Informatics Platform for Mammalian Gene Regulation at Isoform-level

异构体水平的哺乳动物基因调控信息学平台

• 批准号: 10273985
• 负责人: RAMANA V DAVULURI
• 金额: $ 34.36万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-11-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10273985
• 关键词: Address; Adopted; Algorithmic Analysis; Algorithms; Alleles; Alternative Splicing; Architecture; Bioinformatics; Biological Assay; Bipolar Disorder; Brain Neoplasms; CRISPR/Cas technology; Cancer Patient; Cell Line; Cells; Classification; Cluster Analysis; Computer software; Computing Methodologies; DNA-Binding Proteins; Data; Data Set; Derivation procedure; Development; Diagnosis; Disease; Epigenetic Process; Exons; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Transcription; Genome; Genomics; Glioblastoma; Goals; Human; Human Genome; Informatics; Knowledge; Label; Luciferases; Malignant Neoplasms; Mammalian Cell; Methods; Molecular; Mus; Mutation; Neurodegenerative Disorders; Normal tissue morphology; Parkinson Disease; Pattern; Phenotype; Protein Isoforms; Quantitative Trait Loci; RNA Splicing; Regulation; Regulator Genes; Reporter; Research; Sampling; Schizophrenia; Signal Pathway; Site; Specimen; Statistical Data Interpretation; Statistical Methods; Stratification; Tissue Sample; Transcript; Update; Variant; autism spectrum disorder; base; bioinformatics tool; cancer subtypes; chromatin immunoprecipitation; classification algorithm; data integration; data mining; diagnostic biomarker; discrete data; feature selection; gene function; gene product; genetic signature; genome editing; genome-wide analysis; high dimensionality; improved; in silico; innovation; molecular diagnostics; multiple omics; neuropsychiatric disorder; novel; open source; outcome forecast; platform-independent; prediction algorithm; programs; promoter; protein function; public health relevance; therapeutic target; tool; transcriptome; transcriptome sequencing; tumor; user friendly software; user-friendly

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.

总结 随着遗传学的每一个连续的发现，人类基因组的真正动态复杂性已经成为 这一点越来越明显，需要对“基因”一词的技术定义进行相对一致的更新。是 现在理解了“一个基因产生一种蛋白质，在一个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 （转录物变体和蛋白质同种型），通过选择性转录和/或选择性剪接。 因此，我们的中心假设是，亚型水平的基因产物-“转录变体”和“蛋白质 “同种型”是哺乳动物细胞中的基本功能单位，因此， 对正常细胞和疾病细胞的基因调控数据的管理和分析应采用“基因异构体 以“基因为中心”而不是“基因为中心”的方法。朝着广泛影响基因调控的目标， 在基因异构体水平的功能研究，我们一直在开发新的算法分析基因组， 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 基因水平的正向定位到外显子和转录水平的定位， 老鼠基因通过在公共数据集上应用这些新算法，我们观察到了显著的表达， 不同样本组之间的差异（例如，发育阶段，癌症亚型，正常vs癌症） 在同种型水平上而不是在整体基因水平上对许多基因进行了研究，并通过实验验证了 在独立生物样本中使用RT-qPCR的“显著”同种型。虽然这些算法的应用 导致了诊断胶质母细胞瘤或其亚型的新方法的发展， 水平的转录组分析结果也导致了一些具有挑战性的问题-例如- 基因的替代启动子显示开关样相反的活性模式（当一个启动子在上游时， 另一种在一种情况下与另一种情况下下调），以及基因的不同剪接变体如何显示 在癌症和正常组织样本中的相反表达模式？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）， 外显子阵列和RNA-seq异构体水平基因表达信息整合聚类分析 平台，（2）用于鉴定异质性癌症样品中的差异转录物/同种型使用，以及 (3)用于鉴定肿瘤中的选择性转录/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）这些算法的新颖预测将是 通过进行染色质免疫沉淀（ChIP）、双荧光素酶报告基因测定进行实验验证 以及U87和A172细胞中的CRISPR/Cas9基因组编辑。由此开发的新型生物信息学方法 该项目将有助于计算机发现和研究，以加速表型和基因组的联系 信息，在基因异构体水平。