UCLA

Environmental stressors come in many different forms and affect every level of ecological organization. In a natural environment, organisms experience mixtures of stressors at various doses or strengths of exposure that could be constantly changing, both temporally and spatially. Complex environmental regimes and interactions among multiple stressors can have a profound effect on both the short-term fitness of organisms and their long-term evolutionary dynamics. This dissertation uses a bacterium-drug system to study drug-drug, drug-temperature, and gene-gene interactions. The first question we ask is how bacteria evolve to combat new stressors. Through an interaction network clustering approach followed by transcriptomic analysis, we show that Escherichia coli may have co-opted its cellular response to temperature stress for antibiotic stress because these stressors share similar physiological effects. We also found that antibiotic stress modifies the thermal response of E. coli by altering both its optimal growth temperature and temperature breadth. Next, we ask how we can identify and quantify dose-dependent drug interactions, including interactions among more than two components, i.e., higher-order interactions. To do this, we introduce a novel visual representation termed interaction landscape to directly map local dose-dependent interactions and the transitions between different interaction classes. Finally, we ask how gene-gene interactions change in fluctuating environments. We showed that changes in the type and magnitude of environmental fluctuations could affect fitness due to the differences in epistatic interactions of mutations. We quantify structural features of fitness landscapes by calculating the ruggedness across broad concentration gradients of various antibiotics. We show that fluctuating environments frequently lead to epistasis sign switches (from negative to positive or vice versa) on the pairwise level, as a potential mechanism to either promote specialization or maintain genetic variation. Overall, this dissertation combines experimental, mathematical, and computational biology to identify and understand the structures and patterns of interactions at different scales and their effects on the fitness and eco-evolutionary dynamics of bacterial populations.