Promoter and enhancer RNAs regulate chromatin reorganization and activation of miR-10b/HOXD locus, and neoplastic transformation in glioma.

miR-10b is silenced in normal neuroglial cells of the brain but commonly activated in glioma, where it assumes an essential tumor-promoting role. We demonstrate that the entire miR-10b-hosting HOXD locus is activated in glioma via the cis-acting mechanism involving 3D chromatin reorganization and CTCF-cohesin-mediated looping. This mechanism requires two interacting lncRNAs, HOXD-AS2 and LINC01116, one associated with HOXD3/HOXD4/miR-10b promoter and another with the remote enhancer. Knockdown of either lncRNA in glioma cells alters CTCF and cohesin binding, abolishes chromatin looping, inhibits the expression of all genes within HOXD locus, and leads to glioma cell death. Conversely, in cortical astrocytes, enhancer activation is sufficient for HOXD/miR-10b locus reorganization, gene derepression, and neoplastic cell transformation. LINC01116 RNA is essential for this process. Our results demonstrate the interplay of two lncRNAs in the chromatin folding and concordant regulation of miR-10b and multiple HOXD genes normally silenced in astrocytes and triggering the neoplastic glial transformation.

HiConfidence: a novel approach uncovering the biological signal in Hi-C data affected by technical biases.

The chromatin interaction assays, particularly Hi-C, enable detailed studies of genome architecture in multiple organisms and model systems, resulting in a deeper understanding of gene expression regulation mechanisms mediated by epigenetics. However, the analysis and interpretation of Hi-C data remain challenging due to technical biases, limiting direct comparisons of datasets obtained in different experiments and laboratories. As a result, removing biases from Hi-C-generated chromatin contact matrices is a critical data analysis step. Our novel approach, HiConfidence, eliminates biases from the Hi-C data by weighing chromatin contacts according to their consistency between replicates so that low-quality replicates do not substantially influence the result. The algorithm is effective for the analysis of global changes in chromatin structures such as compartments and topologically associating domains. We apply the HiConfidence approach to several Hi-C datasets with significant technical biases, that could not be analyzed effectively using existing methods, and obtain meaningful biological conclusions. In particular, HiConfidence aids in the study of how changes in histone acetylation pattern affect chromatin organization in Drosophila melanogaster S2 cells. The method is freely available at GitHub: https://github.com/victorykobets/HiConfidence.

Bioframe: operations on genomic intervals in Pandas dataframes.

MOTIVATION: Genomic intervals are one of the most prevalent data structures in computational genome biology, and used to represent features ranging from genes, to DNA binding sites, to disease variants. Operations on genomic intervals provide a language for asking questions about relationships between features. While there are excellent interval arithmetic tools for the command line, they are not smoothly integrated into Python, one of the most popular general-purpose computational and visualization environments. RESULTS: Bioframe is a library to enable flexible and performant operations on genomic interval dataframes in Python. Bioframe extends the Python data science stack to use cases for computational genome biology by building directly on top of two of the most commonly-used Python libraries, NumPy and Pandas. The bioframe API enables flexible name and column orders, and decouples operations from data formats to avoid unnecessary conversions, a common scourge for bioinformaticians. Bioframe achieves these goals while maintaining high performance and a rich set of features. AVAILABILITY AND IMPLEMENTATION: Bioframe is open-source under MIT license, cross-platform, and can be installed from the Python Package Index. The source code is maintained by Open2C on GitHub at https://github.com/open2c/bioframe.

Pairtools: From sequencing data to chromosome contacts.

The field of 3D genome organization produces large amounts of sequencing data from Hi-C and a rapidly-expanding set of other chromosome conformation protocols (3C+). Massive and heterogeneous 3C+ data require high-performance and flexible processing of sequenced reads into contact pairs. To meet these challenges, we present pairtools-a flexible suite of tools for contact extraction from sequencing data. Pairtools provides modular command-line interface (CLI) tools that can be flexibly chained into data processing pipelines. The core operations provided by pairtools are parsing of.sam alignments into Hi-C pairs, sorting and removal of PCR duplicates. In addition, pairtools provides auxiliary tools for building feature-rich 3C+ pipelines, including contact pair manipulation, filtration, and quality control. Benchmarking pairtools against popular 3C+ data pipelines shows advantages of pairtools for high-performance and flexible 3C+ analysis. Finally, pairtools provides protocol-specific tools for restriction-based protocols, haplotype-resolved contacts, and single-cell Hi-C. The combination of CLI tools and tight integration with Python data analysis libraries makes pairtools a versatile foundation for a broad range of 3C+ pipelines.

Cooltools: Enabling high-resolution Hi-C analysis in Python.

Chromosome conformation capture (3C) technologies reveal the incredible complexity of genome organization. Maps of increasing size, depth, and resolution are now used to probe genome architecture across cell states, types, and organisms. Larger datasets add challenges at each step of computational analysis, from storage and memory constraints to researchers' time; however, analysis tools that meet these increased resource demands have not kept pace. Furthermore, existing tools offer limited support for customizing analysis for specific use cases or new biology. Here we introduce cooltools (https://github.com/open2c/cooltools), a suite of computational tools that enables flexible, scalable, and reproducible analysis of high-resolution contact frequency data. Cooltools leverages the widely-adopted cooler format which handles storage and access for high-resolution datasets. Cooltools provides a paired command line interface (CLI) and Python application programming interface (API), which respectively facilitate workflows on high-performance computing clusters and in interactive analysis environments. In short, cooltools enables the effective use of the latest and largest genome folding datasets.

RedChIP identifies noncoding RNAs associated with genomic sites occupied by Polycomb and CTCF proteins.

Nuclear noncoding RNAs (ncRNAs) are key regulators of gene expression and chromatin organization. The progress in studying nuclear ncRNAs depends on the ability to identify the genome-wide spectrum of contacts of ncRNAs with chromatin. To address this question, a panel of RNA-DNA proximity ligation techniques has been developed. However, neither of these techniques examines proteins involved in RNA-chromatin interactions. Here, we introduce RedChIP, a technique combining RNA-DNA proximity ligation and chromatin immunoprecipitation for identifying RNA-chromatin interactions mediated by a particular protein. Using antibodies against architectural protein CTCF and the EZH2 subunit of the Polycomb repressive complex 2, we identify a spectrum of cis- and trans-acting ncRNAs enriched at Polycomb- and CTCF-binding sites in human cells, which may be involved in Polycomb-mediated gene repression and CTCF-dependent chromatin looping. By providing a protein-centric view of RNA-DNA interactions, RedChIP represents an important tool for studies of nuclear ncRNAs.

Comparison of genome architecture at two stages of male germline cell differentiation in Drosophila.

Eukaryotic chromosomes are spatially segregated into topologically associating domains (TADs). Some TADs are attached to the nuclear lamina (NL) through lamina-associated domains (LADs). Here, we identified LADs and TADs at two stages of Drosophila spermatogenesis - in bamΔ86 mutant testes which is the commonly used model of spermatogonia (SpG) and in larval testes mainly filled with spermatocytes (SpCs). We found that initiation of SpC-specific transcription correlates with promoters' detachment from the NL and with local spatial insulation of adjacent regions. However, this insulation does not result in the partitioning of inactive TADs into sub-TADs. We also revealed an increased contact frequency between SpC-specific genes in SpCs implying their de novo gathering into transcription factories. In addition, we uncovered the specific X chromosome organization in the male germline. In SpG and SpCs, a single X chromosome is stronger associated with the NL than autosomes. Nevertheless, active chromatin regions in the X chromosome interact with each other more frequently than in autosomes. Moreover, despite the absence of dosage compensation complex in the male germline, randomly inserted SpG-specific reporter is expressed higher in the X chromosome than in autosomes, thus evidencing that non-canonical dosage compensation operates in SpG.

Single-cell Hi-C data analysis: safety in numbers.

Over the past decade, genome-wide assays for chromatin interactions in single cells have enabled the study of individual nuclei at unprecedented resolution and throughput. Current chromosome conformation capture techniques survey contacts for up to tens of thousands of individual cells, improving our understanding of genome function in 3D. However, these methods recover a small fraction of all contacts in single cells, requiring specialised processing of sparse interactome data. In this review, we highlight recent advances in methods for the interpretation of single-cell genomic contacts. After discussing the strengths and limitations of these methods, we outline frontiers for future development in this rapidly moving field.

Perspectives for the reconstruction of 3D chromatin conformation using single cell Hi-C data.

Construction of chromosomes 3D models based on single cell Hi-C data constitute an important challenge. We present a reconstruction approach, DPDchrom, that incorporates basic knowledge whether the reconstructed conformation should be coil-like or globular and spring relaxation at contact sites. In contrast to previously published protocols, DPDchrom can naturally form globular conformation due to the presence of explicit solvent. Benchmarking of this and several other methods on artificial polymer models reveals similar reconstruction accuracy at high contact density and DPDchrom advantage at low contact density. To compare 3D structures insensitively to spatial orientation and scale, we propose the Modified Jaccard Index. We analyzed two sources of the contact dropout: contact radius change and random contact sampling. We found that the reconstruction accuracy exponentially depends on the number of contacts per genomic bin allowing to estimate the reconstruction accuracy in advance. We applied DPDchrom to model chromosome configurations based on single-cell Hi-C data of mouse oocytes and found that these configurations differ significantly from a random one, that is consistent with other studies.

Studying RNA-DNA interactome by Red-C identifies noncoding RNAs associated with various chromatin types and reveals transcription dynamics.

Non-coding RNAs (ncRNAs) participate in various biological processes, including regulating transcription and sustaining genome 3D organization. Here, we present a method termed Red-C that exploits proximity ligation to identify contacts with the genome for all RNA molecules present in the nucleus. Using Red-C, we uncovered the RNA-DNA interactome of human K562 cells and identified hundreds of ncRNAs enriched in active or repressed chromatin, including previously undescribed RNAs. Analysis of the RNA-DNA interactome also allowed us to trace the kinetics of messenger RNA production. Our data support the model of co-transcriptional intron splicing, but not the hypothesis of the circularization of actively transcribed genes.

Evolution of the Genome 3D Organization: Comparison of Fused and Segregated Globin Gene Clusters.

The genomes are folded in a complex three-dimensional (3D) structure. Some features of this organization are common for all eukaryotes, but little is known about its evolution. Here, we have studied the 3D organization and regulation of zebrafish globin gene domain and compared its organization and regulation with those of other vertebrate species. In birds and mammals, the α- and ß-globin genes are segregated into separate clusters located on different chromosomes and organized into chromatin domains of different types, whereas in cold-blooded vertebrates, including Danio rerio, α- and ß-globin genes are organized into common clusters. The major globin gene locus of Danio rerio is of particular interest as it is located in a genomic area that is syntenic in vertebrates and is controlled by a conserved enhancer. We have found that the major globin gene locus of Danio rerio is structurally and functionally segregated into two spatially distinct subloci harboring either adult or embryo-larval globin genes. These subloci demonstrate different organization at the level of chromatin domains and different modes of spatial organization, which appears to be due to selective interaction of the upstream enhancer with the sublocus harboring globin genes of the adult type. These data are discussed in terms of evolution of linear and 3D organization of gene clusters in vertebrates.

Pairtools: from sequencing data to chromosome contacts.

The field of 3D genome organization produces large amounts of sequencing data from Hi-C and a rapidly-expanding set of other chromosome conformation protocols (3C+). Massive and heterogeneous 3C+ data require high-performance and flexible processing of sequenced reads into contact pairs. To meet these challenges, we present pairtools - a flexible suite of tools for contact extraction from sequencing data. Pairtools provides modular command-line interface (CLI) tools that can be flexibly chained into data processing pipelines. Pairtools provides both crucial core tools as well as auxiliary tools for building feature-rich 3C+ pipelines, including contact pair manipulation, filtration, and quality control. Benchmarking pairtools against popular 3C+ data pipelines shows advantages of pairtools for high-performance and flexible 3C+ analysis. Finally, pairtools provides protocol-specific tools for multi-way contacts, haplotype-resolved contacts, and single-cell Hi-C. The combination of CLI tools and tight integration with Python data analysis libraries makes pairtools a versatile foundation for a broad range of 3C+ pipelines.

Extrusion fountains are hallmarks of chromosome organization emerging upon zygotic genome activation.

The first activation of gene expression during development (zygotic genome activation, ZGA) is accompanied by massive changes in chromosome organization. The connection between these two processes remains unknown. Using Hi-C for zebrafish embryos, we found that chromosome folding starts by establishing "fountains", novel elements of chromosome organization, emerging selectively at enhancers upon ZGA. Using polymer simulations, we demonstrate that fountains can emerge as sites of targeted cohesin loading and require two-sided, yet desynchronized, loop extrusion. Specific loss of fountains upon loss of pioneer transcription factors that drive ZGA reveals a causal connection between enhancer activity and fountain formation. Finally, we show that fountains emerge in early Medaka and Xenopus embryos; moreover, we found cohesin-dependent fountain pattern on enhancers of mouse embryonic stem cells. Taken together, fountains are the first enhancer-specific elements of chromosome organization; they constitute starting points of chromosome folding during early development, likely serving as sites of targeted cohesin loading.

RNA-DNA interactomes of three prokaryotes uncovered by proximity ligation.

Proximity ligation approaches, which are widely used to study the spatial organization of the genome, also make it possible to reveal patterns of RNA-DNA interactions. Here, we use RedC, an RNA-DNA proximity ligation approach, to assess the distribution of major RNA types along the genomes of E. coli, B. subtilis, and thermophilic archaeon T. adornatum. We find that (i) messenger RNAs preferentially interact with their cognate genes and the genes located downstream in the same operon, which is consistent with polycistronic transcription; (ii) ribosomal RNAs preferentially interact with active protein-coding genes in both bacteria and archaea, indicating co-transcriptional translation; and (iii) 6S noncoding RNA, a negative regulator of bacterial transcription, is depleted from active genes in E. coli and B. subtilis. We conclude that the RedC data provide a rich resource for studying both transcription dynamics and the function of noncoding RNAs in microbial organisms.

Order and stochasticity in the folding of individual Drosophila genomes.

Mammalian and Drosophila genomes are partitioned into topologically associating domains (TADs). Although this partitioning has been reported to be functionally relevant, it is unclear whether TADs represent true physical units located at the same genomic positions in each cell nucleus or emerge as an average of numerous alternative chromatin folding patterns in a cell population. Here, we use a single-nucleus Hi-C technique to construct high-resolution Hi-C maps in individual Drosophila genomes. These maps demonstrate chromatin compartmentalization at the megabase scale and partitioning of the genome into non-hierarchical TADs at the scale of 100 kb, which closely resembles the TAD profile in the bulk in situ Hi-C data. Over 40% of TAD boundaries are conserved between individual nuclei and possess a high level of active epigenetic marks. Polymer simulations demonstrate that chromatin folding is best described by the random walk model within TADs and is most suitably approximated by a crumpled globule build of Gaussian blobs at longer distances. We observe prominent cell-to-cell variability in the long-range contacts between either active genome loci or between Polycomb-bound regions, suggesting an important contribution of stochastic processes to the formation of the Drosophila 3D genome.

Cumulative contact frequency of a chromatin region is an intrinsic property linked to its function.

Regulation of gene transcription is a complex process controlled by many factors, including the conformation of chromatin in the nucleus. Insights into chromatin conformation on both local and global scales can be provided by the Hi-C (high-throughput chromosomes conformation capture) method. One of the drawbacks of Hi-C analysis and interpretation is the presence of systematic biases, such as different accessibility to enzymes, amplification, and mappability of DNA regions, which all result in different visibility of the regions. Iterative correction (IC) is one of the most popular techniques developed for the elimination of these systematic biases. IC is based on the assumption that all chromatin regions have an equal number of observed contacts in Hi-C. In other words, the IC procedure is equalizing the experimental visibility approximated by the cumulative contact frequency (CCF) for all genomic regions. However, the differences in experimental visibility might be explained by biological factors such as chromatin openness, which is characteristic of distinct chromatin states. Here we show that CCF is positively correlated with active transcription. It is associated with compartment organization, since compartment A demonstrates higher CCF and gene expression levels than compartment B. Notably, this observation holds for a wide range of species, including human, mouse, and Drosophila. Moreover, we track the CCF state for syntenic blocks between human and mouse and conclude that active state assessed by CCF is an intrinsic property of the DNA region, which is independent of local genomic and epigenomic context. Our findings establish a missing link between Hi-C normalization procedures removing CCF from the data and poorly investigated and possibly relevant biological factors contributing to CCF.

A machine learning framework for the prediction of chromatin folding in Drosophila using epigenetic features.

Technological advances have lead to the creation of large epigenetic datasets, including information about DNA binding proteins and DNA spatial structure. Hi-C experiments have revealed that chromosomes are subdivided into sets of self-interacting domains called Topologically Associating Domains (TADs). TADs are involved in the regulation of gene expression activity, but the mechanisms of their formation are not yet fully understood. Here, we focus on machine learning methods to characterize DNA folding patterns in Drosophila based on chromatin marks across three cell lines. We present linear regression models with four types of regularization, gradient boosting, and recurrent neural networks (RNN) as tools to study chromatin folding characteristics associated with TADs given epigenetic chromatin immunoprecipitation data. The bidirectional long short-term memory RNN architecture produced the best prediction scores and identified biologically relevant features. Distribution of protein Chriz (Chromator) and histone modification H3K4me3 were selected as the most informative features for the prediction of TADs characteristics. This approach may be adapted to any similar biological dataset of chromatin features across various cell lines and species. The code for the implemented pipeline, Hi-ChiP-ML, is publicly available: https://github.com/MichalRozenwald/Hi-ChIP-ML.

Nuclear lamina integrity is required for proper spatial organization of chromatin in Drosophila.

How the nuclear lamina (NL) impacts on global chromatin architecture is poorly understood. Here, we show that NL disruption in Drosophila S2 cells leads to chromatin compaction and repositioning from the nuclear envelope. This increases the chromatin density in a fraction of topologically-associating domains (TADs) enriched in active chromatin and enhances interactions between active and inactive chromatin. Importantly, upon NL disruption the NL-associated TADs become more acetylated at histone H3 and less compact, while background transcription is derepressed. Two-colour FISH confirms that a TAD becomes less compact following its release from the NL. Finally, polymer simulations show that chromatin binding to the NL can per se compact attached TADs. Collectively, our findings demonstrate a dual function of the NL in shaping the 3D genome. Attachment of TADs to the NL makes them more condensed but decreases the overall chromatin density in the nucleus by stretching interphase chromosomes.

Activation of the alpha-globin gene expression correlates with dramatic upregulation of nearby non-globin genes and changes in local and large-scale chromatin spatial structure.

BACKGROUND: In homeotherms, the alpha-globin gene clusters are located within permanently open genome regions enriched in housekeeping genes. Terminal erythroid differentiation results in dramatic upregulation of alpha-globin genes making their expression comparable to the rRNA transcriptional output. Little is known about the influence of the erythroid-specific alpha-globin gene transcription outburst on adjacent, widely expressed genes and large-scale chromatin organization. Here, we have analyzed the total transcription output, the overall chromatin contact profile, and CTCF binding within the 2.7 Mb segment of chicken chromosome 14 harboring the alpha-globin gene cluster in cultured lymphoid cells and cultured erythroid cells before and after induction of terminal erythroid differentiation. RESULTS: We found that, similarly to mammalian genome, the chicken genomes is organized in TADs and compartments. Full activation of the alpha-globin gene transcription in differentiated erythroid cells is correlated with upregulation of several adjacent housekeeping genes and the emergence of abundant intergenic transcription. An extended chromosome region encompassing the alpha-globin cluster becomes significantly decompacted in differentiated erythroid cells, and depleted in CTCF binding and CTCF-anchored chromatin loops, while the sub-TAD harboring alpha-globin gene cluster and the upstream major regulatory element (MRE) becomes highly enriched with chromatin interactions as compared to lymphoid and proliferating erythroid cells. The alpha-globin gene domain and the neighboring loci reside within the A-like chromatin compartment in both lymphoid and erythroid cells and become further segregated from the upstream gene desert upon terminal erythroid differentiation. CONCLUSIONS: Our findings demonstrate that the effects of tissue-specific transcription activation are not restricted to the host genomic locus but affect the overall chromatin structure and transcriptional output of the encompassing topologically associating domain.