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: 8331443
• 负责人: Xiaole Shirley Liu
• 金额: $ 34.13万
• 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/8331443
• 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; RNA; 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

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）：破译哺乳动物系统中控制生物过程的转录调控网络（TRN）对于我们理解正常生理学以及疾病病因学的基本机制至关重要。这是一项艰巨的任务，因为 TRN 中有太多未知的链接。 ChIP 芯片和 ChIP-seq 技术的出现使得人们能够绘制许多转录因子 (TF) 的全基因组结合位点图谱，这些转录因子是生物过程中的关键调节因子。然而，这两种技术仅限于具有 ChIP 质量抗体的已知调节器。我们发现 TF 结合通常与动态组蛋白标记特征相关，并且可以通过全基因组组蛋白标记动态进行计算预测。因此，我们假设通过一些信息丰富的组蛋白标记的时程核小体分辨率 ChIP-seq 和基因表达的 RNA-seq 数据以及有效的计算模型，我们可以推断哺乳动物生物过程中的 TRN。具体来说，我们建议开发有效的计算算法来实现目标1：首先，从核小体分辨率组蛋白标记动态中预测 TF 结合；其次，从 TF 结合、组蛋白标记和基因表达谱中识别靶基因；第三，推断一段时间内的 TRN。我们还建议在目标 2 中将上述算法应用于两个生物系统。一个是小鼠成肌细胞系 C2C12 分化为骨骼、脂肪或肌肉，另一个是人顶泌乳腺癌细胞系 MDA-MB-453 可逆用维生素 D 治疗对上皮细胞进行重编程。通过时程核小体分辨率组蛋白标记 ChIP-seq 和 RNA-seq 分析，我们将通过计算推断并通过实验验证这两个系统中的 TRN。