The goal of this project is to understand how the genetic material of a cell, its DNA, controls the amount of protein each cell produces, how a cell's environment stimulates it to ramp protein production up or down, and how this response differs between individuals. To function, an organism copies specific genetic information from genes in its DNA to molecules called RNA and then uses that RNA as templates to make proteins. Proteins are the molecular machines that carry out the work of cells and give cells their identities. It is not clear what causes the amount of protein that cells make to differ between individuals. It is known that this can be caused by differences in how much RNA is made. However, individuals may differ in how fast they make protein from the same amount of RNA and also how quickly they recycle protein when it gets old. The research will measure RNA and protein levels in two different strains of budding yeast as they respond to mating pheromone. It will involve both broad surveys of many proteins and detailed measurements of a targeted set. While engaging in this research, students at a primarily undergraduate institution and a postdoctoral fellow will gain interdisciplinary training in the emerging field of evolutionary systems biology. Students from a junior high school will also be engaged in scientific discovery; they will collect wild yeasts from the air and from tree bark and identify their relationships to each other in the fungal tree of life, fulfilling the requirements of the Next Generation Science Standards recently adopted by California and Washington.
Specifically, the project seeks to systematically characterize the causes of divergence in protein expression temporal dynamics using a unique combination of high-throughput phenotyping, modeling, cellular resolution quantitative trait mapping, and targeted functional analysis. The mating pheromone response network of the budding yeast Saccharomyces cerevisiae will be used as a model system. The research will identify the contribution of variation in mRNA levels, protein production rates, and protein degradation rates to protein levels. Applying this approach to a network of genes will elucidate the general trends in how protein expression variation arises. Identifying the causal polymorphisms underlying protein expression variation will point to the molecular mechanisms for such variation. These project outcomes will have far reaching effects in the fields of evolutionary genetics and systems biology.
