Dynamically compartmentalized control of gene expression by messenger ribonucleoprotein granules

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Project Summary Cells survive rapidly changing environments through adaptation mediated by sophisticated signaling and gene regulatory processes. Posttranscriptional regulation by messenger ribonucleoprotein (mRNP) granules plays an important role in the modulation of the proteome in response to environmental changes. Processing bodies (PBs) and stress granules (SGs) are stress-induced mRNP granules, conserved from yeast to mammals, that coordinate to regulate the localization, translation, degradation and storage of mRNAs.Given their diverse effects on gene expression, PBs and SGs are implicated in many diseases, especially neurodegenerative diseases and cancers. Although PBs/SGs are highly dynamic in nature, which proves crucial for their functions, the majority of previous studies have focused only on the biochemical characteristics of these granules with measurements made at static time points. How PBs/SGs are dynamically regulated and their functional roles under physiological conditions remain largely unclear. Our recent results revealed that the protein kinase A (PKA)-regulated formation of PBs and SGs plays a central role in regulating stress responsive gene expression, promoting a long-lasting cellular memory to facilitate future stress adaptation. Building upon these findings, we will combine experiments with modeling to systematically investigate how PBs/SGs process dynamic inputs and control gene expression and long-term stress responses in yeast cells. In Aim 1, we will track the dynamics of PBs/SGs, mRNAs, and proteins for representative stress responsive genes in single cells in response to various dynamic environmental/signaling inputs. Based on these dynamic data, we will develop a computational model to simulate and predict how PBs/SGs decode input dynamics and control the mRNA fates and protein expression dynamics under rapidly changing environments. In Aim 2, we will track inheritance of PBs/SGs from mother cells by their progenies over many generations using our recently- developed yeast mother device, and will evaluate the role of granule inheritance in gene expression and stress resistance in cell lineages. Using these data, we will construct a stochastic model to quantitatively evaluate the contributions of mRNP granule inheritance to the heterogeneity across cell lineages and in clonal populations. In Aim 3, we will systematically characterize the yeast proteome dynamics in response to environmental changes and evaluate the roles of PBs and SGs in controlling these dynamics using a high-throughput "2K DynOMICS" microfluidic platform. These data will be used to develop a systems-level dynamic model of gene expression control by PBs/SGs. The completion of these aims will significantly advance our understanding about how PBs/SGs operate and function under rapidly changing environments and will lead to the generation of predictive models that will provide mechanistic insights into the PB/SG-mediated control of gene expression. !