| Abstract |
Mobile genetic elements (MGEs) play a key role in bacterial evolution by moving genes between bacterial strains and species, which can dramatically alter bacterial phenotypes, such as their resistance to antimicrobials and virulence traits. In view of the emerging antibiotic resistance crisis, there is a pressing need to better understand how MGEs spread and evolve in complex microbial communities. I propose to combine experimental, bioinformatics and theoretical approaches to examine the relative importance of different bacterial defences in restricting MGE transmission between species and across communities. I will focus on the "ESKAPEE" pathogens, which cause hard to treat infections due to their rapid acquisition of antibiotic resistance genes that are vectored by different types of MGEs. In my bioinformatics analyses, I will analyse >62,000 ESKAPEE genome sequences to identify host defence genes whose presence correlates with reduced MGE loads. I will then carry out fluorescence-activated cell sorting experiments with a large collection of clinical isolates and their MGEs to directly measure the contribution of host defence genes in driving variation in MGE infection success and maintenance. I will apply both reverse and forward genetics approaches to unambiguously demonstrate causality between defence genes and MGE uptake and stability. Finally, I will measure the spread of MGEs between hundreds of pairwise combinations of isolates, both within species as well as between species, to understand how host defence genes shape the infection network within a community. I will use machine learning to predict how the network structure shapes the spread of MGEs through the community, which will be tested experimentally using synthetic communities. This research program will make key contributions to our understanding of the spread of different MGEs through microbial communities in complex environments. |
