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

Identifying high-confidence capture Hi-C interactions using CHiCANE

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

Molecular and computational approaches to map regulatory elements in 3D chromatin structure

Epigenetic marks do not change the sequence of DNA but affect gene expression in a cell-type specific manner by altering the activities of regulatory elements. Development of new molecular biology assays, sequencing technologies, and computational approaches enables us to profile the human epigenome in three-dimensional structure genome-wide. Here we describe various molecular biology techniques and bioinformatic tools that have been developed to measure the activities of regulatory elements and their chromatin interactions. Moreover, we list currently available three-dimensional epigenomic data sets that are generated in various human cell types and tissues to assist in the design and analysis of research projects.

Using functional genomics to advance the understanding of psoriatic arthritis

Psoriatic arthritis (PsA) is a complex disease where susceptibility is determined by genetic and environmental risk factors. Clinically, PsA involves inflammation of the joints and the skin, and, if left untreated, results in irreversible joint damage. There is currently no cure and the few treatments available to alleviate symptoms do not work in all patients. Over the past decade, genome-wide association studies (GWAS) have uncovered a large number of disease-associated loci but translating these findings into functional mechanisms and novel targets for therapeutic use is not straightforward. Most variants have been predicted to affect primarily long-range regulatory regions such as enhancers. There is now compelling evidence to support the use of chromatin conformation analysis methods to discover novel genes that can be affected by disease-associated variants. Here, we will review the studies published in the field that have given us a novel understanding of gene regulation in the context of functional genomics and how this relates to the study of PsA and its underlying disease mechanism.

PsychENCODE and beyond: transcriptomics and epigenomics of brain development and organoids

Crucial decisions involving cell fate and connectivity that shape the distinctive development of the human brain occur in the embryonic and fetal stages-stages that are difficult to access and investigate in humans. The last decade has seen an impressive increase in resources-from atlases and databases to biological models-that is progressively lifting the curtain on this critical period. In this review, we describe the current state of genomic, transcriptomic, and epigenomic datasets charting the development of normal human brain with a particular focus on recent single-cell technologies. We discuss the emergence of brain organoids generated from pluripotent stem cells as a model to compensate for the limited availability of fetal tissue. Indeed, comparisons of neural lineages, transcriptional dynamics, and noncoding element activity between fetal brain and organoids have helped identify gene regulatory networks functioning at early stages of brain development. Altogether, we argue that large multi-omics investigations have pushed brain development into the "big data" era, and that current and future transversal approaches needed to leverage both fetal brain and organoid resources promise to answer major questions of brain biology and psychiatry.

Proximity Ligation-Assisted ChIP-Seq (PLAC-Seq)

Proximity ligation-assisted ChIP-Seq (PLAC-Seq), also known as HiChIP, is a method to detect and quantify chromatin contacts anchored at genomic regions bound by specific proteins or histone modifications. By combining in situ Hi-C and chromatin immunoprecipitation (ChIP) using antibodies against transcription factors (TFs) or histone marks of interest, the method achieves targeted interrogation of chromatin organization at a subset of genomic regions. PLAC-Seq is able to identify long-range chromatin interactions at kilobase-scale resolution with significantly reduced sequencing cost.

Predicting enhancer-promoter interactions by deep learning and matching heuristic

Enhancer-promoter interactions (EPIs) play an important role in transcriptional regulation. Recently, machine learning-based methods have been widely used in the genome-scale identification of EPIs due to their promising predictive performance. In this paper, we propose a novel method, termed EPI-DLMH, for predicting EPIs with the use of DNA sequences only. EPI-DLMH consists of three major steps. First, a two-layer convolutional neural network is used to learn local features, and an bidirectional gated recurrent unit network is used to capture long-range dependencies on the sequences of promoters and enhancers. Second, an attention mechanism is used for focusing on relatively important features. Finally, a matching heuristic mechanism is introduced for the exploration of the interaction between enhancers and promoters. We use benchmark datasets in evaluating and comparing the proposed method with existing methods. Comparative results show that our model is superior to currently existing models in multiple cell lines. Specifically, we found that the matching heuristic mechanism introduced into the proposed model mainly contributes to the improvement of performance in terms of overall accuracy. Additionally, compared with existing models, our model is more efficient with regard to computational speed.

A modified protocol of Capture-C allows affordable and flexible high-resolution promoter interactome analysis

Large-scale epigenomic projects have mapped hundreds of thousands of potential regulatory sites in the human genome, but only a small proportion of these elements are proximal to transcription start sites. It is believed that the majority of these sequences are remote promoter-activating genomic sites scattered within several hundreds of kilobases from their cognate promoters and referred to as enhancers. It is still unclear what principles, aside from relative closeness in the linear genome, determine which promoter(s) is controlled by a given enhancer; however, this understanding is of great fundamental and clinical relevance. In recent years, C-methods (chromosome conformation capture-based methods) have become a powerful tool for the identification of enhancer-promoter spatial contacts that, in most cases, reflect their functional link. Here, we describe a new hybridisation-based promoter Capture-C protocol that makes use of biotinylated dsDNA probes generated by PCR from a custom pool of long oligonucleotides. The described protocol allows high-resolution promoter interactome description, providing a flexible and cost-effective alternative to the existing promoter Capture-C modifications. Based on the obtained data, we propose several tips on probe design that could potentially improve the results of future experiments.

Molecular Insights Into Regulatory T-Cell Adaptation to Self, Environment, and Host Tissues: Plasticity or Loss of Function in Autoimmune Disease

There has been much interest in the ability of regulatory T cells (Treg) to switch function in vivo, either as a result of genetic risk of disease or in response to environmental and metabolic cues. The relationship between levels of FOXP3 and functional fitness plays a significant part in this plasticity. There is an emerging role for Treg in tissue repair that may be less dependent on FOXP3, and the molecular mechanisms underpinning this are not fully understood. As a result of detailed, high-resolution functional genomics, the gene regulatory networks and key functional mediators of Treg phenotype downstream of FOXP3 have been mapped, enabling a mechanistic insight into Treg function. This transcription factor-driven programming of T-cell function to generate Treg requires the switching on and off of key genes that form part of the Treg gene regulatory network and raises the possibility that this is reversible. It is plausible that subtle shifts in expression levels of specific genes, including transcription factors and non-coding RNAs, change the regulation of the Treg gene network. The subtle skewing of gene expression initiates changes in function, with the potential to promote chronic disease and/or to license appropriate inflammatory responses. In the case of autoimmunity, there is an underlying genetic risk, and the interplay of genetic and environmental cues is complex and impacts gene regulation networks frequently involving promoters and enhancers, the regulatory elements that control gene expression levels and responsiveness. These promoter–enhancer interactions can operate over long distances and are highly cell type specific. In autoimmunity, the genetic risk can result in changes in these enhancer/promoter interactions, and this mainly impacts genes which are expressed in T cells and hence impacts Treg/conventional T-cell (Tconv) function. Genetic risk may cause the subtle alterations to the responsiveness of gene regulatory networks which are controlled by or control FOXP3 and its target genes, and the application of assays of the 3D organization of chromatin, enabling the connection of non-coding regulatory regions to the genes they control, is revealing the direct impact of environmental/metabolic/genetic risk on T-cell function and is providing mechanistic insight into susceptibility to inflammatory and autoimmune conditions.

Analysis of chromatin organization and gene expression in T cells identifies functional genes for rheumatoid arthritis

Genome-wide association studies have identified genetic variation contributing to complex disease risk. However, assigning causal genes and mechanisms has been more challenging because disease-associated variants are often found in distal regulatory regions with cell-type specific behaviours. Here, we collect ATAC-seq, Hi-C, Capture Hi-C and nuclear RNA-seq data in stimulated CD4+ T cells over 24 h, to identify functional enhancers regulating gene expression. We characterise changes in DNA interaction and activity dynamics that correlate with changes in gene expression, and find that the strongest correlations are observed within 200 kb of promoters. Using rheumatoid arthritis as an example of T cell mediated disease, we demonstrate interactions of expression quantitative trait loci with target genes, and confirm assigned genes or show complex interactions for 20% of disease associated loci, including FOXO1, which we confirm using CRISPR/Cas9.

The landscape of accessible chromatin in bovine oocytes and early embryos

Chromatin reorganization governs the regulation of gene expression during preimplantation development. However, the landscape of chromatin dynamics in this period has not been explored in bovine. In this study, we constructed a genome-wide map of accessible chromatin in bovine oocytes and early embryos using an improved assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) which revealed unique features of the accessible chromatin during bovine early embryo development. We found that chromatin accessibility is low in oocytes and 2-/4-cell embryos, followed by a significant increase in embryos during major embryonic genome activation (EGA), and peaked in elongating day 14 embryos. Genome-wide characteristics of open chromatin showed that ATAC-seq signals in both transcription start sites (TSS) and transcription end sites (TES) were strong. Additionally, the distal ATAC-seq peaks were enriched in repeat elements in a type-specific and stage-specific manner. We further unveiled a series of transcription factor (TF) motifs with distinct variation of enrichment from distal ATAC-seq peaks. By integrated analysis of chromatin accessibility with transcriptomes and DNA methylomes in bovine early embryos, we showed that promoter accessibility was positively correlated with gene expression, especially during major EGA, and was strongly correlated to DNA methylation and CpG density. Finally, we identified the critical chromatin signatures and TFs that differ between in vivo and in vitro derived blastocysts, which provides insights to the potential mechanisms leading to low quality of embryos produced in vitro. Together, this comprehensive analysis revealed critical features of chromatin landscape and epigenetic reprogramming during bovine preimplantation embryo development.

Hierarchical chromatin organization detected by TADpole

The rapid development of Chromosome Conformation Capture (3C-based techniques), as well as imaging together with bioinformatics analyses, has been fundamental for unveiling that chromosomes are organized into the so-called topologically associating domains or TADs. While TADs appear as nested patterns in the 3C-based interaction matrices, the vast majority of available TAD callers are based on the hypothesis that TADs are individual and unrelated chromatin structures. Here we introduce TADpole, a computational tool designed to identify and analyze the entire hierarchy of TADs in intra-chromosomal interaction matrices. TADpole combines principal component analysis and constrained hierarchical clustering to provide a set of significant hierarchical chromatin levels in a genomic region of interest. TADpole is robust to data resolution, normalization strategy and sequencing depth. Domain borders defined by TADpole are enriched in main architectural proteins (CTCF and cohesin complex subunits) and in the histone mark H3K4me3, while their domain bodies, depending on their activation-state, are enriched in either H3K36me3 or H3K27me3, highlighting that TADpole is able to distinguish functional TAD units. Additionally, we demonstrate that TADpole's hierarchical annotation, together with the new DiffT score, allows for detecting significant topological differences on Capture Hi-C maps between wild-type and genetically engineered mouse.

Galaxy HiCExplorer 3: a web server for reproducible Hi-C, capture Hi-C and single-cell Hi-C data analysis, quality control and visualization

The Galaxy HiCExplorer provides a web service at https://hicexplorer.usegalaxy.eu. It enables the integrative analysis of chromosome conformation by providing tools and computational resources to pre-process, analyse and visualize Hi-C, Capture Hi-C (cHi-C) and single-cell Hi-C (scHi-C) data. Since the last publication, Galaxy HiCExplorer has been expanded considerably with new tools to facilitate the analysis of cHi-C and to provide an in-depth analysis of Hi-C data. Moreover, it supports the analysis of scHi-C data by offering a broad range of tools. With the help of the standard graphical user interface of Galaxy, presented workflows, extensive documentation and tutorials, novices as well as Hi-C experts are supported in their Hi-C data analysis with Galaxy HiCExplorer.

Dynamics of the 4D genome during in vivo lineage specification and differentiation

Mammalian gene expression patterns are controlled by regulatory elements, which interact within topologically associating domains (TADs). The relationship between activation of regulatory elements, formation of structural chromatin interactions and gene expression during development is unclear. Here, we present Tiled-C, a low-input chromosome conformation capture (3C) technique. We use this approach to study chromatin architecture at high spatial and temporal resolution through in vivo mouse erythroid differentiation. Integrated analysis of chromatin accessibility and single-cell expression data shows that regulatory elements gradually become accessible within pre-existing TADs during early differentiation. This is followed by structural re-organization within the TAD and formation of specific contacts between enhancers and promoters. Our high-resolution data show that these enhancer-promoter interactions are not established prior to gene expression, but formed gradually during differentiation, concomitant with progressive upregulation of gene activity. Together, these results provide new insight into the close, interdependent relationship between chromatin architecture and gene regulation during development.

Regulation of KLF4 by posttranslational modification circuitry in endocrine resistance

KLF4 plays an important role in orchestrating a variety of cellular events, including cell-fate decision, genome stability and apoptosis. Its deregulation is correlated with human diseases such as breast cancer and gastrointestinal cancer. Results from recent biochemical studies have revealed that KLF4 is tightly regulated by posttranslational modifications. Here we report a new finding that KLF4 orchestrates estrogen receptor signaling and facilitates endocrine resistance. We also uncovered the underlying mechanism that alteration of KLF4 by posttranslational modifications such as phosphorylation and ubiquitylation changes tumor cell response to endocrine therapy drugs. IHC analyses using based on human breast cancer specimens showed the accumulation of KLF4 protein in ER-positive breast cancer tissues. Elevated KLF4 expression significantly correlated with prognosis and endocrine resistance. Our drug screening for suppressing KLF4 protein expression led to identification of Src kinase to be a critical player in modulating KLF4-mediated tamoxifen resistance. Depletion of VHL (von Hippel-Lindau tumor suppressor), a ubiquitin E3 ligase for KLF4, reduces tumor cell sensitivity to tamoxifen. We demonstrated phosphorylation of VHL by Src enhances proteolysis of VHL that in turn leads to upregulation of KLF4 and increases endocrine resistance. Suppression of Src-VHL-KLF4 cascade by Src inhibitor or enhancement of VHL-KLF4 ubiquitination by TAT-KLF4 (371-420AAa) peptides re-sensitizes tamoxifen-resistant breast cancer cells to tamoxifen treatment. Taken together, our findings demonstrate a novel role for KLF4 in modulating endocrine resistance via the Src-VHL-KLF4 axis.

Prospecting for Breast Cancer Blood Biomarkers: Death-Associated Protein Kinase 1 (DAPK1) as a Potential Candidate

BACKGROUND

Breast cancer is the commonest malignancy in women worldwide. It is estimated to affect approximately 1.5 million women annually and responsible for the greatest number of cancer-related mortalities among women. In 2018, breast cancer mortalities stood at 627,000 women representing approximately 15% of all cancer deaths among women. In Ghana, breast cancer is the second leading cause of cancer deaths, with an incidence of 2,900 cases annually; one of eight women with the disease die. This gives impetus to the fight for improved early detection, treatment, and/management. In this light, we investigated the potential of death-associated protein kinase 1 (DAPK1) as a biomarker for breast cancer. As a tumour suppressor, its expression is activated by several carcinogens to influence cellular pathways that result in apoptosis, autophagy, immune response, and proliferation.

AIM

To investigate DAPK1 as a blood biomarker for breast cancer.

METHODS

Blood samples of participants diagnosed with breast cancer and healthy controls were collected and processed to obtain serum. Information on age, treatment, diagnosis, and pathology numbers was retrieved from folders. Pathology numbers were used to retrieve breast tissue blocks of patients at the Department of Pathology of the KBTH. Tissue blocks were sectioned and immunohistochemically stained with anti-DAPK1 and counterstained with hematoxylin to determine the DAPK1 expression levels. DAKP1 levels in blood sera were quantified using a commercial anti-DAPK1 ELISA kit. Case and control group means were compared using one-way ANOVA and Chi-square test. Statistical significance was set at p ≤ 0.05. Results and Discussion. DAPK1 levels were higher in sera and breast tissues of breast cancer patients than controls. The augmented DAPK1 expression can be interpreted as a stress response survival mechanism to remediate ongoing deleterious events in the cells orchestrated by carcinogenesis. In the presence of abundant DAPK1, the proliferative power of cells (both cancerous and noncancerous) is increased. This may explain why high DAPK1 expression strongly associates with aggressive breast cancer phenotypes like the ER-negative breast cancers, especially the triple-negative breast cancers (TNBC) which are the most aggressive, fast-growing, and highly metastatic.

CONCLUSION

DAPK1 is highly expressed in sera and breast tissues of breast cancer patients than nonbreast cancer participants. The elevated expression of DAKP1 in circulation rather than in breast tissues makes it a candidate for use as a blood biomarker and potential use as therapeutic target in drug development.

OncoBase: a platform for decoding regulatory somatic mutations in human cancers

Whole-exome and whole-genome sequencing have revealed millions of somatic mutations associated with different human cancers, and the vast majority of them are located outside of coding sequences, making it challenging to directly interpret their functional effects. With the rapid advances in high-throughput sequencing technologies, genome-scale long-range chromatin interactions were detected, and distal target genes of regulatory elements were determined using three-dimensional (3D) chromatin looping. Herein, we present OncoBase (http://www.oncobase.biols.ac.cn/), an integrated database for annotating 81 385 242 somatic mutations in 68 cancer types from more than 120 cancer projects by exploring their roles in distal interactions between target genes and regulatory elements. OncoBase integrates local chromatin signatures, 3D chromatin interactions in different cell types and reconstruction of enhancer-target networks using state-of-the-art algorithms. It employs informative visualization tools to display the integrated local and 3D chromatin signatures and effects of somatic mutations on regulatory elements. Enhancer-promoter interactions estimated from chromatin interactions are integrated into a network diffusion system that quantitatively prioritizes somatic mutations and target genes from a large pool. Thus, OncoBase is a useful resource for the functional annotation of regulatory noncoding regions and systematically benchmarking the regulatory effects of embedded noncoding somatic mutations in human carcinogenesis.

Mapping DNA interaction landscapes in psoriasis susceptibility loci highlights KLF4 as a target gene in 9q31

BACKGROUND

Genome-wide association studies (GWAS) have uncovered many genetic risk loci for psoriasis, yet many remain uncharacterised in terms of the causal gene and their biological mechanism in disease. This is largely a result of the findings that over 90% of GWAS variants map outside of protein-coding DNA and instead are enriched in cell type- and stimulation-specific gene regulatory regions.

RESULTS

Here, we use a disease-focused Capture Hi-C (CHi-C) experiment to link psoriasis-associated variants with their target genes in psoriasis-relevant cell lines (HaCaT keratinocytes and My-La CD8+ T cells). We confirm previously assigned genes, suggest novel candidates and provide evidence for complexity at psoriasis GWAS loci. For one locus, uniquely, we combine further epigenomic evidence to demonstrate how a psoriasis-associated region forms a functional interaction with the distant (> 500 kb) KLF4 gene. This interaction occurs between the gene and active enhancers in HaCaT cells, but not in My-La cells. We go on to investigate this long-distance interaction further with Cas9 fusion protein-mediated chromatin modification (CRISPR activation) coupled with RNA-seq, demonstrating how activation of the psoriasis-associated enhancer upregulates KLF4 and its downstream targets, relevant to skin cells and apoptosis.

CONCLUSIONS

This approach utilises multiple functional genomic techniques to follow up GWAS-associated variants implicating relevant cell types and causal genes in each locus; these are vital next steps for the translation of genetic findings into clinical benefit.

Functional effects of variation in transcription factor binding highlight long-range gene regulation by epromoters

Identifying DNA cis-regulatory modules (CRMs) that control the expression of specific genes is crucial for deciphering the logic of transcriptional control. Natural genetic variation can point to the possible gene regulatory function of specific sequences through their allelic associations with gene expression. However, comprehensive identification of causal regulatory sequences in brute-force association testing without incorporating prior knowledge is challenging due to limited statistical power and effects of linkage disequilibrium. Sequence variants affecting transcription factor (TF) binding at CRMs have a strong potential to influence gene regulatory function, which provides a motivation for prioritizing such variants in association testing. Here, we generate an atlas of CRMs showing predicted allelic variation in TF binding affinity in human lymphoblastoid cell lines and test their association with the expression of their putative target genes inferred from Promoter Capture Hi-C and immediate linear proximity. We reveal >1300 CRM TF-binding variants associated with target gene expression, the majority of them undetected with standard association testing. A large proportion of CRMs showing associations with the expression of genes they contact in 3D localize to the promoter regions of other genes, supporting the notion of 'epromoters': dual-action CRMs with promoter and distal enhancer activity.

A Multi-Omics Approach to Liver Diseases: Integration of Single Nuclei Transcriptomics with Proteomics and HiCap Bulk Data in Human Liver

The liver is the largest solid organ and a primary metabolic hub. In recent years, intact cell nuclei were used to perform single-nuclei RNA-seq (snRNA-seq) for tissues difficult to dissociate and for flash-frozen archived tissue samples to discover unknown and rare cell subpopulations. In this study, we performed snRNA-seq of a liver sample to identify subpopulations of cells based on nuclear transcriptomics. In 4282 single nuclei, we detected, on average, 1377 active genes and we identified seven major cell types. We integrated data from 94,286 distal interactions (p < 0.05) for 7682 promoters from a targeted chromosome conformation capture technique (HiCap) and mass spectrometry proteomics for the same liver sample. We observed a reasonable correlation between proteomics and in silico bulk snRNA-seq (r = 0.47) using tissue-independent gene-specific protein abundancy estimation factors. We specifically looked at genes of medical importance. The DPYD gene is involved in the pharmacogenetics of fluoropyrimidine toxicity and some of its variants are analyzed for clinical purposes. We identified a new putative polymorphic regulatory element, which may contribute to variation in toxicity. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and we investigated all known risk genes. We identified a complex regulatory landscape for the SLC2A2 gene with 16 candidate enhancers. Three of them harbor somatic motif breaking and other mutations in HCC in the Pan Cancer Analysis of Whole Genomes dataset and are candidates to contribute to malignancy. Our results highlight the potential of a multi-omics approach in the study of human diseases.

Three-dimensional genome: developmental technologies and applications in precision medicine

In the 20th century, our familiar structure of DNA was the double helix. Due to technical limitations, we do not have a good way to understand the finer structure of the genome, let alone its transcriptional regulation. Until the advent of 3C technologies, we were no longer blind to this one. Three-dimensional (3D) genomics is a new subject, which mainly studies the 3D structure and transcriptional regulation of eukaryotic genomes. Now, this field mainly has Hi-C series and CHIA-PET series technologies. Through 3D genomics, we can understand the basic structure of DNA, understand the growth and development of organisms and the occurrence of diseases, so as to promote human medical and health undertakings. The review introduces the main research techniques of 3D genomics and their characteristics, the latest development of 3D genome structure, the relationship between diseases and 3D genome structure, the applications of 3D genome in precision medicine, and the development of the 4D nucleome project.

Exploring 3D chromatin contacts in gene regulation: The evolution of approaches for the identification of functional enhancer-promoter interaction

Mechanisms underlying gene regulation are key to understand how multicellular organisms with various cell types develop from the same genetic blueprint. Dynamic interactions between enhancers and genes are revealed to play central roles in controlling gene transcription, but the determinants to link functional enhancer-promoter pairs remain elusive. A major challenge is the lack of reliable approach to detect and verify functional enhancer-promoter interactions (EPIs). In this review, we summarized the current methods for detecting EPIs and described how developing techniques facilitate the identification of EPI through assessing the merits and drawbacks of these methods. We also reviewed recent state-of-art EPI prediction methods in terms of their rationale, data usage and characterization. Furthermore, we briefly discussed the evolved strategies for validating functional EPIs.

Liver gene regulatory networks: Contributing factors to nonalcoholic fatty liver disease

Metabolic diseases such as nonalcoholic fatty liver disease (NAFLD) result from complex interactions between intrinsic and extrinsic factors, including genetics and exposure to obesogenic environments. These risk factors converge in aberrant gene expression patterns in the liver, which are underlined by altered cis-regulatory networks. In homeostasis and in disease states, liver cis-regulatory networks are established by coordinated action of liver-enriched transcription factors (TFs), which define enhancer landscapes, activating broad gene programs with spatiotemporal resolution. Recent advances in DNA sequencing have dramatically expanded our ability to map active transcripts, enhancers and TF cistromes, and to define the 3D chromatin topology that contains these elements. Deployment of these technologies has allowed investigation of the molecular processes that regulate liver development and metabolic homeostasis. Moreover, genomic studies of NAFLD patients and NAFLD models have demonstrated that the liver undergoes pervasive regulatory rewiring in NAFLD, which is reflected by aberrant gene expression profiles. We have therefore achieved an unprecedented level of detail in the understanding of liver cis-regulatory networks, particularly in physiological conditions. Future studies should aim to map active regulatory elements with added levels of resolution, addressing how the chromatin landscapes of different cell lineages contribute to and are altered in NAFLD and NAFLD-associated metabolic states. Such efforts would provide additional clues into the molecular factors that trigger this disease. This article is categorized under: Biological Mechanisms > Metabolism Biological Mechanisms > Regulatory Biology Laboratory Methods and Technologies > Genetic/Genomic Methods.

Identification of recurrent noncoding mutations in B-cell lymphoma using capture Hi-C

The identification of driver mutations is fundamental to understanding oncogenesis. Although genes frequently mutated in B-cell lymphoma have been identified, the search for driver mutations has largely focused on the coding genome. Here we report an analysis of the noncoding genome using whole-genome sequencing data from 117 patients with B-cell lymphoma. Using promoter capture Hi-C data in naive B cells, we define cis-regulatory elements, which represent an enriched subset of the noncoding genome in which to search for driver mutations. Regulatory regions were identified whose mutation significantly alters gene expression, including copy number variation at cis-regulatory elements targeting CD69, IGLL5, and MMP14, and single nucleotide variants in a cis-regulatory element for TPRG1 We also show the commonality of pathways targeted by coding and noncoding mutations, exemplified by MMP14, which regulates Notch signaling, a pathway important in lymphomagenesis and whose expression is associated with patient survival. This study provides an enhanced understanding of lymphomagenesis and describes the advantages of using chromosome conformation capture to decipher noncoding mutations relevant to cancer biology.

Chromatin interactome mapping at 139 independent breast cancer risk signals

BACKGROUND

Genome-wide association studies have identified 196 high confidence independent signals associated with breast cancer susceptibility. Variants within these signals frequently fall in distal regulatory DNA elements that control gene expression.

RESULTS

We designed a Capture Hi-C array to enrich for chromatin interactions between the credible causal variants and target genes in six human mammary epithelial and breast cancer cell lines. We show that interacting regions are enriched for open chromatin, histone marks for active enhancers, and transcription factors relevant to breast biology. We exploit this comprehensive resource to identify candidate target genes at 139 independent breast cancer risk signals and explore the functional mechanism underlying altered risk at the 12q24 risk region.

CONCLUSIONS

Our results demonstrate the power of combining genetics, computational genomics, and molecular studies to rationalize the identification of key variants and candidate target genes at breast cancer GWAS signals.

Epigenetics Analysis and Integrated Analysis of Multiomics Data, Including Epigenetic Data, Using Artificial Intelligence in the Era of Precision Medicine

To clarify the mechanisms of diseases, such as cancer, studies analyzing genetic mutations have been actively conducted for a long time, and a large number of achievements have already been reported. Indeed, genomic medicine is considered the core discipline of precision medicine, and currently, the clinical application of cutting-edge genomic medicine aimed at improving the prevention, diagnosis and treatment of a wide range of diseases is promoted. However, although the Human Genome Project was completed in 2003 and large-scale genetic analyses have since been accomplished worldwide with the development of next-generation sequencing (NGS), explaining the mechanism of disease onset only using genetic variation has been recognized as difficult. Meanwhile, the importance of epigenetics, which describes inheritance by mechanisms other than the genomic DNA sequence, has recently attracted attention, and, in particular, many studies have reported the involvement of epigenetic deregulation in human cancer. So far, given that genetic and epigenetic studies tend to be accomplished independently, physiological relationships between genetics and epigenetics in diseases remain almost unknown. Since this situation may be a disadvantage to developing precision medicine, the integrated understanding of genetic variation and epigenetic deregulation appears to be now critical. Importantly, the current progress of artificial intelligence (AI) technologies, such as machine learning and deep learning, is remarkable and enables multimodal analyses of big omics data. In this regard, it is important to develop a platform that can conduct multimodal analysis of medical big data using AI as this may accelerate the realization of precision medicine. In this review, we discuss the importance of genome-wide epigenetic and multiomics analyses using AI in the era of precision medicine.

Topological structure analysis of chromatin interaction networks

BACKGROUND

Current Hi-C technologies for chromosome conformation capture allow to understand a broad spectrum of functional interactions between genome elements. Although significant progress has been made into analysis of Hi-C data to identify biologically significant features, many questions still remain open, in particular regarding potential biological significance of various topological features that are characteristic for chromatin interaction networks.

RESULTS

It has been previously observed that promoter capture Hi-C (PCHi-C) interaction networks tend to separate easily into well-defined connected components that can be related to certain biological functionality, however, such evidence was based on manual analysis and was limited. Here we present a novel method for analysis of chromatin interaction networks aimed towards identifying characteristic topological features of interaction graphs and confirming their potential significance in chromatin architecture. Our method automatically identifies all connected components with an assigned significance score above a given threshold. These components can be subjected afterwards to different assessment methods for their biological role and/or significance. The method was applied to the largest PCHi-C data set available to date that contains interactions for 17 haematopoietic cell types. The results demonstrate strong evidence of well-pronounced component structure of chromatin interaction networks and provide some characterisation of this component structure. We also performed an indicative assessment of potential biological significance of identified network components with the results confirming that the network components can be related to specific biological functionality.

CONCLUSIONS

The obtained results show that the topological structure of chromatin interaction networks can be well described in terms of isolated connected components of the network and that formation of these components can be often explained by biological features of functionally related gene modules. The presented method allows automatic identification of all such components and evaluation of their significance in PCHi-C dataset for 17 haematopoietic cell types. The method can be adapted for exploration of other chromatin interaction data sets that include information about sufficiently large number of different cell types, and, in principle, also for analysis of other kinds of cell type-specific networks.

From Loops to Looks: Transcription Factors and Chromatin Organization Shaping Terminal B Cell Differentiation

B lymphopoiesis is tightly regulated at the level of gene transcription. In recent years, investigators have shed light on the transcription factor networks and the epigenetic machinery involved at all differentiation steps of mammalian B cell development. During terminal differentiation, B cells undergo dramatic changes in gene transcriptional programs to generate germinal center B cells, plasma cells and memory B cells. Recent evidence indicates that mature B cell formation involves an essential contribution from 3D chromatin conformations through its interplay with transcription factors and epigenetic machinery. Here, we provide an up-to-date overview of the coordination between transcription factors, epigenetic changes, and chromatin architecture during terminal B cell differentiation, focusing on recent discoveries and technical advances for studying 3D chromatin structures.

The MITF-SOX10 regulated long non-coding RNA DIRC3 is a melanoma tumour suppressor

The MITF and SOX10 transcription factors regulate the expression of genes important for melanoma proliferation, invasion and metastasis. Despite growing evidence of the contribution of long noncoding RNAs (lncRNAs) in cancer, including melanoma, their functions within MITF-SOX10 transcriptional programmes remain poorly investigated. Here we identify 245 candidate melanoma associated lncRNAs whose loci are co-occupied by MITF-SOX10 and that are enriched at active enhancer-like regions. Our work suggests that one of these, Disrupted In Renal Carcinoma 3 (DIRC3), may be a clinically important MITF-SOX10 regulated tumour suppressor. DIRC3 depletion in human melanoma cells leads to increased anchorage-independent growth, a hallmark of malignant transformation, whilst melanoma patients classified by low DIRC3 expression have decreased survival. DIRC3 is a nuclear lncRNA that activates expression of its neighbouring IGFBP5 tumour suppressor through modulating chromatin structure and suppressing SOX10 binding to putative regulatory elements within the DIRC3 locus. In turn, DIRC3 dependent regulation of IGFBP5 impacts the expression of genes involved in cancer associated processes and is needed for DIRC3 control of anchorage-independent growth. Our work indicates that lncRNA components of MITF-SOX10 networks are an important new class of melanoma regulators and candidate therapeutic targets that can act not only as downstream mediators of MITF-SOX10 function but as feedback regulators of MITF-SOX10 activity.

A compendium of promoter-centered long-range chromatin interactions in the human genome

A large number of putative cis-regulatory sequences have been annotated in the human genome, but the genes they control remain poorly defined. To bridge this gap, we generate maps of long-range chromatin interactions centered on 18,943 well-annotated promoters for protein-coding genes in 27 human cell/tissue types. We use this information to infer the target genes of 70,329 candidate regulatory elements, and suggest potential regulatory function for 27,325 non-coding sequence variants associated with 2,117 physiological traits and diseases. Integrative analysis of these promoter-centered interactome maps reveals widespread enhancer-like promoters involved in gene regulation and common molecular pathways underlying distinct groups of human traits and diseases.

A simple convolutional neural network for prediction of enhancer-promoter interactions with DNA sequence data

MOTIVATION

Enhancer-promoter interactions (EPIs) in the genome play an important role in transcriptional regulation. EPIs can be useful in boosting statistical power and enhancing mechanistic interpretation for disease- or trait-associated genetic variants in genome-wide association studies. Instead of expensive and time-consuming biological experiments, computational prediction of EPIs with DNA sequence and other genomic data is a fast and viable alternative. In particular, deep learning and other machine learning methods have been demonstrated with promising performance.

RESULTS

First, using a published human cell line dataset, we demonstrate that a simple convolutional neural network (CNN) performs as well as, if no better than, a more complicated and state-of-the-art architecture, a hybrid of a CNN and a recurrent neural network. More importantly, in spite of the well-known cell line-specific EPIs (and corresponding gene expression), in contrast to the standard practice of training and predicting for each cell line separately, we propose two transfer learning approaches to training a model using all cell lines to various extents, leading to substantially improved predictive performance.

AVAILABILITY AND IMPLEMENTATION

Computer code is available at https://github.com/zzUMN/Combine-CNN-Enhancer-and-Promoters.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Heterogeneous Loop Model to Infer 3D Chromosome Structures from Hi-C

Adapting a well-established formalism in polymer physics, we develop a minimalist approach to infer three-dimensional folding of chromatin from Hi-C data. The three-dimensional chromosome structures generated from our heterogeneous loop model (HLM) are used to visualize chromosome organizations that can substantiate the measurements from fluorescence in situ hybridization, chromatin interaction analysis by paired-end tag sequencing, and RNA-seq signals. We demonstrate the utility of the HLM with several case studies. Specifically, the HLM-generated chromosome structures, which reproduce the spatial distribution of topologically associated domains from fluorescence in situ hybridization measurement, show the phase segregation between two types of topologically associated domains explicitly. We discuss the origin of cell-type-dependent gene-expression level by modeling the chromatin globules of α-globin and SOX2 gene loci for two different cell lines. We also use the HLM to discuss how the chromatin folding and gene-expression level of Pax6 loci, associated with mouse neural development, are modulated by interactions with two enhancers. Finally, HLM-generated structures of chromosome 19 of mouse embryonic stem cells, based on single-cell Hi-C data collected over each cell-cycle phase, visualize changes in chromosome conformation along the cell-cycle. Given a contact frequency map between chromatic loci supplied from Hi-C, HLM is a computationally efficient and versatile modeling tool to generate chromosome structures that can complement interpreting other experimental data.

C-TALE, a new cost-effective method for targeted enrichment of Hi-C/3C-seq libraries

Studies performed using Hi-C and other high-throughput whole-genome C-methods have demonstrated that 3D organization of eukaryotic genomes is functionally relevant. Unfortunately, ultra-deep sequencing of Hi-C libraries necessary to detect loop structures in large vertebrate genomes remains rather expensive. However, many studies are in fact aimed at determining the fine-scale 3D structure of comparatively small genomic regions up to several Mb in length. Such studies typically focus on the spatial structure of domains of coregulated genes, molecular mechanisms of loop formation, and interrogation of functional significance of GWAS-revealed polymorphisms. Therefore, a handful of molecular techniques based on Hi-C have been developed to address such issues. These techniques commonly rely on in-solution hybridization of Hi-C/3C-seq libraries with pools of biotinylated baits covering the region of interest, followed by deep sequencing of the enriched library. Here, we describe a new protocol of this kind, C-TALE (Chromatin TArget Ligation Enrichment). Preparation of hybridization probes from bacterial artificial chromosomes and an additional round of enrichment make C-TALE a cost-effective alternative to existing many-versus-all C-methods.

Chromatin interactions reveal novel gene targets for drug repositioning in rheumatic diseases

Objectives

There is a need to identify effective treatments for rheumatic diseases, and while genetic studies have been successful it is unclear which genes contribute to the disease. Using our existing Capture Hi-C data on three rheumatic diseases, we can identify potential causal genes which are targets for existing drugs and could be repositioned for use in rheumatic diseases.

Methods

High confidence candidate causal genes were identified using Capture Hi-C data from B cells and T cells. These genes were used to interrogate drug target information from DrugBank to identify existing treatments, which could be repositioned to treat these diseases. The approach was refined using Ingenuity Pathway Analysis to identify enriched pathways and therefore further treatments relevant to the disease.

Results

Overall, 454 high confidence genes were identified. Of these, 48 were drug targets (108 drugs) and 11 were existing therapies used in the treatment of rheumatic diseases. After pathway analysis refinement, 50 genes remained, 13 of which were drug targets (33 drugs). However considering targets across all enriched pathways, a further 367 drugs were identified for potential repositioning.

Conclusion

Capture Hi-C has the potential to identify therapies which could be repositioned to treat rheumatic diseases. This was particularly successful for rheumatoid arthritis, where six effective, biologic treatments were identified. This approach may therefore yield new ways to treat patients, enhancing their quality of life and reducing the economic impact on healthcare providers. As additional cell types and other epigenomic data sets are generated, this prospect will improve further.

ECRG4 acts as a tumor suppressor gene frequently hypermethylated in human breast cancer

Human breast cancer is a malignant form of tumor with a relatively high mortality rate. Although esophageal cancer-related gene 4 (ECRG4) is thought to be a possible potent tumor suppressor gene that acts to suppress breast cancer, its precise role in this disease is not understood. Herein, we assess the correlation between ECRG4 expression and DNA methylation, probing the potential epigenetic regulation of ECRG4 in breast cancer. We analyzed ECRG4 promoter methylation via methylation-specific PCR (MSPCR), bisulfite sequencing, and a promoter reporter assay in human breast cancer cell lines and samples. Gene expression was assessed by quantitative real-time PCR (qPCR), while protein levels were assessed by Western blotting. CCK8 assays were used to quantify cell growth; Esophageal cancer-related gene 4 wound healing assays were used to assess cellular migration, while flow cytometry was used to assess apoptosis and cell cycle progression. Apoptosome formation was validated via CO-IP and Western blotting. We found that human breast cancer samples exhibited increased methylation of the ECRG4 promoter and decreased ECRG4 expression. Remarkably, the down-regulation of ECRG4 was highly associated with promoter methylation, and its expression could be re-activated via 5-aza-2′-deoxycytidine treatment to induce demethylation. ECRG4 overexpression impaired breast cancer cell proliferation and migration, and led to G0/G1 cell cycle phase arrest. Moreover, ECRG4 induced the formation of the Cytc/Apaf-1/caspase-9 apoptosome and promoted breast cancer cell apoptosis. ECRG4 is silenced in human breast cancer cells and cell lines, likely owing to promoter hypermethylation. ECRG4 may act as a tumor suppressor, inhibiting proliferation and migration, inducing G0/G1 phase arrest and apoptosis via the mitochondrial apoptotic pathway.

Higher order genomic organization and epigenetic control maintain cellular identity and prevent breast cancer

Cells establish and sustain structural and functional integrity of the genome to support cellular identity and prevent malignant transformation. In this review, we present a strategic overview of epigenetic regulatory mechanisms including histone modifications and higher order chromatin organization (HCO) that are perturbed in breast cancer onset and progression. Implications for dysfunctions that occur in hormone regulation, cell cycle control, and mitotic bookmarking in breast cancer are considered, with an emphasis on epithelial-to-mesenchymal transition and cancer stem cell activities. The architectural organization of regulatory machinery is addressed within the contexts of translating cancer-compromised genomic organization to advances in breast cancer risk assessment, diagnosis, prognosis, and identification of novel therapeutic targets with high specificity and minimal off target effects.

Identification of rheumatoid arthritis causal genes using functional genomics

Over the past decade, genome-wide association studies have contributed a wealth of knowledge to our understanding of polygenic disorders such as rheumatoid arthritis. As the size of sample cohorts has improved so too have the computational and experimental methods used to robustly define variants associated with disease susceptibility. The challenge now remains to translate these findings into improved understanding of disease aetiology and patient care. Whilst much of the focus of translating the findings of genome-wide association studies has been on global analysis of all variants identified, careful functional study of individual disease susceptibility loci will be required in order to refine our understanding of how individual variants contribute to disease risk. Here, we present the argument behind such an approach and describe some of the novel tools being used to investigate risk loci. This includes the use of chromosomal conformation capture techniques and modifications of the CRISPR-Cas9 system, with several examples of their implementation being described.

Allele specific chromatin signals, 3D interactions, and motif predictions for immune and B cell related diseases

Several Genome Wide Association Studies (GWAS) have reported variants associated to immune diseases. However, the identified variants are rarely the drivers of the associations and the molecular mechanisms behind the genetic contributions remain poorly understood. ChIP-seq data for TFs and histone modifications provide snapshots of protein-DNA interactions allowing the identification of heterozygous SNPs showing significant allele specific signals (AS-SNPs). AS-SNPs can change a TF binding site resulting in altered gene regulation and are primary candidates to explain associations observed in GWAS and expression studies. We identified 17,293 unique AS-SNPs across 7 lymphoblastoid cell lines. In this set of cell lines we interrogated 85% of common genetic variants in the population for potential regulatory effect and we identified 237 AS-SNPs associated to immune GWAS traits and 714 to gene expression in B cells. To elucidate possible regulatory mechanisms we integrated long-range 3D interactions data to identify putative target genes and motif predictions to identify TFs whose binding may be affected by AS-SNPs yielding a collection of 173 AS-SNPs associated to gene expression and 60 to B cell related traits. We present a systems strategy to find functional gene regulatory variants, the TFs that bind differentially between alleles and novel strategies to detect the regulated genes.

Spatial Organization of the Gene Regulatory Program: An Information Theoretical Approach to Breast Cancer Transcriptomics

Gene regulation may be studied from an information-theoretic perspective. Gene regulatory programs are representations of the complete regulatory phenomenon associated to each biological state. In diseases such as cancer, these programs exhibit major alterations, which have been associated with the spatial organization of the genome into chromosomes. In this work, we analyze intrachromosomal, or cis-, and interchromosomal, or trans-gene regulatory programs in order to assess the differences that arise in the context of breast cancer. We find that using information theoretic approaches, it is possible to differentiate cis-and trans-regulatory programs in terms of the changes that they exhibit in the breast cancer context, indicating that in breast cancer there is a loss of trans-regulation. Finally, we use these programs to reconstruct a possible spatial relationship between chromosomes.

Identifying cis Elements for Spatiotemporal Control of Mammalian DNA Replication

The temporal order of DNA replication (replication timing [RT]) is highly coupled with genome architecture, but cis-elements regulating either remain elusive. We created a series of CRISPR-mediated deletions and inversions of a pluripotency-associated topologically associating domain (TAD) in mouse ESCs. CTCF-associated domain boundaries were dispensable for RT. CTCF protein depletion weakened most TAD boundaries but had no effect on RT or A/B compartmentalization genome-wide. By contrast, deletion of three intra-TAD CTCF-independent 3D contact sites caused a domain-wide early-to-late RT shift, an A-to-B compartment switch, weakening of TAD architecture, and loss of transcription. The dispensability of TAD boundaries and the necessity of these "early replication control elements" (ERCEs) was validated by deletions and inversions at additional domains. Our results demonstrate that discrete cis-regulatory elements orchestrate domain-wide RT, A/B compartmentalization, TAD architecture, and transcription, revealing fundamental principles linking genome structure and function.

Functional Variant rs4442975 Modulating FOXA1 Binding Affinity Can Influence Bone Marrow Suppression during Neoadjuvant Chemotherapy for Luminal A Type Breast Cancer

The expression of the transcription factor FOXA1 is associated with the prognosis of estrogen receptor (ER)-positive breast cancer, and the genetic variant rs4442975 can affect FOXA1 function. Therefore, we investigated the association between rs4442975 and the efficacy of neoadjuvant chemotherapy for luminal A type breast cancer and evaluated its toxic side effects in a Chinese population. One hundred seventy-five patients with luminal A type breast cancer receiving neoadjuvant chemotherapy with a combination protocol of epirubicin and docetaxel (ET protocol) were enrolled in the study. Genotyping was performed in a randomized manner to identify candidate genetic variants. Unconditional logistic regression analysis was used to analyze the association of the variant with the efficacy and side effects of neoadjuvant chemotherapy. The results did not reveal any positive association with the efficacy of neoadjuvant chemotherapy, with an odds ratio (OR) of 0.73 (95% confidence interval = 0.27-1.94) in the additive model. However, analysis of the toxic side effects of neoadjuvant chemotherapy showed that rs4442975 was associated with bone marrow suppression, with an OR of 0.38 (95% confidence interval = 0.17-0.73, p = 0.005) in the dominant model. In summary, the functional genetic variant rs4442975 was associated with bone marrow suppression during neoadjuvant chemotherapy for luminal A type breast cancer. These results may help establish reliable molecular markers for predicting the prognosis of personalized treatment for luminal A type breast cancer and thereby contribute to the development of appropriate therapies.

Fine mapping chromatin contacts in capture Hi-C data

BACKGROUND

Hi-C and capture Hi-C (CHi-C) are used to map physical contacts between chromatin regions in cell nuclei using high-throughput sequencing. Analysis typically proceeds considering the evidence for contacts between each possible pair of fragments independent from other pairs. This can produce long runs of fragments which appear to all make contact with the same baited fragment of interest.

RESULTS

We hypothesised that these long runs could result from a smaller subset of direct contacts and propose a new method, based on a Bayesian sparse variable selection approach, which attempts to fine map these direct contacts. Our model is conceptually novel, exploiting the spatial pattern of counts in CHi-C data. Although we use only the CHi-C count data in fitting the model, we show that the fragments prioritised display biological properties that would be expected of true contacts: for bait fragments corresponding to gene promoters, we identify contact fragments with active chromatin and contacts that correspond to edges found in previously defined enhancer-target networks; conversely, for intergenic bait fragments, we identify contact fragments corresponding to promoters for genes expressed in that cell type. We show that long runs of apparently co-contacting fragments can typically be explained using a subset of direct contacts consisting of <10% of the number in the full run, suggesting that greater resolution can be extracted from existing datasets.

CONCLUSIONS

Our results appear largely complementary to those from a per-fragment analytical approach, suggesting that they provide an additional level of interpretation that may be used to increase resolution for mapping direct contacts in CHi-C experiments.

GOPHER: Generator Of Probes for capture Hi-C Experiments at high Resolution

BACKGROUND

Target enrichment combined with chromosome conformation capturing methodologies such as capture Hi-C (CHC) can be used to investigate spatial layouts of genomic regions with high resolution and at scalable costs. A common application of CHC is the investigation of regulatory elements that are in contact with promoters, but CHC can be used for a range of other applications. Therefore, probe design for CHC needs to be adapted to experimental needs, but no flexible tool is currently available for this purpose.

RESULTS

We present a Java desktop application called GOPHER (Generator Of Probes for capture Hi-C Experiments at high Resolution) that implements three strategies for CHC probe design. GOPHER's simple approach is similar to the probe design of previous approaches that employ CHC to investigate all promoters, with one probe being placed at each margin of a single digest that overlaps the transcription start site (TSS) of each promoter. GOPHER's simple-patched approach extends this methodology with a heuristic that improves coverage of viewpoints in which the TSS is located near to one of the boundaries of the digest. GOPHER's extended approach is intended mainly for focused investigations of smaller gene sets. GOPHER can also be used to design probes for regions other than TSS such as GWAS hits or large blocks of genomic sequence. GOPHER additionally provides a number of features that allow users to visualize and edit viewpoints, and outputs a range of files useful for documentation, ordering probes, and downstream analysis.

CONCLUSION

GOPHER is an easy-to-use and robust desktop application for CHC probe design. Source code and a precompiled executable can be downloaded from the GOPHER GitHub page at https://github.com/TheJacksonLaboratory/Gopher .

Endogenous DNA Double-Strand Breaks during DNA Transactions: Emerging Insights and Methods for Genome-Wide Profiling

DNA double-strand breaks (DSBs) jeopardize genome integrity and can-when repaired unfaithfully-give rise to structural rearrangements associated with cancer. Exogenous agents such as ionizing radiation or chemotherapy can invoke DSBs, but a vast amount of breakage arises during vital endogenous DNA transactions, such as replication and transcription. Additionally, chromatin looping involved in 3D genome organization and gene regulation is increasingly recognized as a possible contributor to DSB events. In this review, we first discuss insights into the mechanisms of endogenous DSB formation, showcasing the trade-off between essential DNA transactions and the intrinsic challenges that these processes impose on genomic integrity. In the second part, we highlight emerging methods for genome-wide profiling of DSBs, and discuss future directions of research that will help advance our understanding of genome-wide DSB formation and repair.

Deletion of the murine ortholog of the 8q24 gene desert has anti-cancer effects in transgenic mammary cancer models

Background

The gene desert on human chromosomal band 8q24 harbors multiple genetic variants associated with common cancers, including breast cancer. The locus, including the gene desert and its flanking genes, MYC, PVT1 and FAM84B, is also frequently amplified in human breast cancer. We generated a megadeletion (MD) mouse model lacking 430-Kb of sequence orthologous to the breast cancer-associated region in the gene desert. The goals were to examine the effect of the deletion on mammary cancer development and on transcript level regulation of the candidate genes within the locus.

Methods

The MD allele was engineered using the MICER system in embryonic stem cells and bred onto 3 well-characterized transgenic models for breast cancer, namely MMTV-PyVT, MMTV-neu and C3(1)-TAg. Mammary tumor growth, latency, multiplicity and metastasis were compared between homozygous MD and wild type mice carrying the transgenes. A reciprocal mammary gland transplantation assay was conducted to distinguish mammary cell-autonomous from non-mammary cell-autonomous anti-cancer effects. Gene expression analysis was done using quantitative real-time PCR. Chromatin interactions were evaluated by 3C. Gene-specific patient outcome data were analysed using the METABRIC and TCGA data sets through the cBioPortal website.

Results

Mice homozygous for the MD allele are viable, fertile, lactate sufficiently to nourish their pups, but maintain a 10% lower body weight mainly due to decreased adiposity. The deletion interferes with mammary tumorigenesis in mouse models for luminal and basal breast cancer. In the MMTV-PyVT model the mammary cancer-reducing effects of the allele are mammary cell-autonomous. We found organ-specific effects on transcript level regulation, with Myc and Fam84b being downregulated in mammary gland, prostate and mammary tumor samples. Through analysis using the METABRIC and TCGA datasets, we provide evidence that MYC and FAM84B are frequently co-amplified in breast cancer, but in contrast with MYC, FAM84B is frequently overexpressed in the luminal subtype, whereas MYC activity affect basal breast cancer outcomes.

Conclusion

Deletion of a breast cancer-associated non-protein coding region affects mammary cancer development in 3 transgenic mouse models. We propose Myc as a candidate susceptibility gene, regulated by the gene desert locus, and a potential role for Fam84b in modifying breast cancer development.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-5109-8) contains supplementary material, which is available to authorized users.

Chromatin conformation analysis of primary patient tissue using a low input Hi-C method

Chromatin conformation constitutes a fundamental level of eukaryotic genome regulation. However, our ability to examine its biological function and role in disease is limited by the large amounts of starting material required to perform current experimental approaches. Here, we present Low-C, a Hi-C method for low amounts of input material. By systematically comparing Hi-C libraries made with decreasing amounts of starting material we show that Low-C is highly reproducible and robust to experimental noise. To demonstrate the suitability of Low-C to analyse rare cell populations, we produce Low-C maps from primary B-cells of a diffuse large B-cell lymphoma patient. We detect a common reciprocal translocation t(3;14)(q27;q32) affecting the BCL6 and IGH loci and abundant local structural variation between the patient and healthy B-cells. The ability to study chromatin conformation in primary tissue will be fundamental to fully understand the molecular pathogenesis of diseases and to eventually guide personalised therapeutic strategies.

Chromatin conformation studies are limited by the large amounts of starting material required to perform current protocols. Here the authors present Low-C, a Hi-C method for low amounts of input material and produce Low-C maps from primary B-cells of a diffuse large B-cell lymphoma patient, demonstrating the suitability of Low-C to analyse rare cell populations.

Similarities and differences in the regulation of HoxD genes during chick and mouse limb development

In all tetrapods examined thus far, the development and patterning of limbs require the activation of gene members of the HoxD cluster. In mammals, they are regulated by a complex bimodal process that controls first the proximal patterning and then the distal structure. During the shift from the former to the latter regulation, this bimodal regulatory mechanism allows the production of a domain with low Hoxd gene expression, at which both telomeric (T-DOM) and centromeric regulatory domains (C-DOM) are silent. These cells generate the future wrist and ankle articulations. We analyzed the implementation of this regulatory mechanism in chicken, i.e., in an animal for which large morphological differences exist between fore- and hindlimbs. We report that although this bimodal regulation is globally conserved between the mouse and the chick, some important modifications evolved at least between these two model systems, in particular regarding the activity of specific enhancers, the width of the TAD boundary separating the two regulations, and the comparison between the forelimb versus hindlimb regulatory controls. At least one aspect of these regulations seems to be more conserved between chick and bats than with mouse, which may relate to the extent to which forelimbs and hindlimbs of these various animals differ in their morphologies.

A comparison of Hox gene regulation during the development of limbs in birds and mammals reveals that whereas the characteristic bimodal regulatory system, based on large chromatin domains, is largely conserved between these morphologically distinct structures, some differences are revealed in the way this is implemented in various vertebrates.

The shapes of limbs vary greatly among tetrapod species, even between the forelimbs and hindlimbs of the same animal. Hox genes regulate the proper growth and patterning of tetrapod limbs. In order to evaluate whether variations in the complex regulation of a cluster of Hox genes—the Hoxd genes—during limb development contribute to the differences in limb shape, we compared their transcriptional control during limb bud development in the forelimbs and hindlimbs of mouse and chicken embryos. We found that the regulatory mechanism underlying Hoxd gene expression is highly conserved, but some clear differences exist. For instance, we observed a variation in the topologically associating domain (TAD; a self-interacting genomic region) boundary interval between the mouse and the chick, as well as differences in the activity of a conserved enhancer element situated within the telomeric regulatory domain. In contrast to the mouse, the chicken enhancer has a stronger activity in the forelimb buds than in the hindlimb buds, which is correlated with the striking differences in the mRNA levels of the genes. We conclude that differences in both the timing and duration of TAD activities and in the width of their boundary may parallel the important decrease in Hoxd gene transcription in chick hindlimb buds versus forelimb buds. These differences may also account for the slightly distinct regulatory strategies implemented by mammals and birds at this locus.