Quantitative Modeling for Chromatin Regulation of Gene Expression in Cancer

项目总结 染色质在转录调控中起着至关重要的作用。染色质相关基因经常发生突变 在癌症方面。剖析染色质在基因调控中的功能对于理解 肿瘤发生和肿瘤进展的分子机制。作为一名经验丰富的计算生物学家 在芯片序列生物信息学和表观遗传学方面的专业知识，我的研究重点是开发计算 关于染色质调节的高通量基因组数据分析和计算建模方法 关于基因表达的。随着更多的癌症生物学独立研究培训，我将发展我的研究 计算机癌症表观遗传学计划，并发展独立的学术生涯。 最近的研究证明了将染色质调节剂靶向于活跃的开放区域的可行性 基因组作为癌症治疗的新疗法。然而，染色质的特定于上下文的底物 染色质如何调控基因表达的调节机制和调控机制在很大程度上尚不清楚。使用 基于下一代测序的高通量基因组技术的出现包括芯片序列， DNase-seq和atac-seq，提供了大量的基因组图谱数据，使之成为可能 用综合计算方法系统地破译基因调控机制。 这个项目的目标是开发新的定量和计算方法来研究 表观遗传基因调控和染色质调节剂在癌症中的作用。具体来说，我们建议 开发综合计算方法，利用丰富的公共芯片-SEQ，DNASE-SEQ， 以及用于预测功能调节元件和TF的ATAC-SEQ数据。首先(目标1)，我们将发展 一种预测功能增强子元件和相关转录因子的方法，该方法给定任何基因集 组蛋白标记多种细胞类型芯片序列数据。第二(目标2)，我们将开发一个量化模型来 从配对末端DNase-seq或atac-seq中鉴定核苷酸分辨染色质可及性动态 修正了数据中固有偏差的数据。最后(目标3)，我们将整合公开可用的DNase- 综合数据库中的SEQ、ATAC-SEQ和CHIP-SEQ数据，并系统地描述 染色质调节剂的功能，重点是EZH2在几个癌症系统中的功能，包括抗去势的 前列腺癌(CRPC)细胞和恶性周围神经鞘瘤(MPNSTs)。这些计算量 方法是对现有生物信息学方法的补充，将在生物信息学研究中有广泛的应用 癌症表观遗传学和基因调控。拟议中的研究将填补致癌基因之间的知识空白 驱动程序和下游的基因表达计划，并可以为发展提供机制支持 癌症精准医学的新靶向疗法。

PROJECT SUMMARY Chromatin plays an essential role in transcriptional regulation. Chromatin-related genes are frequently mutated in cancers. Dissecting the functions of chromatin in gene regulation is important for understanding the molecular mechanisms of oncogenesis and tumor progression. As an experienced computational biologist with expertise on ChIP-seq bioinformatics and epigenetics, my research has focused on developing computational methodologies for high-throughput genomic data analysis and computational modeling on chromatin regulation of gene expression. With more independent research training in cancer biology, I will develop my research program on computational cancer epigenetics and develop an independent academic career. Recent studies have demonstrated the feasibility of targeting chromatin regulators for active open regions in the genome as novel therapeutics for cancer treatment. However, the context-specific substrates of chromatin regulators and the mechanisms underlying how chromatin regulates gene expression are largely unclear.. With the advent of next-generation sequencing based high-throughput genomic techniques including ChIP-seq, DNase-seq, and ATAC-seq, a large amount of for genomic profiling data became available, making it possible to systematically decipher the gene regulatory mechanisms with an integrative computational approach. The objective of this project is to develop novel quantitative and computational methodologies for studying epigenetic gene regulation and the functions of chromatin regulators in cancer. Specifically, we propose to develop integrative computational methods that leverage the abundant public ChIP-seq, DNase-seq, and ATAC-seq data for predicting functional regulatory elements and TFs. First (Aim 1), we will develop a method that predicts the functional enhancer elements and associated TFs given any gene set using public histone mark ChIP-seq data across multiple cell types. Second (Aim 2), we will develop a quantitative model to identify the nucleotide-resolution chromatin accessibility dynamics from paired-end DNase-seq or ATAC-seq data with correction of intrinsic biases in the data. Finally (Aim 3), we will integrate publicly available DNase- seq, ATAC-seq, and ChIP-seq data in a comprehensive database and systematically characterize the functions of chromatin regulators with a focus on EZH2 in a few cancer systems, including castration-resistant prostate cancer (CRPC) cells, and malignant peripheral nerve sheath tumors (MPNSTs). These computational methods complement existing bioinformatics methodologies and will have broad applications in the study of cancer epigenetics and gene regulation. The proposed research will fill the knowledge gap between oncogenic drivers and downstream gene expression program, and could provide mechanistic support for development of novel targeted therapeutics for cancer precision medicine.