Spatiotemporal clustering of the epigenome reveals rules of dynamic gene regulation

Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine

Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.

Broodstock nutritional programming differentially affects the hepatic transcriptome and genome-wide DNA methylome of farmed gilthead sea bream (Sparus aurata) depending on genetic background

BACKGROUND

Broodstock nutritional programming improves the offspring utilization of plant-based diets in gilthead sea bream through changes in hepatic metabolism. Attention was initially focused on fatty acid desaturases, but it can involve a wide range of processes that remain largely unexplored. How all this can be driven by a different genetic background is hardly underlined, and the present study aimed to assess how broodstock nutrition affects differentially the transcriptome and genome-wide DNA methylome of reference and genetically selected fish within the PROGENSA® selection program.

RESULTS

After the stimulus phase with a low fish oil diet, two offspring subsets of each genetic background received a control or a FUTURE-based diet. This highlighted a different hepatic transcriptome (RNA-seq) and genome-wide DNA methylation (MBD-seq) pattern depending on the genetic background. The number of differentially expressed transcripts following the challenge phase varied from 323 in reference fish to 2,009 in genetically selected fish. The number of discriminant transcripts, and associated enriched functions, were also markedly higher in selected fish. Moreover, correlation analysis depicted a hyper-methylated and down-regulated gene expression state in selected fish with the FUTURE diet, whereas the opposite pattern appeared in reference fish. After filtering for highly represented functions in selected fish, 115 epigenetic markers were retrieved in this group. Among them, lipid metabolism genes (23) were the most reactive following ordering by fold-change in expression, rendering a final list of 10 top markers with a key role on hepatic lipogenesis and fatty acid metabolism (cd36, pitpna, cidea, fasn, g6pd, lipt1, scd1a, acsbg2, acsl14, acsbg2).

CONCLUSIONS

Gene expression profiles and methylation signatures were dependent on genetic background in our experimental model. Such assumption affected the magnitude, but also the type and direction of change. Thus, the resulting epigenetic clock of reference fish might depict an older phenotype with a lower methylation for the epigenetically responsive genes with a negative methylation-expression pattern. Therefore, epigenetic markers will be specific of each genetic lineage, serving the broodstock programming in our selected fish to prevent and mitigate later in life the risk of hepatic steatosis through changes in hepatic lipogenesis and fatty acid metabolism.

SETDB1 acts as a topological accessory to Cohesin via an H3K9me3-independent, genomic shunt for regulating cell fates

SETDB1 is a key regulator of lineage-specific genes and endogenous retroviral elements (ERVs) through its deposition of repressive H3K9me3 mark. Apart from its H3K9me3 regulatory role, SETDB1 has seldom been studied in terms of its other potential regulatory roles. To investigate this, a genomic survey of SETDB1 binding in mouse embryonic stem cells across multiple libraries was conducted, leading to the unexpected discovery of regions bereft of common repressive histone marks (H3K9me3, H3K27me3). These regions were enriched with the CTCF motif that is often associated with the topological regulator Cohesin. Further profiling of these non-H3K9me3 regions led to the discovery of a cluster of non-repeat loci that were co-bound by SETDB1 and Cohesin. These regions, which we named DiSCs (domains involving SETDB1 and Cohesin) were seen to be proximal to the gene promoters involved in embryonic stem cell pluripotency and lineage development. Importantly, it was found that SETDB1-Cohesin co-regulate target gene expression and genome topology at these DiSCs. Depletion of SETDB1 led to localized dysregulation of Cohesin binding thereby locally disrupting topological structures. Dysregulated gene expression trends revealed the importance of this cluster in ES cell maintenance as well as at gene 'islands' that drive differentiation to other lineages. The 'unearthing' of the DiSCs thus unravels a unique topological and transcriptional axis of control regulated chiefly by SETDB1.

CSCS: a chromatin state interface for Chinese Spring bread wheat

A chromosome-level genome assembly of the bread wheat variety Chinese Spring (CS) has recently been published. Genome-wide identification of regulatory elements (REs) responsible for regulating gene activity is key to further mechanistic studies. Because epigenetic activity can reflect RE activity, defining chromatin states based on epigenomic features is an effective way to detect REs. Here, we present the web-based platform Chinese Spring chromatin state (CSCS), which provides CS chromatin signature information. CSCS includes 15 recently published epigenomic data sets including open chromatin and major chromatin marks, which are further partitioned into 15 distinct chromatin states. CSCS curates detailed information about these chromatin states, with trained self-organization mapping (SOM) for segments in all chromatin states and JBrowse visualization for genomic regions or genes. Motif analysis for genomic regions or genes, GO analysis for genes and SOM analysis for new epigenomic data sets are also integrated into CSCS. In summary, the CSCS database contains the combinatorial patterns of chromatin signatures in wheat and facilitates the detection of functional elements and further clarification of regulatory activities. We illustrate how CSCS enables biological insights using one example, demonstrating that CSCS is a highly useful resource for intensive data mining. CSCS is available at http://bioinfo.cemps.ac.cn/CSCS/.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42994-021-00048-z.

The existence of a nonclassical TCA cycle in the nucleus that wires the metabolic-epigenetic circuitry

The scope and variety of the metabolic intermediates from the mitochondrial tricarboxylic acid (TCA) cycle that are engaged in epigenetic regulation of the chromatin function in the nucleus raise an outstanding question about how timely and precise supply/consumption of these metabolites is achieved in the nucleus. We report here the identification of a nonclassical TCA cycle in the nucleus (nTCA cycle). We found that all the TCA cycle-associated enzymes including citrate synthase (CS), aconitase 2 (ACO2), isocitrate dehydrogenase 3 (IDH3), oxoglutarate dehydrogenase (OGDH), succinyl-CoA synthetase (SCS), fumarate hydratase (FH), and malate dehydrogenase 2 (MDH2), except for succinate dehydrogenase (SDH), a component of electron transport chain for generating ATP, exist in the nucleus. We showed that these nuclear enzymes catalyze an incomplete TCA cycle similar to that found in cyanobacteria. We propose that the nTCA cycle is implemented mainly to generate/consume metabolic intermediates, not for energy production. We demonstrated that the nTCA cycle is intrinsically linked to chromatin dynamics and transcription regulation. Together, our study uncovers the existence of a nonclassical TCA cycle in the nucleus that links the metabolic pathway to epigenetic regulation.

Paternal reprogramming-escape histone H3K4me3 marks located within promoters of RNA splicing genes

Motivation

Exposure of mouse embryos to atrazine decreased histone tri-methylation at lysine 4 (H3K4me3) and increased expression of alternatively spliced RNA in the third generation. Specificity protein (SP) family motifs were enriched in the promoters of genes encoding differentially expressed alternative transcripts.

Results

H3K4me3 chromatin immunoprecipitation sequencing (ChIP-seq) of mouse sperm, preimplantation embryo development and male gonad primordial germ cells (PGCs) were analysed to identify the paternal reprogramming-escape H3K4me3 regions (RERs). In total, 251 RERs selected harbour H3K4me3 marks in sperm, with signals occurring in the paternal genome during early development and in male gonad PGCs, and 179 genes had RERs within 1 kb of transcription start sites (TSSs). These genes were significantly enriched in the gene ontology term ‘RNA splicing’, and SP1/SP2/SP3 motifs were enriched in RER-associated H3K4me3 peaks. Overall, the H3K4me3 marks within TSSs of RNA splicing genes survived two rounds of the epigenetic reprogramming process.

Supplementary information

Supplementary data are available at Bioinformatics online.

TET1 upregulation drives cancer cell growth through aberrant enhancer hydroxymethylation of HMGA2 in hepatocellular carcinoma

Ten‐eleven translocation 1 (TET1) is an essential methylcytosine dioxygenase of the DNA demethylation pathway. Despite its dysregulation being known to occur in human cancer, the role of TET1 remains poorly understood. In this study, we report that TET1 promotes cell growth in human liver cancer. The transcriptome analysis of 68 clinical liver samples revealed a subgroup of TET1‐upregulated hepatocellular carcinoma (HCC), demonstrating hepatoblast‐like gene expression signatures. We performed comprehensive cytosine methylation and hydroxymethylation (5‐hmC) profiling and found that 5‐hmC was aberrantly deposited preferentially in active enhancers. TET1 knockdown in hepatoma cell lines decreased hmC deposition with cell growth suppression. HMGA2 was highly expressed in a TET1^high subgroup of HCC, associated with the hyperhydroxymethylation of its intronic region, marked as histone H3K4–monomethylated, where the H3K27‐acetylated active enhancer chromatin state induced interactions with its promoter. Collectively, our findings point to a novel type of epigenetic dysregulation, methylcytosine dioxygenase TET1, which promotes cell proliferation via the ectopic enhancer of its oncogenic targets, HMGA2, in hepatoblast‐like HCC.

The SAM domain-containing protein 1 (SAMD1) acts as a repressive chromatin regulator at unmethylated CpG islands

CpG islands (CGIs) are key regulatory DNA elements at most promoters, but how they influence the chromatin status and transcription remains elusive. Here, we identify and characterize SAMD1 (SAM domain-containing protein 1) as an unmethylated CGI-binding protein. SAMD1 has an atypical winged-helix domain that directly recognizes unmethylated CpG-containing DNA via simultaneous interactions with both the major and the minor groove. The SAM domain interacts with L3MBTL3, but it can also homopolymerize into a closed pentameric ring. At a genome-wide level, SAMD1 localizes to H3K4me3-decorated CGIs, where it acts as a repressor. SAMD1 tethers L3MBTL3 to chromatin and interacts with the KDM1A histone demethylase complex to modulate H3K4me2 and H3K4me3 levels at CGIs, thereby providing a mechanism for SAMD1-mediated transcriptional repression. The absence of SAMD1 impairs ES cell differentiation processes, leading to misregulation of key biological pathways. Together, our work establishes SAMD1 as a newly identified chromatin regulator acting at unmethylated CGIs.

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.

Inferring time series chromatin states for promoter-enhancer pairs based on Hi-C data

BACKGROUND

Co-localized combinations of histone modifications ("chromatin states") have been shown to correlate with promoter and enhancer activity. Changes in chromatin states over multiple time points ("chromatin state trajectories") have previously been analyzed at promoter and enhancers separately. With the advent of time series Hi-C data it is now possible to connect promoters and enhancers and to analyze chromatin state trajectories at promoter-enhancer pairs.

RESULTS

We present TimelessFlex, a framework for investigating chromatin state trajectories at promoters and enhancers and at promoter-enhancer pairs based on Hi-C information. TimelessFlex extends our previous approach Timeless, a Bayesian network for clustering multiple histone modification data sets at promoter and enhancer feature regions. We utilize time series ATAC-seq data measuring open chromatin to define promoters and enhancer candidates. We developed an expectation-maximization algorithm to assign promoters and enhancers to each other based on Hi-C interactions and jointly cluster their feature regions into paired chromatin state trajectories. We find jointly clustered promoter-enhancer pairs showing the same activation patterns on both sides but with a stronger trend at the enhancer side. While the promoter side remains accessible across the time series, the enhancer side becomes dynamically more open towards the gene activation time point. Promoter cluster patterns show strong correlations with gene expression signals, whereas Hi-C signals get only slightly stronger towards activation. The code of the framework is available at https://github.com/henriettemiko/TimelessFlex .

CONCLUSIONS

TimelessFlex clusters time series histone modifications at promoter-enhancer pairs based on Hi-C and it can identify distinct chromatin states at promoter and enhancer feature regions and their changes over time.

Footprints of global change in marine life: Inferring past environment based on DNA methylation and gene expression marks

Ocean global warming affects the distribution, life history and physiology of marine life. Extreme events, like marine heatwaves, are increasing in frequency and intensity. During sensitive stages of early fish development, the consequences may be long-lasting and mediated by epigenetic mechanisms. Here, we used European sea bass as a model to study the possible long-lasting effects of a marine heatwave during early development. We measured DNA methylation and gene expression in four tissues (brain, muscle, liver and testis) and detected differentially methylated regions (DMRs). Six genes were differentially expressed and contained DMRs three years after exposure to increased temperature, indicating direct phenotypic consequences and representing persistent changes. Interestingly, nine genes contained DMRs around the same genomic regions across tissues, therefore consisting of common footprints of developmental temperature in environmentally responsive loci. These loci are, to our knowledge, the first metastable epialleles (MEs) described in fish. MEs may serve as biomarkers to infer past life history events linked with persistent consequences. These results highlight the importance of subtle phenotypic changes mediated by epigenetics to extreme weather events during sensitive life stages. Also, to our knowledge, it is the first time the molecular effects of a marine heatwave during the lifetime of individuals are assessed. MEs could be used in surveillance programs aimed at determining the footprints of climate change on marine life. Our study paves the way for the identification of conserved MEs that respond equally to environmental perturbations across species. Conserved MEs would constitute a tool of assessment of global change effects in marine life at a large scale.

Machine learning analyses of methylation profiles uncovers tissue-specific gene expression patterns in wheat

DNA methylation is a mechanism of epigenetic modification in eukaryotic organisms. Generally, methylation within genes promoter inhibits regulatory protein binding and represses transcription, whereas gene body methylation is associated with actively transcribed genes. However, it remains unclear whether there is interaction between methylation levels across genic regions and which site has the biggest impact on gene regulation. We investigated and used the methylation patterns of the bread wheat cultivar Chinese Spring to uncover differentially expressed genes (DEGs) between roots and leaves, using six machine learning algorithms and a deep neural network. As anticipated, genes with higher expression in leaves were mainly involved in photosynthesis and pigment biosynthesis processes whereas genes that were not differentially expressed between roots and leaves were involved in protein processes and membrane structures. Methylation occurred preponderantly (60%) in the CG context, whereas 35 and 5% of methylation occurred in CHG and CHH contexts, respectively. Methylation levels were highly correlated (r = 0.7 to 0.9) between all genic regions, except within the promoter (r = 0.4 to 0.5). Machine learning models gave a high (0.81) prediction accuracy of DEGs. There was a strong correlation (p-value = 9.20×10^-10 ) between all features and gene expression, suggesting that methylation across all genic regions contribute to gene regulation. However, the methylation of the promoter, the CDS and the exon in CG context was the most impactful. Our study provides more insights into the interplay between DNA methylation and gene expression and paves the way for identifying tissue-specific genes using methylation profiles.

EpiAlign: an alignment-based bioinformatic tool for comparing chromatin state sequences

The availability of genome-wide epigenomic datasets enables in-depth studies of epigenetic modifications and their relationships with chromatin structures and gene expression. Various alignment tools have been developed to align nucleotide or protein sequences in order to identify structurally similar regions. However, there are currently no alignment methods specifically designed for comparing multi-track epigenomic signals and detecting common patterns that may explain functional or evolutionary similarities. We propose a new local alignment algorithm, EpiAlign, designed to compare chromatin state sequences learned from multi-track epigenomic signals and to identify locally aligned chromatin regions. EpiAlign is a dynamic programming algorithm that novelly incorporates varying lengths and frequencies of chromatin states. We demonstrate the efficacy of EpiAlign through extensive simulations and studies on the real data from the NIH Roadmap Epigenomics project. EpiAlign is able to extract recurrent chromatin state patterns along a single epigenome, and many of these patterns carry cell-type-specific characteristics. EpiAlign can also detect common chromatin state patterns across multiple epigenomes, and it will serve as a useful tool to group and distinguish epigenomic samples based on genome-wide or local chromatin state patterns.

Whsc1 links pluripotency exit with mesendoderm specification

How pluripotent stem cells differentiate into the main germ layers is a key question of developmental biology. Here, we show that the chromatin-related factor Whsc1 (also known as Nsd2 and MMSET) has a dual role in pluripotency exit and germ layer specification of embryonic stem cells. On induction of differentiation, a proportion of Whsc1-depleted embryonic stem cells remain entrapped in a pluripotent state and fail to form mesendoderm, although they are still capable of generating neuroectoderm. These functions of Whsc1 are independent of its methyltransferase activity. Whsc1 binds to enhancers of the mesendodermal regulators Gata4, T (Brachyury), Gata6 and Foxa2, together with Brd4, and activates the expression of these genes. Depleting each of these regulators also delays pluripotency exit, suggesting that they mediate the effects observed with Whsc1. Our data indicate that Whsc1 links silencing of the pluripotency regulatory network with activation of mesendoderm lineages.

Genome-wide analysis of epigenetic dynamics across human developmental stages and tissues

BACKGROUND

Epigenome is highly dynamic during the early stages of embryonic development. Epigenetic modifications provide the necessary regulation for lineage specification and enable the maintenance of cellular identity. Given the rapid accumulation of genome-wide epigenomic modification maps across cellular differentiation process, there is an urgent need to characterize epigenetic dynamics and reveal their impacts on differential gene regulation.

METHODS

We proposed DiffEM, a computational method for differential analysis of epigenetic modifications and identified highly dynamic modification sites along cellular differentiation process. We applied this approach to investigating 6 epigenetic marks of 20 kinds of human early developmental stages and tissues, including hESCs, 4 hESC-derived lineages and 15 human primary tissues.

RESULTS

We identified highly dynamic modification sites where different cell types exhibit distinctive modification patterns, and found that these highly dynamic sites enriched in the genes related to cellular development and differentiation. Further, to evaluate the effectiveness of our method, we correlated the dynamics scores of epigenetic modifications with the variance of gene expression, and compared the results of our method with those of the existing algorithms. The comparison results demonstrate the power of our method in evaluating the epigenetic dynamics and identifying highly dynamic regions along cell differentiation process.

Predicting enhancers in mammalian genomes using supervised hidden Markov models

BACKGROUND

Eukaryotic gene regulation is a complex process comprising the dynamic interaction of enhancers and promoters in order to activate gene expression. In recent years, research in regulatory genomics has contributed to a better understanding of the characteristics of promoter elements and for most sequenced model organism genomes there exist comprehensive and reliable promoter annotations. For enhancers, however, a reliable description of their characteristics and location has so far proven to be elusive. With the development of high-throughput methods such as ChIP-seq, large amounts of data about epigenetic conditions have become available, and many existing methods use the information on chromatin accessibility or histone modifications to train classifiers in order to segment the genome into functional groups such as enhancers and promoters. However, these methods often do not consider prior biological knowledge about enhancers such as their diverse lengths or molecular structure.

RESULTS

We developed enhancer HMM (eHMM), a supervised hidden Markov model designed to learn the molecular structure of promoters and enhancers. Both consist of a central stretch of accessible DNA flanked by nucleosomes with distinct histone modification patterns. We evaluated the performance of eHMM within and across cell types and developmental stages and found that eHMM successfully predicts enhancers with high precision and recall comparable to state-of-the-art methods, and consistently outperforms those in terms of accuracy and resolution.

CONCLUSIONS

eHMM predicts active enhancers based on data from chromatin accessibility assays and a minimal set of histone modification ChIP-seq experiments. In comparison to other 'black box' methods its parameters are easy to interpret. eHMM can be used as a stand-alone tool for enhancer prediction without the need for additional training or a tuning of parameters. The high spatial precision of enhancer predictions gives valuable targets for potential knockout experiments or downstream analyses such as motif search.

From Genotype to Phenotype: Through Chromatin

Advances in sequencing technologies have enabled the exploration of the genetic basis for several clinical disorders by allowing identification of causal mutations in rare genetic diseases. Sequencing technology has also facilitated genome-wide association studies to gather single nucleotide polymorphisms in common diseases including cancer and diabetes. Sequencing has therefore become common in the clinic for both prognostics and diagnostics. The success in follow-up steps, i.e., mapping mutations to causal genes and therapeutic targets to further the development of novel therapies, has nevertheless been very limited. This is because most mutations associated with diseases lie in inter-genic regions including the so-called regulatory genome. Additionally, no genetic causes are apparent for many diseases including neurodegenerative disorders. A complementary approach is therefore gaining interest, namely to focus on epigenetic control of the disease to generate more complete functional genomic maps. To this end, several recent studies have generated large-scale epigenetic datasets in a disease context to form a link between genotype and phenotype. We focus DNA methylation and important histone marks, where recent advances have been made thanks to technology improvements, cost effectiveness, and large meta-scale epigenome consortia efforts. We summarize recent studies unravelling the mechanistic understanding of epigenetic processes in disease development and progression. Moreover, we show how methodology advancements enable causal relationships to be established, and we pinpoint the most important issues to be addressed by future research.

ChromTime: modeling spatio-temporal dynamics of chromatin marks

To model spatial changes of chromatin mark peaks over time we develop and apply ChromTime, a computational method that predicts peaks to be either expanding, contracting, or holding steady between time points. Predicted expanding and contracting peaks can mark regulatory regions associated with transcription factor binding and gene expression changes. Spatial dynamics of peaks provide information about gene expression changes beyond localized signal density changes. ChromTime detects asymmetric expansions and contractions, which for some marks associate with the direction of transcription. ChromTime facilitates the analysis of time course chromatin data in a range of biological systems.

Consistent inverse correlation between DNA methylation of the first intron and gene expression across tissues and species

BACKGROUND

DNA methylation is one of the main epigenetic mechanisms for the regulation of gene expression in eukaryotes. In the standard model, methylation in gene promoters has received the most attention since it is generally associated with transcriptional silencing. Nevertheless, recent studies in human tissues reveal that methylation of the region downstream of the transcription start site is highly informative of gene expression. Also, in some cell types and specific genes it has been found that methylation of the first intron, a gene feature typically rich in enhancers, is linked with gene expression. However, a genome-wide, tissue-independent, systematic comparative analysis of the relationship between DNA methylation in the first intron and gene expression across vertebrates has not been explored yet.

RESULTS

The most important findings of this study are: (1) using different tissues from a modern fish, we show a clear genome-wide, tissue-independent quasi-linear inverse relationship between DNA methylation of the first intron and gene expression. (2) This relationship is conserved across vertebrates, since it is also present in the genomes of a model pufferfish, a model frog and different human tissues. Among the gene features, tissues and species interrogated, the first intron's negative correlation with the gene expression was most consistent. (3) We identified more tissue-specific differentially methylated regions (tDMRs) in the first intron than in any other gene feature. These tDMRs have positive or negative correlation with gene expression, indicative of distinct mechanisms of tissue-specific regulation. (4) Lastly, we identified CpGs in transcription factor binding motifs, enriched in the first intron, the methylation of which tended to increase with the distance from the first exon-first intron boundary, with a concomitant decrease in gene expression.

CONCLUSIONS

Our integrative analysis clearly reveals the important and conserved role of the methylation level of the first intron and its inverse association with gene expression regardless of tissue and species. These findings not only contribute to our basic understanding of the epigenetic regulation of gene expression but also identify the first intron as an informative gene feature regarding the relationship between DNA methylation and gene expression where future studies should be focused.

Global H3.3 dynamic deposition defines its bimodal role in cell fate transition

H3.3 is a histone variant, which is deposited on genebodies and regulatory elements, by Hira, marking active transcription. Moreover, H3.3 is deposited on heterochromatin by Atrx/Daxx complex. The exact role of H3.3 in cell fate transition remains elusive. Here, we investigate the dynamic changes in the deposition of the histone variant H3.3 during cellular reprogramming. H3.3 maintains the identities of the parental cells during reprogramming as its removal at early time-point enhances the efficiency of the process. We find that H3.3 plays a similar role in transdifferentiation to hematopoietic progenitors and neuronal differentiation from embryonic stem cells. Contrastingly, H3.3 deposition on genes associated with the newly reprogrammed lineage is essential as its depletion at the later phase abolishes the process. Mechanistically, H3.3 deposition by Hira, and its K4 and K36 modifications are central to the role of H3.3 in cell fate conversion. Finally, H3.3 safeguards fibroblast lineage by regulating Mapk cascade and collagen synthesis.

Transcription factors, coregulators, and epigenetic marks are linearly correlated and highly redundant

The DNA microstates that regulate transcription include sequence-specific transcription factors (TFs), coregulatory complexes, nucleosomes, histone modifications, DNA methylation, and parts of the three-dimensional architecture of genomes, which could create an enormous combinatorial complexity across the genome. However, many proteins and epigenetic marks are known to colocalize, suggesting that the information content encoded in these marks can be compressed. It has so far proved difficult to understand this compression in a systematic and quantitative manner. Here, we show that simple linear models can reliably predict the data generated by the ENCODE and Roadmap Epigenomics consortia. Further, we demonstrate that a small number of marks can predict all other marks with high average correlation across the genome, systematically revealing the substantial information compression that is present in different cell lines. We find that the linear models for activating marks are typically cell line-independent, while those for silencing marks are predominantly cell line-specific. Of particular note, a nuclear receptor corepressor, transducin beta-like 1 X-linked receptor 1 (TBLR1), was highly predictive of other marks in two hematopoietic cell lines. The methodology presented here shows how the potentially vast complexity of TFs, coregulators, and epigenetic marks at eukaryotic genes is highly redundant and that the information present can be compressed onto a much smaller subset of marks. These findings could be used to efficiently characterize cell lines and tissues based on a small number of diagnostic marks and suggest how the DNA microstates, which regulate the expression of individual genes, can be specified.

Chromatin-state discovery and genome annotation with ChromHMM

Noncoding DNA regions have central roles in human biology, evolution, and disease. ChromHMM helps to annotate the noncoding genome using epigenomic information across one or multiple cell types. It combines multiple genome-wide epigenomic maps, and uses combinatorial and spatial mark patterns to infer a complete annotation for each cell type. ChromHMM learns chromatin-state signatures using a multivariate hidden Markov model (HMM) that explicitly models the combinatorial presence or absence of each mark. ChromHMM uses these signatures to generate a genome-wide annotation for each cell type by calculating the most probable state for each genomic segment. ChromHMM provides an automated enrichment analysis of the resulting annotations to facilitate the functional interpretations of each chromatin state. ChromHMM is distinguished by its modeling emphasis on combinations of marks, its tight integration with downstream functional enrichment analyses, its speed, and its ease of use. Chromatin states are learned, annotations are produced, and enrichments are computed within 1 d.

Chromatin module inference on cellular trajectories identifies key transition points and poised epigenetic states in diverse developmental processes

Changes in chromatin state play important roles in cell fate transitions. Current computational approaches to analyze chromatin modifications across multiple cell types do not model how the cell types are related on a lineage or over time. To overcome this limitation, we developed a method called Chromatin Module INference on Trees (CMINT), a probabilistic clustering approach to systematically capture chromatin state dynamics across multiple cell types. Compared to existing approaches, CMINT can handle complex lineage topologies, capture higher quality clusters, and reliably detect chromatin transitions between cell types. We applied CMINT to gain novel insights in two complex processes: reprogramming to induced pluripotent stem cells (iPSCs) and hematopoiesis. In reprogramming, chromatin changes could occur without large gene expression changes, different combinations of activating marks were associated with specific reprogramming factors, there was an order of acquisition of chromatin marks at pluripotency loci, and multivalent states (comprising previously undetermined combinations of activating and repressive histone modifications) were enriched for CTCF. In the hematopoietic system, we defined critical decision points in the lineage tree, identified regulatory elements that were enriched in cell-type–specific regions, and found that the underlying chromatin state was achieved by specific erasure of preexisting chromatin marks in the precursor cell or by de novo assembly. Our method provides a systematic approach to model the dynamics of chromatin state to provide novel insights into the relationships among cell types in diverse cell-fate specification processes.

Inference of cell type specific regulatory networks on mammalian lineages

Transcriptional regulatory networks are at the core of establishing cell type specific gene expression programs. In mammalian systems, such regulatory networks are determined by multiple levels of regulation, including by transcription factors, chromatin environment, and three-dimensional organization of the genome. Recent efforts to measure diverse regulatory genomic datasets across multiple cell types and tissues offer unprecedented opportunities to examine the context-specificity and dynamics of regulatory networks at a greater resolution and scale than before. In parallel, numerous computational approaches to analyze these data have emerged that serve as important tools for understanding mammalian cell type specific regulation. In this article, we review recent computational approaches to predict the expression and sequence-based regulators of a gene's expression level and examine long-range gene regulation. We highlight promising approaches, insights gained, and open challenges that need to be overcome to build a comprehensive picture of cell type specific transcriptional regulatory networks.

iHMS: a database integrating human histone modification data across developmental stages and tissues

Background

Differences in chromatin states are critical to the multiplicity of cell states. Recently genome-wide histone modification maps of diverse human developmental stages and tissues have been charted.

Description

To facilitate the investigation of epigenetic dynamics and regulatory mechanisms in cellular differentiation processes, we developed iHMS, an integrated human histone modification database that incorporates massive histone modification maps spanning different developmental stages, lineages and tissues (http://www.tongjidmb.com/human/index.html). It also includes genome-wide expression data of different conditions, reference gene annotations, GC content and CpG island information. By providing an intuitive and user-friendly query interface, iHMS enables comprehensive query and comparative analysis based on gene names, genomic region locations, histone modification marks and cell types. Moreover, it offers an efficient browser that allows users to visualize and compare multiple genome-wide histone modification maps and related expression profiles across different developmental stages and tissues.

Conclusion

iHMS is of great helpfulness to understand how global histone modification state transitions impact cellular phenotypes across different developmental stages and tissues in the human genome. This extensive catalog of histone modification states thus presents an important resource for epigenetic and developmental studies.

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.

A Multi-step Transcriptional and Chromatin State Cascade Underlies Motor Neuron Programming from Embryonic Stem Cells

Direct cell programming via overexpression of transcription factors (TFs) aims to control cell fate with the degree of precision needed for clinical applications. However, the regulatory steps involved in successful terminal cell fate programming remain obscure. We have investigated the underlying mechanisms by looking at gene expression, chromatin states, and TF binding during the uniquely efficient Ngn2, Isl1, and Lhx3 motor neuron programming pathway. Our analysis reveals a highly dynamic process in which Ngn2 and the Isl1/Lhx3 pair initially engage distinct regulatory regions. Subsequently, Isl1/Lhx3 binding shifts from one set of targets to another, controlling regulatory region activity and gene expression as cell differentiation progresses. Binding of Isl1/Lhx3 to later motor neuron enhancers depends on the Ebf and Onecut TFs, which are induced by Ngn2 during the programming process. Thus, motor neuron programming is the product of two initially independent transcriptional modules that converge with a feedforward transcriptional logic.

Exposure to the widely used herbicide atrazine results in deregulation of global tissue-specific RNA transcription in the third generation and is associated with a global decrease of histone trimethylation in mice

The epigenetic events imposed during germline reprogramming and affected by harmful exposure can be inherited and transferred to subsequent generations via gametes inheritance. In this study, we examine the transgenerational effects promoted by widely used herbicide atrazine (ATZ). We exposed pregnant outbred CD1 female mice and the male progeny was crossed for three generations with untreated females. We demonstrate here that exposure to ATZ affects meiosis, spermiogenesis and reduces the spermatozoa number in the third generation (F3) male mice. We suggest that changes in testis cell types originate from modified transcriptional network in undifferentiated spermatogonia. Importantly, exposure to ATZ dramatically increases the number of transcripts with novel transcription initiation sites, spliced variants and alternative polyadenylation sites. We found the global decrease in H3K4me3 occupancy in the third generation males. The regions with altered H3K4me3 occupancy in F3 ATZ-derived males correspond to altered H3K4me3 occupancy of F1 generation and 74% of changed peaks in F3 generation are associated with enhancers. The regions with altered H3K4me3 occupancy are enriched in SP family and WT1 transcription factor binding sites. Our data suggest that the embryonic exposure to ATZ affects the development and the changes induced by ATZ are transferred up to three generations.

Integrative analysis of methylome and transcriptome in human blood identifies extensive sex- and immune cell-specific differentially methylated regions

The relationship between DNA methylation and gene expression is complex and elusive. To further elucidate these relations, we performed an integrative analysis of the methylome and transcriptome of 4 circulating immune cell subsets (B cells, monocytes, CD4(+), and CD8(+) T cells) from healthy females. Additionally, in light of the known sex bias in the prevalence of several immune-mediated diseases, the female datasets were compared with similar public available male data sets. Immune cell-specific differentially methylated regions (DMRs) were found to be highly similar between sexes, with an average correlation coefficient of 0.82; however, numerous sex-specific DMRs, shared by the cell subsets, were identified, mainly on autosomal chromosomes. This provides a list of highly interesting candidate genes to be studied in disorders with sexual dimorphism, such as autoimmune diseases. Immune cell-specific DMRs were mainly located in the gene body and intergenic region, distant from CpG islands but overlapping with enhancer elements, indicating that distal regulatory elements are important in immune cell specificity. In contrast, sex-specific DMRs were overrepresented in CpG islands, suggesting that the epigenetic regulatory mechanisms of sex and immune cell specificity may differ. Both positive and, more frequently, negative correlations between subset-specific expression and methylation were observed, and cell-specific DMRs of both interactions were associated with similar biological pathways, while sex-specific DMRs were linked to networks of early development or estrogen receptor and immune-related molecules. Our findings of immune cell- and sex-specific methylome and transcriptome profiles provide novel insight on their complex regulatory interactions and may particularly contribute to research of immune-mediated diseases.

Integrative epigenomic analysis reveals unique epigenetic signatures involved in unipotency of mouse female germline stem cells

Background

Germline stem cells play an essential role in establishing the fertility of an organism. Although extensively characterized, the regulatory mechanisms that govern the fundamental properties of mammalian female germline stem cells remain poorly understood.

Results

We generate genome-wide profiles of the histone modifications H3K4me1, H3K27ac, H3K4me3, and H3K27me3, DNA methylation, and RNA polymerase II occupancy and perform transcriptome analysis in mouse female germline stem cells. Comparison of enhancer regions between embryonic stem cells and female germline stem cells identifies the lineage-specific enhancers involved in germline stem cell features. Additionally, our results indicate that DNA methylation primarily contributes to female germline stem cell unipotency by suppressing the somatic program and is potentially involved in maintenance of sexual identity when compared with male germline stem cells. Moreover, we demonstrate down-regulation of Prmt5 triggers differentiation and thus uncover a role for Prmt5 in maintaining the undifferentiated status of female germline stem cells.

Conclusions

The genome-wide epigenetic signatures and the transcription regulators identified here provide an invaluable resource for understanding the fundamental features of mouse female germline stem cells.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-016-1023-z) contains supplementary material, which is available to authorized users.

Mapping RNA-RNA interactome and RNA structure in vivo by MARIO

The pervasive transcription of our genome presents a possibility of revealing new genomic functions by investigating RNA interactions. Current methods for mapping RNA–RNA interactions have to rely on an ‘anchor' protein or RNA and often require molecular perturbations. Here we present the MARIO (Mapping RNA interactome in vivo) technology to massively reveal RNA–RNA interactions from unperturbed cells. We mapped tens of thousands of endogenous RNA–RNA interactions from mouse embryonic stem cells and brain. We validated seven interactions by RNA antisense purification and one interaction using single-molecule RNA–FISH. The experimentally derived RNA interactome is a scale-free network, which is not expected from currently perceived promiscuity in RNA–RNA interactions. Base pairing is observed at the interacting regions between long RNAs, including transposon transcripts, suggesting a class of regulatory sequences acting in trans. In addition, MARIO data reveal thousands of intra-molecule interactions, providing in vivo data on high-order RNA structures.

Current methods for mapping RNA-RNA interactions have to rely on an ‘anchor' protein or RNA. Here, the authors report the MARIO (Mapping RNA interactome in vivo) technology that can massively reveal RNA-RNA interactions and RNA structure from unperturbed cells.

CpG island erosion, polycomb occupancy and sequence motif enrichment at bivalent promoters in mammalian embryonic stem cells

In embryonic stem (ES) cells, developmental regulators have a characteristic bivalent chromatin signature marked by simultaneous presence of both activation (H3K4me3) and repression (H3K27me3) signals and are thought to be in a 'poised' state for subsequent activation or silencing during differentiation. We collected eleven pairs (H3K4me3 and H3K27me3) of ChIP sequencing datasets in human ES cells and eight pairs in murine ES cells, and predicted high-confidence (HC) bivalent promoters. Over 85% of H3K27me3 marked promoters were bivalent in human and mouse ES cells. We found that (i) HC bivalent promoters were enriched for developmental factors and were highly likely to be differentially expressed upon transcription factor perturbation; (ii) murine HC bivalent promoters were occupied by both polycomb repressive component classes (PRC1 and PRC2) and grouped into four distinct clusters with different biological functions; (iii) HC bivalent and active promoters were CpG rich while H3K27me3-only promoters lacked CpG islands. Binding enrichment of distinct sets of regulators distinguished bivalent from active promoters. Moreover, a 'TCCCC' sequence motif was specifically enriched in bivalent promoters. Finally, this analysis will serve as a resource for future studies to further understand transcriptional regulation during embryonic development.

A computational method for studying the relation between alternative splicing and DNA methylation

Alternative splicing is an important mechanism in eukaryotes that expands the transcriptome and proteome significantly. It plays an important role in a number of biological processes. Understanding its regulation is hence an important challenge. Recently, increasing evidence has been collected that supports an involvement of intragenic DNA methylation in the regulation of alternative splicing. The exact mechanisms of regulation, however, are largely unknown, and speculated to be complex: different methylation profiles might exist, each of which could be associated with a different regulation mechanism. We present a computational technique that is able to determine such stable methylation patterns and allows to correlate these patterns with inclusion propensity of exons. Pattern detection is based on dynamic time warping (DTW) of methylation profiles, a sophisticated similarity measure for signals that can be non-trivially transformed. We design a flexible self-organizing map approach to pattern grouping. Exemplary application on available data sets indicates that stable patterns which correlate non-trivially with exon inclusion do indeed exist. To improve the reliability of these predictions, further studies on larger data sets will be required. We have thus taken great care that our software runs efficiently on modern hardware, so that it can support future studies on large-scale data sets.

Mammalian genome evolution as a result of epigenetic regulation of transposable elements

Transposable elements (TEs) make up a large proportion of mammalian genomes and are a strong evolutionary force capable of rewiring regulatory networks and causing genome rearrangements. Additionally, there are many eukaryotic epigenetic defense mechanisms able to transcriptionally silence TEs. Furthermore, small RNA molecules that target TE DNA sequences often mediate these epigenetic defense mechanisms. As a result, epigenetic marks associated with TE silencing can be reestablished after epigenetic reprogramming - an event during the mammalian life cycle that results in widespread loss of parental epigenetic marks. Furthermore, targeted epigenetic marks associated with TE silencing may have an impact on nearby gene expression. Therefore, TEs may have driven species evolution via their ability to heritably alter the epigenetic regulation of gene expression in mammals.

Ctbp2 Modulates NuRD-Mediated Deacetylation of H3K27 and Facilitates PRC2-Mediated H3K27me3 in Active Embryonic Stem Cell Genes During Exit from Pluripotency

For cells to exit from pluripotency and commit to a lineage, the circuitry of a core transcription factor (CTF) network must be extinguished in an orderly manner through epigenetic modifications. However, how this choreographed epigenetic remodeling at active embryonic stem cell (ESC) genes occurs during differentiation is poorly understood. In this study, we demonstrate that C-terminal binding protein 2 (Ctbp2) regulates nucleosome remodeling and deacetylation (NuRD)-mediated deacetylation of H3K27 and facilitates recruitment of polycomb repressive complex 2 (PRC2)-mediated H3K27me3 in active ESC genes for exit from pluripotency during differentiation. By genomewide analysis, we found that Ctbp2 resides in active ESC genes and co-occupies regions with ESC CTFs in undifferentiated ESCs. Furthermore, ablation of Ctbp2 effects inappropriate gene silencing in ESCs by sustaining high levels of H3K27ac and impeding H3K27me3 in active ESC genes, thereby sustaining ESC maintenance during differentiation. Thus, Ctbp2 preoccupies regions in active genes with the NuRD complex in undifferentiated ESCs that are directed toward H3K27me3 by PRC2 to induce stable silencing, which is pivotal for natural lineage commitment.

A cohesin-OCT4 complex mediates Sox enhancers to prime an early embryonic lineage

Short- and long-scales intra- and inter-chromosomal interactions are linked to gene transcription, but the molecular events underlying these structures and how they affect cell fate decision during embryonic development are poorly understood. One of the first embryonic cell fate decisions (that is, mesendoderm determination) is driven by the POU factor OCT4, acting in concert with the high-mobility group genes Sox-2 and Sox-17. Here we report a chromatin-remodelling mechanism and enhancer function that mediate cell fate switching. OCT4 alters the higher-order chromatin structure at both Sox-2 and Sox-17 loci. OCT4 titrates out cohesin and switches the Sox-17 enhancer from a locked (within an inter-chromosomal Sox-2 enhancer/CCCTC-binding factor CTCF/cohesin loop) to an active (within an intra-chromosomal Sox-17 promoter/enhancer/cohesin loop) state. SALL4 concomitantly mobilizes the polycomb complexes at the Soxs loci. Thus, OCT4/SALL4-driven cohesin- and polycombs-mediated changes in higher-order chromatin structure mediate instruction of early cell fate in embryonic cells.

Inferring dynamic gene regulatory networks in cardiac differentiation through the integration of multi-dimensional data

Background

Decoding the temporal control of gene expression patterns is key to the understanding of the complex mechanisms that govern developmental decisions during heart development. High-throughput methods have been employed to systematically study the dynamic and coordinated nature of cardiac differentiation at the global level with multiple dimensions. Therefore, there is a pressing need to develop a systems approach to integrate these data from individual studies and infer the dynamic regulatory networks in an unbiased fashion.

Results

We developed a two-step strategy to integrate data from (1) temporal RNA-seq, (2) temporal histone modification ChIP-seq, (3) transcription factor (TF) ChIP-seq and (4) gene perturbation experiments to reconstruct the dynamic network during heart development. First, we trained a logistic regression model to predict the probability (LR score) of any base being bound by 543 TFs with known positional weight matrices. Second, four dimensions of data were combined using a time-varying dynamic Bayesian network model to infer the dynamic networks at four developmental stages in the mouse [mouse embryonic stem cells (ESCs), mesoderm (MES), cardiac progenitors (CP) and cardiomyocytes (CM)]. Our method not only infers the time-varying networks between different stages of heart development, but it also identifies the TF binding sites associated with promoter or enhancers of downstream genes.

The LR scores of experimentally verified ESCs and heart enhancers were significantly higher than random regions (p <10⁻¹⁰⁰), suggesting that a high LR score is a reliable indicator for functional TF binding sites. Our network inference model identified a region with an elevated LR score approximately −9400 bp upstream of the transcriptional start site of Nkx2-5, which overlapped with a previously reported enhancer region (−9435 to −8922 bp). TFs such as Tead1, Gata4, Msx2, and Tgif1 were predicted to bind to this region and participate in the regulation of Nkx2-5 gene expression. Our model also predicted the key regulatory networks for the ESC-MES, MES-CP and CP-CM transitions.

Conclusion

We report a novel method to systematically integrate multi-dimensional -omics data and reconstruct the gene regulatory networks. This method will allow one to rapidly determine the cis-modules that regulate key genes during cardiac differentiation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0460-0) contains supplementary material, which is available to authorized users.

A hyper-dynamic nature of bivalent promoter states underlies coordinated developmental gene expression modules

Background

Chromatin remodeling is crucial for proper programing of developmental gene expression. Recent work provides a dynamic view of post-translational histone modifications during differentiation; however there is little insight on the evolution of combinatorial genome-wide patterns of chromatin marks, excluding an essential aspect of developmental gene regulation.

Results

We report here a 15-chromatin state Hidden Markov Model which describes changes in chromatin signatures in relation to transcription profiles during differentiation of human pre-adipocytes into adipocytes. We identify nineteen modules of gene expression reflecting multiple waves of transcriptional up- and down-regulation which characterize adipogenic differentiation. From our model, we developed chromatin state matrices fitting each of these transcription modules to show how the complexity and dynamic nature of chromatin signatures relate to expression patterns. Spatial relationships between chromatin states underlie a high-order chromatin organization in differentiating adipocytes. We show the importance of gene expression level in generating diversity in chromatin signatures, and show that the hyper-dynamic nature of H3K4me2/H3K27me3-marked ‘bivalent’ promoter states underlies many of the gene expression patterns associated with adipogenic differentiation.

Conclusions

Our results reveal the highly dynamic nature of bivalent promoter states within the adipogenic lineage. The data constitute a valuable resource enabling the assessment of possibilities to alter the adipogenic program.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1186) contains supplementary material, which is available to authorized users.

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.

Characterization of the neural stem cell gene regulatory network identifies OLIG2 as a multifunctional regulator of self-renewal

The gene regulatory network (GRN) that supports neural stem cell (NS cell) self-renewal has so far been poorly characterized. Knowledge of the central transcription factors (TFs), the noncoding gene regulatory regions that they bind to, and the genes whose expression they modulate will be crucial in unlocking the full therapeutic potential of these cells. Here, we use DNase-seq in combination with analysis of histone modifications to identify multiple classes of epigenetically and functionally distinct cis-regulatory elements (CREs). Through motif analysis and ChIP-seq, we identify several of the crucial TF regulators of NS cells. At the core of the network are TFs of the basic helix-loop-helix (bHLH), nuclear factor I (NFI), SOX, and FOX families, with CREs often densely bound by several of these different TFs. We use machine learning to highlight several crucial regulatory features of the network that underpin NS cell self-renewal and multipotency. We validate our predictions by functional analysis of the bHLH TF OLIG2. This TF makes an important contribution to NS cell self-renewal by concurrently activating pro-proliferation genes and preventing the untimely activation of genes promoting neuronal differentiation and stem cell quiescence.

5-hydroxymethylcytosine represses the activity of enhancers in embryonic stem cells: a new epigenetic signature for gene regulation

Background

Recent mapping of 5-hydroxymethylcytosine (5hmC) provides a genome-wide view of the distribution of this important chromatin mark. However, the role of 5hmC in specific regulatory regions is not clear, especially at enhancers.

Results

We found a group of distal transcription factor binding sites highly enriched for 5-hdroxymethylcytosine (5hmC), but lacking any known activating histone marks and being depleted for nascent transcripts, suggesting a repressive role for 5hmC in mouse embryonic stem cells (mESCs). 5-formylcytosine (5fC), which is known to mark poised enhancers where H3K4me1 is enriched, is also observed at these sites. Furthermore, the 5hmC levels were inversely correlated with RNA polymerase II (PolII) occupancy in mESCs as well as in fully differentiated adipocytes. Interestingly, activating H3K4me1/2 histone marks were enriched at these sites when the associated genes become activated following lineage specification. These putative enhancers were shown to be functional in embryonic stem cells when unmethylated. Together, these data suggest that 5hmC suppresses the activity of this group of enhancers, which we termed “silenced enhancers”.

Conclusions

Our findings indicate that 5hmC has a repressive role at specific proximal and distal regulatory regions in mESCs, and suggest that 5hmC is a new epigenetic mark for silenced enhancers.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-670) contains supplementary material, which is available to authorized users.

A wavelet approach to detect enriched regions and explore epigenomic landscapes

Epigenetic landscapes represent how cells regulate gene activity. To understand their effect on gene regulation, it is important to detect their occupancy in the genome. Unlike transcription factors whose binding regions are limited to narrow regions, histone modification marks are enriched over broader areas. The stochastic characteristics unique to each mark make it hard to detect their enrichment. Classically, a predefined window has been used to detect their enrichment. However, these approaches heavily rely on the predetermined parameters. Also, the window-based approaches cannot handle the enrichment of multiple marks. We propose a novel algorithm, called SeqW, to detect enrichment of multiple histone modification marks. SeqW applies a zooming approach to detect a broadly enriched domain. The zooming approach helps domain detection by increasing signal-to-noise ratio. The borders of the domains are detected by studying the characteristics of signals in the wavelet domain. We show that SeqW outperformed previous predictors in detecting broad peaks. Also, we applied SeqW in studying spatial combinations of histone modification patterns.

Cotranscriptional histone H2B monoubiquitylation is tightly coupled with RNA polymerase II elongation rate

Various histone modifications decorate nucleosomes within transcribed genes. Among these, monoubiquitylation of histone H2B (H2Bub1) and methylation of histone H3 on lysines 36 (H3K36me2/3) and 79 (H3K79me2/3) correlate positively with gene expression. By measuring the progression of the transcriptional machinery along genes within live cells, we now report that H2B monoubiquitylation occurs cotranscriptionally and accurately reflects the advance of RNA polymerase II (Pol II). In contrast, H3K36me3 and H3K79me2 are less dynamic and represent Pol II movement less faithfully. High-resolution ChIP-seq reveals that H2Bub1 levels are selectively reduced at exons and decrease in an exon-dependent stepwise manner toward the 3' end of genes. Exonic depletion of H2Bub1 in gene bodies is highly correlated with Pol II pausing at exons, suggesting elongation rate changes associated with intron-exon structure. In support of this notion, H2Bub1 levels were found to be significantly correlated with transcription elongation rates measured in various cell lines. Overall, our data shed light on the organization of H2Bub1 within transcribed genes and single out H2Bub1 as a reliable marker for ongoing transcription elongation.

Comparative epigenomics: defining and utilizing epigenomic variations across species, time-course, and individuals

Epigenomic profiling, by revealing genome-wide distributions of epigenetic modifications, generated a large amount of structural information about the chromosomes. Epigenomic analysis has quickly become a big data science, posing tremendous challenges on its translation into knowledge. To meet this challenge, comparative analysis of epigenomes, dubbed comparative epigenomics, has emerged as an active research area. Here, we summarize the recent developments in comparative epigenomic analyses into three major directions, namely the comparisons across species, the time-course of a biological process, and individuals. We review the main ideas, methods, and findings in each direction, and discuss the implications to understanding the regulatory functions of the genomes.

CONFLICT OF INTEREST

The authors have declared no conflicts of interest for this article.

Applications of alignment-free methods in epigenomics

Epigenetic mechanisms play an important role in the regulation of cell type-specific gene activities, yet how epigenetic patterns are established and maintained remains poorly understood. Recent studies have supported a role of DNA sequences in recruitment of epigenetic regulators. Alignment-free methods have been applied to identify distinct sequence features that are associated with epigenetic patterns and to predict epigenomic profiles. Here, we review recent advances in such applications, including the methods to map DNA sequence to feature space, sequence comparison and prediction models. Computational studies using these methods have provided important insights into the epigenetic regulatory mechanisms.

PTRcombiner: mining combinatorial regulation of gene expression from post-transcriptional interaction maps

Background

The progress in mapping RNA-protein and RNA-RNA interactions at the transcriptome-wide level paves the way to decipher possible combinatorial patterns embedded in post-transcriptional regulation of gene expression.

Results

Here we propose an innovative computational tool to extract clusters of mRNA trans-acting co-regulators (RNA binding proteins and non-coding RNAs) from pairwise interaction annotations. In addition the tool allows to analyze the binding site similarity of co-regulators belonging to the same cluster, given their positional binding information. The tool has been tested on experimental collections of human and yeast interactions, identifying modules that coordinate functionally related messages.

Conclusions

This tool is an original attempt to uncover combinatorial patterns using all the post-transcriptional interaction data available so far. PTRcombiner is available at http://disi.unitn.it/~passerini/software/PTRcombiner/.

From genomes to societies: a holistic view of determinants of human health

Both biological and social sciences have identified contributing factors to human health. However, health outcomes are unlikely to equal a simple sum of these identified factors. This article makes an attempt to put together the information, methods, and technologies that relate to health outcomes from biological, behavioral, and social disciplines. Much of this information was obtained by controlling for the variations of the factors in 'other' disciplines. For example, genetic factors were controlled for in identifying the behavioral determinants of health. Looking forward, better understandings of health outcomes may require exploiting the interactions of health determinants that were identified from different disciplines. We propose the concept of 'systems health' studies, which take health outcomes as the outputs of a system, where the inputs and their interactions from multiple disciplines are considered.

Rapid and pervasive changes in genome-wide enhancer usage during mammalian development

Enhancers are distal regulatory elements that can activate tissue-specific gene expression and are abundant throughout mammalian genomes. Although substantial progress has been made toward genome-wide annotation of mammalian enhancers, their temporal activity patterns and global contributions in the context of developmental in vivo processes remain poorly explored. Here we used epigenomic profiling for H3K27ac, a mark of active enhancers, coupled to transgenic mouse assays to examine the genome-wide utilization of enhancers in three different mouse tissues across seven developmental stages. The majority of the ∼90,000 enhancers identified exhibited tightly temporally restricted predicted activity windows and were associated with stage-specific biological functions and regulatory pathways in individual tissues. Comparative genomic analysis revealed that evolutionary conservation of enhancers decreases following midgestation across all tissues examined. The dynamic enhancer activities uncovered in this study illuminate rapid and pervasive temporal in vivo changes in enhancer usage that underlie processes central to development and disease.

Understanding variation in transcription factor binding by modeling transcription factor genome-epigenome interactions

Despite explosive growth in genomic datasets, the methods for studying epigenomic mechanisms of gene regulation remain primitive. Here we present a model-based approach to systematically analyze the epigenomic functions in modulating transcription factor-DNA binding. Based on the first principles of statistical mechanics, this model considers the interactions between epigenomic modifications and a cis-regulatory module, which contains multiple binding sites arranged in any configurations. We compiled a comprehensive epigenomic dataset in mouse embryonic stem (mES) cells, including DNA methylation (MeDIP-seq and MRE-seq), DNA hydroxymethylation (5-hmC-seq), and histone modifications (ChIP-seq). We discovered correlations of transcription factors (TFs) for specific combinations of epigenomic modifications, which we term epigenomic motifs. Epigenomic motifs explained why some TFs appeared to have different DNA binding motifs derived from in vivo (ChIP-seq) and in vitro experiments. Theoretical analyses suggested that the epigenome can modulate transcriptional noise and boost the cooperativity of weak TF binding sites. ChIP-seq data suggested that epigenomic boost of binding affinities in weak TF binding sites can function in mES cells. We showed in theory that the epigenome should suppress the TF binding differences on SNP-containing binding sites in two people. Using personal data, we identified strong associations between H3K4me2/H3K9ac and the degree of personal differences in NFκB binding in SNP-containing binding sites, which may explain why some SNPs introduce much smaller personal variations on TF binding than other SNPs. In summary, this model presents a powerful approach to analyze the functions of epigenomic modifications. This model was implemented into an open source program APEG (Affinity Prediction by Epigenome and Genome, http://systemsbio.ucsd.edu/apeg).