MARS: Minimizing Antibiotic-induced Resistant Superinfections

Antibiotics have transformed medicine, saving millions of lives since they were first used to treat a bacterial infection over 80-years ago. But the use of antibiotics also comes with significant personal risks. Firstly, antibiotics prescribed to treat a specific infection also act on the commensal species living within the patient, leading to loss of colonization resistance.

Secondly, the use of antibiotics selects for drug-resistance, which can cause overgrowth of resistant strains residing in the patient's microbiota leading to hard-to-treat superinfections. Ever increasing rates of antibiotic resistance have led to a high risk that patients harbour potential pathogens that are resistant to antibiotics within their microbiota prior to therapy, making it a vital priority to understand the fundamental mechanisms that lead to blooms of resistant pathogens during antibiotic treatment and develop strategies to minimize this phenomenon.

Here, I propose a unique interdisciplinary approach to determine the fundamental rules that describe and predict how antibiotic treatment will perturb a specific microbiota containing pre-existing resistant pathogens. We will focus on extra-intestinal pathogenic Escherichia coli (ExPEC), which is commonly resistant and can persist in the gut microbiota asymptomatically over long periods.

In particular, we will systematically deconvolve the effect of antibiotics in causing dysbiosis to commensal species and specifically selecting for the overgrowth of resistant strains. Combining novel quantitative experimental techniques and mathematical analysis we will use this data to build a predictive model to identify patients with microbiomes at high risk of antibiotic-induced resistant-ExPEC overgrowth.

Finally, we will test pre-emptive approaches to reduce ExPEC colonization and minimize antibiotic-induced resistant-ExPEC blooms.