MEtaGenome-informed Antimicrobial resistance Surveillance: Harnessing long-read sequencing for an analytical, indicator and risk assessment framework
Lead Research Organisation:
Quadram Institute
Department Name: Microbes in the Food Chain
Abstract
Antimicrobial resistance (AMR) is a threat to humans, animals, and crops, and requires a coordinated One Health approach. AMR affects many of the goals of the WHO 2030 Agenda for Sustainable Development. Levels of AMR are generally quantified by culturing indicator organisms like E. coli, and determining its resistance. Looking at only one indicator organism in all human, animal, food and environmental samples is like studying ocean life by looking at a single droplet of seawater with a microscope. Interesting differences can be found between droplets but is it really the entire picture?
Metagenomic sequencing is a technique that allows identification and quantification of AMR genes and species in a sample without culturing. Currently the use of short read sequencing is more common but limited when AMR quantification is required. One can only quantify genes, not where they are. On a chromosome? Or in a transmittable plasmid or transposon, which can easily spread to pathogens?
We propose to use long-read Nanopore sequencing to develop a 'one stop shop' surveillance method. Long reads contain both resistance genes and flanking sequences, thereby identifying the original organism or mobile genetic element, the location, but also genes associated with high transmission risk. We propose integrating into traditional methods to expand surveillance into non-model-organisms.
By working with stakeholders in government, industry and academia, this project will support translation of long-read metagenome data into actionable surveillance information for the reduction of risk to human health.
Metagenomic sequencing is a technique that allows identification and quantification of AMR genes and species in a sample without culturing. Currently the use of short read sequencing is more common but limited when AMR quantification is required. One can only quantify genes, not where they are. On a chromosome? Or in a transmittable plasmid or transposon, which can easily spread to pathogens?
We propose to use long-read Nanopore sequencing to develop a 'one stop shop' surveillance method. Long reads contain both resistance genes and flanking sequences, thereby identifying the original organism or mobile genetic element, the location, but also genes associated with high transmission risk. We propose integrating into traditional methods to expand surveillance into non-model-organisms.
By working with stakeholders in government, industry and academia, this project will support translation of long-read metagenome data into actionable surveillance information for the reduction of risk to human health.
Technical Summary
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.
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.
People |
ORCID iD |
| Alison Mather (Principal Investigator) |