DISSERTATION RESEARCH: The Evolution of Plant Drought Tolerance and Gene Function Across Historic Frequency Gradients

点击翻译按钮获取中文摘要

This research will investigate the function of genes possibly involved in drought adaptation in wild populations of the plant Arabidopsis thaliana. Droughts are one of nature?s most devastating forces, affecting millions of people each year. Yet as agents of strong selection on plant populations, they are also valuable for understanding basic evolutionary processes, such as discovering the genes involved in adaptation. Furthermore, studying plant evolution in the context of drought may produce the critical knowledge needed to develop drought tolerant crops. Using a global map of historic drought frequency based on remote sensing data, populations were identified that experience very different drought frequencies. From data on genetic differences among those populations, 20 genes were identified that will be studied for their molecular and phenotypic effects. This work is expected to yield key insight into the evolution of gene function, and to confirm genes underlying drought tolerance traits. The results of studying these genes may be of great value to breeding programs working to improve drought tolerance in crop species. In addition, the project will involve mentoring both undergraduate and high school students, including individuals from groups that are underrepresented in the sciences. Historic drought frequency significantly predicts important drought tolerance traits, flowering time and water use efficiency, among natural accessions of Arabidopsis thaliana. The project will investigate the effects of loss of function alleles on 20 genes involved in flowering time or water use efficiency. A reverse genetics approach based on T-DNA gene knockouts will be used to artificially induce mutations in these genes in a common genomic background. The project will quantify the effect sizes of these hypothesized genes on flowering time and water use efficiency. It will also characterize effects on whole genome expression profiles and abiotic signaling cascades using RNAseq.