Building AMR monitoring capacity through expanding NGS capabilities

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PAR-18-604 Vet-LIRN Network Capacity-Building Project Michigan State University Project Title: Building AMR monitoring capacity through expanding NGS capabilities Project Summary/Abstract The overall objective of this proposal is to enhance the Michigan State University Veterinary Diagnostic Laboratory (VDL)’s capability and capacity for surveillance of antimicrobial resistance (AMR) in pathogens deemed to be of high consequence for both animal and public health. The VDL is a full-service veterinary diagnostic laboratory staffed and equipped to provide complete pathological, microbiological, molecular testing for FDA, CVM, and federal, state, or local agencies. The 150 VDL faculty and staff run more than one million tests a year on samples from all 50 states and more than 25 countries. The VDL is in the process of expanding its next generation molecular testing capabilities for diseases affecting domestic and wildlife species of Michigan. For several years, the VDL has maintained ongoing collaborations the Veterinary Laboratory Investigation and Response Network, FDA (Vet-LIRN) to support WGS of bacterial isolates as part of the national initiative to Combating Antibiotic Resistant Bacteria (CARB). Given the MSU VDL’s high caseload of companion animal cases from Midwest and across the United States, the laboratory is uniquely poised to support local, regional, and national AMR surveillance activities. The MSU VDL sees a significant number of antibiotic-resistant Pseudomonas aeruginosa and Enterococcus faecalis and Enterococcus faecium as part of our routine caseload, all considered to be leading causes of resistant infections in these animals. These infections have important animal and public health impacts, given the frequency of treatment failure and the potential for zoonotic transmission in these cases. Using the VDL’s recently expanded next-generation capabilities both in terms of equipment (NextSeq 1000) and staff, the VDL is prepared to perform WGS on up to 400 bacterial isolates (roughly 200 pseudomonas and 200 enterococcus). The addition of additional equipment (QuBit and OT-2 NGS Workstation) will allow for more rapid, higher-throughput and more cost-effective sequencing. The data generated will help track the antibiotic resistant genes in these bacteria and monitor for new resistant traits. Comparing the sequence data to phenotypic resistance data and clinical data (when available) can be used to predict treatment outcomes.