Our water environment is greatly impacted by the presence of microbial contaminants which is a great concern it terms of public health exposure. Full-scale conventional and state-of-the-art wastewater utilities have been found to release pathogens and antibiotic resistant bacteria in the environment. Management and minimization of microbial pathogens and resistant bacteria in wastewater treatment plants is critical since the spread of pathogens and antibiotic resistant genes in the environment poses a significant challenge to diverse aspects of our global community. The overall aim of this study is to provide metagenomic insights into bacterial, viral and phage diversity and resistance to antibiotics and metal compounds in wastewater utilities. Samples were collected from two different wastewater treatment systems, a conventional activated sludge utility and a membrane bioreactor (MBR), in Michigan. Metagenomic analyses were conducted on Illumina Miseq and Hiseq generated sequences using MGRAST and METAVIR analysis software. The findings suggest that there is a substantial shift in the phage community over the course of the activated sludge process. Phage populations are dynamic and phage DNA was associated with antibiotic resistant genes in wastewater. It was observed that there are differences in the abundance of functional genes related to resistance between bacterial, virus and phage communities. Genes coding for antibiotic resistance were identified in all bacterial, virus and phage communities, whereas genes coding for resistance to metals were mostly observed in bacterial and virus communities. The MBR utility samples showed slightly higher number of hits for all the functional categories compared to conventional wastewater treatment samples. Diverse viral and bacterial human pathogens were observed in treated wastewater samples. Diversity analysis does not provide quantitative data on pathogen loads or infectivity but it provides a list of potentially pathogenic viruses and bacteria that need to be considered during treatment management decisions. This study provided a bioinformatics approach for identifying microbial diversity in different wastewater treatment stages and technologies. The results of this work provide significant information that will contribute to sustainable wastewater management decisions.
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