Each individual's genome gives rise to a large array of cellular phenotypes depending on which genes are expressed. Gene expression is thus an essential step in converting genotypes into phenotypes. The regulation of gene expression is responsive to the environment, allowing changes in phenotypes that can help an organism adjust to a variety of living conditions. To date, most studies of genetic variation affecting gene expression have been conducted within a single environment, making it difficult to know how genetic changes affect the environment-dependent aspects of gene regulation. This project will fill this knowledge gap by examining differences in regulatory networks that exist among strains and species of yeast. Experimental and computational advances developed during the course of this work will help other researchers who study gene expression and the evolution of regulatory networks. A museum exhibit will also be developed during the course of this work and will help communicate the importance of evolutionary biology to the general public.
Mutations in either cis-regulatory DNA sequences or trans-regulatory factors that interact with these cis-regulatory sequences can alter gene expression. Over the last 15 years, genomic studies of gene expression have investigated the relative contributions of cis- and trans-regulatory changes to expression differences within and between species, but these studies have focused almost exclusively on levels of gene expression assayed at one point in time, under one set of conditions. Consequently, the impact of cis- and trans-regulatory changes on dynamic aspects of gene expression, such as how expression changes in different environments or throughout the cell cycle, remains less understood. The proposed project will fill this knowledge gap by identifying the genetic mechanisms responsible for variation in dynamic gene expression within and between species. To achieve this goal, the researchers will work at multiple evolutionary scales, employ both genomic and single-gene methods, integrate cutting-edge empirical and computational tools, and harness the complementary resources and expertise of two labs that have been studying gene expression for more than a decade. More specifically, a panel of budding yeast strains, species and hybrids characterized in the Wittkopp lab will be profiled under a wide array of conditions using a setup available in the Barkai lab. Data analysis will proceed through tight collaboration: the Barkai lab will perform the initial processing and quality controls of the data, and establish a data structure amendable for high-throughput analysis and visualization. Both labs will then contribute to the establishment of a bioinformatics platform that will integrate the data with existing genomics and functional genomics data. Relying on its expertise in evolutionary theory, the Wittkopp lab will perform in-depth biological analysis of the data to identify recurrent principles governing the variation between networks and define specific predictions exemplifying these principles. Both labs will contribute to follow-up experiments designed to validate specific predictions.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
