It is increasingly recognized that mammalian biologic complexity is amplified enormously by the regulation of RNA complexity. This regulation is mediated by hundreds of RNA-binding proteins (RBPs) and microRNAs (miRNAs) through sequence-specific interactions with their targets. Misregulation of RNAs can cause a number of genetic diseases. Despite their critical roles, efforts to study RNA complexity are largely impeded by the difficulty to accurately infer global RNA-regulatory networks, due to deficiencies of high-throughput experimental technologies and effective computational methods. This application describes a combination of experimental and computational approaches to advance the understanding of in vivo RNA regulation in mammalian brains at the systems level. On the experimental side, I will take advantage of the mouse genetic systems and high-throughput technologies established in the Robert Darnell laboratory at Rockefeller University, where the mentored phase of research will be performed. HITS-CLIP will be used to generate genome-wide maps of biochemical footprints of several important neuronal RBPs;exon-junction microarrays and RNA-Seq will be used to generate nucleotide-resolution transcriptome profiles for comparative analysis of wild type brains and brains lacking individual RBPs, and of developing mouse brains. On the computational side, integrative modeling techniques, such as hidden Markov models (HMMs) and Bayesian networks, will be employed to probabilistically model biochemical, structural, genomic, and evolutionary information from multiple data sources, so that highly predictive models of RBP/miRNA target sites and RNA-regulatory networks can be developed. Substantial preliminary data have been obtained from the analysis of RNA splicing regulation by Nova, which demonstrates the effectiveness of such an integrative genomic strategy to define accurate and comprehensive RNA-regulatory networks and obtain novel biological insights. Here I propose to further improve these methods, and extend the strategy to other RBPs, individually or in combination, and other steps of RNA regulation. The regulatory mechanisms discovered in genetically engineered systems will be further extended to study the combinatorial and dynamic RNA regulation in developing brains and in different brain regions. While I have received extensive training in machine learning and computational studies of RNA regulation, this career development award will allow me to expand my existing skills and continue to develop my experimental skills. The excellent environment in the Darnell lab and Rockefeller University will greatly facilitate not only the mentored research, but also my transition to an independent academic position. Together, the proposed study will pave the road to launch my future investigations that aim to decode rules governing RNA regulation in normal biological processes and human diseases.

Public Health Relevance

RNA regulation is critical for the diversification of mammalian gene expression, organism complexity, and human diseases. The proposed study is to investigate the fundamentals of neuronal RNA complexity at the systems level, using a combination of high-throughput experimental technologies and novel integrative computational methods.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Career Transition Award (K99)
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Special Emphasis Panel (ZGM1-BRT-9 (KR))
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Hamlet, Michelle R
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Rockefeller University
Internal Medicine/Medicine
Other Domestic Higher Education
New York
United States
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Moore, Michael J; Zhang, Chaolin; Gantman, Emily Conn et al. (2014) Mapping Argonaute and conventional RNA-binding protein interactions with RNA at single-nucleotide resolution using HITS-CLIP and CIMS analysis. Nat Protoc 9:263-93
Wu, Jie; Anczukow, Olga; Krainer, Adrian R et al. (2013) OLego: fast and sensitive mapping of spliced mRNA-Seq reads using small seeds. Nucleic Acids Res 41:5149-63
Zhang, Chaolin; Lee, Kuang-Yung; Swanson, Maurice S et al. (2013) Prediction of clustered RNA-binding protein motif sites in the mammalian genome. Nucleic Acids Res 41:6793-807
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