High prevalence of antimicrobial resistant (AMR) pathogens is undoubtedly an emergent global health crisis. AMR is exacerbated by factors such as the overuse and misuse of antimicrobial drugs and a changing climate. Overusing antimicrobial drugs causes selective pressure that leads to favorable mutations of bacteria. Through mutations, bacteria acquire mechanisms that interfere with the function and effectiveness of antimicrobial drugs. AMR is a threat to global health because, with ineffective last line of defense antimicrobial drugs, we will be unable to treat the most severe infections. Our research focuses on developing pipelines that detect low abundance antibiotic resistance genes (ARGs) on a local scale. This work contributes to the broader context of AMR on a global scale because applications of AMR surveillance across the globe can inform us about the nature of AMR. In my work, I am examining the implications of seasonality in AMR alleles in Seattle. During the dry season in Seattle from July to September, we may find a higher diversity of AMR genes and in particular, unique alleles of AMR genes. The amount of rainfall influences the concentration of bacteria carrying AMR genes. I hypothesize that higher rainfall typically occurring from October to March will lead to a lower diversity of AMR genes. I am assisting in developing a workflow that uses a two-step, unique molecular identifier (UMI) PCR to enrich AMR genes in wastewater. After amplification, the PCR product undergoes long-read Nanopore sequencing and through bioinformatic analysis, I can identify what AMR alleles are present. By gathering data regarding environmental conditions such as rainfall, and wastewater flow rate, in addition to using our current workflow of amplification PCR and Nanopore sequencing, I can identify what AMR alleles are present in relation to season.

High quality surveillance of antimicrobial resistance genes (ARGs) is critical for addressing the threat antimicrobial resistance poses to global health. However, the existing surveillance systems are centered around individual-level sampling in clinical settings. Hence, they are limited in that they do not reflect dynamics in community settings and require culturing for detection. The purpose of the study is to develop a surveillance method in wastewater that provides community level detection of ARGs. We are targeting β-lactam ARGs. DNA extracted from previously collected influent samples from Seattle’s wastewater treatment plant, were seeded with known gene alleles. We then applied Unique Molecular Identifier (UMI) PCR to amplify the alleles, used Nanopore sequencing, and developed bioinformatic pipelines for genomic data analysis. The pipeline translates the nanopore sequencing output (fast5) to genomic sequences (fasta), aligns them with an ARG database to determine the allele types, and graphically represent our alignments and produce interpretable figures from the data. We successfully completed the first allele sequencing and identification of two similar CTX-M alleles (genes for β-lactam resistance) that we inserted into samples. I am evaluating and validating this method with replicates of another β-lactamase ARG - KPC. The expected result at this stage is to successfully identify multiple β-lactamases alleles and test the enrichment of two different gene targets in one reaction. This more efficient and less expensive surveillance method in wastewater will facilitate ARG detection at the community level, providing public health agencies a tool that guides effective and regional-specific monitoring and intervention program design.