A hospital patient with a lung infection carries a population of *Pseudomonas aeruginosa* bacteria that is constantly evolving under the pressure of antibiotic drugs. This matters because antibiotic resistance kills, yet the precise ecological and evolutionary rules that determine whether resistance emerges, persists, or disappears in a single patient remain poorly understood. Doctors cannot currently predict which treatment will drive resistance or which will let it fade away. The project will analyse bacterial samples from a large clinical trial (ASPIRE-ICU), tracking how individual strains mutate, acquire resistance from other bacteria, or die off under different antibiotic regimes. By combining patient data with lab-based experimental evolution, the team will directly test whether high fitness costs—the biological price bacteria pay for being resistant—can reliably accelerate the loss of resistance when antibiotics are removed. If successful, this work could give clinicians a predictive framework: a way to choose antibiotic regimens that minimise the chance of resistance emerging, and to identify when stopping treatment might actually clear resistant strains faster. The research is fundamental science, but it addresses a concrete clinical gap—the gap between knowing resistance is bad and knowing how to stop it.
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Antibiotic resistance in pathogenic bacteria poses a fundamental threat to human health. The overarching aim of this project is to determine the ecological and evolutionary processes that drive the emergence and loss of antibiotic resistance in hospitalized patients. The first objective of this project is to uncover drivers of resistant infections. Specifically, we will determine the relative contribution of (i) de novo variation, (ii) pre-existing variation and (iii) superinfection to the emergence of resistance as a function ofantibiotic treatment. The second objective of the project is to determine key factors that shape the stability of resistance in patients.Specifically, we will (i) measure the cost of resistance, (ii) test for compensatory evolution in resistant populations and (iii) test the hypothesis that high fitness costs accelerate the loss of antibiotic resistance in the absence of antibiotic treatment. The third objective of this project is to determine the impact of antibiotic treatment on genome evolution. Specifically, we will (i) measure the impact of antibiotic treatment on the rate of molecular evolution and (ii) systematically identify genetic drivers of resistance. To achieve these objectives we will use samples and data collected during ASPIRE-ICU, a large scale clinical trial of infections caused by the opportunistic pathogen P.aeruginosa. Characterization of isolates from large numbers of longitudinally sampled patients with varying antibiotic treatment regimes will allow us to study the phenotypic and genomic responses to antibiotic treatment. Experimental evolution with clinical isolates will let us directly test the role of key variables that are predicted to shape antibiotic resistance. The novel combination of clinical sampling, experimental evolution and genomic analyses will allow us to generate unprecedented insights intIn this project we will investigate the ecological and evolutionary drivers of antibiotic resistant infection.
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