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Although genome-wide association studies have identified dozens of loci associated with colorectal cancer (CRC) susceptibility, the causal genes or risk variants within these loci and their biological functions often remain elusive. Recently, the genomic locus 12p13.32, with the tag single-nucleotide polymorphism rs10774214, was identified as a crucial CRC risk locus in Asian populations. However, the functional mechanism of this region has not been fully elucidated. Here, we applied a high-throughput RNA interference approach in CRC cell lines to interrogate the function of genes in this genomic region. Multiple genes were found to affect cell functions, with CCND2 having the most significant effect as an oncogene. Moreover, overexpressed CCND2 could promote CRC cell proliferation. Subsequently, by integrating a fine-mapping analysis and multi-ancestry large-scale population cohorts consisting of 14 358 CRC cases and 34 251 healthy controls, we identified a regulatory variant rs4477507-T that contributed to an increased CRC risk in populations from China (odds ratio = 1.16, 95% confidence interval = 1.11–1.22, P  = 4.45 × 10−10) and Europe (odds ratio = 1.17, 95% confidence interval = 1.12–1.21, P  = 1.65 × 10−14). Functional characterization of the variant demonstrated that it could act as an allele-specific enhancer to distally facilitate the expression of CCND2 mediated by the transcription factor TEAD4. Overall, our study underscores the essential role of CCND2 in CRC development and delineates its regulatory mechanism mediated by rs4477507, establishing an epidemiological and biological link between genetic variation and CRC pathogenesis. [ABSTRACT FROM AUTHOR]

Background: The enhancer–promoter interaction (EPI) is a critical component of gene regulatory networks, playing a significant role in understanding the complexity of gene expression. Traditional EPI prediction methods focus on one-to-one interactions, neglecting more complex one-to-many and many-to-many patterns. To address this gap, we utilize graph neural networks to comprehensively explore all interaction patterns between enhancers and promoters, capturing complex regulatory relationships for more accurate predictions. Methods: In this study, we introduce a novel EPI prediction framework, GATv2EPI, based on dynamic graph attention neural networks. GATv2EPI leverages epigenetic information from enhancers, promoters, and their surrounding regions and organizes interactions into a network to comprehensively explore complex EPI regulatory patterns, including one-to-one, one-to-many, and many-to-many relationships. To avoid overfitting and ensure diverse data representation, we implemented a connectivity-based sampling method for dataset partitioning, which constructs graphs for each chromosome and assigns entire connected subgraphs to training or test sets, thereby preventing information leakage and ensuring comprehensive chromosomal representation. Results: In experiments conducted on four cell lines—NHEK, IMR90, HMEC, and K562—GATv2EPI demonstrated superior EPI recognition accuracy compared to existing similar methods, with a training time improvement of 95.29% over TransEPI. Conclusions: GATv2EPI enhances EPI prediction accuracy by capturing complex topological structure information from gene regulatory networks through graph neural networks. Additionally, our results emphasize the importance of epigenetic features surrounding enhancers and promoters in EPI prediction. [ABSTRACT FROM AUTHOR]

Enhancer-promoter interactions (EPIs) are crucial in gene transcription regulation and cell differentiation. Traditional biological experiments are costly and time-consuming, motivating the development of computational prediction methods. However, existing EPI prediction methods inadequately capture the intricate direct interactions between enhancer and promoter sequences, which limits their prediction performance to some extent. In this work, we propose an innovative attention-based approach RAEPI, which uses convolutional neural networks to extract initial features of enhancers and promoters, combined with a specially designed Restricted Attention mechanism with Query-Key-Value constrained to simulate the interactions between them for further feature extraction. To improve cross-cell line prediction, we employ a transfer learning strategy for pre-training. Furthermore, we extracted sequence motifs to evaluate the RAEPI's effectiveness from a visualization perspective. Experimental results show that RAEPI achieves competitive prediction performance to existing methods on the benchmark dataset. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract 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.

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. Synopsis: Integration of genomics datasets and patient data reveals estrogen receptor agonist-mediated restoration of molecular and physiological changes and identifies a TEAD autopalmitoylation inhibitor as a potential therapeutic for metabolic dysfunction-associated steatotic liver disease (MASLD). Selective activation of nuclear ERs recovers high fat diet (HFD)-induced molecular and physiological liver phenotypes, whereby ERβ represses fibrogenic factors. HFD and systemic ER activation alter core liver pathways, beyond lipid metabolism, that are maintained between mice and primates. ER-sensitive enhancers govern central regulatory and metabolic genes with significance in MASLD patients, including TEAD1. TEAD inhibition improves steatosis in primary human hepatocyte spheroids by suppressing lipogenic pathways. Integration of genomics datasets and patient data reveals estrogen receptor agonist-mediated restoration of molecular and physiological changes and identifies a TEAD autopalmitoylation inhibitor as a potential therapeutic for metabolic dysfunction-associated steatotic liver disease (MASLD). [ABSTRACT FROM AUTHOR]

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

Abstract 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 $$> 0.99$$ > 0.99 ). 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.

Abstract Background Prioritizing genes that underlie complex brain disorders poses a considerable challenge. Despite previous studies have found that they shared symptoms and heterogeneity, it remained difficult to systematically identify the risk genes associated with them. Methods By using the CAGE (Cap Analysis of Gene Expression) read alignment files for 439 human cell and tissue types (including primary cells, tissues and cell lines) from FANTOM5 project, we predicted enhancer-promoter interactions (EPIs) of 439 cell and tissue types in human, and examined their reliability. Then we evaluated the genetic heritability of 17 diverse brain disorders and behavioral-cognitive phenotypes in each neural cell type, brain region, and developmental stage. Furthermore, we prioritized genes associated with brain disorders and phenotypes by leveraging the EPIs in each neural cell and tissue type, and analyzed their pleiotropy and functionality for different categories of disorders and phenotypes. Finally, we characterized the spatiotemporal expression dynamics of these associated genes in cells and tissues. Results We found that identified EPIs showed activity specificity and network aggregation in cell and tissue types, and enriched TF binding in neural cells played key roles in synaptic plasticity and nerve cell development, i.e., EGR1 and SOX family. We also discovered that most neurological disorders exhibit heritability enrichment in neural stem cells and astrocytes, while psychiatric disorders and behavioral-cognitive phenotypes exhibit enrichment in neurons. Furthermore, our identified genes recapitulated well-known risk genes, which exhibited widespread pleiotropy between psychiatric disorders and behavioral-cognitive phenotypes (i.e., FOXP2), and indicated expression specificity in neural cell types, brain regions, and developmental stages associated with disorders and phenotypes. Importantly, we showed the potential associations of brain disorders with brain regions and developmental stages that have not been well studied. Conclusions Overall, our study characterized the gene-enhancer regulatory networks and genetic mechanisms in the human neural cells and tissues, and illustrated the value of reanalysis of publicly available genomic datasets.

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 > 0.99 ). 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. [ABSTRACT FROM AUTHOR]

Background: Prioritizing genes that underlie complex brain disorders poses a considerable challenge. Despite previous studies have found that they shared symptoms and heterogeneity, it remained difficult to systematically identify the risk genes associated with them. Methods: By using the CAGE (Cap Analysis of Gene Expression) read alignment files for 439 human cell and tissue types (including primary cells, tissues and cell lines) from FANTOM5 project, we predicted enhancer-promoter interactions (EPIs) of 439 cell and tissue types in human, and examined their reliability. Then we evaluated the genetic heritability of 17 diverse brain disorders and behavioral-cognitive phenotypes in each neural cell type, brain region, and developmental stage. Furthermore, we prioritized genes associated with brain disorders and phenotypes by leveraging the EPIs in each neural cell and tissue type, and analyzed their pleiotropy and functionality for different categories of disorders and phenotypes. Finally, we characterized the spatiotemporal expression dynamics of these associated genes in cells and tissues. Results: We found that identified EPIs showed activity specificity and network aggregation in cell and tissue types, and enriched TF binding in neural cells played key roles in synaptic plasticity and nerve cell development, i.e., EGR1 and SOX family. We also discovered that most neurological disorders exhibit heritability enrichment in neural stem cells and astrocytes, while psychiatric disorders and behavioral-cognitive phenotypes exhibit enrichment in neurons. Furthermore, our identified genes recapitulated well-known risk genes, which exhibited widespread pleiotropy between psychiatric disorders and behavioral-cognitive phenotypes (i.e., FOXP2), and indicated expression specificity in neural cell types, brain regions, and developmental stages associated with disorders and phenotypes. Importantly, we showed the potential associations of brain disorders with brain regions and developmental stages that have not been well studied. Conclusions: Overall, our study characterized the gene-enhancer regulatory networks and genetic mechanisms in the human neural cells and tissues, and illustrated the value of reanalysis of publicly available genomic datasets. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: As parts of the cis-regulatory mechanism of the human genome, interactions between distal enhancers and proximal promoters play a crucial role. Enhancers, promoters, and enhancer-promoter interactions (EPIs) can be detected using many sequencing technologies and computation models. However, a systematic review that summarizes these EPI identification methods and that can help researchers apply and optimize them is still needed. Results: In this review, we first emphasize the role of EPIs in regulating gene expression and describe a generic framework for predicting enhancer-promoter interaction. Next, we review prediction methods for enhancers, promoters, loops, and enhancer-promoter interactions using different data features that have emerged since 2010, and we summarize the websites available for obtaining enhancers, promoters, and enhancer-promoter interaction datasets. Finally, we review the application of the methods for identifying EPIs in diseases such as cancer. Conclusions: The advance of computer technology has allowed traditional machine learning, and deep learning methods to be used to predict enhancer, promoter, and EPIs from genetic, genomic, and epigenomic features. In the past decade, models based on deep learning, especially transfer learning, have been proposed for directly predicting enhancer-promoter interactions from DNA sequences, and these models can reduce the parameter training time required of bioinformatics researchers. We believe this review can provide detailed research frameworks for researchers who are beginning to study enhancers, promoters, and their interactions. [ABSTRACT FROM AUTHOR]

Expression of Abdominal-B (Abd-B) in abdominal segments A5–A8 is controlled by four regulatory domains, iab-5–iab-8. Each domain has an initiator element (which sets the activity state), elements that maintain this state and tissue-specific enhancers. To ensure their functional autonomy, each domain is bracketed by boundary elements (Mcp, Fab-7, Fab-7 and Fab-8). In addition to blocking crosstalk between adjacent regulatory domains, the Fab boundaries must also have bypass activity so the relevant regulatory domains can ‘jump over’ intervening boundaries and activate the Abd-B promoter. In the studies reported here we have investigated the parameters governing bypass activity. We find that the bypass elements in the Fab-7 and Fab-8 boundaries must be located in the regulatory domain that is responsible for driving Abd-B expression. We suggest that bypass activity may also be subject to regulation.

Introduction: The physical interactions between enhancers and promoters are often involved in gene transcriptional regulation. High tissue-specific enhancerpromoter interactions (EPIs) are responsible for the differential expression of genes. Experimental methods are time-consuming and labor-intensive in measuring EPIs. An alternative approach, machine learning, has been widely used to predict EPIs. However, most existing machine learning methods require a large number of functional genomic and epigenomic features as input, which limits the application to different cell lines. Methods: In this paper, we developed a random forest model, HARD (H3K27ac, ATAC-seq, RAD21, and Distance), to predict EPI using only four types of features. Results: Independent tests on a benchmark dataset showed that HARD outperforms other models with the fewest features. Discussion: Our results revealed that chromatin accessibility and the binding of cohesin are important for cell-line-specific EPIs. Furthermore, we trained the HARD model in the GM12878 cell line and performed testing in the HeLa cell line. The cross-cell-lines prediction also performs well, suggesting it has the potential to be applied to other cell lines. [ABSTRACT FROM AUTHOR]

Abstract 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.

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. [ABSTRACT FROM AUTHOR]

Cellular senescence is a stable cell growth arrest. Senescent cells are metabolically active, as exemplified by the secretion of inflammatory cytokines, chemokines, and growth factors, which is termed senescence-associated secretory phenotype (SASP). The SASP exerts a range of functions in both normal health and pathology, which is possibly best characterized in cancers and physical aging. Recent studies demonstrated that chromatin is instrumental in regulating the SASP both through nuclear transcription and via the innate immune cyclic GMP–AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway in the cytoplasm. Here, we will review these regulatory mechanisms, with an emphasis on most recent developments in the field. We will highlight the challenges and opportunities in developing intervention approaches, such as targeting chromatin regulatory mechanisms, to alter the SASP as an emerging approach to combat cancers and achieve healthy aging. Two most important characteristics of senescent cells, senescence-associated stable growth arrest and the senescence-associated secretory phenotype, are coordinated at the chromatin levels. High-order chromatin structure controls the SASP transcriptionally in the nucleus of senescent cells. Rewiring of enhancer and promoter interaction and reorganization of chromatin loop represent a new layer of the SASP regulation. Cytoplasmic chromatin promotes the SASP through the cGAS-STING innate immune pathway. Therapeutic targeting of the SASP is an attractive alternative to eliminate senescent cells in combating aging and age-associated diseases such as cancer. [ABSTRACT FROM AUTHOR]

The pairwise interaction between transcription factors (TFs) plays an important role in enhancer-promoter loop formation. Although thousands of TFs in the human genome have been found, only a few TF pairs have been demonstrated to be related to loop formation. It is still a challenge to determine which TF pairs could be involved in the enhancer-promoter regulation network. This work describes a computational framework to identify TF pairs in enhancer-promoter regulation. By integrating different levels of data derived from Promoter Capture Hi-C, chromatin immunoprecipitation sequencing (ChIP-seq) of histone marks, RNA-seq, protein-protein interaction (PPI), and TF motif, we identified 361 significant TF pairs and constructed a TF interaction network. From the network, we found several hub-TFs, which may have important roles in the regulation of long-range interactions. Our studies extended TF pairs identified in other experimental and computational approaches. These findings will help the further study of long-range interactions between enhancers and promoters.

Background: Doxorubicin resistance remains a major therapeutic challenge leading to poor survival prognosis and treatment failure in breast cancer. Although doxorubicin induces massive changes in the transcriptional landscape are well known, potential diagnostic or therapeutic targets associated with the reorganization of three-dimensional (3D) chromatin architecture have not yet been systematically investigated.Methods: Here we performed in situ high-throughput chromosome conformation capture (Hi-C) on parental and doxorubicin-resistant MCF7 (MCF7-DR) human breast cancer cells, followed by integrative analysis of HiC, ATAC-seq, RNA-seq and TCGA data.Results: It revealed that A/B compartment switching was positively correlated to genome-wide differential gene expression. The genome of MCF7-DR cells was spatially reorganized into smaller topologically associating domains (TADs) and chromatin loops. We also revealed the contribution of increased chromatin accessibility and potential transcription factor families, including CTCF, AP-1 and bHLH, to gained TADs or loops. Intriguingly, we observed two condensed genomic regions (∼20 kb) with decreased chromatin accessibility flanking TAD boundaries, which might play a critical role in the formation or maintenance of TADs. Finally, combining data from TCGA, we identified a number of gained and lost enhancer-promoter interactions and their corresponding differentially expressed genes involved in chromatin organization and breast cancer signaling pathways, including FA2H, FOXA1 and JRKL, which might serve as potential treatment targets for breast cancer.Conclusion: These data uncovered a close connection between 3D genome reorganization, chromatin accessibility as well as gene transcription and provide novel insights into the epigenomic mechanisms involving doxorubicin resistance in breast cancer.

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. [ABSTRACT FROM AUTHOR]

Cis-regulatory elements such as enhancers can be located even a million base pairs away from their cognate promoter and yet modulate gene transcription. Indeed, the 3D organisation of chromatin enables the establishment of long-range enhancer-promoter communication. The observation of long-range enhancer-promoter chromatin loops at active genes originally led to a model in which enhancers and promoters form physical contacts between each other to control transcription. Yet, recent microscopy data has challenged this prevailing activity-by-contact model of enhancer-promoter communication in transcriptional activation. Live single-cell imaging approaches do not systematically reveal a correlation between enhancer-proximity and transcriptional activation. We therefore discuss the need to move from a static to a dynamic view of enhancer-promoter relationships. We highlight recent studies that not only reveal considerable chromatin movement in specific cell types, but suggest links between chromatin compaction, chromatin movement and transcription. We describe the interplay between enhancer-promoter proximity within the context of biomolecular condensates and the need to understand how condensate microenvironments influence the chromatin binding kinetics of proteins that bind at cis-regulatory elements to activate transcription. Finally, given the complex multi-scale interplay between regulatory proteins, enhancer-promoter proximity and movement, we propose the need to integrate information from complementary single-cell next-generation sequencing and live-cell imaging approaches to derive unified 3D theoretical models of enhancer-promoter communication that are ultimately predictive of transcriptional output and cell fate. In time, improved models will shed light on how tissues grow and diseases emerge.

JMJD1C, a member of the lysine demethylase 3 (KDM3) family, is universally required for the survival of several types of acute myeloid leukemia (AML) cells with different genetic mutations, representing a therapeutic opportunity with broad application. Yet how JMJD1C regulates the leukemic programs of various AML cells is largely unexplored. Here we show that JMJD1C interacts with the master hematopoietic transcription factor RUNX1, which thereby recruits JMJD1C to the genome to facilitate a RUNX1-driven transcriptional program that supports leukemic cell survival. The underlying mechanism hinges on the long N-terminal disordered region of JMJD1C, which harbors two inseparable abilities: condensate formation and direct interaction with RUNX1. This dual capability of JMJD1C may influence enhancer-promoter contacts crucial for the expression of key leukemic genes regulated by RUNX1. Our findings demonstrate a previously unappreciated role for the non-catalytic function of JMJD1C in transcriptional regulation, underlying a mechanism shared by different types of leukemias., (© The Author(s) 2024. Published by Oxford University Press on behalf of Higher Education Press.)

SUMMARY: 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. Significance Statement: The role of insualtor in the regulation of enhancer‐promoter interaction and topological architecture of genomes has been well characterized in mammals but rarely in plants largely due to a lack of a true insualtor. We report the isolation and characterization of an insualtor from rice genome and its binding protein prominently associated with the boundaries of chromatin loops. Our findings are of significance in understanding the insualtor‐based gene regulation and genome demarcation in plants [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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

DNA molecules are highly compacted in the eukaryotic nucleus where distal regulatory elements reach their targets through three-dimensional chromosomal interactions. G-quadruplexes, stable four-stranded non-canonical DNA structures, can change local chromatin organization through the exclusion of nucleosomes. However, the relationship between G-quadruplexes and higher-order genome organization remains unknown. Here, we found that G-quadruplexes are significantly enriched at boundaries of topological associated domains (TADs). Architectural protein occupancy, which plays critical roles in the formation of TADs, was highly correlated with the content of G-quadruplexes at TAD boundaries. Moreover, adjacent boundaries containing G-quadruplexes frequently interacted with each other because of the high enrichment of architectural protein binding sites. Similar to CCCTC-binding factor (CTCF) binding sites, G-quadruplexes also showed strong insulation ability in the separation of adjacent regions. Additionally, the insulation ability of CTCF binding sites and TAD boundaries was significantly reinforced by G-quadruplexes. Furthermore, G-quadruplex motifs on different strands were associated with the orientation of CTCF binding sites. These findings suggest a potential role for G-quadruplexes in loop extrusion. The enrichment of transcription factor binding sites (TFBSs) around regulatory elements containing G-quadruplexes led to frequent interactions between regulatory elements containing G-quadruplexes. Intriguingly, more than 99% of G-quadruplexes overlapped with TFBSs. The binding sites of CTCF and cohesin proteins were preferentially located surrounding G-quadruplexes. Accordingly, we proposed a new mechanism of long-distance gene regulation in which G-quadruplexes are involved in distal interactions between enhancers and promoters.

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. [ABSTRACT FROM AUTHOR]

More than 98% of the human genome does not encode proteins, and the vast majority of the noncoding regions have not been well studied. Some of these regions contain enhancers and functional non-coding RNAs. Previous research suggested that enhancer transcripts could be potent independent indicators of enhancer activity, and some enhancer lncRNAs (elncRNAs) have been proven to play critical roles in gene regulation. Here, we identified enhancer–promoter interactions from high-throughput chromosome conformation capture (Hi-C) data. We found that elncRNAs were highly enriched surrounding chromatin loop anchors. Additionally, the interaction frequency of elncRNA-associated enhancer–promoter pairs was significantly higher than the interaction frequency of other enhancer–promoter pairs, suggesting that elncRNAs may reinforce the interactions between enhancers and promoters. We also found that elncRNA expression levels were positively correlated with the interaction frequency of enhancer–promoter pairs. The promoters interacting with elncRNA-associated enhancers were rich in RNA polymerase II and YY1 transcription factor binding sites. We clustered enhancer–promoter pairs into different groups to reflect the different ways in which elncRNAs could influence enhancer–promoter pairs. Interestingly, G-quadruplexes were found to potentially mediate some enhancer–promoter interaction pairs, and the interaction frequency of these pairs was significantly higher than that of other enhancer–promoter pairs. We also found that the G-quadruplexes on enhancers were highly related to the expression of elncRNAs. G-quadruplexes located in the promoters of elncRNAs led to high expression of elncRNAs, whereas G-quadruplexes located in the gene bodies of elncRNAs generally resulted in low expression of elncRNAs.

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. [ABSTRACT FROM AUTHOR]

More than 98% of the human genome does not encode proteins, and the vast majority of the noncoding regions have not been well studied. Some of these regions contain enhancers and functional non-coding RNAs. Previous research suggested that enhancer transcripts could be potent independent indicators of enhancer activity, and some enhancer lncRNAs (elncRNAs) have been proven to play critical roles in gene regulation. Here, we identified enhancer–promoter interactions from high-throughput chromosome conformation capture (Hi-C) data. We found that elncRNAs were highly enriched surrounding chromatin loop anchors. Additionally, the interaction frequency of elncRNA-associated enhancer–promoter pairs was significantly higher than the interaction frequency of other enhancer–promoter pairs, suggesting that elncRNAs may reinforce the interactions between enhancers and promoters. We also found that elncRNA expression levels were positively correlated with the interaction frequency of enhancer–promoter pairs. The promoters interacting with elncRNA-associated enhancers were rich in RNA polymerase II and YY1 transcription factor binding sites. We clustered enhancer–promoter pairs into different groups to reflect the different ways in which elncRNAs could influence enhancer–promoter pairs. Interestingly, G-quadruplexes were found to potentially mediate some enhancer–promoter interaction pairs, and the interaction frequency of these pairs was significantly higher than that of other enhancer–promoter pairs. We also found that the G-quadruplexes on enhancers were highly related to the expression of elncRNAs. G-quadruplexes located in the promoters of elncRNAs led to high expression of elncRNAs, whereas G-quadruplexes located in the gene bodies of elncRNAs generally resulted in low expression of elncRNAs. [ABSTRACT FROM AUTHOR]

DNA molecules are highly compacted in the eukaryotic nucleus where distal regulatory elements reach their targets through three-dimensional chromosomal interactions. G-quadruplexes, stable four-stranded non-canonical DNA structures, can change local chromatin organization through the exclusion of nucleosomes. However, the relationship between G-quadruplexes and higher-order genome organization remains unknown. Here, we found that G-quadruplexes are significantly enriched at boundaries of topological associated domains (TADs). Architectural protein occupancy, which plays critical roles in the formation of TADs, was highly correlated with the content of G-quadruplexes at TAD boundaries. Moreover, adjacent boundaries containing G-quadruplexes frequently interacted with each other because of the high enrichment of architectural protein binding sites. Similar to CCCTC-binding factor (CTCF) binding sites, G-quadruplexes also showed strong insulation ability in the separation of adjacent regions. Additionally, the insulation ability of CTCF binding sites and TAD boundaries was significantly reinforced by G-quadruplexes. Furthermore, G-quadruplex motifs on different strands were associated with the orientation of CTCF binding sites. These findings suggest a potential role for G-quadruplexes in loop extrusion. The enrichment of transcription factor binding sites (TFBSs) around regulatory elements containing G-quadruplexes led to frequent interactions between regulatory elements containing G-quadruplexes. Intriguingly, more than 99% of G-quadruplexes overlapped with TFBSs. The binding sites of CTCF and cohesin proteins were preferentially located surrounding G-quadruplexes. Accordingly, we proposed a new mechanism of long-distance gene regulation in which G-quadruplexes are involved in distal interactions between enhancers and promoters. [ABSTRACT FROM AUTHOR]

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; P multiplicative-interaction = 1.20 × 10−22; P additive-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-β, 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. [ABSTRACT FROM AUTHOR]

Background: In the human genome, distal enhancers are involved in regulating target genes through proximal promoters by forming enhancer-promoter interactions. Although recently developed high-throughput experimental approaches have allowed us to recognize potential enhancer-promoter interactions genome-wide, it is still largely unclear to what extent the sequence-level information encoded in our genome help guide such interactions. Methods: Here we report a new computational method (named "SPEID") using deep learning models to predict enhancer-promoter interactions based on sequence-based features only, when the locations of putative enhancers and promoters in a particular cell type are given. Results: Our results across six different cell types demonstrate that SPEID is effective in predicting enhancer-promoter interactions as compared to state-of-the-art methods that only use information from a single cell type. As a proof-of-principle, we also applied SPEID to identify somatic non-coding mutations in melanoma samples that may have reduced enhancer-promoter interactions in tumor genomes. Conclusions: This work demonstrates that deep learning models can help reveal that sequence-based features alone are sufficient to reliably predict enhancer-promoter interactions genome-wide. [ABSTRACT FROM AUTHOR]

Enhancer–promoter (E–P) interaction is an essential component of cis-regulatory regulation for gene expression. However, to comprehensively study the gene expression with the regulation of long-range E–P interactions is a major challenge in the regulatory networks. As these types of gene expression are regulated by diverse genomic signatures, we presented a computational method to study the relationships between gene expression levels and diverse genomic signatures. In this paper, based on the datasets of long-range E–P interactions, we extracted feature parameters from multiple signatures (e.g., epigenetic marks, transcription factors) and used regression models to predict the gene expression levels. In our results, we found that the predicted expression values correlated well with the measured expression values in both the interacting and non-interacting sets, and the correlation values of the interacting set were higher than that of the corresponding non-interacting set in each cell line, which indicated that the distal enhancers would cooperate with diverse genomic signatures to facilitate the expression level of target genes. By comparing the important signature features for the gene expression levels between the interacting and non-interacting sets in the same cell line, we found that the important specific signatures affect the gene expression regulated by distal enhancers. Our research provided additional insights about the roles of diverse signatures in gene expression with the regulation of distal enhancers. [ABSTRACT FROM AUTHOR]

Highlights • DNA structure properties and TF binding motifs are used in EPIs recognition. • DNA structure properties and TF binding motifs contribute greatly to EPIs recognition. • A significantly improved recognition model for long range EPIs was proposed. • The correlation analysis and network models were made among key genomic signatures. • Distribution analysis was made for DNA structure properties and TFs binding motifs. Abstract 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. [ABSTRACT FROM AUTHOR]

We have developed a machine learning approach to predict stimulation-dependent enhancer-promoter interactions using evidence from changes in genomic protein occupancy over time. The occupancy of estrogen receptor alpha (ERα), RNA polymerase (Pol II) and histone marks H2AZ and H3K4me3 were measured over time using ChIP-Seq experiments in MCF7 cells stimulated with estrogen. A Bayesian classifier was developed which uses the correlation of temporal binding patterns at enhancers and promoters and genomic proximity as features to predict interactions. This method was trained using experimentally determined interactions from the same system and was shown to achieve much higher precision than predictions based on the genomic proximity of nearest ERα binding. We use the method to identify a genome-wide confident set of ERα target genes and their regulatory enhancers genome-wide. Validation with publicly available GRO-Seq data demonstrates that our predicted targets are much more likely to show early nascent transcription than predictions based on genomic ERα binding proximity alone.

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. [ABSTRACT FROM AUTHOR]

Earlier, it was reported that the strong cytomegalovirus enhancer can activate the cytomegalovirus promoter in trans , i.e. as a separate plasmid co-transfected with a promoter-reporter gene construct. Here we demonstrate that the ability of enhancers to activate promoters in trans in transient transfection experiments is a property of not only viral regulatory elements but also of various genomic enhancers and promoters. Enhancer-promoter activation in trans is promoter- and cell type-specific, and accompanied by physical interaction between promoter and enhancer as revealed by chromosome conformation capture assays. Thus, promoter activation in transient co-transfection of promoters and enhancers shares a number of important traits with long-distance promoter activation by enhancers in living cells and may therefore serve as a model of this fundamental cellular process. [ABSTRACT FROM AUTHOR]

Enhancer promoter interaction (EPI) involves most of gene transcriptional regulation in the high eukaryotes. Predicting the EPIs from given genomic loci or DNA sequences is not a trivial task. The benchmarking work so far for EPI predictors is more or less empirical and lacks quantitative model-based comparisons, posing challenges for molecular biologists to obtain reliable EPI predictions. Here, we present an EPI prediction platform, namely Delta.EPI. Based on a statistic model of the data integration, Delta.EPI is capable of comprehensively assessing the predictions from four state-of-the-art EPI predictors. Equipped with a user-friendly interface and visualization platform, Delta.EPI presents the sorted results with the confidence of EPI relevance, which may guide the molecular biologists who lack the pre-knowledge of the algorithms of EPI prediction. Last, we showcase the utility of Delta.EPI with a case study. Delta.EPI provides a powerful tool to fuel the gene regulation and 3D genome studies by ease-to-access EPI predictions. Delta.EPI can be freely accessed at https://ngdc.cncb.ac.cn/deltaEPI/., Competing Interests: Conflict of interest The authors declare that they have no competing interests., (Copyright © 2023 Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and Genetics Society of China. Published by Elsevier Ltd. All rights reserved.)