Sensitive and robust assessment of ChIP-seq read distribution using a strand-shift profile

Churros: a Docker-based pipeline for large-scale epigenomic analysis

The epigenome, which reflects the modifications on chromatin or DNA sequences, provides crucial insight into gene expression regulation and cellular activity. With the continuous accumulation of epigenomic datasets such as chromatin immunoprecipitation followed by sequencing (ChIP-seq) data, there is a great demand for a streamlined pipeline to consistently process them, especially for large-dataset comparisons involving hundreds of samples. Here, we present Churros, an end-to-end epigenomic analysis pipeline that is environmentally independent and optimized for handling large-scale data. We successfully demonstrated the effectiveness of Churros by analyzing large-scale ChIP-seq datasets with the hg38 or Telomere-to-Telomere (T2T) human reference genome. We found that applying T2T to the typical analysis workflow has important impacts on read mapping, quality checks, and peak calling. We also introduced a useful feature to study context-specific epigenomic landscapes. Churros will contribute a comprehensive and unified resource for analyzing large-scale epigenomic data.

H3K27me3 spreading organizes canonical PRC1 chromatin architecture to regulate developmental programs

Polycomb Repressive Complex 2 (PRC2)-mediated histone H3K27 tri-methylation (H3K27me3) recruits canonical PRC1 (cPRC1) to maintain heterochromatin. In early development, polycomb-regulated genes are connected through long-range 3D interactions which resolve upon differentiation. Here, we report that polycomb looping is controlled by H3K27me3 spreading and regulates target gene silencing and cell fate specification. Using glioma-derived H3 Lys-27-Met (H3K27M) mutations as tools to restrict H3K27me3 deposition, we show that H3K27me3 confinement concentrates the chromatin pool of cPRC1, resulting in heightened 3D interactions mirroring chromatin architecture of pluripotency, and stringent gene repression that maintains cells in progenitor states to facilitate tumor development. Conversely, H3K27me3 spread in pluripotent stem cells, following neural differentiation or loss of the H3K36 methyltransferase NSD1, dilutes cPRC1 concentration and dissolves polycomb loops. These results identify the regulatory principles and disease implications of polycomb looping and nominate histone modification-guided distribution of reader complexes as an important mechanism for nuclear compartment organization.

Highlights

The confinement of H3K27me3 at PRC2 nucleation sites without its spreading correlates with increased 3D chromatin interactions.The H3K27M oncohistone concentrates canonical PRC1 that anchors chromatin loop interactions in gliomas, silencing developmental programs.Stem and progenitor cells require factors promoting H3K27me3 confinement, including H3K36me2, to maintain cPRC1 loop architecture.The cPRC1-H3K27me3 interaction is a targetable driver of aberrant self-renewal in tumor cells.

Context-dependent perturbations in chromatin folding and the transcriptome by cohesin and related factors

Cohesin regulates gene expression through context-specific chromatin folding mechanisms such as enhancer-promoter looping and topologically associating domain (TAD) formation by cooperating with factors such as cohesin loaders and the insulation factor CTCF. We developed a computational workflow to explore how three-dimensional (3D) structure and gene expression are regulated collectively or individually by cohesin and related factors. The main component is CustardPy, by which multi-omics datasets are compared systematically. To validate our methodology, we generated 3D genome, transcriptome, and epigenome data before and after depletion of cohesin and related factors and compared the effects of depletion. We observed diverse effects on the 3D genome and transcriptome, and gene expression changes were correlated with the splitting of TADs caused by cohesin loss. We also observed variations in long-range interactions across TADs, which correlated with their epigenomic states. These computational tools and datasets will be valuable for 3D genome and epigenome studies.

CohesinDB: a comprehensive database for decoding cohesin-related epigenomes, 3D genomes and transcriptomes in human cells

Cohesin is a multifunctional protein responsible for transcriptional regulation and chromatin organization. Cohesin binds to chromatin at tens of thousands of distinct sites in a conserved or tissue-specific manner, whereas the function of cohesin varies greatly depending on the epigenetic properties of specific chromatin loci. Cohesin also extensively mediates cis-regulatory modules (CRMs) and chromatin loops. Even though next-generation sequencing technologies have provided a wealth of information on different aspects of cohesin, the integration and exploration of the resultant massive cohesin datasets are not straightforward. Here, we present CohesinDB (https://cohesindb.iqb.u-tokyo.ac.jp), a comprehensive multiomics cohesin database in human cells. CohesinDB includes 2043 epigenomics, transcriptomics and 3D genomics datasets from 530 studies involving 176 cell types. By integrating these large-scale data, CohesinDB summarizes three types of 'cohesin objects': 751 590 cohesin binding sites, 957 868 cohesin-related chromatin loops and 2 229 500 cohesin-related CRMs. Each cohesin object is annotated with locus, cell type, classification, function, 3D genomics and cis-regulatory information. CohesinDB features a user-friendly interface for browsing, searching, analyzing, visualizing and downloading the desired information. CohesinDB contributes a valuable resource for all researchers studying cohesin, epigenomics, transcriptional regulation and chromatin organization.

Large-scale multi-omics analysis suggests specific roles for intragenic cohesin in transcriptional regulation

Cohesin, an essential protein complex for chromosome segregation, regulates transcription through a variety of mechanisms. It is not a trivial task to assign diverse cohesin functions. Moreover, the context-specific roles of cohesin-mediated interactions, especially on intragenic regions, have not been thoroughly investigated. Here we perform a comprehensive characterization of cohesin binding sites in several human cell types. We integrate epigenomic, transcriptomic and chromatin interaction data to explore the context-specific functions of intragenic cohesin related to gene activation. We identify a specific subset of cohesin binding sites, decreased intragenic cohesin sites (DICs), which are negatively correlated with transcriptional regulation. A subgroup of DICs is enriched with enhancer markers and RNA polymerase II, while the others are more correlated to chromatin architecture. DICs are observed in various cell types, including cells from patients with cohesinopathy. We also implement machine learning to our data and identified genomic features for isolating DICs from all cohesin sites. These results suggest a previously unidentified function of cohesin on intragenic regions for transcriptional regulation.

Comprehensive assessment of differential ChIP-seq tools guides optimal algorithm selection

Background

The analysis of chromatin binding patterns of proteins in different biological states is a main application of chromatin immunoprecipitation followed by sequencing (ChIP-seq). A large number of algorithms and computational tools for quantitative comparison of ChIP-seq datasets exist, but their performance is strongly dependent on the parameters of the biological system under investigation. Thus, a systematic assessment of available computational tools for differential ChIP-seq analysis is required to guide the optimal selection of analysis tools based on the present biological scenario.

Results

We created standardized reference datasets by in silico simulation and sub-sampling of genuine ChIP-seq data to represent different biological scenarios and binding profiles. Using these data, we evaluated the performance of 33 computational tools and approaches for differential ChIP-seq analysis. Tool performance was strongly dependent on peak size and shape as well as on the scenario of biological regulation.

Conclusions

Our analysis provides unbiased guidelines for the optimized choice of software tools in differential ChIP-seq analysis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02686-y.

Six1 promotes skeletal muscle thyroid hormone response through regulation of the MCT10 transporter

BACKGROUND

The Six1 transcription factor is implicated in controlling the development of several tissue types, notably skeletal muscle. Six1 also contributes to muscle metabolism and its activity is associated with the fast-twitch, glycolytic phenotype. Six1 regulates the expression of certain genes of the fast muscle program by directly stimulating their transcription or indirectly acting through a long non-coding RNA. We hypothesized that additional mechanisms of action of Six1 might be at play.

METHODS

A combined analysis of gene expression profiling and genome-wide location analysis data was performed. Results were validated using in vivo RNA interference loss-of-function assays followed by measurement of gene expression by RT-PCR and transcriptional reporter assays.

RESULTS

The Slc16a10 gene, encoding the thyroid hormone transmembrane transporter MCT10, was identified as a gene with a transcriptional enhancer directly bound by Six1 and requiring Six1 activity for full expression in adult mouse tibialis anterior, a predominantly fast-twitch muscle. Of the various thyroid hormone transporters, MCT10 mRNA was found to be the most abundant in skeletal muscle, and to have a stronger expression in fast-twitch compared to slow-twitch muscle groups. Loss-of-function of MCT10 in the tibialis anterior recapitulated the effect of Six1 on the expression of fast-twitch muscle genes and led to lower activity of a thyroid hormone receptor-dependent reporter gene.

CONCLUSIONS

These results shed light on the molecular mechanisms controlling the tissue expression profile of MCT10 and identify modulation of the thyroid hormone signaling pathway as an additional mechanism by which Six1 influences skeletal muscle metabolism.

NoPeak: k-mer-based motif discovery in ChIP-Seq data without peak calling

MOTIVATION

The discovery of sequence motifs mediating DNA-protein binding usually implies the determination of binding sites using high-throughput sequencing and peak calling. The determination of peaks, however, depends strongly on data quality and is susceptible to noise.

RESULTS

Here, we present a novel approach to reliably identify transcription factor-binding motifs from ChIP-Seq data without peak detection. By evaluating the distributions of sequencing reads around the different k-mers in the genome, we are able to identify binding motifs in ChIP-Seq data that yield no results in traditional pipelines.

AVAILABILITY AND IMPLEMENTATION

NoPeak is published under the GNU General Public License and available as a standalone console-based Java application at https://github.com/menzel/nopeak.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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

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

Histone crotonylation promotes mesoendodermal commitment of human embryonic stem cells

Histone crotonylation is a non-acetyl histone lysine modification that is as widespread as acetylation. However, physiological functions associated with histone crotonylation remain almost completely unknown. Here we report that histone crotonylation is crucial for endoderm differentiation. We demonstrate that key crotonyl-coenzyme A (CoA)-producing enzymes are specifically induced in endodermal cells during differentiation of human embryonic stem cells (hESCs) in vitro and in mouse embryos, where they function to increase histone crotonylation and enhance endodermal gene expression. Chemical enhancement of histone crotonylation promotes endoderm differentiation of hESCs, whereas deletion of crotonyl-CoA-producing enzymes reduces histone crotonylation and impairs meso/endoderm differentiation in vitro and in vivo. Our study uncovers a histone crotonylation-mediated mechanism that promotes endodermal commitment of pluripotent stem cells, which may have important implications for therapeutic strategies against a number of human diseases.

A comparative analysis of chromatin accessibility in cattle, pig, and mouse tissues

Background

Although considerable progress has been made towards annotating the noncoding portion of the human and mouse genomes, regulatory elements in other species, such as livestock, remain poorly characterized. This lack of functional annotation poses a substantial roadblock to agricultural research and diminishes the value of these species as model organisms. As active regulatory elements are typically characterized by chromatin accessibility, we implemented the Assay for Transposase Accessible Chromatin (ATAC-seq) to annotate and characterize regulatory elements in pigs and cattle, given a set of eight adult tissues.

Results

Overall, 306,304 and 273,594 active regulatory elements were identified in pig and cattle, respectively. 71,478 porcine and 47,454 bovine regulatory elements were highly tissue-specific and were correspondingly enriched for binding motifs of known tissue-specific transcription factors. However, in every tissue the most prevalent accessible motif corresponded to the insulator CTCF, suggesting pervasive involvement in 3-D chromatin organization. Taking advantage of a similar dataset in mouse, open chromatin in pig, cattle, and mice were compared, revealing that the conservation of regulatory elements, in terms of sequence identity and accessibility, was consistent with evolutionary distance; whereas pig and cattle shared about 20% of accessible sites, mice and ungulates only had about 10% of accessible sites in common. Furthermore, conservation of accessibility was more prevalent at promoters than at intergenic regions.

Conclusions

The lack of conserved accessibility at distal elements is consistent with rapid evolution of enhancers, and further emphasizes the need to annotate regulatory elements in individual species, rather than inferring elements based on homology. This atlas of chromatin accessibility in cattle and pig constitutes a substantial step towards annotating livestock genomes and dissecting the regulatory link between genome and phenome.

Theoretical characterisation of strand cross-correlation in ChIP-seq

BACKGROUND

Strand cross-correlation profiles are used for both peak calling pre-analysis and quality control (QC) in chromatin immunoprecipitation followed by sequencing (ChIP-seq) analysis. Despite its potential for robust and accurate assessments of signal-to-noise ratio (S/N) because of its peak calling independence, it remains unclear what aspects of quality such strand cross-correlation profiles actually measure.

RESULTS

We introduced a simple model to simulate the mapped read-density of ChIP-seq and then derived the theoretical maximum and minimum of cross-correlation coefficients between strands. The results suggest that the maximum coefficient of typical ChIP-seq samples is directly proportional to the number of total mapped reads and the square of the ratio of signal reads, and inversely proportional to the number of peaks and the length of read-enriched regions. Simulation analysis supported our results and evaluation using 790 ChIP-seq data obtained from the public database demonstrated high consistency between calculated cross-correlation coefficients and estimated coefficients based on the theoretical relations and peak calling results. In addition, we found that the mappability-bias-correction improved sensitivity, enabling differentiation of maximum coefficients from the noise level. Based on these insights, we proposed virtual S/N (VSN), a novel peak call-free metric for S/N assessment. We also developed PyMaSC, a tool to calculate strand cross-correlation and VSN efficiently. VSN achieved most consistent S/N estimation for various ChIP targets and sequencing read depths. Furthermore, we demonstrated that a combination of VSN and pre-existing peak calling results enable the estimation of the numbers of detectable peaks for posterior experiments and assess peak calling results.

CONCLUSIONS

We present the first theoretical insights into the strand cross-correlation, and the results reveal the potential and the limitations of strand cross-correlation analysis. Our quality assessment framework using VSN provides peak call-independent QC and will help in the evaluation of peak call analysis in ChIP-seq experiments.

Comprehensive epigenome characterization reveals diverse transcriptional regulation across human vascular endothelial cells

Background

Endothelial cells (ECs) make up the innermost layer throughout the entire vasculature. Their phenotypes and physiological functions are initially regulated by developmental signals and extracellular stimuli. The underlying molecular mechanisms responsible for the diverse phenotypes of ECs from different organs are not well understood.

Results

To characterize the transcriptomic and epigenomic landscape in the vascular system, we cataloged gene expression and active histone marks in nine types of human ECs (generating 148 genome-wide datasets) and carried out a comprehensive analysis with chromatin interaction data. We developed a robust procedure for comparative epigenome analysis that circumvents variations at the level of the individual and technical noise derived from sample preparation under various conditions. Through this approach, we identified 3765 EC-specific enhancers, some of which were associated with disease-associated genetic variations. We also identified various candidate marker genes for each EC type. We found that the nine EC types can be divided into two subgroups, corresponding to those with upper-body origins and lower-body origins, based on their epigenomic landscape. Epigenomic variations were highly correlated with gene expression patterns, but also provided unique information. Most of the deferentially expressed genes and enhancers were cooperatively enriched in more than one EC type, suggesting that the distinct combinations of multiple genes play key roles in the diverse phenotypes across EC types. Notably, many homeobox genes were differentially expressed across EC types, and their expression was correlated with the relative position of each organ in the body. This reflects the developmental origins of ECs and their roles in angiogenesis, vasculogenesis and wound healing.

Conclusions

This comprehensive analysis of epigenome characterization of EC types reveals diverse transcriptional regulation across human vascular systems. These datasets provide a valuable resource for understanding the vascular system and associated diseases.