Evolutionary consequences of variation in mutation rates for antibiotic resistance

• Applicant: Professor Dr. Hinrich Schulenburg
• Subject Area: Medical Microbiology and Mycology, Hygiene, Molecular Infection Biology ; Evolution, Anthropology ; Microbial Ecology and Applied Microbiology
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 529531135

The continuous spread of antibiotic resistance in bacterial pathogens represents a major threat to global health. Novel strategies are thus urgently required. Evolutionary processes are central to the current threat, because it is the ability of the bacteria to adapt to antibiotic therapy that underlies the evolution and dissemination of resistance. Surprisingly, evolutionary concepts are usually neglected and often completely ignored in the main programs addressing the antibiotic crisis. This is a problem, because ignorance of evolutionary processes can lead to treatment designs that enhance the spread of drug resistance. The proposed project focuses on a basic science approach to generate new ideas that may help to constrain the spread of antibiotic resistance, and more generally, to enhance our understanding of bacterial resistance evolution. In detail, the specific aims of the project are to obtain a systematic understanding of (i) the variation in mutation rates for resistance against different antibiotics, and (ii) the resulting evolutionary trade-offs. Such mutation rates for resistance and the associated trade-offs are central to the initial emergence of resistance and its subsequent spread, yet to date, their influence across pathogen genotypes and antibiotics is largely unexplored. Therefore, the proposed work will fill this current knowledge gap, using a combination of resistance analyses, whole genome sequencing, and evolution experiments, with the Gram-negative bacterium Pseudomonas aeruginosa as a model, one of the WHO priority 1 group of problematic human pathogens. The project thus assesses to what extent mutation rates for resistance vary among pathogen genotypes and different antibiotics. It further assesses to what extent the evolution of resistance associates with trade-offs, such as collateral sensitivity, which describes the phenomenon that evolution of resistance to one antibiotic causes an increase in sensitivity to another antibiotic. The expected systematic data is used as a predictive framework to define treatment protocols that either promote or constrain the evolution of resistance - either universally for P. aeruginosa or in dependence on specific genotypes.

• DFG Programme: Research Grants