Inferring Mammalian Transcriptional Regulatory Networks from Epigenomics

描述(申请人提供)：破译在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和疾病病因的基本机制是必不可少的。这是一项艰巨的任务，因为TRN中有太多的链接是未知的。芯片和芯片序列技术的出现使得许多已知在生物过程中起关键调节作用的转录因子(TF)的全基因组结合位点的图谱得以绘制。然而，这两项技术仅限于具有芯片质量抗体的已知监管机构。我们发现，Tf结合通常与一个动态的组蛋白标记签名有关，并且可以从全基因组的组蛋白标记动态进行计算预测。因此，我们假设，利用几个信息量丰富的组蛋白标记和RNA-SEQ基因表达序列的时间序列芯片，以及有效的计算模型，我们可以推断出哺乳动物生物学过程中的TRN。具体地说，我们建议开发有效的计算算法来实现Aim1：第一，根据核小体分辨的组蛋白标记动力学预测Tf结合；第二，根据Tf结合、组蛋白标记和基因表达谱识别靶基因；第三，推断一段时间过程中的TRN。我们还建议将上述算法应用于目标2中的两个生物系统，一个是小鼠成肌细胞系C2C12向骨、脂肪或肌肉分化，另一个是人顶分泌型乳腺癌细胞系MDA-MB-453，用维生素D处理后可逆重编程为上皮细胞。通过时程核小体分辨组蛋白标记芯片-SEQ和RNA-SEQ图谱，我们将对这两个系统中的TRNS进行计算推断和实验验证。 与公共卫生相关：我们提出了一种系统和公正的方法来推断控制哺乳动物系统中两个生物过程的转录调控网络(TRN)，这可以经济有效地扩展到其他过程。特别是，我们希望我们的方法有助于更好地了解干细胞命运控制和细胞身份重新编程，并促进许多毁灭性疾病和损伤的治疗。从目标2中的两个生物系统获得的TRN将揭开转录和表观遗传调控的新的分子机制。它们可能有助于确定肥胖症和癌症治疗干预的新靶点。目标1中的计算算法和目标2中的时间进程高通量数据将作为生物医学研究社区的宝贵资源公开提供。

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. PUBLIC HEALTH RELEVANCE: We propose a systematic and unbiased approach to infer the transcriptional regulatory networks (TRNs) governing two biological processes in mammalian systems, which can be cost- effectively extended to other processes. In particular, we expect our approaches to benefit the better understanding of stem cell fate control and cell identity reprogramming, and facilitate treatments for many devastating diseases and injuries. The TRNs obtained from the two biological systems in Aim 2 will unravel new molecular mechanisms of transcriptional and epigenetic regulation. They may help identify new targets for therapeutic intervention for obesity and cancer. The resulting computational algorithms in Aim 1 and time-course high throughput data in Aim 2 will be made publicly available as valuable resources for the biomedical research community.