Identifying Antimicrobial Resistant Bacteria in Wastewater Influent Samples

Introduction Antimicrobial resistance (AMR) is a rapidly growing field of research that affects human health more than people may realize. AMR bacteria are characterized by their ability to withstand antibiotic treatment, meaning that the fatality rate of infections is much higher than with regular bacteria. The WHO predicts that AMR related deaths may surpass those caused by cancer by 2050. AMR bacteria develop due to genetic changes in the presence of antibiotics, allowing them to adapt and survive. When AMR genes are present in wastewater, it is an indicator that there may be an increase in AMR bacterial infections. Methods 50 mL samples were collected from three municipal wastewater treatment plants in Amherstview, Ravensview, and Cataraqui Bay. Total coliforms and E. coli were detected from influent samples by culturing bacteria and using colour and fluorescent indicators in a Quantitray assay. E. coli positive Quantitray wells were selected to streak isolates, and their minimum inhibitory concentrations were determined for ampicillin and carbapenem antibiotics. Alongside, the isolates underwent DNA extraction and qPCR analysis with several resistant gene targets. Implications By investigating the phenotypic and genotypic properties of wastewater samples, we can determine the level of resistant bacteria present in treated wastewater considered safe enough to return to the environment. Currently, there are AMR genes present in the final effluent samples, raising concerns about environmental and public health risks. This research shows the need for AMR bacteria removal, as current wastewater treatment does not yet focus on this. Improved strategies should ensure that AMR bacteria from discharged wastewater does not spread in the community. Detection of AMR genes and bacteria in wastewater also warns hospitals to expect more AMR bacterial infections. This builds from research during the COVID-19 pandemic where wastewater was analyzed for SARS-CoV-2 to predict increases of hospitalizations.