathogen identification has relied on culture-based methods that can only identify a limited subset of possible pathogens. Metagenomic DNA/RNA sequencing has the potential to identify many microorganisms present in a sample with a single measurement, providing more comprehensive water quality assessment; however, significant challenges remain to make metagenomic methods quantitative and actionable. One of these challenges with metagenomics analysis is the connection between observed sequences and detection assays dependent on PCR amplification. A bioinformatics tool has been developed to predict polymerase chain reaction (PCR) products directly using metagenomics constructed contigs and targeting primers as input. Finally, while targets can be detected by both metagenomic and PCR methods, PCR methods identify targets as concentration of gene copy per volume (absolute abundance), which is necessary for public health applications. Conversely for metagenomics methods, targets are presented as percentage of targeted sequences counts in total sequences counts (relative abundance). It is essential to bridge relative abundance metagenomic virus identifications to quantitative (i.e., per volume) virus identifications. Subsequently, I developed a tool to convert between these disparate measurements.

Ultimately, my dissertation work advances water quality monitoring by demonstrating novel indicator performance through wastewater treatment processes, and methodologies to enable quantitative metagenomic water quality monitoring.

Attributes

Attribute NameValues
Author Zhenyu Wu
Contributor Kyle J. Bibby, Research Director
Contributor Robert Nerenberg, Committee Member
Contributor Kyle Doudrick, Committee Member
Contributor Joshua Shrout, Committee Member
Degree Level Doctoral Dissertation
Degree Discipline Civil and Environmental Engineering and Earth Sciences
Degree Name Doctor of Philosophy
Banner Code
  • PHD-CEES

Defense Date
  • 2022-08-22

Submission Date 2022-08-27
Subject
  • environmental engineering

Language
  • English

Record Visibility Public
Content License
  • All rights reserved

Departments and Units
Catalog Record

Digital Object Identifier

doi:10.7274/nz805x25b15

This DOI is the best way to cite this doctoral dissertation.

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