Computational approaches for comparative regulatory genomics to decipher long-range gene regulation

比较调控基因组学的计算方法来破译远程基因调控

基本信息

批准号：
10208923
负责人：
Sushmita Roy
金额：
$ 33.29万
依托单位：
UNIVERSITY OF WISCONSIN-MADISON
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-17 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10208923
关键词：
3-Dimensional Address Affect Animal Model Awareness Basic Science Binding Biological Biological Markers CRISPR/Cas technology Cardiovascular Diseases Cell Line Cell model Cells Chromatin Comparative Study Complex Computing Methodologies Data Data Set Development Diabetes Mellitus Dimensions Disease Distal Elements Endothelial Cells Enhancers Gene Expression Gene Expression Regulation Genes Genome Genomic Segment Genomics Goals Graph Hi-C Human Ice Individual Joints Link Machine Learning Malignant Neoplasms Measurement Measures Methods Modeling Obesity Pathway interactions Performance Phenotype Phylogenetic Analysis Play Process Protocols documentation Psychological Transfer Publishing Regulation Regulator Genes Regulatory Element Research Personnel Resolution Resources Role Signal Transduction Software Tools Statistical Models Technology Testing Tissue-Specific Gene Expression Training Translational Research Untranslated RNA Variant base cell type chromosome conformation capture comparative cost epigenome epigenomics experimental study follow-up genomic locus histone modification human disease improved learning classifier markov model multi-task learning multiple datasets multitask novel promoter tool trait transcription factor

项目摘要

Project Abstract/Summary The three-dimensional organization of the genome is a major player in long-range gene regulation, where regulatory elements such as enhancers affect the expression of a gene hundreds of kilobases away. Changes in three-dimensional organization are associated with tissue-specific gene expression and have been implicated in several human diseases including cancer, diabetes and obesity. Advances in chromosome conformation capture (3C) technologies have expanded our repertoire of long-range interactions between enhancers and promoters in model cell lines and have shown that such interactions are established through a complex interplay of chromatin state, transcription factor binding and three-dimensional proximity of genomic regions. However, our current understanding of the dynamics of long-range gene regulation is limited, both across different cell types as well as across different species. This is because of the absence of such datasets in most species and cell types, lack of systematic methods to predict and interpret these interactions, and due to limited approaches to compare both the regions and their interactions across different cell types and especially across species. The overarching goals of this proposal are to develop novel computational methods to jointly identify candidate regulatory elements in multiple species and predict their long- range interactions in new cell types and species where high-throughput 3C datasets are not available or difficult to obtain. In Aim 1, we will develop a phylogenetically aware method of jointly identifying regulatory elements such as enhancers in multiple species. Aim 2 will develop multi-task and transfer learning approaches to predict interactions in new species and cell types by integrating available high-throughput 3C datasets from multiple cell types and 3C platforms. In Aim 3, we will collect a novel multi-species chromatin mark dataset in species-specific endothelial cells to enable a systematic study of long-range gene regulation dynamics. We will apply our computational approaches developed in Aims 1 and 2 on this multi-species epigenomic dataset to identify different regulatory elements and predict long-range interactions in multiple species. We will develop rigorous computational measures to evaluate the quality of predictions from our novel methods and the improvements compared to existing methods based on published 3C datasets. We will further experimentally validate predicted interactions using Capture-HiC in multiple species and using CRISPR/Cas9 experiments. We will examine individual and groups of interactions to identify species-specific, and clade- specific interactions and interpret the corresponding genes in the context of known pathways and curated gene sets associated with cardiovascular diseases. Our methods will be widely applicable to dissect long-range gene regulation in complex phenotypes including diseases. Software tools, resources, original data and experimental protocols developed by this project will be made publicly available.

项目摘要/总结基因组的三维组织是远程基因调控的主要参与者，其中增强子等调控元件影响数百个碱基外的基因表达。变化三维组织中的基因与组织特异性基因表达相关，并且已被证实与多种人类疾病有关，包括癌症、糖尿病和肥胖症。染色体的进展构象捕获（3C）技术扩展了我们之间的长距离相互作用的能力模型细胞系中的增强子和启动子，并表明这种相互作用是通过染色质状态、转录因子结合和基因组三维邻近性之间复杂的相互作用地区。然而，我们目前对远程基因调控动态的理解是有限的，无论是跨越不同的细胞类型以及不同的物种。这是因为缺乏此类数据集在大多数物种和细胞类型中，缺乏系统方法来预测和解释这些相互作用，并且由于比较不同细胞类型的区域及其相互作用的方法有限，尤其是跨物种。该提案的总体目标是开发新颖的计算联合识别多个物种中候选调控元件并预测其长期作用的方法无法获得高通量 3C 数据集的新细胞类型和物种中的一系列相互作用或难以获得。在目标 1 中，我们将开发一种系统发育感知方法来联合识别调节元件，例如多个物种的增强子。目标 2 将开发多任务和迁移学习通过整合可用的高通量 3C 来预测新物种和细胞类型中相互作用的方法来自多种细胞类型和 3C 平台的数据集。在目标 3 中，我们将收集一种新型的多物种染色质标记物种特异性内皮细胞中的数据集，以实现远程基因调控的系统研究动力学。我们将在目标 1 和 2 中开发的计算方法应用于这种多物种表观基因组数据集，用于识别不同的调控元件并预测多个中的远程相互作用物种。我们将制定严格的计算措施来评估我们的小说的预测质量方法以及与基于已发布的 3C 数据集的现有方法相比的改进。我们将进一步使用 Capture-HiC 在多个物种中并使用 CRISPR/Cas9 进行实验验证预测的相互作用实验。我们将检查个体和群体的相互作用，以确定物种特异性和进化枝特定的相互作用并在已知途径和策划基因的背景下解释相应的基因与心血管疾病相关的组。我们的方法将广泛适用于解剖远程包括疾病在内的复杂表型的基因调控。软件工具、资源、原始数据和该项目开发的实验方案将公开。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Competition between transcription and loop extrusion modulates promoter and enhancer dynamics.

转录和环挤出之间的竞争调节启动子和增强子的动态。

DOI：
发表时间：
2023-08-16
期刊：
影响因子：
0
作者：
Sexton, Tom;Platania, Angeliki;Erb, Cathie;Barbieri, Mariano;Molcrette, Bastien;Grandgirard, Erwan;de Kort, Marit;Meabum, Karen;Taylor, Tiegh;Shchuka, Virlana;Kocanova, Silvia;Oliveira, Guilherme;Mitchell, Jennifer;Soutoglou, Evi;Lenstra, Ti
通讯作者：
Lenstra, Ti

In silico prediction of high-resolution Hi-C interaction matrices.

高分辨率 Hi-C 相互作用矩阵的计算机预测。

DOI：
发表时间：
2019
期刊：
影响因子：
16.6
作者：
Zhang, Shilu;Chasman, Deborah;Knaack, Sara;Roy, Sushmita
通讯作者：
Roy, Sushmita

Transcriptional regulation and chromatin architecture maintenance are decoupled functions at the Sox2 locus.

转录调节和染色质结构维护是 Sox2 基因座的解耦功能。

DOI：
发表时间：
2022-06-01
期刊：
影响因子：
10.5
作者：
Taylor, Tiegh;Sikorska, Natalia;Shchuka, Virlana M;Chahar, Sanjay;Ji, Chenfan;Macpherson, Neil N;Moorthy, Sakthi D;de Kort, Marit A C;Mullany, Shanelle;Khader, Nawrah;Gillespie, Zoe E;Langroudi, Lida;Tobias, Ian C;Lenstra, Tineke L;Mitchell
通讯作者：
Mitchell

Enabling Studies of Genome-Scale Regulatory Network Evolution in Large Phylogenies with MRTLE.

利用 MRTLE 实现大系统发育中基因组规模调控网络进化的研究。