Chromatin Interaction Analysis Research Articles

Virus Infection Induces Immune Gene Activation with CTCF Anchored Enhancers and Chromatin Interactions in Pig Genome.

Chromatin organization is important for gene transcription in pig genome. However, its three-dimensional (3D) structure and dynamics are much less investigated than those in human. Here we applied the long-reads chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) method to map the whole-genome chromatin interactions mediated by CCCTC-binding factor (CTCF) and RNA polymerase Ⅱ (RNAPⅡ or POLⅡ) in porcine macrophage cells before and after polyinosinic-polycytidylic acid [Poly(I:C)] induction. Our results revealed that Poly(I:C) induction impacts the 3D genome organization in the 3D4/21 cells at the fine-scale chromatin loop level rather than at the large-scale domain level. Furthermore, our findings underscored the pivotal role of CTCF anchored chromatin interactions in reshaping chromatin architecture during immune responses. Knock-out of the CTCF locus further confirmed that the CTCF anchored enhancers are associated with the activation of immune genes via long-range interactions. Notably, ChIA-PET data also supported the spatial relationship between single nucleotide polymorphisms (SNPs) and the related gene transcription in 3D genome aspect. Our findings in this study provide new clues and potential targets to explore key elements related to diseases in swine and are also likely to shed light on elucidating chromatin organization and dynamics underlying the process of mammalian infectious diseases.

Fine mapping of candidate effector genes for heart rate.

An elevated resting heart rate (RHR) is associated with increased cardiovascular mortality. Genome-wide association studies (GWAS) have identified > 350 loci. Uniquely, in this study we applied genetic fine-mapping leveraging tissue specific chromatin segmentation and colocalization analyses to identify causal variants and candidate effector genes for RHR. We used RHR GWAS summary statistics from 388,237 individuals of European ancestry from UK Biobank and performed fine mapping using publicly available genomic annotation datasets. High-confidence causal variants (accounting for > 75% posterior probability) were identified, and we collated candidate effector genes using a multi-omics approach that combined evidence from colocalisation with molecular quantitative trait loci (QTLs), and long-range chromatin interaction analyses. Finally, we performed druggability analyses to investigate drug repurposing opportunities. The fine mapping pipeline indicated 442 distinct RHR signals. For 90 signals, a single variant was identified as a high-confidence causal variant, of which 22 were annotated as missense. In trait-relevant tissues, 39 signals colocalised with cis-expression QTLs (eQTLs), 3 with cis-protein QTLs (pQTLs), and 75 had promoter interactions via Hi-C. In total, 262 candidate genes were highlighted (79% had promoter interactions, 15% had a colocalised eQTL, 8% had a missense variant and 1% had a colocalised pQTL), and, for the first time, enrichment in nervous system pathways. Druggability analyses highlighted ACHE, CALCRL, MYT1 and TDP1 as potential targets. Our genetic fine-mapping pipeline prioritised 262 candidate genes for RHR that warrant further investigation in functional studies, and we provide potential therapeutic targets to reduce RHR and cardiovascular mortality.

Learning Micro-C from Hi-C with diffusion models.

In the last few years, Micro-C has shown itself as an improved alternative to Hi-C. It replaced the restriction enzymes in Hi-C assays with micrococcal nuclease (MNase), resulting in capturing nucleosome resolution chromatin interactions. The signal-to-noise improvement of Micro-C allows it to detect more chromatin loops than high-resolution Hi-C. However, compared with massive Hi-C datasets available in the literature, there are only a limited number of Micro-C datasets. To take full advantage of these Hi-C datasets, we present HiC2MicroC, a computational method learning and then predicting Micro-C from Hi-C based on the denoising diffusion probabilistic models (DDPM). We trained our DDPM and other regression models in human foreskin fibroblast (HFFc6) cell line and evaluated these methods in six different cell types at 5-kb and 1-kb resolution. Our evaluations demonstrate that both HiC2MicroC and regression methods can markedly improve Hi-C towards Micro-C, and our DDPM-based HiC2MicroC outperforms regression in various terms. First, HiC2MicroC successfully recovers most of the Micro-C loops even those not detected in Hi-C maps. Second, a majority of the HiC2MicroC-recovered loops anchor CTCF binding sites in a convergent orientation. Third, HiC2MicroC loops share genomic and epigenetic properties with Micro-C loops, including linking promoters and enhancers, and their anchors are enriched for structural proteins (CTCF and cohesin) and histone modifications. Lastly, we find our recovered loops are also consistent with the loops identified from promoter capture Micro-C (PCMicro-C) and Chromatin Interaction Analysis by Paired-End Tag Sequencing (ChIA-PET). Overall, HiC2MicroC is an effective tool for further studying Hi-C data with Micro-C as a template. HiC2MicroC is publicly available at https://github.com/zwang-bioinformatics/HiC2MicroC/.

Deciphering cell types by integrating scATAC-seq data with genome sequences.

The single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) technology provides insight into gene regulation and epigenetic heterogeneity at single-cell resolution, but cell annotation from scATAC-seq remains challenging due to high dimensionality and extreme sparsity within the data. Existing cell annotation methods mostly focus on the cell peak matrix without fully utilizing the underlying genomic sequence. Here we propose a method, SANGO, for accurate single-cell annotation by integrating genome sequences around the accessibility peaks within scATAC data. The genome sequences of peaks are encoded into low-dimensional embeddings, and then iteratively used to reconstruct the peak statistics of cells through a fully connected network. The learned weights are considered as regulatory modes to represent cells, and utilized to align the query cells and the annotated cells in the reference data through a graph transformer network for cell annotations. SANGO was demonstrated to consistently outperform competing methods on 55 paired scATAC-seq datasets across samples, platforms and tissues. SANGO was also shown to be able to detect unknown tumor cells through attention edge weights learned by the graph transformer. Moreover, from the annotated cells, we found cell-type-specific peaks that provide functional insights/biological signals through expression enrichment analysis, cis-regulatory chromatin interaction analysis and motif enrichment analysis.

Integrated enhancer regulatory network by enhancer-promoter looping in gastric cancer.

Enhancer cis-regulatory elements play critical roles in gene regulation at many stages of cell growth. Enhancers in cancer cells also regulate the transcription of oncogenes. In this study, we performed a comprehensive analysis of long-range chromatin interactions, histone modifications, chromatin accessibility and expression in two gastric cancer (GC) cell lines compared to normal gastric epithelial cells. We found that GC-specific enhancers marked by histone modifications can activate a population of genes, including some oncogenes, by interacting with their proximal promoters. In addition, motif analysis of enhancer-promoter interacting enhancers showed that GC-specific transcription factors are enriched. Among them, we found that MYB is crucial for GC cell growth and activated by the enhancer with an enhancer-promoter loop and TCF7upregulation. Clinical GC samples showed epigenetic activation of enhancers at the MYB locus and significant upregulation of TCF7 and MYB, regardless of molecular GC subtype and clinicopathological factors. Single-cell RNA sequencingof gastric mucosa with intestinal metaplasia showed high expression of TCF7 and MYB in intestinal stem cells. When we inactivated the loop-forming enhancer at the MYB locus using CRISPRinterference (dCas9-KRAB), GC cell growth was significantly inhibited. In conclusion, we identified MYB as an oncogene activated by a loop-forming enhancer and contributing to GC cell growth.

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

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

The Transcriptional Landscape of Ph+B-ALL Is Orchestrated By Long-Range Enhancer-Promoter Interactions and the Coordinated Action of Phosphorylation-Dependent and Phosphorylation-Independent Transcription Factors

Background and Hypothesis: B-cell precursor leukaemia (B-ALL) subtypes can be distinguished by their initiating genetic lesions. Likewise, each subtype exhibits a unique transcriptional program that drives the malignancy and defines the subtype. This often relates to the initiating genetic alteration that affects genes encoding transcription factors (TFs). However, for B-ALLs not driven by TF alterations it is less clear how the transcriptional program is established. Ph+B-ALL is a historically poor prognosis B-ALL driven by the BCR::ABL1 oncogene, a constitutive tyrosine kinase that activates its targets via phosphorylation. We hypothesised that BCR::ABL1 initiates the transcriptional signature of Ph+B-ALL cells by a two-step mechanism involving phosphorylation-activatable TFs, which then induce the expression of additional TFs that consecutively establish the Ph+B-ALL-defining transcriptome. As gene expression is often modulated by cis-regulatory elements such as enhancers, we further speculated that BCR::ABL1 reprograms the enhancer landscape to promote the transcriptional changes that define Ph+B-ALL. Results: Using an in vitro model of BCR::ABL1-induced transformation of murine B-cell precursors, we show that transcriptional deregulation by BCR::ABL1 is a continuous process evolving until full transformation is established. Only a minority of induced transcriptional changes occur immediately upon BCR::ABL1 expression (3 days), while most affected genes require an extended period of BCR::ABL1 to become deregulated. Furthermore, less than half of deregulated genes in murine Ph+B-ALL and less than 10% of human Ph+B-ALL-defining genes were sensitive to 24h BCR::ABL1 inhibition, suggesting an involvement of phosphorylation-dependent and phosphorylation-independent TFs. TF motif enrichment analysis of active chromatin in human and mouse Ph+B-ALL cells indicated the phosphorylation-dependent TF STAT5 and the phosphorylation-independent TF ETV5 as main mediators. Degron-induced rapid degradation of each TF in Ph+B-ALL cells confirmed their unique and overlapping roles and coordinated action. In parallel to gene expression changes, Ph+B-ALL cells further acquire changes in their enhancer signatures defined by H3K27ac+ at non-promoter regions, and these enhancer signatures can be efficiently used to distinguish Ph+B-ALL from other B-ALL subtypes. Analysis of chromatin interactions of these active enhancers with respective active promoters by Promoter-Capture Hi-C (PCHI-C) in combination with H3K27ac-ChIP-Seq showed that most active genes in Ph+B-ALL cells physically interact with an enhancer, including many genes described to be essential for Ph+B-ALL cells, such as CCND2, BCL2, BCL2L1, and ETV5. Using PCHI-C and H3K27ac-HiChIP we further show that, like gene expression and enhancer signatures, physical contacts of promoters and enhancers can be efficiently used to identify Ph+B-ALL cells and distinguish them from other B-ALLs such as KMT2A-rearranged B-ALL. Subtype-specific chromatin interactions that allow this differentiation directly relate to the transcriptional programs that define the respective B-ALL subtype. Conclusion: We describe here the molecular details of how signals from an oncogenic tyrosine kinase become converted into changes in gene expression that define the B-lineage leukaemia subtype Ph+B-ALL. Our work highlights the complexity of these changes by acknowledging the role of gene regulatory elements/enhancers in this process and further describe for the first time that B-ALL subtypes can be distinguished by their enhancer usage and promoter-enhancer interactions. Uncovering the precise enhancer regions for the genes deregulated in Ph+B-ALL and the TFs recruited to them may open new windows for therapeutic intervention.

Abstract 16731: Post-GWAS Functional Analysis of CBLN2 Locus Reveals the Role of CUX1 in the Inflammatory Pathogenesis of Pulmonary Arterial Hypertension

Introduction: Pulmonary arterial hypertension (PAH) is a complex and deadly pulmonary vascular disease highly relevant to genetic susceptibility and acquired inflammatory triggers. Chr18q22.3 near cerebellin 2 precursor (CBLN2) gene is the first genetic locus identified by Genome-wide Association Study (GWAS) that is significantly associated with PAH risk in the European population. This locus includes closely linked non-coding intergenic single nucleotide polymorphisms (SNPs) downstream of CBLN2. Although CBLN2 is upregulated in PAH and causes pulmonary vascular dysfunction, it remains unknown how these non-coding SNPs remotely affect CBLN2 and in turn, alter the genetic susceptibility of PAH. Methods and Results: We combined Hi-C chromatin interaction analysis, electrophoretic mobility shift assay, and SNP genotype-PAH correlation analysis with the All of Us database (N=20,343). In doing so, we identified rs11151770(A/G) as a PAH-associated functional SNP (fSNP), among 19 haplotype SNPs in linkage disequilibrium with the associated CBLN2 locus. The genomic region containing rs11151770(A/G) exhibited long-range chromatin interaction with the CBLN2 promotor. Proteomic analysis revealed that nuclear transcription factor Cut-like Homeobox 1 (CUX1) binds specifically to the risk allele A of rs11151770 but not to the non-risk allele G, regulating the expression of CBLN2 as an activator in pulmonary arterial endothelial cells. CUX1-CBLN2 was upregulated by acquired inflammatory PAH triggers IL-1β and IL-6. SNP genotyping in PAH patients (N=159) and non-PAH controls (N=87) showed a 2.2-fold enrichment of the risk allele A only in patients with PAH associated with connective tissue disorders (N=77, p=0.036). These findings supported a pathogenic mechanism of an inflammation-sensitive pathway (CUX1-CBLN2) controlled by an fSNP (risk allele A) underlying the reported genetic association of the CBLN2 locus with PAH risk. Conclusion: Our post-GWAS functional genomics analysis defined a pathogenic CUX1-mediated, fSNP rs11151770(A/G)-controlled, and inflammation-responsive pathway that provides a mechanistic explanation for the GWAS-identified association between genetic variants in CBLN2 locus and PAH susceptibility.

Deep Resequencing of the 1q22 Locus in Non-Lobar Intracerebral Hemorrhage.

Genome-wide association studies have identified 1q22 as a susceptibility locus for cerebral small vessel diseases, including non-lobar intracerebral hemorrhage (ICH) and lacunar stroke. In the present study, we performed targeted high-depth sequencing of 1q22 in ICH cases and controls to further characterize this locus and prioritize potential causal mechanisms, which remain unknown. A total of 95,000 base pairs spanning 1q22, including SEMA4A, SLC25A44, and PMF1/PMF1-BGLAP were sequenced in 1,055 spontaneous ICH cases (534 lobar and 521 non-lobar) and 1,078 controls. Firth regression and Rare Variant Influential Filtering Tool analysis were used to analyze common and rare variants, respectively. Chromatin interaction analyses were performed using Hi-C, chromatin immunoprecipitation followed by sequencing, and chromatin interaction analysis with paired-end tag databases. Multivariable Mendelian randomization assessed whether alterations in gene-specific expression relative to regionally co-expressed genes at 1q22 could be causally related to ICH risk. Common and rare variant analyses prioritized variants in SEMA4A 5'-UTR and PMF1 intronic regions, overlapping with active promoter and enhancer regions based on ENCODE annotation. Hi-C data analysis determined that 1q22 is spatially organized in a single chromatin loop, and that the genes therein belong to the same topologically associating domain. Chromatin immunoprecipitation followed by sequencing and chromatin interaction analysis with paired-end tag data analysis highlighted the presence of long-range interactions between the SEMA4A-promoter and PMF1-enhancer regions prioritized by association testing. Multivariable Mendelian randomization analyses demonstrated that PMF1 overexpression could be causally related to non-lobar ICH risk. Altered promoter-enhancer interactions leading to PMF1 overexpression, potentially dysregulating polyamine catabolism, could explain demonstrated associations with non-lobar ICH risk at 1q22, offering a potential new target for prevention of ICH and cerebral small vessel disease. ANN NEUROL 2024;95:325-337.

Recapitulation of patient-specific 3D chromatin conformation using machine learning

Regulatory networks containing enhancer-gene edges define cellular states. Multiple efforts have revealed these networks for reference tissues and cell lines by integrating multi-omics data. However, the methods developed cannot be applied for large patient cohorts due to the infeasibility of chromatin immunoprecipitation sequencing (ChIP-seq) for limited biopsy material. We trained machine-learning models using chromatin interaction analysis with paired-end tagsequencing(ChIA-PET) and high-throughput chromosome conformation capture combined with chromatin immunoprecipitation (HiChIP) data that can predict connections using only assay for transposase-accessible chromatin using sequencing (ATAC-seq) and RNA-seq data as input, which can be generated from biopsies. Our method overcomes limitations of correlation-based approaches that cannot distinguish between distinct target genes of given enhancers or between active vs. poised states in different samples, a hallmark of network rewiring in cancer. Application of our model on 371 samples across 22 cancer types revealed 1,780 enhancer-gene connections for 602 cancer genes. Using CRISPR interference (CRISPRi), we validated enhancers predicted to regulate ESR1 in estrogen receptor (ER)+ breast cancer and A1CF in liver hepatocellular carcinoma.

3D organization of regulatory elements for transcriptional regulation in Arabidopsis

BackgroundAlthough spatial organization of compartments and topologically associating domains at large scale is relatively well studied, the spatial organization of regulatory elements at fine scale is poorly understood in plants.ResultsHere we perform high-resolution chromatin interaction analysis using paired-end tag sequencing approach. We map chromatin interactions tethered with RNA polymerase II and associated with heterochromatic, transcriptionally active, and Polycomb-repressive histone modifications in Arabidopsis. Analysis of the regulatory repertoire shows that distal active cis-regulatory elements are linked to their target genes through long-range chromatin interactions with increased expression of the target genes, while poised cis-regulatory elements are linked to their target genes through long-range chromatin interactions with depressed expression of the target genes. Furthermore, we demonstrate that transcription factor MYC2 is critical for chromatin spatial organization, and propose that MYC2 occupancy and MYC2-mediated chromatin interactions coordinately facilitate transcription within the framework of 3D chromatin architecture. Analysis of functionally related gene-defined chromatin connectivity networks reveals that genes implicated in flowering-time control are functionally compartmentalized into separate subdomains via their spatial activity in the leaf or shoot apical meristem, linking active mark- or Polycomb-repressive mark-associated chromatin conformation to coordinated gene expression.ConclusionThe results reveal that the regulation of gene transcription in Arabidopsis is not only by linear juxtaposition, but also by long-range chromatin interactions. Our study uncovers the fine scale genome organization of Arabidopsis and the potential roles of such organization in orchestrating transcription and development.

DeepChIA-PET: Accurately predicting ChIA-PET from Hi-C and ChIP-seq with deep dilated networks.

Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) can capture genome-wide chromatin interactions mediated by a specific DNA-associated protein. The ChIA-PET experiments have been applied to explore the key roles of different protein factors in chromatin folding and transcription regulation. However, compared with widely available Hi-C and ChIP-seq data, there are not many ChIA-PET datasets available in the literature. A computational method for accurately predicting ChIA-PET interactions from Hi-C and ChIP-seq data is needed that can save the efforts of performing wet-lab experiments. Here we present DeepChIA-PET, a supervised deep learning approach that can accurately predict ChIA-PET interactions by learning the latent relationships between ChIA-PET and two widely used data types: Hi-C and ChIP-seq. We trained our deep models with CTCF-mediated ChIA-PET of GM12878 as ground truth, and the deep network contains 40 dilated residual convolutional blocks. We first showed that DeepChIA-PET with only Hi-C as input significantly outperforms Peakachu, another computational method for predicting ChIA-PET from Hi-C but using random forests. We next proved that adding ChIP-seq as one extra input does improve the classification performance of DeepChIA-PET, but Hi-C plays a more prominent role in DeepChIA-PET than ChIP-seq. Our evaluation results indicate that our learned models can accurately predict not only CTCF-mediated ChIA-ET in GM12878 and HeLa but also non-CTCF ChIA-PET interactions, including RNA polymerase II (RNAPII) ChIA-PET of GM12878, RAD21 ChIA-PET of GM12878, and RAD21 ChIA-PET of K562. In total, DeepChIA-PET is an accurate tool for predicting the ChIA-PET interactions mediated by various chromatin-associated proteins from different cell types.

G-quadruplex DNA contributes to RNA polymerase II-mediated 3D chromatin architecture.

High-order chromatin organization plays an important role in biological processes and disease development. Previous studies revealed a widespread occurrence of guanine quadruplex (G4) structures in the human genome, with enrichment in gene regulatory regions, especially in promoters. However, it remains unclear whether G4 structures contribute to RNA polymerase II (RNAPII)-mediated long-range DNA interactions and transcription activity. In this study, we conducted an intuitive overlapping analysis of previously published RNAPII ChIA-PET (chromatin interaction analysis with paired-end tag) and BG4 ChIP-seq (chromatin immunoprecipitation followed by sequencing using a G4 structure-specific antibody) data. We observed a strong positive correlation between RNAPII-linked DNA loops and G4 structures in chromatin. Additionally, our RNAPII HiChIP-seq (in situ Hi-C followed by ChIP-seq) results showed that treatment of HepG2 cells with pyridostatin (PDS), a small-molecule G4-binding ligand, could diminish RNAPII-linked long-range DNA contacts, with more pronounced diminutions being observed for those contacts involving G4 structure loci. RNA sequencing data revealed that PDS treatment modulates the expression of not only genes with G4 structures in their promoters, but also those with promoters being connected with distal G4sthrough RNAPII-linked long-range DNA interactions. Together, our data substantiate the function of DNA G4s in RNAPII-associated DNA looping and transcription regulation.

Abstract A054: Accurate prediction of cohesin-mediated 3D genome organization using 2D chromatin features

Abstract The three-dimensional (3D) genome organization influences diverse nuclear processes. Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) and High-throughput chromatin conformation capture (Hi-C) are powerful methods used to study the 3D genome organization. However, these two experiments are costly, time-consuming, require tens to hundreds of millions of cells, and challenging to optimize and analyze. Predicting ChIA-PET/Hi-C data using cheaper ChIP-Seq data and other easily obtainable features is a useful alternative. Also, it is well-established that the cohesin protein complex is a key determinant of 3D genome organization. Considering this, we present Chromatin Interaction Predictor (ChIPr), a suite of regression models based on deep neural networks (DNN), random forest, and gradient boosting. ChIPr predicts cohesin-mediated chromatin interaction strength between any two given loci in the genome. It was trained mainly using two top-tiered ENCODE cell lines GM12878 and K562 and tested on other cell lines (H1 and HepG2). Comprehensive tests showed that ChIPr predictions correlated well with the original ChIA-PET data at the peak-level resolution and at bin sizes 25kb and 5kb. In addition, ChIPr accurately captured most of the cell-type-dependent loops identified by both, ChIA-PET and Hi-C data, respectively. Extensive feature testing highlighted genomic distance and RAD21 (a cohesin component) ChIP-Seq signals as the most important inputs contributing to ChIPr, in determining the chromatin interaction strength. A standard ChIPr model requires three experimental inputs: ChIP-Seq signals for RAD21, H3K27ac (enhancer/active chromatin mark), and H3K27me3 (inactive chromatin mark). While a reduced model requires a single experiment input: ChIP-Seq signals for RAD21 and performs equally well. Additionally, the use of ChIPr was further extended to predict the contact maps for several prostate cancer (PCa) cell lines whose ChIA-PET data were not available on the ENCODE portal, for instance, RWPE1 and VCaP. We generated the necessary ChIP-seq data for these cell lines to use as input to the model to further examine the changes in the 3D interactions of PCa driver genes and also their isolated neighbourhoods and decipher useful biological insights about the gene regulatory networks involved. Integrative analysis revealed novel insights into the role of CTCF motif, its orientation, and CTCF binding on the prevalence and strength of cohesin-mediated chromatin interactions (~50% - 80% of the RAD21 interactions were enriched with CTCF, < 15% of the interactions had no CTCF ChIP-seq binding in both of the two peaks). Lastly, we also noticed a subset of RAD21 interactions with CTCF binding in only one or none of the two anchor peaks, which were significantly enriched with enhancers. Taken together, this study outlines the general features of genome folding and opens new avenues to analyze spatial genome organization in specimens with limited cell numbers. Citation Format: Khyati Chandratre. Accurate prediction of cohesin-mediated 3D genome organization using 2D chromatin features [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr A054.

Abstract 220: Post-gwas Functional Genomics Analysis Of Abo Locus To Identify Novel Pathogenic Mechanisms For Chronic Thromboembolic Pulmonary Hypertension

Background: Chronic thromboembolic pulmonary hypertension (CTEPH) is a rare and mysterious complication of pulmonary embolism, characterized by chronic pulmonary arterial thrombosis, occlusions and vasculopathy. Emerging evidence has suggested genetic relevance of CTEPH, particularly to ABO blood type. Genome-wide association study (GWAS) has identified ABO gene as the most significant genetic locus associated with CTEPH. This locus, however, consists only intronic SNPs and there is no mechanistic explanation on how these non-coding SNPs control the risk of CTEPH. Methods: GWAS-identified CTEPH-associated ABO locus was analyzed, and haplotype SNPs were identified based on linkage disequilibrium (LD). Electrophoretic mobility shift assay (EMSA) was applied to screen functional SNPs (fSNPs) based on allele-imbalanced nuclear protein binding. SNP-binding nuclear transcription factors (TFs) were identified by proteomics. Hi-C chromatin-interaction analysis was utilized to determine potential target genes affected by fSNPs. Knockdown of fSNP-associated TFs in human pulmonary artery endothelial cells (hPAECs) was applied to validate the regulation of TFs on target genes. Results: Based on GWAS, the minor allele of tag SNP rs2519093 is associated with higher risk of CTEPH (Odds ratio 2.22). It defines 11 haplotype SNPs in a LD with R 2 > 0.8. Screened by EMSA with hPAEC nuclear proteins, intronic SNPs rs579459 and rs550057 were identified functional given their allele-specific protein binding. Proteomics analysis identified 3 candidate TFs that allele-specifically bind to fSNP rs579459: PBX1, CTCF and SAFB2. Candidate target gene ADAMTS13 is chosen based on its prominent interaction with fSNP revealed by Hi-C analysis, and the potential role in thromboembolism through vWF cleavage. TF knockdown by siRNA downregulates ADAMTS13 in hPAECs, suggesting their regulatory function as activators. Conclusion: Leveraging our post-GWAS functional genomics approach, we identified fSNPs in ABO locus that define a genetic architecture controlling the susceptibility of CTEPH. This genetic architecture, along with the newly identified fSNP-associated TFs and target gene ADAMTS13, provides a biological explanation for the pathogenesis of CTEPH.

Haplotype mapping of H3K27me3-associated chromatin interactions defines topological regulation of gene silencing in rice

Histone modification H3K27me3 is an important chromatin mark that plays vital roles in repressing expression of developmental genes. Here, we construct high-resolution 3D genome maps using long-read chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) and characterize H3K27me3-associated chromatin interactions in an elite rice hybrid, Shanyou 63. We find that many H3K27me3-marked regions may function as silencer-like regulatory elements. The silencer-like elements can come into proximity with distal target genes via forming chromatin loops in 3D space of the nuclei, regulating gene silencing and plant traits. Natural and induced deletion of silencers upregulate expression of distal connected genes. Furthermore, we identify extensive allele-specific chromatin loops. We find that genetic variations alter allelic chromatin topology, thus modulating allelic gene imprinting in rice hybrids. In conclusion, the characterization of silencer-like regulatory elements and haplotype-resolved chromatin interaction maps provide insights into the understanding of molecular mechanisms underlying allelic gene silencing and plant trait controlling.

Mapping Multi-factor-mediated Chromatin Interactions to Assess Dysregulation of Lung Cancer-related Genes

Studies on the lung cancer genome are indispensable for developing a cure for lung cancer. Whole-genome resequencing, genome-wide association studies, and transcriptome sequencing have greatly improved our understanding of the cancer genome. However, dysregulation of long-range chromatin interactions in lung cancer remains poorly described. To better understand the three-dimensional (3D) genomic interaction features of the lung cancer genome, we used the A549 cell line as a model system and generated high-resolution chromatin interactions associated with RNA polymerase II (RNAPII), CCCTC-binding factor (CTCF), enhancer of zeste homolog 2 (EZH2), and histone 3 lysine 27 trimethylation (H3K27me3) using long-read chromatin interaction analysis by paired-end tag sequencing (ChIA-PET). Analysis showed that EZH2/H3K27me3-mediated interactions further repressed target genes, either through loops or domains, and their distributions along the genome were distinct from and complementary to those associated with RNAPII. Cancer-related genes were highly enriched with chromatin interactions, and chromatin interactions specific to the A549 cell line were associated with oncogenes and tumor suppressor genes, such as additional repressive interactions on FOXO4 and promoter–promoter interactions between NF1 and RNF135. Knockout of an anchor associated with chromatin interactions reversed the dysregulation of cancer-related genes, suggesting that chromatin interactions are essential for proper expression of lung cancer-related genes. These findings demonstrate the 3D landscape and gene regulatory relationships of the lung cancer genome.

SnHiC: a complete and simplified snakemake pipeline for grouped Hi-C data analysis.

Genome-wide chromosome conformation capture (Hi-C) is a technique that allows the study of 3D genome organization. Despite being widely used, analysis of Hi-C data is technically challenging and involves several time-consuming steps that often require manual involvement making it error prone, potentially affecting data reproducibility. In order to facilitate and simplify these analyses we implemented snHiC, a snakemake-based pipeline that allows for the generation of contact matrices at multiple resolutions in one single run, aggregation of individual samples into user-specified groups, detection of domains, compartments, loops and stripes and performance of differential compartment and chromatin interaction analyses. Source code is freely available at https://github.com/sebastian-gregoricchio/snHiC. A yaml-formatted file (snHiC/workflow/envs/snHiC_conda_env_stable.yaml) is available to build a compatible conda environment. Supplementary data are available at Bioinformatics Advances online.

MCIBox: a toolkit for single-molecule multi-way chromatin interaction visualization and micro-domains identification.

The emerging ligation-free three-dimensional (3D) genome mapping technologies can identify multiplex chromatin interactions with single-molecule precision. These technologies not only offer new insight into high-dimensional chromatin organization and gene regulation, but also introduce new challenges in data visualization and analysis. To overcome these challenges, we developed MCIBox, a toolkit for multi-way chromatin interaction (MCI) analysis, including a visualization tool and a platform for identifying micro-domains with clustered single-molecule chromatin complexes. MCIBox is based on various clustering algorithms integrated with dimensionality reduction methods that can display multiplex chromatin interactions at single-molecule level, allowing users to explore chromatin extrusion patterns and super-enhancers regulation modes in transcription, and to identify single-molecule chromatin complexes that are clustered into micro-domains. Furthermore, MCIBox incorporates a two-dimensional kernel density estimation algorithm to identify micro-domains boundaries automatically. These micro-domains were stratified with distinctive signatures of transcription activity and contained different cell-cycle-associated genes. Taken together, MCIBox represents an invaluable tool for the study of multiple chromatin interactions and inaugurates a previously unappreciated view of 3D genome structure.

FOXA1 regulates alternative splicing in prostate cancer.

SummaryDysregulation of alternative splicing in prostate cancer is linked to transcriptional programs activated by AR, ERG, FOXA1, and MYC. Here, we show that FOXA1 functions as the primary orchestrator of alternative splicing dysregulation across 500 primary and metastatic prostate cancer transcriptomes. We demonstrate that FOXA1 binds to the regulatory regions of splicing-related genes, including HNRNPK and SRSF1. By controlling trans-acting factor expression, FOXA1 exploits an “exon definition” mechanism calibrating alternative splicing toward dominant isoform production. This regulation especially impacts splicing factors themselves and leads to a reduction of nonsense-mediated decay (NMD)-targeted isoforms. Inclusion of the NMD-determinant FLNA exon 30 by FOXA1-controlled oncogene SRSF1 promotes cell growth in vitro and predicts disease recurrence. Overall, we report a role for FOXA1 in rewiring the alternative splicing landscape in prostate cancer through a cascade of events from chromatin access, to splicing factor regulation, and, finally, to alternative splicing of exons influencing patient survival.