An integrative view of the regulatory and transcriptional landscapes in mouse hematopoiesis

Activating mutations remodel the chromatin accessibility landscape to drive distinct regulatory networks in KMT2A-rearranged acute leukemia

Activating FLT3 and RAS mutations commonly occur in leukemia with KMT2A-gene rearrangements (KMT2A-r). However, how these mutations cooperate with the KMT2A-r to remodel the epigenetic landscape is unknown. Using a retroviral acute myeloid leukemia (AML) mouse model driven by KMT2A::MLLT3, we show that FLT3 ITD , FLT3 N676K , and NRAS G12D remodeled the chromatin accessibility landscape and associated transcriptional networks. Although the activating mutations shared a common core of chromatin changes, each mutation exhibits unique profiles with most opened peaks associating with enhancers in intronic or intergenic regions. Specifically, FLT3 N676K and NRAS G12D rewired similar chromatin and transcriptional networks, distinct from those mediated by FLT3 ITD . Motif analysis uncovered a role for the AP-1 family of transcription factors in KMT2A::MLLT3 leukemia with FLT3 N676K and NRAS G12D , whereas Runx1 and Stat5a/Stat5b were active in the presence of FLT3 ITD . Furthermore, transcriptional programs linked to immune cell regulation were activated in KMT2A-r AML expressing NRAS G12D or FLT3 N676K , and the expression of NKG2D-ligands on KMT2A-r cells rendered them sensitive to CAR T cell-mediated killing. Human KMT2A-r AML cells could be pharmacologically sensitized to NKG2D-CAR T cells by treatment with the histone deacetylase inhibitor LBH589 (panobinostat) which caused upregulation of NKG2D-ligand levels. Co-treatment with LBH589 and NKG2D-CAR T cells enabled robust AML cell killing, and the strongest effect was observed for cells expressing NRAS G12D . Finally, the results were validated and extended to acute leukemia in infancy. Combined, activating mutations induced mutation-specific changes in the epigenetic landscape, leading to changes in transcriptional programs orchestrated by specific transcription factor networks.

Interspecies regulatory landscapes and elements revealed by novel joint systematic integration of human and mouse blood cell epigenomes

Knowledge of locations and activities of cis-regulatory elements (CREs) is needed to decipher basic mechanisms of gene regulation and to understand the impact of genetic variants on complex traits. Previous studies identified candidate CREs (cCREs) using epigenetic features in one species, making comparisons difficult between species. In contrast, we conducted an interspecies study defining epigenetic states and identifying cCREs in blood cell types to generate regulatory maps that are comparable between species, using integrative modeling of eight epigenetic features jointly in human and mouse in our Validated Systematic Integration (VISION) Project. The resulting catalogs of cCREs are useful resources for further studies of gene regulation in blood cells, indicated by high overlap with known functional elements and strong enrichment for human genetic variants associated with blood cell phenotypes. The contribution of each epigenetic state in cCREs to gene regulation, inferred from a multivariate regression, was used to estimate epigenetic state regulatory potential (esRP) scores for each cCRE in each cell type, which were used to categorize dynamic changes in cCREs. Groups of cCREs displaying similar patterns of regulatory activity in human and mouse cell types, obtained by joint clustering on esRP scores, harbor distinctive transcription factor binding motifs that are similar between species. An interspecies comparison of cCREs revealed both conserved and species-specific patterns of epigenetic evolution. Finally, we show that comparisons of the epigenetic landscape between species can reveal elements with similar roles in regulation, even in the absence of genomic sequence alignment.

Parallel Evolution at the Regulatory Base-Pair Level Contributes to Mammalian Interspecific Differences in Polygenic Traits

Parallel evolution occurs when distinct lineages with similar ancestral states converge on a new phenotype. Parallel evolution has been well documented at the organ, gene pathway, and amino acid sequence level but in theory, it can also occur at individual nucleotides within noncoding regions. To examine the role of parallel evolution in shaping the biology of mammalian complex traits, we used data on single-nucleotide polymorphisms (SNPs) influencing human intraspecific variation to predict trait values in other species for 11 complex traits. We found that the alleles at SNP positions associated with human intraspecific height and red blood cell (RBC) count variation are associated with interspecific variation in the corresponding traits across mammals. These associations hold for deeper branches of mammalian evolution as well as between strains of collaborative cross mice. While variation in RBC count between primates uses both ancient and more recently evolved genomic regions, we found that only primate-specific elements were correlated with primate body size. We show that the SNP positions driving these signals are flanked by conserved sequences, maintain synteny with target genes, and overlap transcription factor binding sites. This work highlights the potential of conserved but tunable regulatory elements to be reused in parallel to facilitate evolutionary adaptation in mammals.

Interpretable prediction of mRNA abundance from promoter sequence using contextual regression models

While machine learning models have been successfully applied to predicting gene expression from promoter sequences, it remains a great challenge to derive intuitive interpretation of the model and reveal DNA motif grammar such as motif cooperation and distance constraint between motif sites. Previous interpretation approaches are often time-consuming or have difficulty to learn the combinatory rules. In this work, we designed interpretable neural network models to predict the mRNA expression levels from DNA sequences. By applying the Contextual Regression framework we developed, we extracted weighted features to cluster samples into different groups, which have different gene expression levels. We performed motif analysis in each cluster and found motifs with active or repressive regulation on gene expression. By comparing the co-occurrence locations of discovered motifs, we also uncovered multiple grammars of motif combination including communities of cooperative motifs and distance constraints between motif pairs. These results revealed new insights of the regulatory architecture of promoter sequences.

CUX1 regulates human hematopoietic stem cell chromatin accessibility via the BAF complex

CUX1 is a homeodomain-containing transcription factor that is essential for the development and differentiation of multiple tissues. CUX1 is recurrently mutated or deleted in cancer, particularly in myeloid malignancies. However, the mechanism by which CUX1 regulates gene expression and differentiation remains poorly understood, creating a barrier to understanding the tumor-suppressive functions of CUX1. Here, we demonstrate that CUX1 directs the BAF chromatin remodeling complex to DNA to increase chromatin accessibility in hematopoietic cells. CUX1 preferentially regulates lineage-specific enhancers, and CUX1 target genes are predictive of cell fate in vivo. These data indicate that CUX1 regulates hematopoietic lineage commitment and homeostasis via pioneer factor activity, and CUX1 deficiency disrupts these processes in stem and progenitor cells, facilitating transformation.

Interspecies regulatory landscapes and elements revealed by novel joint systematic integration of human and mouse blood cell epigenomes

Knowledge of locations and activities of cis-regulatory elements (CREs) is needed to decipher basic mechanisms of gene regulation and to understand the impact of genetic variants on complex traits. Previous studies identified candidate CREs (cCREs) using epigenetic features in one species, making comparisons difficult between species. In contrast, we conducted an interspecies study defining epigenetic states and identifying cCREs in blood cell types to generate regulatory maps that are comparable between species, using integrative modeling of eight epigenetic features jointly in human and mouse in our Validated Systematic Integration (VISION) Project. The resulting catalogs of cCREs are useful resources for further studies of gene regulation in blood cells, indicated by high overlap with known functional elements and strong enrichment for human genetic variants associated with blood cell phenotypes. The contribution of each epigenetic state in cCREs to gene regulation, inferred from a multivariate regression, was used to estimate epigenetic state Regulatory Potential (esRP) scores for each cCRE in each cell type, which were used to categorize dynamic changes in cCREs. Groups of cCREs displaying similar patterns of regulatory activity in human and mouse cell types, obtained by joint clustering on esRP scores, harbored distinctive transcription factor binding motifs that were similar between species. An interspecies comparison of cCREs revealed both conserved and species-specific patterns of epigenetic evolution. Finally, we showed that comparisons of the epigenetic landscape between species can reveal elements with similar roles in regulation, even in the absence of genomic sequence alignment.

JMnorm: a novel joint multi-feature normalization method for integrative and comparative epigenomics

Combinatorial patterns of epigenetic features reflect transcriptional states and functions of genomic regions. While many epigenetic features have correlated relationships, most existing data normalization approaches analyze each feature independently. Such strategies may distort relationships between functionally correlated epigenetic features and hinder biological interpretation. We present a novel approach named JMnorm that simultaneously normalizes multiple epigenetic features across cell types, species, and experimental conditions by leveraging information from partially correlated epigenetic features. We demonstrate that JMnorm-normalized data can better preserve cross-epigenetic-feature correlations across different cell types and enhance consistency between biological replicates than data normalized by other methods. Additionally, we show that JMnorm-normalized data can consistently improve the performance of various downstream analyses, which include candidate cis-regulatory element clustering, cross-cell-type gene expression prediction, detection of transcription factor binding and changes upon perturbations. These findings suggest that JMnorm effectively minimizes technical noise while preserving true biologically significant relationships between epigenetic datasets. We anticipate that JMnorm will enhance integrative and comparative epigenomics.

Inferring chromatin accessibility during murine hematopoiesis through phylogenetic analysis

OBJECTIVE

Diversification of cell types and changes in epigenetic states during cell differentiation processes are important for understanding development. Recently, phylogenetic analysis using DNA methylation and histone modification information has been shown useful for inferring these processes. The purpose of this study was to examine whether chromatin accessibility data can help infer these processes in murine hematopoiesis.

RESULTS

Chromatin accessibility data could partially infer the hematopoietic differentiation hierarchy. Furthermore, based on the ancestral state estimation of internal nodes, the open/closed chromatin states of differentiating progenitor cells could be predicted with a specificity of 0.86-0.99 and sensitivity of 0.29-0.72. These results suggest that the phylogenetic analysis of chromatin accessibility could offer important information on cell differentiation, particularly for organisms from which progenitor cells are difficult to obtain.

An NFIX-mediated regulatory network governs the balance of hematopoietic stem and progenitor cells during hematopoiesis

The transcription factor (TF) nuclear factor I-X (NFIX) is a positive regulator of hematopoietic stem and progenitor cell (HSPC) transplantation. Nfix-deficient HSPCs exhibit a severe loss of repopulating activity, increased apoptosis, and a loss of colony-forming potential. However, the underlying mechanism remains elusive. Here, we performed cellular indexing of transcriptomes and epitopes by high-throughput sequencing (CITE-seq) on Nfix-deficient HSPCs and observed a loss of long-term hematopoietic stem cells and an accumulation of megakaryocyte and myelo-erythroid progenitors. The genome-wide binding profile of NFIX in primitive murine hematopoietic cells revealed its colocalization with other hematopoietic TFs, such as PU.1. We confirmed the physical interaction between NFIX and PU.1 and demonstrated that the 2 TFs co-occupy super-enhancers and regulate genes implicated in cellular respiration and hematopoietic differentiation. In addition, we provide evidence suggesting that the absence of NFIX negatively affects PU.1 binding at some genomic loci. Our data support a model in which NFIX collaborates with PU.1 at super-enhancers to promote the differentiation and homeostatic balance of hematopoietic progenitors.

Predicting the impact of sequence motifs on gene regulation using single-cell data

The binding of transcription factors at proximal promoters and distal enhancers is central to gene regulation. Identifying regulatory motifs and quantifying their impact on expression remains challenging. Using a convolutional neural network trained on single-cell data, we infer putative regulatory motifs and cell type-specific importance. Our model, scover, explains 29% of the variance in gene expression in multiple mouse tissues. Applying scover to distal enhancers identified using scATAC-seq from the developing human brain, we identify cell type-specific motif activities in distal enhancers. Scover can identify regulatory motifs and their importance from single-cell data where all parameters and outputs are easily interpretable.

AdaLiftOver: high-resolution identification of orthologous regulatory elements with Adaptive liftOver

MOTIVATION

Elucidating functionally similar orthologous regulatory regions for human and model organism genomes is critical for exploiting model organism research and advancing our understanding of results from genome-wide association studies (GWAS). Sequence conservation is the de facto approach for finding orthologous non-coding regions between human and model organism genomes. However, existing methods for mapping non-coding genomic regions across species are challenged by the multi-mapping, low precision, and low mapping rate issues.

RESULTS

We develop Adaptive liftOver (AdaLiftOver), a large-scale computational tool for identifying functionally similar orthologous non-coding regions across species. AdaLiftOver builds on the UCSC liftOver framework to extend the query regions and prioritizes the resulting candidate target regions based on the conservation of the epigenomic and the sequence grammar features. Evaluations of AdaLiftOver with multiple case studies, spanning both genomic intervals from epigenome datasets across a wide range of model organisms and GWAS SNPs, yield AdaLiftOver as a versatile method for deriving hard-to-obtain human epigenome datasets as well as reliably identifying orthologous loci for GWAS SNPs.

AVAILABILITY AND IMPLEMENTATION

The R package and the data for AdaLiftOver is available from https://github.com/keleslab/AdaLiftOver.

Snapshot: a package for clustering and visualizing epigenetic history during cell differentiation

BACKGROUND

Epigenetic modification of chromatin plays a pivotal role in regulating gene expression during cell differentiation. The scale and complexity of epigenetic data pose significant challenges for biologists to identify the regulatory events controlling cell differentiation.

RESULTS

To reduce the complexity, we developed a package, called Snapshot, for clustering and visualizing candidate cis-regulatory elements (cCREs) based on their epigenetic signals during cell differentiation. This package first introduces a binarized indexing strategy for clustering the cCREs. It then provides a series of easily interpretable figures for visualizing the signal and epigenetic state patterns of the cCREs clusters during the cell differentiation. It can also use different hierarchies of cell types to highlight the epigenetic history specific to any particular cell lineage. We demonstrate the utility of Snapshot using data from a consortium project for ValIdated Systematic IntegratiON (VISION) of epigenomic data in hematopoiesis.

CONCLUSION

The package Snapshot can identify all distinct clusters of genomic locations with unique epigenetic signal patterns during cell differentiation. It outperforms other methods in terms of interpreting and reproducing the identified cCREs clusters. The package of Snapshot is available at GitHub: https://github.com/guanjue/Snapshot .

Dynamic DNA methylation reveals novel cis-regulatory elements in mouse hematopoiesis

Differentiation of hematopoietic stem and progenitor cells to terminally differentiated immune cells is accompanied by large-scale remodeling of the DNA methylation landscape. Although significant insights into the molecular mechanisms of hematopoietic tissue regeneration were derived from mouse models, profiling of DNA methylation has been hampered by high cost or low resolution using available methods. The recent development of the Infinium Mouse Methylation BeadChip (MMBC) array facilitates methylation profiling of the mouse genome at a single CpG resolution at affordable cost. We extended the RnBeads package to provide a computational framework for the analysis of MMBC data. This framework was applied to a newly generated reference map of mouse hematopoiesis encompassing nine different cell types. Analysis of dynamically regulated CpG sites showed progressive and unidirectional DNA methylation changes from hematopoietic stem and progenitor cells to differentiated hematopoietic cells and allowed the identification of lineage- and cell type-specific DNA methylation programs. Comparison with previously published catalogs of cis-regulatory elements (CREs) revealed 12,856 novel putative CREs that were dynamically regulated by DNA methylation (mdCREs). These mdCREs were predominantly associated with patterns of cell type-specific DNA hypomethylation and could be identified as epigenetic control regions regulating the expression of key hematopoietic genes during differentiation. In summary, we established an analysis pipeline for MMBC data sets and provide a DNA methylation atlas of mouse hematopoiesis. This resource allowed us to identify novel putative CREs involved in hematopoiesis and will serve as a platform to study epigenetic regulation of normal and malignant hematopoiesis.

CLIMB: High-dimensional association detection in large scale genomic data

Joint analyses of genomic datasets obtained in multiple different conditions are essential for understanding the biological mechanism that drives tissue-specificity and cell differentiation, but they still remain computationally challenging. To address this we introduce CLIMB (Composite LIkelihood eMpirical Bayes), a statistical methodology that learns patterns of condition-specificity present in genomic data. CLIMB provides a generic framework facilitating a host of analyses, such as clustering genomic features sharing similar condition-specific patterns and identifying which of these features are involved in cell fate commitment. We apply CLIMB to three sets of hematopoietic data, which examine CTCF ChIP-seq measured in 17 different cell populations, RNA-seq measured across constituent cell populations in three committed lineages, and DNase-seq in 38 cell populations. Our results show that CLIMB improves upon existing alternatives in statistical precision, while capturing interpretable and biologically relevant clusters in the data.

PU.1-Dependent Enhancer Inhibition Separates Tet2-Deficient Hematopoiesis from Malignant Transformation

Cytosine hypermethylation in and around DNA-binding sites of master transcription factors, including PU.1, occurs in aging hematopoietic stem cells following acquired loss-of-function mutations of DNA methyl-cytosine dioxygenase ten-eleven translocation-2 (TET2), albeit functional relevance has been unclear. We show that Tet2-deficient mouse hematopoietic stem and progenitor cells undergo malignant transformation upon compromised gene regulation through heterozygous deletion of an upstream regulatory region (UREΔ/WT) of the PU.1 gene. Although compatible with multilineage blood formation at young age, Tet2-deficient PU.1 UREΔ/WT mice develop highly penetrant, transplantable acute myeloid leukemia (AML) during aging. Leukemic stem and progenitor cells show hypermethylation at putative PU.1-binding sites, fail to activate myeloid enhancers, and are hallmarked by a signature of genes with impaired expression shared with human AML. Our study demonstrates that Tet2 and PU.1 jointly suppress leukemogenesis and uncovers a methylation-sensitive PU.1-dependent gene network as a unifying molecular vulnerability associated with AML.

SIGNIFICANCE

We identify moderately impaired PU.1 mRNA expression as a biological modality predisposing Tet2-deficient hematopoietic stem and progenitor cells to malignant transformation. Our study furthermore uncovers a methylation-sensitive PU.1 gene network as a common feature of myeloid leukemia potentially allowing for the identification of patients at risk for malignant transformation. See related commentary by Schleicher and Pietras, p. 378. This article is highlighted in the In This Issue feature, p. 369.

Molecular and cellular mechanisms that regulate human erythropoiesis

To enable effective oxygen transport, ∼200 billion red blood cells (RBCs) need to be produced every day in the bone marrow through the fine-tuned process of erythropoiesis. Erythropoiesis is regulated at multiple levels to ensure that defective RBC maturation or overproduction can be avoided. Here, we provide an overview of different layers of this control, ranging from cytokine signaling mechanisms that enable extrinsic regulation of RBC production to intrinsic transcriptional pathways necessary for effective erythropoiesis. Recent studies have also elucidated the importance of posttranscriptional regulation and highlighted additional gatekeeping mechanisms necessary for effective erythropoiesis. We additionally discuss the insights gained by studying human genetic variation affecting erythropoiesis and highlight the discovery of BCL11A as a regulator of hemoglobin switching through genetic studies. Finally, we provide an outlook of how our ability to measure multiple facets of this process at single-cell resolution, while accounting for the impact of human variation, will continue to refine our knowledge of erythropoiesis and how this process is perturbed in disease. As we learn more about this intricate and important process, additional opportunities to modulate erythropoiesis for therapeutic purposes will undoubtedly emerge.

Uncovering the mesendoderm gene regulatory network through multi-omic data integration

Mesendodermal specification is one of the earliest events in embryogenesis, where cells first acquire distinct identities. Cell differentiation is a highly regulated process that involves the function of numerous transcription factors (TFs) and signaling molecules, which can be described with gene regulatory networks (GRNs). Cell differentiation GRNs are difficult to build because existing mechanistic methods are low throughput, and high-throughput methods tend to be non-mechanistic. Additionally, integrating highly dimensional data composed of more than two data types is challenging. Here, we use linked self-organizing maps to combine chromatin immunoprecipitation sequencing (ChIP-seq)/ATAC-seq with temporal, spatial, and perturbation RNA sequencing (RNA-seq) data from Xenopus tropicalis mesendoderm development to build a high-resolution genome scale mechanistic GRN. We recover both known and previously unsuspected TF-DNA/TF-TF interactions validated through reporter assays. Our analysis provides insights into transcriptional regulation of early cell fate decisions and provides a general approach to building GRNs using highly dimensional multi-omic datasets.

Chromatin-state barriers enforce an irreversible mammalian cell fate decision

Stem and progenitor cells have the capacity to balance self-renewal and differentiation. Hematopoietic myeloid progenitors replenish more than 25 billion terminally differentiated neutrophils every day under homeostatic conditions and can increase this output in response to stress or infection. At what point along the spectrum of maturation do progenitors lose capacity for self-renewal and become irreversibly committed to differentiation? Using a system of conditional myeloid development that can be toggled between self-renewal and differentiation, we interrogate determinants of this "point of no return" in differentiation commitment. Irreversible commitment is due primarily to loss of open regulatory site access and disruption of a positive feedback transcription factor activation loop. Restoration of the transcription factor feedback loop extends the window of cell plasticity and alters the point of no return. These findings demonstrate how the chromatin state enforces and perpetuates cell fate and identify potential avenues for manipulating cell identity.

Tree-Based Co-Clustering Identifies Chromatin Accessibility Patterns Associated With Hematopoietic Lineage Structure

Chromatin accessibility, as measured by ATACseq, varies between hematopoietic cell types in different lineages of the hematopoietic differentiation tree, e.g. T cells vs. B cells, but methods that associate variation in chromatin accessibility to the lineage structure of the differentiation tree are lacking. Using an ATACseq dataset recently published by the ImmGen consortium, we construct associations between chromatin accessibility and hematopoietic cell types using a novel co-clustering approach that accounts for the structure of the hematopoietic, differentiation tree. Under a model in which all loci and cell types within a co-cluster have a shared accessibility state, we show that roughly 80% of cell type associated accessibility variation can be captured through 12 cell type clusters and 20 genomic locus clusters, with the cell type clusters reflecting coherent components of the differentiation tree. Using publicly available ChIPseq datasets, we show that our clustering reflects transcription factor binding patterns with implications for regulation across cell types. We show that traditional methods such as hierarchical and kmeans clusterings lead to cell type clusters that are more dispersed on the tree than our tree-based algorithm. We provide a python package, chromcocluster, that implements the algorithms presented.

Segmentation and genome annotation algorithms for identifying chromatin state and other genomic patterns

Segmentation and genome annotation (SAGA) algorithms are widely used to understand genome activity and gene regulation. These algorithms take as input epigenomic datasets, such as chromatin immunoprecipitation-sequencing (ChIP-seq) measurements of histone modifications or transcription factor binding. They partition the genome and assign a label to each segment such that positions with the same label exhibit similar patterns of input data. SAGA algorithms discover categories of activity such as promoters, enhancers, or parts of genes without prior knowledge of known genomic elements. In this sense, they generally act in an unsupervised fashion like clustering algorithms, but with the additional simultaneous function of segmenting the genome. Here, we review the common methodological framework that underlies these methods, review variants of and improvements upon this basic framework, and discuss the outlook for future work. This review is intended for those interested in applying SAGA methods and for computational researchers interested in improving upon them.

CTCF and transcription influence chromatin structure re-configuration after mitosis

During mitosis, transcription is globally attenuated and chromatin architecture is dramatically reconfigured. We exploited the M- to G1-phase progression to interrogate the contributions of the architectural factor CTCF and the process of transcription to genome re-sculpting in newborn nuclei. Depletion of CTCF during the M- to G1-phase transition alters short-range compartmentalization after mitosis. Chromatin domain boundary re-formation is impaired upon CTCF loss, but a subset of boundaries, characterized by transitions in chromatin states, is established normally. Without CTCF, structural loops fail to form, leading to illegitimate contacts between cis-regulatory elements (CREs). Transient CRE contacts that are normally resolved after telophase persist deeply into G1-phase in CTCF-depleted cells. CTCF loss-associated gains in transcription are often linked to increased, normally illegitimate enhancer-promoter contacts. In contrast, at genes whose expression declines upon CTCF loss, CTCF seems to function as a conventional transcription activator, independent of its architectural role. CTCF-anchored structural loops facilitate formation of CRE loops nested within them, especially those involving weak CREs. Transcription inhibition does not significantly affect global architecture or transcription start site-associated boundaries. However, ongoing transcription contributes considerably to the formation of gene domains, regions of enriched contacts along gene bodies. Notably, gene domains emerge in ana/telophase prior to completion of the first round of transcription, suggesting that epigenetic features in gene bodies contribute to genome reconfiguration prior to transcription. The focus on the de novo formation of nuclear architecture during G1 entry yields insights into the contributions of CTCF and transcription to chromatin architecture dynamics during the mitosis to G1-phase progression.

'Enhancing' red cell fate through epigenetic mechanisms

PURPOSE OF REVIEW

Transcription of erythroid-specific genes is regulated by the three-dimensional (3D) structure and composition of chromatin, which dynamically changes during erythroid differentiation. Chromatin organization and dynamics are regulated by several epigenetic mechanisms involving DNA (de-)methylation, posttranslational modifications (PTMs) of histones, chromatin-associated structural proteins, and higher-order structural changes and interactions. This review addresses examples of recent developments in several areas delineating the interface of chromatin regulation and erythroid-specific lineage transcription.

RECENT FINDINGS

We survey and discuss recent studies that focus on the erythroid chromatin landscape, erythroid enhancer-promotor interactions, super-enhancer functionality, the role of chromatin modifiers and epigenetic crosstalk, as well as the progress in mapping red blood cell (RBC) trait-associated genetic variants within cis-regulatory elements (CREs) identified in genome-wide association study (GWAS) efforts as a step toward determining their impact on erythroid-specific gene expression.

SUMMARY

As one of the best characterized and accessible cell differentiation systems, erythropoiesis has been at the forefront of studies aiming to conceptualize how chromatin dynamics regulate transcription. New emerging technologies that bring a significantly enhanced spatial and temporal resolution of chromatin structure, and allow investigation of small cell numbers, have advanced our understanding of chromatin dynamics during erythroid differentiation in vivo.

Effects of sheared chromatin length on ChIP-seq quality and sensitivity

Chromatin immunoprecipitation followed by massively parallel, high throughput sequencing (ChIP-seq) is the method of choice for genome-wide identification of DNA segments bound by specific transcription factors or in chromatin with particular histone modifications. However, the quality of ChIP-seq datasets varies widely, with a substantial fraction being of intermediate to poor quality. Thus, it is important to discern and control the factors that contribute to variation in ChIP-seq. In this study, we focused on sonication, a user-controlled variable, to produce sheared chromatin. We systematically varied the amount of shearing of fixed chromatin from a mouse erythroid cell line, carefully measuring the distribution of resultant fragment lengths prior to ChIP-seq. This systematic study was complemented with a retrospective analysis of additional experiments. We found that the level of sonication had a pronounced impact on the quality of ChIP-seq signals. Over-sonication consistently reduced quality, while the impact of under-sonication differed among transcription factors, with no impact on sites bound by CTCF but frequently leading to the loss of sites occupied by TAL1 or bound by POL2. The bound sites not observed in low quality datasets were inferred to be a mix of both direct and indirect binding. We leveraged these findings to produce a set of CTCF ChIP-seq datasets in rare, primary hematopoietic progenitor cells. Our observation that the amount of chromatin sonication is a key variable in success of ChIP-seq experiments indicates that monitoring the level of sonication can improve ChIP-seq quality and reproducibility and facilitate ChIP-seq in rare cell types.

S3V2-IDEAS: a package for normalizing, denoising and integrating epigenomic datasets across different cell types

SUMMARY

Epigenetic modifications reflect key aspects of transcriptional regulation, and many epigenomic datasets have been generated under different biological contexts to provide insights into regulatory processes. However, the technical noise in epigenomic datasets and the many dimensions (features) examined make it challenging to effectively extract biologically meaningful inferences from these datasets. We developed a package that reduces noise while normalizing the epigenomic data by a novel normalization method, followed by integrative dimensional reduction by learning and assigning epigenetic states. This package, called S3V2-IDEAS, can be used to identify epigenetic states for multiple features, or identify discretized signal intensity levels and a master peak list across different cell types for a single feature. We illustrate the outputs and performance of S3V2-IDEAS using 137 epigenomics datasets from the VISION project that provides ValIdated Systematic IntegratiON of epigenomic data in hematopoiesis.

AVAILABILITY AND IMPLEMENTATION

S3V2-IDEAS pipeline is freely available as open source software released under an MIT license at: https://github.com/guanjue/S3V2_IDEAS_ESMP.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Data integration for inferring context-specific gene regulatory networks

Transcriptional regulatory networks control context-specific gene expression patterns and play important roles in normal and disease processes. Advances in genomics are rapidly increasing our ability to measure different components of the regulation machinery at the single-cell and bulk population level. An important challenge is to combine different types of regulatory genomic measurements to construct a more complete picture of gene regulatory networks across different disease, environmental, and developmental contexts. In this review, we focus on recent computational methods that integrate regulatory genomic data sets to infer context specificity and dynamics in regulatory networks.

Expanded encyclopaedias of DNA elements in the human and mouse genomes

The human and mouse genomes contain instructions that specify RNAs and proteins and govern the timing, magnitude, and cellular context of their production. To better delineate these elements, phase III of the Encyclopedia of DNA Elements (ENCODE) Project has expanded analysis of the cell and tissue repertoires of RNA transcription, chromatin structure and modification, DNA methylation, chromatin looping, and occupancy by transcription factors and RNA-binding proteins. Here we summarize these efforts, which have produced 5,992 new experimental datasets, including systematic determinations across mouse fetal development. All data are available through the ENCODE data portal (https://www.encodeproject.org), including phase II ENCODE1 and Roadmap Epigenomics2 data. We have developed a registry of 926,535 human and 339,815 mouse candidate cis-regulatory elements, covering 7.9 and 3.4% of their respective genomes, by integrating selected datatypes associated with gene regulation, and constructed a web-based server (SCREEN; http://screen.encodeproject.org) to provide flexible, user-defined access to this resource. Collectively, the ENCODE data and registry provide an expansive resource for the scientific community to build a better understanding of the organization and function of the human and mouse genomes.

The changing mouse embryo transcriptome at whole tissue and single-cell resolution

During mammalian embryogenesis, differential gene expression gradually builds the identity and complexity of each tissue and organ system1. Here we systematically quantified mouse polyA-RNA from day 10.5 of embryonic development to birth, sampling 17 tissues and organs. The resulting developmental transcriptome is globally structured by dynamic cytodifferentiation, body-axis and cell-proliferation gene sets that were further characterized by the transcription factor motif codes of their promoters. We decomposed the tissue-level transcriptome using single-cell RNA-seq (sequencing of RNA reverse transcribed into cDNA) and found that neurogenesis and haematopoiesis dominate at both the gene and cellular levels, jointly accounting for one-third of differential gene expression and more than 40% of identified cell types. By integrating promoter sequence motifs with companion ENCODE epigenomic profiles, we identified a prominent promoter de-repression mechanism in neuronal expression clusters that was attributable to known and novel repressors. Focusing on the developing limb, single-cell RNA data identified 25 candidate cell types that included progenitor and differentiating states with computationally inferred lineage relationships. We extracted cell-type transcription factor networks and complementary sets of candidate enhancer elements by using single-cell RNA-seq to decompose integrative cis-element (IDEAS) models that were derived from whole-tissue epigenome chromatin data. These ENCODE reference data, computed network components and IDEAS chromatin segmentations are companion resources to the matching epigenomic developmental matrix, and are available for researchers to further mine and integrate.

S3norm: simultaneous normalization of sequencing depth and signal-to-noise ratio in epigenomic data

Quantitative comparison of epigenomic data across multiple cell types or experimental conditions is a promising way to understand the biological functions of epigenetic modifications. However, differences in sequencing depth and signal-to-noise ratios in the data from different experiments can hinder our ability to identify real biological variation from raw epigenomic data. Proper normalization is required prior to data analysis to gain meaningful insights. Most existing methods for data normalization standardize signals by rescaling either background regions or peak regions, assuming that the same scale factor is applicable to both background and peak regions. While such methods adjust for differences in sequencing depths, they do not address differences in the signal-to-noise ratios across different experiments. We developed a new data normalization method, called S3norm, that normalizes the sequencing depths and signal-to-noise ratios across different data sets simultaneously by a monotonic nonlinear transformation. We show empirically that the epigenomic data normalized by our method, compared to existing methods, can better capture real biological variation, such as impact on gene expression regulation.