Exon recognition during constitutive pre-mRNA splicing

描述（由申请人提供）：在大多数真核前mRNA分子的转录过程中，外显子剪接在一起是将信息从DNA转移到蛋白质的基本步骤。然而，如何识别剪接位点以启动这种快速和准确的过程一点也不清楚，因为类似于真实的位点的假序列的数量超过后者1或2个数量级，但尚未使用。我们正在寻找更多的信息，指定剪接位点识别在哺乳动物细胞中的前mRNA剪接。在以前的工作中，我们已经使用计算分析发现：1）外显子侧翼50 nt以外的剪接位点共识可以极大地影响剪接，并包含序列，从背景中脱颖而出;和2）有大量的特定的8聚体，可以作为外显子剪接增强子或剪接沉默子。我们将描述这两种类型的序列，定义其碱基要求，范围，位置依赖性和特异性。关于最后一个，我们将开发一种新的详尽的调查方法，使用大规模并行固态测序，以确定每一个可能的8聚体，可以在一个特定的情况下的功能，我们将比较这种配置文件在不同的内含子，外显子和细胞的组成型和选择性剪接外显子的情况。根据其G+C含量，侧翼序列分为2个不同的类别;我们将测试这两个类别的外显子定义规则不同的想法。为了简化外显子定义规则的发现，我们将创建由合成的8聚体模块组成的设计外显子，我们已经确定为增强子，沉默子或中性序列。这些模块的数量和间隔将揭示隐藏在自然序列复杂性中的关系。最后，我们将继续使用计算，包括新的机器学习算法，以更好地定义内含子元件，并发现定义剪接位点的局部序列的两个或多个特征之间的相互作用。我们还将开发一个基于网络的外显子发现程序，整合我们积累的所有信息。前体mRNA剪接在很大比例的人类遗传疾病病例中出错。对剪接位点识别规则的理解有助于设计治疗干预策略来逆转这种不良影响。

DESCRIPTION (provided by applicant): The splicing together of exons during the transcription of most eukaryotic pre-mRNA molecules is a fundamental step in the transfer of information from DNA to protein. However, how the splice sites are recognized to initiate this fast and accurate process in not at all clear, since false sequences that resemble real sites outnumber the latter by 1 or 2 orders of magnitude yet are not used. We are searching for the additional information that specifies splice site recognition during pre-mRNA splicing in mammalian cells. In previous work we have used computational analysis to discover that: 1) that exon flanks 50 nt beyond the splice site consensuses can greatly influence splicing and contain sequences that stand out from the background; and 2) there are a large number of specific 8 mers that can act either as exonic splicing enhancers or splicing silencers. We will characterize both of these types of sequences, defining their base requirements, extent, position dependence, and specificity. With regard to the last, we will develop a novel exhaustive survey method that uses massively parallel solid state sequencing to identify every possible 8-mer that can be functional in a particular context, and we will compare this profile in different intronic, exonic and cellular contexts for constitutive and alternatively spliced exons. The flanking sequences fall into 2 distinct classes according to their G+C contents; we will test the idea that the rules governing exon definition differ for the two classes. To simplify the discovery of rules that govern exon definition, we will create designer exons made up of synthetic 8-mer modules we have identified as enhancers, silencers or neutral sequences. The numbers and spacing of these modules will reveal relationships that are hidden in the complexity of natural sequences. Finally, we will continue to use computation, including new machine learning algorithms, to better define intronic elements and to discover interactions between two or more features of a local sequence that define a splice site. We will also develop a Web-based exon-finding program that integrates all the information we have accumulated. Pre-mRNA splicing goes awry in a large proportion or human genetic disease cases. An understanding of the rules that govern the recognition of splice sites should help in the design of therapeutic intervention strategies to reverse such ill effects.