The emergence of antimicrobial resistance is a major global threat to modern medicine. The rapid dissemination of resistant pathogens and the associated loss of efficacy of many important drugs needs to be met with the development of new antibiotics and alternative treatment options. A better understanding of the evolution of resistance could help in developing strategies to slow down the spread of antimicrobial drug resistance.
In this thesis we investigated the evolution of resistance to two important antibiotics, rifampicin and ciprofloxacin, paying special attention to the resistance patterns occurring with high frequency in clinical isolates.
Rifampicin is a first-line drug in tuberculosis treatment and resistance to this valuable drug limits treatment options. Our work on rifampicin resistance helps to explain the extreme bias seen in the frequency of specific resistance mutations in resistant clinical isolates of M. tuberculosis. We identified an important interplay between the level of resistance, relative fitness and selection of fitness-compensatory mutations among the most common resistant isolates.
Fluoroquinlones are widely used to treat infections with Gram-negatives and the frequency of resistance to these important drugs is increasing. Resistance to fluoroquinolones is the result of a multi-step evolutionary process. Our studies on the development of resistance to the fluoroquinolone drug ciprofloxacin provide insights into the evolutionary trajectories and reveal the order in which susceptible wild-type E. coli acquire multiple mutations leading to high level of resistance. We found that the evolution of ciprofloxacin resistance is strongly influenced by the mutation supply rate and by the relative fitness of competing strains at each successive step in the evolution. Our data show that different classes of resistance mutations arise in a particular, predictable order during drug selection. We also uncovered strong evidence for the existence of a novel class of mutations affecting transcription and translation, which contribute to the evolution of resistance to ciprofloxacin.
In evolution alternative genetic trajectories can potentially lead to similar phenotypic outcomes. However, certain trajectories are preferred over others. These preferences bias the genomes of living organisms and the underlying processes can be observed in ongoing evolution.
We have studied a variety of biases that can be found in bacterial chromosomes and determined the selective causes and functional consequences for the cell. We have quantified codon usage bias in highly expressed genes and shown that it is selected to optimise translational speed. We further demonstrated that the resulting differences in decoding speed can be used to regulate gene expression, and that the use of ‘non-optimal’ codons can be detrimental to reading frame maintenance. Biased gene location on the chromosome favours recombination between genes within gene families and leads to co-evolution. We have shown that such recombinational events can protect these gene families from inactivation by mobile genetic elements, and that chromosome organization can be selectively maintained because inversions can lead to the formation of unstable hybrid operons.
We have used the development of antibiotic resistance to study how different bacterial lifestyles influence evolutionary trajectories. For this we used two distinct pairs of antibiotics and disease-causing bacteria, namely (i) Mycobacterium tuberculosis that is treated with rifampicin and (ii) Escherichia coli that is treated with ciprofloxacin. We have shown that in the slow-growing Mycobacterium tuberculosis, resistance mutations are selected for high-level resistance. Fitness is initially less important, and over time fitness costs can be ameliorated by compensatory mutations. The need for rapid growth causes the selection of ciprofloxacin resistance in Escherichia coli not only to be selected on the basis of high-level resistance but also on high fitness. Compensatory evolution is therefore not required and is not observed.
Taken together, our results show that the evolution of a phenotype is the product of multiple steps and that many factors influence which trajectory is the most likely to occur and be most beneficial. Over time, selection will favour this particular trajectory and lead to biased evolution, affecting genome sequence and organization.