Effective surveillance of antimicrobial resistance (AMR) in microbial pathogens is critical, as increasing AMR poses a major global public health threat

The Department of Agriculture National Institute of Food and Agriculture awarded $294,000 to Sam Houston State University on September 1, 2026, under the Agriculture and Food Research Initiative (AFRI) program (CFDA 10.310) to optimize Oxford Nanopore Technology long-read quasi-metagenomic sequencing for detecting antimicrobial resistance genes in Enterococcus species from retail meats.

The project addresses limitations in the National Antimicrobial Resistance Monitoring System's current protocols, which rely on single-colony whole-genome sequencing and may miss co-occurring resistant species in retail poultry. The research will optimize the ONT-LRQMS approach to enable comprehensive antimicrobial resistance gene detection and precise taxonomic classification across all Enterococcus species present in retail meat samples, with variable enrichment incubation times evaluated to determine their influence on species abundance. Task 1 involves optimizing the ONT-LRQMS methodology with independent validation across two laboratories. Task 2 applies the optimized assay to screen 150 naturally contaminated chicken carcasses. The long-term goal is developing a cost-effective, scalable metagenomic assay for antimicrobial resistance gene prediction implementable globally.

Performance of work occurs in Huntsville, Texas. The period of performance runs from September 1, 2026, through August 31, 2028. This is a Project Grant award under the AFRI Foundational and Applied Science Program.

In the united states, the national antimicrobial resistance monitoring system (narms) monitors AMR in enteric bacteria, including enterococcus, from sources including retail meats. Current narms protocols rely on illumina short-read whole-genome sequencing of single colonies per sample for antimicrobial resistance genes (args) detection, which may overlook CO-occurring resistant species. Our preliminary data showed up to 100% enterococcus contamination in retail poultry and frequent CO-occurrence of multiple species. Given the potential for intra- and inter-species args transfer, comprehensive ARG detection in enterococcus from retail meats is essential. This seed grant aims at optimizing the oxford nanopore technology long-read quasi-metagenomic sequencing (ONT-lrqms) for comprehensive args detection and precise taxonomic classification of enterococcus species in retail meats. We will also evaluate how variable enrichment incubation times influence the relative abundance of CO-occurring species. We hypothesize that the optimized ONT-lrqms approach will enable comprehensive and accurate args prediction across all enterococcus species present in retail meats. Task 1 involves optimizing the ONT-lrqms approach including variable enrichment time, with independent validation in two different laboratories. Task 2 involves applying the optimized assay to screen 150 naturally contaminated chicken carcasses. The long-term goal is to develop a cost-effective, scalable metagenomic assay for ARG prediction that can be implemented globally.