Inferring Mammalian Transcriptional Regulatory Networks from Epigenomics

从表观基因组学推断哺乳动物转录调控网络

• 批准号: 8536867
• 负责人: Xiaole Shirley Liu
• 金额: $ 32.93万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-10 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8536867
• 关键词: Adopted; Algorithms; Antibodies; Automobile Driving; Back; Bacteria; Binding; Binding Sites; Biological Process; Biomedical Research; Breast Cancer Cell; Cancer cell line; Cell Differentiation process; Cell Fate Control; Cell Line; Cells; ChIP-on-chip; ChIP-seq; Communities; Computational algorithm; Computer Simulation; Computing Methodologies; DNA; Data; Disease; Epigenetic Process; Epithelial Cells; Etiology; Event; Excision; Fatty acid glycerol esters; Gene Expression; Gene Targeting; Genes; Genome; Genomics; Histones; Human; Indium; Injury; Knowledge; Link; Location; Malignant Neoplasms; Maps; Methods; Modeling; Molecular; Molecular Profiling; Mus; Muscle; Myoblasts; Nucleosomes; Obesity; Physiology; Positioning Attribute; Process; Regulation; Relative (related person); Resolution; Resources; Signal Transduction; Stimulus; Study models; System; Technology; Testing; Therapeutic Intervention; Time; Transcription factor genes; Vitamin D; Work; Yeasts; biological systems; bone; cost; epigenomics; genome-wide; improved; interest; malignant breast neoplasm; response; small hairpin RNA; stem cell differentiation; stem cell fate; transcription factor; transcriptome sequencing

DESCRIPTION (provided by applicant): Deciphering the transcriptional regulatory network (TRN) governing a biological process in mammalian systems is essential to our understanding of basic mechanisms underlying normal physiology as well as disease etiology. It is a daunting task because too many links in the TRN are unknown. The emergence of ChIP-chip and ChIP-seq technologies has enabled the mapping of the genome-wide binding sites of many transcription factors (TFs) known to be key regulators in a biological process. However, these two technologies are limited to the known regulators with ChIP-quality antibodies. We found that TF binding is often associated with a dynamic histone mark signature and can be computationally predicted from the genome-wide histone mark dynamics. Therefore, we hypothesize that with time-course nucleosome-resolution ChIP-seq of a few informative histone marks and RNA-seq data of gene expression, and effective computational modeling, we could infer the TRNs in mammalian biological processes. Specifically, we propose to develop effective computational algorithms to achieve Aim1: first, predict TF binding from nucleosome-resolution histone mark dynamics; second, identify target genes from TF binding, histone marks and gene expression profiles; and third, infers the TRN over a time course. We also propose to apply the above algorithms in two biological systems in Aim 2. One is the mouse myoblast cell line C2C12 differentiation into bone, fat, or muscle, and the other is the human apocrine breast cancer cell line MDA-MB-453 reversible reprogramming to epithelial cells with vitamin D treatment. Through time-course nucleosome-resolution histone mark ChIP-seq and RNA-seq profiling, we will computationally infer and experimentally validate the TRNs in these two systems.

描述（由申请人提供）：对哺乳动物系统中生物过程的转录调节网络（TRN）的解密对于我们对正常生理和疾病病因的基本机制的理解至关重要。这是一项艰巨的任务，因为TRN中的链接太多是未知的。芯片芯片和CHIP-SEQ技术的出现使许多转录因子（TFS）的基因组结合位点的映射在生物学过程中已知是关键调节剂。但是，这两种技术仅限于已知的具有芯片质量抗体的调节剂。我们发现TF结合通常与动态组蛋白标记标记签名有关，并且可以从全基因组组蛋白标记动力学上进行计算预测。因此，我们假设，随着时间顺序的核小体分辨率芯片序列，具有一些信息性的组蛋白标记和基因表达的RNA-seq数据以及有效的计算建模，我们可以在哺乳动物生物学过程中推断出TRN。具体而言，我们建议开发有效的计算算法以实现AIM1：首先，预测核小体分辨率组蛋白标记动力学的TF结合；其次，从TF结合，组蛋白标记和基因表达谱中鉴定靶基因；第三，在时间课程中渗透TRN。我们还建议将上述算法应用于AIM 2的两个生物系统。一个是小鼠成肌细胞细胞系C2C12将骨，脂肪或肌肉分化为骨，脂肪或肌肉，而另一种是人类基本乳腺癌细胞系MDA-MB-453可逆性地对上皮细胞进行维生素D处理。通过时间表核小体分辨率组蛋白标记芯片序列和RNA-Seq分析，我们将在计算中推断和实验验证这两个系统中的TRN。