Enhancer-silencer transitions in the human genome

The contribution of silencer variants to human diseases

BACKGROUND

Although disease-causal genetic variants have been found within silencer sequences, we still lack a comprehensive analysis of the association of silencers with diseases. Here, we profiled GWAS variants in 2.8 million candidate silencers across 97 human samples derived from a diverse panel of tissues and developmental time points, using deep learning models.

RESULTS

We show that candidate silencers exhibit strong enrichment in disease-associated variants, and several diseases display a much stronger association with silencer variants than enhancer variants. Close to 52% of candidate silencers cluster, forming silencer-rich loci, and, in the loci of Parkinson's-disease-hallmark genes TRIM31 and MAL, the associated SNPs densely populate clustered candidate silencers rather than enhancers displaying an overall twofold enrichment in silencers versus enhancers. The disruption of apoptosis in neuronal cells is associated with both schizophrenia and bipolar disorder and can largely be attributed to variants within candidate silencers. Our model permits a mechanistic explanation of causative SNP effects by identifying altered binding of tissue-specific repressors and activators, validated with a 70% of directional concordance using SNP-SELEX. Narrowing the focus of the analysis to individual silencer variants, experimental data confirms the role of the rs62055708 SNP in Parkinson's disease, rs2535629 in schizophrenia, and rs6207121 in type 1 diabetes.

CONCLUSIONS

In summary, our results indicate that advances in deep learning models for the discovery of disease-causal variants within candidate silencers effectively "double" the number of functionally characterized GWAS variants. This provides a basis for explaining mechanisms of action and designing novel diagnostics and therapeutics.

Hidden secrets of the cancer genome: unlocking the impact of non-coding mutations in gene regulatory elements

Discoveries in the field of genomics have revealed that non-coding genomic regions are not merely "junk DNA", but rather comprise critical elements involved in gene expression. These gene regulatory elements (GREs) include enhancers, insulators, silencers, and gene promoters. Notably, new evidence shows how mutations within these regions substantially influence gene expression programs, especially in the context of cancer. Advances in high-throughput sequencing technologies have accelerated the identification of somatic and germline single nucleotide mutations in non-coding genomic regions. This review provides an overview of somatic and germline non-coding single nucleotide alterations affecting transcription factor binding sites in GREs, specifically involved in cancer biology. It also summarizes the technologies available for exploring GREs and the challenges associated with studying and characterizing non-coding single nucleotide mutations. Understanding the role of GRE alterations in cancer is essential for improving diagnostic and prognostic capabilities in the precision medicine era, leading to enhanced patient-centered clinical outcomes.

KSHV genome harbors both constitutive and lytically induced enhancers

Kaposi's sarcoma-associated herpesvirus (KSHV) belongs to the gamma-herpesvirus family and is a well-known human oncogenic virus. In infected cells, the viral genome of 165 kbp is circular DNA wrapped in chromatin. The tight control of gene expression is critical for latency, the transition into the lytic phase, and the development of viral-associated malignancies. Distal cis-regulatory elements, such as enhancers and silencers, can regulate gene expression in a position- and orientation-independent manner. Open chromatin is another characteristic feature of enhancers. To systematically search for enhancers, we cloned all the open chromatin regions in the KSHV genome downstream of the luciferase gene and tested their enhancer activity in infected and uninfected cells. A silencer was detected upstream of the latency-associated nuclear antigen promoter. Two constitutive enhancers were identified in the K12p-OriLyt-R and ORF29 Intron regions, where ORF29 Intron is a tissue-specific enhancer. The following promoters: OriLyt-L, PANp, ALTp, and the terminal repeats (TRs) acted as lytically induced enhancers. The expression of the replication and transcription activator (RTA), the master regulator of the lytic cycle, was sufficient to induce the activity of lytic enhancers in uninfected cells. We propose that the TRs that span about 24 kbp region serve as a "viral super-enhancer" that integrates the repressive effect of the latency-associated nuclear antigen (LANA) with the activating effect of RTA. Utilizing CRISPR activation and interference techniques, we determined the connections between these enhancers and their regulated genes. The silencer and enhancers described here provide an additional layer to the complex gene regulation of herpesviruses.IMPORTANCEIn this study, we performed a systematic functional assay to identify cis-regulatory elements within the genome of the oncogenic herpesvirus, Kaposi's sarcoma-associated herpesvirus (KSHV). Similar to other herpesviruses, KSHV presents both latent and lytic phases. Therefore, our assays were performed in uninfected cells, during latent infection, and under lytic conditions. We identified two constitutive enhancers, one of which seems to be a tissue-specific enhancer. In addition, four lytically induced enhancers, which are all responsive to the replication and transcription activator (RTA), were identified. Furthermore, a silencer was identified between the major latency promoter and the lytic gene locus. Utilizing CRISPR activation and interference techniques, we determined the connections between these enhancers and their regulated genes. The terminal repeats, spanning a region of about 24 kbp, seem like a "viral super-enhancer" that integrates the repressive effect of the latency-associated nuclear antigen (LANA) with the activating effect of RTA to regulate latency to lytic transition.

Determinants of Chromatin Organization in Aging and Cancer-Emerging Opportunities for Epigenetic Therapies and AI Technology

This review article critically examines the pivotal role of chromatin organization in gene regulation, cellular differentiation, disease progression and aging. It explores the dynamic between the euchromatin and heterochromatin, coded by a complex array of histone modifications that orchestrate essential cellular processes. We discuss the pathological impacts of chromatin state misregulation, particularly in cancer and accelerated aging conditions such as progeroid syndromes, and highlight the innovative role of epigenetic therapies and artificial intelligence (AI) in comprehending and harnessing the histone code toward personalized medicine. In the context of aging, this review explores the use of AI and advanced machine learning (ML) algorithms to parse vast biological datasets, leading to the development of predictive models for epigenetic modifications and providing a framework for understanding complex regulatory mechanisms, such as those governing cell identity genes. It supports innovative platforms like CEFCIG for high-accuracy predictions and tools like GridGO for tailored ChIP-Seq analysis, which are vital for deciphering the epigenetic landscape. The review also casts a vision on the prospects of AI and ML in oncology, particularly in the personalization of cancer therapy, including early diagnostics and treatment optimization for diseases like head and neck and colorectal cancers by harnessing computational methods, AI advancements and integrated clinical data for a transformative impact on healthcare outcomes.

Biallelic non-productive enhancer-promoter interactions precede imprinted expression of Kcnk9 during mouse neural commitment

It is only partially understood how constitutive allelic methylation at imprinting control regions (ICRs) interacts with other regulation levels to drive timely parental allele-specific expression along large imprinted domains. The Peg13-Kcnk9 domain is an imprinted domain with important brain functions. To gain insights into its regulation during neural commitment, we performed an integrative analysis of its allele-specific epigenetic, transcriptomic, and cis-spatial organization using a mouse stem cell-based corticogenesis model that recapitulates the control of imprinted gene expression during neurodevelopment. We found that, despite an allelic higher-order chromatin structure associated with the paternally CTCF-bound Peg13 ICR, enhancer-Kcnk9 promoter contacts occurred on both alleles, although they were productive only on the maternal allele. This observation challenges the canonical model in which CTCF binding isolates the enhancer and its target gene on either side and suggests a more nuanced role for allelic CTCF binding at some ICRs.

Functional categorization of gene regulatory variants that cause Mendelian conditions

Much of our current understanding of rare human diseases is driven by coding genetic variants. However, non-coding genetic variants play a pivotal role in numerous rare human diseases, resulting in diverse functional impacts ranging from altered gene regulation, splicing, and/or transcript stability. With the increasing use of genome sequencing in clinical practice, it is paramount to have a clear framework for understanding how non-coding genetic variants cause disease. To this end, we have synthesized the literature on hundreds of non-coding genetic variants that cause rare Mendelian conditions via the disruption of gene regulatory patterns and propose a functional classification system. Specifically, we have adapted the functional classification framework used for coding variants (i.e., loss-of-function, gain-of-function, and dominant-negative) to account for features unique to non-coding gene regulatory variants. We identify that non-coding gene regulatory variants can be split into three distinct categories by functional impact: (1) non-modular loss-of-expression (LOE) variants; (2) modular loss-of-expression (mLOE) variants; and (3) gain-of-ectopic-expression (GOE) variants. Whereas LOE variants have a direct corollary with coding loss-of-function variants, mLOE and GOE variants represent disease mechanisms that are largely unique to non-coding variants. These functional classifications aim to provide a unified terminology for categorizing the functional impact of non-coding variants that disrupt gene regulatory patterns in Mendelian conditions.

Canalizing cell fate by transcriptional repression

Precision in the establishment and maintenance of cellular identities is crucial for the development of multicellular organisms and requires tight regulation of gene expression. While extensive research has focused on understanding cell type-specific gene activation, the complex mechanisms underlying the transcriptional repression of alternative fates are not fully understood. Here, we provide an overview of the repressive mechanisms involved in cell fate regulation. We discuss the molecular machinery responsible for suppressing alternative fates and highlight the crucial role of sequence-specific transcription factors (TFs) in this process. Depletion of these TFs can result in unwanted gene expression and increased cellular plasticity. We suggest that these TFs recruit cell type-specific repressive complexes to their cis-regulatory elements, enabling them to modulate chromatin accessibility in a context-dependent manner. This modulation effectively suppresses master regulators of alternative fate programs and their downstream targets. The modularity and dynamic behavior of these repressive complexes enables a limited number of repressors to canalize and maintain major and minor cell fate decisions at different stages of development.

Methylation-directed regulatory networks determine enhancing and silencing of mutation disease driver genes and explain inter-patient expression variation

BACKGROUND

Common diseases manifest differentially between patients, but the genetic origin of this variation remains unclear. To explore possible involvement of gene transcriptional-variation, we produce a DNA methylation-oriented, driver-gene-wide dataset of regulatory elements in human glioblastomas and study their effect on inter-patient gene expression variation.

RESULTS

In 175 of 177 analyzed gene regulatory domains, transcriptional enhancers and silencers are intermixed. Under experimental conditions, DNA methylation induces enhancers to alter their enhancing effects or convert into silencers, while silencers are affected inversely. High-resolution mapping of the association between DNA methylation and gene expression in intact genomes reveals methylation-related regulatory units (average size = 915.1 base-pairs). Upon increased methylation of these units, their target-genes either increased or decreased in expression. Gene-enhancing and silencing units constitute cis-regulatory networks of genes. Mathematical modeling of the networks highlights indicative methylation sites, which signified the effect of key regulatory units, and add up to make the overall transcriptional effect of the network. Methylation variation in these sites effectively describe inter-patient expression variation and, compared with DNA sequence-alterations, appears as a major contributor of gene-expression variation among glioblastoma patients.

CONCLUSIONS

We describe complex cis-regulatory networks, which determine gene expression by summing the effects of positive and negative transcriptional inputs. In these networks, DNA methylation induces both enhancing and silencing effects, depending on the context. The revealed mechanism sheds light on the regulatory role of DNA methylation, explains inter-individual gene-expression variation, and opens the way for monitoring the driving forces behind deferential courses of cancer and other diseases.

SilenceREIN: seeking silencers on anchors of chromatin loops by deep graph neural networks

Silencers are repressive cis-regulatory elements that play crucial roles in transcriptional regulation. Experimental methods for identifying silencers are always costly and time-consuming. Computational methods, which relies on genomic sequence features, have been introduced as alternative approaches. However, silencers do not have significant epigenomic signature. Therefore, we explore a new way to computationally identify silencers, by incorporating chromatin structural information. We propose the SilenceREIN method, which focuses on finding silencers on anchors of chromatin loops. By using graph neural networks, we extracted chromatin structural information from a regulatory element interaction network. SilenceREIN integrated the chromatin structural information with linear genomic signatures to find silencers. The predictive performance of SilenceREIN is comparable or better than other states-of-the-art methods. We performed a genome-wide scanning to systematically find silencers in human genome. Results suggest that silencers are widespread on anchors of chromatin loops. In addition, enrichment analysis of transcription factor binding motif support our prediction results. As far as we can tell, this is the first attempt to incorporate chromatin structural information in finding silencers. All datasets and source codes of SilenceREIN have been deposited in a GitHub repository (https://github.com/JianHPan/SilenceREIN).

DeepICSH: a complex deep learning framework for identifying cell-specific silencers and their strength from the human genome

Silencers are noncoding DNA sequence fragments located on the genome that suppress gene expression. The variation of silencers in specific cells is closely related to gene expression and cancer development. Computational approaches that exclusively rely on DNA sequence information for silencer identification fail to account for the cell specificity of silencers, resulting in diminished accuracy. Despite the discovery of several transcription factors and epigenetic modifications associated with silencers on the genome, there is still no definitive biological signal or combination thereof to fully characterize silencers, posing challenges in selecting suitable biological signals for their identification. Therefore, we propose a sophisticated deep learning framework called DeepICSH, which is based on multiple biological data sources. Specifically, DeepICSH leverages a deep convolutional neural network to automatically capture biologically relevant signal combinations strongly associated with silencers, originating from a diverse array of biological signals. Furthermore, the utilization of attention mechanisms facilitates the scoring and visualization of these signal combinations, whereas the employment of skip connections facilitates the fusion of multilevel sequence features and signal combinations, thereby empowering the accurate identification of silencers within specific cells. Extensive experiments on HepG2 and K562 cell line data sets demonstrate that DeepICSH outperforms state-of-the-art methods in silencer identification. Notably, we introduce for the first time a deep learning framework based on multi-omics data for classifying strong and weak silencers, achieving favorable performance. In conclusion, DeepICSH shows great promise for advancing the study and analysis of silencers in complex diseases. The source code is available at https://github.com/lyli1013/DeepICSH.

Toward a comprehensive catalog of regulatory elements

Regulatory elements are the genomic regions that interact with transcription factors to control cell-type-specific gene expression in different cellular environments. A precise and complete catalog of functional elements encoded by the human genome is key to understanding mammalian gene regulation. Here, we review the current state of regulatory element annotation. We first provide an overview of assays for characterizing functional elements, including genome, epigenome, transcriptome, three-dimensional chromatin interaction, and functional validation assays. We then discuss computational methods for defining regulatory elements, including peak-calling and other statistical modeling methods. Finally, we introduce several high-quality lists of regulatory element annotations and suggest potential future directions.

Mechanisms of Interaction between Enhancers and Promoters in Three Drosophila Model Systems

In higher eukaryotes, the regulation of developmental gene expression is determined by enhancers, which are often located at a large distance from the promoters they regulate. Therefore, the architecture of chromosomes and the mechanisms that determine the functional interaction between enhancers and promoters are of decisive importance in the development of organisms. Mammals and the model animal Drosophila have homologous key architectural proteins and similar mechanisms in the organization of chromosome architecture. This review describes the current progress in understanding the mechanisms of the formation and regulation of long-range interactions between enhancers and promoters at three well-studied key regulatory loci in Drosophila.

Enhancer-promoter entanglement explains their transcriptional interdependence

Enhancers not only activate target promoters to stimulate messenger RNA (mRNA) synthesis, but they themselves also undergo transcription to produce enhancer RNAs (eRNAs), the significance of which is not well understood. Transcription at the participating enhancer-promoter pair appears coordinated, but it is unclear why and how. Here, we employ cell-free transcription assays using constructs derived from the human GREB1 locus to demonstrate that transcription at an enhancer and its target promoter is interdependent. This interdependence is observable under conditions where direct enhancer-promoter contact (EPC) takes place. We demonstrate that transcription activation at a participating enhancer-promoter pair is dependent on i) the mutual availability of the enhancer and promoter, ii) the state of transcription at both the enhancer and promoter, iii) local abundance of both eRNA and mRNA, and iv) direct EPC. Our results suggest transcriptional interdependence between the enhancer and the promoter as the basis of their transcriptional concurrence and coordination throughout the genome. We propose a model where transcriptional concurrence, coordination and interdependence are possible if the participating enhancer and promoter are entangled in the form of EPC, reside in a proteinaceous bubble, and utilize shared transcriptional resources and regulatory inputs.

Deciphering transcription factors and their corresponding regulatory elements during inhibitory interneuron differentiation using deep neural networks

During neurogenesis, the generation and differentiation of neuronal progenitors into inhibitory gamma-aminobutyric acid-containing interneurons is dependent on the combinatorial activity of transcription factors (TFs) and their corresponding regulatory elements (REs). However, the roles of neuronal TFs and their target REs in inhibitory interneuron progenitors are not fully elucidated. Here, we developed a deep-learning-based framework to identify enriched TF motifs in gene REs (eMotif-RE), such as poised/repressed enhancers and putative silencers. Using epigenetic datasets (e.g., ATAC-seq and H3K27ac/me3 ChIP-seq) from cultured interneuron-like progenitors, we distinguished between active enhancer sequences (open chromatin with H3K27ac) and non-active enhancer sequences (open chromatin without H3K27ac). Using our eMotif-RE framework, we discovered enriched motifs of TFs such as ASCL1, SOX4, and SOX11 in the active enhancer set suggesting a cooperativity function for ASCL1 and SOX4/11 in active enhancers of neuronal progenitors. In addition, we found enriched ZEB1 and CTCF motifs in the non-active set. Using an in vivo enhancer assay, we showed that most of the tested putative REs from the non-active enhancer set have no enhancer activity. Two of the eight REs (25%) showed function as poised enhancers in the neuronal system. Moreover, mutated REs for ZEB1 and CTCF motifs increased their in vivo activity as enhancers indicating a repressive effect of ZEB1 and CTCF on these REs that likely function as repressed enhancers or silencers. Overall, our work integrates a novel framework based on deep learning together with a functional assay that elucidated novel functions of TFs and their corresponding REs. Our approach can be applied to better understand gene regulation not only in inhibitory interneuron differentiation but in other tissue and cell types.

Building integrative functional maps of gene regulation

Every cell in the human body inherits a copy of the same genetic information. The three billion base pairs of DNA in the human genome, and the roughly 50 000 coding and non-coding genes they contain, must thus encode all the complexity of human development and cell and tissue type diversity. Differences in gene regulation, or the modulation of gene expression, enable individual cells to interpret the genome differently to carry out their specific functions. Here we discuss recent and ongoing efforts to build gene regulatory maps, which aim to characterize the regulatory roles of all sequences in a genome. Many researchers and consortia have identified such regulatory elements using functional assays and evolutionary analyses; we discuss the results, strengths and shortcomings of their approaches. We also discuss new techniques the field can leverage and emerging challenges it will face while striving to build gene regulatory maps of ever-increasing resolution and comprehensiveness.

Long-Distance Repression by Human Silencers: Chromatin Interactions and Phase Separation in Silencers

Three-dimensional genome organization represents an additional layer in the epigenetic regulation of gene expression. Active transcription controlled by enhancers or super-enhancers has been extensively studied. Enhancers or super-enhancers can recruit activators or co-activators to activate target gene expression through long-range chromatin interactions. Chromatin interactions and phase separation play important roles in terms of enhancer or super-enhancer functioning. Silencers are another major type of cis-regulatory element that can mediate gene regulation by turning off or reducing gene expression. However, compared to active transcription, silencer studies are still in their infancy. This review covers the current knowledge of human silencers, especially the roles of chromatin interactions and phase separation in silencers. This review also proposes future directions for human silencer studies.