RESUMO
Three-dimensional (3D) chromatin interactions, such as enhancer-promoter interactions (EPIs), loops, topologically associating domains (TADs), and A/B compartments, play critical roles in a wide range of cellular processes by regulating gene expression. Recent development of chromatin conformation capture technologies has enabled genome-wide profiling of various 3D structures, even with single cells. However, current catalogs of 3D structures remain incomplete and unreliable due to differences in technology, tools, and low data resolution. Machine learning methods have emerged as an alternative to obtain missing 3D interactions and/or improve resolution. Such methods frequently use genome annotation data (ChIP-seq, DNAse-seq, etc.), DNA sequencing information (k-mers and transcription factor binding site (TFBS) motifs), and other genomic properties to learn the associations between genomic features and chromatin interactions. In this review, we discuss computational tools for predicting three types of 3D interactions (EPIs, chromatin interactions, and TAD boundaries) and analyze their pros and cons. We also point out obstacles to the computational prediction of 3D interactions and suggest future research directions.
Assuntos
Cromatina , Aprendizado Profundo , Cromatina/genética , Cromatina/metabolismo , Humanos , Biologia Computacional/métodos , Aprendizado de Máquina , Genômica/métodos , Elementos Facilitadores Genéticos , Regiões Promotoras Genéticas , Sítios de Ligação , Genoma , SoftwareRESUMO
Recent analyses revealed the essential function of chromatin structure in maintaining and regulating genomic information. Advancements in microscopy, nuclear structure observation techniques, and the development of methods utilizing next-generation sequencers (NGSs) have significantly progressed these discoveries. Methods utilizing NGS enable genome-wide analysis, which is challenging with microscopy, and have elucidated concepts of important chromatin structures such as a loop structure, a domain structure called topologically associating domains (TADs), and compartments. In this chapter, I introduce chromatin interaction techniques using NGS and outline the principles and features of each method.
Assuntos
Cromatina , Sequenciamento de Nucleotídeos em Larga Escala , Cromatina/genética , Cromatina/metabolismo , Cromatina/química , Humanos , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Genômica/métodos , Estudo de Associação Genômica Ampla/métodos , AnimaisRESUMO
Hi-C is a popular ligation-based technique to detect 3D physical chromosome structure within the nucleus using cross-linking and next-generation sequencing. As an unbiased genome-wide assay based on chromosome conformation capture, it provides rich insights into chromosome structure, dynamic chromosome folding and interactions, and the regulatory state of a cell. Bioinformatics analyses of Hi-C data require dedicated protocols as most genome alignment tools assume that both paired-end reads will map to the same chromosome, resulting in large two-dimensional matrices as processed data. Here, we outline the necessary steps to generate high-quality aligned Hi-C data by separately mapping each read while correcting for biases from restriction enzyme digests. We introduce our own custom open-source pipeline, which enables users to select an aligner of their choosing with high accuracy and performance. This enables users to generate high-resolution datasets with fast turnaround and fewer unmapped reads. Finally, we discuss recent innovations in experimental techniques, bioinformatics techniques, and their applications in clinical testing for diagnostics.
Assuntos
Mapeamento Cromossômico , Biologia Computacional , Sequenciamento de Nucleotídeos em Larga Escala , Software , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Biologia Computacional/métodos , Humanos , Mapeamento Cromossômico/métodos , Cromossomos/genética , Genômica/métodos , Cromatina/genética , Cromatina/químicaRESUMO
Hi-C and Micro-C are the three-dimensional (3D) genome assays that use high-throughput sequencing. In the analysis, the sequenced paired-end reads are mapped to a reference genome to generate a two-dimensional contact matrix for identifying topologically associating domains (TADs), chromatin loops, and chromosomal compartments. On the other hand, the distance distribution of the paired-end mapped reads also provides insight into the 3D genome structure by highlighting global contact frequency patterns at distances indicative of loops, TADs, and compartments. This chapter presents a basic workflow for visualizing and analyzing contact distance distributions from Hi-C data. The workflow can be run on Google Colaboratory, which provides a ready-to-use Python environment accessible through a web browser. The notebook that demonstrates the workflow is available in the GitHub repository at https://github.com/rnakato/Springer_contact_distance_plot.
Assuntos
Sequenciamento de Nucleotídeos em Larga Escala , Software , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Biologia Computacional/métodos , Navegador , Fluxo de Trabalho , Humanos , Cromatina/genética , Genômica/métodosRESUMO
The compartmentalization of chromatin reflects its underlying biological activities. Inferring chromatin sub-compartments using Hi-C data is challenged by data resolution constraints. Consequently, comprehensive characterizations of sub-compartments have been limited to a select number of Hi-C experiments, with systematic comparisons across a wide range of tissues and conditions still lacking. Our original Calder algorithm marked a significant advancement in this field, enabling the identification of multi-scale sub-compartments at various data resolutions and facilitating the inference and comparison of chromatin architecture in over 100 datasets. Building on this foundation, we introduce Calder2, an updated version of Calder that brings notable improvements. These include expanded support for a wider array of genomes and organisms, an optimized bin size selection approach for more accurate chromatin compartment detection, and extended support for input and output formats. Calder2 thus stands as a refined analysis tool, significantly advancing genome-wide studies of 3D chromatin architecture and its functional implications.
Assuntos
Algoritmos , Cromatina , Software , Cromatina/genética , Cromatina/metabolismo , Biologia Computacional/métodos , Humanos , AnimaisRESUMO
Over a decade has passed since the development of the Hi-C method for genome-wide analysis of 3D genome organization. Hi-C utilizes next-generation sequencing (NGS) technology to generate large-scale chromatin interaction data, which has accumulated across a diverse range of species and cell types, particularly in eukaryotes. There is thus a growing need to streamline the process of Hi-C data analysis to utilize these data sets effectively. Hi-C generates data that are much larger compared to other NGS techniques such as chromatin immunoprecipitation sequencing (ChIP-seq) or RNA-seq, making the data reanalysis process computationally expensive. In an effort to bridge this resource gap, the 4D Nucleome (4DN) Data Portal has reanalyzed approximately 600 Hi-C data sets, allowing users to access and utilize the analyzed data. In this chapter, we provide detailed instructions for the implementation of the common workflow language (CWL)-based Hi-C analysis pipeline adopted by the 4DN Data Portal ecosystem. This reproducible and portable pipeline generates standard Hi-C contact matrices in formats such as .hic or .mcool from FASTQ files. It enables users to output their own Hi-C data in the same format as those registered in the 4DN Data portal, facilitating comparative analysis using data registered in the portal. Our custom-made scripts are available on GitHub at https://github.com/kuzobuta/4dn_cwl_pipeline .
Assuntos
Cromatina , Sequenciamento de Nucleotídeos em Larga Escala , Software , Fluxo de Trabalho , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Cromatina/genética , Cromatina/metabolismo , Humanos , Genômica/métodos , Biologia Computacional/métodos , Sequenciamento de Cromatina por Imunoprecipitação/métodosRESUMO
The Hi-C method has emerged as an indispensable tool for analyzing the 3D organization of the genome, becoming increasingly accessible and frequently utilized in chromatin research. To effectively leverage 3D genomics data obtained through advanced technologies, it is crucial to understand what processes are undertaken and what aspects require special attention within the bioinformatics pipeline. This protocol aims to demystify the Hi-C data analysis process for field newcomers. In a step-by-step manner, we describe how to process Hi-C data, from the initial sequencing of the Hi-C library to the final visualization of Hi-C contact data as heatmaps. Each step of the analysis is clearly explained to ensure an understanding of the procedures and their objectives. By the end of this chapter, readers will be equipped with the knowledge to transform raw Hi-C reads into informative visual representations, facilitating a deeper comprehension of the spatial genomic structures critical to cellular functions.
Assuntos
Cromatina , Biologia Computacional , Genômica , Software , Cromatina/genética , Biologia Computacional/métodos , Genômica/métodos , Humanos , Sequenciamento de Nucleotídeos em Larga Escala/métodosRESUMO
Peakachu is a supervised-learning-based approach that identifies chromatin loops from chromatin contact data. Here, we present Peakachu version 2, an updated version that significantly improves extensibility, usability, and computational efficiency compared to its predecessor. It features pretrained models tailored for a wide range of experimental platforms, such as Hi-C, Micro-C, ChIA-PET, HiChIP, HiCAR, and TrAC-loop. This chapter offers a step-by-step tutorial guiding users through the process of training Peakachu models from scratch and utilizing pretrained models to predict chromatin loops across various platforms.
Assuntos
Cromatina , Biologia Computacional , Software , Cromatina/metabolismo , Cromatina/genética , Biologia Computacional/métodos , Humanos , Aprendizado de Máquina Supervisionado , Conformação de Ácido NucleicoRESUMO
Polymer modeling has been playing an increasingly important role in complementing 3D genome experiments, both to aid their interpretation and to reveal the underlying molecular mechanisms. This chapter illustrates an application of Hi-C metainference, a Bayesian approach to explore the 3D organization of a target genomic region by integrating experimental contact frequencies into a prior model of chromatin. The method reconstructs the conformational ensemble of the target locus by combining molecular dynamics simulation and Monte Carlo sampling from the posterior probability distribution given the data. Using prior chromatin models at both 1 kb and nucleosome resolution, we apply this approach to a 30 kb locus of mouse embryonic stem cells consisting of two well-defined domains linking several gene promoters together. Retaining the advantages of both physics-based and data-driven strategies, Hi-C metainference can provide an experimentally consistent representation of the system while at the same time retaining molecular details necessary to derive physical insights.
Assuntos
Teorema de Bayes , Cromatina , Simulação de Dinâmica Molecular , Animais , Camundongos , Cromatina/genética , Cromatina/química , Cromatina/metabolismo , Genoma , Genômica/métodos , Método de Monte Carlo , Células-Tronco Embrionárias Murinas/metabolismoRESUMO
Disentangling the relationship of enhancers and genes is an ongoing challenge in epigenomics. We present STARE, our software to quantify the strength of enhancer-gene interactions based on enhancer activity and chromatin contact data. It implements the generalized Activity-by-Contact (gABC) score, which allows predicting putative target genes of candidate enhancers over any desired genomic distance. The only requirement for its application is a measurement of enhancer activity. In addition to regulatory interactions, STARE calculates transcription factor (TF) affinities on gene level. We illustrate its usage on a public single-cell data set of the human heart by predicting regulatory interactions on cell type level, by giving examples on how to integrate them with other data modalities, and by constructing TF affinity matrices.
Assuntos
Cromatina , Elementos Facilitadores Genéticos , Epigenômica , Software , Humanos , Cromatina/genética , Cromatina/metabolismo , Epigenômica/métodos , Epigenoma , Fatores de Transcrição/metabolismo , Fatores de Transcrição/genética , Biologia Computacional/métodosRESUMO
Hi-C methods reveal 3D genome features but lack correspondence to dynamic chromatin behavior. PHi-C2, Python software, addresses this gap by transforming Hi-C data into polymer models. After the optimization algorithm, it enables us to calculate 3D conformations and conduct dynamic simulations, providing insights into chromatin dynamics, including the mean-squared displacement and rheological properties. This chapter introduces PHi-C2 usage, offering a tutorial for comprehensive 4D genome analysis.
Assuntos
Algoritmos , Cromatina , Software , Cromatina/genética , Cromatina/química , Cromatina/metabolismo , Humanos , Genômica/métodos , Genoma , Biologia Computacional/métodosRESUMO
In order to analyze the three-dimensional genome architecture, it is important to simulate how the genome is structured through the cell cycle progression. In this chapter, we present the usage of our computation codes for simulating how the human genome is formed as the cell transforms from anaphase to interphase. We do not use the global Hi-C data as an input into the genome simulation but represent all chromosomes as linear polymers annotated by the neighboring region contact index (NCI), which classifies the A/B type of each local chromatin region. The simulated mitotic chromosomes heterogeneously expand upon entry to the G1 phase, which induces phase separation of A and B chromatin regions, establishing chromosome territories, compartments, and lamina and nucleolus associations in the interphase nucleus. When the appropriate one-dimensional chromosomal annotation is possible, using the protocol of this chapter, one can quantitatively simulate the three-dimensional genome structure and dynamics of human cells of interest.
Assuntos
Anáfase , Cromatina , Genoma Humano , Interfase , Humanos , Anáfase/genética , Interfase/genética , Cromatina/genética , Cromatina/metabolismo , Simulação por Computador , Cromossomos Humanos/genética , Mitose/genéticaRESUMO
Cohesin is a protein complex that plays a key role in regulating chromosome structure and gene expression. While next-generation sequencing technologies have provided extensive information on various aspects of cohesin, integrating and exploring the vast datasets associated with cohesin are not straightforward. CohesinDB ( https://cohesindb.iqb.u-tokyo.ac.jp ) offers a web-based interface for browsing, searching, analyzing, visualizing, and downloading comprehensive multiomics cohesin information in human cells. In this protocol, we introduce how to utilize CohesinDB to facilitate research on transcriptional regulation and chromatin organization.
Assuntos
Proteínas de Ciclo Celular , Proteínas Cromossômicas não Histona , Coesinas , Navegador , Proteínas Cromossômicas não Histona/metabolismo , Proteínas Cromossômicas não Histona/genética , Proteínas de Ciclo Celular/metabolismo , Proteínas de Ciclo Celular/genética , Humanos , Software , Biologia Computacional/métodos , Genômica/métodos , Bases de Dados Genéticas , Cromatina/metabolismo , Cromatina/genética , Internet , MultiômicaRESUMO
The primed epiblast acts as a transitional stage between the relatively homogeneous naïve epiblast and the gastrulating embryo. Its formation entails coordinated changes in regulatory circuits driven by transcription factors and epigenetic modifications. Using a multi-omic approach in human embryonic stem cell models across the spectrum of peri-implantation development, we demonstrate that the transcription factors ZIC2 and ZIC3 have overlapping but essential roles in opening primed-specific enhancers. Together, they are essential to facilitate progression to and maintain primed pluripotency. ZIC2/3 accomplish this by recruiting SWI/SNF to chromatin and loss of ZIC2/3 or degradation of SWI/SNF both prevent enhancer activation. Loss of ZIC2/3 also results in transcriptome changes consistent with perturbed Polycomb activity and a shift towards the expression of genes linked to differentiation towards the mesendoderm. Additionally, we find an intriguing dependency on the transcriptional machinery for sustained recruitment of ZIC2/3 over a subset of primed-hESC specific enhancers. Taken together, ZIC2 and ZIC3 regulate highly dynamic lineage-specific enhancers and collectively act as key regulators of human primed pluripotency.
Assuntos
Diferenciação Celular , Proteínas de Homeodomínio , Células-Tronco Embrionárias Humanas , Células-Tronco Pluripotentes , Fatores de Transcrição , Humanos , Fatores de Transcrição/metabolismo , Fatores de Transcrição/genética , Proteínas de Homeodomínio/metabolismo , Proteínas de Homeodomínio/genética , Diferenciação Celular/genética , Células-Tronco Pluripotentes/metabolismo , Células-Tronco Pluripotentes/citologia , Células-Tronco Embrionárias Humanas/metabolismo , Células-Tronco Embrionárias Humanas/citologia , Camadas Germinativas/metabolismo , Camadas Germinativas/citologia , Elementos Facilitadores Genéticos , Proteínas Cromossômicas não Histona/metabolismo , Proteínas Cromossômicas não Histona/genética , Regulação da Expressão Gênica no Desenvolvimento , Cromatina/metabolismo , Proteínas NuclearesRESUMO
Progerin causes Hutchinson-Gilford progeria syndrome (HGPS), but how progerin accelerates aging is still an interesting question. Here, we provide evidence linking nuclear envelope (NE) budding and accelerated aging. Mechanistically, progerin disrupts nuclear lamina to induce NE budding in concert with lamin A/C, resulting in transport of chromatin into the cytoplasm where it is removed via autophagy, whereas emerin antagonizes this process. Primary cells from both HGPS patients and mouse models express progerin and display NE budding and chromatin loss, and ectopically expressing progerin in cells can mimic this process. More excitingly, we screen a NE budding inhibitor chaetocin by high-throughput screening, which can dramatically sequester progerin from the NE and prevent this NE budding through sustaining ERK1/2 activation. Chaetocin alleviates NE budding-induced chromatin loss and ameliorates HGPS defects in cells and mice and significantly extends lifespan of HGPS mice. Collectively, we propose that progerin-induced NE budding participates in the induction of progeria, highlight the roles of chaetocin and sustained ERK1/2 activation in anti-aging, and provide a distinct avenue for treating HGPS.
Assuntos
Lamina Tipo A , Membrana Nuclear , Proteínas Nucleares , Progéria , Progéria/metabolismo , Progéria/tratamento farmacológico , Progéria/patologia , Progéria/genética , Animais , Lamina Tipo A/metabolismo , Lamina Tipo A/genética , Camundongos , Humanos , Membrana Nuclear/metabolismo , Proteínas Nucleares/metabolismo , Proteínas Nucleares/genética , Envelhecimento/metabolismo , Envelhecimento/efeitos dos fármacos , Cromatina/metabolismo , Proteínas de Membrana/metabolismo , Proteínas de Membrana/genética , Modelos Animais de Doenças , Autofagia/efeitos dos fármacosRESUMO
The three-dimensional organization of chromatin is influenced by chromatin-binding proteins through both specific and non-specific interactions. However, the roles of chromatin sequence and the interactions between binding proteins in shaping chromatin structure remain elusive. By employing a simple polymer-based model of chromatin that explicitly considers sequence-dependent protein binding and protein-protein interactions, we elucidate a mechanism for chromatin organization. We find that tuning protein-protein interactions and protein concentration is sufficient to either promote or inhibit chromatin compartmentalization. Moreover, chromatin sequence and protein-protein attraction strongly affect the structural and dynamic exponents that describe the spatiotemporal organization of chromatin. Strikingly, our model's predictions for the exponents governing chromatin structure and dynamics successfully capture experimental observations, in sharp contrast to previous chromatin models. Overall, our findings have the potential to reinterpret data obtained from various chromosome conformation capture technologies, laying the groundwork for advancing our understanding of chromatin organization.
Assuntos
Cromatina , Ligação Proteica , Cromatina/química , Cromatina/metabolismo , Modelos Moleculares , Proteínas/química , Proteínas/metabolismoAssuntos
Cromatina , Neoplasias , Humanos , Neoplasias/genética , Neoplasias/terapia , Cromatina/genética , Epigênese GenéticaRESUMO
Involved in mitotic condensation, interaction of transcriptional regulatory elements and isolation of structural domains, loop formation has become a paradigm in the deciphering of chromatin architecture and its functional role. Despite the emergence of increasingly powerful genome visualization techniques, the high variability in cell populations and the randomness of conformations still make loop detection a challenge. We introduce an approach for determining the presence and frequency of loops in a collection of experimental conformations obtained by multiplexed super-resolution imaging. Based on a spectral approach, in conjunction with neural networks, this method offers a powerful tool to detect loops in large experimental data sets, both at the population and single-cell levels. The method's performance is confirmed on experimental FISH data where Hi-C and other loop detection results are available. The method is then applied to recently published experimental data, where it provides a detailed and statistically quantified description of the global architecture of the chromosomal region under study.
Assuntos
Cromatina , Hibridização in Situ Fluorescente , Cromatina/metabolismo , Cromatina/genética , Hibridização in Situ Fluorescente/métodos , Humanos , Animais , Redes Neurais de Computação , Conformação de Ácido Nucleico , Cromossomos/genéticaRESUMO
Epigenetic alterations, such as those in chromatin structure and DNA methylation, have been extensively studied in a number of tumor types. But oral cancer, particularly oral adenocarcinoma, has received far less attention. Here, we combined laser-capture microdissection and muti-omics mini-bulk sequencing to systematically characterize the epigenetic landscape of oral cancer, including chromatin architecture, DNA methylation, H3K27me3 modification, and gene expression. In carcinogenesis, tumor cells exhibit reorganized chromatin spatial structures, including compromised compartment structures and altered gene-gene interaction networks. Notably, some structural alterations are observed in phenotypically non-malignant paracancerous but not in normal cells. We developed transformer models to identify the cancer propensity of individual genome loci, thereby determining the carcinogenic status of each sample. Insights into cancer epigenetic landscapes provide evidence that chromatin reorganization is an important hallmark of oral cancer progression, which is also linked with genomic alterations and DNA methylation reprogramming. In particular, regions of frequent copy number alternations in cancer cells are associated with strong spatial insulation in both cancer and normal samples. Aberrant methylation reprogramming in oral squamous cell carcinomas is closely related to chromatin structure and H3K27me3 signals, which are further influenced by intrinsic sequence properties. Our findings indicate that structural changes are both significant and conserved in two distinct types of oral cancer, closely linked to transcriptomic alterations and cancer development. Notably, the structural changes remain markedly evident in oral adenocarcinoma despite the considerably lower incidence of genomic copy number alterations and lesser extent of methylation alterations compared to squamous cell carcinoma. We expect that the comprehensive analysis of epigenetic reprogramming of different types and subtypes of primary oral tumors can provide additional guidance to the design of novel detection and therapy for oral cancer.