Identification and enrichment of signature regions of antimicrobial resistant pathogen genomes for wastewater disease surveillance

PROJECT SUMMARY/ABSTRACT Antimicrobial resistance is recognized as a global public health threat that makes infections more difficult to treat and increases the risk of other medical procedures. Global and local surveillance of antimicrobial resistance is critical to understanding transmission of resistant pathogens, detecting the emergence of resistance mechanisms, and mitigating the spread of resistant pathogens. Wastewater surveillance represents a powerful, resource-efficient, and comprehensive approach for population-level surveillance of infectious diseases. It has been widely applied to track COVID-19 and other respiratory virus levels in communities, as well as identify circulating variants. However, wastewater surveillance of antimicrobial resistant pathogens has not been widely implemented because of specific challenges that limit its actionability (i.e., the direct use of the surveillance data to inform public health action). This is because antimicrobial resistance is ubiquitous in the environment and even clinically-important antibiotic resistance genes (ARGs) are abundant and widespread in wastewater. Thus, previous approaches that quantified ARGs generated information that was not specific to antimicrobial resistant pathogens. This proposal focuses on the development of foundational computational tools and laboratory methods necessary to identify and detect signature regions of antimicrobial resistant pathogens for wastewater surveillance. Signature regions are defined as genomic regions that are conserved in a pathogen strain but not found in neighboring strains or other genomes. We propose to develop a genomic language model approach to identify signature regions of target antimicrobial resistant pathogens. This will be integrated with a fully automated design pipeline for quantitative assay design. On the wet lab side, we will use microdroplet encapsulation of wastewater microbes to do high-throughput enrichment and isolation of antimicrobial resistant bacteria. This approach will enable sensitive and specific detection of antimicrobial resistant pathogens by encapsulating individual cells and assaying them for signature regions using digital droplet PCR. The proposed computational and laboratory tools will result in software and wet lab methodologies that can be used by public health laboratories and wastewater surveillance programs for specific and sensitive antimicrobial resistance monitoring. Software, assays, and protocols will be made publicly available and can also be applied for other pathogen targets. Advancing the actionability of antimicrobial resistance wastewater monitoring has the potential to enable the prediction of outbreaks, forecast hospitalizations, guide treatment decisions, understand transmission, and evaluate mitigation strategies for antimicrobial resistant infections.