MEtaGenome-informed Antimicrobial resistance Surveillance: Harnessing long-read sequencing for an analytical, indicator and risk assessment framework
( MEGAISurv )
One Health surveillance is essential to understanding sources, reservoirs and transmission of antimicrobial resistance (AMR). Metagenomics is an untargeted, culture-independent method of sequencing all DNA in a given sample, offering unparalleled insights, with new analytical methods required to facilitate its widespread application in AMR surveillance. With the reducing cost of long-read sequencing, we are now able to generate the scale of data allowing a greater understanding of the public health risk associated with AMR independent of its source. In this project, “MEtaGenome-informed Antimicrobial resistance Surveillance: Harnessing long-read sequencing for an analytical, indicator and risk assessment framework” (MEGAISurv), we will 1) provide new analytical tools incorporating both gene and single nucleotide polymorphism (SNP) analysis that will provide accurate quantification of AMR within samples; 2) identify/validate the best AMR indicator organisms for low-cost, high-throughput quantification of AMR across multiple One Health domains; 3) provide a novel computational framework to risk assess the spread of AMR, and 4) evaluate in real-world settings the use of metagenomics in targeting and evaluating interventions to reduce the spread of AMR. Our overall objective is to develop and validate the tools and analytical frameworks necessary to maximise the recent technological advancements in metagenome and long-read sequencing to generate high quality, inter-operable and actionable surveillance data across the One Health paradigm.
- Alison Mather, Quadram Institute Bioscience, United Kingdom (Coordinator)
- Séamus Fanning, University College Dublin, Ireland (Partner)
- Aldert Zomer, Utrecht University, Netherlands (Partner)
- Nick Andrews, Dawn Farm Foods, Ireland (Observer)