A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples.

The role of enhancers, a key class of non-coding regulatory DNA elements, in cancer development has increasingly been appreciated. Here, we present the detection and characterization of a large number of expressed enhancers in a genome-wide analysis of 8928 tumor samples across 33 cancer types using TCGA RNA-seq data. Compared with matched normal tissues, global enhancer activation was observed in most cancers. Across cancer types, global enhancer activity was positively associated with aneuploidy, but not mutation load, suggesting a hypothesis centered on "chromatin-state" to explain their interplay. Integrating eQTL, mRNA co-expression, and Hi-C data analysis, we developed a computational method to infer causal enhancer-gene interactions, revealing enhancers of clinically actionable genes. Having identified an enhancer ∼140 kb downstream of PD-L1, a major immunotherapy target, we validated it experimentally. This study provides a systematic view of enhancer activity in diverse tumor contexts and suggests the clinical implications of enhancers.

A unique histone 3 lysine 14 chromatin signature underlies tissue-specific gene regulation.

Organismal development and cell differentiation critically depend on chromatin state transitions. However, certain developmentally regulated genes lack histone 3 lysine 9 and 27 acetylation (H3K9ac and H3K27ac, respectively) and histone 3 lysine 4 (H3K4) methylation, histone modifications common to most active genes. Here we describe a chromatin state featuring unique histone 3 lysine 14 acetylation (H3K14ac) peaks in key tissue-specific genes in Drosophila and human cells. Replacing H3K14 in Drosophila demonstrates that H3K14 is essential for expression of genes devoid of canonical histone modifications in the embryonic gut and larval wing imaginal disc, causing lethality and defective wing patterning. We find that the SWI/SNF protein Brahma (Brm) recognizes H3K14ac, that brm acts in the same genetic pathway as H3K14R, and that chromatin accessibility at H3K14ac-unique genes is decreased in H3K14R mutants. Our results show that acetylation of a single lysine is essential at genes devoid of canonical histone marks and uncover an important requirement for H3K14 in tissue-specific gene regulation.

CoBATCH for High-Throughput Single-Cell Epigenomic Profiling.

An efficient, generalizable method for genome-wide mapping of single-cell histone modifications or chromatin-binding proteins is lacking. Here, we develop CoBATCH, combinatorial barcoding and targeted chromatin release, for single-cell profiling of genomic distribution of chromatin-binding proteins in cell culture and tissue. Protein A in fusion to Tn5 transposase is enriched through specific antibodies to genomic regions, and Tn5 generates indexed chromatin fragments ready for library preparation and sequencing. Importantly, this strategy enables not only low-input epigenomic profiling in intact tissues but also measures scalable up to tens of thousands of single cells per experiment under both native and cross-linked conditions. CoBATCH produces ∼12,000 reads/cell with extremely low background. Mapping of endothelial cell lineages from ten embryonic mouse organs through CoBATCH allows for efficient deciphering of epigenetic heterogeneity of cell populations and cis-regulatory mechanisms. Thus, obviating specialized devices, CoBATCH is broadly applicable and easily deployable for single-cell profiling of protein-DNA interactions.

Environmental factor reversibly determines cellular identity through opposing Integrators that unify epigenetic and transcriptional pathways.

Organisms must adapt to environmental stresses to ensure their survival and prosperity. Different types of stresses, including thermal, mechanical, and hypoxic stresses, can alter the cellular state that accompanies changes in gene expression but not the cellular identity determined by a chromatin state that remains stable throughout life. Some tissues, such as adipose tissue, demonstrate remarkable plasticity and adaptability in response to environmental cues, enabling reversible cellular identity changes; however, the mechanisms underlying these changes are not well understood. We hypothesized that positive and/or negative "Integrators" sense environmental cues and coordinate the epigenetic and transcriptional pathways required for changes in cellular identity. Adverse environmental factors such as pollution disrupt the coordinated control contributing to disease development. Further research based on this hypothesis will reveal how organisms adapt to fluctuating environmental conditions, such as temperature, extracellular matrix stiffness, oxygen, cytokines, and hormonal cues by changing their cellular identities.

Prediction of cell-type-specific cohesin-mediated chromatin loops based on chromatin state.

Chromatin loop is of crucial importance for the regulation of gene transcription. Cohesin is a type of chromatin-associated protein that mediates the interaction of chromatin through the loop extrusion. Cohesin-mediated chromatin interactions have strong cell-type specificity, posing a challenge for predicting chromatin loops. Existing computational methods perform poorly in predicting cell-type-specific chromatin loops. To address this issue, we propose a random forest model to predict cell-type-specific cohesin-mediated chromatin loops based on chromatin states identified by ChromHMM and the occupancy of related factors. Our results show that chromatin state is responsible for cell-type-specificity of loops. Using only chromatin states as features, the model achieved high accuracy in predicting cell-type-specific loops between two cell types and can be applied to different cell types. Furthermore, when chromatin states are combined with the occurrence frequency of CTCF, RAD21, YY1, and H3K27ac ChIP-seq peaks, more accurate prediction can be achieved. Our feature extraction method provides novel insights into predicting cell-type-specific chromatin loops and reveals the relationship between chromatin state and chromatin loop formation.

Nuclear lamina component KAKU4 regulates chromatin states and transcriptional regulation in the Arabidopsis genome.

BACKGROUND: The nuclear lamina links the nuclear membrane to chromosomes and plays a crucial role in regulating chromatin states and gene expression. However, current knowledge of nuclear lamina in plants is limited compared to animals and humans. RESULTS: This study mainly focused on elucidating the mechanism through which the putative nuclear lamina component protein KAKU4 regulates chromatin states and gene expression in Arabidopsis leaves. Thus, we constructed a network using the association proteins of lamin-like proteins, revealing that KAKU4 is strongly associated with chromatin or epigenetic modifiers. Then, we conducted ChIP-seq technology to generate global epigenomic profiles of H3K4me3, H3K27me3, and H3K9me2 in Arabidopsis leaves for mutant (kaku4-2) and wild-type (WT) plants alongside RNA-seq method to generate gene expression profiles. The comprehensive chromatin state-based analyses indicate that the knockdown of KAKU4 has the strongest effect on H3K27me3, followed by H3K9me2, and the least impact on H3K4me3, leading to significant changes in chromatin states in the Arabidopsis genome. We discovered that the knockdown of the KAKU4 gene caused a transition between two types of repressive epigenetics marks, H3K9me2 and H3K27me3, in some specific PLAD regions. The combination analyses of epigenomic and transcriptomic data between the kaku4-2 mutant and WT suggested that KAKU4 may regulate key biological processes, such as programmed cell death and hormone signaling pathways, by affecting H3K27me3 modification in Arabidopsis leaves. CONCLUSIONS: In summary, our results indicated that KAKU4 is directly and/or indirectly associated with chromatin/epigenetic modifiers and demonstrated the essential roles of KAKU4 in regulating chromatin states, transcriptional regulation, and diverse biological processes in Arabidopsis.

Cancer, metastasis, and the epigenome.

Cancer is the second leading cause of death worldwide and disease burden is expected to increase globally throughout the next several decades, with the majority of cancer-related deaths occurring in metastatic disease. Cancers exhibit known hallmarks that endow them with increased survival and proliferative capacities, frequently as a result of de-stabilizing mutations. However, the genomic features that resolve metastatic clones from primary tumors are not yet well-characterized, as no mutational landscape has been identified as predictive of metastasis. Further, many cancers exhibit no known mutation signature. This suggests a larger role for non-mutational genome re-organization in promoting cancer evolution and dissemination. In this review, we highlight current critical needs for understanding cell state transitions and clonal selection advantages for metastatic cancer cells. We examine links between epigenetic states, genome structure, and misregulation of tumor suppressors and oncogenes, and discuss how recent technologies for understanding domain-scale regulation have been leveraged for a more complete picture of oncogenic and metastatic potential.

Computational methods to explore chromatin state dynamics.

The human genome is marked by several singular and combinatorial histone modifications that shape the different states of chromatin and its three-dimensional organization. Genome-wide mapping of these marks as well as histone variants and open chromatin regions is commonly carried out via profiling DNA-protein binding or via chromatin accessibility methods. After the generation of epigenomic datasets in a cell type, statistical models can be used to annotate the noncoding regions of DNA and infer the combinatorial histone marks or chromatin states (CS). These methods involve partitioning the genome and labeling individual segments based on their CS patterns. Chromatin labels enable the systematic discovery of genomic function and activity and can label the gene body, promoters or enhancers without using other genomic maps. CSs are dynamic and change under different cell conditions, such as in normal, preneoplastic or tumor cells. This review aims to explore the available computational tools that have been developed to capture CS alterations under two or more cellular conditions.

Functional annotation of regulatory elements in cattle genome reveals the roles of extracellular interaction and dynamic change of chromatin states in rumen development during weaning.

We profiled landscapes of bovine regulatory elements and explored dynamic changes of chromatin states in rumen development during weaning. The regulatory elements (15 chromatin states) and their coordinated activities in cattle were defined through genome-wide profiling of four histone modifications, CTCF-binding, DNA accessibility, DNA methylation, and transcriptome in rumen epithelial tissues. Each chromatin state presented specific enrichment for sequence ontology, methylation, trait-associated variants, transcription, gene expression-associated variants, selection signatures, and evolutionarily conserved elements. During weaning, weak enhancers and flanking active transcriptional start sites (TSS) were the most dynamic chromatin states and occurred in tandem with significant variations in gene expression and DNA methylation, significantly associated with stature, production, and reproduction economic traits. By comparing with in vitro cultured epithelial cells and in vivo rumen tissues, we showed the commonness and uniqueness of these results, especially the roles of cell interactions and mitochondrial activities in tissue development.

Systems biology-based analysis indicates that PHO1;H10 positively modulates high light-induced anthocyanin biosynthesis in Arabidopsis leaves.

Arabidopsis PHO1;H10 is a member of the PHO1 gene family with SPX and EXS domains, and its functions remain largely unknown. As shown in PCSD database, the upstream region of PHO1;H10 gene is in the active chromatin states, with high DHS accessibility and binding sites of multiple transcription factors, especially ABI5, SPCH and HY5. Co-expression network and data-mining analyses showed PHO1;H10 and co-expression genes were with activation under high light stress. We did wet-lab experiments, and found that the detached leaves of PHO1;H10 overexpression lines accumulated more anthocyanin than those of WT and mutant under high light treatment. RNA-seq results showed overexpression of PHO1;H10 up-regulated many anthocyanin biosynthetic genes. The GSEA analysis result showed that the functional module related to anthocyanin pathway was significantly enriched. In summary, we conducted systems biology approach, combining dry- and wet-lab analyses, and discovered that PHO1;H10 might play an essential role during modulating high light-induced anthocyanin accumulation in the Arabidopsis detached leaves.

Histone Maps in Gossypium darwinii Reveal Epigenetic Regulation Drives Subgenome Divergence and Cotton Domestication.

The functional annotation of genomes, including chromatin modifications, is essential to understand the intricate architecture of chromatin and the consequential gene regulation. However, such an annotation remains limited for cotton genomes. Here, we conducted chromatin profiling in a wild allotetraploid cotton Gossypium darwinii (AD genome) by integrating the data of histone modification, transcriptome, and chromatin accessibility. We revealed that the A subgenome showed a higher level of active histone marks and lower level of repressive histone marks than the D subgenome, which was consistent with the expression bias between the two subgenomes. We show that the bias in transcription and histone modification between the A and D subgenomes may be caused by genes unique to the subgenome but not by homoeologous genes. Moreover, we integrate histone marks and open chromatin to define six chromatin states (S1-S6) across the cotton genome, which index different genomic elements including genes, promoters, and transposons, implying distinct biological functions. In comparison to the domesticated cotton species, we observed that 23.2% of genes in the genome exhibit a transition from one chromatin state to another at their promoter. Strikingly, the S2 (devoid of epigenetic marks) to S3 (enriched for the mark of open chromatin) was the largest transition group. These transitions occurred simultaneously with changes in gene expression, which were significantly associated with several domesticated traits in cotton. Collectively, our study provides a useful epigenetic resource for research on allopolyploid plants. The domestication-induced chromatin dynamics and associated genes identified here will aid epigenetic engineering, improving polyploid crops.

Epigenetic Landscape Is Largely Shaped by Diversiform Transposons in Aegilops tauschii.

Transposons (TEs) account for more than 80% of the wheat genome, the highest among all known crop species. They play an important role in shaping the elaborate genomic landscape, which is the key to the speciation of wheat. In this study, we analyzed the association between TEs, chromatin states, and chromatin accessibility in Aegilops tauschii, the D genome donor of bread wheat. We found that TEs contributed to the complex but orderly epigenetic landscape as chromatin states showed diverse distributions on TEs of different orders or superfamilies. TEs also contributed to the chromatin state and openness of potential regulatory elements, affecting the expression of TE-related genes. Some TE superfamilies, such as hAT-Ac, carry active/open chromatin regions. In addition, the histone mark H3K9ac was found to be associated with the accessibility shaped by TEs. These results suggest the role of diversiform TEs in shaping the epigenetic landscape and in gene expression regulation in Aegilops tauschii. This has positive implications for understanding the transposon roles in Aegilops tauschii or the wheat D genome.

Methods for ChIP-seq analysis: A practical workflow and advanced applications.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a central method in epigenomic research. Genome-wide analysis of histone modifications, such as enhancer analysis and genome-wide chromatin state annotation, enables systematic analysis of how the epigenomic landscape contributes to cell identity, development, lineage specification, and disease. In this review, we first present a typical ChIP-seq analysis workflow, from quality assessment to chromatin-state annotation. We focus on practical, rather than theoretical, approaches for biological studies. Next, we outline various advanced ChIP-seq applications and introduce several state-of-the-art methods, including prediction of gene expression level and chromatin loops from epigenome data and data imputation. Finally, we discuss recently developed single-cell ChIP-seq analysis methodologies that elucidate the cellular diversity within complex tissues and cancers.

DNA Sequence-Dependent Properties of Nucleosome Positioning in Regions of Distinct Chromatin States in Mouse Embryonic Stem Cells.

Chromatin architecture is orchestrated, and plays crucial roles during the developmental process by regulating gene expression. In embryonic stem cells (ESCs), three types of chromatin states, including active, repressive and poised states, were previously identified and characterized with specific chromatin modification marks and different transcription activity, but it is largely unknown how nucleosomes are organized in these chromatin states. In this study, by using a DNA deformation energy model, we investigated the sequence-dependent nucleosome organization within the chromatin states in mouse ESCs. The results revealed that: (1) compared with poised genes, active genes are characterized with a higher level of nucleosome occupancy around their transcription start sites (TSS) and transcription termination sites (TTS), and both types of genes do not have a nucleosome-depleted region at their TTS, contrasting with the MNase-seq based result; (2) based on our previous DNA bending energy model, we developed an improved model capable of predicting both rotational positioning and nucleosome occupancy determined by a chemical mapping approach; (3) DNA bending-energy-based analyses demonstrated that the fragile nucleosomes positioned at both gene ends could be explained largely by enhanced rotational positioning signals encoded in DNA, but nucleosome phasing around the TSS of active genes was not determined by sequence preference; (4) the nucleosome occupancy landscape around the binding sites of some developmentally important transcription factors known to bind with different chromatin contexts, was also successfully predicted; (5) the difference of nucleosome occupancy around the TSS between CpG-rich and CpG-poor promoters was partly captured by our sequence-dependent model. Taken together, by developing an improved deformation-energy-based model, we revealed some sequence-dependent properties of the nucleosome arrangements in regions of distinct chromatin states in mouse ESCs.

Self-organizing maps with variable neighborhoods facilitate learning of chromatin accessibility signal shapes associated with regulatory elements.

BACKGROUND: Assigning chromatin states genome-wide (e.g. promoters, enhancers, etc.) is commonly performed to improve functional interpretation of these states. However, computational methods to assign chromatin state suffer from the following drawbacks: they typically require data from multiple assays, which may not be practically feasible to obtain, and they depend on peak calling algorithms, which require careful parameterization and often exclude the majority of the genome. To address these drawbacks, we propose a novel learning technique built upon the Self-Organizing Map (SOM), Self-Organizing Map with Variable Neighborhoods (SOM-VN), to learn a set of representative shapes from a single, genome-wide, chromatin accessibility dataset to associate with a chromatin state assignment in which a particular RE is prevalent. These shapes can then be used to assign chromatin state using our workflow. RESULTS: We validate the performance of the SOM-VN workflow on 14 different samples of varying quality, namely one assay each of A549 and GM12878 cell lines and two each of H1 and HeLa cell lines, primary B-cells, and brain, heart, and stomach tissue. We show that SOM-VN learns shapes that are (1) non-random, (2) associated with known chromatin states, (3) generalizable across sets of chromosomes, and (4) associated with magnitude and multimodality. We compare the accuracy of SOM-VN chromatin states against the Clustering Aggregation Tool (CAGT), an unsupervised method that learns chromatin accessibility signal shapes but does not associate these shapes with REs, and we show that overall precision and recall is increased when learning shapes using SOM-VN as compared to CAGT. We further compare enhancer state assignments from SOM-VN in signals above a set threshold to enhancer state assignments from Predicting Enhancers from ATAC-seq Data (PEAS), a deep learning method that assigns enhancer chromatin states to peaks. We show that the precision-recall area under the curve for the assignment of enhancer states is comparable to PEAS. CONCLUSIONS: Our work shows that the SOM-VN workflow can learn relationships between REs and chromatin accessibility signal shape, which is an important step toward the goal of assigning and comparing enhancer state across multiple experiments and phenotypic states.

Chromatin regulatory genes differentially interact in networks to facilitate distinct GAL1 activity and noise profiles.

Controlling chromatin state constitutes a major regulatory step in gene expression regulation across eukaryotes. While global cellular features or processes are naturally impacted by chromatin state alterations, little is known about how chromatin regulatory genes interact in networks to dictate downstream phenotypes. Using the activity of the canonical galactose network in yeast as a model, here, we measured the impact of the disruption of key chromatin regulatory genes on downstream gene expression, genetic noise and fitness. Using Trichostatin A and nicotinamide, we characterized how drug-based modulation of global histone deacetylase activity affected these phenotypes. Performing epistasis analysis, we discovered phenotype-specific genetic interaction networks of chromatin regulators. Our work provides comprehensive insights into how the galactose network activity is affected by protein interaction networks formed by chromatin regulators.

Rewiring of chromatin state and gene expression by transposable elements.

Transposable elements form a major fraction of the genome in various eukaryotic species. Although deleterious effects of transpositions within the genome have been reported, recent findings suggest that transposable elements can function as novel regulatory elements to fine-tune gene expression. Transposable elements can impact the chromatin state through processes such as heterochromatin formation, enhancer-promoter interactions, and chromatin boundary formation, mainly because of the functions of chromatin-based pathways that regulate the expression of transposable elements via DNA methylation and repressive histone modification. Therefore, transposable elements can rewire the chromatin state and gene expression depending on their insertions. Here, we review the findings that reveal the role of transposable elements as modifiers of the chromatin state and gene expression as well as the molecular mechanisms capable of inducing these changes.

Masakari: visualization supported statistical analysis of genome segmentations.

BACKGROUND: In epigenetics, the change of the combination of histone modifications at the same genomic location during cell differentiation is of great interest for understanding the function of these modifications and their combinations. Besides analyzing them locally for individual genomic locations or globally using correlations between different cells types, intermediate level analyses of these changes are of interest. More specifically, the different distributions of these combinations for different cell types, respectively, are compared to gain new insights. RESULTS AND DISCUSSION: We propose a new tool called 'Masakari' that allows segmenting genomes based on lists of ranges having a certain property, e.g., peaks describing histone modifications. It provides a graphical user interface allowing to select all data sets and setting all parameters needed for the segmentation process. Moreover, the graphical user interface provides statistical graphics allowing to assess the quality and suitability of the segmentation and the selected data. CONCLUSION: Masakari provides statistics based visualizations and thus fosters insights into the combination of histone modification marks on genome ranges, and the differences of the distribution of these combinations between different cell types.

JARID2 and the PRC2 complex regulate the cell cycle in skeletal muscle.

JARID2 is a noncatalytic member of the polycomb repressive complex 2 (PRC2) which methylates of histone 3 lysine 27 (H3K27). In this work, we show that JARID2 and the PRC2 complex regulate the cell cycle in skeletal muscle cells to control proliferation and mitotic exit. We found that the stable depletion of JARID2 leads to increased proliferation and cell accumulation in S phase. The regulation of the cell cycle by JARID2 is mediated by direct repression of both cyclin D1 and cyclin E1, both of which are targets of PRC2-mediated H3K27 methylation. Intriguingly, we also find that the retinoblastoma protein (RB1) is a direct target of JARID2 and the PRC2 complex. The depletion of JARID2 is not sufficient to activate RB1. However, the ectopic expression of RB1 can suppress cyclin D1 expression in JARID2-depleted cells. Transient depletion of JARID2 in skeletal muscle cells leads to a transient up-regulation of cyclin D1 that is quickly suppressed with no resulting effect on proliferation, Taken together, we show that JARID2 and the PRC2 complex regulate skeletal muscle proliferation in a precise manner that involves the repression of cyclin D1, thus restraining proliferation and repressing RB1, which is required for mitotic exit and terminal differentiation.

Bivalent genes that undergo transcriptional switching identify networks of key regulators of embryonic stem cell differentiation.

BACKGROUND: Bivalent promoters marked with both H3K27me3 and H3K4me3 histone modifications are characteristic of poised promoters in embryonic stem (ES) cells. The model of poised promoters postulates that bivalent chromatin in ES cells is resolved to monovalency upon differntiation. With the availability of single-cell RNA sequencing (scRNA-seq) data, subsequent switches in transcriptional state at bivalent promoters can be studied more closely. RESULTS: We develop an approach for capturing genes undergoing transcriptional switching by detecting 'bimodal' gene expression patterns from scRNA-seq data. We integrate the identification of bimodal genes in ES cell differentiation with analysis of chromatin state, and identify clear cell-state dependent patterns of bimodal, bivalent genes. We show that binarization of bimodal genes can be used to identify differentially expressed genes from fractional ON/OFF proportions. In time series data from differentiating cells, we build a pseudotime approximation and use a hidden Markov model to infer gene activity switching pseudotimes, which we use to infer a regulatory network. We identify pathways of switching during differentiation, novel details of those pathway, and transcription factor coordination with downstream targets. CONCLUSIONS: Genes with expression levels too low to be informative in conventional scRNA analysis can be used to infer transcriptional switching networks that connect transcriptional activity to chromatin state. Since chromatin bivalency is a hallmark of gene promoters poised for activity, this approach provides an alternative that complements conventional scRNA-seq analysis while focusing on genes near the ON/OFF boundary of activity. This offers a novel and productive means of inferring regulatory networks from scRNA-seq data.