Spatially resolved epigenomic profiling of single cells in complex tissues.

The recent development of spatial omics methods has enabled single-cell profiling of the transcriptome and 3D genome organization with high spatial resolution. Expanding the repertoire of spatial omics tools, a spatially resolved single-cell epigenomics method will accelerate understanding of the spatial regulation of cell and tissue functions. Here, we report a method for spatially resolved epigenomic profiling of single cells using in situ tagmentation and transcription followed by multiplexed imaging. We demonstrated the ability to profile histone modifications marking active promoters, putative enhancers, and silent promoters in individual cells, and generated high-resolution spatial atlas of hundreds of active promoters and putative enhancers in embryonic and adult mouse brains. Our results suggested putative promoter-enhancer pairs and enhancer hubs regulating developmentally important genes. We envision this approach will be generally applicable to spatial profiling of epigenetic modifications and DNA-binding proteins, advancing our understanding of how gene expression is spatiotemporally regulated by the epigenome.

Regulation of T helper cell differentiation by the interplay between histone modification and chromatin interaction.

The development and function of the immune system are controlled by temporospatial gene expression programs, which are regulated by cis-regulatory elements, chromatin structure, and trans-acting factors. In this study, we cataloged the dynamic histone modifications and chromatin interactions at regulatory regions during T helper (Th) cell differentiation. Our data revealed that the H3K4me1 landscape established by MLL4 in naive CD4+ T cells is critical for restructuring the regulatory interaction network and orchestrating gene expression during the early phase of Th differentiation. GATA3 plays a crucial role in further configuring H3K4me1 modification and the chromatin interaction network during Th2 differentiation. Furthermore, we demonstrated that HSS3-anchored chromatin loops function to restrict the activity of the Th2 locus control region (LCR), thus coordinating the expression of Th2 cytokines. Our results provide insights into the mechanisms of how the interplay between histone modifications, chromatin looping, and trans-acting factors contributes to the differentiation of Th cells.

H3K27ac bookmarking promotes rapid post-mitotic activation of the pluripotent stem cell program without impacting 3D chromatin reorganization.

During self-renewal, cell-type-defining features are drastically perturbed in mitosis and must be faithfully reestablished upon G1 entry, a process that remains largely elusive. Here, we characterized at a genome-wide scale the dynamic transcriptional and architectural resetting of mouse pluripotent stem cells (PSCs) upon mitotic exit. We captured distinct waves of transcriptional reactivation with rapid induction of stem cell genes and transient activation of lineage-specific genes. Topological reorganization at different hierarchical levels also occurred in an asynchronous manner and showed partial coordination with transcriptional resetting. Globally, rapid transcriptional and architectural resetting associated with mitotic retention of H3K27 acetylation, supporting a bookmarking function. Indeed, mitotic depletion of H3K27ac impaired the early reactivation of bookmarked, stem-cell-associated genes. However, 3D chromatin reorganization remained largely unaffected, suggesting that these processes are driven by distinct forces upon mitotic exit. This study uncovers principles and mediators of PSC molecular resetting during self-renewal.

Robust Hi-C Maps of Enhancer-Promoter Interactions Reveal the Function of Non-coding Genome in Neural Development and Diseases.

Genome-wide mapping of chromatin interactions at high resolution remains experimentally and computationally challenging. Here we used a low-input "easy Hi-C" protocol to map the 3D genome architecture in human neurogenesis and brain tissues and also demonstrated that a rigorous Hi-C bias-correction pipeline (HiCorr) can significantly improve the sensitivity and robustness of Hi-C loop identification at sub-TAD level, especially the enhancer-promoter (E-P) interactions. We used HiCorr to compare the high-resolution maps of chromatin interactions from 10 tissue or cell types with a focus on neurogenesis and brain tissues. We found that dynamic chromatin loops are better hallmarks for cellular differentiation than compartment switching. HiCorr allowed direct observation of cell-type- and differentiation-specific E-P aggregates spanning large neighborhoods, suggesting a mechanism that stabilizes enhancer contacts during development. Interestingly, we concluded that Hi-C loop outperforms eQTL in explaining neurological GWAS results, revealing a unique value of high-resolution 3D genome maps in elucidating the disease etiology.

SRC-3 Coactivator Governs Dynamic Estrogen-Induced Chromatin Looping Interactions during Transcription.

Enhancers are thought to activate transcription by physically contacting promoters via looping. However, direct assays demonstrating these contacts are required to mechanistically verify such cellular determinants of enhancer function. Here, we present versatile cell-free assays to further determine the role of enhancer-promoter contacts (EPCs). We demonstrate that EPC is linked to mutually stimulatory transcription at the enhancer and promoter in vitro. SRC-3 was identified as a critical looping determinant for the estradiol-(E2)-regulated GREB1 locus. Surprisingly, the GREB1 enhancer and promoter contact two internal gene body SRC-3 binding sites, GBS1 and GBS2, which stimulate their transcription. Utilizing time-course 3C assays, we uncovered SRC-3-dependent dynamic chromatin interactions involving the enhancer, promoter, GBS1, and GBS2. Collectively, these data suggest that the enhancer and promoter remain "poised" for transcription via their contacts with GBS1 and GBS2. Upon E2 induction, GBS1 and GBS2 disengage from the enhancer, allowing direct EPC for active transcription.

Estrogen receptor activation remodels TEAD1 gene expression to alleviate hepatic steatosis.

Sex-based differences in obesity-related hepatic malignancies suggest the protective roles of estrogen. Using a preclinical model, we dissected estrogen receptor (ER) isoform-driven molecular responses in high-fat diet (HFD)-induced liver diseases of male and female mice treated with or without an estrogen agonist by integrating liver multi-omics data. We found that selective ER activation recovers HFD-induced molecular and physiological liver phenotypes. HFD and systemic ER activation altered core liver pathways, beyond lipid metabolism, that are consistent between mice and primates. By including patient cohort data, we uncovered that ER-regulated enhancers govern central regulatory and metabolic genes with clinical significance in metabolic dysfunction-associated steatotic liver disease (MASLD) patients, including the transcription factor TEAD1. TEAD1 expression increased in MASLD patients, and its downregulation by short interfering RNA reduced intracellular lipid content. Subsequent TEAD small molecule inhibition improved steatosis in primary human hepatocyte spheroids by suppressing lipogenic pathways. Thus, TEAD1 emerged as a new therapeutic candidate whose inhibition ameliorates hepatic steatosis.

EPI-Trans: an effective transformer-based deep learning model for enhancer promoter interaction prediction.

BACKGROUND: Recognition of enhancer-promoter Interactions (EPIs) is crucial for human development. EPIs in the genome play a key role in regulating transcription. However, experimental approaches for classifying EPIs are too expensive in terms of effort, time, and resources. Therefore, more and more studies are being done on developing computational techniques, particularly using deep learning and other machine learning techniques, to address such problems. Unfortunately, the majority of current computational methods are based on convolutional neural networks, recurrent neural networks, or a combination of them, which don't take into consideration contextual details and the long-range interactions between the enhancer and promoter sequences. A new transformer-based model called EPI-Trans is presented in this study to overcome the aforementioned limitations. The multi-head attention mechanism in the transformer model automatically learns features that represent the long interrelationships between enhancer and promoter sequences. Furthermore, a generic model is created with transferability that can be utilized as a pre-trained model for various cell lines. Moreover, the parameters of the generic model are fine-tuned using a particular cell line dataset to improve performance. RESULTS: Based on the results obtained from six benchmark cell lines, the average AUROC for the specific, generic, and best models is 94.2%, 95%, and 95.7%, while the average AUPR is 80.5%, 66.1%, and 79.6% respectively. CONCLUSIONS: This study proposed a transformer-based deep learning model for EPI prediction. The comparative results on certain cell lines show that EPI-Trans outperforms other cutting-edge techniques and can provide superior performance on the challenge of recognizing EPI.

Redundant enhancers in the iab-5 domain cooperatively activate Abd-B in the A5 and A6 abdominal segments of Drosophila.

The Abdominal-B (Abd-B) gene belongs to the bithorax complex and its expression is controlled by four regulatory domains, iab-5, iab-6, iab-7 and iab-8, each of which is thought to be responsible for directing the expression of Abd-B in one of the abdominal segments from A5 to A8. A variety of experiments have supported the idea that BX-C regulatory domains are functionally autonomous and that each domain is both necessary and sufficient to orchestrate the development of the segment they specify. Unexpectedly, we discovered that this model does not always hold. Instead, we find that tissue-specific enhancers located in the iab-5 domain are required for the proper activation of Abd-B not only in A5 but also in A6. Our findings indicate that the functioning of the iab-5 and iab-6 domains in development of the adult cuticle A5 and A6 in males fit better with an additive model, much like that first envisioned by Ed Lewis.

Capturing large genomic contexts for accurately predicting enhancer-promoter interactions.

Enhancer-promoter interaction (EPI) is a key mechanism underlying gene regulation. EPI prediction has always been a challenging task because enhancers could regulate promoters of distant target genes. Although many machine learning models have been developed, they leverage only the features in enhancers and promoters, or simply add the average genomic signals in the regions between enhancers and promoters, without utilizing detailed features between or outside enhancers and promoters. Due to a lack of large-scale features, existing methods could achieve only moderate performance, especially for predicting EPIs in different cell types. Here, we present a Transformer-based model, TransEPI, for EPI prediction by capturing large genomic contexts. TransEPI was developed based on EPI datasets derived from Hi-C or ChIA-PET data in six cell lines. To avoid over-fitting, we evaluated the TransEPI model by testing it on independent test datasets where the cell line and chromosome are different from the training data. TransEPI not only achieved consistent performance across the cross-validation and test datasets from different cell types but also outperformed the state-of-the-art machine learning and deep learning models. In addition, we found that the improved performance of TransEPI was attributed to the integration of large genomic contexts. Lastly, TransEPI was extended to study the non-coding mutations associated with brain disorders or neural diseases, and we found that TransEPI was also useful for predicting the target genes of non-coding mutations.

Network Analysis of Enhancer-Promoter Interactions Highlights Cell-Type-Specific Mechanisms of Transcriptional Regulation Variation.

Gene expression is orchestrated by a complex array of gene regulatory elements that govern transcription in a cell-type-specific manner. Though previously studied, the ability to utilize regulatory elements to identify disrupting variants remains largely elusive. To identify important factors within these regions, we generated enhancer-promoter interaction (EPI) networks and investigated the presence of disease-associated variants that fall within these regions. Our study analyzed six neuronal cell types across neural differentiation, allowing us to examine closely related cell types and across differentiation stages. Our results expand upon previous findings of cell-type specificity of enhancer, promoter, and transcription factor binding sites. Notably, we find that regulatory regions within EPI networks can identify the enrichment of variants associated with neuropsychiatric disorders within specific cell types and network sub-structures. This enrichment within sub-structures can allow for a better understanding of potential mechanisms by which variants may disrupt transcription. Together, our findings suggest that EPIs can be leveraged to better understand cell-type-specific regulatory architecture and used as a selection method for disease-associated variants to be tested in future functional assays. Combined with these future functional characterization assays, EPIs can be used to better identify and characterize regulatory variants' effects on such networks and model their mechanisms of gene regulation disruption across different disorders. Such findings can be applied in practical settings, such as diagnostic tools and drug development.

Rice RS2-9, which is bound by transcription factor OSH1, blocks enhancer-promoter interactions in plants.

Insulators characterized in Drosophila and mammals have been shown to play a key role in the restriction of promiscuous enhancer-promoter interactions, as well as reshaping the topological landscape of chromosomes. Yet the role of insulators in plants remains poorly understood, in large part because of a lack of well-characterized insulators and binding factor(s). In this study, we isolated a 1.2-kb RS2-9 insulator from the Oryza sativa (rice) genome that can, when interposed between an enhancer and promoter, efficiently block the activation function of both constitutive and floral organ-specific enhancers in transgenic Arabidopsis and Nicotiana tabacum (tobacco). In the rice genome, the genes flanking RS2-9 exhibit an absence of mutual transcriptional interactions, as well as a lack of histone modification spread. We further determined that O. sativa Homeobox 1 (OSH1) bound two regions of RS2-9, as well as over 50 000 additional sites in the rice genome, the majority of which resided in intergenic regions. Mutation of one of the two OSH1-binding sites in RS2-9 impaired insulation activity by up to 60%, whereas the mutation of both binding sites virtually abolished insulator function. We also demonstrated that OSH1 binding sites were associated with 72% of the boundaries of topologically associated domains (TADs) identified in the rice genome, which is comparable to the 77% of TAD boundaries bound by the insulator CCCTC-binding factor (CTCF) in mammals. Taken together, our findings indicate that OSH1-RS2-9 acts as a true insulator in plants, and highlight a potential role for OSH1 in gene insulation and topological organization in plant genomes.

CapsNetYY1: identifying YY1-mediated chromatin loops based on a capsule network architecture.

BACKGROUND: Previous studies have identified that chromosome structure plays a very important role in gene control. The transcription factor Yin Yang 1 (YY1), a multifunctional DNA binding protein, could form a dimer to mediate chromatin loops and active enhancer-promoter interactions. The deletion of YY1 or point mutations at the YY1 binding sites significantly inhibit the enhancer-promoter interactions and affect gene expression. To date, only a few computational methods are available for identifying YY1-mediated chromatin loops. RESULTS: We proposed a novel model named CapsNetYY1, which was based on capsule network architecture to identify whether a pair of YY1 motifs can form a chromatin loop. Firstly, we encode the DNA sequence using one-hot encoding method. Secondly, multi-scale convolution layer is used to extract local features of the sequence, and bidirectional gated recurrent unit is used to learn the features across time steps. Finally, capsule networks (convolution capsule layer and digital capsule layer) used to extract higher level features and recognize YY1-mediated chromatin loops. Compared with DeepYY1, the only prediction for YY1-mediated chromatin loops, our model CapsNetYY1 achieved the better performance on the independent datasets (AUC [Formula: see text]). CONCLUSION: The results indicate that CapsNetYY1 is an excellent method for identifying YY1-mediated chromatin loops. We believe that the CapsNetYY1 method will be used for predictive classification of other DNA sequences.

DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops.

The protein Yin Yang 1 (YY1) could form dimers that facilitate the interaction between active enhancers and promoter-proximal elements. YY1-mediated enhancer-promoter interaction is the general feature of mammalian gene control. Recently, some computational methods have been developed to characterize the interactions between DNA elements by elucidating important features of chromatin folding; however, no computational methods have been developed for identifying the YY1-mediated chromatin loops. In this study, we developed a deep learning algorithm named DeepYY1 based on word2vec to determine whether a pair of YY1 motifs would form a loop. The proposed models showed a high prediction performance (AUCs$\ge$0.93) on both training datasets and testing datasets in different cell types, demonstrating that DeepYY1 has an excellent performance in the identification of the YY1-mediated chromatin loops. Our study also suggested that sequences play an important role in the formation of YY1-mediated chromatin loops. Furthermore, we briefly discussed the distribution of the replication origin site in the loops. Finally, a user-friendly web server was established, and it can be freely accessed at http://lin-group.cn/server/DeepYY1.

HSPA12A was identified as a key driver in colorectal cancer GWAS loci 10q26.12 and modulated by an enhancer-promoter interaction.

Although genome-wide association studies (GWASs) have identified over 100 colorectal cancer (CRC) risk loci, an understanding of causal genes or risk variants and their biological functions in these loci remain unclear. Recently, genomic loci 10q26.12 with lead SNP rs1665650 was identified as an essential CRC risk loci of Asian populations. However, the functional mechanism of this region has not been fully clarified. Here, we applied an RNA interfering-based on-chip approach to screen for the genes essential for cell proliferation in the CRC risk loci 10q26.12. Notably, HSPA12A had the most significant effect among the identified genes and functioned as a crucial oncogene facilitating cell proliferation. Moreover, we conducted an integrative fine-mapping analysis to identify putative casual variants and further explored their association with CRC risk in a large-scale Chinese population consisting of 4054 cases and 4054 controls and also independently validated in 5208 cases and 20,832 controls from the UK biobank cohort. We identified a risk SNP rs7093835 in the intron of HSPA12A that was significantly associated with an increased risk of CRC (OR 1.23, 95% CI 1.08-1.41, P = 1.92 × 10-3). Mechanistically, the risk variant could facilitate an enhancer-promoter interaction mediated by the transcriptional factor (TF) GRHL1 and ultimately upregulate HSPA12A expression, which provides functional evidence to support our population findings. Collectively, our study reveals the important role of HSPA12A in CRC development and illustrates a novel enhancer-promoter interaction module between HSPA12A and its regulatory elements rs7093835, providing new insights into the etiology of CRC.

StackEPI: identification of cell line-specific enhancer-promoter interactions based on stacking ensemble learning.

BACKGROUND: Understanding the regulatory role of enhancer-promoter interactions (EPIs) on specific gene expression in cells contributes to the understanding of gene regulation, cell differentiation, etc., and its identification has been a challenging task. On the one hand, using traditional wet experimental methods to identify EPIs often means a lot of human labor and time costs. On the other hand, although the currently proposed computational methods have good recognition effects, they generally require a long training time. RESULTS: In this study, we studied the EPIs of six human cell lines and designed a cell line-specific EPIs prediction method based on a stacking ensemble learning strategy, which has better prediction performance and faster training speed, called StackEPI. Specifically, by combining different encoding schemes and machine learning methods, our prediction method can extract the cell line-specific effective information of enhancer and promoter gene sequences comprehensively and in many directions, and make accurate recognition of cell line-specific EPIs. Ultimately, the source code to implement StackEPI and experimental data involved in the experiment are available at https://github.com/20032303092/StackEPI.git . CONCLUSIONS: The comparison results show that our model can deliver better performance on the problem of identifying cell line-specific EPIs and outperform other state-of-the-art models. In addition, our model also has a more efficient computation speed.

Systematic Functional Interrogation of Genes in GWAS Loci Identified ATF1 as a Key Driver in Colorectal Cancer Modulated by a Promoter-Enhancer Interaction.

Genome-wide association studies (GWASs) have identified approximately 100 colorectal cancer (CRC) risk loci. However, the causal genes in these loci have not been systematically interrogated. We conducted a high-throughput RNA-interference functional screen to identify the genes essential for proliferation in the CRC risk loci of Asian populations. We found that ATF1, located in the 12q13.12 region, functions as an oncogene that facilitates cell proliferation; ATF1 has the most significant effect of the identified genes and promotes CRC xenograft growth by affecting cell apoptosis. Next, by integrating a fine-mapping analysis, a two-stage affected-control study consisting of 6,213 affected individuals and 10,388 controls, and multipronged experiments, we elucidated that two risk variants, dbSNP: rs61926301 and dbSNP: rs7959129, that located in the ATF1 promoter and first intron, respectively, facilitate a promoter-enhancer interaction, mediated by the synergy of SP1 and GATA3, to upregulate ATF1 expression, thus synergistically predisposing to CRC risk (OR = 1.77, 95% CI = 1.42-2.21, p = 3.16 × 10-7; Pmultiplicative-interaction = 1.20 × 10-22; Padditive-interaction = 6.50 × 10-3). Finally, we performed RNA-seq and ChIP-seq assays in CRC cells treated with ATF1 overexpression in order to dissect the target programs of ATF1. Results showed that ATF1 activates a subset of genes, including BRAF, NRAS, MYC, BIRC2, DAAM1, MAML2, STAT1, ID1, and NKD2, related to apoptosis, Wnt, TGF-ß, and MAPK pathways, and these effects could cooperatively increase the risk of CRC. These findings reveal the clinical potential of ATF1 in CRC development and illuminate a promoter-enhancer interaction module between the ATF1 regulatory elements dbSNP: rs61926301 and dbSNP: rs7959129, and they bring us closer to understanding the molecular drivers of cancer.

Three functional variants were identified to affect RPS24 expression and significantly associated with risk of colorectal cancer.

GWAS-identified 10q22.3 loci with lead SNP rs704017 are significantly associated with CRC risk in both Asian and European populations. However, the functional mechanism of this region is unclear. In this study, we performed a fine-mapping analysis to identify the causal SNPs. To identify potential functional SNPs in linkage disequilibrium with the lead SNP, we searched for the potential target genes using a Hi-C database and an RNA interfering-based on-chip approach. The results indicated that rs12263636 (r2 = 0.41) showed the highest potential to be functional. It resided in a region with enhancer markers and a topologically associating domain. We found that RPS24 was the only gene that significantly promoted the proliferation rate of CRC cells and might have promoter-enhancer interaction with rs12263636. Dual-luciferase reporter assays confirmed that the risk alleles of two variants (rs3740253 and rs7071351) in RPS24 promoter could increase the expression of luciferase. Case control study consisting of 1134 cases and 2039 health controls confirmed that both the two variants were associated with risk of CRC (rs3740253: P = 0.0079, OR = 1.15, 95% CI 1.04-1.28; rs7071351: P = 0.0085, OR = 1.15, 95% CI 1.04-1.28). And plasmid containing mutant haplotypes containing all the three mutations (rs12263636 or rs3740253 and rs7071351) could most significantly increase luciferase expression, compared with any haplotype of the three mutations. The study explained the functional mechanism for the 10q22.3 loci and provided new insights into the prevention and treatment of CRC.

On TADs and LADs: Spatial Control Over Gene Expression.

The combinatorial action of transcription factors drives cell-type-specific gene expression patterns. However, transcription factor binding and gene regulation occur in the context of chromatin, which modulates DNA accessibility. High-resolution chromatin interaction maps have defined units of chromatin that are in spatial proximity, called topologically associated domains (TADs). TADs can be further classified based on expression activity, replication timing, or the histone marks or non-histone proteins associated with them. Independently, other chromatin domains have been defined by their likelihood to interact with non-DNA structures, such as the nuclear lamina. Lamina-associated domains (LADs) correlate with low gene expression and late replication timing. TADs and LADs have recently been evaluated with respect to cell-type-specific gene expression. The results shed light on the relevance of these forms of chromatin organization for transcriptional regulation, and address specifically how chromatin sequestration influences cell fate decisions during organismal development.

Recognition of the long range enhancer-promoter interactions by further adding DNA structure properties and transcription factor binding motifs in human cell lines.

The enhancer-promoter interactions (EPIs) with strong tissue-specificity play an important role in cis-regulatory mechanism of human cell lines. However, it still remains a challenging work to predict these interactions so far. Due to that these interactions are regulated by the cooperativeness of diverse functional genomic signatures, DNA spatial structure and DNA sequence elements. In this paper, by adding DNA structure properties and transcription factor binding motifs, we presented an improved computational method to predict EPIs in human cell lines. In comparison with the results of other group on the same datasets, our best accuracies by cross-validation test were about 15%-24% higher in the same cell lines, and the accuracies by independent test were about 11%-15% higher in new cell lines. Meanwhile, we found that transcription factor binding motifs and DNA structure properties have important information that would largely determine long range EPIs prediction. From the distribution comparisons, we also found their distinct differences between interacting and non-interacting sets in each cell line. Then, the correlation analysis and network models for relationships among top-ranked functional genomic signatures indicated that diverse genomic signatures would cooperatively establish a complex regulatory network to facilitate long range EPIs. The experimental results provided additional insights about the roles of DNA intrinsic properties and functional genomic signatures in EPIs prediction.

Recognition of long-range enhancer-promoter interactions by adding genomic signatures of segmented regulatory regions.

Enhancer-promoter interaction (EPI) is an important cis-regulatory mechanism in the regulation of tissue-specific gene expression. However, it still has limitation to precisely identity these interactions so far. In this paper, using diverse genomic features for various regulatory regions, we presented a computational approach to predict EPIs with improved accuracies. Meanwhile, we comprehensively studied more potential regulatory factors that are important to EPIs prediction, such as nucleosome occupancy, enhancer RNA; and found the cell line-specificity and region-specificity of the contributions of diverse regulatory signatures. By adding genomic signatures of segmented regulatory regions, our best accuracies of cross-validation test were about 11%-16% higher than the previous results, indicating the location-specificity of genomic signatures in a regulatory region for predicting EPIs. Additionally, more training samples and related features can provide reliable performances in new cell lines. Consequently, our study provided additional insights into the roles of diverse signature features for predicting long-range EPIs.