Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C

Enhancers with tissue-specific activity are enriched in intronic regions

Tissue function and homeostasis reflect the gene expression signature by which the combination of ubiquitous and tissue-specific genes contribute to the tissue maintenance and stimuli-responsive function. Enhancers are central to control this tissue-specific gene expression pattern. Here, we explore the correlation between the genomic location of enhancers and their role in tissue-specific gene expression. We find that enhancers showing tissue-specific activity are highly enriched in intronic regions and regulate the expression of genes involved in tissue-specific functions, whereas housekeeping genes are more often controlled by intergenic enhancers, common to many tissues. Notably, an intergenic-to-intronic active enhancers continuum is observed in the transition from developmental to adult stages: the most differentiated tissues present higher rates of intronic enhancers, whereas the lowest rates are observed in embryonic stem cells. Altogether, our results suggest that the genomic location of active enhancers is key for the tissue-specific control of gene expression.

Integration of Multimodal Data for Deciphering Brain Disorders

The accumulation of vast amounts of multimodal data for the human brain, in both normal and disease conditions, has provided unprecedented opportunities for understanding why and how brain disorders arise. Compared with traditional analyses of single datasets, the integration of multimodal datasets covering different types of data (i.e., genomics, transcriptomics, imaging, etc.) has shed light on the mechanisms underlying brain disorders in greater detail across both the microscopic and macroscopic levels. In this review, we first briefly introduce the popular large datasets for the brain. Then, we discuss in detail how integration of multimodal human brain datasets can reveal the genetic predispositions and the abnormal molecular pathways of brain disorders. Finally, we present an outlook on how future data integration efforts may advance the diagnosis and treatment of brain disorders.

Transcriptional and Epigenetic Landscape of Cardiac Pacemaker Cells: Insights Into Cellular Specialization in the Sinoatrial Node

Cardiac pacemaker cells differentiate and functionally specialize early in embryonic development through activation of critical gene regulatory networks. In general, cellular specification and differentiation require that combinations of cell type-specific transcriptional regulators activate expression of key effector genes by binding to DNA regulatory elements including enhancers and promoters. However, because genomic DNA is tightly packaged by histones that must be covalently modified in order to render DNA regulatory elements and promoters accessible for transcription, the process of development and differentiation is intimately connected to the epigenetic regulation of chromatin accessibility. Although the difficulty of obtaining sufficient quantities of pure populations of pacemaker cells has limited progress in this field, the advent of low-input genomic technologies has the potential to catalyze a rapid growth of knowledge in this important area. The goal of this review is to outline the key transcriptional networks that control pacemaker cell development, with particular attention to our emerging understanding of how chromatin accessibility is modified and regulated during pacemaker cell differentiation. In addition, we will discuss the relevance of these findings to adult sinus node function, sinus node diseases, and origins of genetic variation in heart rhythm. Lastly, we will outline the current challenges facing this field and promising directions for future investigation.

Epigenetic rewiring of skeletal muscle enhancers after exercise training supports a role in whole-body function and human health

Objectives

Regular physical exercise improves health by reducing the risk of a plethora of chronic disorders. We hypothesized that endurance exercise training remodels the activity of gene enhancers in skeletal muscle and that this remodeling contributes to the beneficial effects of exercise on human health.

Methods and results

By studying changes in histone modifications, we mapped the genome-wide positions and activities of enhancers in skeletal muscle biopsies collected from young sedentary men before and after 6 weeks of endurance exercise. We identified extensive remodeling of enhancer activities after exercise training, with a large subset of the remodeled enhancers located in the proximity of genes transcriptionally regulated after exercise. By overlapping the position of enhancers with genetic variants, we identified an enrichment of disease-associated genetic variants within the exercise-remodeled enhancers.

Conclusion

Our data provide evidence of a functional link between epigenetic rewiring of enhancers to control their activity after exercise training and the modulation of disease risk in humans.

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Exercise training changes in skeletal muscle gene expression is enriched for secreted factors.

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The activity of skeletal muscle enhancers undergoes substantial remodeling after exercise training.

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Skeletal muscle enhancer activity and gene transcription are strongly associated.

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Exercise training-remodeled enhancer regions are enriched for GWAS SNPs associated with human traits and diseases.

Reorganization of chromatin architecture during prenatal development of porcine skeletal muscle

Myofibres (primary and secondary myofibre) are the basic structure of muscle and the determinant of muscle mass. To explore the skeletal muscle developmental processes from primary myofibres to secondary myofibres in pigs, we conducted an integrative three-dimensional structure of genome and transcriptomic characterization of longissimus dorsi muscle of pig from primary myofibre formation stage [embryonic Day 35 (E35)] to secondary myofibre formation stage (E80). In the hierarchical genomic structure, we found that 11.43% of genome switched compartment A/B status, 14.53% of topologically associating domains are changed intradomain interactions (D-scores) and 2,730 genes with differential promoter–enhancer interactions and (or) enhancer activity from E35 to E80. The alterations of genome architecture were found to correlate with expression of genes that play significant roles in neuromuscular junction, embryonic morphogenesis, skeletal muscle development or metabolism, typically, NEFL, MuSK, SLN, Mef2D and GCK. Significantly, Sox6 and MATN2 play important roles in the process of primary to secondary myofibres formation and increase the regulatory potential score and genes expression in it. In brief, we reveal the genomic reorganization from E35 to E80 and construct genome-wide high-resolution interaction maps that provide a resource for studying long-range control of gene expression from E35 to E80.

Integrative analysis of genomic and epigenomic data reveals underlying super-enhancer-mediated microRNA regulatory network for human bone mineral density

Bone mineral density (BMD) is a highly heritable complex trait and is a key indicator for diagnosis and treatment for osteoporosis. In the last decade, numerous susceptibility loci for BMD and fracture have been identified by genome wide association studies (GWAS); however, fine mapping of these loci is challengeable. Here, we proposed a new long-range fine-mapping approach that combined super-enhancers (SEs) and microRNAs (miRNAs) data, which were two important factors in control of cell identity and specific differentiation, with the GWAS summary datasets in cell-type-restricted way. Genome-wide SE-based analysis found that the BMD-related variants were significantly enriched in the osteoblast SE regions, indicative of potential long-range effects of such SNPs. With the SNP-mapped SEs (mSEs), 13 accessible long-range mSE-interacted miRNAs (mSE-miRNAs) were identified by integrating osteoblast Hi-C and ATAC-seq data, including three known bone-related miRNAs (miR-132-3p, miR-212-3p and miR-125b-5p). The putative targets of the two newly identified mSE-miRNAs (miR-548aj-3p and miR-190a-3p) were found largely enriched in osteogenic-related pathway and processes, suggesting that these mSE-miRNAs could be functional in the regulation of osteoblast differentiation. Furthermore, we identified 54 genes with the long-range 'mSE-miRNA' approach, and 24 of them were previously reported to be related to skeletal development. Besides, enrichment analysis found that these genes were specifically enriched in the post-transcriptional regulation and bone formation processes. This study provided a new insight into the approach of fine-mapping of GWAS loci. A tool was provided for the genome-wide SE-based analysis and the detection of long-range osteoblast-restricted mSE-miRNAs (https://github.com/Zheng-Lab-Westlake/Osteo-Fine-Mapp-SNP2SE2miRNA).

Polycomb-dependent differential chromatin compartmentalization determines gene coregulation in Arabidopsis

In animals, distant H3K27me3-marked Polycomb targets can establish physical interactions forming repressive chromatin hubs. In plants, growing evidence suggests that H3K27me3 acts directly or indirectly to regulate chromatin interactions, although how this histone modification modulates 3D chromatin architecture remains elusive. To decipher the impact of the dynamic deposition of H3K27me3 on the Arabidopsis thaliana nuclear interactome, we combined genetics, transcriptomics, and several 3D epigenomic approaches. By analyzing mutants defective for histone H3K27 methylation or demethylation, we uncovered the crucial role of this chromatin mark in short- and previously unnoticed long-range chromatin loop formation. We found that a reduction in H3K27me3 levels led to a decrease in the interactions within Polycomb-associated repressive domains. Regions with lower H3K27me3 levels in the H3K27 methyltransferase clf mutant established new interactions with regions marked with H3K9ac, a histone modification associated with active transcription, indicating that a reduction in H3K27me3 levels induces a global reconfiguration of chromatin architecture. Altogether, our results reveal that the 3D genome organization is tightly linked to reversible histone modifications that govern chromatin interactions. Consequently, nuclear organization dynamics shapes the transcriptional reprogramming during plant development and places H3K27me3 as a key feature in the coregulation of distant genes.

Genome folding through loop extrusion by SMC complexes

Genomic DNA is folded into loops and topologically associating domains (TADs), which serve important structural and regulatory roles. It has been proposed that these genomic structures are formed by a loop extrusion process, which is mediated by structural maintenance of chromosomes (SMC) protein complexes. Recent single-molecule studies have shown that the SMC complexes condensin and cohesin are indeed able to extrude DNA into loops. In this Review, we discuss how the loop extrusion hypothesis can explain key features of genome architecture; cellular functions of loop extrusion, such as separation of replicated DNA molecules, facilitation of enhancer-promoter interactions and immunoglobulin gene recombination; and what is known about the mechanism of loop extrusion and its regulation, for example, by chromatin boundaries that depend on the DNA binding protein CTCF. We also discuss how the loop extrusion hypothesis has led to a paradigm shift in our understanding of both genome architecture and the functions of SMC complexes.

Three-Dimensional Genome Interactions Identify Potential Adipocyte Metabolism-Associated Gene STON1 and Immune-Correlated Gene FSHR at the rs13405728 Locus in Polycystic Ovary Syndrome

Background

rs13405728 was identified as one of the most prevalent susceptibility loci for polycystic ovary syndrome (PCOS) in Han Chinese and Caucasian women. However, the target genes and potential mechanisms of the rs13405728 locus remain to be determined.

Methods

Three-dimensional (3D) genome interactions from the ovary tissue were characterized via high-through chromosome conformation capture (Hi-C) and Capture Hi-C technologies to identify putative targets at the rs13405728 locus. Combined analyses of eQTL, RNA-Seq, DNase-Seq, ChIP-Seq, and sing-cell sequencing were performed to explore the molecular roles of these target genes in PCOS. PCOS-like mice were applied to verify the expression patterns.

Results

Generally, STON1 and FSHR were identified as potential targets of the rs13405728 locus in 3D genomic interactions with epigenomic regulatory peaks, with STON1 (P=0.0423) and FSHR (P=0.0013) being highly expressed in PCOS patients. STON1 co-expressed genes were associated with metabolic processes (P=0.0008) in adipocytes (P=0.0001), which was validated in the fat tissue (P<0.0001) and ovary (P=0.0035) from fat-diet mice. The immune system process (GO:0002376) was enriched in FSHR co-expressed genes (P=0.0002) and PCOS patients (P=0.0002), with CD4 high expression in PCOS patients (P=0.0316) and PCOS-like models (P=0.0079). Meanwhile, FSHR expression was positively correlated with CD4 expression in PCOS patients (P=0.0252) and PCOS-like models (P=0.0178). Furthermore, androgen receptor (AR) was identified as the common transcription factor for STON1 and FSHR and positively correlated with the expression of STON1 (P=0.039) and FSHR (P=4e-06) in ovary tissues and PCOS-like mice.

Conclusion

Overall, we identified STON1 and FSHR as potential targets for the rs13405728 locus and their roles in the processes of adipocyte metabolism and CD4 immune expression in PCOS, which provides 3D genomic insight into the pathogenesis of PCOS.

Genome-Wide Association Analyses Identify Variants in IRF4 Associated With Acute Myeloid Leukemia and Myelodysplastic Syndrome Susceptibility

The role of common genetic variation in susceptibility to acute myeloid leukemia (AML), and myelodysplastic syndrome (MDS), a group of rare clonal hematologic disorders characterized by dysplastic hematopoiesis and high mortality, remains unclear. We performed AML and MDS genome-wide association studies (GWAS) in the DISCOVeRY-BMT cohorts (2,309 cases and 2,814 controls). Association analysis based on subsets (ASSET) was used to conduct a summary statistics SNP-based analysis of MDS and AML subtypes. For each AML and MDS case and control we used PrediXcan to estimate the component of gene expression determined by their genetic profile and correlate this imputed gene expression level with risk of developing disease in a transcriptome-wide association study (TWAS). ASSET identified an increased risk for de novo AML and MDS (OR = 1.38, 95% CI, 1.26-1.51, Pmeta = 2.8 × 10-12) in patients carrying the T allele at s12203592 in Interferon Regulatory Factor 4 (IRF4), a transcription factor which regulates myeloid and lymphoid hematopoietic differentiation. Our TWAS analyses showed increased IRF4 gene expression is associated with increased risk of de novo AML and MDS (OR = 3.90, 95% CI, 2.36-6.44, Pmeta = 1.0 × 10-7). The identification of IRF4 by both GWAS and TWAS contributes valuable insight on the role of genetic variation in AML and MDS susceptibility.

Plant 3D genomics: the exploration and application of chromatin organization

Eukaryotic genomes are highly folded for packing into higher-order chromatin structures in the nucleus. With the emergence of state-of-the-art chromosome conformation capture methods and microscopic imaging techniques, the spatial organization of chromatin and its functional implications have been interrogated. Our knowledge of 3D chromatin organization in plants has improved dramatically in the past few years, building on the early advances in animal systems. Here, we review recent advances in 3D genome mapping approaches, our understanding of the sophisticated organization of spatial structures, and the application of 3D genomic principles in plants. We also discuss directions for future developments in 3D genomics in plants.

Advances in higher-order chromatin architecture: the move towards 4D genome

In eukaryotes, the genome is hierarchically packed inside the nucleus, which facilitates physical contact between cis-regulatory elements (CREs), such as enhancers and promoters. Accumulating evidence highlights the critical role of higher-order chromatin structure in precise regulation of spatiotemporal gene expression under diverse biological contexts including lineage commitment and cell activation by external stimulus. Genomics and imaging-based technologies, such as Hi-C and DNA fluorescence in situ hybridization (FISH), have revealed the key principles of genome folding, while newly developed tools focus on improvement in resolution, throughput and modality at single-cell and population levels, and challenge the knowledge obtained through conventional approaches. In this review, we discuss recent advances in our understanding of principles of higher-order chromosome conformation and technologies to investigate 4D chromatin interactions.

Inferring Single-Cell 3D Chromosomal Structures Based on the Lennard-Jones Potential

Reconstructing three-dimensional (3D) chromosomal structures based on single-cell Hi-C data is a challenging scientific problem due to the extreme sparseness of the single-cell Hi-C data. In this research, we used the Lennard-Jones potential to reconstruct both 500 kb and high-resolution 50 kb chromosomal structures based on single-cell Hi-C data. A chromosome was represented by a string of 500 kb or 50 kb DNA beads and put into a 3D cubic lattice for simulations. A 2D Gaussian function was used to impute the sparse single-cell Hi-C contact matrices. We designed a novel loss function based on the Lennard-Jones potential, in which the ε value, i.e., the well depth, was used to indicate how stable the binding of every pair of beads is. For the bead pairs that have single-cell Hi-C contacts and their neighboring bead pairs, the loss function assigns them stronger binding stability. The Metropolis-Hastings algorithm was used to try different locations for the DNA beads, and simulated annealing was used to optimize the loss function. We proved the correctness and validness of the reconstructed 3D structures by evaluating the models according to multiple criteria and comparing the models with 3D-FISH data.

covNorm: An R package for coverage based normalization of Hi-C and capture Hi-C data

Hi-C and capture Hi-C have greatly advanced our understanding of the principles of higher-order chromatin structure. In line with the evolution of the Hi-C protocols, there is a demand for an advanced computational method that can be applied to the various forms of Hi-C protocols and effectively remove innate biases. To resolve this issue, we developed an implicit normalization method named “covNorm” and implemented it as an R package. The proposed method can perform a complete procedure of data processing for Hi-C and its variants. Starting from the negative binomial model-based normalization for DNA fragment coverages, removal of genomic distance-dependent background and calling of the significant interactions can be applied sequentially. The performance evaluation of covNorm showed enhanced or similar reproducibility in terms of HiC-spector score, correlation of compartment A/B profiles, and detection of reproducible significant long-range chromatin contacts compared to baseline methods in the benchmark datasets. The developed method is powerful in terms of effective normalization of Hi-C and capture Hi-C data, detection of long-range chromatin contacts, and readily extendibility to the other derivative Hi-C protocols. The covNorm R package is freely available at GitHub: https://github.com/kaistcbfg/covNormRpkg.

Chrom-Lasso: a lasso regression-based model to detect functional interactions using Hi-C data

Hi-C is a genome-wide assay based on Chromosome Conformation Capture and high-throughput sequencing to decipher 3D chromatin organization in the nucleus. However, computational methods to detect functional interactions utilizing Hi-C data face challenges including the correction for various sources of biases and the identification of functional interactions with low counts of interacting fragments. We present Chrom-Lasso, a lasso linear regression model that removes complex biases assumption-free and identifies functional interacting loci with increased power by combining information of local reads distribution surrounding the area of interest. We showed that interacting regions identified by Chrom-Lasso are more enriched for 5C validated interactions and functional GWAS hits than that of GOTHiC and Fit-Hi-C. To further demonstrate the ability of Chrom-Lasso to detect interactions of functional importance, we performed time-series Hi-C and RNA-seq during T cell activation and exhaustion. We showed that the dynamic changes in gene expression and chromatin interactions identified by Chrom-Lasso were largely concordant with each other. Finally, we experimentally confirmed Chrom-Lasso’s finding that Erbb3 was co-regulated with distinct neighboring genes at different states during T cell activation. Our results highlight Chrom-Lasso’s utility in detecting weak functional interaction between cis-regulatory elements, such as promoters and enhancers.

HiCEnterprise: identifying long range chromosomal contacts in Hi-C data

Motivation

Computational analysis of chromosomal contact data is currently gaining popularity with the rapid advance in experimental techniques providing access to a growing body of data. An important problem in this area is the identification of long range contacts between distinct chromatin regions. Such loops were shown to exist at different scales, either mediating relatively short range interactions between enhancers and promoters or providing interactions between much larger, distant chromosome domains. A proper statistical analysis as well as availability to a wide research community are crucial in a tool for this task.

Results

We present HiCEnterprise, a first freely available software tool for identification of long range chromatin contacts not only between small regions, but also between chromosomal domains. It implements four different statistical tests for identification of significant contacts for user defined regions or domains as well as necessary functions for input, output and visualization of chromosome contacts.

Availability

The software and the corresponding documentation are available at: github.com/regulomics/HiCEnterprise.

Supplementary information

Supplemental data are available in the online version of the article and at the website regulomics.mimuw.edu.pl/wp/hicenterprise.

A model of active transcription hubs that unifies the roles of active promoters and enhancers

An essential questions of gene regulation is how large number of enhancers and promoters organize into gene regulatory loops. Using transcription-factor binding enrichment as an indicator of enhancer strength, we identified a portion of H3K27ac peaks as potentially strong enhancers and found a universal pattern of promoter and enhancer distribution: At actively transcribed regions of length of ∼200-300 kb, the numbers of active promoters and enhancers are inversely related. Enhancer clusters are associated with isolated active promoters, regardless of the gene's cell-type specificity. As the number of nearby active promoters increases, the number of enhancers decreases. At regions where multiple active genes are closely located, there are few distant enhancers. With Hi-C analysis, we demonstrate that the interactions among the regulatory elements (active promoters and enhancers) occur predominantly in clusters and multiway among linearly close elements and the distance between adjacent elements shows a preference of ∼30 kb. We propose a simple rule of spatial organization of active promoters and enhancers: Gene transcriptions and regulations mainly occur at local active transcription hubs contributed dynamically by multiple elements from linearly close enhancers and/or active promoters. The hub model can be represented with a flower-shaped structure and implies an enhancer-like role of active promoters.

Genome-wide search for genes affecting the age at diagnosis of type 1 diabetes

BACKGROUND

Type 1 diabetes (T1D) is an autoimmune disease affecting individuals in the early years of life. Although previous studies have identified genetic loci influencing T1D diagnosis age, these studies did not investigate the genome with high resolution.

OBJECTIVE AND METHODS

We performed a genome-wide meta-analysis for age at diagnosis with cohorts from Finland (Finnish Diabetic Nephropathy Study), the United Kingdom (UK Genetic Resource Investigating Diabetes) and Sardinia. Through SNP associations, transcriptome-wide association analysis linked T1D diagnosis age and gene expression.

RESULTS

We identified two chromosomal regions associated with T1D diagnosis age: multiple independent variants in the HLA region on chromosome 6 and a locus on chromosome 17q12. We performed gene-level association tests with transcriptome prediction models from two whole blood datasets, lymphocyte cell line, spleen, pancreas and small intestine tissues. Of the non-HLA genes, lower PNMT expression in whole blood, and higher IKZF3 and ZPBP2, and lower ORMDL3 and GSDMB transcription levels in multiple tissues were associated with lower T1D diagnosis age (FDR = 0.05). These genes lie on chr17q12 which is associated with T1D, other autoimmune diseases, and childhood asthma. Additionally, higher expression of PHF20L1, a gene not previously implicated in T1D, was associated with lower diagnosis age in lymphocytes, pancreas, and spleen. Altogether, the non-HLA associations were enriched in open chromatin in various blood cells, blood vessel tissues and foetal thymus tissue.

CONCLUSION

Multiple genes on chr17q12 and PHF20L1 on chr8 were associated with T1D diagnosis age and only further studies may elucidate the role of these genes for immunity and T1D onset.

Diverse Molecular Mechanisms Contribute to Differential Expression of Human Duplicated Genes

Emerging evidence links genes within human-specific segmental duplications (HSDs) to traits and diseases unique to our species. Strikingly, despite being nearly identical by sequence (>98.5%), paralogous HSD genes are differentially expressed across human cell and tissue types, though the underlying mechanisms have not been examined. We compared cross-tissue mRNA levels of 75 HSD genes from 30 families between humans and chimpanzees and found expression patterns consistent with relaxed selection on or neofunctionalization of derived paralogs. In general, ancestral paralogs exhibited greatest expression conservation with chimpanzee orthologs, though exceptions suggest certain derived paralogs may retain or supplant ancestral functions. Concordantly, analysis of long-read isoform sequencing data sets from diverse human tissues and cell lines found that about half of derived paralogs exhibited globally lower expression. To understand mechanisms underlying these differences, we leveraged data from human lymphoblastoid cell lines (LCLs) and found no relationship between paralogous expression divergence and post-transcriptional regulation, sequence divergence, or copy-number variation. Considering cis-regulation, we reanalyzed ENCODE data and recovered hundreds of previously unidentified candidate CREs in HSDs. We also generated large-insert ChIP-sequencing data for active chromatin features in an LCL to better distinguish paralogous regions. Some duplicated CREs were sufficient to drive differential reporter activity, suggesting they may contribute to divergent cis-regulation of paralogous genes. This work provides evidence that cis-regulatory divergence contributes to novel expression patterns of recent gene duplicates in humans.

The Toolbox for Untangling Chromosome Architecture in Immune Cells

The code of life is not only encrypted in the sequence of DNA but also in the way it is organized into chromosomes. Chromosome architecture is gradually being recognized as an important player in regulating cell activities (e.g., controlling spatiotemporal gene expression). In the past decade, the toolbox for elucidating genome structure has been expanding, providing an opportunity to explore this under charted territory. In this review, we will introduce the recent advancements in approaches for mapping spatial organization of the genome, emphasizing applications of these techniques to immune cells, and trying to bridge chromosome structure with immune cell activities.

HCMB: A stable and efficient algorithm for processing the normalization of highly sparse Hi-C contact data

The high-throughput genome-wide chromosome conformation capture (Hi-C) method has recently become an important tool to study chromosomal interactions where one can extract meaningful biological information including P(s) curve, topologically associated domains, A/B compartments, and other biologically relevant signals. Normalization is a critical pre-processing step of downstream analyses for the elimination of systematic and technical biases from chromatin contact matrices due to different mappability, GC content, and restriction fragment lengths. Especially, the problem of high sparsity puts forward a huge challenge on the correction, indicating the urgent need for a stable and efficient method for Hi-C data normalization. Recently, some matrix balancing methods have been developed to normalize Hi-C data, such as the Knight-Ruiz (KR) algorithm, but it failed to normalize contact matrices with high sparsity. Here, we presented an algorithm, Hi-C Matrix Balancing (HCMB), based on an iterative solution of equations, combining with linear search and projection strategy to normalize the Hi-C original interaction data. Both the simulated and experimental data demonstrated that HCMB is robust and efficient in normalizing Hi-C data and preserving the biologically relevant Hi-C features even facing very high sparsity. HCMB is implemented in Python and is freely accessible to non-commercial users at GitHub: https://github.com/HUST-DataMan/HCMB.

A genome-wide association study identifies susceptibility loci for primary central nervous system lymphoma at 6p25.3 and 3p22.1: a LOC Network study

BACKGROUND

Primary central nervous system lymphoma (PCNSL) is a rare form of extra-nodal non-Hodgkin lymphoma. PCNSL is a distinct subtype of non-Hodgkin lymphoma, with over 95% of tumors belonging to the diffuse large B-cell lymphoma (DLBCL) group. We have conducted a genome-wide association study (GWAS) on immunocompetent patients to address the possibility that common genetic variants influence the risk of developing PCNSL.

METHODS

We performed a meta-analysis of 2 new GWASs of PCNSL totaling 475 cases and 1134 controls of European ancestry. To increase genomic resolution, we imputed >10 million single nucleotide polymorphisms using the 1000 Genomes Project combined with UK10K as reference. In addition we performed a transcription factor binding disruption analysis and investigated the patterns of local chromatin by Capture Hi-C data.

RESULTS

We identified independent risk loci at 3p22.1 (rs41289586, ANO10, P = 2.17 × 10-8) and 6p25.3 near EXOC2 (rs116446171, P = 1.95 x 10-13). In contrast, the lack of an association between rs41289586 and DLBCL suggests distinct germline predisposition to PCNSL and DLBCL. We found looping chromatin interactions between noncoding regions at 6p25.3 (rs11646171) with the IRF4 promoter and at 8q24.21 (rs13254990) with the MYC promoter, both genes with strong relevance to B-cell tumorigenesis.

CONCLUSION

To our knowledge this is the first study providing insight into the genetic predisposition to PCNSL. Our findings represent an important step in defining the contribution of common genetic variation to the risk of developing PCNSL.

Evolutionary genetics of skin pigmentation in African populations

Skin color is a highly heritable human trait, and global variation in skin pigmentation has been shaped by natural selection, migration and admixture. Ethnically diverse African populations harbor extremely high levels of genetic and phenotypic diversity, and skin pigmentation varies widely across Africa. Recent genome-wide genetic studies of skin pigmentation in African populations have advanced our understanding of pigmentation biology and human evolutionary history. For example, novel roles in skin pigmentation for loci near MFSD12 and DDB1 have recently been identified in African populations. However, due to an underrepresentation of Africans in human genetic studies, there is still much to learn about the evolutionary genetics of skin pigmentation. Here, we summarize recent progress in skin pigmentation genetics in Africans and discuss the importance of including more ethnically diverse African populations in future genetic studies. In addition, we discuss methods for functional validation of adaptive variants related to skin pigmentation.

Integration of Multiple Resolution Data in 3D Chromatin Reconstruction Using ChromStruct

The three-dimensional structure of chromatin in the cellular nucleus carries important information that is connected to physiological and pathological correlates and dysfunctional cell behaviour. As direct observation is not feasible at present, on one side, several experimental techniques have been developed to provide information on the spatial organization of the DNA in the cell; on the other side, several computational methods have been developed to elaborate experimental data and infer 3D chromatin conformations. The most relevant experimental methods are Chromosome Conformation Capture and its derivatives, chromatin immunoprecipitation and sequencing techniques (CHIP-seq), RNA-seq, fluorescence in situ hybridization (FISH) and other genetic and biochemical techniques. All of them provide important and complementary information that relate to the three-dimensional organization of chromatin. However, these techniques employ very different experimental protocols and provide information that is not easily integrated, due to different contexts and different resolutions. Here, we present an open-source tool, which is an expansion of the previously reported code ChromStruct, for inferring the 3D structure of chromatin that, by exploiting a multilevel approach, allows an easy integration of information derived from different experimental protocols and referred to different resolution levels of the structure, from a few kilobases up to Megabases. Our results show that the introduction of chromatin modelling features related to CTCF CHIA-PET data, histone modification CHIP-seq, and RNA-seq data produce appreciable improvements in ChromStruct’s 3D reconstructions, compared to the use of HI-C data alone, at a local level and at a very high resolution.

Genetic perturbation of PU.1 binding and chromatin looping at neutrophil enhancers associates with autoimmune disease

Neutrophils play fundamental roles in innate immune response, shape adaptive immunity, and are a potentially causal cell type underpinning genetic associations with immune system traits and diseases. Here, we profile the binding of myeloid master regulator PU.1 in primary neutrophils across nearly a hundred volunteers. We show that variants associated with differential PU.1 binding underlie genetically-driven differences in cell count and susceptibility to autoimmune and inflammatory diseases. We integrate these results with other multi-individual genomic readouts, revealing coordinated effects of PU.1 binding variants on the local chromatin state, enhancer-promoter contacts and downstream gene expression, and providing a functional interpretation for 27 genes underlying immune traits. Collectively, these results demonstrate the functional role of PU.1 and its target enhancers in neutrophil transcriptional control and immune disease susceptibility.

A compendium and comparative epigenomics analysis of cis-regulatory elements in the pig genome

Although major advances in genomics have initiated an exciting new era of research, a lack of information regarding cis-regulatory elements has limited the genetic improvement or manipulation of pigs as a meat source and biomedical model. Here, we systematically characterize cis-regulatory elements and their functions in 12 diverse tissues from four pig breeds by adopting similar strategies as the ENCODE and Roadmap Epigenomics projects, which include RNA-seq, ATAC-seq, and ChIP-seq. In total, we generate 199 datasets and identify more than 220,000 cis-regulatory elements in the pig genome. Surprisingly, we find higher conservation of cis-regulatory elements between human and pig genomes than those between human and mouse genomes. Furthermore, the differences of topologically associating domains between the pig and human genomes are associated with morphological evolution of the head and face. Beyond generating a major new benchmark resource for pig epigenetics, our study provides basic comparative epigenetic data relevant to using pigs as models in human biomedical research.

Application of Hi-C and other omics data analysis in human cancer and cell differentiation research

With the development of 3C (chromosome conformation capture) and its derivative technology Hi-C (High-throughput chromosome conformation capture) research, the study of the spatial structure of the genomic sequence in the nucleus helps researchers understand the functions of biological processes such as gene transcription, replication, repair, and regulation. In this paper, we first introduce the research background and purpose of Hi-C data visualization analysis. After that, we discuss the Hi-C data analysis methods from genome 3D structure, A/B compartment, TADs (topologically associated domain), and loop detection. We also discuss how to apply genome visualization technologies to the identification of chromosome feature structures. We continue with a review of correlation analysis differences among multi-omics data, and how to apply Hi-C and other omics data analysis into cancer and cell differentiation research. Finally, we summarize the various problems in joint analyses based on Hi-C and other multi-omics data. We believe this review can help researchers better understand the progress and applications of 3D genome technology.

Widespread reorganisation of pluripotent factor binding and gene regulatory interactions between human pluripotent states

The transition from naive to primed pluripotency is accompanied by an extensive reorganisation of transcriptional and epigenetic programmes. However, the role of transcriptional enhancers and three-dimensional chromatin organisation in coordinating these developmental programmes remains incompletely understood. Here, we generate a high-resolution atlas of gene regulatory interactions, chromatin profiles and transcription factor occupancy in naive and primed human pluripotent stem cells, and develop a network-graph approach to examine the atlas at multiple spatial scales. We uncover highly connected promoter hubs that change substantially in interaction frequency and in transcriptional co-regulation between pluripotent states. Small hubs frequently merge to form larger networks in primed cells, often linked by newly-formed Polycomb-associated interactions. We identify widespread state-specific differences in enhancer activity and interactivity that correspond with an extensive reconfiguration of OCT4, SOX2 and NANOG binding and target gene expression. These findings provide multilayered insights into the chromatin-based gene regulatory control of human pluripotent states.

Identifying high-confidence capture Hi-C interactions using CHiCANE

The ability to identify regulatory interactions that mediate gene expression changes through distal elements, such as risk loci, is transforming our understanding of how genomes are spatially organized and regulated. Capture Hi-C (CHi-C) is a powerful tool to delineate such regulatory interactions. However, primary analysis and downstream interpretation of CHi-C profiles remains challenging and relies on disparate tools with ad-hoc input/output formats and specific assumptions for statistical modeling. Here we present a data processing and interaction calling toolkit (CHiCANE), specialized for the analysis and meaningful interpretation of CHi-C assays. In this protocol, we demonstrate applications of CHiCANE to region capture Hi-C (rCHi-C) and promoter capture Hi-C (pCHi-C) libraries, followed by quality assessment of interaction peaks, as well as downstream analysis specific to rCHi-C and pCHi-C to aid functional interpretation. For a typical rCHi-C/pCHi-C dataset this protocol takes up to 3 d for users with a moderate understanding of R programming and statistical concepts, although this is dependent on dataset size and compute power available. CHiCANE is freely available at https://cran.r-project.org/web/packages/chicane .

3D reconstruction of genomic regions from sparse interaction data

Chromosome conformation capture (3C) technologies measure the interaction frequency between pairs of chromatin regions within the nucleus in a cell or a population of cells. Some of these 3C technologies retrieve interactions involving non-contiguous sets of loci, resulting in sparse interaction matrices. One of such 3C technologies is Promoter Capture Hi-C (pcHi-C) that is tailored to probe only interactions involving gene promoters. As such, pcHi-C provides sparse interaction matrices that are suitable to characterize short- and long-range enhancer-promoter interactions. Here, we introduce a new method to reconstruct the chromatin structural (3D) organization from sparse 3C-based datasets such as pcHi-C. Our method allows for data normalization, detection of significant interactions and reconstruction of the full 3D organization of the genomic region despite of the data sparseness. Specifically, it builds, with as low as the 2-3% of the data from the matrix, reliable 3D models of similar accuracy of those based on dense interaction matrices. Furthermore, the method is sensitive enough to detect cell-type-specific 3D organizational features such as the formation of different networks of active gene communities.

On the importance of evolutionary constraint for regulatory sequence identification

Regulation of gene expression relies on the activity of specialized genomic elements, enhancers or silencers, distributed over sometimes large distance from their target gene promoters. A significant part of vertebrate genomes consists in such regulatory elements, but their identification and that of their target genes remains challenging, due to the lack of clear signature at the nucleotide level. For many years the main hallmark used for identifying functional elements has been their sequence conservation between genomes of distant species, indicative of purifying selection. More recently, genome-wide biochemical assays have opened new avenues for detecting regulatory regions, shifting attention away from evolutionary constraints. Here, we review the respective contributions of comparative genomics and biochemical assays for the definition of regulatory elements and their targets and advocate that both sequence conservation and preserved synteny, taken as signature of functional constraint, remain essential tools in this task.

Nucleotide spacing distribution analysis for human genome

The distribution of nucleotides spacing in human genome was investigated. An analysis of the frequency of occurrence in the human genome of different sequence lengths flanked by one type of nucleotide was carried out showing that the distribution has no self-similar (fractal) structure. The results nevertheless revealed several characteristic features: (i) the distribution for short-range spacing is quite similar to the purely stochastic sequences; (ii) the distribution for long-range spacing essentially deviates from the random sequence distribution, showing strong long-range correlations; (iii) the differences between (A, T) and (C, G) nucleotides are quite significant; (iv) the spacing distribution displays tiny oscillations.

A computer-guided design tool to increase the efficiency of cellular conversions

Human cell conversion technology has become an important tool for devising new cell transplantation therapies, generating disease models and testing gene therapies. However, while transcription factor over-expression-based methods have shown great promise in generating cell types in vitro, they often endure low conversion efficiency. In this context, great effort has been devoted to increasing the efficiency of current protocols and the development of computational approaches can be of great help in this endeavor. Here we introduce a computer-guided design tool that combines a computational framework for prioritizing more efficient combinations of instructive factors (IFs) of cellular conversions, called IRENE, with a transposon-based genomic integration system for efficient delivery. Particularly, IRENE relies on a stochastic gene regulatory network model that systematically prioritizes more efficient IFs by maximizing the agreement of the transcriptional and epigenetic landscapes between the converted and target cells. Our predictions substantially increased the efficiency of two established iPSC-differentiation protocols (natural killer cells and melanocytes) and established the first protocol for iPSC-derived mammary epithelial cells with high efficiency.

Detection of subgenome bias using an anchored syntenic approach in Eleusine coracana (finger millet)

Background

Finger millet (Eleusine coracana 2n = 4x = 36) is a hardy, nutraceutical, climate change tolerant, orphan crop that is consumed throughout eastern Africa and India. Its genome has been sequenced multiple times, but A and B subgenomes could not be separated because no published genome for E. indica existed. The classification of A and B subgenomes is important for understanding the evolution of this crop and provide a means to improve current and future breeding programs.

Results

We produced subgenome calls for 704 syntenic blocks and inferred A or B subgenomic identity for 59,377 genes 81% of the annotated genes. Phylogenetic analysis of a super matrix containing 455 genes shows high support for A and B divergence within the Eleusine genus. Synonymous substitution rates between A and B genes support A and B calls. The repetitive content on highly supported B contigs is higher than that on similar A contigs. Analysis of syntenic singletons showed evidence of biased fractionation showed a pattern of A genome dominance, with 61% A, 37% B and 1% unassigned, and was further supported by the pattern of loss observed among cyto-nuclear interacting genes.

Conclusion

The evidence of individual gene calls within each syntenic block, provides a powerful tool for inference for subgenome classification. Our results show the utility of a draft genome in resolving A and B subgenomes calls, primarily it allows for the proper polarization of A and B syntenic blocks. There have been multiple calls for the use of phylogenetic inference in subgenome classification, our use of synteny is a practical application in a system that has only one parental genome available.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07447-y.

Heart Enhancers: Development and Disease Control at a Distance

Bound by lineage-determining transcription factors and signaling effectors, enhancers play essential roles in controlling spatiotemporal gene expression profiles during development, homeostasis and disease. Recent synergistic advances in functional genomic technologies, combined with the developmental biology toolbox, have resulted in unprecedented genome-wide annotation of heart enhancers and their target genes. Starting with early studies of vertebrate heart enhancers and ending with state-of-the-art genome-wide enhancer discovery and testing, we will review how studying heart enhancers in metazoan species has helped inform our understanding of cardiac development and disease.

Interaction between GALNT12 and C1GALT1 Associates with Galactose-Deficient IgA1 and IgA Nephropathy

BACKGROUND

Galactose-deficient IgA1 plays a key role in the pathogenesis of IgA nephropathy, the most common primary GN worldwide. Although serum levels of galactose-deficient IgA1 have a strong genetic component, the genetic link between this molecule and IgA nephropathy has not yet been clearly established.

METHODS

To identify novel loci associated with galactose-deficient IgA1, we performed a quantitative genome-wide association study for serum galactose-deficient IgA1 levels, on the basis of two different genome-wide association study panels conducted in 1127 patients with IgA nephropathy. To test genetic associations with susceptibility to IgA nephropathy, we also enrolled 2352 patients with biopsy-diagnosed IgA nephropathy and 2632 healthy controls. Peripheral blood samples from 59 patients and 27 healthy controls were also collected for gene expression analysis.

RESULTS

We discovered two loci, in C1GALT1 and GALNT12, that achieved genome-wide significance, explaining about 3.7% and 3.4% of variance in serum galactose-deficient IgA1 levels, respectively. We confirmed the previously reported association of C1GALT1 with serum galactose-deficient IgA1 levels, but with a different lead single-nucleotide polymorphism (rs10238682; β=0.26, P=1.20×10^-9); the locus we identified at GALNT12 (rs7856182; β=0.73, P=2.38×10^-9) was novel. Of more interest, we found that GALNT12 exhibits genetic interactions with C1GALT1 in both galactose-deficient IgA1 levels (P=1.40×10^-2) and disease risk (P=6.55×10^-3). GALNT12 mRNA expression in patients with IgA nephropathy was significantly lower compared with healthy controls.

CONCLUSIONS

Our data identify GALNT12 as a novel gene associated with galactose-deficient IgA1 and suggest novel genetic interactions. These findings support a key role of genetically conferred dysregulation of galactose-deficient IgA1 in the development of IgA nephropathy.

Identifying Novel Susceptibility Genes for Colorectal Cancer Risk From a Transcriptome-Wide Association Study of 125,478 Subjects

BACKGROUND AND AIMS

Susceptibility genes and the underlying mechanisms for the majority of risk loci identified by genome-wide association studies (GWAS) for colorectal cancer (CRC) risk remain largely unknown. We conducted a transcriptome-wide association study (TWAS) to identify putative susceptibility genes.

METHODS

Gene-expression prediction models were built using transcriptome and genetic data from the 284 normal transverse colon tissues of European descendants from the Genotype-Tissue Expression (GTEx), and model performance was evaluated using data from The Cancer Genome Atlas (n = 355). We applied the gene-expression prediction models and GWAS data to evaluate associations of genetically predicted gene-expression with CRC risk in 58,131 CRC cases and 67,347 controls of European ancestry. Dual-luciferase reporter assays and knockdown experiments in CRC cells and tumor xenografts were conducted.

RESULTS

We identified 25 genes associated with CRC risk at a Bonferroni-corrected threshold of P < 9.1 × 10-6, including genes in 4 novel loci, PYGL (14q22.1), RPL28 (19q13.42), CAPN12 (19q13.2), MYH7B (20q11.22), and MAP1L3CA (20q11.22). In 9 known GWAS-identified loci, we uncovered 9 genes that have not been reported previously, whereas 4 genes remained statistically significant after adjusting for the lead risk variant of the locus. Through colocalization analysis in GWAS loci, we additionally identified 12 putative susceptibility genes that were supported by TWAS analysis at P < .01. We showed that risk allele of the lead risk variant rs1741640 affected the promoter activity of CABLES2. Knockdown experiments confirmed that CABLES2 plays a vital role in colorectal carcinogenesis.

CONCLUSIONS

Our study reveals new putative susceptibility genes and provides new insight into the biological mechanisms underlying CRC development.

A sequence-based deep learning approach to predict CTCF-mediated chromatin loop

Three-dimensional (3D) architecture of the chromosomes is of crucial importance for transcription regulation and DNA replication. Various high-throughput chromosome conformation capture-based methods have revealed that CTCF-mediated chromatin loops are a major component of 3D architecture. However, CTCF-mediated chromatin loops are cell type specific, and most chromatin interaction capture techniques are time-consuming and labor-intensive, which restricts their usage on a very large number of cell types. Genomic sequence-based computational models are sophisticated enough to capture important features of chromatin architecture and help to identify chromatin loops. In this work, we develop Deep-loop, a convolutional neural network model, to integrate k-tuple nucleotide frequency component, nucleotide pair spectrum encoding, position conservation, position scoring function and natural vector features for the prediction of chromatin loops. By a series of examination based on cross-validation, Deep-loop shows excellent performance in the identification of the chromatin loops from different cell types. The source code of Deep-loop is freely available at the repository https://github.com/linDing-group/Deep-loop.

Iterative epigenomic analyses in the same single cell

Gene expression in individual cells is epigenetically regulated by DNA modifications, histone modifications, transcription factors, and other DNA-binding proteins. It has been shown that multiple histone modifications can predict gene expression and reflect future responses of bulk cells to extracellular cues. However, the predictive ability of epigenomic analysis is still limited for mechanistic research at a single cell level. To overcome this limitation, it would be useful to acquire reliable signals from multiple epigenetic marks in the same single cell. Here, we propose a new approach and a new method for analysis of several components of the epigenome in the same single cell. The new method allows reanalysis of the same single cell. We found that reanalysis of the same single cell is feasible, provides confirmation of the epigenetic signals, and allows application of statistical analysis to identify reproduced reads using data sets generated only from the single cell. Reanalysis of the same single cell is also useful to acquire multiple epigenetic marks from the same single cells. The method can acquire at least five epigenetic marks: H3K27ac, H3K27me3, mediator complex subunit 1, a DNA modification, and a DNA-interacting protein. We can predict active signaling pathways in K562 single cells using the epigenetic data and confirm that the predicted results strongly correlate with actual active signaling pathways identified by RNA-seq results. These results suggest that the new method provides mechanistic insights for cellular phenotypes through multilayered epigenome analysis in the same single cells.

LINE retrotransposons characterize mammalian tissue-specific and evolutionarily dynamic regulatory regions

BACKGROUND

To investigate the mechanisms driving regulatory evolution across tissues, we experimentally mapped promoters, enhancers, and gene expression in the liver, brain, muscle, and testis from ten diverse mammals.

RESULTS

The regulatory landscape around genes included both tissue-shared and tissue-specific regulatory regions, where tissue-specific promoters and enhancers evolved most rapidly. Genomic regions switching between promoters and enhancers were more common across species, and less common across tissues within a single species. Long Interspersed Nuclear Elements (LINEs) played recurrent evolutionary roles: LINE L1s were associated with tissue-specific regulatory regions, whereas more ancient LINE L2s were associated with tissue-shared regulatory regions and with those switching between promoter and enhancer signatures across species.

CONCLUSIONS

Our analyses of the tissue-specificity and evolutionary stability among promoters and enhancers reveal how specific LINE families have helped shape the dynamic mammalian regulome.

From the Genetics of Ankylosing Spondylitis to New Biology and Drug Target Discovery

Genome-wide association studies (GWAS) have identified 113 single nucleotide polymorphisms (SNPs) affecting the risk of developing ankylosing spondylitis (AS), and an on-going GWAS study will likely identify 100+ new risk loci. The translation of genetic findings to novel disease biology and treatments has been difficult due to the following challenges: (1) difficulties in determining the causal genes regulated by disease-associated SNPs, (2) difficulties in determining the relevant cell-type(s) that causal genes exhibit their function(s), (3) difficulties in determining appropriate cellular contexts to interrogate the functional role of causal genes in disease biology. This review will discuss recent progress and unanswered questions with a focus on these challenges. Additionally, we will review the investigation of biology and the development of drugs related to the IL-23/IL-17 pathway, which has been partially driven by the AS genetics, and discuss what can be learned from these studies for the future functional and translational study of AS-associated genes.

Three-dimensional chromatin organization in cardiac development and disease

Recent technological advancements in the field of chromatin biology have rewritten the textbook on nuclear organization. We now appreciate that the folding of chromatin in the three-dimensional space (i.e. its 3D "architecture") is non-random, hierarchical, and highly complex. While 3D chromatin structure is partially encoded in the primary sequence and thereby broadly conserved across cell types and states, a substantial portion of the genome seems to be dynamic during development or in disease. Moreover, there is growing evidence that at least some of the 3D structure of chromatin is functionally linked to gene regulation, both being modulated by and impacting on multiple nuclear processes (including DNA replication, transcription, and RNA splicing). In recent years, these new concepts have nourished several investigations about the functional role of 3D chromatin topology dynamics in the heart during development and disease. This review aims to provide a comprehensive overview of our current understanding in this field, and to discuss how this knowledge can inform further research as well as clinical practice.

H3K27me3-rich genomic regions can function as silencers to repress gene expression via chromatin interactions

The mechanisms underlying gene repression and silencers are poorly understood. Here we investigate the hypothesis that H3K27me3-rich regions of the genome, defined from clusters of H3K27me3 peaks, may be used to identify silencers that can regulate gene expression via proximity or looping. We find that H3K27me3-rich regions are associated with chromatin interactions and interact preferentially with each other. H3K27me3-rich regions component removal at interaction anchors by CRISPR leads to upregulation of interacting target genes, altered H3K27me3 and H3K27ac levels at interacting regions, and altered chromatin interactions. Chromatin interactions did not change at regions with high H3K27me3, but regions with low H3K27me3 and high H3K27ac levels showed changes in chromatin interactions. Cells with H3K27me3-rich regions knockout also show changes in phenotype associated with cell identity, and altered xenograft tumor growth. Finally, we observe that H3K27me3-rich regions-associated genes and long-range chromatin interactions are susceptible to H3K27me3 depletion. Our results characterize H3K27me3-rich regions and their mechanisms of functioning via looping.

VARAdb: a comprehensive variation annotation database for human

With the study of human diseases and biological processes increasing, a large number of non-coding variants have been identified and facilitated. The rapid accumulation of genetic and epigenomic information has resulted in an urgent need to collect and process data to explore the regulation of non-coding variants. Here, we developed a comprehensive variation annotation database for human (VARAdb, http://www.licpathway.net/VARAdb/), which specifically considers non-coding variants. VARAdb provides annotation information for 577,283,813 variations and novel variants, prioritizes variations based on scores using nine annotation categories, and supports pathway downstream analysis. Importantly, VARAdb integrates a large amount of genetic and epigenomic data into five annotation sections, which include ‘Variation information’, ‘Regulatory information’, ‘Related genes’, ‘Chromatin accessibility’ and ‘Chromatin interaction’. The detailed annotation information consists of motif changes, risk SNPs, LD SNPs, eQTLs, clinical variant-drug-gene pairs, sequence conservation, somatic mutations, enhancers, super enhancers, promoters, transcription factors, chromatin states, histone modifications, chromatin accessibility regions and chromatin interactions. This database is a user-friendly interface to query, browse and visualize variations and related annotation information. VARAdb is a useful resource for selecting potential functional variations and interpreting their effects on human diseases and biological processes.

High-resolution targeted 3C interrogation of cis-regulatory element organization at genome-wide scale

Chromosome conformation capture (3C) provides an adaptable tool for studying diverse biological questions. Current 3C methods generally provide either low-resolution interaction profiles across the entire genome, or high-resolution interaction profiles at limited numbers of loci. Due to technical limitations, generation of reproducible high-resolution interaction profiles has not been achieved at genome-wide scale. Here, to overcome this barrier, we systematically test each step of 3C and report two improvements over current methods. We show that up to 30% of reporter events generated using the popular in situ 3C method arise from ligations between two individual nuclei, but this noise can be almost entirely eliminated by isolating intact nuclei after ligation. Using Nuclear-Titrated Capture-C, we generate reproducible high-resolution genome-wide 3C interaction profiles by targeting 8055 gene promoters in erythroid cells. By pairing high-resolution 3C interaction calls with nascent gene expression we interrogate the role of promoter hubs and super-enhancers in gene regulation.

Insights into the Links between MYC and 3D Chromatin Structure and Epigenetics Regulation: Implications for Cancer Therapy

MYC is embedded in the transcriptional oasis of the 8q24 gene desert. A plethora of genomic elements has roles in MYC aberrant expression in cancer development by interacting with transcription factors and epigenetics regulators as well as altering the structure of chromatin at the MYC locus and tissue-specific long-range enhancer-promoter contacts. Furthermore, MYC is a master regulator of several human cancers by modulating the transcription of numerous cancer-related genes through epigenetic mechanisms. This review provides a comprehensive overview of the three-dimensional genomic organization around MYC and the role of epigenetic machinery in transcription and function of MYC as well as discusses various epigenetic-targeted therapeutic strategies in MYC-driven cancers.

3DIV update for 2021: a comprehensive resource of 3D genome and 3D cancer genome

Three-dimensional (3D) genome organization is tightly coupled with gene regulation in various biological processes and diseases. In cancer, various types of large-scale genomic rearrangements can disrupt the 3D genome, leading to oncogenic gene expression. However, unraveling the pathogenicity of the 3D cancer genome remains a challenge since closer examinations have been greatly limited due to the lack of appropriate tools specialized for disorganized higher-order chromatin structure. Here, we updated a 3D-genome Interaction Viewer and database named 3DIV by uniformly processing ∼230 billion raw Hi-C reads to expand our contents to the 3D cancer genome. The updates of 3DIV are listed as follows: (i) the collection of 401 samples including 220 cancer cell line/tumor Hi-C data, 153 normal cell line/tissue Hi-C data, and 28 promoter capture Hi-C data, (ii) the live interactive manipulation of the 3D cancer genome to simulate the impact of structural variations and (iii) the reconstruction of Hi-C contact maps by user-defined chromosome order to investigate the 3D genome of the complex genomic rearrangement. In summary, the updated 3DIV will be the most comprehensive resource to explore the gene regulatory effects of both the normal and cancer 3D genome. ‘3DIV’ is freely available at http://3div.kr.

BET inhibition disrupts transcription but retains enhancer-promoter contact

Enhancers are DNA sequences that enable complex temporal and tissue-specific regulation of genes in higher eukaryotes. Although it is not entirely clear how enhancer-promoter interactions can increase gene expression, this proximity has been observed in multiple systems at multiple loci and is thought to be essential for the maintenance of gene expression. Bromodomain and Extra-Terminal domain (BET) and Mediator proteins have been shown capable of forming phase condensates and are thought to be essential for super-enhancer function. Here, we show that targeting of cells with inhibitors of BET proteins or pharmacological degradation of BET protein Bromodomain-containing protein 4 (BRD4) has a strong impact on transcription but very little impact on enhancer-promoter interactions. Dissolving phase condensates reduces BRD4 and Mediator binding at enhancers and can also strongly affect gene transcription, without disrupting enhancer-promoter interactions. These results suggest that activation of transcription and maintenance of enhancer-promoter interactions are separable events. Our findings further indicate that enhancer-promoter interactions are not dependent on high levels of BRD4 and Mediator, and are likely maintained by a complex set of factors including additional activator complexes and, at some sites, CTCF and cohesin.

Long-distance chromatin interaction of IGF1 during embryonic and postnatal development in the liver of Sus scrofa

The dynamics of chromatin have been the focus of studies aimed at characterizing gene regulation. Among various chromosome conformation capture methods, 4C-seq is a powerful technique to identify genome-wide interactions with a single locus of interest. Insulin-like growth factor 1 (IGF1) is a member of the somatotropin axis that plays a significant role in cell proliferation and growth. Determining the IGF1-involved genome-wide chromatin interaction profile at different growth stages not only is important for understanding IGF1 transcriptional regulation but also provides a representation of genome-wide chromatin transformation during development. Using the IGF1 promoter as a "bait", we identified genome-wide interactomes of embryonic (E70) and postnatal (P1 and P70) pig liver cells by 4C-seq. The IGF1 promoter interactomes varied significantly among the three developmental stages. The most active chromatin interaction was observed in the P1 stage, while the highest interaction variability was observed in the P70 stage. The identified 4C sites were enriched around transcription start sites, CpG sites and functional pig QTLs. In addition, the genes located in the interacting regions and the involved pathways were also analysed. Overall, our work reveals a distinct long-distance regulatory pattern in pig liver during development for the first time, and the identified interacting sites and genes may serve as candidate targets in further transcriptional mechanism studies and effective molecular markers for functional traits.

A primer on single-cell genomics in myeloid biology

PURPOSE OF REVIEW

Understanding the fast-moving field of single-cell technologies, as applied to myeloid biology, requires an appreciation of basic molecular, informatics, and biological concepts. Here, we highlight both key and recent articles to illustrate basic concepts for those new to molecular single-cell analyses in myeloid hematology.

RECENT FINDINGS

Recent studies apply single-cell omics to discover novel cell populations, construct relationships between cell populations, reconfigure the organization of hematopoiesis, and study hematopoietic lineage tree and fate choices. Accompanying development of technologies, new informatic tools have emerged, providing exciting new insights.

SUMMARY

Hematopoietic stem and progenitor cells are regulated by complex intrinsic and extrinsic factors to produce blood cell types. In this review, we discuss recent advances in single-cell omics to profile these cells, methods to infer cell type identify, and trajectories from molecular omics data to ultimately derive new insights into hematopoietic stem and progenitor cell biology. We further discuss future applications of these technologies to understand hematopoietic cell interactions, function, and development. The goal is to offer a comprehensive overview of current single-cell technologies and their impact on our understanding of myeloid cell development for those new to single-cell analyses.