We are funded by the NIH, NSF and the Dupont Corporation.


1R01GM107466-01 REGULATION OF QUIESCENCE IN EUKARYOTIC CELLS

DESCRIPTION (provided by applicant): Regulated exit from cell division and initiation of a non-proliferative quiescent state is a criticl requirement in all organisms. Failure to maintain quiescence and inappropriate reinitiation of proliferative cell growth underlies many human cancers. Conversely, subpopulations of quiescent tumor cells may play critical roles in resistance to chemotherapy and tumor recurrence as cancer drugs typically target processes active during cell growth. Similarly, quiescent pathogenic microbes are frequently insensitive to standard drug treatments. We will use the single-celled eukaryotic microbes, Saccharomyces cerevisiae (budding yeast) and Schizosaccharomyces pombe (fission yeast) to identify the conserved networks that regulate cell quiescence. Microbes and some tumor cells enter quiescent states in response to nutrient depletion and are able to survive for prolonged periods of nutrient starvation. Our preliminary studies demonstrate that initiation of quiescence in response to defined nutrient starvation is actively regulated by conserved signaling pathways including the TORC1, Ras/Protein kinase A (PKA) and AMPK pathways. In Aim 1 we will define the conserved genetic program that controls cell quiescence by quantifying the defect in quiescence attributable to loss of function mutations in each gene in both budding and fission yeast in three quiescence-inducing conditions: carbon, nitrogen and phosphorous starvation. We will complement this genetic approach with studies of the phenotypic hallmarks of quiescence in wildtype and mutant cells to identify processes defective in quiescent mutants. In Aim 2 we will study how signaling pathways integrate environmental information to initiate the quiescence program by identifying targets of quiescence-regulating pathways and interactions between pathways using genome-wide genetic interaction mapping in quiescent conditions in both species. These experiments will allow us to identify conserved functional interactions that enable the cell to initiate quiescence n response to specific pro-quiescence signals while simultaneously receiving pro-growth signals that activate parallel pathways. We hypothesize that one means of coordinating signaling pathways is by dynamic subcellular localization of their components and we will test this hypothesis using mutants in which signaling components are mislocalized. In Aim 3 we will quantify variation in mRNA synthesis and degradation rates as cells enter quiescence using in vivo metabolic labeling of mRNAs coupled with RNA-Seq. We will use this method to test whether cells alter the stability of specific transcripts as cell growth slows and they enter quiescence. We will then identify conserved determinants of mRNA degradation variation using computational methods. By focusing on conserved signaling pathways and cellular processes that regulate quiescence we will enhance our understanding of quiescence in both normal and diseased human cells as well as microbial pathogens. A detailed understanding of cell quiescence will ultimately enable new therapeutic strategies that specifically target quiescent cells in a variety of pathological settings including cancer and microbial infections.


MCB 1244219  The Functional Basis of Genetic Interactions Underlying Quantitative Trait Variation

Most inherited traits are quantitative and determined by variation in multiple genes dispersed throughout the genome. Genetic variation in these different genes can interact in ways that are difficult to predict. The project aims to identify principles governing relationships between genes that underlie quantitative trait variation. This project will study genetic interactions in a model experimental system for naturally occurring quantitative trait variation and identify general principles regarding the functional relationships between interacting genes. The research entails selecting yeast mutants that have increased cell growth rates by performing evolution experiments in carefully defined environments. Experiments will be designed so that mutants accumulate a small number of mutations relative to the founding strain. This project will identify all the acquired genetic variation using whole genome resequencing and construct a panel of strains carrying individual mutations and all possible combinations of mutations. It will quantify growth rates for each genotype combination and determine the nature of the interactions between the gene variants. It will then study the functional relationships between genes that interact in different ways using the extensive functional annotation available for the yeast genome. Just as genes and their products are conserved across the kingdoms of life, interactions between genes, and the principles that govern the outcome of those interactions, are likely to be conserved. Thus, findings from the study will inform our understanding of the genetic architecture of quantitative traits in model and non-model organisms.  The project will contribute to an understanding of how genes interact with potential applications to improving agricultural breeding practices. This work will have a number of broader impacts in undergraduate and science education, inclusion of underrepresented groups, and enhancement of scientific understanding. The project will include undergraduates, underrepresented minorities and students from primarily undergraduate institutions. It will involve training scientists in genetics, computational biology and microbiology at the graduate and undergraduate level. In addition, the project will include an outreach program that provides high school students with the opportunity to work in a laboratory environment. High school students from diverse backgrounds will be provided with training in experimental and computational biology and undertake an independent research project in the laboratory. The project will provide mentorship so that high school students can submit their research to national science competitions. Concomitant with the proposed project, a new integrative course for undergraduate students will be developed that combines instruction in statistics, genetics and computing.