One of the largest problems facing modern medicine is drug resistance. Many classes of drugs can be rendered ineffective if their target is able to acquire beneficial mutations. While this is an excellent showcase of the power of evolution, it necessitates the development of increasingly stronger drugs to combat resistant pathogens. Not only is this strategy costly and time consuming, it is also unsustainable. To contend with this problem, many multi-drug treatment strategies are being explored. Previous studies have shown that resistance to some drug combinations is not possible, for example, resistance to a common antifungal drug, fluconazole, seems impossible in the presence of radicicol. We believe that in order to understand the viability of multi-drug strategies in combating drug resistance, we must understand the full spectrum of resistance mutations that an organism can develop, not just the most common ones. It is possible that rare mutations exist that are resistant to both drugs. Knowing the frequency of such mutations is important for making predictions about how problematic they will be when multi-drug strategies are used to treat human disease. This experiment aims to expand on previous research on the evolution of drug resistance in S. cerevisiae by using molecular barcodes to track ~100,000 evolving lineages simultaneously. The barcoded cells were evolved with serial transfers for seven weeks (200 generations) in three concentrations of the antifungal Fluconazole, three concentrations of the Hsp90 inhibitor Radicicol, and in four combinations of Fluconazole and Radicicol. Sequencing data was used to track barcode frequencies over the course of the evolution, allowing us to observe resistant lineages as they rise and quantify differences in resistance evolution across the different conditions. We were able to successfully observe over 100,000 replicates simultaneously, revealing many adaptive lineages in all conditions. Our results also show clear differences across drug concentrations and combinations, with the highest drug concentrations exhibiting distinct behaviors.

Protein misfolding is a problem across all organisms, but the reasons behind misfolded protein (MP) toxicity to cells are largely unknown. To better understand toxicity, I investigate if toxicity from MPs affects all cells equally or affects some cell subpopulations more than others, such as older cells. To define cell subpopulations, I optimized a cutting-edge single-cell RNA sequencing platform (scRNAseq) for yeast. By using scRNAseq in yeast, I studied the expression variability of many genes across populations of thousands of cells. I studied how the transcriptomes of single cells differ from one another in various conditions: at different stages in the growth phase and with different engineered MPs. Differences in gene expression between strains expressing misfolded vs. properly folded proteins were found, confirming previous proteomic data. Further, I found a greater number of cell subpopulations in a MP expressing strain compared to a properly folded protein expressing strain, implying more differentiated subpopulations, potentially in response to toxicity from MPs. This observation is consistent with previous observations that heterogeneity within microbial populations can be beneficial to their fitness by allowing that population to thrive in stressful environments. Thus, my data provide insights about evolutionary biology and how strains respond to stress. Further, after identifying subpopulations with a more severe transcriptional response to MPs, I studied the cells’ physiology to gain insights about why that subpopulation is sensitive to MPs and found an upregulation of markers of aging, stress response, and shortening of lifespan. Observing characteristics of cell subpopulations, I also found differences dependent on stages of the cell cycle. Overall, this study provides insights on the gene regulatory responses associated with MP toxicity by revealing which type of cells are most sensitive to this intracellular threat.