Disease-causing bacteria, such as E. coli, are becoming increasingly difficult to treat with antibiotics. This is because the bacteria are evolving by picking up genes that make them resistant to these drugs. This can happen very quickly, because the resistance genes responsible can hop from one bacterial cell to another by hitching a ride with small rings of DNA called plasmids.
Because the plasmids, and the resistance genes they carry, can spread independently of the bacteria, it is very difficult to track and monitor how they are moving. This can pose a problem in hospitals, where plasmids can spread rapidly from one cell to another resulting in an ‘outbreak’ of infections resistant to antibiotics. It also makes it much more challenging to understand how resistance spreads in environmental settings like farms or rivers, where the presence of antibiotics might pose a risk to human and animal health by selecting for more resistant strains.
This network brings together experts from different disciplines with research expertise that addresses different aspects of this complex problem; this includes bioinformaticians and software developers, clinical and environmental epidemiologists, and evolutionary biologists.
The aims of this network are twofold; first to establish a conceptual ‘roadmap’ that presents possible solutions, whilst accounting for the rapid evolution of plasmids. Second, to provide proof-of-principal of novel approaches through the development of prototype software tools.
- Development of a proof-of-concept prototype linking pathogen.watch with the bespoke plasmid.watch database. This will inform on a new roadmap for plasmid epidemiology, and will be accompanied by a perspective article.
- The network prepares a positional article outlining the requirements and utility of a plasmid typing system and how this might be used alongside standard strain typing. To include a consideration of how plasmid typing might impact on infection control strategies in health care settings.
- Benchmarking and harmonisation to compare different tools and how each performs using short read (vs hybrid) assemblies.
- The networks develop and validates novel typing approaches based on combining a plurality of ‘backbone’ and Mash distance methods, validated against complete plasmid assemblies. This is a pivotal step in enhancing AMR surveillance, and if validated successfully will be written up as short bioinformatics note.
- Bioinformatic analysis on existing hybrid assemblies (from the JPI-AMR SpARK data and other partner projects) to determine plasmid transmission networks across different settings (informed by WS2). This will help to resolve the question as to extent to which AMR plasmids can transfer freely over complex One-Health landscapes.
- Analysis of One-Health metagenomics data using tools developed by partners to ascertain the feasibility of applying typing tools to these data and thus aligning with evidence from single-colony WGS analysis.
- Edward Feil, University of Bath, United Kingdom (Coordinator)
This network includes 24 partners from 11 countries: Belgium, Brazil, France, Germany, Nigeria, Norway, Pakistan, Portugal, Spain, the Netherlands and the United Kingdom.