Unbiased analysis of potential targets of breast cancer susceptibility loci by Capture Hi-C

Mechanistic analysis of enhancer sequences in the estrogen receptor transcriptional program

Estrogen Receptor α (ERα) is a major lineage determining transcription factor (TF) in mammary gland development. Dysregulation of ERα-mediated transcriptional program results in cancer. Transcriptomic and epigenomic profiling of breast cancer cell lines has revealed large numbers of enhancers involved in this regulatory program, but how these enhancers encode function in their sequence remains poorly understood. A subset of ERα-bound enhancers are transcribed into short bidirectional RNA (enhancer RNA or eRNA), and this property is believed to be a reliable marker of active enhancers. We therefore analyze thousands of ERα-bound enhancers and build quantitative, mechanism-aware models to discriminate eRNAs from non-transcribing enhancers based on their sequence. Our thermodynamics-based models provide insights into the roles of specific TFs in ERα-mediated transcriptional program, many of which are supported by the literature. We use in silico perturbations to predict TF-enhancer regulatory relationships and integrate these findings with experimentally determined enhancer-promoter interactions to construct a gene regulatory network. We also demonstrate that the model can prioritize breast cancer-related sequence variants while providing mechanistic explanations for their function. Finally, we experimentally validate the model-proposed mechanisms underlying three such variants.

Osteoarthritis as an Enhanceropathy: Gene Regulation in Complex Musculoskeletal Disease

PURPOSE OF REVIEW

Osteoarthritis is a complex and highly polygenic disease. Over 100 reported osteoarthritis risk variants fall in non-coding regions of the genome, ostensibly conferring functional effects through the disruption of regulatory elements impacting target gene expression. In this review, we summarise the progress that has advanced our knowledge of gene enhancers both within the field of osteoarthritis and more broadly in complex diseases.

RECENT FINDINGS

Advances in technologies such as ATAC-seq have facilitated our understanding of chromatin states in specific cell types, bolstering the interpretation of GWAS and the identification of effector genes. Their application to osteoarthritis research has revealed enhancers as the principal regulatory element driving disease-associated changes in gene expression. However, tissue-specific effects in gene regulatory mechanisms can contribute added complexity to biological interpretation. Understanding gene enhancers and their altered activity in specific cell and tissue types is the key to unlocking the genetic complexity of osteoarthritis. The use of single-cell technologies in osteoarthritis research is still in its infancy. However, such tools offer great promise in improving our functional interpretation of osteoarthritis GWAS and the identification of druggable targets. Large-scale collaborative efforts will be imperative to understand tissue and cell-type specific molecular mechanisms underlying enhancer function in disease.

A chromosome-level haplotype-resolved genome assembly of oriental tobacco budworm (Helicoverpa assulta)

Oriental tobacco budworm (Helicoverpa assulta) and cotton bollworm (Helicoverpa armigera) are two closely related species within the genus Helicoverpa. They have similar appearances and consistent damage patterns, often leading to confusion. However, the cotton bollworm is a typical polyphagous insect, while the oriental tobacco budworm belongs to the oligophagous insects. In this study, we used Nanopore, PacBio, and Illumina platforms to sequence the genome of H. assulta and used Hifiasm to create a haplotype-resolved draft genome. The Hi-C technique helped anchor 33 primary contigs to 32 chromosomes, including two sex chromosomes, Z and W. The final primary haploid genome assembly was approximately 415.19 Mb in length. BUSCO analysis revealed a high degree of completeness, with 99.0% gene coverage in this genome assembly. The repeat sequences constituted 38.39% of the genome assembly, and we annotated 17093 protein-coding genes. The high-quality genome assembly of the oriental tobacco budworm serves as a valuable genetic resource that enhances our comprehension of how they select hosts in a complex odour environment. It will also aid in developing an effective control policy.

Functional Genomics and Insights into the Pathogenesis and Treatment of Psoriasis

Psoriasis is a lifelong, systemic, immune mediated inflammatory skin condition, affecting 1-3% of the world's population, with an impact on quality of life similar to diseases like cancer or diabetes. Genetics are the single largest risk factor in psoriasis, with Genome-Wide Association (GWAS) studies showing that many psoriasis risk genes lie along the IL-23/Th17 axis. Potential psoriasis risk genes determined through GWAS can be annotated and characterised using functional genomics, allowing the identification of novel drug targets and the repurposing of existing drugs. This review is focused on the IL-23/Th17 axis, providing an insight into key cell types, cytokines, and intracellular signaling pathways involved. This includes examination of currently available biological treatments, time to relapse post drug withdrawal, and rates of primary/secondary drug failure, showing the need for greater understanding of the underlying genetic mechanisms of psoriasis and how they can impact treatment. This could allow for patient stratification towards the treatment most likely to reduce the burden of disease for the longest period possible.

3D epigenomics and 3D epigenopathies

Mammalian genomes are intricately compacted to form sophisticated 3-dimensional structures within the tiny nucleus, so called 3D genome folding. Despite their shapes reminiscent of an entangled yarn, the rapid development of molecular and next-generation sequencing technologies (NGS) has revealed that mammalian genomes are highly organized in a hierarchical order that delicately affects transcription activities. An increasing amount of evidence suggests that 3D genome folding is implicated in diseases, giving us a clue on how to identify novel therapeutic approaches. In this review, we will study what 3D genome folding means in epigenetics, what types of 3D genome structures there are, how they are formed, and how the technologies have developed to explore them. We will also discuss the pathological implications of 3D genome folding. Finally, we will discuss how to leverage 3D genome folding and engineering for future studies. [BMB Reports 2024; 57(5): 216-231].

Investigation of inherited noncoding genetic variation impacting the pharmacogenomics of childhood acute lymphoblastic leukemia treatment

Defining genetic factors impacting chemotherapy failure can help to better predict response and identify drug resistance mechanisms. However, there is limited understanding of the contribution of inherited noncoding genetic variation on inter-individual differences in chemotherapy response in childhood acute lymphoblastic leukemia (ALL). Here we map inherited noncoding variants associated with treatment outcome and/or chemotherapeutic drug resistance to ALL cis-regulatory elements and investigate their gene regulatory potential and target gene connectivity using massively parallel reporter assays and three-dimensional chromatin looping assays, respectively. We identify 54 variants with transcriptional effects and high-confidence gene connectivity. Additionally, functional interrogation of the top variant, rs1247117, reveals changes in chromatin accessibility, PU.1 binding affinity and gene expression, and deletion of the genomic interval containing rs1247117 sensitizes cells to vincristine. Together, these data demonstrate that noncoding regulatory variants associated with diverse pharmacological traits harbor significant effects on allele-specific transcriptional activity and impact sensitivity to antileukemic agents.

Increased enhancer-promoter interactions during developmental enhancer activation in mammals

Remote enhancers are thought to interact with their target promoters via physical proximity, yet the importance of this proximity for enhancer function remains unclear. Here we investigate the three-dimensional (3D) conformation of enhancers during mammalian development by generating high-resolution tissue-resolved contact maps for nearly a thousand enhancers with characterized in vivo activities in ten murine embryonic tissues. Sixty-one percent of developmental enhancers bypass their neighboring genes, which are often marked by promoter CpG methylation. The majority of enhancers display tissue-specific 3D conformations, and both enhancer-promoter and enhancer-enhancer interactions are moderately but consistently increased upon enhancer activation in vivo. Less than 14% of enhancer-promoter interactions form stably across tissues; however, these invariant interactions form in the absence of the enhancer and are likely mediated by adjacent CTCF binding. Our results highlight the general importance of enhancer-promoter physical proximity for developmental gene activation in mammals.

Transcription induces context-dependent remodeling of chromatin architecture during differentiation

Metazoan chromosomes are organized into discrete spatial domains (TADs), believed to contribute to the regulation of transcriptional programs. Despite extensive correlation between domain organization and gene activity, a direct mechanistic link is unclear, with perturbation studies often showing little effect. To follow chromatin architecture changes during development, we used Capture Hi-C to interrogate the domains around key differentially expressed genes during mouse thymocyte maturation, uncovering specific remodeling events. Notably, one TAD boundary was broadened to accommodate RNA polymerase elongation past the border, and subdomains were formed around some activated genes without changes in CTCF binding. The ectopic induction of some genes was sufficient to recapitulate domain formation in embryonic stem cells, providing strong evidence that transcription can directly remodel chromatin structure. These results suggest that transcriptional processes drive complex chromosome folding patterns that can be important in certain genomic contexts.

Through the lens of phase separation: intrinsically unstructured protein and chromatin looping

The establishment, maintenance and dynamic regulation of three-dimensional (3D) chromatin structures provide an important means for partitioning of genome into functionally distinctive domains, which helps to define specialized gene expression programs associated with developmental stages and cell types. Increasing evidence supports critical roles for intrinsically disordered regions (IDRs) harbored within transcription factors (TFs) and chromatin-modulatory proteins in inducing phase separation, a phenomenon of forming membrane-less condensates through partitioning of biomolecules. Such a process is also critically involved in the establishment of high-order chromatin structures and looping. IDR- and phase separation-driven 3D genome (re)organization often goes wrong in disease such as cancer. This review discusses about recent advances in understanding how phase separation of intrinsically disordered proteins (IDPs) modulates chromatin looping and gene expression.

HiCAR: Analysis of Open Chromatin Associated Long-range Chromatin Interaction Using Low-Input Materials

Cis-regulatory elements (cREs) and their long-range interactions are crucial for spatial-temporal gene regulation. While cREs can be characterized as accessible chromatin sequences, comprehensively identifying their spatial interactions remains a challenge. We recently developed a method, HiCAR (Hi-C on Accessible Regulatory DNA), which combines Tn5 transposase and chromatin proximity ligation to analyze open chromatin-anchored interactions in low-input cells. Application of HiCAR in human embryonic stem cells and lymphoblastoid cells reveals high-resolution chromatin contacts with efficiency comparable to in situ Hi-C across various distance ranges. Moreover, HiCAR was successfully applied to 30,000 primary human muscle stem cells, showcasing its potential for analyzing chromatin accessibility and looping in low-input primary cells and clinical samples. Here, we provide a detailed step-by-step protocol to perform the updated HiCAR experiments. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Tn5 Transposase Assembly Basic Protocol 2: HiCAR Library Preparation.

Intra- and interchromosomal contact mapping reveals the Igh locus has extensive conformational heterogeneity and interacts with B-lineage genes

To produce a diverse antibody repertoire, immunoglobulin heavy-chain (Igh) loci undergo large-scale alterations in structure to facilitate juxtaposition and recombination of spatially separated variable (VH), diversity (DH), and joining (JH) genes. These chromosomal alterations are poorly understood. Uncovering their patterns shows how chromosome dynamics underpins antibody diversity. Using tiled Capture Hi-C, we produce a comprehensive map of chromatin interactions throughout the 2.8-Mb Igh locus in progenitor B cells. We find that the Igh locus folds into semi-rigid subdomains and undergoes flexible looping of the VH genes to its 3' end, reconciling two views of locus organization. Deconvolution of single Igh locus conformations using polymer simulations identifies thousands of different structures. This heterogeneity may underpin the diversity of V(D)J recombination events. All three immunoglobulin loci also participate in a highly specific, developmentally regulated network of interchromosomal interactions with genes encoding B cell-lineage factors. This suggests a model of interchromosomal coordination of B cell development.

A multiple super-enhancer region establishes inter-TAD interactions and controls Hoxa function in cranial neural crest

Enhancer-promoter interactions preferentially occur within boundary-insulated topologically associating domains (TADs), limiting inter-TAD interactions. Enhancer clusters in linear proximity, termed super-enhancers (SEs), ensure high target gene expression levels. Little is known about SE topological regulatory impact during craniofacial development. Here, we identify 2232 genome-wide putative SEs in mouse cranial neural crest cells (CNCCs), 147 of which target genes establishing CNCC positional identity during face formation. In second pharyngeal arch (PA2) CNCCs, a multiple SE-containing region, partitioned into Hoxa Inter-TAD Regulatory Element 1 and 2 (HIRE1 and HIRE2), establishes long-range inter-TAD interactions selectively with Hoxa2, that is required for external and middle ear structures. HIRE2 deletion in a Hoxa2 haploinsufficient background results in microtia. HIRE1 deletion phenocopies the full homeotic Hoxa2 knockout phenotype and induces PA3 and PA4 CNCC abnormalities correlating with Hoxa2 and Hoxa3 transcriptional downregulation. Thus, SEs can overcome TAD insulation and regulate anterior Hoxa gene collinear expression in a CNCC subpopulation-specific manner during craniofacial development.

Low input capture Hi-C (liCHi-C) identifies promoter-enhancer interactions at high-resolution

Long-range interactions between regulatory elements and promoters are key in gene transcriptional control; however, their study requires large amounts of starting material, which is not compatible with clinical scenarios nor the study of rare cell populations. Here we introduce low input capture Hi-C (liCHi-C) as a cost-effective, flexible method to map and robustly compare promoter interactomes at high resolution. As proof of its broad applicability, we implement liCHi-C to study normal and malignant human hematopoietic hierarchy in clinical samples. We demonstrate that the dynamic promoter architecture identifies developmental trajectories and orchestrates transcriptional transitions during cell-state commitment. Moreover, liCHi-C enables the identification of disease-relevant cell types, genes and pathways potentially deregulated by non-coding alterations at distal regulatory elements. Finally, we show that liCHi-C can be harnessed to uncover genome-wide structural variants, resolve their breakpoints and infer their pathogenic effects. Collectively, our optimized liCHi-C method expands the study of 3D chromatin organization to unique, low-abundance cell populations, and offers an opportunity to uncover factors and regulatory networks involved in disease pathogenesis.

A global high-density chromatin interaction network reveals functional long-range and trans-chromosomal relationships

BACKGROUND

Chromatin contacts are essential for gene-expression regulation; however, obtaining a high-resolution genome-wide chromatin contact map is still prohibitively expensive owing to large genome sizes and the quadratic scale of pairwise data. Chromosome conformation capture (3C)-based methods such as Hi-C have been extensively used to obtain chromatin contacts. However, since the sparsity of these maps increases with an increase in genomic distance between contacts, long-range or trans-chromatin contacts are especially challenging to sample.

RESULTS

Here, we create a high-density reference genome-wide chromatin contact map using a meta-analytic approach. We integrate 3600 human, 6700 mouse, and 500 fly Hi-C experiments to create species-specific meta-Hi-C chromatin contact maps with 304 billion, 193 billion, and 19 billion contacts in respective species. We validate that meta-Hi-C contact maps are uniquely powered to capture functional chromatin contacts in both cis and trans. We find that while individual dataset Hi-C networks are largely unable to predict any long-range coexpression (median 0.54 AUC), meta-Hi-C networks perform comparably in both cis and trans (0.65 AUC vs 0.64 AUC). Similarly, for long-range expression quantitative trait loci (eQTL), meta-Hi-C contacts outperform all individual Hi-C experiments, providing an improvement over the conventionally used linear genomic distance-based association. Assessing between species, we find patterns of chromatin contact conservation in both cis and trans and strong associations with coexpression even in species for which Hi-C data is lacking.

CONCLUSIONS

We have generated an integrated chromatin interaction network which complements a large number of methodological and analytic approaches focused on improved specificity or interpretation. This high-depth "super-experiment" is surprisingly powerful in capturing long-range functional relationships of chromatin interactions, which are now able to predict coexpression, eQTLs, and cross-species relationships. The meta-Hi-C networks are available at https://labshare.cshl.edu/shares/gillislab/resource/HiC/ .

Building integrative functional maps of gene regulation

Every cell in the human body inherits a copy of the same genetic information. The three billion base pairs of DNA in the human genome, and the roughly 50 000 coding and non-coding genes they contain, must thus encode all the complexity of human development and cell and tissue type diversity. Differences in gene regulation, or the modulation of gene expression, enable individual cells to interpret the genome differently to carry out their specific functions. Here we discuss recent and ongoing efforts to build gene regulatory maps, which aim to characterize the regulatory roles of all sequences in a genome. Many researchers and consortia have identified such regulatory elements using functional assays and evolutionary analyses; we discuss the results, strengths and shortcomings of their approaches. We also discuss new techniques the field can leverage and emerging challenges it will face while striving to build gene regulatory maps of ever-increasing resolution and comprehensiveness.

Transcriptional enhancers at 40: evolution of a viral DNA element to nuclear architectural structures

Gene regulation by transcriptional enhancers is the dominant mechanism driving cell type- and signal-specific transcriptional diversity in metazoans. However, over four decades since the original discovery, how enhancers operate in the nuclear space remains largely enigmatic. Recent multidisciplinary efforts combining real-time imaging, genome sequencing, and biophysical strategies provide insightful but conflicting models of enhancer-mediated gene control. Here, we review the discovery and progress in enhancer biology, emphasizing the recent findings that acutely activated enhancers assemble regulatory machinery as mesoscale architectural structures with distinct physical properties. These findings help formulate novel models that explain several mysterious features of the assembly of transcriptional enhancers and the mechanisms of spatial control of gene expression.

Interpretation of the role of germline and somatic non-coding mutations in cancer: expression and chromatin conformation informed analysis

BACKGROUND

There has been extensive scrutiny of cancer driving mutations within the exome (especially amino acid altering mutations) as these are more likely to have a clear impact on protein functions, and thus on cell biology. However, this has come at the neglect of systematic identification of regulatory (non-coding) variants, which have recently been identified as putative somatic drivers and key germline risk factors for cancer development. Comprehensive understanding of non-coding mutations requires understanding their role in the disruption of regulatory elements, which then disrupt key biological functions such as gene expression.

MAIN BODY

We describe how advancements in sequencing technologies have led to the identification of a large number of non-coding mutations with uncharacterized biological significance. We summarize the strategies that have been developed to interpret and prioritize the biological mechanisms impacted by non-coding mutations, focusing on recent annotation of cancer non-coding variants utilizing chromatin states, eQTLs, and chromatin conformation data.

CONCLUSION

We believe that a better understanding of how to apply different regulatory data types into the study of non-coding mutations will enhance the discovery of novel mechanisms driving cancer.

Haplotype-specific chromatin looping reveals genetic interactions of regulatory regions modulating gene expression in 8p23.1

A major goal of genetics research is to elucidate mechanisms explaining how genetic variation contributes to phenotypic variation. The genetic variants identified in genome-wide association studies (GWASs) generally explain only a small proportion of heritability of phenotypic traits, the so-called missing heritability problem. Recent evidence suggests that additional common variants beyond lead GWAS variants contribute to phenotypic variation; however, their mechanistic underpinnings generally remain unexplored. Herein, we undertake a study of haplotype-specific mechanisms of gene regulation at 8p23.1 in the human genome, a region associated with a number of complex diseases. The FAM167A-BLK locus in this region has been consistently found in the genome-wide association studies (GWASs) of systemic lupus erythematosus (SLE) in all major ancestries. Our haplotype-specific chromatin interaction (Hi-C) experiments, allele-specific enhancer activity measurements, genetic analyses, and epigenome editing experiments revealed that: 1) haplotype-specific long-range chromatin interactions are prevalent in 8p23.1; 2) BLK promoter and cis-regulatory elements cooperatively interact with haplotype-specificity; 3) genetic variants at distal regulatory elements are allele-specific modifiers of the promoter variants at FAM167A-BLK; 4) the BLK promoter interacts with and, as an enhancer-like promoter, regulates FAM167A expression and 5) local allele-specific enhancer activities are influenced by global haplotype structure due to chromatin looping. Although systemic lupus erythematosus causal variants at the FAM167A-BLK locus are thought to reside in the BLK promoter region, our results reveal that genetic variants at distal regulatory elements modulate promoter activity, changing BLK and FAM167A gene expression and disease risk. Our results suggest that global haplotype-specific 3-dimensional chromatin looping architecture has a strong influence on local allelic BLK and FAM167A gene expression, providing mechanistic details for how regional variants controlling the BLK promoter may influence disease risk.

scHiCEmbed: Bin-Specific Embeddings of Single-Cell Hi-C Data Using Graph Auto-Encoders

Most publicly accessible single-cell Hi-C data are sparse and cannot reach a higher resolution. Therefore, learning latent representations (bin-specific embeddings) of sparse single-cell Hi-C matrices would provide us with a novel way of mining valuable information hidden in the limited number of single-cell Hi-C contacts. We present scHiCEmbed, an unsupervised computational method for learning bin-specific embeddings of single-cell Hi-C data, and the computational system is applied to the tasks of 3D structure reconstruction of whole genomes and detection of topologically associating domains (TAD). The only input of scHiCEmbed is a raw or scHiCluster-imputed single-cell Hi-C matrix. The main process of scHiCEmbed is to embed each node/bin in a higher dimensional space using graph auto-encoders. The learned n-by-3 bin-specific embedding/latent matrix is considered the final reconstructed 3D genome structure. For TAD detection, we use constrained hierarchical clustering on the latent matrix to classify bins: S_Dbw is used to determine the optimal number of clusters, and each cluster is considered as one potential TAD. Our reconstructed 3D structures for individual chromatins at different cell stages reveal the expanding process of chromatins during the cell cycle. We observe that the TADs called from single-cell Hi-C data are not shared across individual cells and that the TAD boundaries called from raw or imputed single-cell Hi-C are significantly different from those called from bulk Hi-C, confirming the cell-to-cell variability in terms of TAD definitions. The source code for scHiCEmbed is publicly available, and the URL can be found in the conclusion section.

Hodge theory-based biomolecular data analysis

Hodge theory reveals the deep intrinsic relations of differential forms and provides a bridge between differential geometry, algebraic topology, and functional analysis. Here we use Hodge Laplacian and Hodge decomposition models to analyze biomolecular structures. Different from traditional graph-based methods, biomolecular structures are represented as simplicial complexes, which can be viewed as a generalization of graph models to their higher-dimensional counterparts. Hodge Laplacian matrices at different dimensions can be generated from the simplicial complex. The spectral information of these matrices can be used to study intrinsic topological information of biomolecular structures. Essentially, the number (or multiplicity) of k-th dimensional zero eigenvalues is equivalent to the k-th Betti number, i.e., the number of k-th dimensional homology groups. The associated eigenvectors indicate the homological generators, i.e., circles or holes within the molecular-based simplicial complex. Furthermore, Hodge decomposition-based HodgeRank model is used to characterize the folding or compactness of the molecular structures, in particular, the topological associated domain (TAD) in high-throughput chromosome conformation capture (Hi-C) data. Mathematically, molecular structures are represented in simplicial complexes with certain edge flows. The HodgeRank-based average/total inconsistency (AI/TI) is used for the quantitative measurements of the folding or compactness of TADs. This is the first quantitative measurement for TAD regions, as far as we know.

Somatic point mutations are enriched in non-coding RNAs with possible regulatory function in breast cancer

Non-coding RNAs (ncRNAs) form a large portion of the mammalian genome. However, their biological functions are poorly characterized in cancers. In this study, using a newly developed tool, SomaGene, we analyze de novo somatic point mutations from the International Cancer Genome Consortium (ICGC) whole-genome sequencing data of 1,855 breast cancer samples. We identify 1030 candidates of ncRNAs that are significantly and explicitly mutated in breast cancer samples. By integrating data from the ENCODE regulatory features and FANTOM5 expression atlas, we show that the candidate ncRNAs significantly enrich active chromatin histone marks (1.9 times), CTCF binding sites (2.45 times), DNase accessibility (1.76 times), HMM predicted enhancers (2.26 times) and eQTL polymorphisms (1.77 times). Importantly, we show that the 1030 ncRNAs contain a much higher level (3.64 times) of breast cancer-associated genome-wide association (GWAS) single nucleotide polymorphisms (SNPs) than genome-wide expectation. Such enrichment has not been seen with GWAS SNPs from other cancers. Using breast cell line related Hi-C data, we then show that 82% of our candidate ncRNAs (1.9 times) significantly interact with the promoter of protein-coding genes, including previously known cancer-associated genes, suggesting the critical role of candidate ncRNA genes in the activation of essential regulators of development and differentiation in breast cancer. We provide an extensive web-based resource (https://www.ihealthe.unsw.edu.au/research) to communicate our results with the research community. Our list of breast cancer-specific ncRNA genes has the potential to provide a better understanding of the underlying genetic causes of breast cancer. Lastly, the tool developed in this study can be used to analyze somatic mutations in all cancers.

The SomaGene tool is developed to identify non-coding RNAs (ncRNAs) mutated in breast cancer but can be used for other cancers. Candidate ncRNAs are shown to be enriched for regulatory features and to contain specific trait loci polymorphisms.

LncRNA PVT1 contributes to invasion and doxorubicin resistance of bladder cancer cells through promoting MDM2 expression and AURKB-mediated p53 ubiquitination

In most bladder cancer (BC) patients, cancer cells will eventually develop chemical resistance causing increased mortality. This study aimed to explore the mechanism of lncRNA plasmacytoma variant translocation 1 (PVT1) in regulating doxorubicin (ADM) resistance of BC cells. We observed that PVT1 expression was upregulated in ADM-resistant BC cells compared with ADM-sensitive BC cells. Downregulation of PVT1 suppressed ADM-resistant BC cell proliferation and invasion, promoted apoptosis, and increased sensitivity to ADM, while PVT1 overexpression promoted ADM-sensitive BC cell growth and their resistance to ADM. Further study uncovered that PVT1 could interact with and promote mouse double minute 2 (MDM2) expression, and upregulated MDM2-mediated Aurora kinase B (AURKB). Furthermore, Nutlin-3, an inhibitor of MDM2, could counteract the promotive effects of PVT1 overexpression on ADM resistance of ADM-sensitive BC cell, the expression of multidrug-resistance-related proteins, and the inhibition of p53-mediated tumor suppressor genes. And, overexpression of MDM2 or AURKB reversed the promotive effects of PVT1 silence on the ADM sensitivity of ADM-resistant BC cell, and the inhibitory effect on expression multidrug resistance proteins. Mechanically, AURKB increased MDM2-mediated p53 ubiquitination. Taken together, PVT1 promoted BC cell proliferation and drug resistance via elevating MDM2 expression and AURKB-mediated p53 ubiquitination.

Analysis of sub-kilobase chromatin topology reveals nano-scale regulatory interactions with variable dependence on cohesin and CTCF

Enhancers and promoters predominantly interact within large-scale topologically associating domains (TADs), which are formed by loop extrusion mediated by cohesin and CTCF. However, it is unclear whether complex chromatin structures exist at sub-kilobase-scale and to what extent fine-scale regulatory interactions depend on loop extrusion. To address these questions, we present an MNase-based chromosome conformation capture (3C) approach, which has enabled us to generate the most detailed local interaction data to date (20 bp resolution) and precisely investigate the effects of cohesin and CTCF depletion on chromatin architecture. Our data reveal that cis-regulatory elements have distinct internal nano-scale structures, within which local insulation is dependent on CTCF, but which are independent of cohesin. In contrast, we find that depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Together, our data show that loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression.

The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases

Background

The remarkable growth of genome-wide association studies (GWAS) has created a critical need to experimentally validate the disease-associated variants, 90% of which involve non-coding variants.

Methods

To determine how the field is addressing this urgent need, we performed a comprehensive literature review identifying 36,676 articles. These were reduced to 1454 articles through a set of filters using natural language processing and ontology-based text-mining. This was followed by manual curation and cross-referencing against the GWAS catalog, yielding a final set of 286 articles.

Results

We identified 309 experimentally validated non-coding GWAS variants, regulating 252 genes across 130 human disease traits. These variants covered a variety of regulatory mechanisms. Interestingly, 70% (215/309) acted through cis-regulatory elements, with the remaining through promoters (22%, 70/309) or non-coding RNAs (8%, 24/309). Several validation approaches were utilized in these studies, including gene expression (n = 272), transcription factor binding (n = 175), reporter assays (n = 171), in vivo models (n = 104), genome editing (n = 96) and chromatin interaction (n = 33).

Conclusions

This review of the literature is the first to systematically evaluate the status and the landscape of experimentation being used to validate non-coding GWAS-identified variants. Our results clearly underscore the multifaceted approach needed for experimental validation, have practical implications on variant prioritization and considerations of target gene nomination. While the field has a long way to go to validate the thousands of GWAS associations, we show that progress is being made and provide exemplars of validation studies covering a wide variety of mechanisms, target genes, and disease areas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01216-w.

Functional annotation of breast cancer risk loci: current progress and future directions

Genome-wide association studies coupled with large-scale replication and fine-scale mapping studies have identified more than 150 genomic regions that are associated with breast cancer risk. Here, we review efforts to translate these findings into a greater understanding of disease mechanism. Our review comes in the context of a recently published fine-scale mapping analysis of these regions, which reported 352 independent signals and a total of 13,367 credible causal variants. The vast majority of credible causal variants map to noncoding DNA, implicating regulation of gene expression as the mechanism by which functional variants influence risk. Accordingly, we review methods for defining candidate-regulatory sequences, methods for identifying putative target genes and methods for linking candidate-regulatory sequences to putative target genes. We provide a summary of available data resources and identify gaps in these resources. We conclude that while much work has been done, there is still much to do. There are, however, grounds for optimism; combining statistical data from fine-scale mapping with functional data that are more representative of the normal "at risk" breast, generated using new technologies, should lead to a greater understanding of the mechanisms that influence an individual woman's risk of breast cancer.

Polymer physics reveals a combinatorial code linking 3D chromatin architecture to 1D chromatin states

The mammalian genome has a complex, functional 3D organization. However, it remains largely unknown how DNA contacts are orchestrated by chromatin organizers. Here, we infer from only Hi-C the cell-type-specific arrangement of DNA binding sites sufficient to recapitulate, through polymer physics, contact patterns genome wide. Our model is validated by its predictions in a set of duplications at Sox9 against available independent data. The binding site types fall in classes that well match chromatin states from segmentation studies, yet they have an overlapping, combinatorial organization along chromosomes necessary to accurately explain contact specificity. The chromatin signatures of the binding site types return a code linking chromatin states to 3D architecture. The code is validated by extensive de novo predictions of Hi-C maps in an independent set of chromosomes. Overall, our results shed light on how 3D information is encrypted in 1D chromatin via the specific combinatorial arrangement of binding sites.

HiCAR is a robust and sensitive method to analyze open-chromatin-associated genome organization

The long-range interactions of cis-regulatory elements (cREs) play a central role in gene regulation. cREs can be characterized as accessible chromatin sequences. However, it remains technically challenging to comprehensively identify their spatial interactions. Here, we report a new method HiCAR (Hi-C on accessible regulatory DNA), which utilizes Tn5 transposase and chromatin proximity ligation, for the analysis of open-chromatin-anchored interactions with low-input cells. By applying HiCAR in human embryonic stem cells and lymphoblastoid cells, we demonstrate that HiCAR identifies high-resolution chromatin contacts with an efficiency comparable with that of in situ Hi-C over all distance ranges. Interestingly, we found that the "poised" gene promoters exhibit silencer-like function to repress the expression of distal genes via promoter-promoter interactions. Lastly, we applied HiCAR to 30,000 primary human muscle stem cells and demonstrated that HiCAR is capable of analyzing chromatin accessibility and looping using low-input primary cells and clinical samples.

Whole-genome methods to define DNA and histone accessibility and long-range interactions in chromatin

Defining the genome-wide chromatin landscape has been a goal of experimentalists for decades. Here we review highlights of these efforts, from seminal experiments showing discontinuities in chromatin structure related to gene activation to extensions of these methods elucidating general features of chromatin related to gene states by exploiting deep sequencing methods. We also review chromatin conformational capture methods to identify patterns in long-range interactions between genomic loci.

Capture-C: a modular and flexible approach for high-resolution chromosome conformation capture

Chromosome conformation capture (3C) methods measure the spatial proximity between DNA elements in the cell nucleus. Many methods have been developed to sample 3C material, including the Capture-C family of protocols. Capture-C methods use oligonucleotides to enrich for interactions of interest from sequencing-ready 3C libraries. This approach is modular and has been adapted and optimized to work for sampling of disperse DNA elements (NuTi Capture-C), including from low cell inputs (LI Capture-C), as well as to generate Hi-C like maps for specific regions of interest (Tiled-C) and to interrogate multiway interactions (Tri-C). We present the design, experimental protocol and analysis pipeline for NuTi Capture-C in addition to the variations for generation of LI Capture-C, Tiled-C and Tri-C data. The entire procedure can be performed in 3 weeks and requires standard molecular biology skills and equipment, access to a next-generation sequencing platform, and basic bioinformatic skills. Implemented with other sequencing technologies, these methods can be used to identify regulatory interactions and to compare the structural organization of the genome in different cell types and genetic models.

Fine mapping with epigenetic information and 3D structure

Since 2005, thousands of genome-wide association studies (GWAS) have been published, identifying hundreds of thousands of genetic variants that increase risk of complex traits such as autoimmune diseases. This wealth of data has the potential to improve patient care, through personalized medicine and the identification of novel drug targets. However, the potential of GWAS for clinical translation has not been fully achieved yet, due to the fact that the functional interpretation of risk variants and the identification of causal variants and genes are challenging. The past decade has seen the development of great advances that are facilitating the overcoming of these limitations, by utilizing a plethora of genomics and epigenomics tools to map and characterize regulatory elements and chromatin interactions, which can be used to fine map GWAS loci, and advance our understanding of the biological mechanisms that cause disease.

Contribution of 3D genome topological domains to genetic risk of cancers: a genome-wide computational study

Background

Genome-wide association studies have identified statistical associations between various diseases, including cancers, and a large number of single-nucleotide polymorphisms (SNPs). However, they provide no direct explanation of the mechanisms underlying the association. Based on the recent discovery that changes in three-dimensional genome organization may have functional consequences on gene regulation favoring diseases, we investigated systematically the genome-wide distribution of disease-associated SNPs with respect to a specific feature of 3D genome organization: topologically associating domains (TADs) and their borders.

Results

For each of 449 diseases, we tested whether the associated SNPs are present in TAD borders more often than observed by chance, where chance (i.e., the null model in statistical terms) corresponds to the same number of pointwise loci drawn at random either in the entire genome, or in the entire set of disease-associated SNPs listed in the GWAS catalog. Our analysis shows that a fraction of diseases displays such a preferential localization of their risk loci. Moreover, cancers are relatively more frequent among these diseases, and this predominance is generally enhanced when considering only intergenic SNPs. The structure of SNP-based diseasome networks confirms that localization of risk loci in TAD borders differs between cancers and non-cancer diseases. Furthermore, different TAD border enrichments are observed in embryonic stem cells and differentiated cells, consistent with changes in topological domains along embryogenesis and delineating their contribution to disease risk.

Conclusions

Our results suggest that, for certain diseases, part of the genetic risk lies in a local genetic variation affecting the genome partitioning in topologically insulated domains. Investigating this possible contribution to genetic risk is particularly relevant in cancers. This study thus opens a way of interpreting genome-wide association studies, by distinguishing two types of disease-associated SNPs: one with an effect on an individual gene, the other acting in interplay with 3D genome organization.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-022-00375-2.

3D genome organization links non-coding disease-associated variants to genes

Genome sequencing has revealed over 300 million genetic variations in human populations. Over 90% of variants are single nucleotide polymorphisms (SNPs), the remainder include short deletions or insertions, and small numbers of structural variants. Hundreds of thousands of these variants have been associated with specific phenotypic traits and diseases through genome wide association studies which link significant differences in variant frequencies with specific phenotypes among large groups of individuals. Only 5% of disease-associated SNPs are located in gene coding sequences, with the potential to disrupt gene expression or alter of the function of encoded proteins. The remaining 95% of disease-associated SNPs are located in non-coding DNA sequences which make up 98% of the genome. The role of non-coding, disease-associated SNPs, many of which are located at considerable distances from any gene, was at first a mystery until the discovery that gene promoters regularly interact with distal regulatory elements to control gene expression. Disease-associated SNPs are enriched at the millions of gene regulatory elements that are dispersed throughout the non-coding sequences of the genome, suggesting they function as gene regulation variants. Assigning specific regulatory elements to the genes they control is not straightforward since they can be millions of base pairs apart. In this review we describe how understanding 3D genome organization can identify specific interactions between gene promoters and distal regulatory elements and how 3D genomics can link disease-associated SNPs to their target genes. Understanding which gene or genes contribute to a specific disease is the first step in designing rational therapeutic interventions.

Targeted Chromosome Conformation Capture (HiCap)

Targeted chromosome conformation capture (HiCap) is an experimental method for detecting spatial interactions of genomic features such as promoters and/or enhancers. The protocol first describes the design of sequence capture probes. After that, it provides details on the chromosome conformation capture adapted for next-generation sequencing (Hi-C). Finally, the methodology for coupling Hi-C with sequence capture technology is described.

Biological effects and regulation of IGFBP5 in breast cancer

The insulin-like growth factor receptor (IGF1R) pathway plays an important role in cancer progression. In breast cancer, the IGF1R pathway is linked to estrogen-dependent signaling. Regulation of IGF1R activity is complex and involves the actions of its ligands IGF1 and IGF2 and those of IGF-binding proteins (IGFBPs). Six IGFBPs are known that share the ability to form complexes with the IGFs, by which they control the bioavailability of these ligands. Besides, each of the IGFBPs have specific features. In this review, the focus lies on the biological effects and regulation of IGFBP5 in breast cancer. In breast cancer, estrogen is a critical regulator of IGFBP5 transcription. It exerts its effect through an intergenic enhancer loop that is part of the chromosomal breast cancer susceptibility region 2q35. The biological effects of IGFBP5 depend upon the cellular context. By inhibiting or promoting IGF1R signaling, IGFBP5 can either act as a tumor suppressor or promoter. Additionally, IGFBP5 possesses IGF-independent activities, which contribute to the complexity by which IGFBP5 interferes with cancer cell behavior.

Plant 3D Chromatin Organization: Important Insights from Chromosome Conformation Capture Analyses of the Last 10 Years

Over the past few decades, eukaryotic linear genomes and epigenomes have been widely and extensively studied for understanding gene expression regulation. More recently, the three-dimensional (3D) chromatin organization was found to be important for determining genome functionality, finely tuning physiological processes for appropriate cellular responses. With the development of visualization techniques and chromatin conformation capture (3C)-based techniques, increasing evidence indicates that chromosomal architecture characteristics and chromatin domains with different epigenetic modifications in the nucleus are correlated with transcriptional activities. Subsequent studies have further explored the intricate interplay between 3D genome organization and the function of interacting regions. In this review, we summarize spatial distribution patterns of chromatin, including chromatin positioning, configurations and domains, with a particular focus on the effect of a unique form of interaction between varieties of factors that shape the 3D genome conformation in plants. We further discuss the methods, advantages and limitations of various 3C-based techniques, highlighting the applications of these technologies in plants to identify chromatin domains, and address their dynamic changes and functional implications in evolution, and adaptation to development and changing environmental conditions. Moreover, the future implications and emerging research directions of 3D genome organization are discussed.

Sequential in cis mutagenesis in vivo reveals various functions for CTCF sites at the mouse HoxD cluster

Mammalian Hox gene clusters contain a range of CTCF binding sites. In addition to their importance in organizing a TAD border, which isolates the most posterior genes from the rest of the cluster, the positions and orientations of these sites suggest that CTCF may be instrumental in the selection of various subsets of contiguous genes, which are targets of distinct remote enhancers located in the flanking regulatory landscapes. We examined this possibility by producing an allelic series of cumulative in cis mutations in these sites, up to the abrogation of CTCF binding in the five sites located on one side of the TAD border. In the most impactful alleles, the global chromatin architecture of the locus was modified, yet not drastically, illustrating that CTCF sites located on one side of a strong TAD border are sufficient to organize at least part of this insulation. Spatial colinearity in the expression of these genes along the major body axis was nevertheless maintained, despite abnormal expression boundaries. In contrast, strong effects were scored in the selection of target genes responding to particular enhancers, leading to the misregulation of Hoxd genes in specific structures. Altogether, while most enhancer-promoter interactions can occur in the absence of this series of CTCF sites, the binding of CTCF in the Hox cluster is required to properly transform a rather unprecise process into a highly discriminative mechanism of interactions, which is translated into various patterns of transcription accompanied by the distinctive chromatin topology found at this locus. Our allelic series also allowed us to reveal the distinct functional contributions for CTCF sites within this Hox cluster, some acting as insulator elements, others being necessary to anchor or stabilize enhancer-promoter interactions, and some doing both, whereas they all together contribute to the formation of a TAD border. This variety of tasks may explain the amazing evolutionary conservation in the distribution of these sites among paralogous Hox clusters or between various vertebrates.

Predict long-range enhancer regulation based on protein-protein interactions between transcription factors

Long-range regulation by distal enhancers plays critical roles in cell-type specific transcriptional programs. Computational predictions of genome-wide enhancer-promoter interactions are still challenging due to limited accuracy and the lack of knowledge on the molecular mechanisms. Based on recent biological investigations, the protein-protein interactions (PPIs) between transcription factors (TFs) have been found to participate in the regulation of chromatin loops. Therefore, we developed a novel predictive model for cell-type specific enhancer-promoter interactions by leveraging the information of TF PPI signatures. Evaluated by a series of rigorous performance comparisons, the new model achieves superior performance over other methods. The model also identifies specific TF PPIs that may mediate long-range regulatory interactions, revealing new mechanistic understandings of enhancer regulation. The prioritized TF PPIs are associated with genes in distinct biological pathways, and the predicted enhancer-promoter interactions are strongly enriched with cis-eQTLs. Most interestingly, the model discovers enhancer-mediated trans-regulatory links between TFs and genes, which are significantly enriched with trans-eQTLs. The new predictive model, along with the genome-wide analyses, provides a platform to systematically delineate the complex interplay among TFs, enhancers and genes in long-range regulation. The novel predictions also lead to mechanistic interpretations of eQTLs to decode the genetic associations with gene expression.

Heterogeneous graph attention network based on meta-paths for lncRNA-disease association prediction

MOTIVATION

Discovering long noncoding RNA (lncRNA)-disease associations is a fundamental and critical part in understanding disease etiology and pathogenesis. However, only a few lncRNA-disease associations have been identified because of the time-consuming and expensive biological experiments. As a result, an efficient computational method is of great importance and urgently needed for identifying potential lncRNA-disease associations. With the ability of exploiting node features and relationships in network, graph-based learning models have been commonly utilized by these biomolecular association predictions. However, the capability of these methods in comprehensively fusing node features, heterogeneous topological structures and semantic information is distant from optimal or even satisfactory. Moreover, there are still limitations in modeling complex associations between lncRNAs and diseases.

RESULTS

In this paper, we develop a novel heterogeneous graph attention network framework based on meta-paths for predicting lncRNA-disease associations, denoted as HGATLDA. At first, we conduct a heterogeneous network by incorporating lncRNA and disease feature structural graphs, and lncRNA-disease topological structural graph. Then, for the heterogeneous graph, we conduct multiple metapath-based subgraphs and then utilize graph attention network to learn node embeddings from neighbors of these homogeneous and heterogeneous subgraphs. Next, we implement attention mechanism to adaptively assign weights to multiple metapath-based subgraphs and get more semantic information. In addition, we combine neural inductive matrix completion to reconstruct lncRNA-disease associations, which is applied for capturing complicated associations between lncRNAs and diseases. Moreover, we incorporate cost-sensitive neural network into the loss function to tackle the commonly imbalance problem in lncRNA-disease association prediction. Finally, extensive experimental results demonstrate the effectiveness of our proposed framework.

Spatial genome architecture and the emergence of malignancy

Human chromosomes are large spatially and hierarchically structured entities, the integrity of which needs to be preserved throughout the lifespan of the cell and in conjunction with cell cycle progression. Preservation of chromosomal structure is important for proper deployment of cell type-specific gene expression programs. Thus, aberrations in the integrity and structure of chromosomes will predictably lead to disease, including cancer. Here, we provide an updated standpoint with respect to chromatin misfolding and the emergence of various cancer types. We discuss recent studies implicating the disruption of topologically associating domains, switching between active and inactive compartments, rewiring of promoter-enhancer interactions in malignancy as well as the effects of single nucleotide polymorphisms in non-coding regions involved in long-range regulatory interactions. In light of these findings, we argue that chromosome conformation studies may now also be useful for patient diagnosis and drug target discovery.

Seeing the forest through the trees: prioritising potentially functional interactions from Hi-C

Eukaryotic genomes are highly organised within the nucleus of a cell, allowing widely dispersed regulatory elements such as enhancers to interact with gene promoters through physical contacts in three-dimensional space. Recent chromosome conformation capture methodologies such as Hi-C have enabled the analysis of interacting regions of the genome providing a valuable insight into the three-dimensional organisation of the chromatin in the nucleus, including chromosome compartmentalisation and gene expression. Complicating the analysis of Hi-C data, however, is the massive amount of identified interactions, many of which do not directly drive gene function, thus hindering the identification of potentially biologically functional 3D interactions. In this review, we collate and examine the downstream analysis of Hi-C data with particular focus on methods that prioritise potentially functional interactions. We classify three groups of approaches: structural-based discovery methods, e.g. A/B compartments and topologically associated domains, detection of statistically significant chromatin interactions, and the use of epigenomic data integration to narrow down useful interaction information. Careful use of these three approaches is crucial to successfully identifying potentially functional interactions within the genome.

Chromatin Looping Links Target Genes with Genetic Risk Loci for Dermatological Traits

Chromatin looping between regulatory elements and gene promoters presents a potential mechanism whereby disease risk variants affect their target genes. In this study, we use H3K27ac HiChIP, a method for assaying the active chromatin interactome in two cell lines: keratinocytes and skin lymphoma-derived CD8+ T cells. We integrate public datasets for a lymphoblastoid cell line and primary CD4+ T cells and identify gene targets at risk loci for skin-related disorders. Interacting genes enrich for pathways of known importance in each trait, such as cytokine response (psoriatic arthritis and psoriasis) and replicative senescence (melanoma). We show examples of how our analysis can inform changes in the current understanding of multiple psoriasis-associated risk loci. For example, the variant rs10794648, which is generally assigned to IFNLR1, was linked to GRHL3, a gene essential in skin repair and development, in our dataset. Our findings, therefore, indicate a renewed importance of skin-related factors in the risk of disease.

Short- and long-range cis interactions between integrated HPV genomes and cellular chromatin dysregulate host gene expression in early cervical carcinogenesis

Development of cervical cancer is directly associated with integration of human papillomavirus (HPV) genomes into host chromosomes and subsequent modulation of HPV oncogene expression, which correlates with multi-layered epigenetic changes at the integrated HPV genomes. However, the process of integration itself and dysregulation of host gene expression at sites of integration in our model of HPV16 integrant clone natural selection has remained enigmatic. We now show, using a state-of-the-art 'HPV integrated site capture' (HISC) technique, that integration likely occurs through microhomology-mediated repair (MHMR) mechanisms via either a direct process, resulting in host sequence deletion (in our case, partially homozygously) or via a 'looping' mechanism by which flanking host regions become amplified. Furthermore, using our 'HPV16-specific Region Capture Hi-C' technique, we have determined that chromatin interactions between the integrated virus genome and host chromosomes, both at short- (<500 kbp) and long-range (>500 kbp), appear to drive local host gene dysregulation through the disruption of host:host interactions within (but not exceeding) host structures known as topologically associating domains (TADs). This mechanism of HPV-induced host gene expression modulation indicates that integration of virus genomes near to or within a 'cancer-causing gene' is not essential to influence their expression and that these modifications to genome interactions could have a major role in selection of HPV integrants at the early stage of cervical neoplastic progression.

Polymer models are a versatile tool to study chromatin 3D organization

The development of new experimental technologies is opening the way to a deeper investigation of the three-dimensional organization of chromosomes inside the cell nucleus. Genome architecture is linked to vital functional purposes, yet a full comprehension of the mechanisms behind DNA folding is still far from being accomplished. Theoretical approaches based on polymer physics have been employed to understand the complexity of chromatin architecture data and to unveil the basic mechanisms shaping its structure. Here, we review some recent advances in the field to discuss how Polymer Physics, combined with numerical Molecular Dynamics simulation and Machine Learning based inference, can capture important aspects of genome organization, including the description of tissue-specific structural rearrangements, the detection of novel, regulatory-linked architectural elements and the structural variability of chromatin at the single-cell level.

Three-dimensional genome organization in the central nervous system, implications for neuropsychological disorders

Chromosomes in eukaryotic cell nuclei are highly compacted and finely organized into hierarchical three-dimensional (3D) configuration. In recent years, scientists have gained deeper understandings of 3D genome structures and revealed novel evidence linking 3D genome organization to various important cell events on the molecular level. Most importantly, alteration of 3D genome architecture has emerged as an intriguing higher order mechanism that connects disease-related genetic variants in multiple heterogenous and polygenic neuropsychological disorders, delivering novel insights into the etiology. In this review, we provide a brief overview of the hierarchical structures of 3D genome and two proposed regulatory models, loop extrusion and phase separation. We then focus on recent Hi-C data in the central nervous system and discuss 3D genome alterations during normal brain development and in mature neurons. Most importantly, we make a comprehensive review on current knowledge and discuss the role of 3D genome in multiple neuropsychological disorders, including schizophrenia, repeat expansion disorders, 22q11 deletion syndrome, and others.

Functional annotation of the 2q35 breast cancer risk locus implicates a structural variant in influencing activity of a long-range enhancer element

A combination of genetic and functional approaches has identified three independent breast cancer risk loci at 2q35. A recent fine-scale mapping analysis to refine these associations resulted in 1 (signal 1), 5 (signal 2), and 42 (signal 3) credible causal variants at these loci. We used publicly available in silico DNase I and ChIP-seq data with in vitro reporter gene and CRISPR assays to annotate signals 2 and 3. We identified putative regulatory elements that enhanced cell-type-specific transcription from the IGFBP5 promoter at both signals (30- to 40-fold increased expression by the putative regulatory element at signal 2, 2- to 3-fold by the putative regulatory element at signal 3). We further identified one of the five credible causal variants at signal 2, a 1.4 kb deletion (esv3594306), as the likely causal variant; the deletion allele of this variant was associated with an average additional increase in IGFBP5 expression of 1.3-fold (MCF-7) and 2.2-fold (T-47D). We propose a model in which the deletion allele of esv3594306 juxtaposes two transcription factor binding regions (annotated by estrogen receptor alpha ChIP-seq peaks) to generate a single extended regulatory element. This regulatory element increases cell-type-specific expression of the tumor suppressor gene IGFBP5 and, thereby, reduces risk of estrogen receptor-positive breast cancer (odds ratio = 0.77, 95% CI 0.74-0.81, p = 3.1 × 10-31).

Computational methods for predicting 3D genomic organization from high-resolution chromosome conformation capture data

The advent of high-resolution chromosome conformation capture assays (such as 5C, Hi-C and Pore-C) has allowed for unprecedented sequence-level investigations into the structure-function relationship of the genome. In order to comprehensively understand this relationship, computational tools are required that utilize data generated from these assays to predict 3D genome organization (the 3D genome reconstruction problem). Many computational tools have been developed that answer this need, but a comprehensive comparison of their underlying algorithmic approaches has not been conducted. This manuscript provides a comprehensive review of the existing computational tools (from November 2006 to September 2019, inclusive) that can be used to predict 3D genome organizations from high-resolution chromosome conformation capture data. Overall, existing tools were found to use a relatively small set of algorithms from one or more of the following categories: dimensionality reduction, graph/network theory, maximum likelihood estimation (MLE) and statistical modeling. Solutions in each category are far from maturity, and the breadth and depth of various algorithmic categories have not been fully explored. While the tools for predicting 3D structure for a genomic region or single chromosome are diverse, there is a general lack of algorithmic diversity among computational tools for predicting the complete 3D genome organization from high-resolution chromosome conformation capture data.

How to find genomic regions relevant for gene regulation

Genetic variants associated with human diseases are often located outside the protein coding regions of the genome. Identification and functional characterization of the regulatory elements in the non-coding genome is therefore of crucial importance for understanding the consequences of genetic variation and the mechanisms of disease. The past decade has seen rapid progress in high-throughput analysis and mapping of chromatin accessibility, looping, structure, and occupancy by transcription factors, as well as epigenetic modifications, all of which contribute to the proper execution of regulatory functions in the non-coding genome. Here, we review the current technologies for the definition and functional validation of non-coding regulatory regions in the genome.

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.

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.