正反义转录机制和调控网络的系统研究

Antisense transcription is a highly conserved phenomenon that spans the animal, plant and fungal kingdoms, and constitutes a common mechanism for regulating gene expression. Natural antisense transcripts (NATs) are frequently functional and use diverse transcriptional and post-transcriptional gene regulatory mechanisms to carry out a wide variety of biological roles. Changes in antisense transcription have been implicated in pathogenesis. However, apart from a few experimentally validated cases, the physiological roles of antisense transcription and the underlying mechanisms are largely unknown. A major limitation to the development of NATs assays is the lack of high throughput NATs’ expression data. Standard libraries for RNA-seq do not preserve information about which strand was originally transcribed, and strand specific RNA-seq method is labor intensive and requires substantial amounts of starting material. The main goal of this project is to systematically analyze NATs-associated transcription mechanism and gene expression regulatory network mediated by NATs. To do this, we developed a new bioinformatics method to infer NATs expression level from standard RNA-seq libraries, and its inferences are reasonable. We will focus on one or two biologically important process; integrate genetic, functional genomics, and bioinformatics in a systems biology approach, combine information from a number of databases and RNA-seq data sets, to investigate the relationship between sense and antisense transcription profiles, which aim to elucidate the regulatory mechanism that is in action. The output of this project provides a new method for mining strand-information from standard RNA-seq libraries, and the project is expected to provide some clues for further understanding the complex gene regulatory mechanism mediated by NATs.

天然反义转录本NATs广泛存在于原核和真核生物中。NATs对生物生长发育、疾病发生等生理过程具有广泛的影响，其所介导的调控是基因表达调控的基本因素。从系统的观点研究NATs的转录机制以及调控功能是阐明NATs作用机制的关键。高通量实验方法获得NATs转录信息存在实验技术上的困难，制约着NATs相关研究的深度和广度。本项目利用标准文库构建方法得到的高通量RNA-seq数据，开发新的数据挖掘方法用于获得NATs转录信息；进而采用系统生物学、生物信息学等多种新方法，针对一两个重要生物过程，综合各种组学数据系统分析NATs的转录机制，解析其在该生物过程中的作用。本项目的研究成果可为深入挖掘高通量RNA-seq数据提供新的方法，还可为研究与特定表型相关的NATs表达谱特征及调控功能提供新线索和理论基础。

基因之间的相互作用协调一致地调节基因表达从而精细调控细胞活动，理解时空条件下基因表达调控关系是分子生物学的重要研究课题。重叠转录在基因表达调控中扮演重要作用,正-反义基因转录本在广泛的层次上执行调控功能。标准文库制备方法不能区分重叠基因转录本的转录链信息，制约着重叠转录调控机制研究的深度和广度。分析基因表达关系的差异，从基因表达谱数据中解析调控关系以及调控强度，可为研究基因间的调控机理提供重要线索。为了深入解析基因转录调控机制，我们开发了IAOseq算法，根据转录组测序读段在转录区域的分布特征，区分定位在重叠转录区域的测序读段的来源从而估计重叠基因的转录水平。我们还设计了针对小样本问题的迭代优化SSIO算法，用来从小样本基因表达谱数据中解析非线性基因调控关系。与常用方法相比，SSIO算法的拟合模型与实际观测值残差最小，输出稳定，优化效果明显。我们进一步采用差异共表达分析和差异调控关系分析方法从上游调控水平研究了生物过程中的转录调控机理，分析潜在的调控机制。我们开发的IAOseq方法是对高通量转录组数据分析工具的很好补充，是深入研究重叠基因转录调控机制的有力工具。我们还利用定量非线性相关性的条件互信息来估计基因之间的调控强度，分析基因共表达关系的差异，寻找关键差异调控转录因子。这些工作为后续深入研究基因间乃至重叠基因间的转录调控机理奠定了理论和方法学基础。