Informatics Platform for Mammalian Gene Regulation at Isoform-level

摘要 随着遗传学上的每一次相继发现，人类基因组的真正动态复杂性已经成为 越来越明显，需要相对一致地更新“基因”一词的技术定义。它是 现在人们理解了“一个基因使一种蛋白质在一条信号通路中发挥作用”的概念 人类细胞过于简单化，因为大多数人类基因产生多种功能产物 (转录变体和蛋白质异构体)，通过选择性转录和/或选择性剪接。 因此，我们的中心假设是异构体水平的基因产物--“转录变异体”和“蛋白质” 异构体是哺乳动物细胞中的基本功能单位，因此， 正常细胞和疾病细胞中的基因调控数据的管理和分析都应采用“基因异构体” “以基因为中心”，而不是“以基因为中心”。朝着广泛影响基因调控和 在基因异构体水平上的功能研究，我们一直在开发新的算法来分析基因组- 广泛的转录组(RNA-SEQ和外显子阵列)和蛋白质-DNA结合(CHIP-SEQ)数据，并用于扩展 从基因水平的正交学定位到外显子和转录水平的定位 老鼠的基因。通过在公共数据集上应用这些新算法，我们观察到有意义的表达 不同样本之间的差异(例如，发育阶段、癌症亚型、正常与癌症) 在异构体水平上的大量基因，而不是在整体基因水平上，并通过实验验证了 在独立的生物标本中使用RT-qPCR的“显著”异构体。虽然这些算法的应用 已经导致了诊断胶质母细胞瘤或其亚型的新方法的发展，亚型- 水平转录组分析结果也导致了一些具有挑战性的问题-例如-如何 基因的不同启动子表现出类似开关的相反的活性模式(当一个启动子处于上- 另一个在一种情况下比另一种情况下下调)，以及一个基因的不同剪接变体是如何表现出来的 癌症组织样本与正常组织样本的表达模式相反？我们目前缺乏信息学方法 来解决这些具有挑战性的问题。因此，我们建议开发新的统计方法(1)，用于 外显子阵列和RNA-seq基因表达信息的综合聚类分析 平台，(2)用于鉴定异质性癌症样本中差异转录物/异构体的使用，以及 (3)筛选受基因调控的肿瘤可选择转录/剪接数量性状基因座(SQTL) 体细胞遗传和表观遗传变化。并且，(4)来自这些算法的新预测将是 通过进行染色质免疫沉淀(CHIP)、双荧光素酶报告试验进行实验验证 以及在U87和A172细胞中的CRISPR/Cas9基因组编辑。由此开发的生物信息学新方法 该项目将有助于硅胶的发现和研究，以加速表型和基因组的联系 信息，在基因异构体水平上。

SUMMARY With each successive discovery in genetics, the true dynamic complexity of the human genome has become increasingly apparent, requiring relatively consistent updates to the technical definition of the word “gene”. It is now understood that the notion of “one gene makes one protein that functions in one signaling pathway” in human cells is overly simplistic, because majority of the human genes produce multiple functional products (transcript variants and protein isoforms), through alternative transcription and/or alternative splicing. Therefore, our central hypothesis is that the isoform-level gene products – “transcript variants” and “protein isoforms” are the basic functional units in a mammalian cell, and accordingly, the informatics platforms for managing and analyzing gene regulation data both in normal and disease cells should adopt “gene isoform centric” rather than “gene centric” approaches. Towards the goal of broadly impacting gene regulation and functional studies at gene isoform-level, we have been developing novel algorithms for analyses of genome- wide transcriptome (RNA-seq and exon-array) and protein-DNA binding (ChIP-seq) data, and for extending the gene-level orthology mapping to exon- and transcript-level mapping between the orthologous human and mouse genes. By applying these novel algorithms on public datasets, we have observed significant expression differences between different sample groups (e.g., developmental stages, cancer subtypes, normal vs cancer) for numerous genes at the isoform-level but not at the overall gene-level, and experimentally validated the `significant' isoforms using RT-qPCR in independent bio-specimens. While the application of these algorithms has led to the development of new methods for diagnosis of glioblastoma or a sub-type thereof, the isoform- level transcriptome analyses results also led to some challenging questions – for example – How are the alternative promoters of a gene show switch-like opposing patterns of activity (while one promoter is up- the other is down-regulated in one condition vs the other), and how are different splice-variants of a gene show opposing expression patterns in cancer versus normal tissue samples? We currently lack informatics methods to address these challenging questions. Therefore, we propose to develop novel statistical methods (1) for integrative cluster analysis of isoform-level gene expression information from exon-array and RNA-seq platforms, (2) for identification of differential transcript/isoform usage in heterogeneous cancer samples, and (3) for identification of alternative transcription/splicing quantitative trait locus (sQTL) in tumor adjusted by somatic genetic and epigenetic changes. And, (4) the novel predictions from these algorithms will be experimentally validated by performing Chromatin immunoprecipitation (ChIP), dual-luciferase reporter assay and CRISPR/Cas9 genome editing in U87 and A172 cells. The novel bioinformatics methods developed by this project will help in silico discovery and research for accelerating the linkage of phenotypic and genomic information, at gene-isoform level.