Computational and Experimental Modeling of Alternative Polyadenylation

 描述(由申请人提供)： 选择性多聚腺苷酸化(APA)是一种普遍存在的机制，在不同的生理和病理条件下调节大多数人类基因。通过改变PolyA位点的位置，APA可以缩短或延长含有许多重要顺式调控元件的3‘UTRs，如miRNA结合位点。在这种情况下，APA为microRNA的工作方式增加了一个新的层面，因为3‘UTRs较短的mRNAs将不再是靶标，从而导致更高的表达。APA在癌症等人类疾病中的作用才刚刚开始得到重视。增殖细胞和转化细胞都被证明倾向于缩短3‘UTRs，从而导致原癌基因的激活。此外，我们最近的研究(自然2014)发现，APA的主要调节因子CFIm25是胶质母细胞瘤(GBM)的肿瘤抑制因子，进一步强调了APA在癌症发展中的重要性。然而，在GBM以外的其他疾病模型和癌症类型中，APA的关键靶基因、APA的功能后果和APA的调控机制仍然知之甚少。这主要是因为POLYA分析方法(POLYA-SEQ)没有被广泛采用。相比之下，RNA-SEQ已被广泛用于基因表达分析，但这些RNA-SEQ数据中的大多数还没有以“APA意识”的方式进行分析。尽管有上述限制，我们的初步数据表明，肿瘤和正常之间APA使用的显著变化导致3‘UTR内RNA-SEQ读取密度的局部变化，这很容易被我们新的生物信息学算法DaPars(Natural Commun)检测到。2014年)。因此，我们假设DaPars对现有RNA-SEQ数据的回顾分析可以用于研究大多数癌症模型和患者样本中APA的调节。这项建议的目的是通过利用33种癌症类型中约14,000种肿瘤的现有RNA-SEQ数据，揭示APA靶基因、APA功能后果和APA调节因子，然后在细胞和动物模型中进行实验验证。此外，我们将评估生物信息学方法在功能分析中的有效性，然后可以用来进一步完善我们的分析。结合新的生物信息学方法、令人信服的初步结果、大数据分析和功能验证，这一提议具有独特的地位，可以为我们理解肿瘤发生过程中基因调控的这一新范式做出重大贡献。

 DESCRIPTION (provided by applicant): Alternative polyadenylation (APA) is emerging as a pervasive mechanism in the regulation of most human genes under diverse physiological and pathological conditions. By changing the position of polyA site, APA can either shorten or extend 3' UTRs that contain many important cis-regulatory elements, such as miRNA binding sites. In this context, APA adds a new layer to how microRNA works, as mRNAs with shorter 3' UTRs will no longer be targeted, leading to higher expression. The role of APA in human diseases such as cancer is only beginning to be appreciated. Both proliferating and transformed cells have been shown to favor shortened 3′ UTRs, leading to activation of proto-oncogenes. In addition, our recent study (Nature 2014) identified CFIm25, a master APA regulator, as a glioblastoma (GBM) tumor suppressor, further underscoring the importance of APA in cancer development. However, in other disease models and cancer types beyond GBM, the critical target genes subject to APA, the functional consequences of APA and the mechanisms governing APA remain poorly understood. This is mainly because polyA profiling methods (PolyA-seq) have not been widely adopted. In contrast, RNA-seq has been widely used for gene expression analysis, yet most of these RNA-seq data have not been analyzed in an "APA aware" manner. Despite the above limitations, our preliminary data indicate that significant changes in APA usage between tumor and normal result in localized changes in RNA-seq read density within 3' UTR, which is readily detectable by our novel bioinformatics algorithm DaPars (Nature Commun. 2014). Therefore, we hypothesize that DaPars retrospective analysis of existing RNA-seq data can be used to study APA regulation in most cancer models and patient samples. The objective of this proposal is to reveal APA target genes, APA functional consequences and APA regulators by taking advantage of existing RNA-seq data of ~14,000 tumors across 33 cancer types, followed by experimental validation in cells and animal models. Furthermore, we will evaluate the efficacy of the bioinformatics method when measured in functional assays, which can then be used to further refine our analysis. Together, with novel bioinformatics methods, convincing preliminary results, big data analyses and functional validation, this proposal is uniquely positioned to make significant contributions towards our understanding of this new paradigm of gene regulation during tumorigenesis.