Chromatin Interaction Analysis Research Articles

EagleC: A deep-learning framework for detecting a full range of structural variations from bulk and single-cell contact maps.

The Hi-C technique has been shown to be a promising method to detect structural variations (SVs) in human genomes. However, algorithms that can use Hi-C data for a full-range SV detection have been severely lacking. Current methods can only identify interchromosomal translocations and long-range intrachromosomal SVs (>1 Mb) at less-than-optimal resolution. Therefore, we develop EagleC, a framework that combines deep-learning and ensemble-learning strategies to predict a full range of SVs at high resolution. We show that EagleC can uniquely capture a set of fusion genes that are missed by whole-genome sequencing or nanopore. Furthermore, EagleC also effectively captures SVs in other chromatin interaction platforms, such as HiChIP, Chromatin interaction analysis with paired-end tag sequencing (ChIA-PET), and capture Hi-C. We apply EagleC in more than 100 cancer cell lines and primary tumors and identify a valuable set of high-quality SVs. Last, we demonstrate that EagleC can be applied to single-cell Hi-C and used to study the SV heterogeneity in primary tumors.

Transcriptional regulation and chromatin architecture maintenance are decoupled functions at the Sox2 locus.

How distal regulatory elements control gene transcription and chromatin topology is not clearly defined, yet these processes are closely linked in lineage specification during development. Through allele-specific genome editing and chromatin interaction analyses of the Sox2 locus in mouse embryonic stem cells, we found a striking disconnection between transcriptional control and chromatin architecture. We traced nearly all Sox2 transcriptional activation to a small number of key transcription factor binding sites, whose deletions have no effect on promoter-enhancer interaction frequencies or topological domain organization. Local chromatin architecture maintenance, including at the topologically associating domain (TAD) boundary downstream from the Sox2 enhancer, is widely distributed over multiple transcription factor-bound regions and maintained in a CTCF-independent manner. Furthermore, partial disruption of promoter-enhancer interactions by ectopic chromatin loop formation has no effect on Sox2 transcription. These findings indicate that many transcription factors are involved in modulating chromatin architecture independently of CTCF.

ELOVL2-AS1 inhibits migration of triple negative breast cancer.

In this study, we identified a key enhancer RNA (eRNA) region in breast cancer (BRCA) by applying an integrated analysis method. Reported eRNA region and genes affected by them were selected as presumed target pairs. Kaplan–Meier (KM) survival and correlation analyses were performed to screen valuable eRNA region. Based on the KM value and its correlation with the paired target genes, we carefully selected ELOVL2-AS1 as a potential key eRNA region in BRCA. Subsequently, we analyzed the expression of ELOVL2-AS1 and ELOVL2 in four BRCA subtypes and in different BRCA cell lines. The expression of ELOVL2-AS1 and ELOVL2 in triple negative breast cancer (TNBC) was significantly lower than those in Luminal A. After that, we analyzed the function of genes that are positively correlated with ELOVL2-AS1. We found that the co-expression gene mainly related to cilia and cilia characteristics of TNBC is significantly weaker than that of Luminal A. Considering the stronger invasion and metastasis of TNBC (compared with Luminal A) and the close relationship between decreased cilia and metastasis, we overexpressed ELOVL2-AS1 in TNBC and observed its effect on cell migration. The results show that it can inhibit the migration of TNBC. Finally, we analyzed the assay for transposase-accessible chromatin sequencing data, chromatin interaction analysis with paired-end tag sequencing data, and chromatin immunoprecipitation sequencing data and identified the chromatin interaction between ELOVL2-AS1 and ELOVL2, suggesting a direct regulatory interaction.

Interrogating Global Chromatin Interaction Network by High-Throughput Chromosome Conformation Capture (Hi-C) in Plants.

High-throughput chromosome conformation capture (Hi-C) enables the global quantification of chromatin interaction frequency in eukaryotic nuclei. This method is based on in situ Hi-C, in which chromatin is cross-linked with formaldehyde, then digested with restriction enzyme. Biotin-labeled nucleotide is incorporated before the spatially adjacent DNA ends are ligated, making it possible to enrich specifically the chimeric ligation products for deep sequencing. In this chapter, we describe a modified in situ Hi-C protocol for the global chromatin interaction analysis in plants.

Improving Chromatin-Interaction Prediction Using Single-Cell Open-Chromatin Profiles and Making Insight Into the Cis-Regulatory Landscape of the Human Brain.

Single-cell open-chromatin profiles have the potential to reveal the pattern of chromatin-interaction in a cell type. However, currently available cis-regulatory network prediction methods using single-cell open-chromatin profiles focus more on local chromatin interactions despite the fact that long-range interactions among genomic sites play a significant role in gene regulation. Here, we propose a method that predicts both short and long-range interactions among genomic sites using single-cell open chromatin profiles. Our method, termed as single-cell epigenome based chromatin-interaction analysis (scEChIA) exploits signal imputation and refined L1 regularization. For a few single-cell open-chromatin profiles, scEChIA outperformed other tools even in terms of accuracy of prediction. Using scEChIA, we predicted almost 0.7 million interactions among genomic sites across seven cell types in the human brain. Further analysis revealed cell type for connection between genes and expression quantitative trait locus (eQTL) in the human brain and making insight about target genes of human-accelerated-elements and disease-associated mutations. Our analysis enabled by scEChIA also hints about the possible action of a few transcription factors (TFs), especially through long-range interaction in brain endothelial cells.

Analysis of HiChIP Data.

HiChIP is a novel method for the analysis of chromatin interactions based on in situ Hi-C that adds an immuno-precipitation (ChIP) step for the investigation of chromatin structures driven by specific proteins. This approach has been shown to be very efficient as it reliably reproduces Hi-C results and displays a higher rate of informative reads with a required lower amount of input cells when compared with other ChIP-based techniques (as ChIA-PET). Although HiChIP data preprocessing can be performed with the same methods developed for other Hi-C techniques, the identification of chromatin interactions needs to take into account specific biases introduced by the ChIP step. In this chapter we describe a computational pipeline for the analysis of HiChIP data obtained with the immuno-precipitation of Rad21 (part of the cohesin complex) in human embryonic stem cells before and after heat-shock treatment. We provide a detailed description of the preprocessing of raw data, the identification of chromatin interactions, the evaluation of the alterations induced by treatment, and, finally, the visualization of differential loops.

In situ Chromatin Interaction Analysis Using Paired-End Tag Sequencing.

Chromatin Interaction Analysis Using Paired-End Tag Sequencing (ChIA-PET) is an established method to map protein-mediated chromatin interactions. A limitation, however, is that it requires a hundred million cells per experiment, which hampers its broad application in biomedical research, particularly in studies in which it is impractical to obtain a large number of cells from rare samples. To reduce the required input cell number while retaining high data quality, we developed an in situ ChIA-PET protocol, which requires as few as 1 million cells. Here, we describe detailed step-by-step procedures for performing in situ ChIA-PET from cultured cells, including both an experimental protocol for sample preparation and data generation and a computational protocol for data processing and visualization using the ChIA-PIPE pipeline. As the protocol significantly simplifies the experimental procedure, reduces ligation noise, and decreases the required input of cells compared to previous versions of ChIA-PET protocols, it can be applied to generate high-resolution chromatin contact maps mediated by various protein factors for a wide range of human and mouse primary cells. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Sample preparation and data generation Support Protocol: Bridge linker preparation Basic Protocol 2: Data processing and visualization.

Chromatin loop anchors predict transcript and exon usage.

Epigenomics and transcriptomics data from high-throughput sequencing techniques such as RNA-seq and ChIP-seq have been successfully applied in predicting gene transcript expression. However, the locations of chromatin loops in the genome identified by techniques such as Chromatin Interaction Analysis with Paired End Tag sequencing (ChIA-PET) have never been used for prediction tasks. Here, we developed machine learning models to investigate if ChIA-PET could contribute to transcript and exon usage prediction. In doing so, we used a large set of transcription factors as well as ChIA-PET data. We developed different Gradient Boosting Trees models according to the different tasks with the integrated datasets from three cell lines, including GM12878, HeLaS3 and K562. We validated the models via 10-fold cross validation, chromosome-split validation and cross-cell validation. Our results show that both transcript and splicing-derived exon usage can be effectively predicted with at least 0.7512 and 0.7459 of accuracy, respectively, on all cell lines from all kinds of validations. Examining the predictive features, we found that RNA Polymerase II ChIA-PET was one of the most important features in both transcript and exon usage prediction, suggesting that chromatin loop anchors are predictive of both transcript and exon usage.

Genetic Association of a Gain-of-Function IFNGR1 Polymorphism and the Intergenic Region LNCAROD/DKK1 With Behçet's Disease.

Behçet's disease is a complex systemic inflammatory vasculitis of incompletely understood etiology. This study was undertaken to investigate genetic associations with Behçet's disease in a diverse multiethnic population. A total of 9,444 patients and controls from 7 different populations were included in this study. Genotyping was performed using an Infinium ImmunoArray-24 v.1.0 or v.2.0 BeadChip. Analysis of expression data from stimulated monocytes, and epigenetic and chromatin interaction analyses were performed. We identified 2 novel genetic susceptibility loci for Behçet's disease, including a risk locus in IFNGR1 (rs4896243) (odds ratio [OR] 1.25; P = 2.42 × 10-9 ) and within the intergenic region LNCAROD/DKK1 (rs1660760) (OR 0.78; P = 2.75 × 10-8 ). The risk variants in IFNGR1 significantly increased IFNGR1 messenger RNA expression in lipopolysaccharide-stimulated monocytes. In addition, our results replicated the association (P < 5 × 10-8 ) of 6 previously identified susceptibility loci in Behçet's disease: IL10, IL23R, IL12A-AS1, CCR3, ADO, and LACC1, reinforcing the notion that these loci are strong genetic factors in Behçet's disease shared across ancestries. We also identified >30 genetic susceptibility loci with a suggestive level of association (P < 5 × 10-5 ), which will require replication. Finally, functional annotation of genetic susceptibility loci in Behçet's disease revealed their possible regulatory roles and suggested potential causal genes and molecular mechanisms that could be further investigated. We performed the largest genetic association study in Behçet's disease to date. Our findings reveal novel putative functional variants associated with the disease and replicate and extend the genetic associations in other loci across multiple ancestries.

Global Landscape of 3D Architectural Anchors from Genome-Wide Analysis of Specific Chromatin Interactions

Chromatin folding and interactions play essential roles in fundamental processes such as regulation of gene expression and cellular specialization. While Hi-C measurements often detect numerous interactions (∼108), our previous results showed that only 2-9% of Hi-C interactions, which we call specific interactions, are required to generate ensembles of 3D single-cell chromatin conformations that collectively exhibit the observed Hi-C contact patterns. In this study, we carried out a genome-wide comprehensive analysis of these specific interactions.

Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles.

The exploration of three-dimensional chromatin interaction and organization provides insight into mechanisms underlying gene regulation, cell differentiation and disease development. Advances in chromosome conformation capture technologies, such as high-throughput chromosome conformation capture (Hi-C) and chromatin interaction analysis by paired-end tag (ChIA-PET), have enabled the exploration of chromatin interaction and organization. However, high-resolution Hi-C and ChIA-PET data are only available for a limited number of cell lines, and their acquisition is costly, time consuming, laborious and affected by theoretical limitations. Increasing evidence shows that DNA sequence and epigenomic features are informative predictors of regulatory interaction and chromatin architecture. Based on these features, numerous computational methods have been developed for the prediction of chromatin interaction and organization, whereas they are not extensively applied in biomedical study. A systematical study to summarize and evaluate such methods is still needed to facilitate their application. Here, we summarize 48 computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles, categorize them and compare their performance. Besides, we provide a comprehensive guideline for the selection of suitable methods to predict chromatin interaction and organization based on available data and biological question of interest.

Advances in three-dimensional genomics

Three-dimensional (3D) genomics is an emerging discipline that studies the 3D spatial structure and function of genomes, focusing on the 3D spatial conformation of genome sequences in the nucleus and its biological effects on biological processes such as DNA replication, DNA recombination and gene expression regulation. The invention of chromosome conformation capture (3C) technology speeds up the research on 3D genomics and its related fields. Furthermore, the development of 3C-based technologies, such as the genome-wide chromosome conformation capture (Hi-C) and chromatin interaction analysis using paired-end tag sequencing (ChIA-PET), help scientists get insight into the 3D genomes of various species. Aims of 3D genomics are to reveal the spatial genome organization, chromosomal interaction patterns, mechanisms underlying the transcriptional regulation and formation of biological traits of microorganism, plant, animal. Additionally, the identification of key genes and signaling pathways associated with biological processes and disease via chromosome 3C technology boosts the rapid development of agricultural science, life science and medical science. This paper reviews the research progress of 3D genomics, mainly in the concept of 3D genomics, the development of chromosome 3C technologies and their applications in agricultural science, life science and medical science, specifically in the field of tumor.

ChIAMM: A Mixture Model for Statistical Analysis of Long-Range Chromatin Interactions From ChIA-PET Experiments.

Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is an important experimental method for detecting specific protein-mediated chromatin loops genome-wide at high resolution. Here, we proposed a new statistical approach with a mixture model, chromatin interaction analysis using mixture model (ChIAMM), to detect significant chromatin interactions from ChIA-PET data. The statistical model is cast into a Bayesian framework to consider more systematic biases: the genomic distance, local enrichment, mappability, and GC content. Using different ChIA-PET datasets, we evaluated the performance of ChIAMM and compared it with the existing methods, including ChIA-PET Tool, ChiaSig, Mango, ChIA-PET2, and ChIAPoP. The result showed that the new approach performed better than most top existing methods in detecting significant chromatin interactions in ChIA-PET experiments.

Model-based analysis of chromatin interactions from dCas9-Based CAPTURE-3C-seq.

Deciphering long-range chromatin interactions is critical for understanding temporal and tissue-specific gene expression regulated by cis- and trans-acting factors. By combining the chromosome conformation capture (3C) and biotinylated dCas9 system, we previously established a method CAPTURE-3C-seq to unbiasedly identify high-resolution and locus-specific long-range DNA interactions. Here we present the statistical model and a flexible pipeline, C3S, for analysing CAPTURE-3C-seq or similar experimental data from raw sequencing reads to significantly interacting chromatin loci. C3S provides all steps for data processing, quality control and result illustration. It can automatically define the bin size based on the binding peak of the dCas9-targeted regions. Furthermore, it supports the analysis of intra- and inter-chromosomal interactions for different mammalian cell types. We successfully applied C3S across multiple datasets in human K562 cells and mouse embryonic stem cells (mESC) for detecting known and new chromatin interactions at multiple scales. Integrative and topological analysis of the interacted loci at the human β-globin gene cluster provides new insights into mechanisms in developmental gene regulation and network structure in local chromosomal architecture. Furthermore, computational results in mESCs reveal a role for chromatin interacting loops between enhancers and promoters in regulating alternative transcripts of the pluripotency gene OCT4.

Landscape of cohesin-mediated chromatin loops in the human genome

Physical interactions between distal regulatory elements have a key role in regulating gene expression, but the extent to which these interactions vary between cell types and contribute to cell-type-specific gene expression remains unclear. Here, to address these questions as part of phase III of the Encyclopedia of DNA Elements (ENCODE), we mapped cohesin-mediated chromatin loops, using chromatin interaction analysis by paired-end tag sequencing (ChIA-PET), and analysed gene expression in 24 diverse human cell types, including core ENCODE cell lines. Twenty-eight per cent of all chromatin loops vary across cell types; these variations modestly correlate with changes in gene expression and are effective at grouping cell types according to their tissue of origin. The connectivity of genes corresponds to different functional classes, with housekeeping genes having few contacts, and dosage-sensitive genes being more connected to enhancer elements. This atlas of chromatin loops complements the diverse maps of regulatory architecture that comprise the ENCODE Encyclopedia, and will help to support emerging analyses of genome structure and function.

ChIA-PIPE: A fully automated pipeline for comprehensive ChIA-PET data analysis and visualization.

ChIA-PET (chromatin interaction analysis with paired-end tags) enables genome-wide discovery of chromatin interactions involving specific protein factors, with base pair resolution. Interpretation of ChIA-PET data requires a robust analytic pipeline. Here, we introduce ChIA-PIPE, a fully automated pipeline for ChIA-PET data processing, quality assessment, visualization, and analysis. ChIA-PIPE performs linker filtering, read mapping, peak calling, and loop calling and automates quality control assessment for each dataset. To enable visualization, ChIA-PIPE generates input files for two-dimensional contact map viewing with Juicebox and HiGlass and provides a new dockerized visualization tool for high-resolution, browser-based exploration of peaks and loops. To enable structural interpretation, ChIA-PIPE calls chromatin contact domains, resolves allele-specific peaks and loops, and annotates enhancer-promoter loops. ChIA-PIPE also supports the analysis of other related chromatin-mapping data types.

Free energy-based model of CTCF-mediated chromatin looping in the human genome.

In recent years, high-throughput techniques have revealed considerable structural organization of the human genome with diverse regions of the chromatin interacting with each other in the form of loops. Some of these loops are quite complex and may encompass regions comprised of many interacting chain segments around a central locus. Popular techniques for extracting this information are chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) and high-throughput chromosome conformation capture (Hi-C). Here, we introduce a physics-based method to predict the three-dimensional structure of chromatin from population-averaged ChIA-PET data. The approach uses experimentally-validated data from human B-lymphoblastoid cells to generate 2D meta-structures of chromatin using a dynamic programming algorithm that explores the chromatin free energy landscape. By generating both optimal and suboptimal meta-structures we can calculate both the free energy and additionally the relative thermodynamic probability. A 3D structure prediction program with applied restraints then can be used to generate the tertiary structures. The main advantage of this approach for population-averaged experimental data is that it provides a way to distinguish between the principal and the spurious contacts. This study also finds that euchromatin appear to have rather precisely regulated 2D meta-structures compared to heterochromatin. The program source-code is available at https://github.com/plewczynski/looper.

Global analysis of histone modifications and long-range chromatin interactions revealed the differential cistrome changes and novel transcriptional players in human dilated cardiomyopathy

BackgroundAcetylation and methylation of histones alter the chromatin structure and accessibility that affect transcriptional regulators binding to enhancers and promoters. The binding of transcriptional regulators enables the interaction between enhancers and promoters, thus affecting gene expression. However, our knowledge of these epigenetic alternations in patients with heart failure remains limited. Methods and resultsFrom the comprehensive analysis of major histone modifications, 3-dimensional chromatin interactions, and transcriptome in left ventricular (LV) tissues from dilated cardiomyopathy (DCM) patients and non-heart failure (NF) donors, differential active enhancer and promoter regions were identified between NF and DCM. Moreover, the genome-wide average promoter signal is significantly lower in DCM than in NF. Super-enhancer (SE) analysis revealed that fewer SEs were found in DCM LVs than in NF ones, and three unique SE-associated genes between NF and DCM were identified. Moreover, SEs are enriched within the genomic region associated with long-range chromatin interactions. The differential enhancer-promoter interactions were observed in the known heart failure gene loci and are correlated with the gene expression levels. Motif analysis identified known cardiac factors and possible novel players for DCM. ConclusionsWe have established the cistrome of four histone modifications and chromatin interactome for enhancers and promoters in NF and DCM tissues. Differential histone modifications and enhancer-promoter interactions were found in DCM, which were associated with gene expression levels of a subset of disease-associated genes in human heart failure.

SAT-446 Transcriptome Comparison of a Natural T3 Regulated Process and a Treatment with T3

Thyroid hormones (TH) act mainly on the expression of the genome. It is well accepted that differential gene expression following treatment with TH recapitulates the expression profiles taking place during natural processes induced by these hormones. Although both processes seem to correlate at the scale of a few target genes, this hasn’t been addressed in a systematic manner. Our objectives were first to compare transcriptome variations after TH treatment with transcriptome variations during a TH controlled natural process and second to evaluate the proportion of the direct TH-response. The measurement of gene expression at genome scale (transcriptome) is obtained by sequencing all the messenger RNAs (RNA-seq). Direct response was sorted out by linking the transcription start sites of target genes using RNA-PET analysis with TR binding sites mapping using chromatin interaction analysis by paired-end tag sequencing (ChIA-PET). Indeed, ChIA-PET not only allow to map TR binding sites but also the physical interactions between them and transcription start sites of regulated genes. Our model is one of the striking developmental processes orchestrated by TH: amphibian metamorphosis. Tadpole transformation is marked by dramatic changes including de novo morphogenesis (limb), tissue remodelling (brain, intestine...) and organ resorption through apoptosis (tail). These changes involve cascades of gene regulation initiated by TH and their receptors. Because metamorphosis has close and interesting parallels with the perinatal period in mammals (including human), metamorphosis is thus an attractive model to analyze in a physiological context, the functions and mechanisms of action of TH. Here, we have focused on one Xenopus tropicalis organs, the tail fin skin which will disappear through cell death. We have compared natural metamorphosis with 24h of 10nM triiodothyronine (T3) treatment. We were able to observe several differences between T3 treatment and natural development. First, the genes regulated by T3 only correspond to a proportion of genes differentially expressed during metamorphosis. Second, T3-response genes start to be regulated well before tail regression (several days). Finally, T3-direct target genes represent a few percent of all the genes differentially expressed during tail regression. In conclusion, the comparison of transcriptomes of natural and induced metamorphosis allow us to reach a more precise understanding of TH action

Enhancer occlusion transcripts regulate the activity of human enhancer domains via transcriptional interference: a computational perspective.

Analysis of ENCODE long RNA-Seq and ChIP-seq (Chromatin Immunoprecipitation Sequencing) datasets for HepG2 and HeLa cell lines uncovered 1647 and 1958 transcripts that interfere with transcription factor binding to human enhancer domains. TFBSs (Transcription Factor Binding Sites) intersected by these ‘Enhancer Occlusion Transcripts’ (EOTrs) displayed significantly lower relative transcription factor (TF) binding affinities compared to TFBSs for the same TF devoid of EOTrs. Expression of most EOTrs was regulated in a cell line specific manner; analysis for the same TFBSs across cell lines, i.e. in the absence or presence of EOTrs, yielded consistently higher relative TF/DNA-binding affinities for TFBSs devoid of EOTrs. Lower activities of EOTr-associated enhancer domains coincided with reduced occupancy levels for histone tail modifications H3K27ac and H3K9ac. Similarly, the analysis of EOTrs with allele-specific expression identified lower activities for alleles associated with EOTrs. ChIA-PET (Chromatin Interaction Analysis by Paired-End Tag Sequencing) and 5C (Carbon Copy Chromosome Conformation Capture) uncovered that enhancer domains associated with EOTrs preferentially interacted with poised gene promoters. Analysis of EOTr regions with GRO-seq (Global run-on) data established the correlation of RNA polymerase pausing and occlusion of TF-binding. Our results implied that EOTr expression regulates human enhancer domains via transcriptional interference.