Establishment of dsDNA-dsDNA interactions by the condensin complex.

Condensin is a structural maintenance of chromosomes (SMC) complex family member thought to build mitotic chromosomes by DNA loop extrusion. However, condensin variants unable to extrude loops, yet proficient in chromosome formation, were recently described. Here, we explore how condensin might alternatively build chromosomes. Using bulk biochemical and single-molecule experiments with purified fission yeast condensin, we observe that individual condensins sequentially and topologically entrap two double-stranded DNAs (dsDNAs). Condensin loading transitions through a state requiring DNA bending, as proposed for the related cohesin complex. While cohesin then favors the capture of a second single-stranded DNA (ssDNA), second dsDNA capture emerges as a defining feature of condensin. We provide complementary in vivo evidence for DNA-DNA capture in the form of condensin-dependent chromatin contacts within, as well as between, chromosomes. Our results support a "diffusion capture" model in which condensin acts in mitotic chromosome formation by sequential dsDNA-dsDNA capture.

Mapping Active Gene-Associated Chromatin Loops by ChIA-PET in Rice.

Hierarchical chromatin structures are critical for transcriptional regulation and many biological processes. It has been widely known that the linear genome of many plants and animals is partitioned into various chromatin interacting domains or gene regulatory modules with specific chromatin features, such as H3K4me3-related active interacting domains, H3K27me3 or Polycomb-related repressive domains, and H3K9me2-related heterochromatin domains. ChIA-PET, which combines chromatin immunoprecipitation (ChIP) assay with proximity ligation, can detect gene contact networks that are connected by co-regulated genes by pulling down specific chromatin complexes using an antibody of interest. Here, we describe a detailed, long-read ChIA-PET protocol for mapping promoter-centered active gene modules in plants.

Domains Rearranged Methylase 2 maintains DNA methylation at large DNA hypomethylated shores and long-range chromatin interactions in rice.

DNA methylation plays an important role in gene regulation and genomic stability. However, large DNA hypomethylated regions known as DNA methylation valleys (DMVs) or canyons have also been suggested to serve unique regulatory functions, largely unknown in rice (Oryza sativa). Here, we describe the DMVs in rice seedlings, which were highly enriched with developmental and transcription regulatory genes. Further detailed analysis indicated that grand DMVs (gDMVs) might be derived from nuclear integrants of organelle DNA (NORGs). Furthermore, Domains Rearranged Methylase 2 (OsDRM2) maintained DNA methylation at short DMV (sDMV) shores. Epigenetic maps indicated that sDMVs were marked with H3K4me3 and/or H3K27me3, although the loss of DNA methylation had a negligible effect on histone modification within these regions. In addition, we constructed H3K27me3-associated interaction maps for homozygous T-DNA insertion mutant of the gene (osdrm2) and wild type (WT). From a global perspective, most (90%) compartments were stable between osdrm2 and WT plants. At a high resolution, we observed a dramatic loss of long-range chromatin loops in osdrm2, which suffered an extensive loss of non-CG (CHG and CHH, H = A, T, or C) methylation. From another viewpoint, the loss of non-CG methylation at sDMV shores in osdrm2 could disrupt H3K27me3-mediated chromatin interaction networks. Overall, our results demonstrated that DMVs are a key genomic feature in rice and are precisely regulated by epigenetic modifications, including DNA methylation and histone modifications. OsDRM2 maintained DNA methylation at sDMV shores, while OsDRM2 deficiency strongly affected three-dimensional (3D) genome architectures.

3D organization of regulatory elements for transcriptional regulation in Arabidopsis.

BACKGROUND: Although spatial organization of compartments and topologically associating domains at large scale is relatively well studied, the spatial organization of regulatory elements at fine scale is poorly understood in plants. RESULTS: Here we perform high-resolution chromatin interaction analysis using paired-end tag sequencing approach. We map chromatin interactions tethered with RNA polymerase II and associated with heterochromatic, transcriptionally active, and Polycomb-repressive histone modifications in Arabidopsis. Analysis of the regulatory repertoire shows that distal active cis-regulatory elements are linked to their target genes through long-range chromatin interactions with increased expression of the target genes, while poised cis-regulatory elements are linked to their target genes through long-range chromatin interactions with depressed expression of the target genes. Furthermore, we demonstrate that transcription factor MYC2 is critical for chromatin spatial organization, and propose that MYC2 occupancy and MYC2-mediated chromatin interactions coordinately facilitate transcription within the framework of 3D chromatin architecture. Analysis of functionally related gene-defined chromatin connectivity networks reveals that genes implicated in flowering-time control are functionally compartmentalized into separate subdomains via their spatial activity in the leaf or shoot apical meristem, linking active mark- or Polycomb-repressive mark-associated chromatin conformation to coordinated gene expression. CONCLUSION: The results reveal that the regulation of gene transcription in Arabidopsis is not only by linear juxtaposition, but also by long-range chromatin interactions. Our study uncovers the fine scale genome organization of Arabidopsis and the potential roles of such organization in orchestrating transcription and development.

Predicting enhancer-promoter interaction based on epigenomic signals.

Introduction: The physical interactions between enhancers and promoters are often involved in gene transcriptional regulation. High tissue-specific enhancer-promoter interactions (EPIs) are responsible for the differential expression of genes. Experimental methods are time-consuming and labor-intensive in measuring EPIs. An alternative approach, machine learning, has been widely used to predict EPIs. However, most existing machine learning methods require a large number of functional genomic and epigenomic features as input, which limits the application to different cell lines. Methods: In this paper, we developed a random forest model, HARD (H3K27ac, ATAC-seq, RAD21, and Distance), to predict EPI using only four types of features. Results: Independent tests on a benchmark dataset showed that HARD outperforms other models with the fewest features. Discussion: Our results revealed that chromatin accessibility and the binding of cohesin are important for cell-line-specific EPIs. Furthermore, we trained the HARD model in the GM12878 cell line and performed testing in the HeLa cell line. The cross-cell-lines prediction also performs well, suggesting it has the potential to be applied to other cell lines.

Mapping Multi-factor-mediated Chromatin Interactions to Assess Dysregulation of Lung Cancer-related Genes.

Studies on the lung cancer genome are indispensable for developing a cure for lung cancer. Whole-genome resequencing, genome-wide association studies, and transcriptome sequencing have greatly improved our understanding of the cancer genome. However, dysregulation of long-range chromatin interactions in lung cancer remains poorly described. To better understand the three-dimensional (3D) genomic interaction features of the lung cancer genome, we used the A549 cell line as a model system and generated high-resolution chromatin interactions associated with RNA polymerase II (RNAPII), CCCTC-binding factor (CTCF), enhancer of zeste homolog 2 (EZH2), and histone 3 lysine 27 trimethylation (H3K27me3) using long-read chromatin interaction analysis by paired-end tag sequencing (ChIA-PET). Analysis showed that EZH2/H3K27me3-mediated interactions further repressed target genes, either through loops or domains, and their distributions along the genome were distinct from and complementary to those associated with RNAPII. Cancer-related genes were highly enriched with chromatin interactions, and chromatin interactions specific to the A549 cell line were associated with oncogenes and tumor suppressor genes, such as additional repressive interactions on FOXO4 and promoter-promoter interactions between NF1 and RNF135. Knockout of an anchor associated with chromatin interactions reversed the dysregulation of cancer-related genes, suggesting that chromatin interactions are essential for proper expression of lung cancer-related genes. These findings demonstrate the 3D landscape and gene regulatory relationships of the lung cancer genome.

Long-range interaction within the chromatin domain determines regulatory patterns in porcine skeletal muscle.

Spatial chromatin structure is crucial for understanding the early growth and development of porcine skeletal muscle. However, its characteristic of 3D architecture and elaborate regulation of gene transcription remains unclear. In this study, ChIA-PET method is used to study the changes of early chromatin three-dimensional structure in skeletal muscle of lean type Yorkshire pig and fat type Meishan pig. Integrating the in situ Hi-C data revealed the 3D architecture and long-range interaction of the porcine muscle. The results showed the CTCF/RNAPII mediated long-range interaction shapes the different chromatin architecture and dominates the unique regulation of enhancers. In addition, the results revealed that key myogenic genes like ssc-mir-1 had a unique enhancer regulation function in myogenesis. Interestingly, the FGF6 gene is of breed-specific regulation, implying the difference between two breeds in skeletal muscle development. Our research thus may provide a clue for the porcine genetic improvement of skeletal muscle.

Mapping nucleosome and chromatin architectures: A survey of computational methods.

With ever-growing genomic sequencing data, the data variabilities and the underlying biases of the sequencing technologies pose significant computational challenges ranging from the need for accurately detecting the nucleosome positioning or chromatin interaction to the need for developing normalization methods to eliminate systematic biases. This review mainly surveys the computational methods for mapping the higher-resolution nucleosome and higher-order chromatin architectures. While a detailed discussion of the underlying algorithms is beyond the scope of our survey, we have discussed the methods and tools that can detect the nucleosomes in the genome, then demonstrated the computational methods for identifying 3D chromatin domains and interactions. We further illustrated computational approaches for integrating multi-omics data with Hi-C data and the advance of single-cell (sc)Hi-C data analysis. Our survey provides a comprehensive and valuable resource for biomedical scientists interested in studying nucleosome organization and chromatin structures as well as for computational scientists who are interested in improving upon them.

Whole-genome methods to define DNA and histone accessibility and long-range interactions in chromatin.

Defining the genome-wide chromatin landscape has been a goal of experimentalists for decades. Here we review highlights of these efforts, from seminal experiments showing discontinuities in chromatin structure related to gene activation to extensions of these methods elucidating general features of chromatin related to gene states by exploiting deep sequencing methods. We also review chromatin conformational capture methods to identify patterns in long-range interactions between genomic loci.

Bacon: a comprehensive computational benchmarking framework for evaluating targeted chromatin conformation capture-specific methodologies.

Chromatin conformation capture (3C)-based technologies have enabled the accurate detection of topological genomic interactions, and the adoption of ChIP techniques to 3C-based protocols makes it possible to identify long-range interactions. To analyze these large and complex datasets, computational methods are undergoing rapid and expansive evolution. Thus, a thorough evaluation of these analytical pipelines is necessary to identify which commonly used algorithms and processing pipelines need to be improved. Here we present a comprehensive benchmark framework, Bacon, to evaluate the performance of several computational methods. Finally, we provide practical recommendations for users working with HiChIP and/or ChIA-PET analyses.

3D-Epigenomic Regulation of Gene Transcription in Hepatocellular Carcinoma.

The fundamental cause of transcription dysregulation in hepatocellular carcinoma (HCC) remains elusive. To investigate the underlying mechanisms, comprehensive 3D-epigenomic analyses are performed in cellular models of THLE2 (a normal hepatocytes cell line) and HepG2 (a hepatocellular carcinoma cell line) using integrative approaches for chromatin topology, genomic and epigenomic variation, and transcriptional output. Comparing the 3D-epigenomes in THLE2 and HepG2 reveal that most HCC-associated genes are organized in complex chromatin interactions mediated by RNA polymerase II (RNAPII). Incorporation of genome-wide association studies (GWAS) data enables the identification of non-coding genetic variants that are enriched in distal enhancers connecting to the promoters of HCC-associated genes via long-range chromatin interactions, highlighting their functional roles. Interestingly, CTCF binding and looping proximal to HCC-associated genes appear to form chromatin architectures that overarch RNAPII-mediated chromatin interactions. It is further demonstrated that epigenetic variants by DNA hypomethylation at a subset of CTCF motifs proximal to HCC-associated genes can modify chromatin topological configuration, which in turn alter RNAPII-mediated chromatin interactions and lead to dysregulation of transcription. Together, the 3D-epigenomic analyses provide novel insights of multifaceted interplays involving genetics, epigenetics, and chromatin topology in HCC cells.

Understanding 3D Genome Organization and Its Effect on Transcriptional Gene Regulation Under Environmental Stress in Plant: A Chromatin Perspective.

The genome of a eukaryotic organism is comprised of a supra-molecular complex of chromatin fibers and intricately folded three-dimensional (3D) structures. Chromosomal interactions and topological changes in response to the developmental and/or environmental stimuli affect gene expression. Chromatin architecture plays important roles in DNA replication, gene expression, and genome integrity. Higher-order chromatin organizations like chromosome territories (CTs), A/B compartments, topologically associating domains (TADs), and chromatin loops vary among cells, tissues, and species depending on the developmental stage and/or environmental conditions (4D genomics). Every chromosome occupies a separate territory in the interphase nucleus and forms the top layer of hierarchical structure (CTs) in most of the eukaryotes. While the A and B compartments are associated with active (euchromatic) and inactive (heterochromatic) chromatin, respectively, having well-defined genomic/epigenomic features, TADs are the structural units of chromatin. Chromatin architecture like TADs as well as the local interactions between promoter and regulatory elements correlates with the chromatin activity, which alters during environmental stresses due to relocalization of the architectural proteins. Moreover, chromatin looping brings the gene and regulatory elements in close proximity for interactions. The intricate relationship between nucleotide sequence and chromatin architecture requires a more comprehensive understanding to unravel the genome organization and genetic plasticity. During the last decade, advances in chromatin conformation capture techniques for unravelling 3D genome organizations have improved our understanding of genome biology. However, the recent advances, such as Hi-C and ChIA-PET, have substantially increased the resolution, throughput as well our interest in analysing genome organizations. The present review provides an overview of the historical and contemporary perspectives of chromosome conformation capture technologies, their applications in functional genomics, and the constraints in predicting 3D genome organization. We also discuss the future perspectives of understanding high-order chromatin organizations in deciphering transcriptional regulation of gene expression under environmental stress (4D genomics). These might help design the climate-smart crop to meet the ever-growing demands of food, feed, and fodder.

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 interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences.

Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences. ChINN predicts CTCF- and RNA polymerase II-associated and Hi-C chromatin interactions. ChINN shows good across-sample performances and captures various sequence features for chromatin interaction prediction. We apply ChINN to 6 chronic lymphocytic leukemia (CLL) patient samples and a published cohort of 84 CLL open chromatin samples. Our results demonstrate extensive heterogeneity in chromatin interactions among CLL patient samples.

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.

Oncogenic extrachromosomal DNA functions as mobile enhancers to globally amplify chromosomal transcription.

Extrachromosomal, circular DNA (ecDNA) is emerging as a prevalent yet less characterized oncogenic alteration in cancer genomes. We leverage ChIA-PET and ChIA-Drop chromatin interaction assays to characterize genome-wide ecDNA-mediated chromatin contacts that impact transcriptional programs in cancers. ecDNAs in glioblastoma patient-derived neurosphere and prostate cancer cell cultures are marked by widespread intra-ecDNA and genome-wide chromosomal interactions. ecDNA-chromatin contact foci are characterized by broad and high-level H3K27ac signals converging predominantly on chromosomal genes of increased expression levels. Prostate cancer cells harboring synthetic ecDNA circles composed of characterized enhancers result in the genome-wide activation of chromosomal gene transcription. Deciphering the chromosomal targets of ecDNAs at single-molecule resolution reveals an association with actively expressed oncogenes spatially clustered within ecDNA-directed interaction networks. Our results suggest that ecDNA can function as mobile transcriptional enhancers to promote tumor progression and manifest a potential synthetic aneuploidy mechanism of transcription control in cancer.

Integration of Multiple Resolution Data in 3D Chromatin Reconstruction Using ChromStruct.

The three-dimensional structure of chromatin in the cellular nucleus carries important information that is connected to physiological and pathological correlates and dysfunctional cell behaviour. As direct observation is not feasible at present, on one side, several experimental techniques have been developed to provide information on the spatial organization of the DNA in the cell; on the other side, several computational methods have been developed to elaborate experimental data and infer 3D chromatin conformations. The most relevant experimental methods are Chromosome Conformation Capture and its derivatives, chromatin immunoprecipitation and sequencing techniques (CHIP-seq), RNA-seq, fluorescence in situ hybridization (FISH) and other genetic and biochemical techniques. All of them provide important and complementary information that relate to the three-dimensional organization of chromatin. However, these techniques employ very different experimental protocols and provide information that is not easily integrated, due to different contexts and different resolutions. Here, we present an open-source tool, which is an expansion of the previously reported code ChromStruct, for inferring the 3D structure of chromatin that, by exploiting a multilevel approach, allows an easy integration of information derived from different experimental protocols and referred to different resolution levels of the structure, from a few kilobases up to Megabases. Our results show that the introduction of chromatin modelling features related to CTCF CHIA-PET data, histone modification CHIP-seq, and RNA-seq data produce appreciable improvements in ChromStruct's 3D reconstructions, compared to the use of HI-C data alone, at a local level and at a very high resolution.

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.

Mustache: multi-scale detection of chromatin loops from Hi-C and Micro-C maps using scale-space representation.

We present MUSTACHE, a new method for multi-scale detection of chromatin loops from Hi-C and Micro-C contact maps. MUSTACHE employs scale-space theory, a technical advance in computer vision, to detect blob-shaped objects in contact maps. MUSTACHE is scalable to kilobase-resolution maps and reports loops that are highly consistent between replicates and between Hi-C and Micro-C datasets. Compared to other loop callers, such as HiCCUPS and SIP, MUSTACHE recovers a higher number of published ChIA-PET and HiChIP loops as well as loops linking promoters to regulatory elements. Overall, MUSTACHE enables an efficient and comprehensive analysis of chromatin loops. Available at: https://github.com/ay-lab/mustache .