B-149 Next-generation Sequencing Via Targeted Metagenomics for Broad Antimicrobial Resistance Surveillance

Abstract

Abstract Background Antimicrobial-resistant infections secondary to the overuse of antibiotics are a major public health threat. The standard of care for identifying resistance is antimicrobial susceptibility testing, the phenotypic characterization of minimum inhibitory concentrations (mic) for antibiotics in the presence of bacterial growth, but these methods are labor intensive, have long turnaround times, and face challenges with non-cultivable bacteria. Molecular methods for antimicrobial resistance (AMR) ascertainment are high-throughput, robust, and sensitive and have gained interest as a genotypic complement to traditional phenotypic methods. Since the COVID-19 pandemic, next-generation sequencing has emerged as the leading technology for tracking and surveillance of infectious diseases and antimicrobial resistance, emphasizing the need for flexible sequencing panels that are easily interchangeable and capable of detecting pathogens and AMR genes. Methods In a collaboration between Illumina, a national retail pharmacy, and Aegis Sciences Corporation, residual, self-collected nasal swab samples (n = 4390) submitted for COVID-19 testing from patients across the U.S. were sequenced using the Respiratory Pathogen ID/AMR Panel (RPIP, Illumina – For Research Use Only), a target capture-based next-generation sequencing panel targeting 282 respiratory pathogens and 2097 relevant AMR markers. Sequencing data was analyzed using the Explify RPIP Analysis app (BaseSpace, Illumina) and paired with self-reported patient data submitted via an online questionnaire. Data including pathogens and anti-microbial resistance (AMR) genes detected, age, race, gender, residing state of the patient, collection month, self-reported symptoms, and health risks were collated, imported into Microsoft PowerBI software, and used to create interactive visuals for a surveillance dashboard. Results In this cohort of sequenced specimens, 85.1% (n = 3576) had one or more viral, bacterial, or fungal pathogens present, 41% (n = 1816) had one or more AMR genes present, and 38.4% (n = 1685) had one or more pathogens and AMR genes co-detected. Twenty-six different AMR genes were identified with the most prevalent being Erm 23S ribosomal RNA methyltransferase (65.5%; n = 1115). Erm was identified in samples from all 50 US states and was co-detected with staphylococcus aureus (18.2%; n = 216) and streptococcus pneumoniae (4.9%; n = 57), among others. Following Erm in prevalence, this cohort of samples harbored genes for trimethoprim resistant dihydrofolate reductase (dfr = 27.9%), tetracycline-resistant ribosomal protection protein (tet = 21.0%), and macrolide phosphotransferase (MPH = 14.2%). The data also revealed AMR gene-pathogen co-detections listed as CDC serious threats, such as the mecA gene responsible for methicillin resistance in staphylococcal species. The mecA gene was co-detected in 5.3% (n = 28) of staphylococcus aureus positive samples in this cohort. Finally, although not classified as a serious threat, neuraminidase inhibitor resistance, associated with less susceptibility to anti-virals, was found in 12% (n = 15) of all influenza A (H3N2) infected samples (n = 112). Conclusions Completion of these studies demonstrated that next-generation sequencing using targeted metagenomics with RPIP is a cost effective, high-throughput option for large-scale surveillance of AMR genes in potential respiratory infections.