Department of Chemical and Biological Engineering - PhD Thesis Presentation - Proteomic Study of Antibiotic Tolerance and Resistance in Bacterial Populations from Adaptive Laboratory Evolution

It is known that bacterial populations could quickly adapt to antibiotic treatments by becoming tolerant and resistant to the drug. Recently, adaptive laboratory evolution (ALE) has proven to be a useful strategy to generate mutants that are adapted to different treatment conditions. This strategy opens up a new avenue for studying antibiotic tolerance and resistance since it is robust and can be highly parallelized, generating numerous mutants in a short time. Combining it with proteomics, one can cross-compare multiple resistant and tolerant strains that evolved from the same ancestor to study their differential adaptation strategies. This can be a viable roadmap to mapping the “tolerome”, the collection of genes/proteins that are important for tolerance. This information will be of tremendous clinical value because it has become increasingly recognized that tolerance accelerates the evolution of resistance, and diagnostic tools and suitable therapy for combatting tolerance will also be key to preventing resistance development.
In the first part of the thesis (chapters 2-3), we devised an experimental strategy to study antibiotic tolerance in E. coli model organism by using the combination of adaptive laboratory evolution (ALE) and proteomics for cross-comparison across multiple mutants. We showed that time-course proteomics is also useful to decipher the resuscitation mechanism of the persister cells from one of the tolerant mutants. Resuscitation of persisters is currently still under-studied, and targeting the key players during this process could also be used for therapeutic intervention. In the second part of the thesis (chapters 4-7), we applied this strategy to a more clinically relevant methicillin-resistant S. aureus (MRSA) pathogen and explored how different experimental protocols mimicking clinical conditions affect the evolutionary dynamics of tolerance/resistance. For instance, we evolved MRSA populations using different drug combinations treatment (e.g. daptomycin and rifampin), treated them at different growth phases (exponential and stationary phase), and incorporated population bottlenecks following antibiotic exposure to see how sudden and severe reductions in population size affect the evolution of tolerance and resistance. Through these laboratory evolutions, we generated multiple distinct tolerant and resistant MRSA strains, and by performing deep proteome profiling of each of the mutants, we found the key players that were responsible for tolerance and resistance. In the last part of our study (chapters 8-9), we discovered elasnin as an effective antibiofilm and antibacterial agent to eliminate MRSA pathogen, which mainly interferes with the cell division process and generates cell-wall defective cells that are more sensitive to other antimicrobials.