Cohesin extrusion is thought to play a central role in establishing the architecture of mammalian genomes. However, extrusion has not been visualized in vivo, and thus, its functional impact and energetics are unknown. Using ultra-deep Hi-C, we show that loop domains form by a process that requires cohesin ATPases. Once formed, however, loops and compartments are maintained for hours without energy input. Strikingly, without ATP, we observe the emergence of hundreds of CTCF-independent loops that link regulatory DNA. We also identify architectural "stripes," where a loop anchor interacts with entire domains at high frequency. Stripes often tether super-enhancers to cognate promoters, and in B cells, they facilitate Igh transcription and recombination. Stripe anchors represent major hotspots for topoisomerase-mediated lesions, which promote chromosomal translocations and cancer. In plasmacytomas, stripes can deregulate Igh-translocated oncogenes. We propose that higher organisms have coopted cohesin extrusion to enhance transcription and recombination, with implications for tumor development.

In vivo, the human genome folds into a characteristic ensemble of 3D structures. The mechanism driving the folding process remains unknown. We report a theoretical model for chromatin (Minimal Chromatin Model) that explains the folding of interphase chromosomes and generates chromosome conformations consistent with experimental data. The energy landscape of the model was derived by using the maximum entropy principle and relies on two experimentally derived inputs: a classification of loci into chromatin types and a catalog of the positions of chromatin loops. First, we trained our energy function using the Hi-C contact map of chromosome 10 from human GM12878 lymphoblastoid cells. Then, we used the model to perform molecular dynamics simulations producing an ensemble of 3D structures for all GM12878 autosomes. Finally, we used these 3D structures to generate contact maps. We found that simulated contact maps closely agree with experimental results for all GM12878 autosomes. The ensemble of structures resulting from these simulations exhibited unknotted chromosomes, phase separation of chromatin types, and a tendency for open chromatin to lie at the periphery of chromosome territories.human genome | genome architecture | maximum entropy | molecular dynamics | Hi-C C hromatin comprises a highly flexible polymer composed of nucleosomes, DNA wrapped around histone proteins, connected to one another by a linker region of 20-50 bp. Hundreds of associated structural and regulatory proteins interact with the genetic material, coordinating the way chromatin folds to fit inside the nucleus of eukaryotic cells.The resulting ensemble of partially organized structures brings sections of DNA separated by a great genomic distance into close spatial proximity, and plays an important role in controlling gene transcription (1, 2). Although some of the features of this ensemble can be explained using simple polymer physics (3-6), there is now ample evidence that specific biochemical interactions play a crucial role (7-10). Understanding the interplay between biochemistry, genome architecture, and transcriptional regulation is a major outstanding challenge.For over two decades, molecular biology techniques that combine chromatin fragmentation and proximity ligation have given us quantitative information about how chromatin is organized in vivo (5,(11)(12)(13). In recent years, Hi-C experiments have made it possible to measure the frequency of contact between all pairs of genomic loci using a single experiment.Here, we explore a physical model by which local interactions between genomic loci can lead to the conformations of human chromosomes in interphase. Specifically, we propose a theoretical energy landscape model for chromatin folding, designated the Minimal Chromatin Model (MiChroM), which uses the maximum entropy principle (14, 15) in combination with a minimal number of assumptions to model the structural consequences of the aforementioned biochemical interactions. Importantly, MiChroM can be used to model biochemical inter...

Chromosome organization is crucial for genome function. Here, we present a method for visualizing chromosomal DNA at super-resolution and then integrating Hi-C data to produce three-dimensional models of chromosome organization. Using the super-resolution microscopy methods of OligoSTORM and OligoDNA-PAINT, we trace 8 megabases of human chromosome 19, visualizing structures ranging in size from a few kilobases to over a megabase. Focusing on chromosomal regions that contribute to compartments, we discover distinct structures that, in spite of considerable variability, can predict whether such regions correspond to active (A-type) or inactive (B-type) compartments. Imaging through the depths of entire nuclei, we capture pairs of homologous regions in diploid cells, obtaining evidence that maternal and paternal homologous regions can be differentially organized. Finally, using restraint-based modeling to integrate imaging and Hi-C data, we implement a method–integrative modeling of genomic regions (IMGR)–to increase the genomic resolution of our traces to 10 kb.

We investigated genome folding across the eukaryotic tree of life. We find two types of three-dimensional (3D) genome architectures at the chromosome scale. Each type appears and disappears repeatedly during eukaryotic evolution. The type of genome architecture that an organism exhibits correlates with the absence of condensin II subunits. Moreover, condensin II depletion converts the architecture of the human genome to a state resembling that seen in organisms such as fungi or mosquitoes. In this state, centromeres cluster together at nucleoli, and heterochromatin domains merge. We propose a physical model in which lengthwise compaction of chromosomes by condensin II during mitosis determines chromosome-scale genome architecture, with effects that are retained during the subsequent interphase. This mechanism likely has been conserved since the last common ancestor of all eukaryotes.

SignificanceSeveral active processes operate on eukaryotic genomes, dictating their three-dimensional arrangement and dynamical properties. The combination of structural organization and dynamics is essential to the proper functioning of the cell. We show that an effective energy landscape model for chromatin provides a unifying description of both the structural and dynamical aspects of the genome, recapitulating many of its features. Using this quasi-equilibrium energy landscape model, we demonstrate that the physical interactions accounting for genome architecture also lead to the nontrivial dynamical behavior of genomes previously described in multiple experimental observations.

Inside the cell nucleus, genomes fold into organized structures that are characteristic of cell type. Here, we show that this chromatin architecture can be predicted de novo using epigenetic data derived from chromatin immunoprecipitation-sequencing (ChIP-Seq). We exploit the idea that chromosomes encode a 1D sequence of chromatin structural types. Interactions between these chromatin types determine the 3D structural ensemble of chromosomes through a process similar to phase separation. First, a neural network is used to infer the relation between the epigenetic marks present at a locus, as assayed by ChIP-Seq, and the genomic compartment in which those loci reside, as measured by DNA-DNA proximity ligation (Hi-C). Next, types inferred from this neural network are used as an input to an energy landscape model for chromatin organization [Minimal Chromatin Model (MiChroM)] to generate an ensemble of 3D chromosome conformations at a resolution of 50 kilobases (kb). After training the model, dubbed Maximum Entropy Genomic Annotation from Biomarkers Associated to Structural Ensembles (MEGABASE), on odd-numbered chromosomes, we predict the sequences of chromatin types and the subsequent 3D conformational ensembles for the even chromosomes. We validate these structural ensembles by using ChIP-Seq tracks alone to predict Hi-C maps, as well as distances measured using 3D fluorescence in situ hybridization (FISH) experiments. Both sets of experiments support the hypothesis of phase separation being the driving process behind compartmentalization. These findings strongly suggest that epigenetic marking patterns encode sufficient information to determine the global architecture of chromosomes and that de novo structure prediction for whole genomes may be increasingly possible.epigenetics | machine learning | energy landscape theory | genomic architecture | Hi-C I n the nucleus of eukaryotic cells, the 1D information of the genome is organized in three dimensions (1, 2). It is increasingly evident that genomic spatial organization is a key element of transcriptional regulation (1, 3, 4). During interphase, the 3D arrangement of chromatin brings into close spatial proximity sections of DNA separated by great genomic distance, introducing interactions between genes and regulatory elements. These folding patterns are cell type-specific (5, 6), and their disruption can lead to disease (7-10).The use of high-resolution contact mapping experiments (Hi-C) has revealed that, at the large scale, genome structure is dominated by the segregation of human chromatin into compartments. Initial analysis of Hi-C experiments revealed that loci typically exhibited one of two long-range contact patterns, suggesting the presence of two spatial neighborhoods, dubbed the A and B compartments (11). Subsequently, higher resolution experiments have shown the presence of six distinct long-range patterns, indicating the presence of six subcompartments (A1, A2, B1, B2, B3, and B4) in human lymphoblastoid cells (GM12878) (6). The compartmentalization o...

SignificanceIn the nucleus of eukaryotic cells, the genome is organized in three dimensions in an architecture that depends on cell type. This organization is a key element of transcriptional regulation, and its disruption often leads to disease. We demonstrate that it is possible to predict how a genome will fold based on the epigenetic marks that decorate chromatin. Epigenetic marking patterns are used to predict the corresponding ensemble of 3D structures by leveraging both energy landscape theory and neural network-based machine learning. These predictions are extensively validated by the results of DNA-DNA ligation assays and fluorescence microscopy, which are found to be in exceptionally good agreement with theory.

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