Genome-wide mapping of DNase hypersensitive sites using massively parallel signature sequencing (MPSS)

Spirits in the Material World: Enhancer RNAs in Transcriptional Regulation

Responses to developmental and environmental cues depend on precise spatiotemporal control of gene transcription. Enhancers, which comprise DNA elements bound by regulatory proteins, can activate target genes in response to these external signals. Recent studies have shown that enhancers are transcribed to produce enhancer RNAs (eRNAs). Do eRNAs play a functional role in activating gene expression or are they non-functional byproducts of nearby transcription machinery? The unstable nature of eRNAs and over-reliance on knockdown approaches have made elucidating the possible functions of eRNAs challenging. We focus here on studies using cloned eRNAs to study their function as transcripts, revealing roles for eRNAs in enhancer-promoter looping, recruiting transcriptional machinery, and facilitating RNA polymerase pause-release to regulate gene expression.

Genome-Wide Characterization of DNase I-Hypersensitive Sites and Cold Response Regulatory Landscapes in Grasses

Deep sequencing of DNase-I treated chromatin (DNase-seq) can be used to identify DNase I-hypersensitive sites (DHSs) and facilitates genome-scale mining of de novo cis-regulatory DNA elements. Here, we adapted DNase-seq to generate genome-wide maps of DHSs using control and cold-treated leaf, stem, and root tissues of three widely studied grass species: Brachypodium distachyon, foxtail millet (Setaria italica), and sorghum (Sorghum bicolor). Functional validation demonstrated that 12 of 15 DHSs drove reporter gene expression in transiently transgenic B. distachyon protoplasts. DHSs under both normal and cold treatment substantially differed among tissues and species. Intriguingly, the putative DHS-derived transcription factors (TFs) are largely colocated among tissues and species and include 17 ubiquitous motifs covering all grass taxa and all tissues examined in this study. This feature allowed us to reconstruct a regulatory network that responds to cold stress. Ethylene-responsive TFs SHINE3, ERF2, and ERF9 occurred frequently in cold feedback loops in the tissues examined, pointing to their possible roles in the regulatory network. Overall, we provide experimental annotation of 322,713 DHSs and 93 derived cold-response TF binding motifs in multiple grasses, which could serve as a valuable resource for elucidating the transcriptional networks that function in the cold-stress response and other physiological processes.

Total Functional Score of Enhancer Elements Identifies Lineage-Specific Enhancers That Drive Differentiation of Pancreatic Cells

The differentiation of embryonic stem cells into various lineages is highly dependent on the chromatin state of the genome and patterns of gene expression. To identify lineage-specific enhancers driving the differentiation of progenitors into pancreatic cells, we used a previously described computational framework called Total Functional Score of Enhancer Elements (TFSEE), which integrates multiple genomic assays that probe both transcriptional and epigenomic states. First, we evaluated and compared TFSEE as an enhancer-calling algorithm with enhancers called using GRO-seq-defined enhancer transcripts (method 1) versus enhancers called using histone modification ChIP-seq data (method 2). Second, we used TFSEE to define the enhancer landscape and identify transcription factors (TFs) that maintain the multipotency of a subpopulation of endodermal stem cells during differentiation into pancreatic lineages. Collectively, our results demonstrate that TFSEE is a robust enhancer-calling algorithm that can be used to perform multilayer genomic data integration to uncover cell type-specific TFs that control lineage-specific enhancers.

MTTFsite: cross-cell type TF binding site prediction by using multi-task learning

Motivation

The prediction of transcription factor binding sites (TFBSs) is crucial for gene expression analysis. Supervised learning approaches for TFBS predictions require large amounts of labeled data. However, many TFs of certain cell types either do not have sufficient labeled data or do not have any labeled data.

Results

In this paper, a multi-task learning framework (called MTTFsite) is proposed to address the lack of labeled data problem by leveraging on labeled data available in cross-cell types. The proposed MTTFsite contains a shared CNN to learn common features for all cell types and a private CNN for each cell type to learn private features. The common features are aimed to help predicting TFBSs for all cell types especially those cell types that lack labeled data. MTTFsite is evaluated on 241 cell type TF pairs and compared with a baseline method without using any multi-task learning model and a fully shared multi-task model that uses only a shared CNN and do not use private CNNs. For cell types with insufficient labeled data, results show that MTTFsite performs better than the baseline method and the fully shared model on more than 89% pairs. For cell types without any labeled data, MTTFsite outperforms the baseline method and the fully shared model by more than 80 and 93% pairs, respectively. A novel gene expression prediction method (called TFChrome) using both MTTFsite and histone modification features is also presented. Results show that TFBSs predicted by MTTFsite alone can achieve good performance. When MTTFsite is combined with histone modification features, a significant 5.7% performance improvement is obtained.

Availability and implementation

The resource and executable code are freely available at http://hlt.hitsz.edu.cn/MTTFsite/ and http://www.hitsz-hlt.com:8080/MTTFsite/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Simultaneous quantification of protein-DNA interactions and transcriptomes in single cells with scDam&T-seq

Protein-DNA interactions are essential for establishing cell type-specific chromatin architecture and gene expression. We recently developed scDam&T-seq, a multi-omics method that can simultaneously quantify protein-DNA interactions and the transcriptome in single cells. The method effectively combines two existing methods: DNA adenine methyltransferase identification (DamID) and CEL-Seq2. DamID works through the tethering of a protein of interest (POI) to the Escherichia coli DNA adenine methyltransferase (Dam). Upon expression of this fusion protein, DNA in proximity to the POI is methylated by Dam and can be selectively digested and amplified. CEL-Seq2, in contrast, makes use of poly-dT primers to reverse transcribe mRNA, followed by linear amplification through in vitro transcription. scDam&T-seq is the first technique capable of providing a combined readout of protein-DNA contact and transcription from single-cell samples. Once suitable cell lines have been established, the protocol can be completed in 5 d, with a throughput of hundreds to thousands of cells. The processing of raw sequencing data takes an additional 1-2 d. Our method can be used to understand the transcriptional changes a cell undergoes upon the DNA binding of a POI. It can be performed in any laboratory with access to FACS, robotic and high-throughput-sequencing facilities.

Peax: Interactive Visual Pattern Search in Sequential Data Using Unsupervised Deep Representation Learning

We present Peax, a novel feature-based technique for interactive visual pattern search in sequential data, like time series or data mapped to a genome sequence. Visually searching for patterns by similarity is often challenging because of the large search space, the visual complexity of patterns, and the user's perception of similarity. For example, in genomics, researchers try to link patterns in multivariate sequential data to cellular or pathogenic processes, but a lack of ground truth and high variance makes automatic pattern detection unreliable. We have developed a convolutional autoencoder for unsupervised representation learning of regions in sequential data that can capture more visual details of complex patterns compared to existing similarity measures. Using this learned representation as features of the sequential data, our accompanying visual query system enables interactive feedback-driven adjustments of the pattern search to adapt to the users' perceived similarity. Using an active learning sampling strategy, Peax collects user-generated binary relevance feedback. This feedback is used to train a model for binary classification, to ultimately find other regions that exhibit patterns similar to the search target. We demonstrate Peax's features through a case study in genomics and report on a user study with eight domain experts to assess the usability and usefulness of Peax. Moreover, we evaluate the effectiveness of the learned feature representation for visual similarity search in two additional user studies. We find that our models retrieve significantly more similar patterns than other commonly used techniques.

Identifying branch-specific positive selection throughout the regulatory genome using an appropriate proxy neutral

BACKGROUND

Adaptive changes in cis-regulatory elements are an essential component of evolution by natural selection. Identifying adaptive and functional noncoding DNA elements throughout the genome is therefore crucial for understanding the relationship between phenotype and genotype.

RESULTS

We used ENCODE annotations to identify appropriate proxy neutral sequences and demonstrate that the conservativeness of the test can be modulated during the filtration of reference alignments. We applied the method to noncoding Human Accelerated Elements as well as open chromatin elements previously identified in 125 human tissues and cell lines to demonstrate its utility. Then, we evaluated the impact of query region length, proxy neutral sequence length, and branch count on test sensitivity and specificity. We found that the length of the query alignment can vary between 150 bp and 1 kb without affecting the estimation of selection, while for the reference alignment, we found that a length of 3 kb is adequate for proper testing. We also simulated sequence alignments under different classes of evolution and validated our ability to distinguish positive selection from relaxation of constraint and neutral evolution. Finally, we re-confirmed that a quarter of all non-coding Human Accelerated Elements are evolving by positive selection.

CONCLUSION

Here, we introduce a method we called adaptiPhy, which adds significant improvements to our earlier method that tests for branch-specific directional selection in noncoding sequences. The motivation for these improvements is to provide a more sensitive and better targeted characterization of directional selection and neutral evolution across the genome.

The how and why of lncRNA function: An innate immune perspective

Next-generation sequencing has provided a more complete picture of the composition of the human transcriptome indicating that much of the "blueprint" is a vastness of poorly understood non-protein-coding transcripts. This includes a newly identified class of genes called long noncoding RNAs (lncRNAs). The lack of sequence conservation for lncRNAs across species meant that their biological importance was initially met with some skepticism. LncRNAs mediate their functions through interactions with proteins, RNA, DNA, or a combination of these. Their functions can often be dictated by their localization, sequence, and/or secondary structure. Here we provide a review of the approaches typically adopted to study the complexity of these genes with an emphasis on recent discoveries within the innate immune field. Finally, we discuss the challenges, as well as the emergence of new technologies that will continue to move this field forward and provide greater insight into the biological importance of this class of genes. This article is part of a Special Issue entitled: ncRNA in control of gene expression edited by Kotb Abdelmohsen.

Genetic Insights Into Smooth Muscle Cell Contributions to Coronary Artery Disease

Coronary artery disease is a complex cardiovascular disease involving an interplay of genetic and environmental influences over a lifetime. Although considerable progress has been made in understanding lifestyle risk factors, genetic factors identified from genome-wide association studies may capture additional hidden risk undetected by traditional clinical tests. These genetic discoveries have highlighted many candidate genes and pathways dysregulated in the vessel wall, including those involving smooth muscle cell phenotypic modulation and injury responses. Here, we summarize experimental evidence for a few genome-wide significant loci supporting their roles in smooth muscle cell biology and disease. We also discuss molecular quantitative trait locus mapping as a powerful discovery and fine-mapping approach applied to smooth muscle cell and coronary artery disease-relevant tissues. We emphasize the critical need for alternative genetic strategies, including cis/trans-regulatory network analysis, genome editing, and perturbations, as well as single-cell sequencing in smooth muscle cell tissues and model organisms, under both normal and disease states. By integrating multiple experimental and analytical modalities, these multidimensional datasets should improve the interpretation of coronary artery disease genome-wide association studies and molecular quantitative trait locus signals and inform candidate targets for therapeutic intervention or risk prediction.

Efficient chromatin profiling of H3K4me3 modification in cotton using CUT&Tag

BACKGROUND

In 2019, Kaya-Okur et al. reported on the cleavage under targets and tagmentation (CUT&Tag) technology for efficient profiling of epigenetically modified DNA fragments. It was used mainly for cultured cell lines and was especially effective for small samples and single cells. This strategy generated high-resolution and low-background-noise chromatin profiling data for epigenomic analysis. CUT&Tag is well suited to be used in plant cells, especially in tissues from which small samples are taken, such as ovules, anthers, and fibers.

RESULTS

Here, we present a CUT&Tag protocol step by step using plant nuclei. In this protocol, we quantified the nuclei that can be used in each CUT&Tag reaction, and compared the efficiency of CUT&Tag with chromatin immunoprecipitation with sequencing (ChIP-seq) in the leaves of cotton. A general workflow for the bioinformatic analysis of CUT&Tag is also provided. Results indicated that, compared with ChIP-seq, the CUT&Tag procedure was faster and showed a higher-resolution, lower-background signal than did ChIP.

CONCLUSION

A CUT&Tag protocol has been refined for plant cells using intact nuclei that have been isolated.

Genomic methods in profiling DNA accessibility and factor localization

Recent advancements in next-generation sequencing technologies and accompanying reductions in cost have led to an explosion of techniques to examine DNA accessibility and protein localization on chromatin genome-wide. Generally, accessible regions of chromatin are permissive for factor binding and are therefore hotspots for regulation of gene expression; conversely, genomic regions that are highly occupied by histone proteins are not permissive for factor binding and are less likely to be active regulatory regions. Identifying regions of differential accessibility can be useful to uncover putative gene regulatory regions, such as enhancers, promoters, and insulators. In addition, DNA-binding proteins, such as transcription factors that preferentially bind certain DNA sequences and histone proteins that form the core of the nucleosome, play essential roles in all DNA-templated processes. Determining the genomic localization of chromatin-bound proteins is therefore essential in determining functional roles, sequence motifs important for factor binding, and regulatory networks controlling gene expression. In this review, we discuss techniques for determining DNA accessibility and nucleosome positioning (DNase-seq, FAIRE-seq, MNase-seq, and ATAC-seq) and techniques for detecting and functionally characterizing chromatin-bound proteins (ChIP-seq, DamID, and CUT&RUN). These methods have been optimized to varying degrees of resolution, specificity, and ease of use. Here, we outline some advantages and disadvantages of these techniques, their general protocols, and a brief discussion of their development. Together, these complimentary approaches have provided an unparalleled view of chromatin architecture and functional gene regulation.

An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome

Simultaneous profiling of transcriptome and chromatin accessibility within single cells is a powerful approach to dissect gene regulatory programs in complex tissues. However, current tools are limited by modest throughput. We now describe an ultra high-throughput method, Paired-seq, for parallel analysis of transcriptome and accessible chromatin in millions of single cells. We demonstrate the utility of Paired-seq for analyzing the dynamic and cell-type-specific gene regulatory programs in complex tissues by applying it to mouse adult cerebral cortex and fetal forebrain. The joint profiles of a large number of single cells allowed us to deconvolute the transcriptome and open chromatin landscapes in the major cell types within these brain tissues, infer putative target genes of candidate enhancers, and reconstruct the trajectory of cellular lineages within the developing forebrain.

Global prediction of chromatin accessibility using small-cell-number and single-cell RNA-seq

Conventional high-throughput genomic technologies for mapping regulatory element activities in bulk samples such as ChIP-seq, DNase-seq and FAIRE-seq cannot analyze samples with small numbers of cells. The recently developed low-input and single-cell regulome mapping technologies such as ATAC-seq and single-cell ATAC-seq (scATAC-seq) allow analyses of small-cell-number and single-cell samples, but their signals remain highly discrete or noisy. Compared to these regulome mapping technologies, transcriptome profiling by RNA-seq is more widely used. Transcriptome data in single-cell and small-cell-number samples are more continuous and often less noisy. Here, we show that one can globally predict chromatin accessibility and infer regulatory element activities using RNA-seq. Genome-wide chromatin accessibility predicted by RNA-seq from 30 cells can offer better accuracy than ATAC-seq from 500 cells. Predictions based on single-cell RNA-seq (scRNA-seq) can more accurately reconstruct bulk chromatin accessibility than using scATAC-seq. Integrating ATAC-seq with predictions from RNA-seq increases the power and value of both methods. Thus, transcriptome-based prediction provides a new tool for decoding gene regulatory circuitry in samples with limited cell numbers.

Going low to reach high: Small-scale ChIP-seq maps new terrain

Chromatin immunoprecipitation (ChIP) enables mapping of specific histone modifications or chromatin-associated factors in the genome and represents a powerful tool in the study of chromatin and genome regulation. Importantly, recent technological advances that couple ChIP with whole-genome high-throughput sequencing (ChIP-seq) now allow the mapping of chromatin factors throughout the genome. However, the requirement for large amounts of ChIP-seq input material has long made it challenging to assess chromatin profiles of cell types only available in limited numbers. For many cell types, it is not feasible to reach high numbers when collecting them as homogeneous cell populations in vivo. Nonetheless, it is an advantage to work with pure cell populations to reach robust biological conclusions. Here, we review (a) how ChIP protocols have been scaled down for use with as little as a few hundred cells; (b) which considerations to be aware of when preparing small-scale ChIP-seq and analyzing data; and (c) the potential of small-scale ChIP-seq datasets for elucidating chromatin dynamics in various biological systems, including some examples such as oocyte maturation and preimplantation embryo development. This article is categorized under: Laboratory Methods and Technologies > Genetic/Genomic Methods Developmental Biology > Developmental Processes in Health and Disease Biological Mechanisms > Cell Fates.

Microfluidic epigenomic mapping technologies for precision medicine

Epigenomic mapping of tissue samples generates critical insights into genome-wide regulations of gene activities and expressions during normal development and disease processes. Epigenomic profiling using a low number of cells produced by patient and mouse samples presents new challenges to biotechnologists. In this review, we first discuss the rationale and premise behind profiling epigenomes for precision medicine. We then examine the existing literature on applying microfluidics to facilitate low-input and high-throughput epigenomic profiling, with emphasis on technologies enabling interfacing with next-generation sequencing. We detail assays on studies of histone modifications, DNA methylation, 3D chromatin structures and non-coding RNAs. Finally, we discuss what the future may hold in terms of method development and translational potential.

FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data

Due to the large numbers of transcription factors (TFs) and cell types, querying binding profiles of all valid TF/cell type pairs is not experimentally feasible. To address this issue, we developed a convolutional-recurrent neural network model, called FactorNet, to computationally impute the missing binding data. FactorNet trains on binding data from reference cell types to make predictions on testing cell types by leveraging a variety of features, including genomic sequences, genome annotations, gene expression, and signal data, such as DNase I cleavage. FactorNet implements several convenient strategies to reduce runtime and memory consumption. By visualizing the neural network models, we can interpret how the model predicts binding. We also investigate the variables that affect cross-cell type accuracy, and offer suggestions to improve upon this field. Our method ranked among the top teams in the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge, achieving first place on six of the 13 final round evaluation TF/cell type pairs, the most of any competing team. The FactorNet source code is publicly available, allowing users to reproduce our methodology from the ENCODE-DREAM Challenge.

Benchmark and integration of resources for the estimation of human transcription factor activities

The prediction of transcription factor (TF) activities from the gene expression of their targets (i.e., TF regulon) is becoming a widely used approach to characterize the functional status of transcriptional regulatory circuits. Several strategies and data sets have been proposed to link the target genes likely regulated by a TF, each one providing a different level of evidence. The most established ones are (1) manually curated repositories, (2) interactions derived from ChIP-seq binding data, (3) in silico prediction of TF binding on gene promoters, and (4) reverse-engineered regulons from large gene expression data sets. However, it is not known how these different sources of regulons affect the TF activity estimations and, thereby, downstream analysis and interpretation. Here we compared the accuracy and biases of these strategies to define human TF regulons by means of their ability to predict changes in TF activities in three reference benchmark data sets. We assembled a collection of TF-target interactions for 1541 human TFs and evaluated how different molecular and regulatory properties of the TFs, such as the DNA-binding domain, specificities, or mode of interaction with the chromatin, affect the predictions of TF activity. We assessed their coverage and found little overlap on the regulons derived from each strategy and better performance by literature-curated information followed by ChIP-seq data. We provide an integrated resource of all TF-target interactions derived through these strategies, with confidence scores, as a resource for enhanced prediction of TF activities.

Unveiling the gene regulatory landscape in diseases through the identification of DNase I-hypersensitive sites

DNase I-hypersensitive sites (DHSs) serve key roles in the regulation of gene transcription as markers of cis-regulatory elements (CREs). Recent advances in next-generation sequencing have enabled the genome-wide location and annotation of DHSs in a variety of cells. Numerous studies have confirmed that DHSs are involved in several processes in cell fate decision and development. DHSs have also been indicated in cancer and inherited diseases as driver distal regulatory elements. Here, the definition of DHSs is reviewed, in addition to high-throughput methods of DHS identification. Furthermore, the function of DHSs in gene expression is probed. The roles of DHSs in disease occurrence are also reviewed and discussed. Concomitant advances in the identification of essential roles of DHSs will assist in disclosing the underlying molecular mechanisms, supplementing gene transcription and enlarging the molecular basis of DHS-related bioprocesses, phenotypes, distinct traits and diseases.

Genome-wide enhancer annotations differ significantly in genomic distribution, evolution, and function

Background

Non-coding gene regulatory enhancers are essential to transcription in mammalian cells. As a result, a large variety of experimental and computational strategies have been developed to identify cis-regulatory enhancer sequences. Given the differences in the biological signals assayed, some variation in the enhancers identified by different methods is expected; however, the concordance of enhancers identified by different methods has not been comprehensively evaluated. This is critically needed, since in practice, most studies consider enhancers identified by only a single method. Here, we compare enhancer sets from eleven representative strategies in four biological contexts.

Results

All sets we evaluated overlap significantly more than expected by chance; however, there is significant dissimilarity in their genomic, evolutionary, and functional characteristics, both at the element and base-pair level, within each context. The disagreement is sufficient to influence interpretation of candidate SNPs from GWAS studies, and to lead to disparate conclusions about enhancer and disease mechanisms. Most regions identified as enhancers are supported by only one method, and we find limited evidence that regions identified by multiple methods are better candidates than those identified by a single method. As a result, we cannot recommend the use of any single enhancer identification strategy in all settings.

Conclusions

Our results highlight the inherent complexity of enhancer biology and identify an important challenge to mapping the genetic architecture of complex disease. Greater appreciation of how the diverse enhancer identification strategies in use today relate to the dynamic activity of gene regulatory regions is needed to enable robust and reproducible results.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5779-x) contains supplementary material, which is available to authorized users.

Cold stress induces enhanced chromatin accessibility and bivalent histone modifications H3K4me3 and H3K27me3 of active genes in potato

BACKGROUND

Cold stress can greatly affect plant growth and development. Plants have developed special systems to respond to and tolerate cold stress. While plant scientists have discovered numerous genes involved in responses to cold stress, few studies have been dedicated to investigation of genome-wide chromatin dynamics induced by cold or other abiotic stresses.

RESULTS

Genomic regions containing active cis-regulatory DNA elements can be identified as DNase I hypersensitive sites (DHSs). We develop high-resolution DHS maps in potato (Solanum tuberosum) using chromatin isolated from tubers stored under room (22 °C) and cold (4 °C) conditions. We find that cold stress induces a large number of DHSs enriched in genic regions which are frequently associated with differential gene expression in response to temperature variation. Surprisingly, active genes show enhanced chromatin accessibility upon cold stress. A large number of active genes in cold-stored tubers are associated with the bivalent H3K4me3-H3K27me3 mark in gene body regions. Interestingly, upregulated genes associated with the bivalent mark are involved in stress response, whereas downregulated genes with the bivalent mark are involved in developmental processes. In addition, we observe that the bivalent mark-associated genes are more accessible than others upon cold stress.

CONCLUSIONS

Collectively, our results suggest that cold stress induces enhanced chromatin accessibility and bivalent histone modifications of active genes. We hypothesize that in cold-stored tubers, the bivalent H3K4me3-H3K27me3 mark represents a distinct chromatin environment with greater accessibility, which may facilitate the access of regulatory proteins required for gene upregulation or downregulation in response to cold stress.

Translating GWAS in rheumatic disease: approaches to establishing mechanism and function for genetic associations with ankylosing spondylitis

Ankylosing spondylitis (AS) is a highly heritable chronic inflammatory arthritis characterized by osteoproliferation, fusion of affected joints and systemic manifestations. Many disease associations for AS have been reported through genome-wide association studies; however, identifying modulated genes and functional mechanism remains challenging. This review summarizes current genetic associations involving AS and describes strategic approaches for functional follow-up of disease-associated variants. Fine mapping using methods leveraging Bayesian approaches are outlined. Evidence highlighting the importance of context specificity for regulatory variants is reviewed, noting current evidence in AS for the relevant cell and tissue type to conduct such analyses. Technological advances for understanding the regulatory landscape within which functional variants may act are discussed using exemplars. Approaches include defining regulatory elements based on chromatin accessibility, effects of variants on genes at a distance through evidence of physical interactions (chromatin conformation capture), expression quantitative trait loci mapping and single-cell methodologies. Opportunities for mechanistic studies to investigate the function of specific variants, regulatory elements and genes enabled by genome editing using clustered regularly interspaced short palindromic repeats/Cas9 are also described. Further progress in our understanding of the genetics of AS through functional genomic and epigenomic approaches offers new opportunities to understand mechanism and develop innovative treatments.

iDHS-DMCAC: identifying DNase I hypersensitive sites with balanced dinucleotide-based detrending moving-average cross-correlation coefficient

DNase I hypersensitive sites (DHSs) are associated with regulatory DNA elements, so their good understanding is significant for both the biomedical research and the discovery of new drugs. Traditional experimental methods are laborious, time consuming and an inaccurately task to detect DHSs. More importantly, with the avalanche of genome sequences in the postgenomic age, it is highly essential to develop cost-effective computational approaches to identify DHSs. In this paper, we develop a statistical feature extraction model using the detrended moving-average cross-correlation (DMCA) coefficient descriptor based on dinucleotide property matrix generated by the 15 DNA dinucleotide properties, and this model is named iDHS-DMCAC. A 105-dimensional feature vector is constructed for a certain window on the two class imbalanced benchmark datasets, with over-sampling and support vector machine algorithms. Rigorous cross-validations indicate that our predictor remarkably outperforms the existing models in both accuracy and stability. We anticipate that iDHS-DMCAC will become a very useful high throughput tool, or at the very least, a complementary tool to the existing methods of identifying DNase I hypersensitive sites. The datasets and source codes of the proposed model are freely available at https://github.com/shengli0201/Datasets .

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.

Identification of transcription factor binding sites using ATAC-seq

Transposase-Accessible Chromatin followed by sequencing (ATAC-seq) is a simple protocol for detection of open chromatin. Computational footprinting, the search for regions with depletion of cleavage events due to transcription factor binding, is poorly understood for ATAC-seq. We propose the first footprinting method considering ATAC-seq protocol artifacts. HINT-ATAC uses a position dependency model to learn the cleavage preferences of the transposase. We observe strand-specific cleavage patterns around transcription factor binding sites, which are determined by local nucleosome architecture. By incorporating all these biases, HINT-ATAC is able to significantly outperform competing methods in the prediction of transcription factor binding sites with footprints.

Anchor: trans-cell type prediction of transcription factor binding sites

The ENCyclopedia of DNA Elements (ENCODE) consortium has generated transcription factor (TF) binding ChIP-seq data covering hundreds of TF proteins and cell types; however, due to limits on time and resources, only a small fraction of all possible TF-cell type pairs have been profiled. One solution is to build machine learning models trained on currently available epigenomic data sets that can be applied to the remaining missing pairs. A major challenge is that TF binding sites are cell-type-specific, which can be attributed to cellular contexts such as chromatin accessibility. Meanwhile, indirect TF-DNA binding and interactions between TFs complicate this regulatory process. Technical issues such as sequencing biases and batch effects render the prediction task even more challenging. Many pioneering efforts have been made to predict TF binding profiles based on DNA sequence and DNase-seq footprints, but to what extent a model can be generalized to completely untested cell conditions remains unknown. In this study, we describe our first place solution to the 2017 ENCODE-DREAM in vivo TF binding site prediction challenge. By carefully addressing multisource biases and information imbalance across cell types, we created a pipeline that significantly outperforms the current state-of-the-art methods. The proposed method is sufficiently complex enough to model nonlinear interactions between TF binding motifs and chromatin accessibility information up to 1500 bp from the genomic region of interest.

Enhancer Associated Long Non-coding RNA Transcription and Gene Regulation in Experimental Models of Rickettsial Infection

Recent discovery that much of the mammalian genome does not encode protein-coding genes (PCGs) has brought widespread attention to long noncoding RNAs (lncRNAs) as a novel layer of biological regulation. Enhancer lnc (elnc) RNAs from the enhancer regions of the genome carry the capacity to regulate PCGs in cis or in trans. Spotted fever rickettsioses represent the consequence of host infection with Gram-negative, obligate intracellular bacteria in the Genus Rickettsia. Despite being implicated in the pathways of infection and inflammation, the roles of lncRNAs in host response to Rickettsia species have remained a mystery. We have profiled the expression of host lncRNAs during infection of susceptible mice with R. conorii as a model closely mimicking the pathogenesis of human spotted fever rickettsioses. RNA sequencing on the lungs of infected hosts yielded reads mapping to 74,964 non-coding RNAs, 206 and 277 of which were determined to be significantly up- and down-regulated, respectively, in comparison to uninfected controls. Following removal of short non-coding RNAs and ambiguous transcripts, remaining transcripts underwent in-depth analysis of mouse lung epigenetic signatures H3K4Me1 and H3K4Me3, active transcript markers (POLR2A, p300, CTCF), and DNaseI hypersensitivity sites to identify two potentially active and highly up-regulated elncRNAs NONMMUT013718 and NONMMUT024103. Using Hi-3C sequencing resource, we further determined that genomic loci of NONMMUT013718 and NONMMUT024103 might interact with and regulate the expression of nearby PCGs, namely Id2 (inhibitor of DNA binding 2) and Apol10b (apolipoprotein 10b), respectively. Heterologous reporter assays confirmed the activity of elncRNAs as the inducers of their predicted PCGs. In the lungs of infected mice, expression of both elncRNAs and their targets was significantly higher than mock-infected controls. Induced expression of NONMMUT013718/Id2 in murine macrophages and NONMMUT024103/Apol10b in endothelial cells was also clearly evident during R. conorii infection in vitro. Finally, shRNA mediated knock-down of NONMMUT013718 and NONMMUT024103 elncRNAs resulted in reduced expression of endogenous Id2 and Apl10b, demonstrating the regulatory roles of these elncRNAs on their target PCGs. Our results provide very first experimental evidence suggesting altered expression of pulmonary lncRNAs and elncRNA-mediated regulation of PCGs involved in immunity and during host interactions with pathogenic rickettsiae.

Genome-wide DNA methylation analysis reveals a prognostic classifier for non-metastatic colorectal cancer (ProMCol classifier)

OBJECTIVE

Pathological staging used for the prediction of patient survival in colorectal cancer (CRC) provides only limited information.

DESIGN

Here, a genome-wide study of DNA methylation was conducted for two cohorts of patients with non-metastatic CRC (screening cohort (n=572) and validation cohort (n=274)). A variable screening for prognostic CpG sites was performed in the screening cohort using marginal testing based on a Cox model and subsequent adjustment of the p-values via independent hypothesis weighting using the methylation difference between 34 pairs of tumour and normal mucosa tissue as auxiliary covariate. From the 1000 CpG sites with the smallest adjusted p-value, 20 CpG sites with the smallest Brier score for overall survival (OS) were selected. Applying principal component analysis, we derived a prognostic methylation-based classifier for patients with non-metastatic CRC (ProMCol classifier).

RESULTS

This classifier was associated with OS in the screening (HR 0.51, 95% CI 0.41 to 0.63, p=6.2E-10) and the validation cohort (HR 0.61, 95% CI 0.45 to 0.82, p=0.001). The independent validation of the ProMCol classifier revealed a reduction of the prediction error for 3-year OS from 0.127, calculated only with standard clinical variables, to 0.120 combining the clinical variables with the classifier and for 4-year OS from 0.153 to 0.140. All results were confirmed for disease-specific survival.

CONCLUSION

The ProMCol classifier could improve the prognostic accuracy for patients with non-metastatic CRC.

Deep learning for DNase I hypersensitive sites identification

Background

The DNase I hypersensitive sites (DHSs) are associated with the cis-regulatory DNA elements. An efficient method of identifying DHSs can enhance the understanding on the accessibility of chromatin. Despite a multitude of resources available on line including experimental datasets and computational tools, the complex language of DHSs remains incompletely understood.

Methods

Here, we address this challenge using an approach based on a state-of-the-art machine learning method. We present a novel convolutional neural network (CNN) which combined Inception like networks with a gating mechanism for the response of multiple patterns and longterm association in DNA sequences to predict multi-scale DHSs in Arabidopsis, rice and Homo sapiens.

Results

Our method obtains 0.961 area under curve (AUC) on Arabidopsis, 0.969 AUC on rice and 0.918 AUC on Homo sapiens.

Conclusions

Our method provides an efficient and accurate way to identify multi-scale DHSs sequences by deep learning.

Genome-wide analysis of chromatin accessibility using ATAC-seq

Programs of gene transcription are controlled by cis-acting DNA elements, including enhancers, silencers, and promoters. Local accessibility of chromatin has proven to be a highly informative structural feature for identifying such regulatory elements, which tend to be relatively open due to their interactions with proteins. Recently, ATAC-seq (assay for transposase-accessible chromatin using sequencing) has emerged as one of the most powerful approaches for genome-wide chromatin accessibility profiling. This method assesses DNA accessibility using hyperactive Tn5 transposase, which simultaneously cuts DNA and inserts sequencing adaptors, preferentially in regions of open chromatin. ATAC-seq is a relatively simple procedure which can be applied to only a few thousand cells. It is well-suited to developing embryos of sea urchins and other echinoderms, which are a prominent experimental model for understanding the genomic control of animal development. In this chapter, we present a protocol for applying ATAC-seq to embryonic cells of sea urchins.

ChIP-Atlas: a data-mining suite powered by full integration of public ChIP-seq data

We have fully integrated public chromatin chromatin immunoprecipitation sequencing (ChIP-seq) and DNase-seq data (n > 70,000) derived from six representative model organisms (human, mouse, rat, fruit fly, nematode, and budding yeast), and have devised a data-mining platform-designated ChIP-Atlas (http://chip-atlas.org). ChIP-Atlas is able to show alignment and peak-call results for all public ChIP-seq and DNase-seq data archived in the NCBI Sequence Read Archive (SRA), which encompasses data derived from GEO, ArrayExpress, DDBJ, ENCODE, Roadmap Epigenomics, and the scientific literature. All peak-call data are integrated to visualize multiple histone modifications and binding sites of transcriptional regulators (TRs) at given genomic loci. The integrated data can be further analyzed to show TR-gene and TR-TR interactions, as well as to examine enrichment of protein binding for given multiple genomic coordinates or gene names. ChIP-Atlas is superior to other platforms in terms of data number and functionality for data mining across thousands of ChIP-seq experiments, and it provides insight into gene regulatory networks and epigenetic mechanisms.

The enhancer RNA lnc-SLC4A1-1 epigenetically regulates unexplained recurrent pregnancy loss (URPL) by activating CXCL8 and NF-kB pathway

Background

Enhancer RNAs (eRNAs) are a group of lncRNAs transcribed from enhancers, whose regulatory effects on gene expression are an emerging area of interest. However, the role of eRNAs in regulating trophoblast cells and unexplained recurrent pregnancy loss (URPL) remains elusive.

Methods

We profiled eRNAs in villi from URPL patients and matched controls by RNA-seq. Functions of URPL-related eRNAs were further investigated in vitro.

Results

We identified lnc-SLC4A1-1, which was transcribed from an active enhancer marked with H3K27ac and H3K4me1 and so-called eRNA, highly expressed in URPL patients. Gain-of-function experiments indicated that lnc-SLC4A1-1 facilitated trophoblast cell migration and apoptosis. Mechanistically, as an eRNA, lnc-SLC4A1-1 was retained in the nuclei and recruited transcription factor NF-κB to bind to CXCL8, resulting in increased H3K27ac in the CXCL8 promoter and subsequent elevation of CXCL8 expression. Activation of CXCL8 exacerbated inflammatory reactions in trophoblast cells by inducing TNF-α and IL-1β, which could be blocked by an antagonist of lnc-SLC4A1-1.

Interpretation

These findings indicate that an eRNA, lnc-SLC4A1-1, alters trophoblast function via activation of immune responses and by regulating the NF-κB/CXCL8 axis. Our study provides new insights in understanding lncRNA/eRNA function in pathological pregnancy, potentially informing on therapeutic strategies for URPL.

Fund

National Natural Science Foundation of China, Natural Science Foundation of Jiangsu Province, National Key Research and Development Program, the Priority Academic Program for the Development of Jiangsu Higher Education Institutions.

iPro70-FMWin: identifying Sigma70 promoters using multiple windowing and minimal features

In bacterial DNA, there are specific sequences of nucleotides called promoters that can bind to the RNA polymerase. Sigma70 ([Formula: see text]) is one of the most important promoter sequences due to its presence in most of the DNA regulatory functions. In this paper, we identify the most effective and optimal sequence-based features for prediction of [Formula: see text] promoter sequences in a bacterial genome. We used both short-range and long-range DNA sequences in our proposed method. A very small number of effective features are selected from a large number of the extracted features using multi-window of different sizes within the DNA sequences. We call our prediction method iPro70-FMWin and made it freely accessible online via a web application established at http://ipro70.pythonanywhere.com/server for the sake of convenience of the researchers. We have tested our method using a standard benchmark dataset. In the experiments, iPro70-FMWin has achieved an area under the curve of the receiver operating characteristic and accuracy of 0.959 and 90.57%, respectively, which significantly outperforms the state-of-the-art predictors.

Gene editing in the context of an increasingly complex genome

The reporting of the first draft of the human genome in 2000 brought with it much hope for the future in what was felt as a paradigm shift toward improved health outcomes. Indeed, we have now mapped the majority of variation across human populations with landmark projects such as 1000 Genomes; in cancer, we have catalogued mutations across the primary carcinomas; whilst, for other diseases, we have identified the genetic variants with strongest association. Despite this, we are still awaiting the genetic revolution in healthcare to materialise and translate itself into the health benefits for which we had hoped. A major problem we face relates to our underestimation of the complexity of the genome, and that of biological mechanisms, generally. Fixation on DNA sequence alone and a 'rigid' mode of thinking about the genome has meant that the folding and structure of the DNA molecule -and how these relate to regulation- have been underappreciated. Projects like ENCODE have additionally taught us that regulation at the level of RNA is just as important as that at the spatiotemporal level of chromatin.In this review, we chart the course of the major advances in the biomedical sciences in the era pre- and post the release of the first draft sequence of the human genome, taking a focus on technology and how its development has influenced these. We additionally focus on gene editing via CRISPR/Cas9 as a key technique, in particular its use in the context of complex biological mechanisms. Our aim is to shift the mode of thinking about the genome to that which encompasses a greater appreciation of the folding of the DNA molecule, DNA- RNA/protein interactions, and how these regulate expression and elaborate disease mechanisms.Through the composition of our work, we recognise that technological improvement is conducive to a greater understanding of biological processes and life within the cell. We believe we now have the technology at our disposal that permits a better understanding of disease mechanisms, achievable through integrative data analyses. Finally, only with greater understanding of disease mechanisms can techniques such as gene editing be faithfully conducted.

Pan-Cancer Analysis Reveals Differential Susceptibility of Bidirectional Gene Promoters to DNA Methylation, Somatic Mutations, and Copy Number Alterations

Bidirectional gene promoters affect the transcription of two genes, leading to the hypothesis that they should exhibit protection against genetic or epigenetic changes in cancer. Therefore, they provide an excellent opportunity to learn about promoter susceptibility to somatic alteration in tumors. We tested this hypothesis using data from genome-scale DNA methylation (14 cancer types), simple somatic mutation (10 cancer types), and copy number variation profiling (14 cancer types). For DNA methylation, the difference in rank differential methylation between tumor and tumor-adjacent normal matched samples based on promoter type was tested by the Wilcoxon rank sum test. Logistic regression was used to compare differences in simple somatic mutations. For copy number alteration, a mixed effects logistic regression model was used. The change in methylation between non-diseased tissues and their tumor counterparts was significantly greater in single compared to bidirectional promoters across all 14 cancer types examined. Similarly, the extent of copy number alteration was greater in single gene compared to bidirectional promoters for all 14 cancer types. Furthermore, among 10 cancer types with available simple somatic mutation data, bidirectional promoters were slightly more susceptible. These results suggest that selective pressures related with specific functional impacts during carcinogenesis drive the susceptibility of promoter regions to somatic alteration.

Prenatal immune activation alters the adult neural epigenome but can be partly stabilised by a n-3 polyunsaturated fatty acid diet

An unstable epigenome is implicated in the pathophysiology of neurodevelopmental disorders such as schizophrenia and autism. This is important because the epigenome is potentially modifiable. We have previously reported that adult offspring exposed to maternal immune activation (MIA) prenatally have significant global DNA hypomethylation in the hypothalamus. However, what genes had altered methylation state, their functional effects on gene expression and whether these changes can be moderated, have not been addressed. In this study, we used next-generation sequencing (NGS) for methylome profiling in a MIA rodent model of neurodevelopmental disorders. We assessed whether differentially methylated regions (DMRs) affected the chromatin state by mapping known DNase I hypersensitivity sites (DHSs), and selected overlapping genes to confirm a functional effect of MIA on gene expression using qPCR. Finally, we tested whether methylation differences elicited by MIA could be limited by post-natal dietary (omega) n-3 polyunsaturated fatty acid (PUFA) supplementation. These experiments were conducted using hypothalamic brain tissue from 12-week-old offspring of mice injected with viral analogue PolyI:C on gestation day 9 of pregnancy or saline on gestation day 9. Half of the animals from each group were fed a diet enriched with n-3 PUFA from weaning (MIA group, n = 12 units, n = 39 mice; Control group, n = 12 units, n = 38 mice). The results confirmed our previous finding that adult offspring exposed to MIA prenatally had significant global DNA hypomethylation. Furthermore, genes linked to synaptic plasticity were over-represented among differentially methylated genes following MIA. More than 80% of MIA-induced hypomethylated sites, including those affecting chromatin state and MECP2 binding, were stabilised by the n-3 PUFA intervention. MIA resulted in increased expression of two of the 'top five' genes identified from an integrated analysis of DMRs, DHSs and MECP2 binding sites, namely Abat (t = 2.46, p < 0.02) and Gnas9 (t = 2.96, p < 0.01), although these changes were not stabilised by dietary intervention. Thus, prenatal MIA exposure impacts upon the epigenomic regulation of gene pathways linked to neurodevelopmental conditions; and many of the changes can be attenuated by a low-cost dietary intervention.

Towards a map of cis-regulatory sequences in the human genome

Accumulating evidence indicates that transcription factor (TF) binding sites, or cis-regulatory elements (CREs), and their clusters termed cis-regulatory modules (CRMs) play a more important role than do gene-coding sequences in specifying complex traits in humans, including the susceptibility to common complex diseases. To fully characterize their roles in deriving the complex traits/diseases, it is necessary to annotate all CREs and CRMs encoded in the human genome. However, the current annotations of CREs and CRMs in the human genome are still very limited and mostly coarse-grained, as they often lack the detailed information of CREs in CRMs. Here, we integrated 620 TF ChIP-seq datasets produced by the ENCODE project for 168 TFs in 79 different cell/tissue types and predicted an unprecedentedly completely map of CREs in CRMs in the human genome at single nucleotide resolution. The map includes 305 912 CRMs containing a total of 1 178 913 CREs belonging to 736 unique TF binding motifs. The predicted CREs and CRMs tend to be subject to either purifying selection or positive selection, thus are likely to be functional. Based on the results, we also examined the status of available ChIP-seq datasets for predicting the entire regulatory genome of humans.

Single-cell regulome data analysis by SCRAT

Summary

Emerging single-cell technologies (e.g. single-cell ATAC-seq, DNase-seq or ChIP-seq) have made it possible to assay regulome of individual cells. Single-cell regulome data are highly sparse and discrete. Analyzing such data is challenging. User-friendly software tools are still lacking. We present SCRAT, a Single-Cell Regulome Analysis Toolbox with a graphical user interface, for studying cell heterogeneity using single-cell regulome data. SCRAT can be used to conveniently summarize regulatory activities according to different features (e.g. gene sets, transcription factor binding motif sites, etc.). Using these features, users can identify cell subpopulations in a heterogeneous biological sample, infer cell identities of each subpopulation, and discover distinguishing features such as gene sets and transcription factors that show different activities among subpopulations.

Availability and implementation

SCRAT is freely available at https://zhiji.shinyapps.io/scrat as an online web service and at https://github.com/zji90/SCRAT as an R package.

Contact

hji@jhu.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding

Motivation

Experimental techniques for measuring chromatin accessibility are expensive and time consuming, appealing for the development of computational approaches to predict open chromatin regions from DNA sequences. Along this direction, existing methods fall into two classes: one based on handcrafted k-mer features and the other based on convolutional neural networks. Although both categories have shown good performance in specific applications thus far, there still lacks a comprehensive framework to integrate useful k-mer co-occurrence information with recent advances in deep learning.

Results

We fill this gap by addressing the problem of chromatin accessibility prediction with a convolutional Long Short-Term Memory (LSTM) network with k-mer embedding. We first split DNA sequences into k-mers and pre-train k-mer embedding vectors based on the co-occurrence matrix of k-mers by using an unsupervised representation learning approach. We then construct a supervised deep learning architecture comprised of an embedding layer, three convolutional layers and a Bidirectional LSTM (BLSTM) layer for feature learning and classification. We demonstrate that our method gains high-quality fixed-length features from variable-length sequences and consistently outperforms baseline methods. We show that k-mer embedding can effectively enhance model performance by exploring different embedding strategies. We also prove the efficacy of both the convolution and the BLSTM layers by comparing two variations of the network architecture. We confirm the robustness of our model to hyper-parameters by performing sensitivity analysis. We hope our method can eventually reinforce our understanding of employing deep learning in genomic studies and shed light on research regarding mechanisms of chromatin accessibility.

Availability and implementation

The source code can be downloaded from https://github.com/minxueric/ismb2017_lstm.

Supplementary information

Supplementary materials are available at Bioinformatics online.

Denoising genome-wide histone ChIP-seq with convolutional neural networks

Motivation

Chromatin immune-precipitation sequencing (ChIP-seq) experiments are commonly used to obtain genome-wide profiles of histone modifications associated with different types of functional genomic elements. However, the quality of histone ChIP-seq data is affected by many experimental parameters such as the amount of input DNA, antibody specificity, ChIP enrichment and sequencing depth. Making accurate inferences from chromatin profiling experiments that involve diverse experimental parameters is challenging.

Results

We introduce a convolutional denoising algorithm, Coda, that uses convolutional neural networks to learn a mapping from suboptimal to high-quality histone ChIP-seq data. This overcomes various sources of noise and variability, substantially enhancing and recovering signal when applied to low-quality chromatin profiling datasets across individuals, cell types and species. Our method has the potential to improve data quality at reduced costs. More broadly, this approach-using a high-dimensional discriminative model to encode a generative noise process-is generally applicable to other biological domains where it is easy to generate noisy data but difficult to analytically characterize the noise or underlying data distribution.

Availability and implementation

https://github.com/kundajelab/coda .

Contact

akundaje@stanford.edu.

Epigenetic control of CD8+ T cell differentiation

Upon stimulation, small numbers of naive CD8+ T cells proliferate and differentiate into a variety of memory and effector cell types. CD8+ T cells can persist for years and kill tumour cells and virally infected cells. The functional and phenotypic changes that occur during CD8+ T cell differentiation are well characterized, but the epigenetic states that underlie these changes are incompletely understood. Here, we review the epigenetic processes that direct CD8+ T cell differentiation and function. We focus on epigenetic modification of DNA and associated histones at genes and their regulatory elements. We also describe structural changes in chromatin organization that affect gene expression. Finally, we examine the translational potential of epigenetic interventions to improve CD8+ T cell function in individuals with chronic infections and cancer.

Targeted in situ genome-wide profiling with high efficiency for low cell numbers

Cleavage under targets and release using nuclease (CUT&RUN) is an epigenomic profiling strategy in which antibody-targeted controlled cleavage by micrococcal nuclease releases specific protein-DNA complexes into the supernatant for paired-end DNA sequencing. As only the targeted fragments enter into solution, and the vast majority of DNA is left behind, CUT&RUN has exceptionally low background levels. CUT&RUN outperforms the most widely used chromatin immunoprecipitation (ChIP) protocols in resolution, signal-to-noise ratio and depth of sequencing required. In contrast to ChIP, CUT&RUN is free of solubility and DNA accessibility artifacts and has been used to profile insoluble chromatin and to detect long-range 3D contacts without cross-linking. Here, we present an improved CUT&RUN protocol that does not require isolation of nuclei and provides high-quality data when starting with only 100 cells for a histone modification and 1,000 cells for a transcription factor. From cells to purified DNA, CUT&RUN requires less than a day at the laboratory bench and requires no specialized skills.

Global analysis of primary mesenchyme cell cis-regulatory modules by chromatin accessibility profiling

Background

The developmental gene regulatory network (GRN) that underlies skeletogenesis in sea urchins and other echinoderms is a paradigm of GRN structure, function, and evolution. This transcriptional network is deployed selectively in skeleton-forming primary mesenchyme cells (PMCs) of the early embryo. To advance our understanding of this model developmental GRN, we used genome-wide chromatin accessibility profiling to identify and characterize PMC cis-regulatory modules (CRMs).

Results

ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) analysis of purified PMCs provided a global picture of chromatin accessibility in these cells. We used both ATAC-seq and DNase-seq (DNase I hypersensitive site sequencing) to identify > 3000 sites that exhibited increased accessibility in PMCs relative to other embryonic cell lineages, and provide both computational and experimental evidence that a large fraction of these sites represent bona fide skeletogenic CRMs. Putative PMC CRMs were preferentially located near genes differentially expressed by PMCs and consensus binding sites for two key transcription factors in the PMC GRN, Alx1 and Ets1, were enriched in these CRMs. Moreover, a high proportion of candidate CRMs drove reporter gene expression specifically in PMCs in transgenic embryos. Surprisingly, we found that PMC CRMs were partially open in other embryonic lineages and exhibited hyperaccessibility as early as the 128-cell stage.

Conclusions

Our work provides a comprehensive picture of chromatin accessibility in an early embryonic cell lineage. By identifying thousands of candidate PMC CRMs, we significantly enhance the utility of the sea urchin skeletogenic network as a general model of GRN architecture and evolution. Our work also shows that differential chromatin accessibility, which has been used for the high-throughput identification of enhancers in differentiated cell types, is a powerful approach for the identification of CRMs in early embryonic cells. Lastly, we conclude that in the sea urchin embryo, CRMs that control the cell type-specific expression of effector genes are hyperaccessible several hours in advance of gene activation.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4542-z) contains supplementary material, which is available to authorized users.

CATaDa reveals global remodelling of chromatin accessibility during stem cell differentiation in vivo

During development eukaryotic gene expression is coordinated by dynamic changes in chromatin structure. Measurements of accessible chromatin are used extensively to identify genomic regulatory elements. Whilst chromatin landscapes of pluripotent stem cells are well characterised, chromatin accessibility changes in the development of somatic lineages are not well defined. Here we show that cell-specific chromatin accessibility data can be produced via ectopic expression of E. coli Dam methylase in vivo, without the requirement for cell-sorting (CATaDa). We have profiled chromatin accessibility in individual cell-types of Drosophila neural and midgut lineages. Functional cell-type-specific enhancers were identified, as well as novel motifs enriched at different stages of development. Finally, we show global changes in the accessibility of chromatin between stem-cells and their differentiated progeny. Our results demonstrate the dynamic nature of chromatin accessibility in somatic tissues during stem cell differentiation and provide a novel approach to understanding gene regulatory mechanisms underlying development.

For an embryo to successfully develop into an adult animal, specific genes must act in different types of cells. Though all the cells have the same genes encoded within their DNA, looking at the way that the DNA is packaged can indicate which parts of the DNA are important for that particular cell type. If regions of DNA are “open” one can infer that those regions are actively involved in gene regulation, whereas “closed” regions are considered less important.

It is currently difficult to determine which parts of the DNA are open within an individual cell type in a complex organ, such as the brain. Existing methods require the cells to be physically isolated from the tissue, which is technically challenging.

To overcome this issue, Aughey et al. have now developed a method that does not require isolation of the cells. The new technique involves using genetic engineering to introduce an enzyme called Dam into specific cell types in living fruit flies. This enzyme adds a chemical label on regions of open DNA, which can then be detected. Aughey et al. tested this technique on various cells of the developing brain and gut, and were able to see differences in the openness of DNA that corresponded to the action of genes that are important in each cell type. The data also contain trends that help to understand the role of open DNA in development. For example, mature cells were shown to overall have less open DNA than the stem cells that divide to generate them.

Aughey et al. hope their new technique will be of use to other researchers working with either fruit flies or mammalian tissues. The knowledge that scientists will gain from identifying how open DNA contributes to gene regulation, in both healthy and diseased tissues, will further our understanding of human development and the biology of diseases such as cancer.