Bacterial infections are becoming increasingly hard to treat due to resistance to at least one antibiotic, termed antimicrobial resistance (AMR). One strategy to limit the emergence of resistance is to ensure the antibiotic chosen to treat an infection will be successful. To support this, diagnostic tests and tools are being developed to predict resistance through the detection of AMR genes or mutations which confer AMR from genomic data. However, mutations to AMR gene or changes in how they are expressed can result in a mismatch between predicted and actual AMR, limiting the usefulness of these tests. Therefore, it is important to identify and characterise mutations which change the function of resistance genes and/or increases their expression. However, it is often difficult to identify these mutations before the antibiotics are used in clinical practice due to the use of nutrient rich growth media, not representative of the human body. An important example of this is resistance to β-lactam/β-lactamase inhibitor combinations. The mechanism of action of these antibiotics involves the β-lactamase inhibitor binding to the β-lactamase, rendering the cell susceptible to the β-lactam. Often, resistance is conferred by mutations which reduces the affinity of the β-lactamase inhibitor to the β-lactamase or by increasing the expression of the β-lactamase, overcoming the action of the β-lactamase inhibitor.
In this project, we will first use a targeted, mutagenesis approach to identify all mutations in clinically important β-lactamases resulting in β-lactam/β-lactamase inhibitor combinations. The effect of these mutations on growth, enzyme activity, and resistance to other antibiotics will be determined. Secondly, we will investigate the link between hyperproduction of β-lactamases and the corresponding decrease in susceptibility to β-lactam/β-lactamase inhibitor combinations. These will initially be performed in growth media and then replicated in in vitro models which represent the human body to produce robust, clinically relevant data.
It is anticipated that data from this project will be used to inform a bioinformatic tool to predict resistance to β-lactam/β-lactamase inhibitor combinations using genomic data. This will directly improve the development of diagnostic tests to predict AMR to β-lactam/β-lactamase inhibitor combinations. As well as inform treatment options for bacterial infections, reducing the emergence of AMR and improve treatment outcomes.
The project will include molecular biology, cell culture, microbiology, next generation sequencing and bioinformatics. Full training will be offered for all aspect of the project.