Modelling to predict the evolution of biofilm mutants and antibiotic resistance in Pseudomonas bacteria
Our group uses experimental evolution and modelling to predict the evolution of biofilm mutants and antibiotic resistance in Pseudomonas bacteria.
Evolutionary processes are of central importance for understanding infectious diseases, including the development of resistance to antimicrobial drugs, vaccines and the spread of new diseases from animals to humans. We study hiw bacteria evolve to form biofilms and resistance to antibiotics in the laboratory to test basic questions about the predictability of evolution. Because we use bacterial species that cause serious infections in humans and where the problems of resistance are greatest, our studies can be used to inform clinical research and contribute to developing new antibiotics and identifying new resistance mechanisms. Since the bacteria cannot adapt to future conditions, we hope to be able to stay one step ahead of the bacteria to lure them into evolutionary dead ends.
The increasing antibiotic resistance of various pathogenic bacteria threatens the entire modern healthcare system and risks making common uncomplicated infections deadly once again. Since few new antibiotics are developed, the existing antibiotics must be used in an optimal way to reduce the risk of resistance. This is a particularly difficult challenge when several different antibiotics are used for a long time, for example in the treatment of chronic lung infections in people with cystic fibrosis or in people with weakened immune systems, as it leads to multi-resistance and increased formation of biofilms through a variety of mutations that are unique to each patient. Biofilms are formed by microorganisms by increased adhesion to surfaces and other cells and the formation of an extracellular matrix consisting of various extracellular polymeric substances. This leads to increased antibiotic resistance and makes it more difficult for the immune system to fight the microorganisms, which can lead to chronic infections. The risk of future resistance to another antibiotic and how virulent these mutants depend on the previous evolution of the bacterium. If it was possible to predict evolution and understand why certain evolutionary pathways are chosen, this knowledge could be used to guide and control the evolutionary process to make treatments more effective.
We use a combination of mathematical modeling and experimental evolution to attempt to predict and control the evolution of multi-resistance and biofilms. We have previously developed mathematical models of molecular networks that can predict how Pseudomonas bacteria can mutate to form biofilms and have shown that these models can be used to predict the evolution of different species of Pseudomonas bacteria.
We are now developing new mathematical models to predict and control the evolution of multi-resistance to antibiotics in pathogenic Pseudomonas and Acinetobacter bacteria. The models are used to make simulations of how different mutations affect the growth rate, which is crucial for the bacterium's continued spread, and if there are unusual evolutionary pathways to increased antibiotic resistance that are usually missed in laboratory experiments. These predictions are then tested in the laboratory through experimental evolution in the presence of various clinically relevant antibiotics. The bacteria then develop increased multiresistance through mutations that are identified by sequencing their genome. The multiresistant bacteria are then characterized with respect to the level of resistance to various antibiotics and growth ability, and the results are used to continuously improve the models. Finally, we will use our improved models to predict how we can control the bacteria evolution to dead ends where the bacteria can no longer develop increased resistance without a large cost in growth rate and how different combinations of antibiotics can be used in the best way.