Thesis (Ph.D.)--University of Washington, 2022CHAPTER 1. To increase our basic understanding of the ecology and evolution of conjugative plasmids, we need reliable estimates of their rate of transfer between bacterial cells. Current assays to measure transfer rate are based on deterministic modeling frameworks. However, some cell numbers in these assays can be very small, making estimates that rely on these numbers prone to noise. Here we take a different approach to estimate plasmid transfer rate, which explicitly embraces this noise. Inspired by the classic fluctuation analysis of Luria and Delbrück, our method is grounded in a stochastic modeling framework. In addition to capturing the random nature of plasmid conjugation, our new methodology, the Luria-Delbrück method (‘LDM’), can be used on a diverse set of bacterial systems, including cases for which current approaches are inaccurate. A notable example involves plasmid transfer between different strains or species where the rate that one type of cell donates the plasmid is not equal to the rate at which the other cell type donates. Asymmetry in these rates has the potential to bias or constrain current transfer estimates, thereby limiting our capabilities for estimating transfer in microbial communities. In contrast, the LDM overcomes obstacles of traditional methods by avoiding restrictive assumptions about growth and transfer rates for each population within the assay. Using stochastic simulations and experiments, we show that the LDM has high accuracy and precision for estimation of transfer rates compared to the most widely used methods, which can produce estimates that differ from the LDM estimate by orders of magnitude. CHAPTER 2. Genes that undergo horizontal gene transfer (HGT) evolve in dramatically different genomic backgrounds as they move between hosts, which is in stark contrast to genes that evolve under strict vertical inheritance. Given the ubiquity of HGT in microbial communities, it is notable that the effects of host-switching on gene evolution have been largely understudied. Here, we present a novel framework to examine the consequences of host switching on gene evolution depending on the existence and form of host-dependent mutational effects. We started exploring the effects of HGT on gene evolution by focusing on a well-known antibiotic resistance gene (encoding a beta-lactamase) commonly encoded on conjugative plasmids found in Enterobacteriaceae pathogens. By reconstructing the resistance landscape for a small set of mutationally connected alleles in three species (Escherichia coli, Salmonella enterica, and Klebsiella pneumoniae), we uncovered that the landscape topography was overwhelmingly aligned with very low levels of host-dependent mutational effects. By simulating gene evolution with and without HGT using the species-specific empirical landscapes, we found that evolutionary outcomes were similar despite HGT. These findings suggest that mobile genes adapting in one species can lead to adaptation in another species. In such a case, vehicles of cross-species HGT enable a distributed form of genetic evolution across a bacterial community, where species can ‘crowdsource’ adaptation from other community members
