Conformational heterogeneity in human interphase chromosome organization reconciles the FISH and Hi-C paradox

Delayed Excitations Induce Polymer Looping and Coherent Motion

We consider inhomogeneous polymers driven by energy-consuming active processes which encode temporal patterns of athermal kicks. We find that such temporal excitation programs, propagated by tension along the polymer, can effectively couple distinct polymer loci. Consequently, distant loci exhibit correlated motions that fold the polymer into specific conformations, as set by the local actions of the active processes and their distribution along the polymer. Interestingly, active kicks that are canceled out by a time-delayed echo can induce strong compaction of the active polymer.

Topological Spectra and Entropy of Chromatin Loop Networks

The 3D folding of a mammalian gene can be studied by a polymer model, where the chromatin fiber is represented by a semiflexible polymer which interacts with multivalent proteins, representing complexes of DNA-binding transcription factors and RNA polymerases. This physical model leads to the natural emergence of clusters of proteins and binding sites, accompanied by the folding of chromatin into a set of topologies, each associated with a different network of loops. Here, we combine numerics and analytics to first classify these networks and then find their relative importance or statistical weight, when the properties of the underlying polymer are those relevant to chromatin. Unlike polymer networks previously studied, our chromatin networks have finite average distances between successive binding sites, and this leads to giant differences between the weights of topologies with the same number of edges and nodes but different wiring. These weights strongly favor rosettelike structures with a local cloud of loops with respect to more complicated nonlocal topologies. Our results suggest that genes should overwhelmingly fold into a small fraction of all possible 3D topologies, which can be robustly characterized by the framework we propose here.

Chromatin structure from high resolution microscopy: Scaling laws and microphase separation

Recent advances in experimental fluorescence microscopy allow high accuracy determination (resolution of 50 nm) of the three-dimensional physical location of multiple (up to ∼10^{2}) tagged regions of the chromosome. We investigate publicly available microscopy data for two loci of the human Chr21 obtained from multiplexed fluorescence in situ hybridization (FISH) methods for different cell lines and treatments. Inspired by polymer physics models, our analysis centers around distance distributions between different tags with the aim being to unravel the chromatin conformational arrangements. We show that for any specific genomic site, there are (at least) two different conformational arrangements of chromatin, implying coexisting distinct topologies which we refer to as phase α and phase β. These two phases show different scaling behaviors: the former is consistent with a crumpled globule, while the latter indicates a confined, but more extended conformation, such as a looped domain. The identification of these distinct phases sheds light on the coexistence of multiple chromatin topologies and provides insights into the effects of cellular context and/or treatments on chromatin structure.

Large-scale data-driven and physics-based models offer insights into the relationships among the structures, dynamics, and functions of chromosomes

The organized three-dimensional chromosome architecture in the cell nucleus provides scaffolding for precise regulation of gene expression. When the cell changes its identity in the cell-fate decision-making process, extensive rearrangements of chromosome structures occur accompanied by large-scale adaptations of gene expression, underscoring the importance of chromosome dynamics in shaping genome function. Over the last two decades, rapid development of experimental methods has provided unprecedented data to characterize the hierarchical structures and dynamic properties of chromosomes. In parallel, these enormous data offer valuable opportunities for developing quantitative computational models. Here, we review a variety of large-scale polymer models developed to investigate the structures and dynamics of chromosomes. Different from the underlying modeling strategies, these approaches can be classified into data-driven ('top-down') and physics-based ('bottom-up') categories. We discuss their contributions to offering valuable insights into the relationships among the structures, dynamics, and functions of chromosomes and propose the perspective of developing data integration approaches from different experimental technologies and multidisciplinary theoretical/simulation methods combined with different modeling strategies.

Network models of chromatin structure

Increasing numbers of datasets and experimental assays that capture the organization of chromatin inside the nucleus warrant an effort to develop tools to visualize and analyze these structures. Alongside polymer physics or constraint-based modeling, network theory approaches to describe 3D epigenome organization have gained in popularity. Representing genomic regions as nodes in a network enables visualization of 1D epigenomics datasets in the context of chromatin structure maps, while network theory metrics can be used to describe 3D epigenome organization and dynamics. In this review, we summarize the most salient applications of network theory to the study of chromatin contact maps, demonstrating its potential in revealing epigenomic patterns and relating them to cellular phenotypes.

Structural changes in chromosomes driven by multiple condensin motors during mitosis

We create a computational framework that utilizes loop extrusion (LE) by multiple condensin I/II motors to predict changes in chromosome organization during mitosis. The theory accurately reproduces the experimental contact probability profiles for the mitotic chromosomes in HeLa and DT40 cells. The LE rate is smaller at the start of mitosis and increases as the cells approach metaphase. Condensin II-mediated mean loop size is about six times larger than loops because of condensin I. The loops, which overlap each other, are stapled to a central dynamically changing helical scaffold formed by the motors during the LE process. A polymer physics-based data-driven method that uses the Hi-C contact map as the only input shows that the helix is characterized as random helix perversions (RHPs) in which the handedness changes randomly along the scaffold. The theoretical predictions, which are testable using imaging experiments, do not contain any parameters.

Compartmentalization with nuclear landmarks yields random, yet precise, genome organization

The 3D organization of eukaryotic genomes plays an important role in genome function. While significant progress has been made in deciphering the folding mechanisms of individual chromosomes, the principles of the dynamic large-scale spatial arrangement of all chromosomes inside the nucleus are poorly understood. We use polymer simulations to model the diploid human genome compartmentalization relative to nuclear bodies such as nuclear lamina, nucleoli, and speckles. We show that a self-organization process based on a cophase separation between chromosomes and nuclear bodies can capture various features of genome organization, including the formation of chromosome territories, phase separation of A/B compartments, and the liquid property of nuclear bodies. The simulated 3D structures quantitatively reproduce both sequencing-based genomic mapping and imaging assays that probe chromatin interaction with nuclear bodies. Importantly, our model captures the heterogeneous distribution of chromosome positioning across cells while simultaneously producing well-defined distances between active chromatin and nuclear speckles. Such heterogeneity and preciseness of genome organization can coexist due to the nonspecificity of phase separation and the slow chromosome dynamics. Together, our work reveals that the cophase separation provides a robust mechanism for us to produce functionally important 3D contacts without requiring thermodynamic equilibration that can be difficult to achieve.

A maximum-entropy model to predict 3D structural ensembles of chromatin from pairwise distances with applications to interphase chromosomes and structural variants

The principles that govern the organization of genomes, which are needed for an understanding of how chromosomes are packaged and function in eukaryotic cells, could be deciphered if the three-dimensional (3D) structures are known. Recently, single-cell imaging techniques have been developed to determine the 3D coordinates of genomic loci in vivo. Here, we introduce a computational method (Distance Matrix to Ensemble of Structures, DIMES), based on the maximum entropy principle, with experimental pairwise distances between loci as constraints, to generate a unique ensemble of 3D chromatin structures. Using the ensemble of structures, we quantitatively account for the distribution of pairwise distances, three-body co-localization, and higher-order interactions. The DIMES method can be applied to both small and chromosome-scale imaging data to quantify the extent of heterogeneity and fluctuations in the shapes across various length scales. We develop a perturbation method in conjunction with DIMES to predict the changes in 3D structures from structural variations. Our method also reveals quantitative differences between the 3D structures inferred from Hi-C and those measured in imaging experiments. Finally, the physical interpretation of the parameters extracted from DIMES provides insights into the origin of phase separation between euchromatin and heterochromatin domains.

On the Advent of Super-Resolution Microscopy in the Realm of Polycomb Proteins

The genomes of metazoans are organized at multiple spatial scales, ranging from the double helix of DNA to whole chromosomes. The intermediate genomic scale of kilobases to megabases, which corresponds to the 50-300 nm spatial scale, is particularly interesting, as the 3D arrangement of chromatin is implicated in multiple regulatory mechanisms. In this context, polycomb group (PcG) proteins stand as major epigenetic modulators of chromatin function, acting prevalently as repressors of gene transcription by combining chemical modifications of target histones with physical crosslinking of distal genomic regions and phase separation. The recent development of super-resolution microscopy (SRM) has strongly contributed to improving our comprehension of several aspects of nano-/mesoscale (10-200 nm) chromatin domains. Here, we review the current state-of-the-art SRM applied to PcG proteins, showing that the application of SRM to PcG activity and organization is still quite limited and mainly focused on the 3D assembly of PcG-controlled genomic loci. In this context, SRM approaches have mostly been applied to multilabel fluorescence in situ hybridization (FISH). However, SRM data have complemented the maps obtained from chromosome capture experiments and have opened a new window to observe how 3D chromatin topology is modulated by PcGs.

Dissecting the cosegregation probability from genome architecture mapping

Genome architecture mapping (GAM) is a recently developed methodology that offers the cosegregation probability of two genomic segments from an ensemble of thinly sliced nuclear profiles, enabling us to probe and decipher three-dimensional chromatin organization. The cosegregation probability from GAM binned at 1 Mb, which thus probes the length scale associated with the genomic separation greater than 1 Mb, is, however, not identical to the contact probability obtained from Hi-C, and its correlation with interlocus distance measured with fluorescence in situ hybridization is not so good as the contact probability. In this study, by using a polymer-based model of chromatins, we derive a theoretical expression of the cosegregation probability as well as that of the contact probability and carry out quantitative analyses of how they differ from each other. The results from our study, validated with in silico GAM analysis on three-dimensional genome structures from fluorescence in situ hybridization, suggest that to attain strong correlation with the interlocus distance, a properly normalized version of cosegregation probability needs to be calculated based on a large number of nuclear slices (n>103).

Heterogeneous interactions and polymer entropy decide organization and dynamics of chromatin domains

Chromatin is known to be organized into multiple domains of varying sizes and compaction. While these domains are often imagined as static structures, they are highly dynamic and show cell-to-cell variability. Since processes such as gene regulation and DNA replication occur in the context of these domains, it is important to understand their organization, fluctuation, and dynamics. To simulate chromatin domains, one requires knowledge of interaction strengths among chromatin segments. Here, we derive interaction-strength parameters from experimentally known contact maps and use them to predict chromatin organization and dynamics. Taking two domains on the human chromosome as examples, we investigate its three-dimensional organization, size/shape fluctuations, and dynamics of different segments within a domain, accounting for hydrodynamic effects. Considering different cell types, we quantify changes in interaction strengths and chromatin shape fluctuations in different epigenetic states. Perturbing the interaction strengths systematically, we further investigate how epigenetic-like changes can alter the spatio-temporal nature of the domains. Our results show that heterogeneous weak interactions are crucial in determining the organization of the domains. Computing effective stiffness and relaxation times, we investigate how perturbations in interactions affect the solid- and liquid-like nature of chromatin domains. Quantifying dynamics of chromatin segments within a domain, we show how the competition between polymer entropy and interaction energy influence the timescales of loop formation and maintenance of stable loops.

A Liquid State Perspective on Dynamics of Chromatin Compartments

The interior of the eukaryotic cell nucleus has a crowded and heterogeneous environment packed with chromatin polymers, regulatory proteins, and RNA molecules. Chromatin polymer, assisted by epigenetic modifications, protein and RNA binders, forms multi-scale compartments which help regulate genes in response to cellular signals. Furthermore, chromatin compartments are dynamic and tend to evolve in size and composition in ways that are not fully understood. The latest super-resolution imaging experiments have revealed a much more dynamic and stochastic nature of chromatin compartments than was appreciated before. An emerging mechanism explaining chromatin compartmentalization dynamics is the phase separation of protein and nucleic acids into membraneless liquid condensates. Consequently, concepts and ideas from soft matter and polymer systems have been rapidly entering the lexicon of cell biology. In this respect, the role of computational models is crucial for establishing a rigorous and quantitative foundation for the new concepts and disentangling the complex interplay of forces that contribute to the emergent patterns of chromatin dynamics and organization. Several multi-scale models have emerged to address various aspects of chromatin dynamics, ranging from equilibrium polymer simulations, hybrid non-equilibrium simulations coupling protein binding and chromatin folding, and mesoscopic field-theoretic models. Here, we review these emerging theoretical paradigms and computational models with a particular focus on chromatin’s phase separation and liquid-like properties as a basis for nuclear organization and dynamics.

Chromatin as self-returning walks: From population to single cell and back

With a growing understanding of the chromatin structure, many efforts remain focused on bridging the gap between what is suggested by population-averaged data and what is visualized for single cells. A popular approach to traversing these scales is to fit a polymer model to Hi-C contact data. However, Hi-C is an average of millions to billions of cells, and each cell may not contain all population-averaged contacts. Therefore, we employ a novel approach of summing individual chromosome trajectories-determined by our Self-Returning Random Walk model-to create populations of cells. We allow single cells to consist of disparate structures and reproduce a variety of experimentally relevant contact maps. We show that the amount of shared topology between cells, and their mechanism of formation, changes the population-averaged structure. Therefore, we present a modeling technique that, with few constraints and little oversight, can be used to understand which single-cell chromatin structures underlie population-averaged behavior.

Physics of biomolecular recognition and conformational dynamics

Biomolecular recognition usually leads to the formation of binding complexes, often accompanied by large-scale conformational changes. This process is fundamental to biological functions at the molecular and cellular levels. Uncovering the physical mechanisms of biomolecular recognition and quantifying the key biomolecular interactions are vital to understand these functions. The recently developed energy landscape theory has been successful in quantifying recognition processes and revealing the underlying mechanisms. Recent studies have shown that in addition to affinity, specificity is also crucial for biomolecular recognition. The proposed physical concept of intrinsic specificity based on the underlying energy landscape theory provides a practical way to quantify the specificity. Optimization of affinity and specificity can be adopted as a principle to guide the evolution and design of molecular recognition. This approach can also be used in practice for drug discovery using multidimensional screening to identify lead compounds. The energy landscape topography of molecular recognition is important for revealing the underlying flexible binding or binding-folding mechanisms. In this review, we first introduce the energy landscape theory for molecular recognition and then address four critical issues related to biomolecular recognition and conformational dynamics: (1) specificity quantification of molecular recognition; (2) evolution and design in molecular recognition; (3) flexible molecular recognition; (4) chromosome structural dynamics. The results described here and the discussions of the insights gained from the energy landscape topography can provide valuable guidance for further computational and experimental investigations of biomolecular recognition and conformational dynamics.

FISH Going Meso-Scale: A Microscopic Search for Chromatin Domains

The intimate relationships between genome structure and function direct efforts toward deciphering three-dimensional chromatin organization within the interphase nuclei at different genomic length scales. For decades, major insights into chromatin structure at the level of large-scale euchromatin and heterochromatin compartments, chromosome territories, and subchromosomal regions resulted from the evolution of light microscopy and fluorescence in situ hybridization. Studies of nanoscale nucleosomal chromatin organization benefited from a variety of electron microscopy techniques. Recent breakthroughs in the investigation of mesoscale chromatin structures have emerged from chromatin conformation capture methods (C-methods). Chromatin has been found to form hierarchical domains with high frequency of local interactions from loop domains to topologically associating domains and compartments. During the last decade, advances in super-resolution light microscopy made these levels of chromatin folding amenable for microscopic examination. Here we are reviewing recent developments in FISH-based approaches for detection, quantitative measurements, and validation of contact chromatin domains deduced from C-based data. We specifically focus on the design and application of Oligopaint probes, which marked the latest progress in the imaging of chromatin domains. Vivid examples of chromatin domain FISH-visualization by means of conventional, super-resolution light and electron microscopy in different model organisms are provided.

Molecular Karyotyping on Populus simonii × P. nigra and the Derived Doubled Haploid

The molecular karyotype could represent the basic genetic make-up in a cell nucleus of an organism or species. A doubled haploid (DH) is a genotype formed from the chromosome doubling of haploid cells. In the present study, molecular karyotype analysis of the poplar hybrid Populus simonii × P. nigra (P. xiaohei) and the derived doubled haploids was carried out with labeled telomeres, rDNA, and two newly repetitive sequences as probes by fluorescence in situ hybridization (FISH). The tandem repeats, pPC349_XHY and pPD284_XHY, with high-sequence homology were used, and the results showed that they presented the colocalized distribution signal in chromosomes. For P. xiaohei, pPD284_XHY produced hybridizations in chromosomes 1, 5, 8, and 9 in the hybrid. The combination of pPD284_XHY, 45S rDNA, and 5S rDNA distinctly distinguished six pairs of chromosomes, and the three pairs of chromosomes showed a significant difference in the hybridization between homologous chromosomes. The repeat probes used produced similar FISH hybridizations in the DH; nevertheless, pPD284_XHY generated an additional hybridization site in the telomere region of chromosome 14. Moreover, two pairs of chromosomes showed differential hybridization distributions between homologous chromosomes. Comparisons of the distinguished chromosomes between hybrid and DH poplar showed that three pairs of chromosomes in the DH presented hybridization patterns that varied from those of the hybrid. The No. 8 chromosome in DH and one of the homologous chromosomes in P. xiaohei shared highly similar FISH patterns, which suggested the possibility of intact or mostly partial transfer of the chromosome between the hybrid and DH. Our study will contribute to understanding the genetic mechanism of chromosomal variation in P. xiaohei and derived DH plants.

Multiscale modeling of genome organization with maximum entropy optimization

Three-dimensional (3D) organization of the human genome plays an essential role in all DNA-templated processes, including gene transcription, gene regulation, and DNA replication. Computational modeling can be an effective way of building high-resolution genome structures and improving our understanding of these molecular processes. However, it faces significant challenges as the human genome consists of over 6 × 109 base pairs, a system size that exceeds the capacity of traditional modeling approaches. In this perspective, we review the progress that has been made in modeling the human genome. Coarse-grained models parameterized to reproduce experimental data via the maximum entropy optimization algorithm serve as effective means to study genome organization at various length scales. They have provided insight into the principles of whole-genome organization and enabled de novo predictions of chromosome structures from epigenetic modifications. Applications of these models at a near-atomistic resolution further revealed physicochemical interactions that drive the phase separation of disordered proteins and dictate chromatin stability in situ. We conclude with an outlook on the opportunities and challenges in studying chromosome dynamics.

CBCR: A Curriculum Based Strategy For Chromosome Reconstruction

In this paper, we introduce a novel algorithm that aims to estimate chromosomes’ structure from their Hi-C contact data, called Curriculum Based Chromosome Reconstruction (CBCR). Specifically, our method performs this three dimensional reconstruction using cis-chromosomal interactions from Hi-C data. CBCR takes intra-chromosomal Hi-C interaction frequencies as an input and outputs a set of xyz coordinates that estimate the chromosome’s three dimensional structure in the form of a .pdb file. The algorithm relies on progressively training a distance-restraint-based algorithm with a strategy we refer to as curriculum learning. Curriculum learning divides the Hi-C data into classes based on contact frequency and progressively re-trains the distance-restraint algorithm based on the assumed importance of each curriculum in predicting the underlying chromosome structure. The distance-restraint algorithm relies on a modification of a Gaussian maximum likelihood function that scales probabilities based on the importance of features. We evaluate the performance of CBCR on both simulated and actual Hi-C data and perform validation on FISH, HiChIP, and ChIA-PET data as well. We also compare the performance of CBCR to several current methods. Our analysis shows that the use of curricula affects the rate of convergence of the optimization while decreasing the computational cost of our distance-restraint algorithm. Also, CBCR is more robust to increases in data resolution and therefore yields superior reconstruction accuracy of higher resolution data than all other methods in our comparison.

A unified framework for inferring the multi-scale organization of chromatin domains from Hi-C

Chromosomes are giant chain molecules organized into an ensemble of three-dimensional structures characterized with its genomic state and the corresponding biological functions. Despite the strong cell-to-cell heterogeneity, the cell-type specific pattern demonstrated in high-throughput chromosome conformation capture (Hi-C) data hints at a valuable link between structure and function, which makes inference of chromatin domains (CDs) from the pattern of Hi-C a central problem in genome research. Here we present a unified method for analyzing Hi-C data to determine spatial organization of CDs over multiple genomic scales. By applying statistical physics-based clustering analysis to a polymer physics model of the chromosome, our method identifies the CDs that best represent the global pattern of correlation manifested in Hi-C. The multi-scale intra-chromosomal structures compared across different cell types uncover the principles underlying the multi-scale organization of chromatin chain: (i) Sub-TADs, TADs, and meta-TADs constitute a robust hierarchical structure. (ii) The assemblies of compartments and TAD-based domains are governed by different organizational principles. (iii) Sub-TADs are the common building blocks of chromosome architecture. Our physically principled interpretation and analysis of Hi-C not only offer an accurate and quantitative view of multi-scale chromatin organization but also help decipher its connections with genome function.

An array of square blocks and checkerboard patterns characteristic to Hi-C data reflects the multi-scale organization of the chromatin chain. Deciphering the structures of chromatin domains from Hi-C and associating them with genome function are open problems of great importance in genome research. However, most existing methods are specialized in finding domains at different scales, making it difficult to integrate the solutions. Here we develop a unified framework for modeling and inferring domain structures over multiple scales, based on a physical model of the chromosome that reflects its nature as a three-dimensional object. Our method efficiently explores the space of domain solutions at different genomic scales, and systematically infers the chromatin domains over multiple scales from Hi-C data by employing a single tuning parameter. Our principled interpretation of Hi-C not only offers a quantitative view of multi-scale chromatin organization but also helps understand its connections with genome function.

Revisiting the organization of Polycomb-repressed domains: 3D chromatin models from Hi-C compared with super-resolution imaging

The accessibility of target gene, a factor critical for gene regulation, is controlled by epigenetic fine-tuning of chromatin organization. While there are multiple experimental techniques to study change of chromatin architecture with its epigenetic state, measurements from them are not always complementary. A qualitative discrepancy is noted between recent super-resolution imaging studies, particularly on Polycomb-group protein repressed domains in Drosophila cell. One of the studies shows that Polycomb-repressed domains are more compact than inactive domains and are segregated from neighboring active domains, whereas Hi-C and chromatin accessibility assay as well as the other super-resolution imaging studies paint a different picture. To examine this issue in detail, we analyzed Hi-C libraries of Drosophila chromosomes as well as distance constraints from one of the imaging studies, and modeled different epigenetic domains by employing a polymer-based approach. According to our chromosome models, both Polycomb-repressed and inactive domains are featured with a similar degree of intra-domain packaging and significant intermixing with adjacent active domains. The epigenetic domains explicitly visualized by our polymer model call for extra attention to the discrepancy of the super-resolution imaging with other measurements, although its precise physicochemical origin still remains to be elucidated.

The Self-Organizing Genome: Principles of Genome Architecture and Function

Genomes have complex three-dimensional architectures. The recent convergence of genetic, biochemical, biophysical, and cell biological methods has uncovered several fundamental principles of genome organization. They highlight that genome function is a major driver of genome architecture and that structural features of chromatin act as modulators, rather than binary determinants, of genome activity. The interplay of these principles in the context of self-organization can account for the emergence of structural chromatin features, the diversity and single-cell heterogeneity of nuclear architecture in cell types and tissues, and explains evolutionarily conserved functional features of genomes, including plasticity and robustness.

Characterizing chromatin folding coordinate and landscape with deep learning

Genome organization is critical for setting up the spatial environment of gene transcription, and substantial progress has been made towards its high-resolution characterization. The underlying molecular mechanism for its establishment is much less understood. We applied a deep-learning approach, variational autoencoder (VAE), to analyze the fluctuation and heterogeneity of chromatin structures revealed by single-cell imaging and to identify a reaction coordinate for chromatin folding. This coordinate connects the seemingly random structures observed in individual cohesin-depleted cells as intermediate states along a folding pathway that leads to the formation of topologically associating domains (TAD). We showed that folding into wild-type-like structures remain energetically favorable in cohesin-depleted cells, potentially as a result of the phase separation between the two chromatin segments with active and repressive histone marks. The energetic stabilization, however, is not strong enough to overcome the entropic penalty, leading to the formation of only partially folded structures and the disappearance of TADs from contact maps upon averaging. Our study suggests that machine learning techniques, when combined with rigorous statistical mechanical analysis, are powerful tools for analyzing structural ensembles of chromatin.

Heterogeneous fluid-like movements of chromatin and their implications to transcription

Eukaryotic chromatin is a complex of genome DNA and associated proteins, and its structure and dynamics play a crucial role in regulating DNA functions. Chromatin takes rather irregular structures in the nucleus and exhibits heterogeneous sub-diffusive movements as polymers fluctuating in a fluid state. Using genome-wide single-nucleosome tracking data, heterogeneity of movements was statistically analyzed, which categorized chromatin into two types: slow chromatin that moves under structurally constrained environments and fast chromatin that moves with less constraints. Interactions of chromatin to various protein factors determine the motional constraints. For example, loss of the cohesin complex that bundles the chromatin chains reduces the motional constraints and increases the population of fast chromatin. Another example is the transcriptional machinery. While it was previously thought that the transcriptional activity is associated with more open and dynamic chromatin structure, recent studies suggested a more nuanced role of transcription in chromatin dynamics: dynamic association/dissociation of active RNA polymerase II (RNAPII) and other transcription factors and Mediators (TF-Meds) transiently bridges transcriptionally active DNA regions, which forms a loose network of chromatin and constrains chromatin movement, enhancing the slow chromatin population. This new view on the dynamical effects of transcription urges a reflection on the traditional model of transcription factories and invites the more recent models of condensates/phase-separated liquid droplets of RNAPII, transcription factors, and Mediators. The combined procedure of genome-wide single-nucleosome tracking and its statistical analysis would unveil heterogeneity in the chromatin movement, which should provide a key to understanding the relations among chromatin dynamics, structure, and function.

CTCF modulates allele-specific sub-TAD organization and imprinted gene activity at the mouse Dlk1-Dio3 and Igf2-H19 domains

BACKGROUND

Genomic imprinting is essential for mammalian development and provides a unique paradigm to explore intra-cellular differences in chromatin configuration. So far, the detailed allele-specific chromatin organization of imprinted gene domains has mostly been lacking. Here, we explored the chromatin structure of the two conserved imprinted domains controlled by paternal DNA methylation imprints-the Igf2-H19 and Dlk1-Dio3 domains-and assessed the involvement of the insulator protein CTCF in mouse cells.

RESULTS

Both imprinted domains are located within overarching topologically associating domains (TADs) that are similar on both parental chromosomes. At each domain, a single differentially methylated region is bound by CTCF on the maternal chromosome only, in addition to multiple instances of bi-allelic CTCF binding. Combinations of allelic 4C-seq and DNA-FISH revealed that bi-allelic CTCF binding alone, on the paternal chromosome, correlates with a first level of sub-TAD structure. On the maternal chromosome, additional CTCF binding at the differentially methylated region adds a further layer of sub-TAD organization, which essentially hijacks the existing paternal-specific sub-TAD organization. Perturbation of maternal-specific CTCF binding site at the Dlk1-Dio3 locus, using genome editing, results in perturbed sub-TAD organization and bi-allelic Dlk1 activation during differentiation.

CONCLUSIONS

Maternal allele-specific CTCF binding at the imprinted Igf2-H19 and the Dlk1-Dio3 domains adds an additional layer of sub-TAD organization, on top of an existing three-dimensional configuration and prior to imprinted activation of protein-coding genes. We speculate that this allele-specific sub-TAD organization provides an instructive or permissive context for imprinted gene activation during development.