Cells rely on spatially and temporally precise expression of proteins to drive key biological processes. Thus, decoding the underlying regulatory genome is a crucial step towards developing realistic and accurate models of cellular behavior. While transcriptional networks have been widely studied, post-transcriptional regulatory programs remain largely uncharacterized. RNA-mediated interactions are highly influenced by local secondary structures and a full characterization of post-transcriptional regulatory programs requires capturing information provided by both the secondary structure and its underlying sequence. The main objective of this K99/R00 proposal is to develop a principled integrated approach for a systematic dissection of post-transcriptional regulation in both normal and pathologic states. I have previously described a computational approach (TEISER), which has been successfully employed to reveal regulatory programs that modulate transcript stability in mammalian cells. Guided by these findings, this proposed research aims to contribute an integrated framework for studying various post-transcriptional regulatory mechanisms and to further elucidate their role in disease progression. Understanding and quantitatively modeling the regulatory networks underlying complex cellular behaviors is crucial for the successful development of promising therapeutic interventions. Recently, I discovered a structural RNA element significantly enriched among the transcripts destabilized in metastatic breast cancer cells. I identified the double-stranded RNA-binding protein TARBP2 as the factor that binds this element and modulates the stability of its targets. Further characterization of thi regulon led to the discovery of a novel TARBP2-mediated pro-metastatic regulatory program. My first goal, as part of the mentored phase of this proposal (K99), is to functionally characteriz the molecular mechanisms through which TARBP2 affects transcript stability. Based on extensive TARBP2 binding to intronic regions of its targets, I have hypothesized that TARBP2 binding results in intron-retention in bound transcripts, which in turn increases RNA degradation. While my preliminary results and recently published findings support this hypothesis, focused experiments and analyses are required to establish the regulatory mechanisms through which TARBP2 modulates its targets. While multidisciplinary in nature, my research throughout my career has been rooted in computational biology. I believe that the scientific and practical lessons that I will learn from this project, in conjunction with training received from my mentor (Dr. Tavazoie) and co-mentor (Dr. Bieniasz) and their respective labs here at Rockefeller University, will further expand my expertise as an experimental biologist and form a strong foundation for my future research. A part of my proposal, spanning both the mentored and independent phases, focuses on the experimental and computational components that are essential for enabling TEISER to become a truly integrated framework, henceforth referred to as iTEISER. Recent advances in computational power and experimental methodologies enable a more systematic approach for discovery of structural elements. On the experimental side, I will adopt the recently developed transcriptome-wide secondary structure mapping approaches based on differential nuclease digestion (or DMS-based modifications) and high-throughput sequencing to gain information from the secondary structure of cellular RNA in vitro and in vivo. I will also include an in silico folding step to assign a probability to the local formation of a gven structure. A Bayesian model can then be employed to assess the likelihood of structural elements forming based on both in vivo, in vitro, and in silico data. Combining a preliminary version of iTEISER, which relies on a simple RNA folding algorithm, with in vitro differential nuclease digestion patterns to assess the presence of a given structural element, I re-analyzed the differential transcript stability measurements in breast cancer lines. Remarkably, I discovered an A-rich stem-loop element over-represented in the transcripts destabilized in metastatic cells, which previously fell below the sensitivity of TEISER. My preliminary results, based on in-culture titration experiments, strongly support the functionality of this novel element Additionally, as part of the R00 phase, I will focus on expanding the approach outlined above to regulation of alternative splicing in breast cancer models. Regulation of splicing is a complex process involving many RNA-protein interactions that can be effectively dissected through the approach outlined in this proposal. I will take advantage of deep RNA sequencing data from metastatic and non-metastatic cells to identify the exons that are differentially spliced across multiple cell lines. I will then scan these exons and their flanking intronic sequences for common structural/linear splicing elements that explain the observed deregulations. My preliminary findings indicate that not only can these splicing elements be discovered, but also that a number of them are implicated in multiple cancer types. iTEISER will enable the identification of such elements in a systematic and unbiased manner. It would be of particular interest to identify regulatory networks of alternative splicing and study their role in disease progression. The framework outlined here has the potential to provide substantial momentum towards studying different aspects of post-transcriptional regulation (e.g. RNA localization and translation in addition to transcript stability and splicing). These, in turn, will serve as the foundation of an 01 proposal to be prepared upon the completion of the main stages of this research plan.

Public Health Relevance

Gene expression regulation plays a key role in all aspects of biology. The goal of this project is to develop an integrated framework for discovery and dissection of post-transcriptional regulatory programs and to study their role in cancer metastasis. Focused application of this approach to human disease progression enables a systems-level understanding of pathologic states and may in turn reveal novel therapeutic targets based on disrupting key regulatory interactions in living cells.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Career Transition Award (K99)
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Study Section
Subcommittee G - Education (NCI)
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Schmidt, Michael K
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Rockefeller University
Other Domestic Higher Education
New York
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Alkallas, Rached; Fish, Lisa; Goodarzi, Hani et al. (2017) Inference of RNA decay rate from transcriptional profiling highlights the regulatory programs of Alzheimer's disease. Nat Commun 8:909
Goodarzi, Hani; Nguyen, Hoang C B; Zhang, Steven et al. (2016) Modulated Expression of Specific tRNAs Drives Gene Expression and Cancer Progression. Cell 165:1416-1427
Alarcón, Claudio R; Goodarzi, Hani; Lee, Hyeseung et al. (2015) HNRNPA2B1 Is a Mediator of m(6)A-Dependent Nuclear RNA Processing Events. Cell 162:1299-308