Antibiotic resistance is a quickly emerging public health crisis, accounting for more than 700,000 annual global deaths. Global human antibiotic overuse and misuse has significantly expedited the rate at which bacteria become resistant to antibiotics. A renewed focus on discovering new antibiotics is one approach to addressing this crisis. However, it alone cannot solve the problem: historically, the introduction of a new antibiotic has consistently, and at times rapidly, been followed by the appearance and dissemination of resistant bacteria. It is thus crucial to develop strategies to improve how we select and deploy antibiotics so that we can control and prevent the emergence and transmission of antibiotic resistance. Current gold-standard antibiotic susceptibility tests measure bacterial growth, which can take up to 72 hours. However, bacteria exhibit more immediate measurable phenotypes of antibiotic susceptibility, including changes in transcription, after brief antibiotic exposure. In this dissertation I develop a framework for building a paper-based cell-free toehold sensor antibiotic susceptibility test that can detect differential mRNA expression. I also explore how long-term lab evolution experiments can be used to prospectively uncover transcriptional signatures of antibiotic susceptibility. Paper-based cell-free systems provide an opportunity for developing clinically tractable nucleic-acid based diagnostics that are low-cost, rapid, and sensitive. I develop a computational workflow to rapidly and easily design toehold switch sensors, amplification primers, and synthetic RNAs. I develop an experimental workflow, based on existing paper-based cell-free technology, for screening toehold sensors, amplifying bacterial mRNA, and deploying sensors for differential mRNA detection. I combine this work to introduce a paper-based cell-free toehold sensor antibiotic susceptibility test that can detect fluoroquinolone-susceptible E. coli. Next, I describe a methodology for long-term lab evolution and how it can be used to explore the relationship between a phenotype, such as gene expression, and antibiotic resistance acquisition. Using a set of E. coli strains evolved to acquire tetracycline resistance, I explore how each strain's transcriptome changes as resistance increases. Together, this work provides a set of computational and experimental methods that can be used to study the emergence of antibiotic resistance, and improve upon available methods for properly selecting and deploying antibiotics. / 2023-03-17T00:00:00Z

To combat the ever-growing clinical burden imposed by antibiotic-resistant pathogens, multiple-antibiotic treatments are increasingly being considered as promising treatment options. The impact of multiple-antibiotic treatments on the evolution of resistance is not well understood however, and debate is ongoing about the effectiveness of various multiple-antibiotic treatments. In this thesis, I investigate how aspects of multiple-antibiotic treatments impact the rate of evolution of antibiotic resistance in the opportunistic human pathogen Pseudomonas aeruginosa. In particular, I look at the impact of interactions between antibiotics in combination on the evolution of resistance, and how creating heterogeneity in the antibiotic environment by rotating the antibiotics used may change the rate of evolution of resistance. I characterise the interactions present in 120 combinations of antibiotics and find that the type of interaction can be predicted by the mechanism of action of the antibiotics involved. I investigate the effect of a subset of these combinations on the evolution of antibiotic resistance. My results refute the influential but poorly-evidenced hypothesis that synergistic combinations accelerate the evolution of resistance, even when synergistic combinations have the same inhibitory effect on sensitive bacteria as additive or antagonistic antibiotic combinations. I focus on a combination of the antibiotics ceftriaxone and sulfamethoxazole and test whether it is more effective in preventing the evolution of resistance than predicted by the inhibitory effect of the combination on sensitive bacteria. I do not find the combination to be more effective than predicted. Finally, I create heterogeneous antibiotic environments by rotating the antibiotic present at different rates. For the first time in a laboratory setting, I test how varying the rate of fluctuation in the antibiotics present in a heterogeneous antibiotic environment impacts the rate of evolution of resistance. Unexpectedly, I find the rate of evolution of resistance increases with increasing levels of antibiotic heterogeneity.