UCLA

The prevalence and strength of multi-drug antibiotic resistance have resulted in an arms race between the development of new treatment options and the evolution of resistance in bacteria. The combination of drug therapies and antibiotic cycling presents possible solutions to this problem. However, these solutions introduce new factors to consider (see Chapter 1). This dissertation uses experimental evolution to broaden our understanding of resistance evolution to multiple antibiotics used in combination. It was found that the range of antibiotic concentrations that can select for resistant mutants widens once resistance has evolved (Chapter Two). In addition, this work investigated how the genetic background of resistant strains affects the viability of four effective 3-drug combinations and each of the individual drugs that make up the combination (Chapter Three). This work also evaluated the presence and persistence of an understudied type of drug interaction, hidden suppression, in 3-, 4-, and 5- drug combinations (Chapter Four). Hidden suppression occurs when the combined effects of multiple antibiotics result in more bacterial growth than the effects of a smaller subset of those same antibiotics. Finally, this work asked if the drug interactions within a 3-drug combination can affect the rate at which resistance evolves in Staphylococcus epidermidis (Chapter Five). Using antibiotic resistance as a model system not only helps to fill the knowledge gap to solve a public health crisis but also allows me to address fundamental questions in evolutionary biology. For example, this work directly addresses how a combination of stressors affects the evolution of an entire population with varying genetic backgrounds. Overall, this research integrates this evolutionary perspective to determine how different antibiotic treatments affect the adaptation rates, adaptation frequencies, and resistance strengths of bacterial populations.