Kruisresistentie en collaterale gevoeligheid aan antibiotica in bacteriën

The emergence of antibiotic resistance is outpacing new antibiotic discovery, costing human lives. An additional problem is that, by acquiring or evolving resistance to one antibiotic, bacteria can also become resistant to a second one, due to shared underlying mechanisms. This is called cross-resistance (XR) and further limits therapeutic choices. The opposite scenario, in which initial resistance leads to sensitivity to a second antibiotic, is termed collateral sensitivity (CS) and can inform cycling or combinatorial treatments. Despite their clinical relevance, our current knowledge of such interactions and understanding of the underlying mechanisms are limited. Thus, I worked on two projects, both of which aim to map XR and CS, but using different bacteria, drugs and methods: (i) high-throughput detection of antibiotic XR & CS in E. coli and their underlying mechanisms using chemical genetics on genome-wide mutant libraries to facilitate a rational design of antibiotic combinations; and (ii) assessing the impact of non-antibiotic drugs on antibiotic resistance (XR) or sensitivity (CS) of anaerobic human gut bacteria, which could be overlooked contributor to antibiotic resistance. In the first project (chapter 2), the aim was to address limited knowledge of XR and CS due to their measurement constraints by proposing high-throughput cost- and time-efficient methods of chemical genetics. To do so, we used published chemical genetics data on the impact of all Escherichia coli non-essential genes on resistance/sensitivity to 40 antibiotics. We devised a metric that robustly discriminates between known XR and CS antibiotic interactions. This metric, based on chemical-genetic profile (dis)similarity between two drugs, allowed us to infer 404 XR and 267 CS interactions, thereby expanding the number of currently known interactions for E. coli by 3-fold. We benchmarked our results by validating inferred interactions via experimental evolution. By identifying mutants driving XR and CS interactions in chemical genetics, we recapitulated known and uncovered previously unknown mechanisms and demonstrated that a given drug pair can exhibit both interactions depending on the resistance mechanism. Data on all XR and CS interactions are made available via the user-friendly Shiny app to facilitate research in understanding their mechanisms. Finally, we applied CS drug pairs in combination to reduce antibiotic resistance development in vitro. Altogether, this approach provided a systematic framework to map XR/CS interactions and their mechanisms, paving the way for the development of rationally designed antibiotic combination treatments. The gut microbiome consists of bacteria, archaea, viruses, and eukaryotic microbes, which play important roles in human health and disease (breaking down food, protecting against pathogens, and training the immune system). Changes in the composition and diversity of the microbiome towards dysbiosis have been associated with a long list of diseases. Drugs, and antibiotics, in particular, impact the gut microbiome. Surprisingly, both human cohorts and in vitro studies found that non-antibiotic drugs, which target human cells and are not prescribed as antibiotics, have been shown to directly inhibit bacterial growth at physiological concentrations, affecting a microbiome composition. This poses two main challenges - the impact of non-antibiotic drugs on dysbiosis and the contribution to the emergence/spread of antibiotic resistance. Moreover, systematic knowledge of non-antibiotic drugs ability to promote resistance (XR - resistance to a non-antibiotic provides sensitivity to a given antibiotic) or sensitivity (CS - resistance to a non-antibiotic provides resistance to a given antibiotic) to antibiotics in gut bacteria is still lacking. Whether gut bacteria can evolve high levels of resistance to non-antibiotic drugs, mechanisms of resistance, and their impact on antibiotic resistance or sensitivity remains unknown. In the second project (chapter 3), we decided to address the gaps described above using experiential evolution. This is because genome-wide libraries for bacterial species of our interest have only started to be made recently, which are essential for chemical genetics. In a collaborative project with Camille Goemans and Samir Giri, postdocs in Typas lab, we evolved resistance of eight gut bacterial species, which are commensal, pathogenic, abundant, and prevalent, to 12 commonly used non-antibiotic drugs in four lineages. This resulted in both mild and high levels of resistance, resistance to 5-Fluorouracil, a chemotherapy medication, being very high (>128-fold) and rapid (>32-fold resistance achieved in 48 hours) in several of the species. Next, we assessed the impact of the non-antibiotic drug resistance of eight gut bacteria on resistance (XR) or sensitivity (CS) to 12 chemically diverse antibiotics. As expected, the majority of interactions were neutral, but, surprisingly, CS was more frequently observed (~15% of tested interactions) than XR (~10% of tested interactions). Among non-antibiotics, resistance to toremifene, an antiestrogenic drug, resulted in the broadest CS to antibiotics. We illustrated that treatment of stool-derived communities with toremifene and subsequent antibiotic treatment result in a significant loss of community richness and diversity, potentially leading to gut dysbiosis. Although antibiotics are thought to be the main contributor to antimicrobial resistance, our work describes that non-antibiotic drugs can also promote both antibiotic resistance and sensitivity. Understanding the interplay between non-antibiotics and antibiotics is critical to minimizing the risk of selecting resistant bacterial strains, losing non-antibiotic drugs' efficacy, and promoting human gut dysbiosis. Overall, this PhD dissertation introduces a new framework for the systematic detection of XR/CS, and mechanism deconvolution, offering insights for rationally designed antibiotic combination therapies. Our findings on the impact of non-antibiotic drugs on gut bacteria emphasize the complex interplay between antibiotic and non-antibiotic resistance/sensitivity, revealing the broader implications of drug use on microbial dynamics and gut health. By expanding our understanding of resistance mechanisms and their interactions with various drugs, the research described in this PhD dissertation paves the way for more effective strategies to address antibiotic resistance and maintain the balance of the gut microbiome.