染色质可及性分析的研究进展

在细胞核内,染色质可及性模式会随着外部刺激和发育线索的改变而发生动态变化。染色质可及性重构对于基因表达调控至关重要,在建立和维持细胞特性等方面发挥着重要作用。因此开展染色质可及性的研究对染色质功能上的三维解析具有十分重要的意义。近几年,随着高通量测序技术的进步以及测序成本的降低,基于高通量测序技术的染色质可及性分析方法得到了迅速发展。目前观察和分析全基因组染色质开放与否的常见技术主要有脱氧核糖核酸酶I超敏位点测序（DNase-seq）、微球菌核酸酶测序（MNase-seq）、甲醛辅助分离调控元件测序（FAIRE-seq）以及转座酶可及性测序（ATAC-seq）。本文比较了这4种染色质可及性分析技术的优缺点,详细介绍了它们的原理及主要实验流程,并简要讨论了它们的发展及相关技术的应用,期望通过这些互补的方法为染色质分析领域的未来发展提供一些借鉴和思路。     

Malaria: Could its unusual epigenome be the weak spot?

The epigenetic contribution to the regulation and maintenance of gene expression patterns by histone modifications is well established in eukaryotes. In Plasmodium falciparum, the mechanisms and factors regulating gene expression during progression through its infected red blood cell cycle (iRBC) and underlying mutually exclusive expression of antigenic variation genes involved in immune evasion are far from understood. Recently, the first comprehensive analyses of the P. falciparum chromatin landscape at different iRBC stages have been performed. These studies uncovered the existence of well-defined heterochromatic regions within a generally euchromatic epigenome. Notably, silencing of genes encoding for virulence determinants such as var genes, appears to be orchestrated by the concerted action of the Sir2 and HP1 orthologs and the presence of the histone mark, H3K9me3. Epigenetic speciation could make the parasite exquisitely vulnerable to epigenetic drug treatment, unless this deadly parasite still has a number of tricks up his sleeves.     

Flexible copula model for integrating correlated multi-omics data from single-cell experiments

With recent advances in technologies to profile multi-omics data at the single-cell level, integrative multi-omics data analysis has been increasingly popular. It is increasingly common that information such as methylation changes, chromatin accessibility, and gene expression are jointly collected in a single-cell experiment. In biomedical studies, it is often of interest to study the associations between various data types and to examine how these associations might change according to other factors such as cell types and gene regulatory components. However, since each data type usually has a distinct marginal distribution, joint analysis of these changes of associations using multi-omics data is statistically challenging. In this paper, we propose a flexible copula-based framework to model covariate-dependent correlation structures independent of their marginals. In addition, the proposed approach could jointly combine a wide variety of univariate marginal distributions, either discrete or continuous, including the class of zero-inflated distributions. The performance of the proposed framework is demonstrated through a series of simulation studies. Finally, it is applied to a set of experimental data to investigate the dynamic relationship between single-cell RNA sequencing, chromatin accessibility, and DNA methylation at different germ layers during mouse gastrulation.     

Single Cell Visualization of Yeast Gene Expression Shows Correlation of Epigenetic Switching between Multiple Heterochromatic Regions through Multiple Generations

Differences in gene expression between individual cells can be mediated by epigenetic regulation; thus, methods that enable detailed analyses of single cells are crucial to understanding this phenomenon. In this study, genomic silencing regions of Saccharomyces cerevisiae that are subject to epigenetic regulation, including the HMR, HML, and telomere regions, were investigated using a newly developed single cell analysis method. This method uses fluorescently labeled proteins to track changes in gene expression over multiple generations of a single cell. Epigenetic control of gene expression differed depending on the specific silencing region at which the reporter gene was inserted. Correlations between gene expression at the HMR-left and HMR-right regions, as well as the HMR-right and HML-right regions, were observed in the single-cell level; however, no such correlations involving the telomere region were observed. Deletion of the histone acetyltransferase GCN5 gene from a yeast strain carrying a fluorescent reporter gene at the HMR-left region reduced the frequency of changes in gene expression over a generation. The results presented here suggest that epigenetic control within an individual cell is reversible and can be achieved via regulation of histone acetyltransferase activity.     

A glimpse into the epigenetic landscape of gene regulation

Post-translational modifications to histone proteins and methylation of DNA comprise the epigenome of a cell. The epigenome, which changes through development, controls access to our genes. Recent advances in DNA sequencing technology has led to genome-wide distribution data for a limited number of histone modifications in mammalian stem cells and some differentiated lineages. These studies reveal predictive correlations between histone modifications, different classes of gene and chromosomal features. Moreover, this glimpse into our epigenome challenges current ideas about regulation of gene expression. Many genes in stem cells are poised for expression with initiated RNA polymerase II at the promoter. This state is maintained by an epigenetic mark through multiple lineages until the gene is expressed.     

Combinatorial chromatin modification patterns in the human genome revealed by subspace clustering

Chromatin modifications, such as post-translational modification of histone proteins and incorporation of histone variants, play an important role in regulating gene expression. Joint analyses of multiple histone modification maps are starting to reveal combinatorial patterns of modifications that are associated with functional DNA elements, providing support to the 'histone code' hypothesis. However, due to the lack of analytical methods, only a small number of chromatin modification patterns have been discovered so far. Here, we introduce a scalable subspace clustering algorithm, coherent and shifted bicluster identification (CoSBI), to exhaustively identify the set of combinatorial modification patterns across a given epigenome. Performance comparisons demonstrate that CoSBI can generate biclusters with higher intra-cluster coherency and biological relevance. We apply our algorithm to a compendium of 39 genome-wide chromatin modification maps in human CD4(+) T cells. We identify 843 combinatorial patterns that recur at >0.1% of the genome. A total of 19 chromatin modifications are observed in the combinatorial patterns, 10 of which occur in more than half of the patterns. We also identify combinatorial modification signatures for eight classes of functional DNA elements. Application of CoSBI to epigenome maps of different cells and developmental stages will aid in understanding how chromatin structure helps regulate gene expression.     

Model-Based Deconvolution of Cell Cycle Time-Series Data Reveals Gene Expression Details at High Resolution

In both prokaryotic and eukaryotic cells, gene expression is regulated across the cell cycle to ensure “just-in-time” assembly of select cellular structures and molecular machines. However, present in all time-series gene expression measurements is variability that arises from both systematic error in the cell synchrony process and variance in the timing of cell division at the level of the single cell. Thus, gene or protein expression data collected from a population of synchronized cells is an inaccurate measure of what occurs in the average single-cell across a cell cycle. Here, we present a general computational method to extract “single-cell”-like information from population-level time-series expression data. This method removes the effects of 1) variance in growth rate and 2) variance in the physiological and developmental state of the cell. Moreover, this method represents an advance in the deconvolution of molecular expression data in its flexibility, minimal assumptions, and the use of a cross-validation analysis to determine the appropriate level of regularization. Applying our deconvolution algorithm to cell cycle gene expression data from the dimorphic bacterium Caulobacter crescentus, we recovered critical features of cell cycle regulation in essential genes, including ctrA and ftsZ, that were obscured in population-based measurements. In doing so, we highlight the problem with using population data alone to decipher cellular regulatory mechanisms and demonstrate how our deconvolution algorithm can be applied to produce a more realistic picture of temporal regulation in a cell.     

染色质结构之美——染色质可及性

染色质可及性(chromatin accessibility)作为一种衡量染色质结合因子与染色质DNA结合能力高低的染色质属性,是评价染色质结构稳态的重要指标之一,在多种细胞核进程中扮演重要角色,包括基因转录调控以及DNA损伤修复等。该属性的异常调控与多种疾病的发生发展密切相关,包括肿瘤以及神经退行性疾病等。对于该属性探究已经成为生命科学与疾病领域的热点。伴随越来越多的新技术应运而生,例如染色质构象捕获技术、高通量测序技术以及两种技术的结合等。随着技术的进步,多种参与调控染色质可及性的因素被发现和总结,包括核小体占位、组蛋白修饰以及非编码RNA等。多项大规模的染色质组学数据绘制了多种疾病的染色质可及性图谱,为揭示疾病的发生发展与染色质可及性之间的关系提供了数据支持。同时,随着单细胞染色质可及性测序技术的发展,实现了对细胞类型染色质层面的划分,弥补了单纯依赖基因表达划分细胞类型的不足。本文将从染色质的组成与可及性、影响染色质可及性的因素、染色质可及性的检测方法,以及染色质可及性与癌症的关系等方面简要阐述染色质可及性的研究进展。     

Epigenetic Mechanisms Regulate Stem Cell Expressed Genes Pou5f1 and Gfra1 in a Male Germ Cell Line

Male fertility is declining and an underlying cause may be due to environment-epigenetic interactions in developing sperm, yet nothing is known of how the epigenome controls gene expression in sperm development. Histone methylation and acetylation are dynamically regulated in spermatogenesis and are sensitive to the environment. Our objectives were to determine how histone H3 methylation and acetylation contribute to the regulation of key genes in spermatogenesis. A germ cell line, GC-1, was exposed to either the control, or the chromatin modifying drugs tranylcypromine (T), an inhibitor of the histone H3 demethylase KDM1 (lysine specific demethylase 1), or trichostatin (TSA), an inhibitor of histone deacetylases, (HDAC). Quantitative PCR (qPCR) was used to identify genes that were sensitive to treatment. As a control for specificity the Myod1 (myogenic differentiation 1) gene was analyzed. Chromatin immunoprecipitation (ChIP) followed by qPCR was used to measure histone H3 methylation and acetylation at the promoters of target genes and the control, Myod1. Remarkably, the chromatin modifying treatment specifically induced the expression of spermatogonia expressed genes Pou5f1 and Gfra1. ChIP-qPCR revealed that induction of gene expression was associated with a gain in gene activating histone H3 methylation and acetylation in Pou5f1 and Gfra1 promoters, whereas CpG DNA methylation was not affected. Our data implicate a critical role for histone H3 methylation and acetylation in the regulation of genes expressed by spermatogonia – here, predominantly mediated by HDAC-containing protein complexes.     

Emerging patterns of epigenomic variation

Fuelled by new sequencing technologies, epigenome mapping projects are revealing epigenomic variation at all levels of biological complexity, from species to cells. Comparisons of methylation profiles among species reveal evolutionary conservation of gene body methylation patterns, pointing to the fundamental role of epigenomes in gene regulation. At the human population level, epigenomic changes provide footprints of the effects of genomic variants within the vast nonprotein-coding fraction of the genome, and comparisons of the epigenomes of parents and their offspring point to quantitative epigenomic parent-of-origin effects confounding classical Mendelian genetics. At the organismal level, comparisons of epigenomes from diverse cell types provide insights into cellular differentiation. Finally, comparisons of epigenomes from monozygotic twins help dissect genetic and environmental influences on human phenotypes and longitudinal comparisons reveal aging-associated epigenomic drift. The development of new bioinformatic frameworks for comparative epigenome analysis is putting epigenome maps within the reach of researchers across a wide spectrum of biological disciplines.