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Oxford University co-lead: Dr Nicole Stoesser

UKHSA co-lead: Dr Katie Hopkins 

We aim to understand how healthcare associated and antimicrobial resistant infections can be affected by what happens on farms, and in both the general and hospital environment. Our strategy is to increase our understanding on what the important factors to drive AMR and HAI infections,  how these factors are influenced by different environments and how they are passed onto and disseminated in humans.  We hope this will provide understanding on how AMR genes are shared between humans, animals and the environment, identify what systems contribute to the to the spread of microbes in hospitals and work towards minimising this and optimise resistance prediction for certain microbes using whole genome sequencing and molecular techniques.


Reducing the influence of wastewater and the hospital environment on HAI & AMR  

We are currently conducting a systematic review to summarise the impact of hospital wastewater-associated interventions on HAI, including drug-resistant infections. Ongoing work has been evaluating the impact of different types of disinfectants on colonisation, bioburden control and dissemination of different lineages/species of Enterobacterales, and on the horizontal transfer of drug resistance plasmids in sinks in controlled experiments on the UKHSA modular ward at Porton. 

In collaboration with the National Infection Trainee Collaborative for Audit and Research (NITCAR), we have conducted a national survey of sink drain colonisation in 30 UK hospitals to understand burden of Enterobacterales and AMR gene colonisation and association with: (i) hospital, (ii) sink type, (iii) sink location, (iv) cleaning protocols, (v) infection rates.   



Genomic surveillance of AMR holds substantial potential, but resistance prediction in E. coli and other Gram-negatives is challenging, particularly for beta-lactamase/beta-lactamase inhibitors where for example expression/promotors, gene dosage and multiple interacting mechanisms play important roles. There are few robust studies evaluating key features of resistance prediction, such as the database used, and the bioinformatics algorithms developed to identifying presence/absence of AMR variants using simulated and large-scale “real-world” data as a prerequisite to predicting resistance. Ongoing work is testing new approaches to identifying novel, undiscovered mechanisms/variation using a pangenome-wide gene and unitig presence/absence matrix, and more accurately quantifying the relationship between mechanism and genotype at the MIC-level.  


Genomic surveillance of AMR in wastewater  

We have conducted a systematic review and data synthesis of existing studies using wastewater-based analyses to perform surveillance of AMR in human populations and an evaluation of methods to sample wastewater (grab versus composite) and characterise taxonomic and AMR profiles in samples (metagenomic vs targeted approaches). We are currently evaluating the longitudinal AMR prevalence in wastewater in 5 sewage catchments over a year in Oxfordshire, investigating associations with AMR profiles in sequenced clinical isolates, and evaluating whether antimicrobial prescribing in a catchment is associated with changes in the wastewater load of important AMR genes.