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Using Hi-C, promoter-capture Hi-C (pCHi-C), and other genome-wide approaches in skeletal muscle progenitors that inducibly express a master transcription factor, Pax7, we systematically characterize at high-resolution the spatio-temporal re-organization of compartments and promoter-anchored interactions as a consequence of myogenic commitment and differentiation. We identify key promoter-enhancer interaction motifs, namely, cliques and networks, and interactions that are dependent on Pax7 binding. Remarkably, Pax7 binds to a majority of super-enhancers, and together with a cadre of interacting transcription factors, assembles feed-forward regulatory loops. During differentiation, epigenetic memory and persistent looping are maintained at a subset of Pax7 enhancers in the absence of Pax7. We also identify and functionally validate a previously uncharacterized Pax7-bound enhancer hub that regulates the essential myosin heavy chain cluster during skeletal muscle cell differentiation. Our studies lay the groundwork for understanding the role of Pax7 in orchestrating changes in the three-dimensional chromatin conformation in muscle progenitors.

Dynamic changes in the three-dimensional (3D) organization of chromatin are associated with central biological processes, such as transcription, replication and development. Therefore, the comprehensive identification and quantification of these changes is fundamental to understanding of evolutionary and regulatory mechanisms. Here, we present Comparison of Hi-C Experiments using Structural Similarity (CHESS), an algorithm for the comparison of chromatin contact maps and automatic differential feature extraction. We demonstrate the robustness of CHESS to experimental variability and showcase its biological applications on (1) interspecies comparisons of syntenic regions in human and mouse models; (2) intraspecies identification of conformational changes in Zelda-depleted Drosophila embryos; (3) patient-specific aberrant chromatin conformation in a diffuse large B-cell lymphoma sample; and (4) the systematic identification of chromatin contact differences in high-resolution Capture-C data. In summary, CHESS is a computationally efficient method for the comparison and classification of changes in chromatin contact data.

Here we describe the LifeTime Initiative, which aims to track, understand and target human cells during the onset and progression of complex diseases, and to analyse their response to therapy at single-cell resolution. This mission will be implemented through the development, integration and application of single-cell multi-omics and imaging, artificial intelligence and patient-derived experimental disease models during the progression from health to disease. The analysis of large molecular and clinical datasets will identify molecular mechanisms, create predictive computational models of disease progression, and reveal new drug targets and therapies. The timely detection and interception of disease embedded in an ethical and patient-centred vision will be achieved through interactions across academia, hospitals, patient associations, health data management systems and industry. The application of this strategy to key medical challenges in cancer, neurological and neuropsychiatric disorders, and infectious, chronic inflammatory and cardiovascular diseases at the single-cell level will usher in cell-based interceptive medicine in Europe over the next decade.

There is a need for methods that can image chromosomes with genome-wide coverage, as well as greater genomic and optical resolution. We introduce OligoFISSEQ, a suite of three methods that leverage fluorescence in situ sequencing (FISSEQ) of barcoded Oligopaint probes to enable the rapid visualization of many targeted genomic regions. Applying OligoFISSEQ to human diploid fibroblast cells, we show how four rounds of sequencing are sufficient to produce 3D maps of 36 genomic targets across six chromosomes in hundreds to thousands of cells, implying a potential to image thousands of targets in only five to eight rounds of sequencing. We also use OligoFISSEQ to trace chromosomes at finer resolution, following the path of the X chromosome through 46 regions, with separate studies showing compatibility of OligoFISSEQ with immunocytochemistry. Finally, we combined OligoFISSEQ with OligoSTORM, laying the foundation for accelerated single-molecule super-resolution imaging of large swaths of, if not entire, human genomes.

Three-dimensional organization of the genome is important for transcriptional regulation 1-7 . In mammals, CTCF and the cohesin complex create submegabase structures with elevated internal chromatin contact frequencies, called topologically associating domains (TADs) 8-12 . Although TADs can contribute to transcriptional regulation, ablation of TAD organization by disrupting CTCF or the cohesin complex causes modest gene expression changes 13-16 . In contrast, CTCF is required for cell cycle regulation 17 , embryonic development and formation of various adult cell types 18 . To uncouple the role of CTCF in cell-state transitions and cell proliferation, we studied the effect of CTCF depletion during the conversion of human leukemic B cells into macrophages with minimal cell division. CTCF depletion disrupts TAD organization but not cell transdifferentiation. In contrast, CTCF depletion in induced macrophages impairs the full-blown upregulation of inflammatory genes after exposure to endotoxin. Our results demonstrate that CTCF-dependent genome topology is not strictly required for a functional cell-fate conversion but facilitates a rapid and efficient response to an external stimulus.

Chromosome structure is a crucial regulatory factor for a wide range of nuclear processes. Chromosome conformation capture (3C)-based experiments combined with computational modelling are pivotal for unveiling 3D chromosome structure. Here, we introduce TADdyn, a tool that integrates time-course 3C data, restraint-based modelling, and molecular dynamics to simulate the structural rearrangements of genomic loci in a completely data-driven way. We apply TADdyn on in situ Hi-C time-course experiments studying the reprogramming of murine B cells to pluripotent cells, and characterize the structural rearrangements that take place upon changes in the transcriptional state of 21 genomic loci of diverse expression dynamics. By measuring various structural and dynamical properties, we find that during gene activation, the transcription starting site contacts with open and active regions in 3D chromatin domains. We propose that these 3D hubs of open and active chromatin may constitute a general feature to trigger and maintain gene transcription.

Genetic studies promise to provide insight into the molecular mechanisms underlying type 2 diabetes (T2D). Variants associated with T2D are often located in tissue-specific enhancer clusters or super-enhancers. So far, such domains have been defined through clustering of enhancers in linear genome maps rather than in three-dimensional (3D) space. Furthermore, their target genes are often unknown. We have created promoter capture Hi-C maps in human pancreatic islets. This linked diabetes-associated enhancers to their target genes, often located hundreds of kilobases away. It also revealed >1,300 groups of islet enhancers, super-enhancers and active promoters that form 3D hubs, some of which show coordinated glucose-dependent activity. We demonstrate that genetic variation in hubs impacts insulin secretion heritability, and show that hub annotations can be used for polygenic scores that predict T2D risk driven by islet regulatory variants. Human islet 3D chromatin architecture, therefore, provides a framework for interpretation of T2D genome-wide association study (GWAS) signals.

Genome discoveries at the core of biology are made by visual description and exploration of the cell, from microscopic sketches and biochemical mapping to computational analysis and spatial modeling. We outline the experimental and visualization techniques that have been developed recently which capture the three-dimensional interactions regulating how genes are expressed. We detail the challenges faced in integration of the data to portray the components and organization and their dynamic landscape. The goal is more than a single data-driven representation as interactive visualization for de novo research is paramount to decipher insights on genome organization in space., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

In embryonic stem cells (ESCs), developmental gene promoters are characterized by their bivalent chromatin state, with simultaneous modification by MLL2 and Polycomb complexes. Although essential for embryogenesis, bivalency is functionally not well understood. Here, we show that MLL2 plays a central role in ESC genome organization. We generate a catalog of bona fide bivalent genes in ESCs and demonstrate that loss of MLL2 leads to increased Polycomb occupancy. Consequently, promoters lose accessibility, long-range interactions are redistributed, and ESCs fail to differentiate. We pose that bivalency balances accessibility and long-range connectivity of promoters, allowing developmental gene expression to be properly modulated.

Due to recent advances in experimental and theoretical approaches, the dynamic three-dimensional organization (3D) of the nucleus has become a very active area of research in life sciences. We now understand that the linear genome is folded in ways that may modulate how genes are expressed during the basic functioning of cells. Importantly, it is now possible to build 3D models of how the genome folds within the nucleus and changes over time (4D). Because genome folding influences its function, this opens exciting new possibilities to broaden our understanding of the mechanisms that determine cell fate. However, the rapid evolution of methods and the increasing complexity of data can result in ambiguity and reproducibility challenges, which may hamper the progress of this field. Here, we describe such challenges ahead and provide guidelines to think about strategies for shared standardized validation of experimental 4D nucleome data sets and models.

Two variant cohesin complexes containing SMC1, SMC3, RAD21 and either SA1 (also known as STAG1) or SA2 (also known as STAG2) are present in all cell types. We report here their genomic distribution and specific contributions to genome organization in human cells. Although both variants are found at CCCTC-binding factor (CTCF) sites, a distinct population of the SA2-containing cohesin complexes (hereafter referred to as cohesin-SA2) localize to enhancers lacking CTCF, are linked to tissue-specific transcription and cannot be replaced by the SA1-containing cohesin complex (cohesin-SA1) when SA2 is absent, a condition that has been observed in several tumors. Downregulation of each of these variants has different consequences for gene expression and genome architecture. Our results suggest that cohesin-SA1 preferentially contributes to the stabilization of topologically associating domain boundaries together with CTCF, whereas cohesin-SA2 promotes cell-type-specific contacts between enhancers and promoters independently of CTCF. Loss of cohesin-SA2 rewires local chromatin contacts and alters gene expression. These findings provide insights into how cohesin mediates chromosome folding and establish a novel framework to address the consequences of mutations in cohesin genes in cancer.

Chromosomal architecture is known to influence gene expression, yet its role in controlling cell fate remains poorly understood. Reprogramming of somatic cells into pluripotent stem cells (PSCs) by the transcription factors (TFs) OCT4, SOX2, KLF4 and MYC offers an opportunity to address this question but is severely limited by the low proportion of responding cells. We have recently developed a highly efficient reprogramming protocol that synchronously converts somatic into pluripotent stem cells. Here, we used this system to integrate time-resolved changes in genome topology with gene expression, TF binding and chromatin-state dynamics. The results showed that TFs drive topological genome reorganization at multiple architectural levels, often before changes in gene expression. Removal of locus-specific topological barriers can explain why pluripotency genes are activated sequentially, instead of simultaneously, during reprogramming. Together, our results implicate genome topology as an instructive force for implementing transcriptional programs and cell fate in mammals.

At the kilo- to megabase pair scales, eukaryotic genomes are partitioned into self-interacting modules or topologically associated domains (TADs) that associate to form nuclear compartments. Here, we combine high-content super-resolution microscopies with state-of-the-art DNA-labeling methods to reveal the variability in the multiscale organization of the Drosophila genome. We find that association frequencies within TADs and between TAD borders are below ~10%, independently of TAD size, epigenetic state, or cell type. Critically, despite this large heterogeneity, we are able to visualize nanometer-sized epigenetic domains at the single-cell level. In addition, absolute contact frequencies within and between TADs are to a large extent defined by genomic distance, higher-order chromosome architecture, and epigenetic identity. We propose that TADs and compartments are organized by multiple, small-frequency, yet specific interactions that are regulated by epigenetics and transcriptional state.

Genomic interactions reveal the spatial organization of genomes and genomic domains, which is known to play key roles in cell function. Physical proximity can be represented as two-dimensional heat maps or matrices. From these, three-dimensional (3D) conformations of chromatin can be computed revealing coherent structures that highlight the importance of nonsequential relationships across genomic features. Mainstream genomic browsers have been classically developed to display compact, stacked tracks based on a linear, sequential, per-chromosome coordinate system. Genome-wide comparative analysis demands new approaches to data access and new layouts for analysis. The legibility can be compromised when displaying track-aligned second dimension matrices, which require greater screen space. Moreover, 3D representations of genomes defy vertical alignment in track-based genome browsers. Furthermore, investigation at previously unattainable levels of detail is revealing multiscale, multistate, time-dependent complexity. This article outlines how these challenges are currently handled in mainstream browsers as well as how novel techniques in visualization are being explored to address them. A set of requirements for coherent visualization of novel spatial genomic data is defined and the resulting potential for whole genome visualization is described., (© 2017 The Authors. FEBS Letters published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)

Mating-type switching in yeast occurs through gene conversion between the MAT locus and one of two silent loci (HML or HMR) on opposite ends of the chromosome. MATa cells choose HML as template, whereas MATα cells use HMR. The recombination enhancer (RE) located on the left arm regulates this process. One long-standing hypothesis is that switching is guided by mating-type-specific and possibly RE-dependent chromosome folding. Here, we use Hi-C, 5C, and live-cell imaging to characterize the conformation of chromosome III in both mating types. We discovered a mating-type-specific conformational difference in the left arm. Deletion of a 1-kb subregion within the RE, which is not necessary during switching, abolished mating-type-dependent chromosome folding. The RE is therefore a composite element with one subregion essential for donor selection during switching and a separate region involved in modulating chromosome conformation., (Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.)

Structures of biomolecular systems are increasingly computed by integrative modeling that relies on varied types of experimental data and theoretical information. We describe here the proceedings and conclusions from the first wwPDB Hybrid/Integrative Methods Task Force Workshop held at the European Bioinformatics Institute in Hinxton, UK, on October 6 and 7, 2014. At the workshop, experts in various experimental fields of structural biology, experts in integrative modeling and visualization, and experts in data archiving addressed a series of questions central to the future of structural biology. How should integrative models be represented? How should the data and integrative models be validated? What data should be archived? How should the data and models be archived? What information should accompany the publication of integrative models?, (Copyright © 2015 Elsevier Ltd. All rights reserved.)

How DNA is organized in three dimensions inside the cell nucleus and how this affects the ways in which cells access, read and interpret genetic information are among the longest standing questions in cell biology. Using newly developed molecular, genomic and computational approaches based on the chromosome conformation capture technology (such as 3C, 4C, 5C and Hi-C), the spatial organization of genomes is being explored at unprecedented resolution. Interpreting the increasingly large chromatin interaction data sets is now posing novel challenges. Here we describe several types of statistical and computational approaches that have recently been developed to analyse chromatin interaction data.

Recent advances in determination of the high-resolution structure of membrane proteins now enable analysis of the main features of amino acids in transmembrane (TM) segments in comparison with amino acids in water-soluble helices. In this work, we conducted a large-scale analysis of the prevalent locations of amino acids by using a data set of 170 structures of integral membrane proteins obtained from the MPtopo database and 930 structures of water-soluble helical proteins obtained from the protein data bank. Large hydrophobic amino acids (Leu, Val, Ile, and Phe) plus Gly were clearly prevalent in TM helices whereas polar amino acids (Glu, Lys, Asp, Arg, and Gln) were less frequent in this type of helix. The distribution of amino acids along TM helices was also examined. As expected, hydrophobic and slightly polar amino acids are commonly found in the hydrophobic core of the membrane whereas aromatic (Trp and Tyr), Pro, and the hydrophilic amino acids (Asn, His, and Gln) occur more frequently in the interface regions. Charged amino acids are also statistically prevalent outside the hydrophobic core of the membrane, and whereas acidic amino acids are frequently found at both cytoplasmic and extra-cytoplasmic interfaces, basic amino acids cluster at the cytoplasmic interface. These results strongly support the experimentally demonstrated biased distribution of positively charged amino acids (that is, the so-called the positive-inside rule) with structural data.

The three-dimensional (3D) architecture of a genome determines the spatial localization of regulatory elements and the genes they regulate. Thus, elucidating the 3D structure of a genome may result in significant insights about how genes are regulated. The current state-of-the art in experimental methods, including light microscopy and cell/molecular biology, are now able to provide detailed information on the position of genes and their interacting partners. However, such methods by themselves are not able to determine the high-resolution 3D structure of genomes or genomic domains. Here we describe a computational module of the Integrative Modeling Platform (IMP, http://www.integrativemodeling.org) that uses chromosome conformation capture data to determine the 3D architecture of genomic domains and entire genomes at unprecedented resolutions. This approach, through the visualization of looping interactions between distal regulatory elements, allows characterizing global chromatin features and their relation to gene expression. We illustrate our work by outlining the determination of the 3D architecture of the α-globin domain in the human genome., (Copyright © 2012 Elsevier Inc. All rights reserved.)

N-glycosylation is the most common and versatile protein modification. In eukaryotic cells, this modification is catalyzed cotranslationally by the enzyme oligosaccharyltransferase, which targets the β-amide of the asparagine in an Asn-Xaa-Ser/Thr consensus sequon (where Xaa is any amino acid but proline) in nascent proteins as they enter the endoplasmic reticulum. Because modification of the glycosylation acceptor site on membrane proteins occurs in a compartment-specific manner, the presence of glycosylation is used to indicate membrane protein topology. Moreover, glycosylation sites can be added to gain topological information. In this study, we explored the determinants of N-glycosylation with the in vitro transcription/translation of a truncated model protein in the presence of microsomes and surveyed 25,488 glycoproteins, of which 2,533 glycosylation sites had been experimentally validated. We found that glycosylation efficiency was dependent on both the distance to the C-terminus and the nature of the amino acid that preceded the consensus sequon. These findings establish a broadly applicable method for membrane protein tagging in topological studies.

The three-dimensional (3D) architecture of a genome is non-random and known to facilitate the spatial colocalization of regulatory elements with the genes they regulate. Determining the 3D structure of a genome may therefore probe an essential step in characterizing how genes are regulated. Currently, there are several experimental and theoretical approaches that aim at determining the 3D structure of genomes and genomic domains; however, approaches integrating experiments and computation to identify the most likely 3D folding of a genome at medium to high resolutions have not been widely explored. Here, we review existing methodologies and propose that the integrative modeling platform (http://www.integrativemodeling.org), a computational package developed for structurally characterizing protein assemblies, could be used for integrating diverse experimental data towards the determination of the 3D architecture of genomic domains and entire genomes at unprecedented resolution. Our approach, through the visualization of looping interactions between distal regulatory elements, will allow for the characterization of global chromatin features and their relation to gene expression. We illustrate our work by outlining the recent determination of the 3D architecture of the α-globin domain in the human genome.

The accuracy of an alignment between two protein sequences can be improved by including other detectably related sequences in the comparison. We optimize and benchmark such an approach that relies on aligning two multiple sequence alignments, each one including one of the two protein sequences. Thirteen different protocols for creating and comparing profiles corresponding to the multiple sequence alignments are implemented in the SALIGN command of MODELLER. A test set of 200 pairwise, structure-based alignments with sequence identities below 40% is used to benchmark the 13 protocols as well as a number of previously described sequence alignment methods, including heuristic pairwise sequence alignment by BLAST, pairwise sequence alignment by global dynamic programming with an affine gap penalty function by the ALIGN command of MODELLER, sequence-profile alignment by PSI-BLAST, Hidden Markov Model methods implemented in SAM and LOBSTER, pairwise sequence alignment relying on predicted local structure by SEA, and multiple sequence alignment by CLUSTALW and COMPASS. The alignment accuracies of the best new protocols were significantly better than those of the other tested methods. For example, the fraction of the correctly aligned residues relative to the structure-based alignment by the best protocol is 56%, which can be compared with the accuracies of 26%, 42%, 43%, 48%, 50%, 49%, 43%, and 43% for the other methods, respectively. The new method is currently applied to large-scale comparative protein structure modeling of all known sequences.

Autoantibodies against beta(2)-glycoprotein I (beta(2)GPI) appear to be a critical feature of the antiphospholipid syndrome (APS). As determined using domain deletion mutants, human autoantibodies bind to the first of five domains present in beta(2)GPI. In this study the fine detail of the domain I epitope has been examined using 10 selected mutants of whole beta(2)GPI containing single point mutations in the first domain. The binding to beta(2)GPI was significantly affected by a number of single point mutations in domain I, particularly by mutations in the region of aa 40-43. Molecular modeling predicted these mutations to affect the surface shape and electrostatic charge of a facet of domain I. Mutation K19E also had an effect, albeit one less severe and involving fewer patients. Similar results were obtained in two different laboratories using affinity-purified anti-beta(2)GPI in a competitive inhibition ELISA and with whole serum in a direct binding ELISA. This study confirms that anti-beta(2)GPI autoantibodies bind to domain I, and that the charged surface patch defined by residues 40-43 contributes to a dominant target epitope.