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

Glycolytic enzymes in non-glycolytic web: functional analysis of the key players

To survive in the tumour microenvironment, cancer cells undergo rapid metabolic reprograming and adaptability. One of the key characteristics of cancer is increased glycolytic selectivity and decreased oxidative phosphorylation (OXPHOS). Apart from ATP synthesis, glycolysis is also responsible for NADH regeneration and macromolecular biosynthesis, such as amino acid biosynthesis and nucleotide biosynthesis. This allows cancer cells to survive and proliferate even in low-nutrient and oxygen conditions, making glycolytic enzymes a promising target for various anti-cancer agents. Oncogenic activation is also caused by the uncontrolled production and activity of glycolytic enzymes. Nevertheless, in addition to conventional glycolytic processes, some glycolytic enzymes are involved in non-canonical functions such as transcriptional regulation, autophagy, epigenetic changes, inflammation, various signaling cascades, redox regulation, oxidative stress, obesity and fatty acid metabolism, diabetes and neurodegenerative disorders, and hypoxia. The mechanisms underlying the non-canonical glycolytic enzyme activities are still not comprehensive. This review summarizes the current findings on the mechanisms fundamental to the non-glycolytic actions of glycolytic enzymes and their intermediates in maintaining the tumor microenvironment.

A sequence-based two-layer predictor for identifying enhancers and their strength through enhanced feature extraction

Enhancers are short regulatory DNA fragments that are bound with proteins called activators. They are free-bound and distant elements, which play a vital role in controlling gene expression. It is challenging to identify enhancers and their strength due to their dynamic nature. Although some machine learning methods exist to accelerate identification process, their prediction accuracy and efficiency will need more improvement. In this regard, we propose a two-layer prediction model with enhanced feature extraction strategy which does feature combination from improved position-specific amino acid propensity (PSTKNC) method along with Enhanced Nucleic Acid Composition (ENAC) and Composition of k-spaced Nucleic Acid Pairs (CKSNAP). The feature sets from all three feature extraction approaches were concatenated and then sent through a simple artificial neural network (ANN) to accurately identify enhancers in the first layer and their strength in the second layer. Experiments are conducted on benchmark chromatin nine cell lines dataset. A 10-fold cross validation method is employed to evaluate model's performance. The results show that the proposed model gives an outstanding performance with 94.50%, 0.8903 of accuracy and Matthew's correlation coefficient (MCC) in predicting enhancers and fairly does well with independent test also when compared with all other existing methods.

Enhancers as potential targets for engineering salinity stress tolerance in crop plants

Enhancers represent noncoding regulatory regions of the genome located distantly from their target genes. They regulate gene expression programs in a context-specific manner via interacting with promoters of one or more target genes and are generally associated with transcription factor binding sites and epi(genomic)/chromatin features, such as regions of chromatin accessibility and histone modifications. The enhancers are difficult to identify due to the modularity of their associated features. Although enhancers have been studied extensively in human and animals, only a handful of them has been identified in few plant species till date due to nonavailability of plant-specific experimental and computational approaches for their discovery. Being an important regulatory component of the genome, enhancers represent potential targets for engineering agronomic traits, including salinity stress tolerance in plants. Here, we provide a review of the available experimental and computational approaches along with the associated sequence and chromatin/epigenetic features for the discovery of enhancers in plants. In addition, we provide insights into the challenges and future prospects of enhancer research in plant biology with emphasis on potential applications in engineering salinity stress tolerance in crop plants.

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.

Transcriptional enhancers: from prediction to functional assessment on a genome-wide scale

Enhancers are cis-regulatory sequences located distally to target genes. These sequences consolidate developmental and environmental cues to coordinate gene expression in a tissue-specific manner. Enhancer function and tissue specificity depend on the expressed set of transcription factors, which recognize binding sites and recruit cofactors that regulate local chromatin organization and gene transcription. Unlike other genomic elements, enhancers are challenging to identify because they function independently of orientation, are often distant from their promoters, have poorly defined boundaries, and display no reading frame. In addition, there are no defined genetic or epigenetic features that are unambiguously associated with enhancer activity. Over recent years there have been developments in both empirical assays and computational methods for enhancer prediction. We review genome-wide tools, CRISPR advancements, and high-throughput screening approaches that have improved our ability to both observe and manipulate enhancers in vitro at the level of primary genetic sequences, chromatin states, and spatial interactions. We also highlight contemporary animal models and their importance to enhancer validation. Together, these experimental systems and techniques complement one another and broaden our understanding of enhancer function in development, evolution, and disease.

TriPCE: A Novel Tri-Clustering Algorithm for Identifying Pan-Cancer Epigenetic Patterns

Epigenetic alteration is a fundamental characteristic of nearly all human cancers. Tumor cells not only harbor genetic alterations, but also are regulated by diverse epigenetic modifications. Identification of epigenetic similarities across different cancer types is beneficial for the discovery of treatments that can be extended to different cancers. Nowadays, abundant epigenetic modification profiles have provided a great opportunity to achieve this goal. Here, we proposed a new approach TriPCE, introducing tri-clustering strategy to integrative pan-cancer epigenomic analysis. The method is able to identify coherent patterns of various epigenetic modifications across different cancer types. To validate its capability, we applied the proposed TriPCE to analyze six important epigenetic marks among seven cancer types, and identified significant cross-cancer epigenetic similarities. These results suggest that specific epigenetic patterns indeed exist among these investigated cancers. Furthermore, the gene functional analysis performed on the associated gene sets demonstrates strong relevance with cancer development and reveals consistent risk tendency among these investigated cancer types.

HebbPlot: an intelligent tool for learning and visualizing chromatin mark signatures

BACKGROUND

Histone modifications play important roles in gene regulation, heredity, imprinting, and many human diseases. The histone code is complex and consists of more than 100 marks. Therefore, biologists need computational tools to characterize general signatures representing the distributions of tens of chromatin marks around thousands of regions.

RESULTS

To this end, we developed a software tool, HebbPlot, which utilizes a Hebbian neural network in learning a general chromatin signature from regions with a common function. Hebbian networks can learn the associations between tens of marks and thousands of regions. HebbPlot presents a signature as a digital image, which can be easily interpreted. Moreover, signatures produced by HebbPlot can be compared quantitatively. We validated HebbPlot in six case studies. The results of these case studies are novel or validating results already reported in the literature, indicating the accuracy of HebbPlot. Our results indicate that promoters have a directional chromatin signature; several marks tend to stretch downstream or upstream. H3K4me3 and H3K79me2 have clear directional distributions around active promoters. In addition, the signatures of high- and low-CpG promoters are different; H3K4me3, H3K9ac, and H3K27ac are the most different marks. When we studied the signatures of enhancers active in eight tissues, we observed that these signatures are similar, but not identical. Further, we identified some histone modifications - H3K36me3, H3K79me1, H3K79me2, and H4K8ac - that are associated with coding regions of active genes. Other marks - H4K12ac, H3K14ac, H3K27me3, and H2AK5ac - were found to be weakly associated with coding regions of inactive genes.

CONCLUSIONS

This study resulted in a novel software tool, HebbPlot, for learning and visualizing the chromatin signature of a genetic element. Using HebbPlot, we produced a visual catalog of the signatures of multiple genetic elements in 57 cell types available through the Roadmap Epigenomics Project. Furthermore, we made a progress toward a functional catalog consisting of 22 histone marks. In sum, HebbPlot is applicable to a wide array of studies, facilitating the deciphering of the histone code.

Histone deacetylase inhibition modulates histone acetylation at gene promoter regions and affects genome-wide gene transcription in Schistosoma mansoni

BACKGROUND

Schistosomiasis is a parasitic disease infecting hundreds of millions of people worldwide. Treatment depends on a single drug, praziquantel, which kills the Schistosoma spp. parasite only at the adult stage. HDAC inhibitors (HDACi) such as Trichostatin A (TSA) induce parasite mortality in vitro (schistosomula and adult worms), however the downstream effects of histone hyperacetylation on the parasite are not known.

METHODOLOGY/PRINCIPAL FINDINGS

TSA treatment of adult worms in vitro increased histone acetylation at H3K9ac and H3K14ac, which are transcription activation marks, not affecting the unrelated transcription repression mark H3K27me3. We investigated the effect of TSA HDACi on schistosomula gene expression at three different time points, finding a marked genome-wide change in the transcriptome profile. Gene transcription activity was correlated with changes on the chromatin acetylation mark at gene promoter regions. Moreover, combining expression data with ChIP-Seq public data for schistosomula, we found that differentially expressed genes having the H3K4me3 mark at their promoter region in general showed transcription activation upon HDACi treatment, compared with those without the mark, which showed transcription down-regulation. Affected genes are enriched for DNA replication processes, most of them being up-regulated. Twenty out of 22 genes encoding proteins involved in reducing reactive oxygen species accumulation were down-regulated. Dozens of genes encoding proteins with histone reader motifs were changed, including SmEED from the PRC2 complex. We targeted SmEZH2 methyltransferase PRC2 component with a new EZH2 inhibitor (GSK343) and showed a synergistic effect with TSA, significantly increasing schistosomula mortality.

CONCLUSIONS/SIGNIFICANCE

Genome-wide gene expression analyses have identified important pathways and cellular functions that were affected and may explain the schistosomicidal effect of TSA HDACi. The change in expression of dozens of histone reader genes involved in regulation of the epigenetic program in S. mansoni can be used as a starting point to look for possible novel schistosomicidal targets.

Identification of recurrent combinatorial patterns of chromatin modifications at promoters across various tissue types

Background

Identification and analysis of recurrent combinatorial patterns of multiple chromatin modifications provide invaluable information for understanding epigenetic regulations. Furthermore, as more data becomes available, it is computationally expensive and unnecessary to study combinatorial patterns of all modifications.

Methods

A novel framework is proposed to investigate recurrent combinatorial patterns of a subset of quantitatively selected chromatin modifications. The framework is based on heirarchical clustering and selects subsets of chromatin modifications that form distinct recurrent patterns at regulatory regions. The identified recurrent combinatorial patterns can be further utilized to discover novel regulatory regions. Data is in the form of genome wide maps of histone acetylations, methylations, and histone variant of human skeletal muscular and B-lymphocyte cells both derived from the ENCODE project.

Results

A case study conducted at promoter regions is presented: four out of twelve chromatin modifications were selected, eight different promoter states were identified and the identified patterns of active promoters were further utilized to discover novel promoter regions. Several previously un-annotated promoters were discovered, further investigations confirm their promoter functions.

Conclusions

This framework is approproiately general and could lead to better understanding of epigenetic regulations by discovering previously unknown regulatory regions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1346-5) contains supplementary material, which is available to authorized users.

Dynamic epigenetic mode analysis using spatial temporal clustering

Background

Differentiation of human embryonic stem cells requires precise control of gene expression that depends on specific spatial and temporal epigenetic regulation. Recently available temporal epigenomic data derived from cellular differentiation processes provides an unprecedented opportunity for characterizing fundamental properties of epigenomic dynamics and revealing regulatory roles of epigenetic modifications.

Results

This paper presents a spatial temporal clustering approach, named STCluster, which exploits the temporal variation information of epigenomes to characterize dynamic epigenetic mode during cellular differentiation. This approach identifies significant spatial temporal patterns of epigenetic modifications along human embryonic stem cell differentiation and cluster regulatory sequences by their spatial temporal epigenetic patterns.

Conclusions

The results show that this approach is effective in capturing epigenetic modification patterns associated with specific cell types. In addition, STCluster allows straightforward identification of coherent epigenetic modes in multiple cell types, indicating the ability in the establishment of the most conserved epigenetic signatures during cellular differentiation process.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1331-z) contains supplementary material, which is available to authorized users.

ChARM: Discovery of combinatorial chromatin modification patterns in hepatitis B virus X-transformed mouse liver cancer using association rule mining

Background

Various chromatin modifications, identified in large-scale epigenomic analyses, are associated with distinct phenotypes of different cells and disease phases. To improve our understanding of these variations, many computational methods have been developed to discover novel sites and cell-specific chromatin modifications. Despite the availability of existing methods, there is still room for further improvement when they are applied to resolve the histone code hypothesis. Hence, we aim to investigate the development of a computational method to provide new insights into de novo combinatorial pattern discovery of chromatin modifications to characterize epigenetic variations in distinct phenotypes of different cells.

Results

We report a new computational approach, ChARM (Combinatorial Chromatin Modification Patterns using Association Rule Mining), that can be employed for the discovery of de novo combinatorial patterns of differential chromatin modifications. We used ChARM to analyse chromatin modification data from the livers of normal (non-cancerous) mice and hepatitis B virus X (HBx)-transgenic mice with hepatocellular carcinoma, and discovered 2,409 association rules representing combinatorial chromatin modification patterns. Among these, the combination of three histone modifications, a loss of H3K4Me3 and gains of H3K27Me3 and H3K36Me3, was the most striking pattern associated with the cancer. This pattern was enriched in functional elements of the mouse genome such as promoters, coding exons and 5′UTR with high CpG content, and CpG islands. It also showed strong correlations with polymerase activity at promoters and DNA methylation levels at gene bodies. We found that 30 % of the genes associated with the pattern were differentially expressed in the HBx compared to the normal, and 78.9 % of these genes were down-regulated. The significant canonical pathways (Wnt/ß-catenin, cAMP, Ras, and Notch signalling) that were enriched in the pattern could account for the pathogenesis of HBx.

Conclusions

ChARM, an unsupervised method for discovering combinatorial chromatin modification patterns, can identify histone modifications that occur globally. ChARM provides a scalable framework that can easily be applied to find various levels of combination patterns, which should reflect a range of globally common to locally rare chromatin modifications.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1307-z) contains supplementary material, which is available to authorized users.

Utility of next-generation RNA-sequencing in identifying chimeric transcription involving human endogenous retroviruses

Several studies have shown that human endogenous retroviruses and endogenous retrovirus-like repeats (here collectively HERVs) impose direct regulation on human genes through enhancer and promoter motifs present in their long terminal repeats (LTRs). Although chimeric transcription in which novel gene isoforms containing retroviral and human sequence are transcribed from viral promoters are commonly associated with disease, regulation by HERVs is beneficial in other settings; for example, in human testis chimeric isoforms of TP63 induced by an ERV9 LTR protect the male germ line upon DNA damage by inducing apoptosis, whereas in the human globin locus the γ- and β-globin switch during normal hematopoiesis is mediated by complex interactions of an ERV9 LTR and surrounding human sequence. The advent of deep sequencing or next-generation sequencing (NGS) has revolutionized the way researchers solve important scientific questions and develop novel hypotheses in relation to human genome regulation. We recently applied next-generation paired-end RNA-sequencing (RNA-seq) together with chromatin immunoprecipitation with sequencing (ChIP-seq) to examine ERV9 chimeric transcription in human reference cell lines from Encyclopedia of DNA Elements (ENCODE). This led to the discovery of advanced regulation mechanisms by ERV9s and other HERVs across numerous human loci including transcription of large gene-unannotated genomic regions, as well as cooperative regulation by multiple HERVs and non-LTR repeats such as Alu elements. In this article, well-established examples of human gene regulation by HERVs are reviewed followed by a description of paired-end RNA-seq, and its application in identifying chimeric transcription genome-widely. Based on integrative analyses of RNA-seq and ChIP-seq, data we then present novel examples of regulation by ERV9s of tumor suppressor genes CADM2 and SEMA3A, as well as transcription of an unannotated region. Taken together, this article highlights the high suitability of contemporary sequencing methods in future analyses of human biology in relation to evolutionary acquired retroviruses in the human genome.

Combinations of Histone Modifications for Pattern Genes

Histone post-translational modifications play important roles in transcriptional regulation. It is known that multiple histone modifications can act in a combinatorial manner. In this study, we investigated the effects of multiple histone modifications on expression levels of five gene categories (four kinds of pattern genes and non-pattern genes) in coding regions. The combinatorial patterns of modifications for the five gene categories were also studied in the regions. Our results indicated that the differences in the expression levels between any two gene categories were significant. There were some corresponding differences in multiple histone modification levels among the five gene categories. Multiple histone modifications jointly impacted expression levels of every gene category. Four mutual combinations of histone modifications were found and analyzed.

Progress and challenges in bioinformatics approaches for enhancer identification

Enhancers are cis-acting DNA elements that play critical roles in distal regulation of gene expression. Identifying enhancers is an important step for understanding distinct gene expression programs that may reflect normal and pathogenic cellular conditions. Experimental identification of enhancers is constrained by the set of conditions used in the experiment. This requires multiple experiments to identify enhancers, as they can be active under specific cellular conditions but not in different cell types/tissues or cellular states. This has opened prospects for computational prediction methods that can be used for high-throughput identification of putative enhancers to complement experimental approaches. Potential functions and properties of predicted enhancers have been catalogued and summarized in several enhancer-oriented databases. Because the current methods for the computational prediction of enhancers produce significantly different enhancer predictions, it will be beneficial for the research community to have an overview of the strategies and solutions developed in this field. In this review, we focus on the identification and analysis of enhancers by bioinformatics approaches. First, we describe a general framework for computational identification of enhancers, present relevant data types and discuss possible computational solutions. Next, we cover over 30 existing computational enhancer identification methods that were developed since 2000. Our review highlights advantages, limitations and potentials, while suggesting pragmatic guidelines for development of more efficient computational enhancer prediction methods. Finally, we discuss challenges and open problems of this topic, which require further consideration.

Identify Critical Genes in Development with Consistent H3K4me2 Patterns across Multiple Tissues

Histone modification is an important epigenetic event which plays essential roles in cell differentiation and tissue development. Recent studies show that a unique dimethylation of lysine 4 residue on histone 3 (H3K4me2) distribution pattern around transcription starting sites (TSS) of genes marks tissue specific genes in human CD4 þ T cells and mouse nervous tissue cells. However, existence of this pattern has not been widely tested and its implication remains unclear. In this paper, we study the H3K4me2 distribution patterns across six different cell lines from five major tissue types (including muscular tissue, nervous tissue, non-blood connective tissue, blood, and epithelial tissue) as well as embryonic stem cells. We define a metric ‘tail length’ to quantitatively describe H3K4me2 distribution patterns around the TSS. While confirming the previous observations, we also identified a group of 217 genes with ubiquitous long-tail H3K4me2 patterns in all the tested tissues and the embryonic stem cells (ESC). Further analyses confirmed that these genes are critical for development, and highly interactive with other tissue specific genes as evinced by protein-protein interaction networks, suggesting their critical regulatory functions. Our results suggest that rich information on gene functions and epigenetic events can be revealed using pattern recognition methods.

Association analysis between the distributions of histone modifications and gene expression in the human embryonic stem cell

It is well known that histone modifications are associated with gene expression. In order to further study this relationship, 16 kinds of Chip-seq histone modification data and mRNA-seq data of the human embryonic stem cell H1 are chosen. The distributions of histone modifications in the regions flanking transcription start sites (TSSs) for highly expressed and lowly expressed genes are computed, respectively. And four types of distributions of histone modifications in regions flanking TSSs and the spatial patterning of the correlations between histone modifications and gene expression are detected. Our results suggest that the correlations between the regions overlapped by peaks are higher than the non-overlapped ones for each histone modification. In addition, to obtain the effect of the cooperative action of histone modification on gene expression, five histone modification clusters are found in highly expressed and lowly expressed genes, histone modification and gene expression interaction network is constructed. To further explore which region is the main target region for the specific histone modification, the human genes are divided into five functional regions. The results indicate that histone modifications are mostly located in the promoters of highly expressed genes versus the exons of lowly expressed genes, and exons have a smaller range of normalized tag counts than other gene elements in the two groups of genes. Finally, the type specificity and regional bias of histone modifications for 11 key transcription factor genes regulating the stem cell renewal are analyzed.

Human endogenous retroviruses sustain complex and cooperative regulation of gene-containing loci and unannotated megabase-sized regions

Background

Evidence suggests that some human endogenous retroviruses and endogenous retrovirus-like repeats (here collectively ERVs) regulate the expression of neighboring genes in normal and disease states; e.g. the human globin locus is regulated by an ERV9 that coordinates long-range gene switching during hematopoiesis and activates also intergenic transcripts. While complex transcription regulation is associated with integration of certain exogenous retroviruses, comparable regulation sustained by ERVs is less understood.

Findings

We analyzed ERV transcription using ERV9 consensus sequences and publically available RNA-sequencing, chromatin immunoprecipitation with sequencing (ChIP-seq) and cap analysis gene expression (CAGE) data from ENCODE. We discovered previously undescribed and advanced transcription regulation mechanisms in several human reference cell lines. We show that regulation by ERVs involves long-ranging activations including complex RNA splicing patterns, and transcription of large unannotated regions ranging in size from several hundred kb to around 1 Mb. Moreover, regulation was found to be cooperatively sustained in some loci by multiple ERVs and also non-LTR repeats.

Conclusion

Our analyses show that endogenous retroviruses sustain advanced transcription regulation in human cell lines, which shows similarities to complex insertional mutagenesis effects exerted by exogenous retroviruses. By exposing previously undescribed regulation effects, this study should prove useful for understanding fundamental transcription mechanisms resulting from evolutionary acquisition of retroviral sequence in the human genome.

Electronic supplementary material

The online version of this article (doi:10.1186/s12977-015-0161-9) contains supplementary material, which is available to authorized users.

Spectacle: fast chromatin state annotation using spectral learning

Epigenomic data from ENCODE can be used to associate specific combinations of chromatin marks with regulatory elements in the human genome. Hidden Markov models and the expectation-maximization (EM) algorithm are often used to analyze epigenomic data. However, the EM algorithm can have overfitting problems in data sets where the chromatin states show high class-imbalance and it is often slow to converge. Here we use spectral learning instead of EM and find that our software Spectacle overcame these problems. Furthermore, Spectacle is able to find enhancer subtypes not found by ChromHMM but strongly enriched in GWAS SNPs. Spectacle is available at https://github.com/jiminsong/Spectacle.

A stationary wavelet entropy-based clustering approach accurately predicts gene expression

Studying epigenetic landscapes is important to understand the condition for gene regulation. Clustering is a useful approach to study epigenetic landscapes by grouping genes based on their epigenetic conditions. However, classical clustering approaches that often use a representative value of the signals in a fixed-sized window do not fully use the information written in the epigenetic landscapes. Clustering approaches to maximize the information of the epigenetic signals are necessary for better understanding gene regulatory environments. For effective clustering of multidimensional epigenetic signals, we developed a method called Dewer, which uses the entropy of stationary wavelet of epigenetic signals inside enriched regions for gene clustering. Interestingly, the gene expression levels were highly correlated with the entropy levels of epigenetic signals. Dewer separates genes better than a window-based approach in the assessment using gene expression and achieved a correlation coefficient above 0.9 without using any training procedure. Our results show that the changes of the epigenetic signals are useful to study gene regulation.

Modelling epigenetic regulation of gene expression in 12 human cell types reveals combinatorial patterns of cell-type-specific genes

The maintenance of the diverse cell types in a multicellular organism is one of the fundamental mysteries of biology. Modelling the dynamic regulatory relationships between the histone modifications and the gene expression across the diverse cell types is essential for the authors to understand the mechanisms of the epigenetic regulation. Here, the authors thoroughly assessed the histone modification enrichment profiles at the promoters and constructed quantitative models between the histone modification abundances and the gene expression in 12 human cell types. The author's results showed that the histone modifications at the promoters exhibited remarkably cell-type-dependent variability in the cell-type-specific (CTS) genes. They demonstrated that the variable profiles of the modifications are highly predictive for the dynamic changes of the gene expression across all the cell types. Their findings revealed the close relationship between the combinatorial patterns of the histone modifications and the CTS gene expression. They anticipate that the findings and the methods they used in this study could provide useful information for the future studies of the regulatory roles of the histone modifications in the CTS genes.

The role of epigenetics in the biology of multiple myeloma

Several recent studies have highlighted the biological complexity of multiple myeloma (MM) that arises as a result of several disrupted cancer pathways. Apart from the central role of genetic abnormalities, epigenetic aberrations have also been shown to be important players in the development of MM, and a lot of research during the past decades has focused on the ways DNA methylation, histone modifications and noncoding RNAs contribute to the pathobiology of MM. This has led to, apart from better understanding of the disease biology, the development of epigenetic drugs, such as histone deacetylase inhibitors that are already used in clinical trials in MM with promising results. This review will present the role of epigenetic abnormalities in MM and how these can affect specific pathways, and focus on the potential of novel 'epidrugs' as future treatment modalities for MM.

Combinatorial epigenetic patterns as quantitative predictors of chromatin biology

Background

Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) is the most widely used method for characterizing the epigenetic states of chromatin on a genomic scale. With the recent availability of large genome-wide data sets, often comprising several epigenetic marks, novel approaches are required to explore functionally relevant interactions between histone modifications. Computational discovery of "chromatin states" defined by such combinatorial interactions enabled descriptive annotations of genomes, but more quantitative approaches are needed to progress towards predictive models.

Results

We propose non-negative matrix factorization (NMF) as a new unsupervised method to discover combinatorial patterns of epigenetic marks that frequently co-occur in subsets of genomic regions. We show that this small set of combinatorial "codes" can be effectively displayed and interpreted. NMF codes enable dimensionality reduction and have desirable statistical properties for regression and classification tasks. We demonstrate the utility of codes in the quantitative prediction of Pol2-binding and the discrimination between Pol2-bound promoters and enhancers. Finally, we show that specific codes can be linked to molecular pathways and targets of pluripotency genes during differentiation.

Conclusions

We have introduced and evaluated a new computational approach to represent combinatorial patterns of epigenetic marks as quantitative variables suitable for predictive modeling and supervised machine learning. To foster widespread adoption of this method we make it available as an open-source software-package – epicode at https://github.com/mcieslik-mctp/epicode.

A nucleosomal approach to inferring causal relationships of histone modifications

Motivation

Histone proteins are subject to various posttranslational modifications (PTMs). Elucidating their functional relationships is crucial toward understanding many biological processes. Bayesian network (BN)-based approaches have shown the advantage of revealing causal relationships, rather than simple cooccurrences, of PTMs. Previous works employing BNs to infer causal relationships of PTMs require that all confounders should be included. This assumption, however, is unavoidably violated given the fact that several modifications are often regulated by a common but unobserved factor. An existing non-parametric method can be applied to tackle the problem but the complexity and inflexibility make it impractical.

Results

We propose a novel BN-based method to infer causal relationships of histone modifications. First, from the evidence that nucleosome organization in vivo significantly affects the activities of PTM regulators working on chromatin substrate, hidden confounders of PTMs are selectively introduced by an information-theoretic criterion. Causal relationships are then inferred from a network model of both PTMs and the derived confounders. Application on human epigenomic data shows the advantage of the proposed method, in terms of computational performance and support from literature. Requiring less strict data assumptions also makes it more practical. Interestingly, analysis of the most significant relationships suggests that the proposed method can recover biologically relevant causal effects between histone modifications, which should be important for future investigation of histone crosstalk.

Discovery of cell-type specific regulatory elements in the human genome using differential chromatin modification analysis

Chromatin modifications have been comprehensively illustrated to play important roles in gene regulation and cell diversity in recent years. Given the rapid accumulation of genome-wide chromatin modification maps across multiple cell types, there is an urgent need for computational methods to analyze multiple maps to reveal combinatorial modification patterns and define functional DNA elements, especially those are specific to cell types or tissues. In this current study, we developed a computational method using differential chromatin modification analysis (dCMA) to identify cell-type-specific genomic regions with distinctive chromatin modifications. We then apply this method to a public data set with modification profiles of nine marks for nine cell types to evaluate its effectiveness. We found cell-type-specific elements unique to each cell type investigated. These unique features show significant cell-type-specific biological relevance and tend to be located within functional regulatory elements. These results demonstrate the power of a differential comparative epigenomic strategy in deciphering the human genome and characterizing cell specificity.

A wavelet-based method to exploit epigenomic language in the regulatory region

MOTIVATION

Epigenetic landscapes in the regulatory regions reflect binding condition of transcription factors and their co-factors. Identifying epigenetic condition and its variation is important in understanding condition-specific gene regulation. Computational approaches to explore complex multi-dimensional landscapes are needed.

RESULTS

To study epigenomic condition for gene regulation, we developed a method, AWNFR, to classify epigenomic landscapes based on the detected epigenomic landscapes. Assuming mixture of Gaussians for a nucleosome, the proposed method captures the shape of histone modification and identifies potential regulatory regions in the wavelet domain. For accuracy estimation as well as enhanced computational speed, we developed a novel algorithm based on down-sampling operation and footprint in wavelet. We showed the algorithmic advantages of AWNFR using the simulated data. AWNFR identified regulatory regions more effectively and accurately than the previous approaches with the epigenome data in mouse embryonic stem cells and human lung fibroblast cells (IMR90). Based on the detected epigenomic landscapes, AWNFR classified epigenomic status and studied epigenomic codes. We studied co-occurring histone marks and showed that AWNFR captures the epigenomic variation across time.

AVAILABILITY AND IMPLEMENTATION

The source code and supplemental document of AWNFR are available at http://wonk.med.upenn.edu/AWNFR.

Discovering and mapping chromatin states using a tree hidden Markov model

New biological techniques and technological advances in high-throughput sequencing are paving the way for systematic, comprehensive annotation of many genomes, allowing differences between cell types or between disease/normal tissues to be determined with unprecedented breadth. Epigenetic modifications have been shown to exhibit rich diversity between cell types, correlate tightly with cell-type specific gene expression, and changes in epigenetic modifications have been implicated in several diseases. Previous attempts to understand chromatin state have focused on identifying combinations of epigenetic modification, but in cases of multiple cell types, have not considered the lineage of the cells in question.

We present a Bayesian network that uses epigenetic modifications to simultaneously model 1) chromatin mark combinations that give rise to different chromatin states and 2) propensities for transitions between chromatin states through differentiation or disease progression. We apply our model to a recent dataset of histone modifications, covering nine human cell types with nine epigenetic modifications measured for each. Since exact inference in this model is intractable for all the scale of the datasets, we develop several variational approximations and explore their accuracy. Our method exhibits several desirable features including improved accuracy of inferring chromatin states, improved handling of missing data, and linear scaling with dataset size. The source code for our model is available at http:// http://github.com/uci-cbcl/tree-hmm

LinkNMF: identification of histone modification modules in the human genome using nonnegative matrix factorization

Histone modifications are ubiquitous processes involved in various cellular mechanisms. Systemic analysis of multiple chromatin modifications has been used to characterize various chromatin states associated with functional DNA elements, gene expression, and specific biological functions. However, identification of modular modification patterns is still required to understand the functional associations between histone modification patterns and specific chromatin/DNA binding factors. To recognize modular modification patterns, we developed a novel algorithm that combines nonnegative matrix factorization (NMF) and a clique-detection algorithm. We applied it, called LinkNMF, to generate a comprehensive modification map in human CD4+ T cell promoter regions. Initially, we identified 11 modules not recognized by conventional approaches. The modules were grouped into two major classes: gene activation and repression. We found that genes targeted by each module were enriched with distinguishable biological functions, suggesting that each modular pattern plays a unique functional role. To explain the formation of modular patterns, we investigated the module-specific binding patterns of chromatin regulators. Application of LinkNMF to histone modification maps of diverse cells and developmental stages will be helpful for understanding how histone modifications regulate gene expression. The algorithm is available on our website at biodb.kaist.ac.kr/LinkNMF.

Redistribution of H3K4me2 on neural tissue specific genes during mouse brain development

Background

Histone modification plays an important role in cell differentiation and tissue development. A recent study has shown that the dimethylation of lysine 4 residue on histone 3 (H3K4me2) marks the gene body area of tissue specific genes in the human CD4+ T cells and neural cells. However, little is known of the H3k4me2 distribution dynamics through the cell differentiation and tissue development.

Results

We applied several clustering methods including K-means, hierarchical and principle component analysis on H3K4me2 ChIP-seq data from embryonic stem cell, neural progenitor cell and whole brain of mouse, trying to identify genes with the H3K4me2 binding on the gene body region in different cell development stage and study their redistribution in different tissue development stages.

A cluster of 356 genes with heavy H3K4me2 labeling in the gene body region was identified in the mouse whole brain tissue using K-means clustering. They are highly enriched with neural system related functions and pathways, and are involved in several central neural system diseases. The distribution of H3K4me2 on neural function related genes follows three distinctive patterns: a group of genes contain constant heavy H3K4me2 marks in the gene body from embryonic stem cell stage through neural progenitor stage to matured brain tissue stage; another group of gene have little H3K4me2 marks until cells mature into brain cells; the majority of the genes acquired H3K4me2 marks in the neural progenitor cell stage, and gain heavy labeling in the matured brain cell stage. Gene ontology enrichment analysis also revealed corresponding gene ontology terms that fit in the scenario of each cell developmental stages.

Conclusions

We investigated the process of the H3K4me2 mark redistribution during tissue specificity development for mouse brain tissue. Our analysis confirmed the previous report that heavy labeling of H3K4me2 in the downstream of TSS marks tissue specific genes. These genes show remarkable enrichment in central neural system related diseases. Furthermore, we have shown that H3K4me2 labeling can be acquired as early as the embryonic stem cell stage, and its distribution is dynamic and progressive throughout cell differentiation and tissue development.

Quantitative analysis reveals increased histone modifications and a broad nucleosome-free region bound by histone acetylases in highly expressed genes in human CD4+ T cells

Genome-wide mapping of nucleosomes and histone modifications revealed meaningful patterns. Despite advances in resolving the associations between chromatin and transcription, quantitative chromatin dynamics have not been well defined. We quantitatively determined differences in histone modifications, nucleosome positions, DNA methylation, and transcription factor binding in highly expressed and repressed genes in human CD4(+) T cells. We showed that the first (-1) nucleosome upstream of the transcription start site (TSS) is shifted to the 5' direction, thus forming a broad nucleosome-free region (NFR) near the TSS in highly expressed genes in CD4(+) T cells. Moreover, the transcription factor YY1 and histone acetyltransferases bind the NFR with high affinity. Most of histone acetylations drastically increase in transcription activation (>5 folds). We also suggested that single nucleotide polymorphisms (SNPs) occur at a much lower frequency in highly expressed genes than in repressed genes. Our analysis quantitatively revealed details of chromatin dynamics.

Chromatin signature discovery via histone modification profile alignments

We report on the development of an unsupervised algorithm for the genome-wide discovery and analysis of chromatin signatures. Our Chromatin-profile Alignment followed by Tree-clustering algorithm (ChAT) employs dynamic programming of combinatorial histone modification profiles to identify locally similar chromatin sub-regions and provides complementary utility with respect to existing methods. We applied ChAT to genomic maps of 39 histone modifications in human CD4⁺ T cells to identify both known and novel chromatin signatures. ChAT was able to detect chromatin signatures previously associated with transcription start sites and enhancers as well as novel signatures associated with a variety of regulatory elements. Promoter-associated signatures discovered with ChAT indicate that complex chromatin signatures, made up of numerous co-located histone modifications, facilitate cell-type specific gene expression. The discovery of novel L1 retrotransposon-associated bivalent chromatin signatures suggests that these elements influence the mono-allelic expression of human genes by shaping the chromatin environment of imprinted genomic regions. Analysis of long gene-associated chromatin signatures point to a role for the H4K20me1 and H3K79me3 histone modifications in transcriptional pause release. The novel chromatin signatures and functional associations uncovered by ChAT underscore the ability of the algorithm to yield novel insight on chromatin-based regulatory mechanisms.

Finding combinatorial histone code by semi-supervised biclustering

Background

Combinatorial histone modification is an important epigenetic mechanism for regulating chromatin state and gene expression. Given the rapid accumulation of genome-wide histone modification maps, there is a pressing need for computational methods capable of joint analysis of multiple maps to reveal combinatorial modification patterns.

Results

We present the Semi-Supervised Coherent and Shifted Bicluster Identification algorithm (SS-CoSBI). It uses prior knowledge of combinatorial histone modifications to guide the biclustering process. Specifically, co-occurrence frequencies of histone modifications characterized by mass spectrometry are used as probabilistic priors to adjust the similarity measure in the biclustering process. Using a high-quality set of transcriptional enhancers and associated histone marks, we demonstrate that SS-CoSBI outperforms its predecessor by finding histone modification and genomic locus biclusters with higher enrichment of enhancers. We apply SS-CoSBI to identify multiple cell-type-specific combinatorial histone modification states associated with human enhancers. We show enhancer histone modification states are correlated with the expression of nearby genes. Further, we find that enhancers with the histone mark H3K4me1 have higher levels of DNA methylation and decreased expression of nearby genes, suggesting a functional interplay between H3K4me1 and DNA methylation that can modulate enhancer activities.

Conclusions

The analysis presented here provides a systematic characterization of combinatorial histone codes of enhancers across three human cell types using a novel semi-supervised biclustering algorithm. As epigenomic maps accumulate, SS-CoSBI will become increasingly useful for understanding combinatorial chromatin modifications by taking advantage of existing knowledge.

Availability and implementation

SS-CoSBI is implemented in C. The source code is freely available at http://www.healthcare.uiowa.edu/labs/tan/SS-CoSBI.gz.

Binding profiles of chromatin-modifying proteins are predictive for transcriptional activity and promoter-proximal pausing

The establishment and maintenance of proper gene expression patterns is essential for stable cell differentiation. Using unsupervised learning techniques, chromatin states have been linked to discrete gene expression states, but these models cannot predict continuous gene expression levels, nor do they reveal detailed insight into the chromatin-based control of gene expression. Here, we employ regularized regression techniques to link, in a quantitative manner, binding profiles of chromatin proteins to gene expression levels and promoter-proximal pausing of RNA polymerase II in Drosophila melanogaster on a genome-wide scale. We apply stability selection to reliably detect interactions of chromatin features and predict several known, suggested, and novel proteins and protein pairs as transcriptional activators or repressors. Our integrative analysis reveals new insights into the complex interplay of transcriptional regulators in the context of gene expression. Supplementary Material is available at www.libertonline.com/cmb.

Linking genome to epigenome

Recent epigenomic studies have identified significant differences between developmental stages and cell types. While the importance of epigenetic regulation has been increasingly recognized, it remains unclear how the global epigenetic patterns are established and maintained. Here I review a number of recent studies with the emphasis on the role of the genomic sequence in shaping the epigenetic landscape. These studies strongly suggest that the sequence information is important not just for controlling target specificity but for orchestrating the diversity of epigenetic patterns among different cell types. The epigenome is maintained by the complex network of a large number of interactions. Integrative approaches are needed to gain insights into these networks.

Histone demethylases in chromatin cross-talks

The 'histone code' hypothesis states that chromatin-based regulation of nuclear processes such as transcription is brought about by the combination of distinct modifications (histone marks) at specific loci. Its correct establishment involves chromatin cross-talks, ensuring an ordered and concerted deposition/removal of a particular set of modifications that act together to give the correct transcriptional outcome. Histone methylation on lysine residues can negatively or positively impact on gene transcription, depending on the residue and on its degree of methylation. Thanks to this complexity and given the number of chromatin 'readers' that can recognize methylated lysine residues, histone methylation plays a very special role in specifying the various chromatin states. The recent discovery of histone demethylases, which represent a large family of enzymes often containing histone modification binding modules, sheds new light on cross-talk mechanisms involving methylated residues. In the present review, after a brief overview of the various families of histone demethylases, we describe the different mechanisms by which they participate in chromatin cross-talks and how these mechanisms are integrated to achieve the mutual exclusion or the link between chromatin marks, leading to the establishment of the correct histone code.