An integrative genomic strategy to infer global RNA regulatory networks

描述（由申请人提供）：越来越多的人认识到哺乳动物的生物复杂性被RNA复杂性的调节极大地放大。这种调节是由数百种rna结合蛋白（rbp）和microrna （mirna）通过序列特异性相互作用与它们的靶标介导的。rna的错误调控会导致许多遗传疾病。尽管它们具有关键作用，但由于缺乏高通量实验技术和有效的计算方法，难以准确推断全球RNA调控网络，这在很大程度上阻碍了研究RNA复杂性的努力。本应用程序描述了实验和计算方法的结合，以推进对哺乳动物大脑系统水平的体内RNA调控的理解。在实验方面，我将利用洛克菲勒大学罗伯特·达内尔实验室建立的小鼠遗传系统和高通量技术，在那里进行指导阶段的研究。HITS-CLIP将用于生成几种重要神经元rbp的生化足迹的全基因组图谱；外显子结微阵列和RNA-Seq将用于生成核苷酸分辨率转录组图谱，用于野生型大脑和缺乏个体rbp的大脑以及发育中的小鼠大脑的比较分析。在计算方面，将采用隐马尔可夫模型（hmm）和贝叶斯网络等综合建模技术，对来自多个数据源的生化、结构、基因组和进化信息进行概率建模，从而开发RBP/miRNA靶点和rna调控网络的高度预测模型。通过Nova对RNA剪接调控的分析获得了大量的初步数据，这证明了这种整合基因组策略在定义准确和全面的RNA调控网络和获得新的生物学见解方面的有效性。在这里，我建议进一步改进这些方法，并将该策略扩展到其他rbp，单独或组合，以及RNA调控的其他步骤。在基因工程系统中发现的调控机制将进一步扩展到研究发育中的大脑和不同大脑区域的组合和动态RNA调控。虽然我在机器学习和RNA调控的计算研究方面接受了广泛的培训，但这个职业发展奖将使我能够扩展我现有的技能，并继续发展我的实验技能。Darnell实验室和Rockefeller University的良好环境不仅会极大地促进我所指导的研究，也会极大地促进我向独立学术职位的过渡。总之，拟议的研究将为我未来的研究铺平道路，旨在解码正常生物过程和人类疾病中控制RNA调控的规则。

DESCRIPTION (provided by applicant): 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.