Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements

CD-Loop: a chromatin loop detection method based on the diffusion model

Motivation

In recent years, there have been significant advances in various chromatin conformation capture techniques, and annotating the topological structure from Hi-C contact maps has become crucial for studying the three-dimensional structure of chromosomes. However, the structure and function of chromatin loops are highly dynamic and diverse, influenced by multiple factors. Therefore, obtaining the three-dimensional structure of the genome remains a challenging task. Among many chromatin loop prediction methods, it is difficult to fully extract features from the contact map and make accurate predictions at low sequencing depths.

Results

In this study, we put forward a deep learning framework based on the diffusion model called CD-Loop for predicting accurate chromatin loops. First, by pre-training the input data, we obtain prior probabilities for predicting the classification of the Hi-C contact map. Then, by combining the denoising process based on the diffusion model and the prior probability obtained by pre-training, candidate loops were predicted from the input Hi-C contact map. Finally, CD-Loop uses a density-based clustering algorithm to cluster the candidate chromatin loops and predict the final chromatin loops. We compared CD-Loop with the currently popular methods, such as Peakachu, Chromosight, and Mustache, and found that in different cell types, species, and sequencing depths, CD-Loop outperforms other methods in loop annotation. We conclude that CD-Loop can accurately predict chromatin loops and reveal cell-type specificity. The code is available at https://github.com/wangyang199897/CD-Loop.

Loop Catalog: a comprehensive HiChIP database of human and mouse samples

HiChIP enables cost-effective and high-resolution profiling of regulatory and structural loops. To leverage the increasing number of publicly available HiChIP datasets from diverse cell lines and primary cells, we developed the Loop Catalog (https://loopcatalog.lji.org), a web-based database featuring HiChIP loop calls for 1319 samples across 133 studies and 44 high-resolution Hi-C loop calls. We demonstrate its utility in interpreting fine-mapped GWAS variants (SNP-to-gene linking), in identifying enriched sequence motifs and motif pairs at loop anchors, and in network-level analysis of loops connecting regulatory elements (community detection). Our comprehensive catalog, spanning over 4M unique 5kb loops, along with the accompanying analysis modalities constitutes an important resource for studies in gene regulation and genome organization.

Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles

Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer-gene maps from disease-relevant tissues. Building enhancer-gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer-gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer-gene maps, essential for defining noncoding variant function.

Enhancer-promoter activation by the Kaposi sarcoma-associated herpesvirus episome maintenance protein LANA

Higher-order genome structure influences the transcriptional regulation of cellular genes through the juxtaposition of regulatory elements, such as enhancers, close to promoters of target genes. While enhancer activation has emerged as an important facet of Kaposi sarcoma-associated herpesvirus (KSHV) biology, the mechanisms controlling enhancer-target gene expression remain obscure. Here, we discover that the KSHV genome tethering protein latency-associated nuclear antigen (LANA) potentiates enhancer-target gene expression in primary effusion lymphoma (PEL), a highly aggressive B cell lymphoma causally associated with KSHV. Genome-wide analyses demonstrate increased levels of enhancer RNA transcription as well as activating chromatin marks at LANA-bound enhancers. 3D genome conformation analyses identified genes critical for latency and tumorigenesis as targets of LANA-occupied enhancers, and LANA depletion results in their downregulation. These findings reveal a mechanism in enhancer-gene coordination and describe a role through which the main KSHV tethering protein regulates essential gene expression in PEL.

Hi-C, a chromatin 3D structure technique advancing the functional genomics of immune cells

The functional performance of immune cells relies on a complex transcriptional regulatory network. The three-dimensional structure of chromatin can affect chromatin status and gene expression patterns, and plays an important regulatory role in gene transcription. Currently available techniques for studying chromatin spatial structure include chromatin conformation capture techniques and their derivatives, chromatin accessibility sequencing techniques, and others. Additionally, the recently emerged deep learning technology can be utilized as a tool to enhance the analysis of data. In this review, we elucidate the definition and significance of the three-dimensional chromatin structure, summarize the technologies available for studying it, and describe the research progress on the chromatin spatial structure of dendritic cells, macrophages, T cells, B cells, and neutrophils.

MMCT-Loop: a mix model-based pipeline for calling targeted 3D chromatin loops

Protein-specific Chromatin Conformation Capture (3C)-based technologies have become essential for identifying distal genomic interactions with critical roles in gene regulation. The standard techniques include Chromatin Interaction Analysis by Paired-End Tag (ChIA-PET), in situ Hi-C followed by chromatin immunoprecipitation (HiChIP) also known as PLAC-seq. To identify chromatin interactions from these data, a variety of computational methods have emerged. Although these state-of-art methods address many issues with loop calling, only few methods can fit different data types simultaneously, and the accuracy as well as the efficiency these approaches remains limited. Here we have generated a pipeline, MMCT-Loop, which ensures the accurate identification of strong loops as well as dynamic or weak loops through a mixed model. MMCT-Loop outperforms existing methods in accuracy, and the detected loops show higher activation functionality. To highlight the utility of MMCT-Loop, we applied it to conformational data derived from neural stem cell (NSCs) and uncovered several previously unidentified regulatory regions for key master regulators of stem cell identity. MMCT-Loop is an accurate and efficient loop caller for targeted conformation capture data, which supports raw data or pre-processed valid pairs as input, the output interactions are formatted and easily uploaded to a genome browser for visualization.

Integrative high-throughput enhancer surveying and functional verification divulges a YY2-condensed regulatory axis conferring risk for osteoporosis

The precise roles of chromatin organization at osteoporosis risk loci remain largely elusive. Here, we combined chromatin interaction conformation (Hi-C) profiling and self-transcribing active regulatory region sequencing (STARR-seq) to qualify enhancer activities of prioritized osteoporosis-associated single-nucleotide polymorphisms (SNPs). We identified 319 SNPs with biased allelic enhancer activity effect (baaSNPs) that linked to hundreds of candidate target genes through chromatin interactions across 146 loci. Functional characterizations revealed active epigenetic enrichment for baaSNPs and prevailing osteoporosis-relevant regulatory roles for their chromatin interaction genes. Further motif enrichment and network mapping prioritized several putative, key transcription factors (TFs) controlling osteoporosis binding to baaSNPs. Specifically, we selected one top-ranked TF and deciphered that an intronic baaSNP (rs11202530) could allele-preferentially bind to YY2 to augment PAPSS2 expression through chromatin interactions and promote osteoblast differentiation. Our results underline the roles of TF-mediated enhancer-promoter contacts for osteoporosis, which may help to better understand the intricate molecular regulatory mechanisms underlying osteoporosis risk loci.

The regulation and differentiation of regulatory T cells and their dysfunction in autoimmune diseases

The discovery of FOXP3+ regulatory T (Treg) cells as a distinct cell lineage with a central role in regulating immune responses provided a deeper understanding of self-tolerance. The transcription factor FOXP3 serves a key role in Treg cell lineage determination and maintenance, but is not sufficient to enable the full potential of Treg cell suppression, indicating that other factors orchestrate the fine-tuning of Treg cell function. Moreover, FOXP3-independent mechanisms have recently been shown to contribute to Treg cell dysfunction. FOXP3 mutations in humans cause lethal fulminant systemic autoinflammation (IPEX syndrome). However, it remains unclear to what degree Treg cell dysfunction is contributing to the pathophysiology of common autoimmune diseases. In this Review, we discuss the origins of Treg cells in the periphery and the multilayered mechanisms by which Treg cells are induced, as well as the FOXP3-dependent and FOXP3-independent cellular programmes that maintain the suppressive function of Treg cells in humans and mice. Further, we examine evidence for Treg cell dysfunction in the context of common autoimmune diseases such as multiple sclerosis, inflammatory bowel disease, systemic lupus erythematosus and rheumatoid arthritis.

Mapping the functional impact of non-coding regulatory elements in primary T cells through single-cell CRISPR screens

BACKGROUND

Drug targets with genetic evidence are expected to increase clinical success by at least twofold. Yet, translating disease-associated genetic variants into functional knowledge remains a fundamental challenge of drug discovery. A key issue is that the vast majority of complex disease associations cannot be cleanly mapped to a gene. Immune disease-associated variants are enriched within regulatory elements found in T-cell-specific open chromatin regions.

RESULTS

To identify genes and molecular programs modulated by these regulatory elements, we develop a CRISPRi-based single-cell functional screening approach in primary human T cells. Our pipeline enables the interrogation of transcriptomic changes induced by the perturbation of regulatory elements at scale. We first optimize an efficient CRISPRi protocol in primary CD4+ T cells via CROPseq vectors. Subsequently, we perform a screen targeting 45 non-coding regulatory elements and 35 transcription start sites and profile approximately 250,000 T -cell single-cell transcriptomes. We develop a bespoke analytical pipeline for element-to-gene (E2G) mapping and demonstrate that our method can identify both previously annotated and novel E2G links. Lastly, we integrate genetic association data for immune-related traits and demonstrate how our platform can aid in the identification of effector genes for GWAS loci.

CONCLUSIONS

We describe "primary T cell crisprQTL" - a scalable, single-cell functional genomics approach for mapping regulatory elements to genes in primary human T cells. We show how this framework can facilitate the interrogation of immune disease GWAS hits and propose that the combination of experimental and QTL-based techniques is likely to address the variant-to-function problem.

Decoding the Epigenetics and Chromatin Loop Dynamics of Androgen Receptor-Mediated Transcription

Androgen receptor (AR)-mediated transcription plays a critical role in normal prostate development and prostate cancer growth. AR drives gene expression by binding to thousands of cis-regulatory elements (CRE) that loop to hundreds of target promoters. With multiple CREs interacting with a single promoter, it remains unclear how individual AR bound CREs contribute to gene expression. To characterize the involvement of these CREs, we investigated the AR-driven epigenetic and chromosomal chromatin looping changes. We collected a kinetic multi-omic dataset comprised of steady-state mRNA, chromatin accessibility, transcription factor binding, histone modifications, chromatin looping, and nascent RNA. Using an integrated regulatory network, we found that AR binding induces sequential changes in the epigenetic features at CREs, independent of gene expression. Further, we showed that binding of AR does not result in a substantial rewiring of chromatin loops, but instead increases the contact frequency of pre-existing loops to target promoters. Our results show that gene expression strongly correlates to the changes in contact frequency. We then proposed and experimentally validated an unbalanced multi-enhancer model where the impact on gene expression of AR-bound enhancers is heterogeneous, and is proportional to their contact frequency with target gene promoters. Overall, these findings provide new insight into AR-mediated gene expression upon acute androgen simulation and develop a mechanistic framework to investigate nuclear receptor mediated perturbations.

Multi-omic analysis of human kidney tissue identified medulla-specific gene expression patterns

The kidney medulla is a specialized region with important homeostatic functions. It has been implicated in genetic and developmental disorders along with ischemic and drug-induced injuries. Despite its role in kidney function and disease, the medulla's baseline gene expression and epigenomic signatures have not been well described in the adult human kidney. Here we generated and analyzed gene expression (RNA-seq), chromatin accessibility (ATAC-seq), chromatin conformation (Hi-C) and spatial transcriptomic data from the adult human kidney cortex and medulla. Tissue samples were obtained from macroscopically dissected cortex and medulla of tumor-adjacent normal material in nephrectomy specimens from five male patients. We used these carefully annotated specimens to reassign incorrectly labeled samples in the larger public Genotype-Tissue Expression (GTEx) Project, and to extract meaningful medullary gene expression signatures. Using integrated analysis of gene expression, chromatin accessibility and conformation profiles, we found insights into medulla development and function and then validated this by spatial transcriptomics and immunohistochemistry. Thus, our datasets provide a valuable resource for functional annotation of variants from genome-wide association studies and are freely accessible through an epigenome browser portal.

Exploring noncoding variants in genetic diseases: from detection to functional insights

Previous studies on genetic diseases predominantly focused on protein-coding variations, overlooking the vast noncoding regions in the human genome. The development of high-throughput sequencing technologies and functional genomics tools has enabled the systematic identification of functional noncoding variants. These variants can impact gene expression, regulation, and chromatin conformation, thereby contributing to disease pathogenesis. Understanding the mechanisms that underlie the impact of noncoding variants on genetic diseases is indispensable for the development of precisely targeted therapies and the implementation of personalized medicine strategies. The intricacies of noncoding regions introduce a multitude of challenges and research opportunities. In this review, we introduce a spectrum of noncoding variants involved in genetic diseases, along with research strategies and advanced technologies for their precise identification and in-depth understanding of the complexity of the noncoding genome. We will delve into the research challenges and propose potential solutions for unraveling the genetic basis of rare and complex diseases.

Chromatin activity identifies differential gene regulation across human ancestries

BACKGROUND

Current evidence suggests that cis-regulatory elements controlling gene expression may be the predominant target of natural selection in humans and other species. Detecting selection acting on these elements is critical to understanding evolution but remains challenging because we do not know which mutations will affect gene regulation.

RESULTS

To address this, we devise an approach to search for lineage-specific selection on three critical steps in transcriptional regulation: chromatin activity, transcription factor binding, and chromosomal looping. Applying this approach to lymphoblastoid cells from 831 individuals of either European or African descent, we find strong signals of differential chromatin activity linked to gene expression differences between ancestries in numerous contexts, but no evidence of functional differences in chromosomal looping. Moreover, we show that enhancers rather than promoters display the strongest signs of selection associated with sites of differential transcription factor binding.

CONCLUSIONS

Overall, our study indicates that some cis-regulatory adaptation may be more easily detected at the level of chromatin than DNA sequence. This work provides a vast resource of genomic interaction data from diverse human populations and establishes a novel selection test that will benefit future study of regulatory evolution in humans and other species.

3D Enhancer-promoter networks provide predictive features for gene expression and coregulation in early embryonic lineages

Mammalian embryogenesis commences with two pivotal and binary cell fate decisions that give rise to three essential lineages: the trophectoderm, the epiblast and the primitive endoderm. Although key signaling pathways and transcription factors that control these early embryonic decisions have been identified, the non-coding regulatory elements through which transcriptional regulators enact these fates remain understudied. Here, we characterize, at a genome-wide scale, enhancer activity and 3D connectivity in embryo-derived stem cell lines that represent each of the early developmental fates. We observe extensive enhancer remodeling and fine-scale 3D chromatin rewiring among the three lineages, which strongly associate with transcriptional changes, although distinct groups of genes are irresponsive to topological changes. In each lineage, a high degree of connectivity, or 'hubness', positively correlates with levels of gene expression and enriches for cell-type specific and essential genes. Genes within 3D hubs also show a significantly stronger probability of coregulation across lineages compared to genes in linear proximity or within the same contact domains. By incorporating 3D chromatin features, we build a predictive model for transcriptional regulation (3D-HiChAT) that outperforms models using only 1D promoter or proximal variables to predict levels and cell-type specificity of gene expression. Using 3D-HiChAT, we identify, in silico, candidate functional enhancers and hubs in each cell lineage, and with CRISPRi experiments, we validate several enhancers that control gene expression in their respective lineages. Our study identifies 3D regulatory hubs associated with the earliest mammalian lineages and describes their relationship to gene expression and cell identity, providing a framework to comprehensively understand lineage-specific transcriptional behaviors.

Stage-specific dynamic reorganization of genome topology shapes transcriptional neighborhoods in developing human retinal organoids

We have generated a high-resolution Hi-C map of developing human retinal organoids to elucidate spatiotemporal dynamics of genomic architecture and its relationship with gene expression patterns. We demonstrate progressive stage-specific alterations in DNA topology and correlate these changes with transcription of cell-type-restricted gene markers during retinal differentiation. Temporal Hi-C reveals a shift toward A compartment for protein-coding genes and B compartment for non-coding RNAs, displaying high and low expression, respectively. Notably, retina-enriched genes are clustered near lost boundaries of topologically associated domains (TADs), and higher-order assemblages (i.e., TAD cliques) localize in active chromatin regions with binding sites for eye-field transcription factors. These genes gain chromatin contacts at their transcription start site as organoid differentiation proceeds. Our study provides a global view of chromatin architecture dynamics associated with diversification of cell types during retinal development and serves as a foundational resource for in-depth functional investigations of retinal developmental traits.

The role of enhancers in psoriasis and atopic dermatitis

Regulatory elements, particularly enhancers, play a crucial role in disease susceptibility and progression. Enhancers are DNA sequences that activate gene expression and can be affected by epigenetic modifications, interactions with transcription factors (TFs) or changes to the enhancer DNA sequence itself. Altered enhancer activity impacts gene expression and contributes to disease. In this review, we define enhancers and the experimental techniques used to identify and characterize them. We also discuss recent studies that examine how enhancers contribute to atopic dermatitis (AD) and psoriasis. Articles in the PubMed database were identified (from 1 January 2010 to 28 February 2023) that were relevant to enhancer variants, enhancer-associated TFs and enhancer histone modifications in psoriasis or AD. Most enhancers associated with these conditions regulate genes affecting epidermal homeostasis or immune function. These discoveries present potential therapeutic targets to complement existing treatment options for AD and psoriasis.

Breakage fusion bridge cycles drive high oncogene copy number, but not intratumoral genetic heterogeneity or rapid cancer genome change

Oncogene amplification is a major driver of cancer pathogenesis. Breakage fusion bridge (BFB) cycles, like extrachromosomal DNA (ecDNA), can lead to high copy numbers of oncogenes, but their impact on intratumoral heterogeneity, treatment response, and patient survival are not well understood due to difficulty in detecting them by DNA sequencing. We describe a novel algorithm that detects and reconstructs BFB amplifications using optical genome maps (OGMs), called OM2BFB. OM2BFB showed high precision (>93%) and recall (92%) in detecting BFB amplifications in cancer cell lines, PDX models and primary tumors. OM-based comparisons demonstrated that short-read BFB detection using our AmpliconSuite (AS) toolkit also achieved high precision, albeit with reduced sensitivity. We detected 371 BFB events using whole genome sequences from 2,557 primary tumors and cancer lines. BFB amplifications were preferentially found in cervical, head and neck, lung, and esophageal cancers, but rarely in brain cancers. BFB amplified genes show lower variance of gene expression, with fewer options for regulatory rewiring relative to ecDNA amplified genes. BFB positive (BFB (+)) tumors showed reduced heterogeneity of amplicon structures, and delayed onset of resistance, relative to ecDNA(+) tumors. EcDNA and BFB amplifications represent contrasting mechanisms to increase the copy numbers of oncogene with markedly different characteristics that suggest different routes for intervention.

Spatial promoter-enhancer hubs in cancer: organization, regulation, and function

Transcriptional dysregulation is a hallmark of cancer and can be driven by altered enhancer landscapes. Recent studies in genome organization have revealed that multiple enhancers and promoters can spatially coalesce to form dynamic topological assemblies, known as promoter-enhancer hubs, which strongly correlate with elevated gene expression. In this review, we discuss the structure and complexity of promoter-enhancer hubs recently identified in multiple cancer types. We further discuss underlying mechanisms driving dysregulation of promoter-enhancer hubs and speculate on their functional role in pathogenesis. Understanding the role of promoter-enhancer hubs in transcriptional dysregulation can provide insight into new therapeutic approaches to target these complex features of genome organization.

CREaTor: zero-shot cis-regulatory pattern modeling with attention mechanisms

Linking cis-regulatory sequences to target genes has been a long-standing challenge. In this study, we introduce CREaTor, an attention-based deep neural network designed to model cis-regulatory patterns for genomic elements up to 2 Mb from target genes. Coupled with a training strategy that predicts gene expression from flanking candidate cis-regulatory elements (cCREs), CREaTor can model cell type-specific cis-regulatory patterns in new cell types without prior knowledge of cCRE-gene interactions or additional training. The zero-shot modeling capability, combined with the use of only RNA-seq and ChIP-seq data, allows for the ready generalization of CREaTor to a broad range of cell types.

SilenceREIN: seeking silencers on anchors of chromatin loops by deep graph neural networks

Silencers are repressive cis-regulatory elements that play crucial roles in transcriptional regulation. Experimental methods for identifying silencers are always costly and time-consuming. Computational methods, which relies on genomic sequence features, have been introduced as alternative approaches. However, silencers do not have significant epigenomic signature. Therefore, we explore a new way to computationally identify silencers, by incorporating chromatin structural information. We propose the SilenceREIN method, which focuses on finding silencers on anchors of chromatin loops. By using graph neural networks, we extracted chromatin structural information from a regulatory element interaction network. SilenceREIN integrated the chromatin structural information with linear genomic signatures to find silencers. The predictive performance of SilenceREIN is comparable or better than other states-of-the-art methods. We performed a genome-wide scanning to systematically find silencers in human genome. Results suggest that silencers are widespread on anchors of chromatin loops. In addition, enrichment analysis of transcription factor binding motif support our prediction results. As far as we can tell, this is the first attempt to incorporate chromatin structural information in finding silencers. All datasets and source codes of SilenceREIN have been deposited in a GitHub repository (https://github.com/JianHPan/SilenceREIN).

Be-1DCNN: a neural network model for chromatin loop prediction based on bagging ensemble learning

The chromatin loops in the three-dimensional (3D) structure of chromosomes are essential for the regulation of gene expression. Despite the fact that high-throughput chromatin capture techniques can identify the 3D structure of chromosomes, chromatin loop detection utilizing biological experiments is arduous and time-consuming. Therefore, a computational method is required to detect chromatin loops. Deep neural networks can form complex representations of Hi-C data and provide the possibility of processing biological datasets. Therefore, we propose a bagging ensemble one-dimensional convolutional neural network (Be-1DCNN) to detect chromatin loops from genome-wide Hi-C maps. First, to obtain accurate and reliable chromatin loops in genome-wide contact maps, the bagging ensemble learning method is utilized to synthesize the prediction results of multiple 1DCNN models. Second, each 1DCNN model consists of three 1D convolutional layers for extracting high-dimensional features from input samples and one dense layer for producing the prediction results. Finally, the prediction results of Be-1DCNN are compared to those of the existing models. The experimental results indicate that Be-1DCNN predicts high-quality chromatin loops and outperforms the state-of-the-art methods using the same evaluation metrics. The source code of Be-1DCNN is available for free at https://github.com/HaoWuLab-Bioinformatics/Be1DCNN.

Ultrafine mapping of chromosome conformation at hundred basepair resolution reveals regulatory genome architecture

The resolution limit of chromatin conformation capture methodologies (3Cs) has restrained their application in detection of fine-level chromatin structure mediated by cis-regulatory elements (CREs). Here, we report two 3C-derived methods, Tri-4C and Tri-HiC, which utilize multirestriction enzyme digestions for ultrafine mapping of targeted and genome-wide chromatin interaction, respectively, at up to one hundred basepair resolution. Tri-4C identified CRE loop interaction networks and quantitatively revealed their alterations underlying dynamic gene control. Tri-HiC uncovered global fine-gauge regulatory interaction networks, identifying >20-fold more enhancer:promoter (E:P) loops than in situ Hi-C. In addition to vastly improved identification of subkilobase-sized E:P loops, Tri-HiC also uncovered interaction stripes and contact domain insulation from promoters and enhancers, revealing their loop extrusion behaviors resembling the topologically associating domain boundaries. Tri-4C and Tri-HiC provide robust approaches to achieve the high-resolution interactome maps required for characterizing fine-gauge regulatory chromatin interactions in analysis of development, homeostasis, and disease.

Single-cell chromatin accessibility and transcriptomic characterization of Behcet's disease

Behect's disease is a chronic vasculitis characterized by complex multi-organ immune aberrations. However, a comprehensive understanding of the gene-regulatory profile of peripheral autoimmunity and the diverse immune responses across distinct cell types in Behcet's disease (BD) is still lacking. Here, we present a multi-omic single-cell study of 424,817 cells in BD patients and non-BD individuals. This study maps chromatin accessibility and gene expression in the same biological samples, unraveling vast cellular heterogeneity. We identify widespread cell-type-specific, disease-associated active and pro-inflammatory immunity in both transcript and epigenomic aspects. Notably, integrative multi-omic analysis reveals putative TF regulators that might contribute to chromatin accessibility and gene expression in BD. Moreover, we predicted gene-regulatory networks within nominated TF activators, including AP-1, NF-kB, and ETS transcript factor families, which may regulate cellular interaction and govern inflammation. Our study illustrates the epigenetic and transcriptional landscape in BD peripheral blood and expands understanding of potential epigenomic immunopathology in this disease.

Epromoters are new players in the regulatory landscape with potential pleiotropic roles

Precise spatiotemporal control of gene expression during normal development and cell differentiation is achieved by the combined action of proximal (promoters) and distal (enhancers) cis-regulatory elements. Recent studies have reported that a subset of promoters, termed Epromoters, works also as enhancers to regulate distal genes. This new paradigm opened novel questions regarding the complexity of our genome and raises the possibility that genetic variation within Epromoters has pleiotropic effects on various physiological and pathological traits by differentially impacting multiple proximal and distal genes. Here, we discuss the different observations pointing to an important role of Epromoters in the regulatory landscape and summarize the evidence supporting a pleiotropic impact of these elements in disease. We further hypothesize that Epromoter might represent a major contributor to phenotypic variation and disease.

Exploring the impact of dexamethasone on gene regulation in myeloma cells

Among glucocorticoids (GCs), dexamethasone (Dex) is widely used in treatment of multiple myelomas. However, despite a definite benefit, all patients relapse. Moreover, the molecular basis of glucocorticoid efficacy remains elusive. To determine genomic response to Dex in myeloma cells, we generated bulk and single-cell multi-omics data and high-resolution contact maps of active enhancers and target genes. We show that a minority of glucocorticoid receptor-binding sites are associated with enhancer activity gains, increased interaction loops, and transcriptional activity. We identified and characterized a predominant enhancer enriched in cohesin (RAD21) and more accessible upon Dex exposure. Analysis of four gene-specific networks revealed the importance of the CTCF-cohesin couple and the synchronization of regulatory sequence openings for efficient transcription in response to Dex. Notably, these epigenomic changes are associated with cell-to-cell transcriptional heterogeneity, in particular, lineage-specific genes. As consequences, BCL2L11-encoding BIM critical for Dex-induced apoptosis and CXCR4 protective from chemotherapy-induced apoptosis are rather up-regulated in different cells. In summary, our work provides new insights into the molecular mechanisms involved in Dex escape.

MethNet: a robust approach to identify regulatory hubs and their distal targets in cancer

Aberrations in the capacity of DNA/chromatin modifiers and transcription factors to bind non-coding regions can lead to changes in gene regulation and impact disease phenotypes. However, identifying distal regulatory elements and connecting them with their target genes remains challenging. Here, we present MethNet, a pipeline that integrates large-scale DNA methylation and gene expression data across multiple cancers, to uncover novel cis regulatory elements (CREs) in a 1Mb region around every promoter in the genome. MethNet identifies clusters of highly ranked CREs, referred to as 'hubs', which contribute to the regulation of multiple genes and significantly affect patient survival. Promoter-capture Hi-C confirmed that highly ranked associations involve physical interactions between CREs and their gene targets, and CRISPRi based scRNA Perturb-seq validated the functional impact of CREs. Thus, MethNet-identified CREs represent a valuable resource for unraveling complex mechanisms underlying gene expression, and for prioritizing the verification of predicted non-coding disease hotspots.

iEnhance: a multi-scale spatial projection encoding network for enhancing chromatin interaction data resolution

Although sequencing-based high-throughput chromatin interaction data are widely used to uncover genome-wide three-dimensional chromatin architecture, their sparseness and high signal-noise-ratio greatly restrict the precision of the obtained structural elements. To improve data quality, we here present iEnhance (chromatin interaction data resolution enhancement), a multi-scale spatial projection and encoding network, to predict high-resolution chromatin interaction matrices from low-resolution and noisy input data. Specifically, iEnhance projects the input data into matrix spaces to extract multi-scale global and local feature sets, then hierarchically fused these features by attention mechanism. After that, dense channel encoding and residual channel decoding are used to effectively infer robust chromatin interaction maps. iEnhance outperforms state-of-the-art Hi-C resolution enhancement tools in both visual and quantitative evaluation. Comprehensive analysis shows that unlike other tools, iEnhance can recover both short-range structural elements and long-range interaction patterns precisely. More importantly, iEnhance can be transferred to data enhancement of other tissues or cell lines of unknown resolution. Furthermore, iEnhance performs robustly in enhancement of diverse chromatin interaction data including those from single-cell Hi-C and Micro-C experiments.

3D chromatin reprogramming primes human memory TH2 cells for rapid recall and pathogenic dysfunction

Memory T cells provide long-lasting defense responses through their ability to rapidly reactivate, but how they efficiently "recall" an inflammatory transcriptional program remains unclear. Here, we show that human CD4⁺ memory T helper 2 (T_H2) cells carry a chromatin landscape synergistically reprogrammed at both one-dimensional (1D) and 3D levels to accommodate recall responses, which is absent in naive T cells. In memory T_H2 cells, recall genes were epigenetically primed through the maintenance of transcription-permissive chromatin at distal (super)enhancers organized in long-range 3D chromatin hubs. Precise transcriptional control of key recall genes occurred inside dedicated topologically associating domains ("memory TADs"), in which activation-associated promoter-enhancer interactions were preformed and exploited by AP-1 transcription factors to promote rapid transcriptional induction. Resting memory T_H2 cells from patients with asthma showed premature activation of primed recall circuits, linking aberrant transcriptional control of recall responses to chronic inflammation. Together, our results implicate stable multiscale reprogramming of chromatin organization as a key mechanism underlying immunological memory and dysfunction in T cells.

Complementary Alu sequences mediate enhancer-promoter selectivity

Enhancers determine spatiotemporal gene expression programs by engaging with long-range promoters^1-4. However, it remains unknown how enhancers find their cognate promoters. We recently developed a RNA in situ conformation sequencing technology to identify enhancer-promoter connectivity using pairwise interacting enhancer RNAs and promoter-derived noncoding RNAs^5,6. Here we apply this technology to generate high-confidence enhancer-promoter RNA interaction maps in six additional cell lines. Using these maps, we discover that 37.9% of the enhancer-promoter RNA interaction sites are overlapped with Alu sequences. These pairwise interacting Alu and non-Alu RNA sequences tend to be complementary and potentially form duplexes. Knockout of Alu elements compromises enhancer-promoter looping, whereas Alu insertion or CRISPR-dCasRx-mediated Alu tethering to unregulated promoter RNAs can create new loops to homologous enhancers. Mapping 535,404 noncoding risk variants back to the enhancer-promoter RNA interaction maps enabled us to construct variant-to-function maps for interpreting their molecular functions, including 15,318 deletions or insertions in 11,677 Alu elements that affect 6,497 protein-coding genes. We further demonstrate that polymorphic Alu insertion at the PTK2 enhancer can promote tumorigenesis. Our study uncovers a principle for determining enhancer-promoter pairing specificity and provides a framework to link noncoding risk variants to their molecular functions.

Impact of supraphysiologic MDM2 expression on chromatin networks and therapeutic responses in sarcoma

Amplification of MDM2 on supernumerary chromosomes is a common mechanism of P53 inactivation across tumors. Here, we investigated the impact of MDM2 overexpression on chromatin, gene expression, and cellular phenotypes in liposarcoma. Three independent regulatory circuits predominate in aggressive, dedifferentiated tumors. RUNX and AP-1 family transcription factors bind mesenchymal gene enhancers. P53 and MDM2 co-occupy enhancers and promoters associated with P53 signaling. When highly expressed, MDM2 also binds thousands of P53-independent growth and stress response genes, whose promoters engage in multi-way topological interactions. Overexpressed MDM2 concentrates within nuclear foci that co-localize with PML and YY1 and could also contribute to P53-independent phenotypes associated with supraphysiologic MDM2. Importantly, we observe striking cell-to-cell variability in MDM2 copy number and expression in tumors and models. Whereas liposarcoma cells are generally sensitive to MDM2 inhibitors and their combination with pro-apoptotic drugs, MDM2-high cells tolerate them and may underlie the poor clinical efficacy of these agents.

Hi-TrAC detects active sub-TADs and reveals internal organizations of super-enhancers

The spatial folding of eukaryotic genome plays a key role in genome function. We report here that our recently developed method, Hi-TrAC, which specializes in detecting chromatin loops among accessible genomic regions, can detect active sub-TADs with a median size of 100 kb, most of which harbor one or two cell specifically expressed genes and regulatory elements such as super-enhancers organized into nested interaction domains. These active sub-TADs are characterized by highly enriched histone mark H3K4me1 and chromatin-binding proteins, including Cohesin complex. Deletion of selected sub-TAD boundaries have different impacts, such as decreased chromatin interaction and gene expression within the sub-TADs or compromised insulation between the sub-TADs, depending on the specific chromatin environment. We show that knocking down core subunit of the Cohesin complex using shRNAs in human cells or decreasing the H3K4me1 modification by deleting the H3K4 methyltransferase Mll4 gene in mouse Th17 cells disrupted the sub-TADs structure. Our data also suggest that super-enhancers exist as an equilibrium globule structure, while inaccessible chromatin regions exist as a fractal globule structure. In summary, Hi-TrAC serves as a highly sensitive and inexpensive approach to study dynamic changes of active sub-TADs, providing more explicit insights into delicate genome structures and functions.

Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data

Resolving chromatin-remodeling-linked gene expression changes at cell-type resolution is important for understanding disease states. Here we describe MAGICAL (Multiome Accessibility Gene Integration Calling and Looping), a hierarchical Bayesian approach that leverages paired single-cell RNA sequencing and single-cell transposase-accessible chromatin sequencing from different conditions to map disease-associated transcription factors, chromatin sites, and genes as regulatory circuits. By simultaneously modeling signal variation across cells and conditions in both omics data types, MAGICAL achieved high accuracy on circuit inference. We applied MAGICAL to study Staphylococcus aureus sepsis from peripheral blood mononuclear single-cell data that we generated from subjects with bloodstream infection and uninfected controls. MAGICAL identified sepsis-associated regulatory circuits predominantly in CD14 monocytes, known to be activated by bacterial sepsis. We addressed the challenging problem of distinguishing host regulatory circuit responses to methicillin-resistant and methicillin-susceptible S. aureus infections. Although differential expression analysis failed to show predictive value, MAGICAL identified epigenetic circuit biomarkers that distinguished methicillin-resistant from methicillin-susceptible S. aureus infections.

Vorinostat-induced acetylation of RUNX3 reshapes transcriptional profile through long-range enhancer-promoter interactions in natural killer cells

Natural killer (NK) cells are an essential part of the innate immune system that helps control infections and tumors. Recent studies have shown that Vorinostat, a histone deacetylase (HDAC) inhibitor, can cause significant changes in gene expression and signaling pathways in NK cells. Since gene expression in eukaryotic cells is closely linked to the complex three-dimensional (3D) chromatin architecture, an integrative analysis of the transcriptome, histone profiling, chromatin accessibility, and 3D genome organization is needed to gain a more comprehensive understanding of how Vorinostat impacts transcription regulation of NK cells from a chromatin-based perspective. The results demonstrate that Vorinostat treatment reprograms the enhancer landscapes of the human NK-92 NK cell line while overall 3D genome organization remains largely stable. Moreover, we identified that the Vorinostat-induced RUNX3 acetylation is linked to the increased enhancer activity, leading to elevated expression of immune response-related genes via long-range enhancerpromoter chromatin interactions. In summary, these findings have important implications in the development of new therapies for cancer and immune-related diseases by shedding light on the mechanisms underlying Vorinostat's impact on transcriptional regulation in NK cells within the context of 3D enhancer network. [BMB Reports 2023; 56(7): 398-403].

Reference panel-guided super-resolution inference of Hi-C data

MOTIVATION

Accurately assessing contacts between DNA fragments inside the nucleus with Hi-C experiment is crucial for understanding the role of 3D genome organization in gene regulation. This challenging task is due in part to the high sequencing depth of Hi-C libraries required to support high-resolution analyses. Most existing Hi-C data are collected with limited sequencing coverage, leading to poor chromatin interaction frequency estimation. Current computational approaches to enhance Hi-C signals focus on the analysis of individual Hi-C datasets of interest, without taking advantage of the facts that (i) several hundred Hi-C contact maps are publicly available and (ii) the vast majority of local spatial organizations are conserved across multiple cell types.

RESULTS

Here, we present RefHiC-SR, an attention-based deep learning framework that uses a reference panel of Hi-C datasets to facilitate the enhancement of Hi-C data resolution of a given study sample. We compare RefHiC-SR against tools that do not use reference samples and find that RefHiC-SR outperforms other programs across different cell types, and sequencing depths. It also enables high-accuracy mapping of structures such as loops and topologically associating domains.

AVAILABILITY AND IMPLEMENTATION

https://github.com/BlanchetteLab/RefHiC.

Integrative dissection of gene regulatory elements at base resolution

Although vast numbers of putative gene regulatory elements have been cataloged, the sequence motifs and individual bases that underlie their functions remain largely unknown. Here, we combine epigenetic perturbations, base editing, and deep learning to dissect regulatory sequences within the exemplar immune locus encoding CD69. We converge on a ∼170 base interval within a differentially accessible and acetylated enhancer critical for CD69 induction in stimulated Jurkat T cells. Individual C-to-T base edits within the interval markedly reduce element accessibility and acetylation, with corresponding reduction of CD69 expression. The most potent base edits may be explained by their effect on regulatory interactions between the transcriptional activators GATA3 and TAL1 and the repressor BHLHE40. Systematic analysis suggests that the interplay between GATA3 and BHLHE40 plays a general role in rapid T cell transcriptional responses. Our study provides a framework for parsing regulatory elements in their endogenous chromatin contexts and identifying operative artificial variants.

Chromatin alternates between A and B compartments at kilobase scale for subgenic organization

Nuclear compartments are prominent features of 3D chromatin organization, but sequencing depth limitations have impeded investigation at ultra fine-scale. CTCF loops are generally studied at a finer scale, but the impact of looping on proximal interactions remains enigmatic. Here, we critically examine nuclear compartments and CTCF loop-proximal interactions using a combination of in situ Hi-C at unparalleled depth, algorithm development, and biophysical modeling. Producing a large Hi-C map with 33 billion contacts in conjunction with an algorithm for performing principal component analysis on sparse, super massive matrices (POSSUMM), we resolve compartments to 500 bp. Our results demonstrate that essentially all active promoters and distal enhancers localize in the A compartment, even when flanking sequences do not. Furthermore, we find that the TSS and TTS of paused genes are often segregated into separate compartments. We then identify diffuse interactions that radiate from CTCF loop anchors, which correlate with strong enhancer-promoter interactions and proximal transcription. We also find that these diffuse interactions depend on CTCF's RNA binding domains. In this work, we demonstrate features of fine-scale chromatin organization consistent with a revised model in which compartments are more precise than commonly thought while CTCF loops are more protracted.

Functional characterization of human genomic variation linked to polygenic diseases

The burden of human disease lies predominantly in polygenic diseases. Since the early 2000s, genome-wide association studies (GWAS) have identified genetic variants and loci associated with complex traits. These have ranged from variants in coding sequences to mutations in regulatory regions, such as promoters and enhancers, as well as mutations affecting mediators of mRNA stability and other downstream regulators, such as 5' and 3'-untranslated regions (UTRs), long noncoding RNA (lncRNA), and miRNA. Recent research advances in genetics have utilized a combination of computational techniques, high-throughput in vitro and in vivo screening modalities, and precise genome editing to impute the function of diverse classes of genetic variants identified through GWAS. In this review, we highlight the vastness of genomic variants associated with polygenic disease risk and address recent advances in how genetic tools can be used to functionally characterize them.

Exploring the genetic basis of coronary artery disease using functional genomics

Genome-wide Association Studies (GWAS) have identified more than 300 loci associated with coronary artery disease (CAD), defining the genetic risk map of the disease. However, the translation of the association signals into biological-pathophysiological mechanisms constitute a major challenge. Through a group of examples of studies focused on CAD, we discuss the rationale, basic principles and outcomes of the main methodologies implemented to prioritize and characterize causal variants and their target genes. Additionally, we highlight the strategies as well as the current methods that integrate association and functional genomics data to dissect the cellular specificity underlying the complexity of disease mechanisms. Despite the limitations of existing approaches, the increasing knowledge generated through functional studies helps interpret GWAS maps and opens novel avenues for the clinical usability of association data.

Integration site-dependent HIV-1 promoter activity shapes host chromatin conformation

HIV-1 integration introduces ectopic transcription factor binding sites into host chromatin. We postulate that the integrated provirus serves as an ectopic enhancer that recruits additional transcription factors to the integration locus, increases chromatin accessibility, changes 3D chromatin interactions, and enhances both retroviral and host gene expression. We used four well-characterized HIV-1-infected cell line clones having unique integration sites and low to high levels of HIV-1 expression. Using single-cell DOGMA-seq, which captured the heterogeneity of HIV-1 expression and host chromatin accessibility, we found that HIV-1 transcription correlated with HIV-1 accessibility and host chromatin accessibility. HIV-1 integration increased local host chromatin accessibility within an ∼5- to 30-kb distance. CRISPRa- and CRISPRi-mediated HIV-1 promoter activation and inhibition confirmed integration site-dependent HIV-1-driven changes of host chromatin accessibility. HIV-1 did not drive chromatin confirmation changes at the genomic level (by Hi-C) or the enhancer connectome (by H3K27ac HiChIP). Using 4C-seq to interrogate HIV-1-chromatin interactions, we found that HIV-1 interacted with host chromatin ∼100-300 kb from the integration site. By identifying chromatin regions having both increased transcription factor activity (by ATAC-seq) and HIV-1-chromatin interaction (by 4C-seq), we identified enrichment of ETS, RUNT, and ZNF-family transcription factor binding that may mediate HIV-1-host chromatin interactions. Our study has found that HIV-1 promoter activity increases host chromatin accessibility, and HIV-1 interacted with host chromatin within the existing chromatin boundaries in an integration site-dependent manner.

Single-nucleus multiregion transcriptomic analysis of brain vasculature in Alzheimer's disease

Cerebrovascular dysregulation is a hallmark of Alzheimer's disease (AD), but the changes that occur in specific cell types have not been fully characterized. Here, we profile single-nucleus transcriptomes in the human cerebrovasculature in six brain regions from 220 individuals with AD and 208 age-matched controls. We annotate 22,514 cerebrovascular cells, including 11 subtypes of endothelial, pericyte, smooth muscle, perivascular fibroblast and ependymal cells. We identify 2,676 differentially expressed genes in AD, including downregulation of PDGFRB in pericytes, and of ABCB1 and ATP10A in endothelial cells, and validate the downregulation of SLC6A1 and upregulation of APOD, INSR and COL4A1 in postmortem AD brain tissues. We detect vasculature, glial and neuronal coexpressed gene modules, suggesting coordinated neurovascular unit dysregulation in AD. Integration with AD genetics reveals 125 AD differentially expressed genes directly linked to AD-associated genetic variants. Lastly, we show that APOE4 genotype-associated differences are significantly enriched among AD-associated genes in capillary and venule endothelial cells, as well as subsets of pericytes and fibroblasts.

Decoding enhancer complexity with machine learning and high-throughput discovery

Enhancers are genomic DNA elements controlling spatiotemporal gene expression. Their flexible organization and functional redundancies make deciphering their sequence-function relationships challenging. This article provides an overview of the current understanding of enhancer organization and evolution, with an emphasis on factors that influence these relationships. Technological advancements, particularly in machine learning and synthetic biology, are discussed in light of how they provide new ways to understand this complexity. Exciting opportunities lie ahead as we continue to unravel the intricacies of enhancer function.

StocSum: stochastic summary statistics for whole genome sequencing studies

Genomic summary statistics, usually defined as single-variant test results from genome-wide association studies, have been widely used to advance the genetics field in a wide range of applications. Applications that involve multiple genetic variants also require their correlations or linkage disequilibrium (LD) information, often obtained from an external reference panel. In practice, it is usually difficult to find suitable external reference panels that represent the LD structure for underrepresented and admixed populations, or rare genetic variants from whole genome sequencing (WGS) studies, limiting the scope of applications for genomic summary statistics. Here we introduce StocSum, a novel reference-panel-free statistical framework for generating, managing, and analyzing stochastic summary statistics using random vectors. We develop various downstream applications using StocSum including single-variant tests, conditional association tests, gene-environment interaction tests, variant set tests, as well as meta-analysis and LD score regression tools. We demonstrate the accuracy and computational efficiency of StocSum using two cohorts from the Trans-Omics for Precision Medicine Program. StocSum will facilitate sharing and utilization of genomic summary statistics from WGS studies, especially for underrepresented and admixed populations.

Extrachromosomal DNA Amplification Contributes to Small Cell Lung Cancer Heterogeneity and Is Associated with Worse Outcomes

Small-cell lung cancer (SCLC) is an aggressive neuroendocrine lung cancer. Oncogenic MYC amplifications drive SCLC heterogeneity, but the genetic mechanisms of MYC amplification and phenotypic plasticity, characterized by neuroendocrine and nonneuroendocrine cell states, are not known. Here, we integrate whole-genome sequencing, long-range optical mapping, single-cell DNA sequencing, and fluorescence in situ hybridization to find extrachromosomal DNA (ecDNA) as a primary source of SCLC oncogene amplifications and driver fusions. ecDNAs bring to proximity enhancer elements and oncogenes, creating SCLC transcription-amplifying units, driving exceptionally high MYC gene dosage. We demonstrate that cell-free nucleosome profiling can noninvasively detect ecDNA amplifications in plasma, facilitating its genome-wide interrogation in SCLC and other cancers. Altogether, our work provides the first comprehensive map of SCLC ecDNA and describes a new mechanism that governs MYC-driven SCLC heterogeneity. ecDNA-enabled transcriptional flexibility may explain the significantly worse survival outcomes of SCLC harboring complex ecDNA amplifications.

SIGNIFICANCE

MYC drives SCLC progression, but the genetic basis of MYC-driven SCLC evolution is unknown. Using SCLC as a paradigm, we report how ecDNA amplifications function as MYC-amplifying units, fostering tumor plasticity and a high degree of tumor heterogeneity. This article is highlighted in the In This Issue feature, p. 799.

Genomic Analysis of Foxp3 Function in Regulatory T Cells

Regulatory T (Treg) cells are critical for tolerance to self-antigens and for preventing autoimmunity. Foxp3 has been identified as a Treg cell lineage-defining transcription factor controlling Treg cell differentiation and function. In this article, we review the current mechanistic and systemic understanding of Foxp3 function enabled by experimental and computational advances in high-throughput genomics.

CRISPR screens identify gene targets at breast cancer risk loci

Background

Genome-wide association studies (GWAS) have identified > 200 loci associated with breast cancer risk. The majority of candidate causal variants are in non-coding regions and likely modulate cancer risk by regulating gene expression. However, pinpointing the exact target of the association, and identifying the phenotype it mediates, is a major challenge in the interpretation and translation of GWAS.

Results

Here, we show that pooled CRISPR screens are highly effective at identifying GWAS target genes and defining the cancer phenotypes they mediate. Following CRISPR mediated gene activation or suppression, we measure proliferation in 2D, 3D, and in immune-deficient mice, as well as the effect on DNA repair. We perform 60 CRISPR screens and identify 20 genes predicted with high confidence to be GWAS targets that promote cancer by driving proliferation or modulating the DNA damage response in breast cells. We validate the regulation of a subset of these genes by breast cancer risk variants.

Conclusions

We demonstrate that phenotypic CRISPR screens can accurately pinpoint the gene target of a risk locus. In addition to defining gene targets of risk loci associated with increased breast cancer risk, we provide a platform for identifying gene targets and phenotypes mediated by risk variants.

BRD9-SMAD2/3 orchestrates stemness and tumorigenesis in pancreatic ductal adenocarcinoma

The dismal prognosis of pancreatic ductal adenocarcinoma (PDAC) is linked to the presence of pancreatic cancer stem-like cells (CSCs) that respond poorly to current chemotherapy regimens. By small molecule compound screening targeting 142 epigenetic enzymes, we identified that bromodomain-containing protein BRD9, a component of the BAF histone remodelling complex, is a key chromatin regulator to orchestrate the stemness of pancreatic CSCs via cooperating with the TGFβ/Activin-SMAD2/3 signalling pathway. Inhibition and genetic ablation of BDR9 block the self-renewal, cell cycle entry into G0 phase and invasiveness of CSCs, and improve the sensitivity of CSCs to gemcitabine treatment. In addition, pharmacological inhibition of BRD9 significantly reduced the tumorigenesis in patient-derived xenografts mouse models and eliminated CSCs in tumours from pancreatic cancer patients. Mechanistically, inhibition of BRD9 disrupts enhancer-promoter looping and transcription of stemness genes in CSCs. Collectively, the data suggest BRD9 as a novel therapeutic target for PDAC treatment via modulation of CSC stemness.

Translating non-coding genetic associations into a better understanding of immune-mediated disease

Genome-wide association studies have identified hundreds of genetic loci that are associated with immune-mediated diseases. Most disease-associated variants are non-coding, and a large proportion of these variants lie within enhancers. As a result, there is a pressing need to understand how common genetic variation might affect enhancer function and thereby contribute to immune-mediated (and other) diseases. In this Review, we first describe statistical and experimental methods to identify causal genetic variants that modulate gene expression, including statistical fine-mapping and massively parallel reporter assays. We then discuss approaches to characterise the mechanisms by which these variants modulate immune function, such as clustered regularly interspaced short palindromic repeats (CRISPR)-based screens. We highlight examples of studies that, by elucidating the effects of disease variants within enhancers, have provided important insights into immune function and uncovered key pathways of disease.

Analysis of Chromatin Interaction and Accessibility by Trac-Looping

Spatial organization of the genome modulates pivotal biological processes. The emerging new technologies have provided novel insights into genome structure and its role in regulating cell activities. To examine the genome-wide chromatin interactions at accessible chromatin regions, we developed a DNA transposase-mediated analysis of chromatin looping (Trac-looping) method for simultaneously detecting chromatin interactions and chromatin accessibility. Here, we describe a detailed protocol of generating Trac-looping libraries.

Widespread contribution of transposable elements to the rewiring of mammalian 3D genomes

Transposable elements (TEs) are major contributors of genetic material in mammalian genomes. These often include binding sites for architectural proteins, including the multifarious master protein, CTCF, which shapes the 3D genome by creating loops, domains, compartment borders, and RNA-DNA interactions. These play a role in the compact packaging of DNA and have the potential to facilitate regulatory function. In this study, we explore the widespread contribution of TEs to mammalian 3D genomes by quantifying the extent to which they give rise to loops and domain border differences across various cell types and species using several 3D genome mapping technologies. We show that specific families and subfamilies of TEs have contributed to lineage-specific 3D chromatin structures across mammalian species. In many cases, these loops may facilitate sustained interaction between distant cis-regulatory elements and target genes, and domains may segregate chromatin state to impact gene expression in a lineage-specific manner. An experimental validation of our analytical findings using CRISPR-Cas9 to delete a candidate TE resulted in disruption of species-specific 3D chromatin structure. Taken together, we comprehensively quantify and selectively validate our finding that TEs contribute to shaping 3D genome organization and may, in some cases, impact gene regulation during the course of mammalian evolution.

Complexity of enhancer networks predicts cell identity and disease genes revealed by single-cell multi-omics analysis

Many enhancers exist as clusters in the genome and control cell identity and disease genes; however, the underlying mechanism remains largely unknown. Here, we introduce an algorithm, eNet, to build enhancer networks by integrating single-cell chromatin accessibility and gene expression profiles. The complexity of enhancer networks is assessed by two metrics: the number of enhancers and the frequency of predicted enhancer interactions (PEIs) based on chromatin co-accessibility. We apply eNet algorithm to a human blood dataset and find cell identity and disease genes tend to be regulated by complex enhancer networks. The network hub enhancers (enhancers with frequent PEIs) are the most functionally important. Compared with super-enhancers, enhancer networks show better performance in predicting cell identity and disease genes. eNet is robust and widely applicable in various human or mouse tissues datasets. Thus, we propose a model of enhancer networks containing three modes: Simple, Multiple and Complex, which are distinguished by their complexity in regulating gene expression. Taken together, our work provides an unsupervised approach to simultaneously identify key cell identity and disease genes and explore the underlying regulatory relationships among enhancers in single cells.