多种真核生物多聚腺苷化信号的识别及其演化研究

Polyadenylation and alternative polyadenylation (APA) have been shown to play an important role in gene expression regulation, which are regulated by poly(A) signals. Poly(A) signals are critical to cleavage, transcription termination and translation. However, the features of more than 20% mammalian poly(A) signals and up to 90% plant poly(A) signals remain to be further characterized. Moreover, the regulation mechanism of the newly found APA sites relevant to read-through transcription and antisense transcription remains unclear. This project will focus on transcriptomic data obtained from Next-Generation Sequencing (NGS), a data source providing greater depths and wider coverage of transcriptomes and enabling us to employ innovative new methodologies in bioinformatics studies of polyadenylation and poly(A) signals. Based on our studies about polyadenylation in the past several years, firstly, we will build an integrated poly(A) signal recognition model for the genome-wide identification of the primary sequence features and the secondary structures, and the exploration of the co-occurring interactions among different elements. Using high-quality and high-magnitude poly(A) data, we then classify APA sites based on their locations, types and biological consequences related to read-through transcription or antisense transcription. Using the poly(A) signal recognition model, we will make a comparative analysis of the poly(A) signals of APA sites in different classes and construct the co-regulated networks of the poly(A) signals. Using the large amount of poly(A) sites collected from different eukaryotic species including algae, yeast, Arabidopsis, rice, mouse, human, etc., we will build a dynamic poly(A) signal analytic model and conduct a comparative analysis for the examination of the evolutionary patterns of poly(A) signals over different species. Finally, we will release our data as an open-access database, make our bioinformatics programs open-source and create an open-access, web-based server for poly(A) signal analysis, so that the larger research community in poly(A) study can benefit from our work. Through this project, we will make significant progress toward understanding the mechanisms of mRNA 3' end processing and APA regulation, and the phenomena of read-through transcription and antisense transcription. The comparative study will provide valuable insights into the origin and evolution of poly(A) signals, further our understanding of evolutionary mechanisms regulating eukaryotic mRNA polyadenylation process and the complicated mechanism of post-transcriptional regulation, and also provide better research capabilities for finding cures to many diseases related to 3' end processing.

多聚腺苷化[poly(A)]及选择性多聚腺苷化(APA)是基因表达调控的重要途径，是由poly(A)信号调控的。Poly(A)信号对剪切、转录终止及翻译起关键作用。仍有超过20%的动物及高达90%的植物非典型poly(A)信号的特征有待识别，且新发现的与反向转录或转录通读有关的APA位点的调控机制也尚不清晰。基于多年研究基础，本项目拟从全基因组水平分析海量测序数据，识别poly(A)信号的一级序列及二级结构特征；分析新发现不同类型APA位点的poly(A)信号，构建poly(A)信号共调网络；建立poly(A)信号动态分析模型，系统研究不同真核生物poly(A)信号演化模式；开发poly(A)信号识别与分析的生物信息可视化平台。此研究有利于从反向转录和通读等新视角探索APA机制，能加深对poly(A)信号起源与进化的认识，也有助于3'末端加工相关疾病的治疗及更深入了解复杂的转录后调控机制。

mRNA多聚腺苷化[polyadenylation, poly(A)]是真核细胞的一个重要后转录调控过程，对细胞中mRNA的生命周期起决定作用，影响mRNA的稳定性、输出及翻译。如果一个基因有多个poly(A)位点，将可通过选择性多聚腺苷化(Alternative Polyadenylation, APA)选择不同位点来调节基因表达，如人类易栓症、地中海贫血、癌细胞周期调控、神经细胞发育，植物表观遗传调控、开花时间、及抗病抗逆等本项目从全基因组水平探索典型和非典型poly(A)信号的一级序列特征及结构特征，分析不同类型APA位点的poly(A)信号及差异，并系统研究多种真核生物的poly(A)信号演化，最终建立高效便捷的生物信息可视化平台供相关科研人员使用。本项目对不同真核生物poly(A)信号的识别及其演化的研究将有利于深入理解全基因组转录后调控及基因表达调控，并促进调控真核生物mRNA多聚腺苷化过程的分子、生物及进化机制的研究。..在人才培养方面，本项目培养了生物信息学领域的多名研究生与博士生，如项目组成员叶从庭和李磊曾赴美国迈阿密大学交流一年，归来后已顺利完成博士学业。在论著方面，本项目已资助发表了10篇SCI论文，以及3篇EI论文。其中项目负责人为通讯或第一的SCI论文有7篇（JCR 2区4篇，JCR 3区2篇，JCR 4区1篇），项目负责人通讯作者的EI期刊论文有3篇。另有3篇论文在准备投稿中（负责人为第一或通讯作者）。此外，负责人参与编著Springer出版社出版的《Polyadenylation in Plants: Methods and Protocols》书中的4章。在网站平台方面，顺利搭建了公开发表了用于poly(A)研究的VAAPA平台（http://bmi.xmu.edu.cn:8001/vaapa）和PASPA平台（http://bmi.xmu.edu.cn/paspa）。另外，正在搭建的多种植物poly(A)位点可视化及分析平台PlantAPA（暂定http://bmi.xmu.edu.cn/front）也即将投入使用。这些平台将为有关poly(A)相关的分析提供丰富的资源及有助于生物实验筛选高质量的候选基因或位点，促进APA机制的研究。