Computational and Experimental Modeling of Alternative Polyadenylation

 描述（由申请人提供）： 选择性多聚腺苷酸化（阿帕）是一种在多种生理和病理条件下普遍存在的基因调控机制。通过改变polyA位点的位置，阿帕可以缩短或延长含有许多重要顺式调控元件（如miRNA结合位点）的3'UTR。在这种情况下，阿帕为microRNA的工作方式增加了一个新的层次，因为具有较短3'UTR的mRNA将不再被靶向，从而导致更高的表达。阿帕在癌症等人类疾病中的作用才刚刚开始被认识。增殖和转化的细胞都显示出有利于缩短的3′ UTR，导致原癌基因的激活。此外，我们最近的研究（Nature 2014）确定了CFIm 25，一种主要的阿帕调节剂，作为胶质母细胞瘤（GBM）肿瘤抑制剂，进一步强调了阿帕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，阿帕的关键靶基因，阿帕的功能后果和阿帕的调控机制仍然知之甚少。这主要是因为polyA分析方法（PolyA-seq）尚未被广泛采用。相比之下，RNA-seq已被广泛用于基因表达分析，但这些RNA-seq数据中的大多数尚未以“阿帕感知”的方式进行分析。尽管有上述限制，我们的初步数据表明，肿瘤和正常之间阿帕使用的显著变化导致3' UTR内RNA-seq读数密度的局部变化，这可通过我们的新型生物信息学算法DaPars（Nature Commun. 2014年）。因此，我们假设DaPars对现有RNA-seq数据的回顾性分析可用于研究大多数癌症模型和患者样本中的阿帕调节。该提案的目的是利用现有的33种癌症类型约14，000种肿瘤的RNA-seq数据，揭示阿帕靶基因，阿帕功能后果和阿帕调节因子，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测定中测量时的有效性，然后可以用于进一步完善我们的分析。结合新颖的生物信息学方法、令人信服的初步结果、大数据分析和功能验证，该提案具有独特的优势，可以为我们理解肿瘤发生过程中基因调控的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.