Capture Hi-C Data Research Articles

Investigating the role of WT1 in Brugada syndrome pathophysiology

Abstract Background Brugada syndrome (BrS) is a heritable arrhythmic disorder presumably caused by conduction slowing in the right ventricular (RV) outflow tract (RVOT). A recent genome-wide association study found an association between common variants on chromosome 11 near WT1, tagged by the SNP rs72905083, and BrS susceptibility. Purpose To study whether WT1 affects cardiac conduction and could therefore represents the underlying association of the chr11 locus with BrS susceptibility. Methods We conducted in silico analyses of omics datasets to identify the likely causal gene at the chr11 BrS locus. Next, we conducted electrophysiological studies in heterozygous young adult (4 months old) Wt1-deficient (Wt1+/-) mice and wild-type littermates to explore the effect of Wt1 on cardiac conduction. To study the potential role of Wt1 in the setting of reduced conduction reserve, we compared aged Scn5a haploinsufficient mice (Scn5a+/-;Wt1+/+, 17 months) with littermates haploinsufficient for both Wt1 and Scn5a (Scn5a+/-;Wt1+/-). Optical mapping was performed in Langendorff-perfused hearts, where conduction was further challenged by acute ajmaline administration or by pacing at the effective refractory period (ERP). Transcriptomic analyses were conducted in Wt1+/- and wild-type mice by RNAseq of PCM1-sorted cardiomyocytes. Results Analysis of omics datasets uncovered WT1 as the likely causal gene since WT1 was the closest gene to, and in fact overlapped, the BrS-associated region; promoter capture Hi-C data in human tissue showed chromatin contact between the promoter of WT1 and the associated region; and H3K27ac ChIP-seq signals (i.e. enhancer activity) of putative cardiac enhancers located within the associated region were correlated with WT1 transcript abundance across multiple tissues (Z-score = 4.32, p=1.57e-05). No differences were observed in ECG parameters or optically mapped epicardial conduction between Wt1+/- mice and wild-type littermates. However, comparison of aged Scn5a+/-;Wt1+/- and Scn5a+/-; Wt1+/+ hearts, in the setting of acute ajmaline challenge or pacing at the ERP, showed faster conduction in RV/RVOT in the presence of Wt1 haploinsufficiency (genotype effect: p=0.057 and p=0.041 for RV and RVOT epicardial conduction, respectively, during ajmaline challenge; p=0.034 for RV epicardial conduction during pacing at ERP). RNAseq of RV/RVOT cardiomyocytes revealed higher Scn5a expression in Wt1+/- vs wild-type (p=0.006). Conclusion We identified WT1 as the gene that most likely underlies the effect of the chr11 BrS susceptibility locus. Functional data revealed protective effects of reduced Wt1 expression in the setting of reduced conduction reserve. Transcriptomic data suggest that this occurs through a transcriptional effect on Scn5a expression.

Enhlink infers distal and context-specific enhancer–promoter linkages

Enhlink is a computational tool for scATAC-seq data analysis, facilitating precise interrogation of enhancer function at the single-cell level. It employs an ensemble approach incorporating technical and biological covariates to infer condition-specific regulatory DNA linkages. Enhlink can integrate multi-omic data for enhanced specificity, when available. Evaluation with simulated and real data, including multi-omic datasets from the mouse striatum and novel promoter capture Hi-C data, demonstrate that Enhlink outperfoms alternative methods. Coupled with eQTL analysis, it identified a putative super-enhancer in striatal neurons. Overall, Enhlink offers accuracy, power, and potential for revealing novel biological insights in gene regulation.

RELA governs a network of islet-specific metabolic genes necessary for beta cell function

Aims/hypothesisNF-κB activation unites metabolic and inflammatory responses in many diseases yet less is known about the role that NF-κB plays in normal metabolism. In this study we investigated how RELA impacts the beta cell transcriptional landscape and provides network control over glucoregulation.MethodsWe generated novel mouse lines harbouring beta cell-specific deletion of either the Rela gene, encoding the canonical NF-κB transcription factor p65 (βp65KO mice), or the Ikbkg gene, encoding the NF-κB essential modulator NEMO (βNEMOKO mice), as well as βA20Tg mice that carry beta cell-specific and forced transgenic expression of the NF-κB-negative regulator gene Tnfaip3, which encodes the A20 protein. Mouse studies were complemented by bioinformatics analysis of human islet chromatin accessibility (assay for transposase-accessible chromatin with sequencing [ATAC-seq]), promoter capture Hi-C (pcHi-C) and p65 binding (chromatin immunoprecipitation–sequencing [ChIP-seq]) data to investigate genome-wide control of the human beta cell metabolic programme.ResultsRela deficiency resulted in complete loss of stimulus-dependent inflammatory gene upregulation, consistent with its known role in governing inflammation. However, Rela deletion also rendered mice glucose intolerant because of functional loss of insulin secretion. Glucose intolerance was intrinsic to beta cells as βp65KO islets failed to secrete insulin ex vivo in response to a glucose challenge and were unable to restore metabolic control when transplanted into secondary chemical-induced hyperglycaemic recipients. Maintenance of glucose tolerance required Rela but was independent of classical NF-κB inflammatory cascades, as blocking NF-κB signalling in vivo by beta cell knockout of Ikbkg (NEMO), or beta cell overexpression of Tnfaip3 (A20), did not cause severe glucose intolerance. Thus, basal p65 activity has an essential and islet-intrinsic role in maintaining normal glucose homeostasis. Genome-wide bioinformatic mapping revealed the presence of p65 binding sites in the promoter regions of specific metabolic genes and in the majority of islet enhancer hubs (~70% of ~1300 hubs), which are responsible for shaping beta cell type-specific gene expression programmes. Indeed, the islet-specific metabolic genes Slc2a2, Capn9 and Pfkm identified within the large network of islet enhancer hub genes showed dysregulated expression in βp65KO islets.Conclusions/interpretationThese data demonstrate an unappreciated role for RELA as a regulator of islet-specific transcriptional programmes necessary for the maintenance of healthy glucose metabolism. These findings have clinical implications for the use of anti-inflammatories, which influence NF-κB activation and are associated with diabetes.Graphical

P152 Allele-associated chromatin interactions in primary immune cells uncover mechanisms of gene regulation

Abstract Background/Aims Genome-wide association studies studies have identified many genetic variants associated with the risk of developing common rheumatological conditions. These variants primarily affect regulatory elements of the genome which can affect the expression of genes located far away from their genomic location through chromatin interaction mechanisms. Furthermore, common genetic variants have been associated with specific changes in chromatin interactions in cell lines. Studying chromatin interactions can improve our understanding of these diseases. Here, we aim to study chromatin interactions and how they are affected by genetic variation in primary immune cells that are relevant for rheumatological conditions. Methods We generated promoter Capture Hi-C (pCHi-C) data for primary CD4+ T cells and CD14+ monocytes isolated from peripheral blood of 10 systemic sclerosis patients and 5 healthy controls. We developed a new computational pipeline to explore how genetic variation affects chromatin interactions. Briefly, genotypes were called from the pCHi-C data using a modified GLIMPSE pipeline. Genotypes were phased using the integrated phasing pipeline which combined population haplotype information with the pCHi-C data. Reads were then assigned to the correct allele using SNPsplit and assigned to chromatin loops (CHiCAGO score > 5). A test for allelic imbalance was carried out using a binomial test, and results were consolidated using Fisher’s method. Results We identified 171 and 139 allele-associated loops (FDR < 0.05) in CD4+ T cells and CD14+ monocytes respectively. These interactions are cell-type specific and highly associated with expression quantitative trait loci (eQTLs). We found that 80% of the variants involved in allele-associated loops were eQTLs for at least one gene. These interactions link to the promoters of 62 and 57 distal genes in CD4+ T cells and CD14+ monocytes respectively. Of these, 38 and 31 respectively were also eQTL for those genes. Many of these allele-associated interactions affect genomic regions that are highly significant for the function of these cell populations. For example, a region with variants affecting T cell-specific chromatin interactions is the TRAF3IP3/IRF6/UTP25 locus. In this region there are separate groups of variants which affect gene expression with a complex mechanism. A group of variants overlapping the UTP25 promoter increases interaction strength with the TRAF3IP3 and IRF6 gene bodies. Interestingly, these are also associated with an increase in UTP25 expression but a decrease in TRAF3IP3 and IRF6 expression. Another group of variants located near the IRF6 promoter is associated with increased interactions with the UTP25 promoter. In contrast, these variants are associated with a reduction of UTP25 expression but an increase in TRAF3IP3 and IRF6 expression. Conclusion We created a new pipeline that allows the identification of allele-associated chromatin interactions in primary cells. These new data will be helpful in uncovering mechanisms of gene regulation in loci highly significant to autoimmune conditions. Disclosure D. González-Serna: None. C. Shi: None. M. Kerick: None. J. Hankinson: None. J. Ding: None. A. McGovern: None. M. Tutino: None. G. Villanueva Martin: None. N. Ortego-Centeno: None. J. Luis Callejas: None. J. Martin: None. G. Orozco: None.

Widespread allele-specific topological domains in the human genome are not confined to imprinted gene clusters

BackgroundThere is widespread interest in the three-dimensional chromatin conformation of the genome and its impact on gene expression. However, these studies frequently do not consider parent-of-origin differences, such as genomic imprinting, which result in monoallelic expression. In addition, genome-wide allele-specific chromatin conformation associations have not been extensively explored. There are few accessible bioinformatic workflows for investigating allelic conformation differences and these require pre-phased haplotypes which are not widely available.ResultsWe developed a bioinformatic pipeline, “HiCFlow,” that performs haplotype assembly and visualization of parental chromatin architecture. We benchmarked the pipeline using prototype haplotype phased Hi-C data from GM12878 cells at three disease-associated imprinted gene clusters. Using Region Capture Hi-C and Hi-C data from human cell lines (1-7HB2, IMR-90, and H1-hESCs), we can robustly identify the known stable allele-specific interactions at the IGF2-H19 locus. Other imprinted loci (DLK1 and SNRPN) are more variable and there is no “canonical imprinted 3D structure,” but we could detect allele-specific differences in A/B compartmentalization. Genome-wide, when topologically associating domains (TADs) are unbiasedly ranked according to their allele-specific contact frequencies, a set of allele-specific TADs could be defined. These occur in genomic regions of high sequence variation. In addition to imprinted genes, allele-specific TADs are also enriched for allele-specific expressed genes. We find loci that have not previously been identified as allele-specific expressed genes such as the bitter taste receptors (TAS2Rs).ConclusionsThis study highlights the widespread differences in chromatin conformation between heterozygous loci and provides a new framework for understanding allele-specific expressed genes.

Algorithmic considerations when analysing capture Hi-C data.

Chromosome conformation capture methodologies have provided insight into the effect of 3D genomic architecture on gene regulation. Capture Hi-C (CHi-C) is a recent extension of Hi-C that improves the effective resolution of chromatin interactions by enriching for defined regions of biological relevance. The varying targeting efficiency between capture regions, however, introduces bias not present in conventional Hi-C, making analysis more complicated. Here we consider salient features of an algorithm that should be considered in evaluating the performance of a program used to analyse CHi-C data in order to infer meaningful interactions. We use the program CHICAGO to analyse promoter capture Hi-C data generated on 28 different cell lines as a case study.

Genome-wide association study in minority children with asthma implicates DNAH5 in bronchodilator responsiveness

Variability in response to short-acting β2-agonists (e.g., albuterol) among patients with asthma from diverse racial/ethnic groups may contribute to asthma disparities. We sought to identify genetic variants associated with bronchodilator response (BDR) to identify potential mechanisms of drug response and risk factors for worse asthma outcomes. Genome-wide association studies of bronchodilator response (BDR) were performed using TOPMed Whole Genome Sequencing data of the Asthma Translational Genomic Collaboration (ATGC), which corresponded to 1136 Puerto Rican, 656 Mexican and 4337 African American patients with asthma. With the population-specific GWAS results, a trans-ethnic meta-analysis was performed to identify BDR-associated variants shared across the three populations. Replication analysis was carried out in three pediatric asthma cohorts, including CAMP (Childhood Asthma Management Program; n = 560), GACRS (Genetics of Asthma in Costa Rica Study; n = 967) and HPR (Hartford-Puerto Rico; n = 417). A genome-wide significant locus (rs35661809; P = 3.61 × 10–8) in LINC02220, a non-coding RNA gene, was identified in Puerto Ricans. While this region was devoid of protein-coding genes, capture Hi-C data showed a distal interaction with the promoter of the DNAH5 gene in lung tissue. In replication analysis, the GACRS cohort yielded a nominal association (1-tailed P < 0.05). No genetic variant was associated with BDR at the genome-wide significant threshold in Mexicans and African Americans. Our findings help inform genetic underpinnings of BDR for understudied minority patients with asthma, but the limited availability of genetic data for racial/ethnic minority children with asthma remains a paramount challenge.

MaxHiC: A robust background correction model to identify biologically relevant chromatin interactions in Hi-C and capture Hi-C experiments.

Hi-C is a genome-wide chromosome conformation capture technology that detects interactions between pairs of genomic regions and exploits higher order chromatin structures. Conceptually Hi-C data counts interaction frequencies between every position in the genome and every other position. Biologically functional interactions are expected to occur more frequently than transient background and artefactual interactions. To identify biologically relevant interactions, several background models that take biases such as distance, GC content and mappability into account have been proposed. Here we introduce MaxHiC, a background correction tool that deals with these complex biases and robustly identifies statistically significant interactions in both Hi-C and capture Hi-C experiments. MaxHiC uses a negative binomial distribution model and a maximum likelihood technique to correct biases in both Hi-C and capture Hi-C libraries. We systematically benchmark MaxHiC against major Hi-C background correction tools including Hi-C significant interaction callers (SIC) and Hi-C loop callers using published Hi-C, capture Hi-C, and Micro-C datasets. Our results demonstrate that 1) Interacting regions identified by MaxHiC have significantly greater levels of overlap with known regulatory features (e.g. active chromatin histone marks, CTCF binding sites, DNase sensitivity) and also disease-associated genome-wide association SNPs than those identified by currently existing models, 2) the pairs of interacting regions are more likely to be linked by eQTL pairs and 3) more likely to link known regulatory features including known functional enhancer-promoter pairs validated by CRISPRi than any of the existing methods. We also demonstrate that interactions between different genomic region types have distinct distance distributions only revealed by MaxHiC. MaxHiC is publicly available as a python package for the analysis of Hi-C, capture Hi-C and Micro-C data.

Discovery and Functional Characterization of Two Regulatory Variants Underlying Lupus Susceptibility at 2p13.1.

Genome-wide association studies have identified 2p13.1 as a prominent susceptibility locus for systemic lupus erythematosus (SLE)—a complex, multisystem autoimmune disease. However, the identity of underlying causal variant (s) and molecular mechanisms for increasing disease susceptibility are poorly understood. Using meta-analysis (cases = 10,252, controls = 21,604) followed by conditional analysis, bioinformatic annotation, and eQTL and 3D-chromatin interaction analyses, we computationally prioritized potential functional variants and subsequently experimentally validated their effects. Ethnicity-specific meta-analysis revealed striking allele frequency differences between Asian and European ancestries, but with similar odds ratios. We identified 20 genome-wide significant (p < 5 × 10−8) variants, and conditional analysis pinpointed two potential functional variants, rs6705628 and rs2272165, likely to explain the association. The two SNPs are near DGUOK, mitochondrial deoxyguanosine kinase, and its associated antisense RNA DGUOK-AS1. Using luciferase reporter gene assays, we found significant cell type- and allele-specific promoter activity at rs6705628 and enhancer activity at rs2272165. This is supported by ChIP-qPCR showing allele-specific binding with three histone marks (H3K27ac, H3K4me3, and H3K4me1), RNA polymerase II (Pol II), transcriptional coactivator p300, CCCTC-binding factor (CTCF), and transcription factor ARID3A. Transcriptome data across 28 immune cell types from Asians showed both SNPs are cell-type-specific but only in B-cells. Splicing QTLs showed strong regulation of DGUOK-AS1. Genotype-specific DGOUK protein levels are supported by Western blots. Promoter capture Hi-C data revealed long-range chromatin interactions between rs2272165 and several nearby promoters, including DGUOK. Taken together, we provide mechanistic insights into how two noncoding variants underlie SLE risk at the 2p13.1 locus.

Super interactive promoters provide insight into cell type-specific regulatory networks in blood lineage cell types.

Existing studies of chromatin conformation have primarily focused on potential enhancers interacting with gene promoters. By contrast, the interactivity of promoters per se, while equally critical to understanding transcriptional control, has been largely unexplored, particularly in a cell type-specific manner for blood lineage cell types. In this study, we leverage promoter capture Hi-C data across a compendium of blood lineage cell types to identify and characterize cell type-specific super-interactive promoters (SIPs). Notably, promoter-interacting regions (PIRs) of SIPs are more likely to overlap with cell type-specific ATAC-seq peaks and GWAS variants for relevant blood cell traits than PIRs of non-SIPs. Moreover, PIRs of cell-type-specific SIPs show enriched heritability of relevant blood cell trait (s), and are more enriched with GWAS variants associated with blood cell traits compared to PIRs of non-SIPs. Further, SIP genes tend to express at a higher level in the corresponding cell type. Importantly, SIP subnetworks incorporating cell-type-specific SIPs and ATAC-seq peaks help interpret GWAS variants. Examples include GWAS variants associated with platelet count near the megakaryocyte SIP gene EPHB3 and variants associated lymphocyte count near the native CD4 T-Cell SIP gene ETS1. Interestingly, around 25.7% ~ 39.6% blood cell traits GWAS variants residing in SIP PIR regions disrupt transcription factor binding motifs. Importantly, our analysis shows the potential of using promoter-centric analyses of chromatin spatial organization data to identify biologically important genes and their regulatory regions.

Comparison of Capture Hi-C Analytical Pipelines.

It is now evident that DNA forms an organized nuclear architecture, which is essential to maintain the structural and functional integrity of the genome. Chromatin organization can be systematically studied due to the recent boom in chromosome conformation capture technologies (e.g., 3C and its successors 4C, 5C and Hi-C), which is accompanied by the development of computational pipelines to identify biologically meaningful chromatin contacts in such data. However, not all tools are applicable to all experimental designs and all structural features. Capture Hi-C (CHi-C) is a method that uses an intermediate hybridization step to target and select predefined regions of interest in a Hi-C library, thereby increasing effective sequencing depth for those regions. It allows researchers to investigate fine chromatin structures at high resolution, for instance promoter-enhancer loops, but it introduces additional biases with the capture step, and therefore requires specialized pipelines. Here, we compare multiple analytical pipelines for CHi-C data analysis. We consider the effect of retaining multi-mapping reads and compare the efficiency of different statistical approaches in both identifying reproducible interactions and determining biologically significant interactions. At restriction fragment level resolution, the number of multi-mapping reads that could be rescued was negligible. The number of identified interactions varied widely, depending on the analytical method, indicating large differences in type I and type II error rates. The optimal pipeline depends on the project-specific tolerance level of false positive and false negative chromatin contacts.

Chromatin accessibility and gene expression during adipocyte differentiation identify context-dependent effects at cardiometabolic GWAS loci.

Chromatin accessibility and gene expression in relevant cell contexts can guide identification of regulatory elements and mechanisms at genome-wide association study (GWAS) loci. To identify regulatory elements that display differential activity across adipocyte differentiation, we performed ATAC-seq and RNA-seq in a human cell model of preadipocytes and adipocytes at days 4 and 14 of differentiation. For comparison, we created a consensus map of ATAC-seq peaks in 11 human subcutaneous adipose tissue samples. We identified 58,387 context-dependent chromatin accessibility peaks and 3,090 context-dependent genes between all timepoint comparisons (log2 fold change>1, FDR<5%) with 15,919 adipocyte- and 18,244 preadipocyte-dependent peaks. Adipocyte-dependent peaks showed increased overlap (60.1%) with Roadmap Epigenomics adipocyte nuclei enhancers compared to preadipocyte-dependent peaks (11.5%). We linked context-dependent peaks to genes based on adipocyte promoter capture Hi-C data, overlap with adipose eQTL variants, and context-dependent gene expression. Of 16,167 context-dependent peaks linked to a gene, 5,145 were linked by two or more strategies to 1,670 genes. Among GWAS loci for cardiometabolic traits, adipocyte-dependent peaks, but not preadipocyte-dependent peaks, showed significant enrichment (LD score regression P<0.005) for waist-to-hip ratio and modest enrichment (P < 0.05) for HDL-cholesterol. We identified 659 peaks linked to 503 genes by two or more approaches and overlapping a GWAS signal, suggesting a regulatory mechanism at these loci. To identify variants that may alter chromatin accessibility between timepoints, we identified 582 variants in 454 context-dependent peaks that demonstrated allelic imbalance in accessibility (FDR<5%), of which 55 peaks also overlapped GWAS variants. At one GWAS locus for palmitoleic acid, rs603424 was located in an adipocyte-dependent peak linked to SCD and exhibited allelic differences in transcriptional activity in adipocytes (P = 0.003) but not preadipocytes (P = 0.09). These results demonstrate that context-dependent peaks and genes can guide discovery of regulatory variants at GWAS loci and aid identification of regulatory mechanisms.

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

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

Algorithmic considerations when analysing capture Hi-C data.

Chromosome conformation capture methodologies have provided insight into the effect of 3D genomic architecture on gene regulation. Capture Hi-C (CHi-C) is a recent extension of Hi-C that improves the effective resolution of chromatin interactions by enriching for defined regions of biological relevance. The varying targeting efficiency between capture regions, however, introduces bias not present in conventional Hi-C, making analysis more complicated. Here we consider salient features of an algorithm that should be considered in evaluating the performance of a program used to analyse CHi-C data in order to infer meaningful interactions. We use the program CHICAGO to analyse promotor capture Hi-C data generated on 28 different cell lines as a case study.

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

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

Algorithmic considerations when analysing capture Hi-C data

Algorithmic considerations when analysing capture Hi-C data

Algorithmic considerations when analysing capture Hi-C data

Algorithmic considerations when analysing capture Hi-C data

Single-Cell Transcriptome Analysis Reveals Dynamic Cell Populations and Differential Gene Expression Patterns in Control and Aneurysmal Human Aortic Tissue.

Ascending thoracic aortic aneurysm (ATAA) is caused by the progressive weakening and dilatation of the aortic wall and can lead to aortic dissection, rupture, and other life-threatening complications. To improve our understanding of ATAA pathogenesis, we aimed to comprehensively characterize the cellular composition of the ascending aortic wall and to identify molecular alterations in each cell population of human ATAA tissues. We performed single-cell RNA sequencing analysis of ascending aortic tissues from 11 study participants, including 8 patients with ATAA (4 women and 4 men) and 3 control subjects (2 women and 1 man). Cells extracted from aortic tissue were analyzed and categorized with single-cell RNA sequencing data to perform cluster identification. ATAA-related changes were then examined by comparing the proportions of each cell type and the gene expression profiles between ATAA and control tissues. We also examined which genes may be critical for ATAA by performing the integrative analysis of our single-cell RNA sequencing data with publicly available data from genome-wide association studies. We identified 11 major cell types in human ascending aortic tissue; the high-resolution reclustering of these cells further divided them into 40 subtypes. Multiple subtypes were observed for smooth muscle cells, macrophages, and T lymphocytes, suggesting that these cells have multiple functional populations in the aortic wall. In general, ATAA tissues had fewer nonimmune cells and more immune cells, especially T lymphocytes, than control tissues did. Differential gene expression data suggested the presence of extensive mitochondrial dysfunction in ATAA tissues. In addition, integrative analysis of our single-cell RNA sequencing data with public genome-wide association study data and promoter capture Hi-C data suggested that the erythroblast transformation-specific related gene(ERG) exerts an important role in maintaining normal aortic wall function. Our study provides a comprehensive evaluation of the cellular composition of the ascending aortic wall and reveals how the gene expression landscape is altered in human ATAA tissue. The information from this study makes important contributions to our understanding of ATAA formation and progression.

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.

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.