Project Snapshot

To expand information on the role of mobile genetic elements (MGE) in the spread and persistence of AMR from a “One Health” perspective and to develop machine learning biotools for quantitative AMR prediction and transmission.

MGEs such as plasmids and integrative and conjugative elements (ICE) are integral to the exchange of antimicrobial resistance genes (ARGs) within bacterial populations and converting susceptible bacteria to multidrug resistant (MDR). Regulatory factors that control the mobility and the expression of ARGs are critical for assessing risk and developing AMR policy as part of a “One Health” initiative.

1. Information to detect and manage MGE in human and livestock health settings to reduce the risk of AMR
2. New biotools for AMR prediction and knowledge on the function of MGEs to support risk assessments and the development of AMR policy

1. Can phenotypic antimicrobial resistance be predicted from genomic information?
2. What are the factors that determine the mobility of MGEs?
3. Can methods/tools be developed to reduce the risk that MGEs pose to the development of MDR bacteria?

• Insights will be garnered through the development of machine learning tools that draw heaviliy upon phenotypic, genomic and metagenomic analyses to formulate predictive outcomes for AMR.
• The application of high throughput omics tools with an emphasis on a combination of Illumina short-read and nanopore long read sequencing technologies to understand the entirety of MGEs/ICE and to predict their horizontal transferability intra- and inter species.