Hi-C技术在三维基因组学和疾病致病机理研究中的应用

三维基因组学在基因组序列、基因结构及其调控元件的基础上对细胞核内的染色质的三维空间结构进行研究。染色体的空间交互作用是基因表达调控的重要因素,随着高通量染色体构象捕获(high-throughput chromosome conformation capture,Hi-C)技术及其衍生技术的出现和快速发展,借助Hi-C技术获取高通量三维基因组学数据,对基因表达调控等生物过程进行研究,已成为揭示细胞深层机制、阐明疾病致病机理的重要手段。本文在介绍三维基因组的发展历程和研究技术的基础上,重点总结了近年来Hi-C技术在多种疾病研究、特别是致病机理阐释方面的应用和成果,为深入理解三维基因组学在构建全局基因调控图谱、挖掘疾病致病机理方面的应用提供参考和借鉴。     

利用改进的3DMax算法重构染色体3D结构

近年来,随着高通量染色体构象捕获(Hi-C)等技术的发展和高通量测序成本的降低,全基因组交互作用的数据量快速增长,交互作用图谱分辨率不断提高,促使染色体和基因组三维结构建模的研究取得了很大进展,已经提出了几种从染色体构象捕捉数据中构建单个染色体或整个基因组结构的方法。文中通过对在 Hi-C 数据基础上对染色体三维结构重建的相关文献进行分析,总结了重建染色体三维空间结构的经典算法3DMax的原理,并且提出了一种新的随机梯度上升算法:XNadam,是Nadam优化方法的一个变体,将其应用于3DMax算法中,以便提高3DMax算法的性能,从而用于预测染色体三维结构。     

基于Hi-C数据的染色体三维分析计算方法

真核生物的染色质在细胞核中高度折叠且有序排列,这种三维结构对基因转录和DNA复制等细胞功能发挥至关重要。近年来Hi-C数据的涌现,使得研究染色质三维交互作用及其空间结构成为了可能。自2009年Hi-C被开发以来,已经出现了许多用于处理Hi-C数据的生物信息学工具,从原始序列比对到接触矩阵的可视化,均提供了系统的Hi-C数据处理过程或解决某一个特定问题的方法。本研究全面介绍了传统的Hi-C数据处理工作流程,以及近十年来新兴的Hi-C数据处理工具以及其未来发展方向。     

基于Hi-C技术哺乳动物三维基因组研究进展

基因组DNA在细胞核中并不是呈线性的一字排列,而是以三维结构高度折叠并浓缩成染色质的方式储存于核内,具有特定的高级空间结构和构象。高通量染色体构象捕获(high-througnput chromosome conformationcapture, Hi-C)技术于2009年首次被提出,目前已得到大规模运用,使得人们对于三维基因组学有了更深刻的认识。研究表明,哺乳动物基因组三维层级结构单元由大到小依次为染色体疆域(chromosome territory, CT)、染色质区室(chromatin compartment A/B)、拓扑关联结构域(topological associated domain, TAD)和染色质环(chromatin loop),这些层级结构单元在基因转录和表达调控过程中发挥着重要作用。本文基于Hi-C技术从染色质的三维层级结构划分、构象单元作用以及三维基因组在发育、疾病等方面的应用进行阐述,旨在为更深入地了解哺乳动物三维基因组学研究提供参考。     

下一代测序技术在干细胞转录调控研究中的应用

基因转录调控及其机制分析是后基因组时代生物学研究的重点之一。随着高通量测序技术的发展,人们可以从不同层面研究基因的转录调控行为,从转录组、转录因子结合,到染色质局部结构和整体空间构象,可系统分析转录调控的分子机制。干细胞分化过程的转录调控分析对研究再生医学和理解细胞癌变机制等具有重要意义。本文综述了下一代测序技术在干细胞转录调控研究中的应用,包括：(1)基于基因芯片或RNA测序的转录组分析;(2)基于染色体免疫共沉淀(chromatin immunoprecipitation, ChIP)测序的表观基因组和转录因子结合信息的分析;(3)基于DNase 酶切测序(DNase-Seq)的染色质开放性分析;(4)基于高通量染色质构象捕获(high-throughput chromosome conformation capture, Hi-C)技术的染色体远程相互作用分析。从基因表达谱、转录因子结合和基因组三维结构等层面展开介绍,重点关注了一些多能性转录因子(Oct4、Sox2和Nanog等)在维持干细胞干性和分化中的调控作用,以期为干细胞转录调控的研究提供借鉴和参考。     

欢迎订阅2020年《基因组学与应用生物学》 连续4次入编北大《中文核心期刊要目总览》

正染色体三维结构重构Three-dimensional reconstruction of chromosomes染色体三维重构是近年来基因组学研究的重要手段,利用测序技术获得的染色体空间结构信息,本质是根据染色体二维接触频率数据来预测其在细胞核中的三维形态。基于染色体构象捕获(Chromosome conformation capture,3C)技术的高通量Hi-C(High-throughput/resolution chromosome conformation capture)     

染色质构象解析技术——Hi-C及染色质构象信息提取

真核生物染色质在核内的空间组织形式能影响DNA的空间分布,因而对基因转录、DNA复制等生物学过程具有调节作用。目前对这种空间上高度有序的基因组结构的认识还是粗糙的、碎片式的和不完整的。近年,利用染色质构象捕获技术发展起来的衍生技术——Hi-C技术,是一种研究全基因组范围的染色质相互作用以及探明全基因组的三维结构的分析技术。利用Hi-C技术能够对染色质内部或所有染色质之间的相互作用进行精细分析,从而把基因表达调控引入到空间的、全局性的研究层面,为全面解析与DNA有关的生物学过程的机理开启新的契机。本文主要阐述染色质构象解析技术Hi-C的实验原理、数据处理以及染色质构象信息提取,包括染色质内相互作用情况分析、全基因组基因活性分类、拓扑关联结构域(TAD)和染色质环(chromatin loop),介绍染色质构象信息与基因调控研究方面的国际前沿进展。     

三维基因组数据分析方法进展

对染色质三维结构的探究逐渐成为了解基因组功能与基因调控关系的必要手段.近年来,随着高通量染色体构象捕获(Hi-C)等技术的发展和高通量测序成本的降低,全基因组交互作用的数据量快速增长,交互作用图谱分辨率不断提高.这给三维基因组学发展带来机遇的同时,也给计算建模带来了挑战.当前,三维基因组数据的分析方法覆盖面广,包括了数据前期处理、标准化、可视化、特征提取、三维建模等环节,但是如何从中选择高效、准确的方法却成为制约研究者们开展研究的一项关键因素.本文根据这些方法的适用场景、原理及特点进行系统地归纳,并重点关注了针对新技术或新需求的分析方法,以期促进这一领域中信息学方法的应用和开发,助力三维基因组学的研究.     

辐射诱导的染色质拓扑关联结构域层级变化及其在细胞辐射响应中的作用

基因组三维结构在基因表达调控中发挥重要作用,染色质拓扑关联结构域(topologically associated domain,TAD)是DNA复制和基因转录的基本功能单位,也是DNA损伤修复的功能单元,在辐射诱导的DNA损伤修复中发挥重要作用。近期研究表明,TAD并非是完全独立的结构单元,其内部常呈现多层级结构,对基因表达具有重要调控作用。为探究TAD多层级结构在细胞辐射响应中的作用,本研究使用TAD层级结构识别算法OnTAD对Gene expression omnibus数据库中5Gy X射线照射的淋巴细胞、成纤维细胞和毛细血管扩张性共济失调突变(ataxia telangiectasia mutated,ATM)基因缺陷的成纤维细胞,共26个样本的Hi-C(high-through chromosome conformation capture,Hi-C)数据进行分析,发现辐射后细胞的TAD层级结构出现规律性变化,高层级TAD缺失较多,低层级TAD相对保守;辐射诱导的TAD层级结构变化通过调节基因表达参与细胞辐射响应;ATM是辐射诱导TAD层级结构变化和恢复的重要因子。本研究为从TAD多层级结构角度理解基因组三维结构在细胞辐射响应中的作用提供了新思路。     

三维基因组染色质构象捕获及其衍生技术

线性染色质经过多重折叠凝缩到真核生物的细胞核中,染色质的三维构象直接决定了真核生物的基因表达,因此染色质可以在局部或远程空间上发生互作调控基因转录。折叠成环状构象的染色质可以借助染色质构象捕获 (Chromosome conformation capture,3C) 技术来研究,基于3C技术扩展的4C/5C/Hi-C从单个位点延伸到全基因组捕捉三维构象,在此基础上,染色质构象核心技术可以与免疫共沉淀、核酸分子杂交、单细胞、基因组测序等技术偶联而产生新的衍生技术和应用,这极大地推动了染色质构象技术在基因时空特异性表达调控上的研究。文中将以3C和Hi-C等三维基因组核心技术为基础,重点介绍染色质构象捕获及其衍生技术的原理和前沿应用。     

Advances in technologies for 3D genomics research

The spatial structure of the orderly organized chromatin in the nucleus has important roles in maintaining normal cell function and in regulation of gene expression, and the high-throughput Hi-C and Ch IA-PET methods have been widely used in various biological studies for determining potential spatial genome structures and their functions. However, there are still great difficulties and challenges in three-dimensional(3D) genomics research. More efficient, economical, and unbiased approaches to studying 3D genomics need to be developed for more widespread and easier applications. Here, we review the most recent studies on new 3D genomics research technologies, such as improvements of the traditional Hi-C and Ch IA-PET methods, new approaches based on non-proximal-ligation strategies, and imaging-based methods improved in recent years. Especially, we review the CRISPR-based methods for functional validations in 3D genomics, which could be the forthcoming directions. We hope this review can show some insights into the potential improvements for future 3D genomics.     

A Scalable Computational Approach for Simulating Complexes of Multiple Chromosomes

Significant efforts have been recently made to obtain the three-dimensional (3D) structure of the genome with the goal of understanding how structures may affect gene regulation and expression. Chromosome conformational capture techniques such as Hi-C, have been key in uncovering the quantitative information needed to determine chromatin organization. Complementing these experimental tools, co-polymers theoretical methods are necessary to determine the ensemble of three-dimensional structures associated to the experimental data provided by Hi-C maps. Going beyond just structural information, these theoretical advances also start to provide an understanding of the underlying mechanisms governing genome assembly and function. Recent theoretical work, however, has been focused on single chromosome structures, missing the fact that, in the full nucleus, interactions between chromosomes play a central role in their organization. To overcome this limitation, MiChroM (Minimal Chromatin Model) has been modified to become capable of performing these multi-chromosome simulations. It has been upgraded into a fast and scalable software version, which is able to perform chromosome simulations using GPUs via OpenMM Python API, called Open-MiChroM. To validate the efficiency of this new version, analyses for GM12878 individual autosomes were performed and compared to earlier studies. This validation was followed by multi-chain simulations including the four largest human chromosomes (C1-C4). These simulations demonstrated the full power of this new approach. Comparison to Hi-C data shows that these multiple chromosome interactions are essential for a more accurate agreement with experimental results. Without any changes to the original MiChroM potential, it is now possible to predict experimentally observed inter-chromosome contacts. This scalability of Open-MiChroM allow for more audacious investigations, looking at interactions of multiple chains as well as moving towards higher resolution chromosomes models.     

3D genome and its disorganization in diseases

The chromosomes in eukaryotic cells are highly folded and organized to form dynamic three-dimensional (3D) structures. In recent years, many technologies including chromosome conformation capture (3C) and 3C-based technologies (Hi-C, ChIA-PET) have been developed to investigate the 3D structure of chromosomes. These technologies are enabling research on how gene regulatory events are affected by the 3D genome structure, which is increasingly implicated in the regulation of gene expression and cellular functions. Importantly, many diseases are associated with genetic variations, most of which are located in non-coding regions. However, it is difficult to determine the mechanisms by which these variations lead to diseases. With 3D genome technologies, we can now better determine the consequences of non-coding genome alterations via their impact on chromatin interactions and structures in cancer and other diseases. In this review, we introduce the various 3D genome technologies, with a focus on their application to cancer and disease research, as well as future developments to extend their utility.     

染色质构象捕获及其衍生技术

染色质的构象在基因表达调节方面起重要作用．介绍了染色质构象捕获、环状染色质构象捕获、3C碳拷贝、ChIP-loop、ChIA-PET和Hi-C等技术的基本原理及发展历程,对影响实验结果准确性的主要因素进行了分析．目的是为在三维层面研究基因的表达调控介绍新的研究手段,为功能研究提供新思路,也为相关技术的应用提供理论参考．     

三维基因组学研究进展

三维基因组学是一门研究基因组三维空间结构与功能的新兴学科,主要研究基因组序列在细胞核内的三维空间构象,及其对DNA复制、DNA重组、基因表达调控等生物过程的生物学效应。自染色质构象捕获技术 (3C)出现后,三维基因组学相关研究领域飞速发展。借助于3C及其衍生技术、Hi-C和ChIA-PET等技术,科学家能对各类物种的三维基因组进行更为深入的研究,从而揭示微生物、植物和动物基因组的空间构象、染色质的相互作用模式、转录调控以及不同生物学性状的形成机制;挖掘与生命活动和疾病相关的关键基因和信号通路;推动农业科学、生命科学和医学等领域的快速发展。文中就三维基因组学研究进展作一综述,主要阐述三维基因组学的概念和研究技术的发展及其在农业科学、生命科学和医学等领域的应用,尤其是肿瘤领域所取得的阶段性研究成果。     

Large-scale reconstruction of 3D structures of human chromosomes from chromosomal contact data

Chromosomes are not positioned randomly within a nucleus, but instead, they adopt preferred spatial conformations to facilitate necessary long-range gene–gene interactions and regulations. Thus, obtaining the 3D shape of chromosomes of a genome is critical for understanding how the genome folds, functions and how its genes interact and are regulated. Here, we describe a method to reconstruct preferred 3D structures of individual chromosomes of the human genome from chromosomal contact data generated by the Hi-C chromosome conformation capturing technique. A novel parameterized objective function was designed for modeling chromosome structures, which was optimized by a gradient descent method to generate chromosomal structural models that could satisfy as many intra-chromosomal contacts as possible. We applied the objective function and the corresponding optimization method to two Hi-C chromosomal data sets of both a healthy and a cancerous human B-cell to construct 3D models of individual chromosomes at resolutions of 1 MB and 200 KB, respectively. The parameters used with the method were calibrated according to an independent fluorescence in situ hybridization experimental data. The structural models generated by our method could satisfy a high percentage of contacts (pairs of loci in interaction) and non-contacts (pairs of loci not in interaction) and were compatible with the known two-compartment organization of human chromatin structures. Furthermore, structural models generated at different resolutions and from randomly permuted data sets were consistent.