Development of a rapid screening method for Carbapenem-Resistant Enterobacteriaceae using high resolution mass spectrometry

Carbapenem-resistant Enterobacteriaceae are categorised by the World Health Organisation (WHO) as a critical priority for the development of novel drugs and treatment surveillance strategies. Species include Escherichia coli and Klebsiella pneumoniae both of which have developed multi-drug resistant strains. Carbapenems are referred to as the last resort antimicrobial by the WHO, and without immediate action antimicrobial resistance will be the leading cause of death per year by 2050 costing 10 million lives and causing economic damage in excess of $100 trillion.

This project will contribute towards the development of novel screening methods to enable rapid identification of carbapenem-resistant organism or presence of resistance genes. There are currently three classes of carbopenem-resistance enzymes known (Class A carbapenemases, Class B Metallo-B-lactamases, and Class D OXA carbapenemases), which are further divided into several subgroups dependant on the microorganism. Techniques such as MALDI-MS, LC-MS, and GC-MS have already shown to be useful in discriminating bacteria and have been widely adopted by microbiology laboratories. In this research project, the student will use high resolution mass spectrometry to target and characterise the molecular phenotype of drug resistance mechanisms from the extracellular production of proteins, lipids, and metabolites (both volatile and non-volatile) with the aim of discovering metabolite biomarkers with diagnostic potential. The student will sample from axenic cultures and co-cultures of the same species to identify core and resistant microbial footprint, and metabolites produced from dynamic population change in acquiring resistance genes when in co-culture with drug sensitive strains. To confirm association with metabolic pathways, the student will also perform functional analysis by isotopic enrichment of culture media. The student will then perform complex univariate and multivariate analyses to develop robust classification algorithms and compare these to commonly used microbiology assays. Prospective metabolite biomarkers will then directly contribute to the formation of rapid mass-spectrometry or pre-cast identification tests which can be used for clinical identification, environment analysis (e.g. agriculture or wastewater), and applications in the food industry, thereby contributing to global surveillance of AMR.