Developing a Quantitative Metagenomic Approach to Explore Viral Community Dynamics Through Wastewater Treatment

Abstract

Municipal wastewater treatment removes carbon and nutrients from sewage by harnessing a dense microbial community in a biological treatment process. The dynamics of the viral community structure and function through wastewater treatment is not well understood. Viruses are expected to play critical roles in biological wastewater treatment because they are highly abundant, exhibit complex host interactions ranging from predatory to symbiotic, and accelerate host evolution. The lack of rigorous methods for isolating viral communities from environmental samples and quantitative methods for measuring and interpreting viral metagenomes has hindered our understanding of the roles of viruses in the environment, in general, and biological wastewater treatment, in particular. The overall goal of this dissertation research was to develop and apply metagenomic and in silico approaches to explore viral community dynamics through biological wastewater treatment and probe the roles that viruses play on the dissemination and emergence of antibiotic resistance. This dissertation developed rigorous methodologies for studying environmental viromes. To address the issue of virus enrichment from environmental samples, an ultrafiltration approach was compared with an iron chloride flocculation method. Next, to measure the absolute abundances of target viruses in wastewater samples before and after treatment, a rigorous quantitative viral metagenomic method was developed. Specifically, dsDNA and ssDNA standards were added to viral DNA extracts to relate relative and absolute abundances. A bioinformatic pipeline, QuantMeta, was developed to calculate concentrations of targets (e.g., contigs or sequences from databases) and assess target-specific detection thresholds and detect and correct non-specific mapping and assembly errors. QuantMeta was applied to quantitative viromes from wastewater samples and improved quantification confidence and accuracy. QuantMeta is not specific to wastewater viromes and is applicable to whole metagenomes and other environments. These methods were applied to three samples of wastewater influent and secondary effluent collected in December 2020 from a municipal wastewater treatment plant. The wastewater viromes were highly purified for viruses with 75.5-78% of contigs classified as viral. Mean total virus concentrations in influent and secondary effluent were 10.3 and 10.6 log10 gc/mL, respectively, approximately two-orders of magnitude higher than previous concentrations made with viral particle counting-based methods. 12.9% of influent viral populations persisted and replicated through biological treatment to be 10.3 log10 gc/mL more abundant in secondary effluent. Viruses rarely carried antibiotic resistance genes, with only 59 viral populations identified. Finally, compounding effects of phage-host coevolution and antibiotic stress on antibiotic resistance emergence and expression in chemostat environments, such as biological treatment and the gut, was assessed using in silico evolution experiments. An Avida environment was developed that simulated an antibiotic with an evolvable trait to confer antibiotic resistance. Experiments demonstrated that phage-host coevolution accelerated the emergence of antibiotic resistance and the presence of phages and antibiotics occasionally resulted in decreased susceptibility to antibiotics. The results indicate that phages alter outcomes of antibiotic resistance evolution. Overall, this dissertation provides critical tools for quantitative studies of viromes. Their application provides insight on viral community dynamics through wastewater treatment, including their overall abundances, diversity, and potential role in the spread of antimicrobial resistance. The tools developed here can be applied in future studies of viral and microbial communities in metagenomes to directly compare between samples.