Speaker
Description
There is great deal of interest in exploiting evolutionary tradeoffs to combat drug resistance. Instances of drug resistance have been steadily increasing creating considerable human health and economic impacts. In the United States, the CDC reports that ~35,000 deaths and $55 billion can be attributed to drug resistant infections per year. Collateral sensitivity (CS), where developing resistance to drug A results in sensitivity to drug B, is a type of evolutionary tradeoff that can be exploited to counter drug resistance. While some studies suggest that CS is common, others, in both cancer cells and bacteria populations, have demonstrated that CS is unpredictable and nonrepeatable. This is a problem if our goal is to predict how cell populations evolve to drug A in order to select the most effective drug B. One reason that previous work has not reached a consensus on the likelihood of evolutionary tradeoffs, like CS, that that most work utilizes small experiments with low replicate numbers and cannot survey the wide array of mutations that can contribute to drug resistance. Here, we use a barcoded S. cerevisiae system to track a large population of yeast strains as they develop resistance to different drugs at varying concentrations (drug A; n=12). This system tracked hundreds of thousands of replicate yeast lineages, such that it has the potential to reveal many different adaptive mutants that protect against each drug. From these evolved populations, ~24,000 strains were then subsequently challenged by a second drug (drug B; n=12) and mutants with interesting CS profiles were sequenced. These experiments provide a quantitative understanding of the likelihood of CS, how this likelihood changes depending on drug concentration, and whether patterns of CS are predictable across different mutants. Finally, we are developing a new high-throughput single cell DNA sequencing method that allows us to characterize the genetic basis of drug resistance and CS directly from pooled samples without having to physically isolate individual mutants. We hope this technology will dramatically increase the throughput with which we and others can interrogate the genetic basis of adaptation.
