3D Chromosome Structure Research Articles

Reconstructing high-resolution chromosome three-dimensional structures by Hi-C complex networks

BackgroundHi-C data have been widely used to reconstruct chromosomal three-dimensional (3D) structures. One of the key limitations of Hi-C is the unclear relationship between spatial distance and the number of Hi-C contacts. Many methods used a fixed parameter when converting the number of Hi-C contacts to wish distances. However, a single parameter cannot properly explain the relationship between wish distances and genomic distances or the locations of topologically associating domains (TADs).ResultsWe have addressed one of the key issues of using Hi-C data, that is, the unclear relationship between spatial distances and the number of Hi-C contacts, which is crucial to understand significant biological functions, such as the enhancer-promoter interactions. Specifically, we developed a new method to infer this converting parameter and pairwise Euclidean distances based on the topology of the Hi-C complex network (HiCNet). The inferred distances were modeled by clustering coefficient and multiple other types of constraints. We found that our inferred distances between bead-pairs within the same TAD were apparently smaller than those distances between bead-pairs from different TADs. Our inferred distances had a higher correlation with fluorescence in situ hybridization (FISH) data, fitted the localization patterns of Xist transcripts on DNA, and better matched 156 pairs of protein-enabled long-range chromatin interactions detected by ChIA-PET. Using the inferred distances and another round of optimization, we further reconstructed 40 kb high-resolution 3D chromosomal structures of mouse male ES cells. The high-resolution structures successfully illustrate TADs and DNA loops (peaks in Hi-C contact heatmaps) that usually indicate enhancer-promoter interactions.ConclusionsWe developed a novel method to infer the wish distances between DNA bead-pairs from Hi-C contacts. High-resolution 3D structures of chromosomes were built based on the newly-inferred wish distances. This whole process has been implemented as a tool named HiCNet, which is publicly available at http://dna.cs.miami.edu/HiCNet/.

Inferring Spatial Organization of Individual Topologically Associated Domains via Piecewise Helical Model.

The recently developed Hi-C technology enables a genome-wide view of chromosome spatial organizations, and has shed deep insights into genome structure and genome function. However, multiple sources of uncertainties make downstream data analysis and interpretation challenging. Specifically, statistical models for inferring three-dimensional (3D) chromosomal structure from Hi-C data are far from their maturity. Most existing methods are highly over-parameterized, lacking clear interpretations, and sensitive to outliers. In this study, we propose a parsimonious, easy to interpret, and robust piecewise helical model for the inference of 3D chromosomal structure of individual topologically associated domain from Hi-C data. When applied to a real Hi-C dataset, the piecewise helical model not only achieves much better model fitting than existing models, but also reveals that geometric properties of chromatin spatial organization are closely related to genome function.

Characterizing the 3D structure and dynamics of chromosomes and proteins in a common contact matrix framework.

Conformational ensembles of biopolymers, whether proteins or chromosomes, can be described using contact matrices. Principal component analysis (PCA) on the contact data has been used to interrogate both protein and chromosome structures and/or dynamics. However, as these fields have developed separately, variants of PCA have emerged. Previously, a variant we hereby term Implicit-PCA (I-PCA) has been applied to chromosome contact matrices and revealed the spatial segregation of active and inactive chromatin. Separately, Explicit-PCA (E-PCA) has previously been applied to proteins and characterized their correlated structure fluctuations. Here, we swapped analysis methods (I-PCA and E-PCA), applying each to a different biopolymer type (chromosome or protein) than the one for which they were initially developed. We find that applying E-PCA to chromosome distance matrices derived from microscopy data can reveal the dominant motion (concerted fluctuation) of these chromosomes. Further, by applying E-PCA to Hi-C data across the human blood cell lineage, we isolated the aspects of chromosome structure that most strongly differentiate cell types. Conversely, when we applied I-PCA to simulation snapshots of proteins, the major component reported the consensus features of the structure, making this a promising approach for future analysis of semi-structured proteins.

3D genome and its disorganization in diseases.

The chromosomes in eukaryotic cells are highly folded and organized to form dynamic three-dimensional (3D) structures. In recent years, many technologies including chromosome conformation capture (3C) and 3C-based technologies (Hi-C, ChIA-PET) have been developed to investigate the 3D structure of chromosomes. These technologies are enabling research on how gene regulatory events are affected by the 3D genome structure, which is increasingly implicated in the regulation of gene expression and cellular functions. Importantly, many diseases are associated with genetic variations, most of which are located in non-coding regions. However, it is difficult to determine the mechanisms by which these variations lead to diseases. With 3D genome technologies, we can now better determine the consequences of non-coding genome alterations via their impact on chromatin interactions and structures in cancer and other diseases. In this review, we introduce the various3D genome technologies, with a focus on their application to cancer and disease research, as well as future developments to extend their utility.

A maximum likelihood algorithm for reconstructing 3D structures of human chromosomes from chromosomal contact data

BackgroundThe development of chromosomal conformation capture techniques, particularly, the Hi-C technique, has made the analysis and study of the spatial conformation of a genome an important topic in bioinformatics and computational biology. Aided by high-throughput next generation sequencing techniques, the Hi-C technique can generate genome-wide, large-scale intra- and inter-chromosomal interaction data capable of describing in details the spatial interactions within a genome. These data can be used to reconstruct 3D structures of chromosomes that can be used to study DNA replication, gene regulation, genome interaction, genome folding, and genome function.ResultsHere, we introduce a maximum likelihood algorithm called 3DMax to construct the 3D structure of a chromosome from Hi-C data. 3DMax employs a maximum likelihood approach to infer the 3D structures of a chromosome, while automatically re-estimating the conversion factor (α) for converting Interaction Frequency (IF) to distance. Our results show that the models generated by 3DMax from a simulated Hi-C dataset match the true models better than most of the existing methods. 3DMax is more robust to structural variability and noise. Compared on a real Hi-C dataset, 3DMax constructs chromosomal models that fit the data better than most methods, and it is faster than all other methods. The models reconstructed by 3DMax were consistent with fluorescent in situ hybridization (FISH) experiments and existing knowledge about the organization of human chromosomes, such as chromosome compartmentalization.Conclusions3DMax is an effective approach to reconstructing 3D chromosomal models. The results, and the models generated for the simulated and real Hi-C datasets are available here: http://sysbio.rnet.missouri.edu/bdm_download/3DMax/. The source code is available here: https://github.com/BDM-Lab/3DMax. A short video demonstrating how to use 3DMax can be found here: https://youtu.be/ehQUFWoHwfo.

A flexible approach to reconstruct the genomic spatial structure by the genetic algorithm

The 3D structures of the chromosomes play fundamental roles in essential cellular functions, e.g., gene regulation, gene expression, evolution. HiC technique provides the interaction density between loci on chromosomes. Several approaches have been developed to reconstruct the 3D model of the chromosomes from HiC data. However, all of the approaches are based on a particular mathematical model and lack of flexibility for new development. We introduce a novel approach using the genetic algorithm. Our approach is flexible to accept any mathematical models to build a 3D chromosomal structure. Also, our approach outperforms current techniques in accuracy.

Analysis of high-resolution 3D intrachromosomal interactions aided by Bayesian network modeling

Long-range intrachromosomal interactions play an important role in 3D chromosome structure and function, but our understanding of how various factors contribute to the strength of these interactions remains poor. In this study we used a recently developed analysis framework for Bayesian network (BN) modeling to analyze publicly available datasets for intrachromosomal interactions. We investigated how 106 variables affect the pairwise interactions of over 10 million 5-kb DNA segments in the B-lymphocyte cell line GB12878. Strictly data-driven BN modeling indicates that the strength of intrachromosomal interactions (hic_strength) is directly influenced by only four types of factors: distance between segments, Rad21 or SMC3 (cohesin components),transcription at transcription start sites (TSS), and the number of CCCTC-binding factor (CTCF)-cohesin complexes between the interacting DNA segments. Subsequent studies confirmed that most high-intensity interactions have a CTCF-cohesin complex in at least one of the interacting segments. However, 46% have CTCF on only one side, and 32% are without CTCF. As expected, high-intensity interactions are strongly dependent on the orientation of the ctcf motif, and, moreover, we find that the interaction between enhancers and promoters is similarly dependent on ctcf motif orientation. Dependency relationships between transcription factors were also revealed, including known lineage-determining B-cell transcription factors (e.g., Ebf1) as well as potential novel relationships. Thus, BN analysis of large intrachromosomal interaction datasets is a useful tool for gaining insight into DNA-DNA, protein-DNA, and protein-protein interactions.

Physical properties of the chromosomes and implications for development.

Remarkable progress has been made in understanding chromosome structures inside the cell nucleus. Recent advances in Hi-C technologies enable the detection of genome-wide chromatin interactions, providing insight into three-dimensional (3D) genome organization. Advancements in the spatial and temporal resolutions of imaging as well as in molecular biological techniques allow the tracking of specific chromosomal loci, improving our understanding of chromosome movements. From these data, we are beginning to understand how the intra-nuclear locations of chromatin loci and the 3D genome structure change during development and differentiation. This emerging field of genome structure and dynamics research requires an interdisciplinary approach including efficient collaborations between experimental biologists and physicists, informaticians, or engineers. Quantitative and mathematical analyses based on polymer physics are becoming increasingly important for processing and interpreting experimental data on 3D chromosome structures and dynamics. In this review, we aim to provide an overview of recent research on the physical aspects of chromosome structure and dynamics oriented for biologists. These studies have mainly focused on chromosomes at the cellular level, using unicellular organisms and cultured cells. However, physical parameters that change during development, such as nuclear size, may impact genome structure and dynamics. Here, we discuss how chromatin dynamics and genome structures in early embryos change during development, which we expect will be a hot topic in the field of chromatin dynamics in the near future. We hope this review helps developmental biologists to quantitatively investigate the physical natures of chromosomes in developmental biology research.

Inferential Structure Determination of Chromosomes from Single-Cell Hi-C Data.

Chromosome conformation capture (3C) techniques have revealed many fascinating insights into the spatial organization of genomes. 3C methods typically provide information about chromosomal contacts in a large population of cells, which makes it difficult to draw conclusions about the three-dimensional organization of genomes in individual cells. Recently it became possible to study single cells with Hi-C, a genome-wide 3C variant, demonstrating a high cell-to-cell variability of genome organization. In principle, restraint-based modeling should allow us to infer the 3D structure of chromosomes from single-cell contact data, but suffers from the sparsity and low resolution of chromosomal contacts. To address these challenges, we adapt the Bayesian Inferential Structure Determination (ISD) framework, originally developed for NMR structure determination of proteins, to infer statistical ensembles of chromosome structures from single-cell data. Using ISD, we are able to compute structural error bars and estimate model parameters, thereby eliminating potential bias imposed by ad hoc parameter choices. We apply and compare different models for representing the chromatin fiber and for incorporating singe-cell contact information. Finally, we extend our approach to the analysis of diploid chromosome data.

3Disease Browser: A Web server for integrating 3D genome and disease-associated chromosome rearrangement data.

Chromosomal rearrangement (CR) events have been implicated in many tumor and non-tumor human diseases. CR events lead to their associated diseases by disrupting gene and protein structures. Also, they can lead to diseases through changes in chromosomal 3D structure and gene expression. In this study, we search for CR-associated diseases potentially caused by chromosomal 3D structure alteration by integrating Hi-C and ChIP-seq data. Our algorithm rediscovers experimentally verified disease-associated CRs (polydactyly diseases) that alter gene expression by disrupting chromosome 3D structure. Interestingly, we find that intellectual disability may be a candidate disease caused by 3D chromosome structure alteration. We also develop a Web server (3Disease Browser, http://3dgb.cbi.pku.edu.cn/disease/) for integrating and visualizing disease-associated CR events and chromosomal 3D structure.

Three-dimensional reconstruction of single-cell chromosome structure using recurrence plots.

Single-cell analysis of the three-dimensional (3D) chromosome structure can reveal cell-to-cell variability in genome activities. Here, we propose to apply recurrence plots, a mathematical method of nonlinear time series analysis, to reconstruct the 3D chromosome structure of a single cell based on information of chromosomal contacts from genome-wide chromosome conformation capture (Hi-C) data. This recurrence plot-based reconstruction (RPR) method enables rapid reconstruction of a unique structure in single cells, even from incomplete Hi-C information.

Computational Identification of Genomic Features That Influence 3D Chromatin Domain Formation.

Recent advances in long-range Hi-C contact mapping have revealed the importance of the 3D structure of chromosomes in gene expression. A current challenge is to identify the key molecular drivers of this 3D structure. Several genomic features, such as architectural proteins and functional elements, were shown to be enriched at topological domain borders using classical enrichment tests. Here we propose multiple logistic regression to identify those genomic features that positively or negatively influence domain border establishment or maintenance. The model is flexible, and can account for statistical interactions among multiple genomic features. Using both simulated and real data, we show that our model outperforms enrichment test and non-parametric models, such as random forests, for the identification of genomic features that influence domain borders. Using Drosophila Hi-C data at a very high resolution of 1 kb, our model suggests that, among architectural proteins, BEAF-32 and CP190 are the main positive drivers of 3D domain borders. In humans, our model identifies well-known architectural proteins CTCF and cohesin, as well as ZNF143 and Polycomb group proteins as positive drivers of domain borders. The model also reveals the existence of several negative drivers that counteract the presence of domain borders including P300, RXRA, BCL11A and ELK1.

MOGEN: a tool for reconstructing 3D models of genomes from chromosomal conformation capturing data

The three-dimensional (3D) conformation of chromosomes and genomes play an important role in cellular processes such as gene regulation, DNA replication and genome methylation. Several methods have been developed to reconstruct 3D structures of individual chromosomes from chromosomal conformation capturing data such as Hi-C data. However, few methods can effectively reconstruct the 3D structures of an entire genome due to the difficulty of handling noisy and inconsistent inter-chromosomal contact data. We generalized a 3D chromosome reconstruction method to make it capable of reconstructing 3D models of genomes from both intra- and inter-chromosomal Hi-C contact data and implemented it as a software tool called MOGEN. We validated MOGEN on synthetic datasets of a polymer worm-like chain model and a yeast genome at first, and then applied it to generate an ensemble of 3D structural models of the genome of human B-cells from a Hi-C dataset. These genome models not only were validated by some known structural patterns of the human genome, such as chromosome compartmentalization, chromosome territories, co-localization of small chromosomes in the nucleus center with the exception of chromosome 18, enriched center-toward inter-chromosomal interactions between elongated or telomere regions of chromosomes, but also demonstrated the intrinsically dynamic orientations between chromosomes. Therefore, MOGEN is a useful tool for converting chromosomal contact data into 3D genome models to provide a better view into the spatial organization of genomes. The software of MOGEN is available at: http://calla.rnet.missouri.edu/mogen/ : chengji@missouri.edu Supplementary data are available at Bioinformatics online.

Iterative reconstruction of three-dimensional models of human chromosomes from chromosomal contact data.

BackgroundThe entire collection of genetic information resides within the chromosomes, which themselves reside within almost every cell nucleus of eukaryotic organisms. Each individual chromosome is found to have its own preferred three-dimensional (3D) structure independent of the other chromosomes. The structure of each chromosome plays vital roles in controlling certain genome operations, including gene interaction and gene regulation. As a result, knowing the structure of chromosomes assists in the understanding of how the genome functions. Fortunately, the 3D structure of chromosomes proves possible to construct through computational methods via contact data recorded from the chromosome. We developed a unique computational approach based on optimization procedures known as adaptation, simulated annealing, and genetic algorithm to construct 3D models of human chromosomes, using chromosomal contact data.ResultsOur models were evaluated using a percentage-based scoring function. Analysis of the scores of the final 3D models demonstrated their effective construction from our computational approach. Specifically, the models resulting from our approach yielded an average score of 80.41 %, with a high of 91 %, across models for all chromosomes of a normal human B-cell. Comparisons made with other methods affirmed the effectiveness of our strategy. Particularly, juxtaposition with models generated through the publicly available method Markov chain Monte Carlo 5C (MCMC5C) illustrated the outperformance of our approach, as seen through a higher average score for all chromosomes. Our methodology was further validated using two consistency checking techniques known as convergence testing and robustness checking, which both proved successful.ConclusionsThe pursuit of constructing accurate 3D chromosomal structures is fueled by the benefits revealed by the findings as well as any possible future areas of study that arise. This motivation has led to the development of our computational methodology. The implementation of our approach proved effective in constructing 3D chromosome models and proved consistent with, and more effective than, some other methods thereby achieving our goal of creating a tool to help advance certain research efforts. The source code, test data, test results, and documentation of our method, Gen3D, are available at our sourceforge site at: http://sourceforge.net/projects/gen3d/.

Modeling Three-Dimensional Chromosome Structures Using Gene Expression Data.

Recent genomic studies have shown that significant chromosomal spatial correlation exists in gene expression of many organisms. Interestingly, coexpression has been observed among genes separated by a fixed interval in specific regions of a chromosome chain, which is likely caused by three-dimensional (3D) chromosome folding structures. Modeling such spatial correlation explicitly may lead to essential understandings of 3D chromosome structures and their roles in transcriptional regulation. In this paper, we explore chromosomal spatial correlation induced by 3D chromosome structures, and propose a hierarchical Bayesian method based on helical structures to formally model and incorporate the correlation into the analysis of gene expression microarray data. It is the first study to quantify and infer 3D chromosome structures in vivo using expression microarrays. Simulation studies show computing feasibility of the proposed method and that, under the assumption of helical chromosome structures, it can lead to precise estimation of structural parameters and gene expression levels. Real data applications demonstrate an intriguing biological phenomenon that functionally associated genes, which are far apart along the chromosome chain, are brought into physical proximity by chromosomal folding in 3D space to facilitate their coexpression. It leads to important biological insight into relationship between chromosome structure and function.

Large-scale reconstruction of 3D structures of human chromosomes from chromosomal contact data

Chromosomes are not positioned randomly within a nucleus, but instead, they adopt preferred spatial conformations to facilitate necessary long-range gene–gene interactions and regulations. Thus, obtaining the 3D shape of chromosomes of a genome is critical for understanding how the genome folds, functions and how its genes interact and are regulated. Here, we describe a method to reconstruct preferred 3D structures of individual chromosomes of the human genome from chromosomal contact data generated by the Hi-C chromosome conformation capturing technique. A novel parameterized objective function was designed for modeling chromosome structures, which was optimized by a gradient descent method to generate chromosomal structural models that could satisfy as many intra-chromosomal contacts as possible. We applied the objective function and the corresponding optimization method to two Hi-C chromosomal data sets of both a healthy and a cancerous human B-cell to construct 3D models of individual chromosomes at resolutions of 1 MB and 200 KB, respectively. The parameters used with the method were calibrated according to an independent fluorescence in situ hybridization experimental data. The structural models generated by our method could satisfy a high percentage of contacts (pairs of loci in interaction) and non-contacts (pairs of loci not in interaction) and were compatible with the known two-compartment organization of human chromatin structures. Furthermore, structural models generated at different resolutions and from randomly permuted data sets were consistent.

3D Chromosome Modeling with Semi-Definite Programming and Hi-C Data

For a long period of time, scientists studied genomes while assuming they are linear. Recently, chromosome conformation capture (3C)-based technologies, such as Hi-C, have been developed that provide the loci contact frequencies among loci pairs in a genome-wide scale. The technology unveiled that two far-apart loci can interact in the tested genome. It indicated that the tested genome forms a three-dimensional (3D) chromosomal structure within the nucleus. With the available Hi-C data, our next challenge is to model the 3D chromosomal structure from the 3C-derived data computationally. This article presents a deterministic method called ChromSDE, which applies semi-definite programming techniques to find the best structure fitting the observed data and uses golden section search to find the correct parameter for converting the contact frequency to spatial distance. Further, we develop a measure called consensus index to indicate if the Hi-C data corresponds to a single structure or a mixture of structures. To the best of our knowledge, ChromSDE is the only method that can guarantee recovering the correct structure in the noise-free case. In addition, we prove that the parameter of conversion from contact frequency to spatial distance will change under different resolutions theoretically and empirically. Using simulation data and real Hi-C data, we showed that ChromSDE is much more accurate and robust than existing methods. Finally, we demonstrated that interesting biological findings can be uncovered from our predicted 3D structure.

Bayesian Inference of Spatial Organizations of Chromosomes

Knowledge of spatial chromosomal organizations is critical for the study of transcriptional regulation and other nuclear processes in the cell. Recently, chromosome conformation capture (3C) based technologies, such as Hi-C and TCC, have been developed to provide a genome-wide, three-dimensional (3D) view of chromatin organization. Appropriate methods for analyzing these data and fully characterizing the 3D chromosomal structure and its structural variations are still under development. Here we describe a novel Bayesian probabilistic approach, denoted as “Bayesian 3D constructor for Hi-C data” (BACH), to infer the consensus 3D chromosomal structure. In addition, we describe a variant algorithm BACH-MIX to study the structural variations of chromatin in a cell population. Applying BACH and BACH-MIX to a high resolution Hi-C dataset generated from mouse embryonic stem cells, we found that most local genomic regions exhibit homogeneous 3D chromosomal structures. We further constructed a model for the spatial arrangement of chromatin, which reveals structural properties associated with euchromatic and heterochromatic regions in the genome. We observed strong associations between structural properties and several genomic and epigenetic features of the chromosome. Using BACH-MIX, we further found that the structural variations of chromatin are correlated with these genomic and epigenetic features. Our results demonstrate that BACH and BACH-MIX have the potential to provide new insights into the chromosomal architecture of mammalian cells.

MP 3 The basal ganglia in perceptual timing: timing performance in multiple system atrophy and Huntington's disease

Transcription machinery depends on the temporal formation of protein-DNA complexes. Recent experiments demonstrated that lifetime of the complex can also affect transcription. In parallel, <i>in vitro</i> single-molecule studies showed that nucleoid-associated proteins (NAPs) leave the DNA rapidly as the bulk concentration of the protein increases via facilitated dissociation (FD). Never-theless, whether such concentration-dependent mechanism is functional in a bacterial cell, in which NAP levels and the 3D chromosomal structure are often coupled, is not clear <i>a priori</i>. Here, by using extensive coarse-grained molecular simulations, we model the unbinding of specific and nonspecific dimeric NAPs from a high-molecular-weight circular DNA molecule in a cylindrical structure mimicking the cellular confinement of a bacterial chromosome. Our simulations show that physiologically relevant peak protein levels (tens of micromolar) lead to highly compact chromosomal structures. This compaction results in rapid off rates (shorter DNA-residence times) but only for specifically DNA-binding NAPs such as the factor for inversion stimulation (Fis). Contrarily, for nonspecific NAPs, the off rates decrease as the protein levels increase, suggesting an inverse FD pattern. The simulations with restrained chromosome models reveal that this inverse response is due to DNA-segmental fluctuations, and that chromosomal compaction is in favor of faster protein dissociation. Overall, our results indicate that cellular-concentration level of a structural DNA-binding protein can be highly intermingled with its DNA-residence time.

Modeling Three-Dimensional Chromosome Structures Using Gene Expression Data

Recent genomic studies have shown that significant chromosomal spatial correlation exists in gene expression of many organisms. Interestingly, coexpression has been observed among genes separated by a fixed interval in specific regions of a chromosome chain, which is likely caused by three-dimensional (3D) chromosome folding structures. Modeling such spatial correlation explicitly may lead to essential understandings of 3D chromosome structures and their roles in transcriptional regulation. In this paper, we explore chromosomal spatial correlation induced by 3D chromosome structures, and propose a hierarchical Bayesian method based on helical structures to formally model and incorporate the correlation into the analysis of gene expression microarray data. It is the first study to quantify and infer 3D chromosome structures in vivo using expression microarrays. Simulation studies show computing feasibility of the proposed method and that, under the assumption of helical chromosome structures, it can lead to precise estimation of structural parameters and gene expression levels. Real data applications demonstrate an intriguing biological phenomenon that functionally associated genes, which are far apart along the chromosome chain, are brought into physical proximity by chromosomal folding in 3D space to facilitate their coexpression. It leads to important biological insight into relationship between chromosome structure and function.