Predicting and preventing antimicrobial resistance through patient-specific infection ecology
Supervisors
Dr Leonardo Mancini, School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow
Dr Justine Rudkin, School of Infection and Immunity, University of Glasgow
Dr Katarina Oravcova, School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow
Prof Sander Granneman, School of Biological Sciences, University of Edinburgh
Summary
In the clinic, antimicrobial treatments are routinely chosen based on an assessment of their immediate efficacy on the most abundant pathogen lineage isolated from an infection. However, the infection microenvironment is often occupied by multiple lineages and multiple microbial species, each with its own antimicrobial susceptibility profile and likelihood to evolve antimicrobial resistance (AMR). Treatments that do not take into account such diversity clear out the main pathogenic lineage, but select pathogen variants and opportunistic pathogens with enhanced capabilities to survive antibiotic treatment. This leads to infection relapse and a progressive reduction of antimicrobial efficacy. In this project, we will leverage experimental (microbiology, microfluidics, microscopy, sequencing) and analytical approaches (computer vision- and machine learning-based image analysis, and bioinformatics) to build a platform for the investigation of AMR evolution across the diversity of microbes present at the infection site of each patient. Our efforts will produce the proof-of-concept for a high-throughput device allowing clinicians to observe ex vivo how different treatments play out within each patient’s infective niche before administering them. This will allow us to identify pathways to AMR ahead of treatment, provide personalized treatments that target the desired microbe, and prevent the development of AMR.
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