Stochastic gene expression in differentiation and development

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The overarching goal of this proposal is to the explore the role of stochastic gene expression during development and cell differentiation using the nematode worm Caenorhabditis elegans and mammalian cell lines as experimental model systems. In the first program of this proposal I describe a single molecule mRNA detection technique that allows accurate measurements of stochastic expression of endogenous genes in developmental and differentiation pathways. We will use this technique to determine expression fluctuations in the C. elegans endodermal specification pathway. Next, we will use this technology to better understand the phenomenon of incomplete penetrance. Many C. elegans mutants exhibit the phenomenon of incomplete penetrance, where only a certain fraction of genetically identical mutants exhibit the defective mutant phenotype, while the remaining organisms are often indistinguishable from the wild-type. We will test the hypothesis that these mutants allow stochastic events in the gene expression program controlling development to influence the organism's ultimate developmental outcome. Finally we will use the mRNA counting method to determine the stochastic component of mammalian cell fate decisions using the glucocorticoid pathway and hemapoietic progenitor cells as experimental model systems. The second program focuses on elucidating the roles of microRNAs in controlling gene expression fluctuations. During the last decade it has become clear that microRNAs play crucial roles in diverse biological processes including development and differentiation. Program 2 will explore the potential roles for microRNAs controlling gene expression fluctuations. First, we will experimentally determine the relationship between the concentration of a microRNA and its target in a single cell. Second, we will test the hypothesis that in order to dampen translational amplification of mRNA fluctuations, miRNAs may be employed to selectively lower the translation rate for genes that can fluctuate significantly. Finally, we will explore if microRNA-mediated feedback networks can lower fluctuations in target gene expression.