Population-based 3D genome structure analysis reveals driving forces in spatial genome organization

The positioning of chromosomes in the nucleus of a eukaryotic cell is highly organized and has a complex and dynamic relationship with gene expression. In the human malaria parasite Plasmodium falciparum, the clustering of a family of virulence genes correlates with their coordinated silencing and has a strong influence on the overall organization of the genome. To identify conserved and species-specific principles of genome organization, we performed Hi-C experiments and generated 3D genome models for five Plasmodium species and two related apicomplexan parasites. Plasmodium species mainly showed clustering of centromeres, telomeres, and virulence genes. In P. falciparum, the heterochromatic virulence gene cluster had a strong repressive effect on the surrounding nuclear space, while this was less pronounced in Plasmodium vivax and Plasmodium berghei, and absent in Plasmodium yoelii In Plasmodium knowlesi, telomeres and virulence genes were more dispersed throughout the nucleus, but its 3D genome showed a strong correlation with gene expression. The Babesia microti genome showed a classical Rabl organization with colocalization of subtelomeric virulence genes, while the Toxoplasma gondii genome was dominated by clustering of the centromeres and lacked virulence gene clustering. Collectively, our results demonstrate that spatial genome organization in most Plasmodium species is constrained by the colocalization of virulence genes. P. falciparum and P. knowlesi, the only two Plasmodium species with gene families involved in antigenic variation, are unique in the effect of these genes on chromosome folding, indicating a potential link between genome organization and gene expression in more virulent pathogens.

The nuclear genome is spatially organized into a three-dimensional (3D) architecture by physical association of large chromosomal domains with subnuclear compartments including the nuclear lamina at the radial periphery and nuclear speckles within the nucleoplasm1–5. However, how spatial genome architecture regulates human brain development has been overlooked owing to technical limitations. Here, we generate high-resolution maps of genomic interactions with the lamina and speckles in cells of the neurogenic lineage isolated from midgestational human cortex, uncovering an intimate association between subnuclear genome compartmentalization, chromatin state and transcription. During cortical neurogenesis, spatial genome organization is extensively remodeled, relocating hundreds of neuronal genes from the lamina to speckles including key neurodevelopmental genes bivalent for H3K27me3 and H3K4me3. At the lamina, bivalent genes have exceptionally low expression, and relocation to speckles enhances resolution of bivalent chromatin to H3K4me3 and increases transcription >7-fold. We further demonstrate that proximity to the nuclear periphery – not the presence of H3K27me3 – is the dominant factor in maintaining the lowly expressed, poised state of bivalent genes embedded in the lamina. In addition to uncovering a critical role of subnuclear genome compartmentalization in neurogenic transcriptional regulation, our results establish a new paradigm in which knowing the spatial location of a gene is necessary to understanding its epigenomic regulation.

Ankylosing Spondylitis (AS) is a chronic inflammatory arthritis of the spine exhibiting a strong genetic background. The mechanistic and functional understanding of the AS-associated genomic loci, identified with Genome Wide Association Studies (GWAS), remains challenging. Chromosome conformation capture (3C) and derivatives are recent techniques which are of great help in elucidating the spatial genome organization and of enormous support in uncover a mechanistic explanation for disease-associated genetic variants. The perturbation of three-dimensional (3D) genome hierarchy may lead to a plethora of human diseases, including rheumatological disorders. Here we illustrate the latest approaches and related findings on the field of genome organization, highlighting how the instability of 3D genome conformation may be among the causes of rheumatological disease phenotypes. We suggest a new perspective on the inclusive potential of a 3C approach to inform GWAS results in rheumatic diseases. 3D genome organization may ultimately lead to a more precise and comprehensive functional interpretation of AS association, which is the starting point for emerging and more specific therapies.

MotivationHigh-resolution Hi-C data are indispensable for the studies of three-dimensional (3D) genome organization at kilobase level. However, generating high-resolution Hi-C data (e.g. 5 kb) by conducting Hi-C experiments needs millions of mammalian cells, which may eventually generate billions of paired-end reads with a high sequencing cost. Therefore, it will be important and helpful if we can enhance the resolutions of Hi-C data by computational methods.ResultsWe developed a new computational method named HiCNN that used a 54-layer very deep convolutional neural network to enhance the resolutions of Hi-C data. The network contains both global and local residual learning with multiple speedup techniques included resulting in fast convergence. We used mean squared errors and Pearson’s correlation coefficients between real high-resolution and computationally predicted high-resolution Hi-C data to evaluate the method. The evaluation results show that HiCNN consistently outperforms HiCPlus, the only existing tool in the literature, when training and testing data are extracted from the same cell type (i.e. GM12878) and from two different cell types in the same or different species (i.e. GM12878 as training with K562 as testing, and GM12878 as training with CH12-LX as testing). We further found that the HiCNN-enhanced high-resolution Hi-C data are more consistent with real experimental high-resolution Hi-C data than HiCPlus-enhanced data in terms of indicating statistically significant interactions. Moreover, HiCNN can efficiently enhance low-resolution Hi-C data, which eventually helps recover two chromatin loops that were confirmed by 3D-FISH.Availability and implementationHiCNN is freely available at http://dna.cs.miami.edu/HiCNN/.Supplementary informationSupplementary data are available at Bioinformatics online.

Chromosome conformation capture technologies such as Hi-C are widely used to investigate the spatial organization of genomes. Because genome structures can vary considerably between individual cells of a population, interpreting ensemble-averaged Hi-C data can be challenging, in particular for long-range and interchromosomal interactions. We pioneered a probabilistic approach for the generation of a population of distinct diploid 3D genome structures consistent with all the chromatin-chromatin interaction probabilities from Hi-C experiments. Each structure in the population is a physical model of the genome in 3D. Analysis of these models yields new insights into the causes and the functional properties of the genome's organization in space and time. We provide a user-friendly software package, called PGS, which runs on local machines (for practice runs) and high-performance computing platforms. PGS takes a genome-wide Hi-C contact frequency matrix, along with information about genome segmentation, and produces an ensemble of 3D genome structures entirely consistent with the input. The software automatically generates an analysis report, and provides tools to extract and analyze the 3D coordinates of specific domains. Basic Linux command-line knowledge is sufficient for using this software. A typical running time of the pipeline is ∼3 d with 300 cores on a computer cluster to generate a population of 1,000 diploid genome structures at topological-associated domain (TAD)-level resolution.

CTCF-Mediated Human 3D Genome Architecture Reveals Chromatin Topology for Transcription

In eukaryotic cells, 3D genome plays an important role in the regulation of gene spatiotemporal expression, which is essential for the biological and developmental processes in a life cycle. In the past decade, the development of high-throughput technologies greatly enhances our ability to map the 3D genome organization, identifies multiple 3D genome structures, and investigates the functional role of 3D genome organization in gene regulation, which facilitates our understandings of cis-regulatory landscape and biological development. Comparing with the comprehensive analyses of 3D genome in mammals and model plants, the progress in soybean is much less. Future development and application of tools to precisely manipulate 3D genome structure at different levels will significantly strengthen the functional genome study and molecular breeding in soybean. Here, we review the recent progresses in 3D genome study and discuss future directions, which may help to improve soybean 3D functional genome study and molecular breeding.

To investigate three-dimensional (3D) genome organization in prokaryotic and eukaryotic cells, three main strategies are employed, namely nuclear proximity ligation-based methods, imaging tools (such as fluorescence in situ hybridization (FISH) and its derivatives), and computational/visualization methods. Proximity ligation-based methods are based on digestion and re-ligation of physically proximal cross-linked chromatin fragments accompanied by massively parallel DNA sequencing to measure the relative spatial proximity between genomic loci. Imaging tools enable direct visualization and quantification of spatial distances between genomic loci, and advanced implementation of (super-resolution) microscopy helps to significantly improve the resolution of images. Computational methods are used to map global 3D genome structures at various scales driven by experimental data, and visualization methods are used to visualize genome 3D structures in virtual 3D space-based on algorithms. In this review, we focus on the introduction of novel imaging and visualization methods to study 3D genomes. First, we introduce the progress made recently in 3D genome imaging in both fixed cell and live cells based on long-probe labeling, short-probe labeling, RNA FISH, and the CRISPR system. As the fluorescence-capturing capability of a particular microscope is very important for the sensitivity of bioimaging experiments, we also introduce two novel super-resolution microscopy methods, SDOM and low-power super-resolution STED, which have potential for time-lapse super-resolution live-cell imaging of chromatin. Finally, we review some software tools developed recently to visualize proximity ligation-based data. The imaging and visualization methods are complementary to each other, and all three strategies are not mutually exclusive. These methods provide powerful tools to explore the mechanisms of gene regulation and transcription in cell nuclei.

Three-dimensional (3D) genome organization becomes altered during development, aging and disease, but the factors regulating chromatin topology are incompletely understood and currently no technology can efficiently screen for new regulators of multi-scale chromatin organization. Here, we developed an image-based high-content screening platform (Perturb-tracing) that combines pooled CRISPR screens, a cellular barcode readout method (BARC-FISH) and chromatin tracing. We performed a loss-of-function screen in human cells, and visualized alterations to their 3D chromatin folding conformations, alongside perturbation-paired barcode readout in the same single cells. We discovered tens of new regulators of chromatin folding at different length scales, ranging from chromatin domains and compartments to chromosome territory. A subset of the regulators exhibited 3D genome effects associated with loop extrusion and A–B compartmentalization mechanisms, while others were largely unrelated to these known 3D genome mechanisms. Finally, we identified new regulators of nuclear architectures and found a functional link between chromatin compaction and nuclear shape. Altogether, our method enables scalable, high-content identification of chromatin and nuclear topology regulators that will stimulate new insights into the 3D genome.

To date, most studies explored changes in 3D-genome organization between different tissues or during differentiation, which involve massive reprogramming of transcriptional programs. Much fewer studies examined alterations in genome organization in response to cellular stress, which involves less pervasive transcriptional modulation. Here, we examined associations between spatial chromatin organization and gene expression in two different biological contexts: transcriptional programs determining cell identity and transcriptional responses to stress, using p53 activation as a model. We selected 10 cell lines of diverse tissues, and in each performed micro-C, RNA-seq, and p53 ChIP-seq, before and after p53 induction. In the comparison between cell types, we delineated marked correlations between gene expression and spatial genome organization and identified hundreds of active enhancer-promoter loops associated with the expression of cell-type marker genes. In contrast, within each cell type, no such links were observed for expression changes induced by p53 activation, even for enhancers and promoters activated by p53 binding. Our analysis points to a fundamental difference between chromatin interactions that define cell identity and those that are established in response to cellular stress. Our results on p53-induced transcriptional responses support the recently proposed TFactivity gradientmodel, which speculated a contact-independent mechanism for enhancer-promoter communication.

Human breast cancer cells exhibit organotropism during metastasis, showing preferential homing to certain organs such as bone, lung, liver, and brain. Spatial genome organization plays a crucial role in oncogenic transformation and progression, but the extent to which chromosome architecture contributes to organ-specific metastatic traits is unclear. This work characterizes chromosome architecture changes associated with organotropic metastatic traits. By comparing a collection of human genomic data from different subtypes of localized and lung metastatic breast cancer cells with both normal and cancerous lung cells, we find important trends of genomic reorganization. The most striking differences in 3D genome compartments segregate cell types according to their epithelial vs. mesenchymal status. This epithelial–mesenchymal transition (EMT) compartment signature occurs at genomic regions distinct from transcription-defined EMT signatures, suggesting a separate layer of regulation. Specifically querying organotropism, we find 3D genome changes consistent with adaptations needed to survive in a new microenvironment, with lung metastatic breast cancer cells exhibiting compartment switch signatures that shift the genome architecture to a lung cell-like conformation and brain metastatic prostate cancer cells showing compartment shifts toward a brain-like state. TCGA patient data reveals gene expression changes concordant with these organ-permissive compartment changes. These results suggest that genome architecture provides an additional level of cell fate specification informing organotropism and enabling survival at the metastatic site.

Hi-C technique is an important tool for the study of 3D genome organization. In the past few years, we have seen an explosion of Hi-C data in a variety of cell/tissue types. While these publicly available data presents an unprecedented opportunity to interrogate chromosomal architecture, how to quantitatively compare Hi-C data from different tissues and identify tissue-specific chromatin interactions remains challenging. Here, we present HiCComp, a comprehensive framework for comparing Hi-C data. HiCComp utilizes convolutional neural networks to extract key features in Hi-C interaction matrices in a fully automatic way. The core component of HiCComp is a triplet network, which contains three identical convolutional neural networks with shared parameters. The inputs to our network are three Hi-C matrices: two of them are biological replicates from the same cell type and the third one is from another cell type. The HiCComp network takes advantages of the two biological replicates to estimate the natural variation in the experiments and further use it to identify significant variations between Hi-C matrices from different cell types. Furthermore, we incorporate systematic occluding method into our framework so that we can identify the dynamic interaction regions from Hi-C maps. Finally, we show that the dynamic regions between two cell types are enriched for transcription factor binding sites and histone modifications that are associated with cis-regulatory functions, suggesting these variations in 3D genome structure are potentially gene regulatory events.

Genome-wide chromatin conformation capture assays provide formidable insights into the spatial organization of genomes. However, due to the complexity of the data structure, their integration in multi-omics workflows remains challenging. We present data structures, computational methods and visualization tools available in Bioconductor to investigate Hi-C, micro-C and other 3C-related data, in R. An online book (https://bioconductor.org/books/OHCA/) further provides prospective end users with a number of workflows to process, import, analyze and visualize any type of chromosome conformation capture data.

P-014: Disruption of chromosome territories in Multiple Myeloma

<h3>Abstract</h3> Somatic mutations arise during the life history of a cell. Mutations occurring in cancer driver genes may ultimately lead to the development of clinically detectable disease. Nascent cancer lineages...