Cells respond to environmental signals by readjusting their physiology through transcriptional, post- transcriptional and post-translational regulation of gene expression. It has become evident that non-coding RNA-mediated post-transcriptional regulation plays important roles in signal transduction, development, and diseases such as cancer. However, we are yet to fully understand when and how cells choose between transcriptional and post-transcriptional regulatory mechanisms. One hypothesis is that the degree to which the two mechanisms are deployed depends on the rapidity with which an organism has to alter and retool its physiology, with rapid responses relying more on post-transcriptional mechanisms. To test this hypothesis, genome-wide dynamics of mRNA synthesis, degradation and translation efficiency will be measured in diverse environmental perturbations to characterize relationships between transcriptional and post-transcriptional regulation (Aim 1). These studies will be conducted in Halobacterium salinarum NRC-1, which is a simple tractable model system with a substantial knowledgebase of genetic and genomic tools, algorithms, software, databases and well characterized physiological responses to diverse environmental changes with associated molecular measurements of diverse systems properties including fitness, transcriptome structure, gene expression, protein-protein interactions, and protein-DNA interactions. The rich information in this knowledgebase will be used to generate biologically meaningful hypotheses regarding functions of specific ncRNAs, which will guide characterization of selected ncRNA mechanisms (Aim 2). Further, a transcriptional regulatory model exists that can explain and predict how this organism responds to changes in diverse environmental factors - including conditions that were not used to construct this model. Here, by integrating transcriptional and translational dynamics on a systems scale, it will now be possible to discover most post- transcriptional regulatory mechanisms operating under selected environmental conditions. Integration of post- transcriptional regulation into the existing gene regulatory network model should significantly improve mechanistic accuracy and predictive power of the model (Aim 3). Iterative model refinement will occur by testing predicted influences of post-transcriptional control. This modeling will enable prediction and explanation of the systems impact of combining transcription factor and ncRNA-based regulation by allowing unprecedented genome-wide analysis of network architecture for sub-circuits involving ncRNAs. Such analysis will address the central question of when and how cells employ transcriptional and post-transcriptional mechanisms. Further, this modeling will drive hypothesis generation regarding the biology of environmental response. By developing approaches for the incorporation of post-transcriptional regulation into network modeling, this work will lay the groundwork for future research leveraging post-transcriptional regulation as a promising tool for network optimization in re-engineering for bioproduction and therapeutics.
I will investigate the function of post-transcriptional regulation in environmental response to establish a fundamental understanding of how transcriptional and post-transcriptional regulation are integrated within regulatory networks. Dysregulation of ncRNAs is involved in disorders spanning human life, from congenital abnormalities to cancer and neurodegeneration, and a strong understanding of the basic biology of how post- transcriptional regulation fits into network structures will be immensely important to select drug targets for maximum efficacy and minimize off-target effects. Further, by developing strategies to integrate transcriptional and post-transcriptional regulation into predictive regulatory network models, this work will provide insight into how the activity of candidate therapeutic ncRNAs can be modeled.
|Beer, Karlyn D; Wurtmann, Elisabeth J; Pinel, Nicolás et al. (2014) Model organisms retain an ""ecological memory"" of complex ecologically relevant environmental variation. Appl Environ Microbiol 80:1821-31|
|Wurtmann, Elisabeth J; Ratushny, Alexander V; Pan, Min et al. (2014) An evolutionarily conserved RNase-based mechanism for repression of transcriptional positive autoregulation. Mol Microbiol 92:369-82|