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.

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.

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.
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.
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.

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.

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.

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.

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.

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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.

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.