Pre-mRNA选择性剪接调控机制研究
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摘要
Pre-mRNA的剪接是真核生物中,基因表达在后转录过程中的最为关键的步骤之一。Pre-mRNA的剪接模式可能多种多样,因而被称为选择性剪接或可变剪接。由于Pre-mRNA选择性剪接的普遍性、复杂性和与疾病的高相关性,其背后的调控机制最近几年越来越得到生命科学领域的重视。然而,以往的研究只局限于少量的基因,不能解释选择性剪接调控的普遍机理。
微阵列技术的深入发展和下一代高通量测序技术的出现给全转录组范围研究pre-mRNA选择性剪接调控机制带来希望。本文总结了该领域目前所遇到的若干瓶颈,基于高通量生物数据,建立数学模型或仿真算法,对这些难点进行逐一研究,主要包括以下几个方面：
组织特异性选择性剪接被不同的组织用来生成组织特异的mRNA和蛋白质变体。然而,如何预测组织特异性选择性剪接的调控因子及其功能一直是该领域的难点。基于外显子芯片数据,论文提出一种数学模型对调控组织特异性pre-mRNA选择性剪接的顺式作用元件及其功能进行预测。在该模型中,首先,将不同组织间表达有显著差异的外显子的剪接指数考虑为该外显子周围四个调控区域中多个调控因子的综合作用,预测造成两种不同组织中外显子表达差异的顺式作用元件及其相对作用水平；然后评估顺式作用元件在各种组织中的组织特异作用水平。在对人类11种组织的外显子芯片数据分析表明该模型是一种可行的预测方法。
顺式作用元件的精确预测有赖于反式作用因子—pre-mRNA剪接起调控作用的RNA蛋白质结合区域特性的研究。如何利用生物实验和计算模型相结合的手段在全转录组范围内准确分析RNA结合蛋白质的性质目前仍是该领域最具挑战的课题之一。针对该难点,论文设计了分析RNA结合蛋白质特性的一系列生物信息学方法。基于SFRS1蛋白质的交联免疫沉淀-高通量测序数据,成功获取SFRS1蛋白质在全基因组中的结合区域及类别和所调控的基因参与的分子生物学过程；预测出SFRS1蛋白质在整个基因组中的结合位点,并分析了SFRS1蛋白质结合位点与剪接位点的距离性质；最后研究了SFRS1蛋白质与遗传疾病之间的关系。
CLIP实验中使用核糖核酸酶对转录物水解成小的RNA片段,但是有关RNase A/T1的具体的RNA序列特异性并不明确。论文从核糖核酸酶对RNA分解的机理出发预测RNA结合蛋白质的结合位点。提出一种统计方法评估在交联免疫沉淀实验中的核糖核酸酶的序列特异性。基于该性质构建RNase分解RNA的仿真算法,通过实验仿真与真实数据之间的相关性来得到最有可能的反式作用因子结合区域。在含有一个和含有两个结合位点的实例中,该算法都取得了较好的预测效果。
传统预测RNA-蛋白质结合位点的算法和模型都忽略了RNA二级结构对蛋白质识别RNA的影响。论文将RNA的二级结构特征引入到RNA结合蛋白质结合位点的预测中来,提出基于统计力学理论的RNA结合蛋白质结合位点的预测模型。采用嵌套的优化算法来搜索模型参数,在外层通过评分函数、内层通过量子粒子群算法分别求出RNA结合蛋白质的最佳模体及其对应的最优参数。基于SFRS1蛋白质的CLIP-seq序列,该模型准确预测出SFRS1蛋白质与RNA结合位点的序列和最优未配对概率,预测的效果比单纯靠序列信息的模型更好。
Pre-mRNA alternative splicing is one of the most pivotal procedures during post-transcriptional gene regulation of eukaryotes. The splicing patterns of the same pre-mRNA could be different, hence the so-called "alternative splicing". Due to the universality, complexity and pathogenicity nature of alternative splicing, the underlying regulatory mechanisms are placed more and more emphasis in Life Science. However, previous studies were always limited within a handful of gene models; therefore, the universal mechanisms of alternative splicing are still unclear.
The in-depth development of microarray technologies and the emerging of next-generation high-throughput sequencing devices make it possible to study the regulatory mechanisms of pre-mRNA alternative splicing on a transcriptome-wide scale. Here we summarize several bottle-necks in this field, and look into each problem based on high-throughput biochemical data and mathematical models or simulation algorithms. The following main topics are included in this thesis:
In different tissues, tissue-specific alternative splicing is applied to generate tissue-specific mRNA and proteins, but how to predict the regulatory factors of tissue-specific alternative splicing and their functions is still an unsolved problem in this area. Based on exon array data, this thesis proposes a mathematical model to predict the cis-acting RNA elements regulating tissue-specific alternative splicing as well as their functions. In this model, we first consider the splicing index of differentially expressed exons between different tissues as the combinatorial effect of multiple regulators located in four nearby regulatory regions, and predict the cis-acting elements and their relative functional levels. Then we estimate the tissue-specific functional levels of predicted cis-acting elements in different tissues. It is proved by the application on exon array data of 11 human tissues that this model has great potential for tissue-specific regulator prediction.
The precise prediction of cis-acting elements depends on the research of the features of binding regions of trans-acting elements—RNA binding proteins that are responsible for pre-mRNA splicing. It remains a challenging topic in this field that how to integrate biochemical experiments and computational models to do accurate analysis of protein-RNA binding patterns transcriptome-widely. To overcome this problem, we developed a series of methods to analyze the binding properties of RNA binding proteins. On the basis of CLIP-seq data for SFRS1 proteins, the genome-wide binding regions and classes of SFRS1 proteins as well as the significant molecular functions of SFRS1-regulated genes were obtained. The binding sites of SFRS1 proteins in the whole genome are predicted, and also the relationships between SFRS1 binding sites with splicing sites are discussed. In the end, we investigate to see if SFRS1 proteins are involved in genetic diseases.
During CLIP experiment, RNase A/T1 is used to digest transcripts into small RNA fragments, but it is unclear whether or not RNase A/T1 has sequence specificities. Based on the digestion mechanism of RNase, we figure out how to predict binding sites of RNA binding proteins. A statistics-based method is proposed to evaluate the sequence specificities of RNase in CLIP experiment. Then a simulation algorithm is invented to retrieve the regions that are most likely bound by trans-acting elements by the correlation coefficient between simulated and real data. In the implementations on genomic regions containing one and two binding sites, the algorithm turned out to have good prediction power.
Traditional algorithms and models for RNA-protein binding sites prediction ignored the impact of RNA secondary structure on the recognition of RNA by proteins. In this study, we bring RNA folding features into binding site prediction of RNA binding proteins, and establish a model based on statistical mechanics to predict RNA-protein binding sites. When searching parameters of the proposed model, we adopt a nested optimization algorithm, in which, the optimal reference motif and its corresponding optimal parameters are optimized at outer and inner optimization layer respectively. Implemented on CLIP-seq sequences of SFRS1 proteins, this model accurately predicted the optimal sequence consensus and the optimal unpaired probabilities of SFRS1 binding sites. The prediction power of our model was proved to perform better than the model which only considered sequence information.
微阵列技术的深入发展和下一代高通量测序技术的出现给全转录组范围研究pre-mRNA选择性剪接调控机制带来希望。本文总结了该领域目前所遇到的若干瓶颈,基于高通量生物数据,建立数学模型或仿真算法,对这些难点进行逐一研究,主要包括以下几个方面：
组织特异性选择性剪接被不同的组织用来生成组织特异的mRNA和蛋白质变体。然而,如何预测组织特异性选择性剪接的调控因子及其功能一直是该领域的难点。基于外显子芯片数据,论文提出一种数学模型对调控组织特异性pre-mRNA选择性剪接的顺式作用元件及其功能进行预测。在该模型中,首先,将不同组织间表达有显著差异的外显子的剪接指数考虑为该外显子周围四个调控区域中多个调控因子的综合作用,预测造成两种不同组织中外显子表达差异的顺式作用元件及其相对作用水平；然后评估顺式作用元件在各种组织中的组织特异作用水平。在对人类11种组织的外显子芯片数据分析表明该模型是一种可行的预测方法。
顺式作用元件的精确预测有赖于反式作用因子—pre-mRNA剪接起调控作用的RNA蛋白质结合区域特性的研究。如何利用生物实验和计算模型相结合的手段在全转录组范围内准确分析RNA结合蛋白质的性质目前仍是该领域最具挑战的课题之一。针对该难点,论文设计了分析RNA结合蛋白质特性的一系列生物信息学方法。基于SFRS1蛋白质的交联免疫沉淀-高通量测序数据,成功获取SFRS1蛋白质在全基因组中的结合区域及类别和所调控的基因参与的分子生物学过程；预测出SFRS1蛋白质在整个基因组中的结合位点,并分析了SFRS1蛋白质结合位点与剪接位点的距离性质；最后研究了SFRS1蛋白质与遗传疾病之间的关系。
CLIP实验中使用核糖核酸酶对转录物水解成小的RNA片段,但是有关RNase A/T1的具体的RNA序列特异性并不明确。论文从核糖核酸酶对RNA分解的机理出发预测RNA结合蛋白质的结合位点。提出一种统计方法评估在交联免疫沉淀实验中的核糖核酸酶的序列特异性。基于该性质构建RNase分解RNA的仿真算法,通过实验仿真与真实数据之间的相关性来得到最有可能的反式作用因子结合区域。在含有一个和含有两个结合位点的实例中,该算法都取得了较好的预测效果。
传统预测RNA-蛋白质结合位点的算法和模型都忽略了RNA二级结构对蛋白质识别RNA的影响。论文将RNA的二级结构特征引入到RNA结合蛋白质结合位点的预测中来,提出基于统计力学理论的RNA结合蛋白质结合位点的预测模型。采用嵌套的优化算法来搜索模型参数,在外层通过评分函数、内层通过量子粒子群算法分别求出RNA结合蛋白质的最佳模体及其对应的最优参数。基于SFRS1蛋白质的CLIP-seq序列,该模型准确预测出SFRS1蛋白质与RNA结合位点的序列和最优未配对概率,预测的效果比单纯靠序列信息的模型更好。
Pre-mRNA alternative splicing is one of the most pivotal procedures during post-transcriptional gene regulation of eukaryotes. The splicing patterns of the same pre-mRNA could be different, hence the so-called "alternative splicing". Due to the universality, complexity and pathogenicity nature of alternative splicing, the underlying regulatory mechanisms are placed more and more emphasis in Life Science. However, previous studies were always limited within a handful of gene models; therefore, the universal mechanisms of alternative splicing are still unclear.
The in-depth development of microarray technologies and the emerging of next-generation high-throughput sequencing devices make it possible to study the regulatory mechanisms of pre-mRNA alternative splicing on a transcriptome-wide scale. Here we summarize several bottle-necks in this field, and look into each problem based on high-throughput biochemical data and mathematical models or simulation algorithms. The following main topics are included in this thesis:
In different tissues, tissue-specific alternative splicing is applied to generate tissue-specific mRNA and proteins, but how to predict the regulatory factors of tissue-specific alternative splicing and their functions is still an unsolved problem in this area. Based on exon array data, this thesis proposes a mathematical model to predict the cis-acting RNA elements regulating tissue-specific alternative splicing as well as their functions. In this model, we first consider the splicing index of differentially expressed exons between different tissues as the combinatorial effect of multiple regulators located in four nearby regulatory regions, and predict the cis-acting elements and their relative functional levels. Then we estimate the tissue-specific functional levels of predicted cis-acting elements in different tissues. It is proved by the application on exon array data of 11 human tissues that this model has great potential for tissue-specific regulator prediction.
The precise prediction of cis-acting elements depends on the research of the features of binding regions of trans-acting elements—RNA binding proteins that are responsible for pre-mRNA splicing. It remains a challenging topic in this field that how to integrate biochemical experiments and computational models to do accurate analysis of protein-RNA binding patterns transcriptome-widely. To overcome this problem, we developed a series of methods to analyze the binding properties of RNA binding proteins. On the basis of CLIP-seq data for SFRS1 proteins, the genome-wide binding regions and classes of SFRS1 proteins as well as the significant molecular functions of SFRS1-regulated genes were obtained. The binding sites of SFRS1 proteins in the whole genome are predicted, and also the relationships between SFRS1 binding sites with splicing sites are discussed. In the end, we investigate to see if SFRS1 proteins are involved in genetic diseases.
During CLIP experiment, RNase A/T1 is used to digest transcripts into small RNA fragments, but it is unclear whether or not RNase A/T1 has sequence specificities. Based on the digestion mechanism of RNase, we figure out how to predict binding sites of RNA binding proteins. A statistics-based method is proposed to evaluate the sequence specificities of RNase in CLIP experiment. Then a simulation algorithm is invented to retrieve the regions that are most likely bound by trans-acting elements by the correlation coefficient between simulated and real data. In the implementations on genomic regions containing one and two binding sites, the algorithm turned out to have good prediction power.
Traditional algorithms and models for RNA-protein binding sites prediction ignored the impact of RNA secondary structure on the recognition of RNA by proteins. In this study, we bring RNA folding features into binding site prediction of RNA binding proteins, and establish a model based on statistical mechanics to predict RNA-protein binding sites. When searching parameters of the proposed model, we adopt a nested optimization algorithm, in which, the optimal reference motif and its corresponding optimal parameters are optimized at outer and inner optimization layer respectively. Implemented on CLIP-seq sequences of SFRS1 proteins, this model accurately predicted the optimal sequence consensus and the optimal unpaired probabilities of SFRS1 binding sites. The prediction power of our model was proved to perform better than the model which only considered sequence information.
引文
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