Computational and Experimental Modeling of Alternative Polyadenylation

替代聚腺苷酸化的计算和实验模型

基本信息

批准号：
9027561
负责人：
Wei Li
金额：
$ 37.45万
依托单位：
BAYLOR COLLEGE OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-12-01 至 2020-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9027561
关键词：
3&apos Untranslated Regions Adopted Algorithms Animal Model Atlases Big Data Binding Binding Sites Bioinformatics Biological Assay Cancer Model Cancer Patient Cells Code Computer Simulation Computer software Computing Methodologies Data Data Analyses Data Reporting Databases Development Disease Disease model Ensure Experimental Models Galaxy Gene Expression Profiling Gene Expression Regulation Gene Targeting Genes Genomics Glioblastoma Human In Vitro Internet KDM5B gene Lead Malignant Neoplasms Malignant neoplasm of lung Measures Mediating Messenger RNA Methods MicroRNAs Modeling Nature Oncogenes PTEN gene Physiological Polyadenylation Positioning Attribute Post-Transcriptional Regulation Proliferating Proto-Oncogenes RNA Reading Regulation Regulatory Element Reporter Repression Role Sample Size Sampling Site System Testing Therapeutic Intervention Transcript Translations Tumor Suppressor Proteins Tumorigenicity Untranslated RNA Untranslated Regions Validation Work base biological systems cancer genomics cancer type cell transformation dark matter density design gene discovery human disease in vivo insight malignant breast neoplasm mathematical model novel patient stratification personalized diagnostics personalized medicine public health relevance transcriptome transcriptome sequencing tumor tumor growth tumorigenesis web interface web portal

项目摘要

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.

描述（由申请人提供）：替代多聚腺苷酸化 (APA) 正在成为多种生理和病理条件下大多数人类基因调节的普遍机制，通过改变多聚腺苷酸位点的位置，APA 可以缩短或延长包含许多重要顺式调节元件的 3' UTR。，例如 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，因为具有较短 3' UTR 的 mRNA 将不再被靶向，从而导致更高的表达量。 APA 在癌症等人类疾病中的作用才刚刚开始受到重视，增殖细胞和转化细胞都被证明有利于缩短 3' UTR，从而导致原癌基因的激活。此外，我们最近的研究（Nature 2014）发现了 CFIm25，作为胶质母细胞瘤 (GBM) 肿瘤抑制因子的主要 APA 调节因子，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，受 APA 影响的关键靶基因、APA 的功能后果以及控制 APA 的机制仍然知之甚少，这主要是因为 PolyA 分析方法 (PolyA-seq) 尚未得到广泛采用，相反，RNA-seq 已得到广泛应用。尽管存在上述限制，但我们的初步数据表明，肿瘤和正常人之间 APA 使用的显着变化会导致 RNA 的局部变化。 -seq 读取3' UTR 内的密度，可以通过我们的新型生物信息学算法 DaPars 轻松检测到（Nature Commun. 2014）。因此，我们追求现有 RNA-seq 数据的 DaPars 回顾性分析可用于研究大多数癌症模型和患者中的 APA 调节。该提案的目的是通过利用 33 种癌症类型的约 14,000 个肿瘤的现有 RNA-seq 数据来揭示 APA 靶基因、APA 功能后果和 APA 调节因子。此外，我们将在功能测定中评估生物信息学方法的有效性，然后与新颖的生物信息学方法一起进一步完善我们的分析，获得令人信服的初步结果。通过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这种新范式做出重大贡献。