Going the distance: a current view of enhancer action

The homie insulator has sub-elements with different insulating and long-range pairing properties

Chromatin insulators are major determinants of chromosome architecture. Specific architectures induced by insulators profoundly influence nuclear processes, including how enhancers and promoters interact over long distances and between homologous chromosomes. Insulators can pair with copies of themselves in trans to facilitate homolog pairing. They can also pair with other insulators, sometimes with great specificity, inducing long-range chromosomal loops. Contrary to their canonical function of enhancer blocking, these loops can bring distant enhancers and promoters together to activate gene expression, while at the same time blocking other interactions in cis. The details of these effects depend on the choice of pairing partner, and on the orientation specificity of pairing, implicating the 3D architecture as a major functional determinant. Here, we dissect the homie insulator from the Drosophila even skipped (eve) locus, to understand its substructure. We test pairing function based on homie-carrying transgenes interacting with endogenous eve. The assay is sensitive to both pairing strength and orientation. Using this assay, we found that a Su(Hw) binding site in homie is required for efficient long-range interaction, although some activity remains without it. This binding site also contributes to the canonical insulator activities of enhancer blocking and barrier function. Based on this and other results from our functional dissection, each of the canonical insulator activities, chromosomal loop formation, enhancer blocking, and barrier activity, are partially separable. Our results show the complexity inherent in insulator functions, which can be provided by an array of different proteins with both shared and distinct properties.

Predicting gene expression changes from chromatin structure modification

Spatial organization of chromatin plays a critical role in gene transcription, but connecting population-averaged HiC data to functional outcomes remains a challenge. We present a computational framework linking HiC contact map to gene transcription. Utilizing a bead-spring polymer model informed by HiC contact maps, we generate an ensemble of 3D conformations for a given genomic locus. These conformations are then coupled to gene transcription levels through a Markov chain model, with transition rates derived from molecular dynamics simulations. The efficacy of this framework is demonstrated by simulating the perturbation of a CTCF-mediated TAD boundary, impacting the expression of sox9 and kcnj2. Our model quantitatively reproduces experimentally observed changes in gene expression, revealing that the increased kcnj2 transcription is a consequence of enhancers within the sox9 TAD becoming accessible upon boundary disruption. Quantifying enhancer impact, our model can also identify functional enhancers. This framework enhances our understanding of the relationship between chromosome spatial architecture and gene regulation.

Genomic Language Model for Predicting Enhancers and Their Allele-Specific Activity in the Human Genome

Predicting and deciphering the regulatory logic of enhancers is a challenging problem, due to the intricate sequence features and lack of consistent genetic or epigenetic signatures that can accurately discriminate enhancers from other genomic regions. Recent machine-learning based methods have spotlighted the importance of extracting nucleotide composition of enhancers but failed to learn the sequence context and perform suboptimally. Motivated by advances in genomic language models, we developed DNABERT-Enhancer, a novel enhancer prediction method, by applying DNABERT pre-trained language model on the human genome. We trained two different models, using large collection of enhancers curated from the ENCODE registry of candidate cis-Regulatory Elements. The best fine-tuned model achieved 88.05% accuracy with Matthews correlation coefficient of 76% on independent set aside data. Further, we present the analysis of the predicted enhancers for all chromosomes of the human genome by comparing with the enhancer regions reported in publicly available databases. Finally, we applied DNABERT-Enhancer along with other DNABERT based regulatory genomic region prediction models to predict candidate SNPs with allele-specific enhancer and transcription factor binding activity. The genome-wide enhancer annotations and candidate loss-of-function genetic variants predicted by DNABERT-Enhancer provide valuable resources for genome interpretation in functional and clinical genomics studies.

Establishment of the REMBAC-cassette, a rapid, efficient and manifold BacMam tool for recombinant protein expression

Efficient recombinant protein production requires mammalian stable cell lines or often relies on inefficient transfection processes. Baculoviral transduction of mammalian cells (BacMam) offers cost-effective and robust gene transfer and straightforward scalability. The advantages over conventional approaches are, no need of high biosafety level laboratories, efficient transduction of various cell types and transfer of large transgenes into host cells. In our study, we aim to develop a high expression cassette to increase yields of baculoviral transduction. The establishment follows a sequential approach by first identifying the strongest promoter, followed by intron and WPRE sequences as enhancer elements for transcription and translation. The resulting REMBAC-cassette was compared to conventional transfection in suspension and adherent cells. Irrespective of the cell line, transduction reached nearly 100 % efficiency and led to almost 10-fold increases of gene expression levels. We confirmed these results in larger scale with batch and fed-batch cultivations. Finally, expression of different soluble proteins with high degrees of complexity confirmed the versatility of our established cassette. Overall, the REMBAC-cassette incorporated into the BacMam platform is a manifold tool offering advantages over standard transfection, in the scalability, efficiency and gene expression, which results in higher yields, shorter cultivation times and consequently cost-effective production processes.

The homie insulator has sub-elements with different insulating and long-range pairing properties

Chromatin insulators are major determinants of chromosome architecture. Specific architectures induced by insulators profoundly influence nuclear processes, including how enhancers and promoters interact over long distances and between homologous chromosomes. Insulators can pair with copies of themselves in trans to facilitate homolog pairing. They can also pair with other insulators, sometimes with great specificity, inducing long-range chromosomal loops. Contrary to their canonical function of enhancer blocking, these loops can bring distant enhancers and promoters together to activate gene expression, while at the same time blocking other interactions in cis. The details of these effects depend on the choice of pairing partner, and on the orientation specificity of pairing, implicating the 3-dimensional architecture as a major functional determinant. Here we dissect the homie insulator from the Drosophila even skipped (eve) locus, to understand its substructure. We test pairing function based on homie-carrying transgenes interacting with endogenous eve. The assay is sensitive to both pairing strength and orientation. Using this assay, we found that a Su(Hw) binding site in homie is required for efficient long-range interaction, although some activity remains without it. This binding site also contributes to the canonical insulator activities of enhancer blocking and barrier function. Based on this and other results from our functional dissection, each of the canonical insulator activities, chromosomal loop formation, enhancer blocking, and barrier activity, are partially separable. Our results show the complexity inherent in insulator functions, which can be provided by an array of different proteins with both shared and distinct properties.

DeepCORE: An interpretable multi-view deep neural network model to detect co-operative regulatory elements

Gene transcription is an essential process involved in all aspects of cellular functions with significant impact on biological traits and diseases. This process is tightly regulated by multiple elements that co-operate to jointly modulate the transcription levels of target genes. To decipher the complicated regulatory network, we present a novel multi-view attention-based deep neural network that models the relationship between genetic, epigenetic, and transcriptional patterns and identifies co-operative regulatory elements (COREs). We applied this new method, named DeepCORE, to predict transcriptomes in various tissues and cell lines, which outperformed the state-of-the-art algorithms. Furthermore, DeepCORE contains an interpreter that extracts the attention values embedded in the deep neural network, maps the attended regions to putative regulatory elements, and infers COREs based on correlated attentions. The identified COREs are significantly enriched with known promoters and enhancers. Novel regulatory elements discovered by DeepCORE showed epigenetic signatures consistent with the status of histone modification marks.

The chromosome folding problem and how cells solve it

Every cell must solve the problem of how to fold its genome. We describe how the folded state of chromosomes is the result of the combined activity of multiple conserved mechanisms. Homotypic affinity-driven interactions lead to spatial partitioning of active and inactive loci. Molecular motors fold chromosomes through loop extrusion. Topological features such as supercoiling and entanglements contribute to chromosome folding and its dynamics, and tethering loci to sub-nuclear structures adds additional constraints. Dramatically diverse chromosome conformations observed throughout the cell cycle and across the tree of life can be explained through differential regulation and implementation of these basic mechanisms. We propose that the first functions of chromosome folding are to mediate genome replication, compaction, and segregation and that mechanisms of folding have subsequently been co-opted for other roles, including long-range gene regulation, in different conditions, cell types, and species.

Translation of genome-wide association study: from genomic signals to biological insights

Since the turn of the 21st century, genome-wide association study (GWAS) have successfully identified genetic signals associated with a myriad of common complex traits and diseases. As we transition from establishing robust genetic associations with diverse phenotypes, the central challenge is now focused on characterizing the underlying functional mechanisms driving these signals. Previous GWAS efforts have revealed multiple variants, each conferring relatively subtle susceptibility, collectively contributing to the pathogenesis of various common diseases. Such variants can further exhibit associations with multiple other traits and differ across ancestries, plus disentangling causal variants from non-causal due to linkage disequilibrium complexities can lead to challenges in drawing direct biological conclusions. Combined with cellular context considerations, such challenges can reduce the capacity to definitively elucidate the biological significance of GWAS signals, limiting the potential to define mechanistic insights. This review will detail current and anticipated approaches for functional interpretation of GWAS signals, both in terms of characterizing the underlying causal variants and the corresponding effector genes.

Designing and optimizing AAV-mediated gene therapy for neurodegenerative diseases: from bench to bedside

Recombinant adeno-associated viruses (rAAVs) have emerged as an attractive tool for gene delivery, and demonstrated tremendous promise in gene therapy and gene editing-therapeutic modalities with potential "one-and-done" treatment benefits compared to conventional drugs. Given their tropisms for the central nervous system (CNS) across various species including humans, rAAVs have been extensively investigated in both pre-clinical and clinical studies targeting neurodegenerative disease. However, major challenges remain in the application of rAAVs for CNS gene therapy, such as suboptimal vector design, low CNS transduction efficiency and specificity, and therapy-induced immunotoxicity. Therefore, continuing efforts are being made to optimize the rAAV vectors from their "core" genetic payloads to their "coat" or capsid structure. In this review, we describe current approaches for rAAV vector design tailored for transgene expression in the CNS, summarize the development of CNS-targeting AAV serotypes, and highlight recent advancements in AAV capsid engineering, aimed at generating a new generation of rAAVs with improved CNS tropism. Additionally, we discuss various administration routes for delivering rAAVs to the CNS and provide an overview of AAV-mediated gene therapies currently under investigation in clinical trials for the treatment of neurodegenerative diseases.

Impact of putatively beneficial genomic loci on gene expression in little brown bats (Myotis lucifugus, Le Conte, 1831) affected by white-nose syndrome

Genome-wide scans for selection have become a popular tool for investigating evolutionary responses in wildlife to emerging diseases. However, genome scans are susceptible to false positives and do little to demonstrate specific mechanisms by which loci impact survival. Linking putatively resistant genotypes to observable phenotypes increases confidence in genome scan results and provides evidence of survival mechanisms that can guide conservation and management efforts. Here we used an expression quantitative trait loci (eQTL) analysis to uncover relationships between gene expression and alleles associated with the survival of little brown bats (Myotis lucifugus) despite infection with the causative agent of white-nose syndrome. We found that 25 of the 63 single-nucleotide polymorphisms (SNPs) associated with survival were related to gene expression in wing tissue. The differentially expressed genes have functional annotations associated with the innate immune system, metabolism, circadian rhythms, and the cellular response to stress. In addition, we observed differential expression of multiple genes with survival implications related to loci in linkage disequilibrium with focal SNPs. Together, these findings support the selective function of these loci and suggest that part of the mechanism driving survival may be the alteration of immune and other responses in epithelial tissue.

Differential impact of quiescent non-coding loci on chromatin entropy

Non-coding regions of the human genome are important for functional regulations, but their mechanisms remain elusive. We used machine learning to guide a CRISPR screening on hubs (i.e. non-coding loci forming many 3D contacts) and significantly increased the discovery rate of hubs essential for cell growth. We found no clear genetic or epigenetic differences between essential and nonessential hubs, but we observed that some neighboring hubs in the linear genome have distinct spatial contacts and opposite effects on cell growth. One such pair in an epigenetically quiescent region showed different impacts on gene expression, chromatin accessibility and chromatin organization. We also found that deleting the essential hub altered the genetic network activity and increased the entropy of chromatin accessibility, more severe than that caused by deletion of the nonessential hub, suggesting that they are critical for maintaining an ordered chromatin structure. Our study reveals new insights into the system-level roles of non-coding regions in the human genome.

ACE gene polymorphism and susceptibility to hypertension in a Jordanian adult population

Hypertension is one of the most common and complicated disorders associated with genetic and environmental risk factors. The angiotensin-converting enzyme (ACE) is important in the renin-angiotensin-system pathway. The gene expression of ACE has been investigated as a possible hypertension marker. This study investigates the association between polymorphisms within the ACE1 and ACE2 genes and hypertension susceptibility in a Jordanian population. The study comprised a total of 200 hypertensive patients and 180 healthy controls. A polymerase chain reaction (PCR) was performed to genotype the candidate polymorphism (rs4646994) of the ACE1gene. The Luminex DNA array technique was used for genotyping SNPs (rs4359, rs4344, rs4341, rs4343, and rs2106809) of the ACE1 and ACE2 genes. Our findings suggest no association between SNPs and hypertension regarding allelic and genotypic frequencies. However, rs4359 was significantly associated with diet (pP = 0.049), know HTN (P = 0.042), and number of years DM (P = 0.003). rs4341 was associated with diet (P = 0.032), peripheral vascular disease (P = 0.005), and chronic kidney disease (p = 0.049). While rs4343 was associated with diet (P = 0.031), diabetes mellitus (P = 0.032), and other medication (P = 0.025). Furthermore, the haplotypes of four SNPs of the ACE1 gene showed no significant association with HTN patients and healthy controls. Our findings indicate no association between the polymorphisms in the ACE gene and the risk of hypertension development in the Jordanian adult population.

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.

The Blueprint of Logical Decisions in a NF-κB Signaling System

Nearly identical cells can exhibit substantially different responses to the same stimulus that causes phenotype diversity. Such interplay between phenotype diversity and the architecture of regulatory circuits is crucial since it determines the state of a biological cell. Here, we theoretically analyze how the circuit blueprints of NF-κB in cellular environments are formed and their role in determining the cells' metabolic state. The NF-κB is a collective name for a developmental conserved family of five different transcription factors that can form homodimers or heterodimers and often promote DNA looping to reprogram the inflammatory gene response. The NF-κB controls many biological functions, including cellular differentiation, proliferation, migration, and survival. Our model shows that nuclear localization of NF-κB differentially promotes logic operations such as AND, NAND, NOR, and OR in its regulatory network. Through the quantitative thermodynamic model of transcriptional regulation and systematic variation of promoter-enhancer interaction modes, we can account for the origin of various logic gates as formed in the NF-κB system. We further show that the interconversion or switching of logic gates yielded under systematic variations of the stimuli activity and DNA looping parameters. Such computation occurs in regulatory and signaling pathways in individual cells at a molecular scale, which one can exploit to design a biomolecular computer.

The dynamic landscape of enhancer-derived RNA during mouse early embryo development

Enhancer-derived RNAs (eRNAs) play critical roles in diverse biological processes by facilitating their target gene expression. However, the abundance and function of eRNAs in early embryos are not clear. Here, we present a comprehensive eRNA atlas by systematically integrating publicly available datasets of mouse early embryos. We characterize the transcriptional and regulatory network of eRNAs and show that different embryo developmental stages have distinct eRNA expression and regulatory profiles. Paternal eRNAs are activated asymmetrically during zygotic genome activation (ZGA). Moreover, we identify an eRNA, MZGAe1, which plays an important function in regulating mouse ZGA and early embryo development. MZGAe1 knockdown leads to a developmental block from 2-cell embryo to blastocyst. We create an online data portal, M2ED2, to query and visualize eRNA expression and regulation. Our study thus provides a systematic landscape of eRNA and reveals the important role of eRNAs in regulating mouse early embryo development.

Pathogenic role of super-enhancers as potential therapeutic targets in lung cancer

Lung cancer is still one of the deadliest malignancies today, and most patients with advanced lung cancer pass away from disease progression that is uncontrollable by medications. Super-enhancers (SEs) are large clusters of enhancers in the genome's non-coding sequences that actively trigger transcription. Although SEs have just been identified over the past 10 years, their intricate structure and crucial role in determining cell identity and promoting tumorigenesis and progression are increasingly coming to light. Here, we review the structural composition of SEs, the auto-regulatory circuits, the control mechanisms of downstream genes and pathways, and the characterization of subgroups classified according to SEs in lung cancer. Additionally, we discuss the therapeutic targets, several small-molecule inhibitors, and available treatment options for SEs in lung cancer. Combination therapies have demonstrated considerable advantages in preclinical models, and we anticipate that these drugs will soon enter clinical studies and benefit patients.

Genomic Exploration: Unraveling the Intricacies of Indica Rice Oryza sativa L. Germin-Like Protein Gene 12-3 (OsGLP12-3) Promoter via Cloning, Sequencing, and In Silico Analysis

Germin and Germin-like proteins (GLPs) are a class of plant proteins that are part of the Cupins superfamily, found in several plant organs including roots, seeds, leaves, and nectar glands. They play a crucial role in plant defense against pathogens and environmental stresses. Herein, this study focused on the promoter analysis of OsGLP12-3 in rice cultivar Swat-1 to elucidate its regulation and functions. The region (1863bp) of the OsGLP12-3 promoter from Swat-1 genomic DNA was amplified, purified, quantified, and cloned using Topo cloning technology, followed by sequencing. Further in silico comparative analysis was conducted between the OsGLP12-3 promoters from Nipponbare and Swat-1 using the Plant CARE database, identifying 24 cis-acting regulatory elements with diverse functions. These elements exhibited distinct distribution patterns in the 2 rice varieties. The OsGLP12-3 promoter revealed an abundance of regulatory elements associated with biotic and abiotic stress responses. Computational tools were employed to analyze the regulatory features of this region. In silico expression analysis of OsGLP12-3, considering various developmental stages, stress conditions, hormones, and expression timing, was performed using the TENOR tool. Pairwise alignment indicated 86% sequence similarity between Nipponbare and Swat-1. Phylogenetic analysis was conducted to explore the evolutionary relationship between the OsGLP12-3 and other plant GLPs. Additionally, 2 unique regulatory elements were modeled and docked, GARE and MBS to understand their hydrogen bonding interactions in gene regulation. The study highlights the importance of OsGLP12-3 in plant defense against biotic and abiotic stresses, supported by its expression patterns in response to various stressors and the presence of specific regulatory elements within its promoter region.

Cytoplasmic Accumulation of Histones Induced by BET Inhibition Protects Cells from C9orf72 Poly(PR)-Induced Cell Death

Repeat dipeptides such as poly(proline-arginine) (polyPR) are generated from the hexanucleotide GGGGCC repeat expansions in the C9orf72 gene. These dipeptides are often considered as the genetic cause of familial amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). In the study, fluorescein isothiocyanate (FITC) labeled PR20 is used to investigate PR20-induced cell death. The findings reveal that the cell death induced by PR20 is dependent on its nuclear distribution and can be blocked by a nuclear import inhibitor called importazole. Further investigation reveals that BRD4 inhibitors, such as JQ-1 and I-BET762, restrict cytoplasmic localization of PR20, thereby reducing its cytotoxic effect. Mechanistically, the inhibition of BRD4 leads to an increase in the expression of numerous histones, resulting in the accumulation of histones in the cytoplasm. These cytoplasmic histones associate with PR20 and limit its distribution within the nucleus. Notably, the ectopic expression of histones alone is enough to confer protection to cells treated with PR20. In addition, phenylephrine (PE) induces cellular hypertrophy and cytoplasmic distribution of histone, which also helps protect cells from PR20-induced cell death. The research suggests that temporarily inducing the presence of cytoplasmic histones may alleviate the neurotoxic effects of dipeptide repeat proteins.

Enhancer mutations modulate the severity of chemotherapy-induced myelosuppression

Non-small cell lung cancer is often diagnosed at advanced stages, and many patients are still treated with classical chemotherapy. The unselective nature of chemotherapy often results in severe myelosuppression. Previous studies showed that protein-coding mutations could not fully explain the predisposition to myelosuppression. Here, we investigate the possible role of enhancer mutations in myelosuppression susceptibility. We produced transcriptome and promoter-interaction maps (using HiCap) of three blood stem-like cell lines treated with carboplatin or gemcitabine. Taking advantage of publicly available enhancer datasets, we validated HiCap results in silico and in living cells using epigenetic CRISPR technology. We also developed a network approach for interactome analysis and detection of differentially interacting genes. Differential interaction analysis provided additional information on relevant genes and pathways for myelosuppression compared with differential gene expression analysis at the bulk level. Moreover, we showed that enhancers of differentially interacting genes are highly enriched for variants associated with differing levels of myelosuppression. Altogether, our work represents a prominent example of integrative transcriptome and gene regulatory datasets analysis for the functional annotation of noncoding mutations.

Multi-omic analysis of human kidney tissue identified medulla-specific gene expression patterns

The kidney medulla is a specialized region with important homeostatic functions. It has been implicated in genetic and developmental disorders along with ischemic and drug-induced injuries. Despite its role in kidney function and disease, the medulla's baseline gene expression and epigenomic signatures have not been well described in the adult human kidney. Here we generated and analyzed gene expression (RNA-seq), chromatin accessibility (ATAC-seq), chromatin conformation (Hi-C) and spatial transcriptomic data from the adult human kidney cortex and medulla. Tissue samples were obtained from macroscopically dissected cortex and medulla of tumor-adjacent normal material in nephrectomy specimens from five male patients. We used these carefully annotated specimens to reassign incorrectly labeled samples in the larger public Genotype-Tissue Expression (GTEx) Project, and to extract meaningful medullary gene expression signatures. Using integrated analysis of gene expression, chromatin accessibility and conformation profiles, we found insights into medulla development and function and then validated this by spatial transcriptomics and immunohistochemistry. Thus, our datasets provide a valuable resource for functional annotation of variants from genome-wide association studies and are freely accessible through an epigenome browser portal.

Super-enhancers: Implications in gastric cancer

Gastric cancer (GC) is the fifth most prevalent malignancy and the third leading cause of cancer-related mortality globally. Despite intensive efforts to enhance the efficiencies of various therapeutics (chemotherapy, surgical interventions, molecular-targeted therapies, immunotherapies), the prognosis for patients with GC remains poor. This might be predominantly due to the limited understanding of the complicated etiology of GC. Importantly, epigenetic modifications and alterations are crucial during GC development. Super-enhancers (SEs) are a large cluster of adjacent enhancers that greatly activate transcription. SEs sustain cell-specific identity by enhancing the transcription of specific oncogenes. In this review, we systematically summarize how SEs are involved in GC development, including the SE landscape in GC, the SE target genes in GC, and the interventions related to SE functions for treating GC.

Mechanisms regulating heterogeneity of hemostatic gene expression in endothelial cells

The hemostatic system involves an array of circulating coagulation factors that work in concert with platelets and the vascular endothelium to promote clotting in a space- and time-defined manner. Despite equal systemic exposure to circulating factors, bleeding and thrombotic diseases tend to prefer specific sites, suggesting an important role for local factors. This may be provided by endothelial heterogeneity. Endothelial cells differ not only between arteries, veins, and capillaries but also between microvascular beds from different organs, which present unique organotypic morphology and functional and molecular profiles. Accordingly, regulators of hemostasis are not uniformly distributed in the vasculature. The establishment and maintenance of endothelial diversity are orchestrated at the transcriptional level. Recent transcriptomic and epigenomic studies have provided a global picture of endothelial cell heterogeneity. In this review, we discuss the organotypic differences in the hemostatic profile of endothelial cells; we focus on 2 major endothelial regulators of hemostasis, namely von Willebrand factor and thrombomodulin, to provide examples of transcriptional mechanisms that control heterogeneity; finally, we consider some of the methodological challenges and opportunities for future studies.

An Enhancer Within Abcb11 Regulates G6pc2 in C57BL/6 Mouse Pancreatic Islets

G6PC2 is predominantly expressed in pancreatic islet β-cells where it encodes a glucose-6-phosphatase catalytic subunit that modulates the sensitivity of insulin secretion to glucose by opposing the action of glucokinase, thereby regulating fasting blood glucose (FBG). Prior studies have shown that the G6pc2 promoter alone is unable to confer sustained islet-specific gene expression in mice, suggesting the existence of distal enhancers that regulate G6pc2 expression. Using information from both mice and humans and knowledge that single nucleotide polymorphisms (SNPs) both within and near G6PC2 are associated with variations in FBG in humans, we identified several putative enhancers 3' of G6pc2. One region, herein referred to as enhancer I, resides in the 25th intron of Abcb11 and binds multiple islet-enriched transcription factors. CRISPR-mediated deletion of enhancer I in C57BL/6 mice had selective effects on the expression of genes near the G6pc2 locus. In isolated islets, G6pc2 and Spc25 expression were reduced ∼50%, and Gm13613 expression was abolished, whereas Cers6 and nostrin expression were unaffected. This partial reduction in G6pc2 expression enhanced islet insulin secretion at basal glucose concentrations but did not affect FBG or glucose tolerance in vivo, consistent with the absence of a phenotype in G6pc2 heterozygous C57BL/6 mice.

ARTICLE HIGHLIGHTS

An enhancer trap system to track developmental dynamics in Marchantia polymorpha

A combination of streamlined genetics, experimental tractability and relative morphological simplicity compared to vascular plants makes the liverwort Marchantia polymorpha an ideal model system for studying many aspects of plant biology. Here we describe a transformation vector combining a constitutive fluorescent membrane marker with a nuclear marker that is regulated by nearby enhancer elements and use this to produce a library of enhancer trap lines for Marchantia. Screening gemmae from these lines allowed the identification and characterization of novel marker lines, including markers for rhizoids and oil cells. The library allowed the identification of a margin tissue running around the thallus edge, highlighted during thallus development. The expression of this marker is correlated with auxin levels. We generated multiple markers for the meristematic apical notch region, which have different spatial expression patterns, reappear at different times during meristem regeneration following apical notch excision and have varying responses to auxin supplementation or inhibition. This reveals that there are proximodistal substructures within the apical notch that could not be observed otherwise. We employed our markers to study Marchantia sporeling development, observing meristem emergence as defining the protonema-to-prothallus stage transition, and subsequent production of margin tissue during the prothallus stage. Exogenous auxin treatment stalls meristem emergence at the protonema stage but does not inhibit cell division, resulting in callus-like sporelings with many rhizoids, whereas pharmacologically inhibiting auxin synthesis and transport does not prevent meristem emergence. This enhancer trap system presents a useful resource for the community and will contribute to future Marchantia research.

Hand2 represses non-cardiac cell fates through chromatin remodeling at cis- regulatory elements

Developmental studies have revealed the importance of the transcription factor Hand2 in cardiac development. Hand2 promotes cardiac progenitor differentiation and epithelial maturation, while repressing other tissue types. The mechanisms underlying the promotion of cardiac fates are far better understood than those underlying the repression of alternative fates. Here, we assess Hand2-dependent changes in gene expression and chromatin remodeling in cardiac progenitors of zebrafish embryos. Cell-type specific transcriptome analysis shows a dual function for Hand2 in activation of cardiac differentiation genes and repression of pronephric pathways. We identify functional cis- regulatory elements whose chromatin accessibility are increased in hand2 mutant cells. These regulatory elements associate with non-cardiac gene expression, and drive reporter gene expression in tissues associated with Hand2-repressed genes. We find that functional Hand2 is sufficient to reduce non-cardiac reporter expression in cardiac lineages. Taken together, our data support a model of Hand2-dependent coordination of transcriptional programs, not only through transcriptional activation of cardiac and epithelial maturation genes, but also through repressive chromatin remodeling at the DNA regulatory elements of non-cardiac genes.

Distinct 3D contacts and phenotypic consequences of adjacent non-coding loci in the epigenetically quiescent regions

Non-coding regions of the human genome are important for functional regulations, but their mechanisms remain elusive. We used machine learning to guide a CRISPR screening on hubs (i.e. non-coding loci forming many 3D contacts) and significantly increased the discovery rate of hubs essential for cell growth. We found no clear genetic or epigenetic differences between essential and nonessential hubs, but we observed that some neighboring hubs in the linear genome have distinct spatial contacts and opposite effects on cell growth. One such pair in an epigenetically quiescent region showed different impacts on gene expression, chromatin accessibility and chromatin organization. We also found that deleting the essential hub altered the genetic network activity and increased the entropy of chromatin accessibility, more severe than that caused by deletion of the nonessential hub, suggesting that they are critical for maintaining an ordered chromatin structure. Our study reveals new insights into the system-level roles of non-coding regions in the human genome.

Cell factory engineering: Challenges and opportunities for synthetic biology applications

The production of high-quality recombinant proteins is critical to maintaining a continuous supply of biopharmaceuticals, such as therapeutic antibodies. Engineering mammalian cell factories presents a number of limitations typically associated with the proteotoxic stress induced upon aberrant accumulation of off-pathway protein folding intermediates, which eventually culminate in the induction of apoptosis. In this review, we will discuss advances in cell engineering and their applications at different hierarchical levels of control of the expression of recombinant proteins, from transcription and translational to posttranslational modifications and subcellular trafficking. We also highlight challenges and unique opportunities to apply modern synthetic biology tools to the design of programmable cell factories for improved biomanufacturing of therapeutic proteins.

AIB1/SRC-3/NCOA3 function in estrogen receptor alpha positive breast cancer

The estrogen receptor alpha (ERα) is a steroid receptor that is pivotal in the initiation and progression of most breast cancers. ERα regulates gene transcription through recruitment of essential coregulators, including the steroid receptor coactivator AIB1 (Amplified in Breast Cancer 1). AIB1 itself is an oncogene that is overexpressed in a subset of breast cancers and is known to play a role in tumor progression and resistance to endocrine therapy through multiple mechanisms. Here we review the normal and pathological functions of AIB1 in regard to its ERα-dependent and ERα-independent actions, as well as its genomic conservation and protein evolution. We also outline the efforts to target AIB1 in the treatment of breast cancer.

Chromosome and protein folding: In search for unified principles

Structural biology has traditionally focused on the structures of proteins, short nucleic acids, small molecules, and their complexes. However, it is now widely recognized that the 3D organization of chromosomes should also be included in this list, despite significant differences in scale and complexity of organization. Here we highlight some notable similarities between the folding processes that shape proteins and chromosomes. Both biomolecules are folded by two types of processes: the affinity-mediated interactions, and by active (ATP-dependent) processes. Both chromosome and proteins in vivo can have partially unstructured and non-equilibrium ensembles with yet to be understood functional roles. By analyzing these biological systems in parallel, we can uncover universal principles of biomolecular organization that transcend specific biopolymers.

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.

Identification of the enhancer RNAs related to tumorgenesis of pituitary neuroendocrine tumors

Background

Pituitary neuroendocrine tumors (PitNETs), which originate from the pituitary gland, account for 10%-15% of all intracranial neoplasms. Recent studies have indicated that enhancer RNAs (eRNAs) exert regulatory effects on tumor growth. However, the mechanisms underlying the eRNA-mediated tumorigenesis of PitNETs have not been elucidated.

Methods

Normal pituitary and PitNETs tissues were used to identify the differentially expressed eRNAs (DEEs). Immune gene sets and hallmarks of cancer gene sets were quantified based on single sample gene set enrichment analysis (ssGSEA) algorithm using GSVA. The perspective of immune cells among all samples was calculated by the CIBERSORT algorithm. Moreover, the regulatory network composed of key DEEs, target genes of eRNAs, hallmarks of cancer gene sets, differentially expressed TF, immune cells and immune gene sets were constructed by Pearson correlation analysis. Small molecular anti-PitNETs drugs were explored by CMap analysis and the accuracy of the study was verified by in vitro and in vivo experiments, ATAC-seq and ChIP-seq.

Results

In this study, data of 134 PitNETs and 107 non-tumorous pituitary samples were retrieved from a public database to identify differentially expressed genes. In total, 1128 differentially expressed eRNAs (DEEs) (494 upregulated eRNAs and 634 downregulated eRNAs) were identified. Next, the correlation of DEEs with cancer-related and immune-related gene signatures was examined to establish a co-expression regulatory network comprising 18 DEEs, 50 potential target genes of DEEs, 5 cancer hallmark gene sets, 2 differentially expressed transcription factors, 4 immune cell types, and 4 immune gene sets. Based on this network, the following four therapeutics for PitNETs were identified using Connectivity Map analysis: ciclopirox, bepridil, clomipramine, and alexidine. The growth-inhibitory effects of these therapeutics were validated using in vitro experiments. Ciclopirox exerted potential growth-inhibitory effects on PitNETs. Among the DEEs, GNLY, HOXB7, MRPL33, PRDM16, TCF7, and ZNF26 were determined to be potential diagnostic and therapeutic biomarkers for PitNETs.

Conclusion

This study illustrated the significant influence of eRNAs on the occurrence and development of PitNETs. By constructing the co-expression regulation network, GNLY, HOXB6, MRPL33, PRDM16, TCF7, and ZNF26 were identified as relatively significant DEEs which were considered as the novel biomarkers of diagnosis and treatment of PitNETs. This study demonstrated the roles of eRNAs in the occurrence and development of PitNETs and revealed that ciclopirox was a potential therapeutic for pituitary adenomas.

A comprehensive revisit of the machine-learning tools developed for the identification of enhancers in the human genome

Enhancers are non-coding DNA elements that play a crucial role in enhancing the transcription rate of a specific gene in the genome. Experiments for identifying enhancers can be restricted by their conditions and involve complicated, time-consuming, laborious, and costly steps. To overcome these challenges, computational platforms have been developed to complement experimental methods that enable high-throughput identification of enhancers. Over the last few years, the development of various enhancer computational tools has resulted in significant progress in predicting putative enhancers. Thus, researchers are now able to use a variety of strategies to enhance and advance enhancer study. In this review, an overview of machine learning (ML)-based prediction methods for enhancer identification and related databases has been provided. The existing enhancer-prediction methods have also been reviewed regarding their algorithms, feature selection processes, validation techniques, and software utility. In addition, the advantages and drawbacks of these ML approaches and guidelines for developing bioinformatic tools have been highlighted for a more efficient enhancer prediction. This review will serve as a useful resource for experimentalists in selecting the appropriate ML tool for their study, and for bioinformaticians in developing more accurate and advanced ML-based predictors.

Dosage balance acts as a time-dependent selective barrier to subfunctionalization

BACKGROUND

Gene duplication is an important process for genome expansion, sometimes allowing for new gene functions to develop. Duplicate genes can be retained through multiple processes, either for intermediate periods of time through processes such as dosage balance, or over extended periods of time through processes such as subfunctionalization and neofunctionalization.

RESULTS

Here, we built upon an existing subfunctionalization Markov model by incorporating dosage balance to describe the interplay between subfunctionalization and dosage balance to explore selective pressures on duplicate copies. Our model incorporates dosage balance using a biophysical framework that penalizes the fitness of genetic states with stoichiometrically imbalanced proteins. These imbalanced states cause increased concentrations of exposed hydrophobic surface areas, which cause deleterious mis-interactions. We draw comparison between our Subfunctionalization + Dosage-Balance Model (Sub + Dos) and the previous Subfunctionalization-Only (Sub-Only) Model. This comparison includes how the retention probabilities change over time, dependent upon the effective population size and the selective cost associated with spurious interaction of dosage-imbalanced partners. We show comparison between Sub-Only and Sub + Dos models for both whole-genome duplication and small-scale duplication events.

CONCLUSION

These comparisons show that following whole-genome duplication, dosage balance serves as a time-dependent selective barrier to the subfunctionalization process, by causing an overall delay but ultimately leading to a larger portion of the genome retained through subfunctionalization. This higher percentage of the genome that is ultimately retained is caused by the alternative competing process, nonfunctionalization, being selectively blocked to a greater extent. In small-scale duplication, the reverse pattern is seen, where dosage balance drives faster rates of subfunctionalization, but ultimately leads to a smaller portion of the genome retained as duplicates. This faster rate of subfunctionalization is because the dosage balance of interacting gene products is negatively affected immediately after duplication and the loss of a duplicate restores the stoichiometric balance. Our findings provide support that the subfunctionalization of genes that are susceptible to dosage balance effects, such as proteins involved in complexes, is not a purely neutral process. With stronger selection against stoichiometrically imbalanced gene partners, the rates of subfunctionalization and nonfunctionalization slow; however, this ultimately leads to a greater proportion of subfunctionalized gene pairs.

DeepCORE: An interpretable multi-view deep neural network model to detect co-operative regulatory elements

Gene transcription is an essential process involved in all aspects of cellular functions with significant impact on biological traits and diseases. This process is tightly regulated by multiple elements that co-operate to jointly modulate the transcription levels of target genes. To decipher the complicated regulatory network, we present a novel multi-view attention-based deep neural network that models the relationship between genetic, epigenetic, and transcriptional patterns and identifies co-operative regulatory elements (COREs). We applied this new method, named DeepCORE, to predict transcriptomes in 25 different cell lines, which outperformed the state-of-the-art algorithms. Furthermore, DeepCORE translates the attention values embedded in the neural network into interpretable information, including locations of putative regulatory elements and their correlations, which collectively implies COREs. These COREs are significantly enriched with known promoters and enhancers. Novel regulatory elements discovered by DeepCORE showed epigenetic signatures consistent with the status of histone modification marks.

PAR-CLIP Assay in Ferroptosis

Ferroptosis is a regulatory cell death process that is accompanied by large amounts of iron ion accumulation and lipid peroxidation. Photoactivated ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) is a method used to identify the binding sites of RNA-binding proteins (RBPs) on target RNAs with high resolution at the nucleotide level. By inserting photosensitive ribonucleoside analogs into new RNA transcripts of living cells, characteristic mutations can be generated during reverse transcription and be used to accurately locate the crosslinking position of RNAs and RBPs. The use of PAR-CLIP to detect interactions and determine precise crosslinking sites between RNAs and RBPs, or to search for RNAs upstream or downstream of ferroptosis pathways genes through known proteins, can help to clarify and verify the occurrence and regulation mechanisms of the various signaling pathways of ferroptosis. Furthermore, it may reveal new targets for ferroptosis detection and improve the treatment efficiency of ferroptosis-related diseases such as cancer and neurodegenerative diseases. Here, we introduce a specific PAR-CLIP protocol for monitoring the ferroptosis process.

StripeDiff: Model-based algorithm for differential analysis of chromatin stripe

Multiple recent studies revealed stripes as an architectural feature of three-dimensional chromatin and found stripes connected to epigenetic regulation of transcription. Whereas a couple of tools are available to define stripes in a single sample, there is yet no reported method to quantitatively measure the dynamic change of each stripe between samples. Here, we developed StripeDiff, a bioinformatics tool that delivers a set of statistical methods to detect differential stripes between samples. StripeDiff showed optimal performance in both simulation data analysis and real Hi-C data analysis. Applying StripeDiff to 12 sets of Hi-C data revealed new insights into the connection between change of chromatin stripe and change of chromatin modification, transcriptional regulation, and cell differentiation. StripeDiff will be a robust tool for the community to facilitate understanding of stripes and their function in numerous biological models.

Gene activation guided by nascent RNA-bound transcription factors

Technologies for gene activation are valuable tools for the study of gene functions and have a wide range of potential applications in bioengineering and medicine. In contrast to existing methods based on recruiting transcriptional modulators via DNA-binding proteins, we developed a strategy termed Narta (nascent RNA-guided transcriptional activation) to achieve gene activation by recruiting artificial transcription factors (aTFs) to transcription sites through nascent RNAs of the target gene. Using Narta, we demonstrate robust activation of a broad range of exogenous and endogenous genes in various cell types, including zebrafish embryos, mouse and human cells. Importantly, the activation is reversible, tunable and specific. Moreover, Narta provides better activation potency of some expressed genes than CRISPRa and, when used in combination with CRISPRa, has an enhancing effect on gene activation. Quantitative imaging illustrated that nascent RNA-directed aTFs could induce the high-density assembly of coactivators at transcription sites, which may explain the larger transcriptional burst size induced by Narta. Overall, our work expands the gene activation toolbox for biomedical research.

Relative importance of composition structures and biologically meaningful logics in bipartite Boolean models of gene regulation

Boolean networks have been widely used to model gene networks. However, such models are coarse-grained to an extent that they abstract away molecular specificities of gene regulation. Alternatively, bipartite Boolean network models of gene regulation explicitly distinguish genes from transcription factors (TFs). In such bipartite models, multiple TFs may simultaneously contribute to gene regulation by forming heteromeric complexes, thus giving rise to composition structures. Since bipartite Boolean models are relatively recent, an empirical investigation of their biological plausibility is lacking. Here, we estimate the prevalence of composition structures arising through heteromeric complexes. Moreover, we present an additional mechanism where composition structures may arise as a result of multiple TFs binding to cis-regulatory regions and provide empirical support for this mechanism. Next, we compare the restriction in BFs imposed by composition structures and by biologically meaningful properties. We find that though composition structures can severely restrict the number of Boolean functions (BFs) driving a gene, the two types of minimally complex BFs, namely nested canalyzing functions (NCFs) and read-once functions (RoFs), are comparatively more restrictive. Finally, we find that composition structures are highly enriched in real networks, but this enrichment most likely comes from NCFs and RoFs.

Functional genomic assays to annotate enhancer-promoter interactions genome wide

Enhancers are pivotal for regulating gene transcription that occurs at promoters. Identification of the interacting enhancer-promoter pairs and understanding the mechanisms behind how they interact and how enhancers modulate transcription can provide fundamental insight into gene regulatory networks. Recently, advances in high-throughput methods in three major areas-chromosome conformation capture assay, such as Hi-C to study basic chromatin architecture, ectopic reporter experiments such as self-transcribing active regulatory region sequencing (STARR-seq) to quantify promoter and enhancer activity, and endogenous perturbations such as clustered regularly interspaced short palindromic repeat interference (CRISPRi) to identify enhancer-promoter compatibility-have further our knowledge about transcription. In this review, we will discuss the major method developments and key findings from these assays.

Potential mechanisms and prognostic model of eRNAs-regulated genes in stomach adenocarcinoma

Gastric Carcinoma is the fourth leading cause of cancer deaths worldwide, in which stomach adenocarcinoma (STAD) is the most common histological type. A growing amount of evidence has suggested the importance of enhancer RNAs (eRNAs) in the cancer. However, the potential mechanism of eRNAs in STAD remains unclear. The eRNAs-regulated genes (eRRGs) were identified through four different enhancer resources. The differentially expressed eRRGs were obtained by ‘DESeq2’ R package. The prognosis prediction model was constructed by Cox and Lasso regression analysis. The ‘ChAMP’ R package and ‘maftools’ R package were used to investigate the multi-omics characters. In this study, combining the concept of contact domain, a total of 9014 eRRGs including 4926 PCGs and 4088 lncRNAs were identified and these eRRGs showed higher and more stable expression. Besides, the functions of these genes were mainly associated with tumor-related biological processes. Then, a prognostic prediction model was constructed and the AUC values of the 1-, 3- and 5-year survival prediction reached 0.76, 0.84 and 0.84, respectively, indicating that this model has a high accuracy. Finally, the difference between high-risk group and low-risk group were investigated using multi-omics data including gene expression, DNA methylation and somatic mutations. Our study provides significant clues for the elucidation of eRNAs in STAD and may help improve the overall survival for STAD patients.

Transcriptional enhancers at 40: evolution of a viral DNA element to nuclear architectural structures

Gene regulation by transcriptional enhancers is the dominant mechanism driving cell type- and signal-specific transcriptional diversity in metazoans. However, over four decades since the original discovery, how enhancers operate in the nuclear space remains largely enigmatic. Recent multidisciplinary efforts combining real-time imaging, genome sequencing, and biophysical strategies provide insightful but conflicting models of enhancer-mediated gene control. Here, we review the discovery and progress in enhancer biology, emphasizing the recent findings that acutely activated enhancers assemble regulatory machinery as mesoscale architectural structures with distinct physical properties. These findings help formulate novel models that explain several mysterious features of the assembly of transcriptional enhancers and the mechanisms of spatial control of gene expression.

A machine learning technique for identifying DNA enhancer regions utilizing CIS-regulatory element patterns

Enhancers regulate gene expression, by playing a crucial role in the synthesis of RNAs and proteins. They do not directly encode proteins or RNA molecules. In order to control gene expression, it is important to predict enhancers and their potency. Given their distance from the target gene, lack of common motifs, and tissue/cell specificity, enhancer regions are thought to be difficult to predict in DNA sequences. Recently, a number of bioinformatics tools were created to distinguish enhancers from other regulatory components and to pinpoint their advantages. However, because the quality of its prediction method needs to be improved, its practical application value must also be improved. Based on nucleotide composition and statistical moment-based features, the current study suggests a novel method for identifying enhancers and non-enhancers and evaluating their strength. The proposed study outperformed state-of-the-art techniques using fivefold and tenfold cross-validation in terms of accuracy. The accuracy from the current study results in 86.5% and 72.3% in enhancer site and its strength prediction respectively. The results of the suggested methodology point to the potential for more efficient and successful outcomes when statistical moment-based features are used. The current study's source code is available to the research community at https://github.com/csbioinfopk/enpred.

Repression of enhancer RNA PHLDA1 promotes tumorigenesis and progression of Ewing sarcoma via decreasing infiltrating T‐lymphocytes: A bioinformatic analysis

Background: The molecular mechanisms of EWS-FLI-mediating target genes and downstream pathways may provide a new way in the targeted therapy of Ewing sarcoma. Meanwhile, enhancers transcript non-coding RNAs, known as enhancer RNAs (eRNAs), which may serve as potential diagnosis markers and therapeutic targets in Ewing sarcoma.

Materials and methods: Differentially expressed genes (DEGs) were identified between 85 Ewing sarcoma samples downloaded from the Treehouse database and 3 normal bone samples downloaded from the Sequence Read Archive database. Included in DEGs, differentially expressed eRNAs (DEeRNAs) and target genes corresponding to DEeRNAs (DETGs), as well as the differentially expressed TFs, were annotated. Then, cell type identification by estimating relative subsets of known RNA transcripts (CIBERSORT) was used to infer portions of infiltrating immune cells in Ewing sarcoma and normal bone samples. To evaluate the prognostic value of DEeRNAs and immune function, cross validation, independent prognosis analysis, and Kaplan–Meier survival analysis were implemented using sarcoma samples from the Cancer Genome Atlas database. Next, hallmarks of cancer by gene set variation analysis (GSVA) and immune gene sets by single-sample gene set enrichment analysis (ssGSEA) were identified to be significantly associated with Ewing sarcoma. After screening by co-expression analysis, most significant DEeRNAs, DETGs and DETFs, immune cells, immune gene sets, and hallmarks of cancer were merged to construct a co-expression regulatory network to eventually identify the key DEeRNAs in tumorigenesis of Ewing sarcoma. Moreover, Connectivity Map Analysis was utilized to identify small molecules targeting Ewing sarcoma. External validation based on multidimensional online databases and scRNA-seq analysis were used to verify our key findings.

Results: A six-different-dimension regulatory network was constructed based on 17 DEeRNAs, 29 DETFs, 9 DETGs, 5 immune cells, 24 immune gene sets, and 8 hallmarks of cancer. Four key DEeRNAs (CCR1, CD3D, PHLDA1, and RASD1) showed significant co-expression relationships in the network. Connectivity Map Analysis screened two candidate compounds, MS-275 and pyrvinium, that might target Ewing sarcoma. PHLDA1 (key DEeRNA) was extensively expressed in cancer stem cells of Ewing sarcoma, which might play a critical role in the tumorigenesis of Ewing sarcoma.

Conclusion: PHLDA1 is a key regulator in the tumorigenesis and progression of Ewing sarcoma. PHLDA1 is directly repressed by EWS/FLI1 protein and low expression of FOSL2, resulting in the deregulation of FOX proteins and CC chemokine receptors. The decrease of infiltrating T‐lymphocytes and TNFA signaling may promote tumorigenesis and progression of Ewing sarcoma.

Genome-Wide Super-Enhancer-Based Analysis: Identification of Prognostic Genes in Oral Squamous Cell Carcinoma

Advanced-stage oral squamous cell carcinoma (OSCC) patients are treated with combination therapies, such as surgery, radiation, chemotherapy, and immunotherapy. However, OSCC cells acquire resistance to these treatments, resulting in local recurrence and distant metastasis. The identification of genes involved in drug resistance is essential for improving the treatment of this disease. In this study, we applied chromatin immunoprecipitation sequencing (ChIP-Seq) to profile active enhancers. For that purpose, we used OSCC cell lines that had been exposed to cetuximab for a prolonged period. In total, 64 chromosomal loci were identified as active super-enhancers (SE) according to active enhancer marker histone H3 lysine 27 acetylation (H3K27ac) ChIP-Seq. In addition, a total of 131 genes were located in SE regions, and 34 genes were upregulated in OSCC tissues by TCGA-OSCC analysis. Moreover, high expression of four genes (C9orf89; p = 0.035, CENPA; p = 0.020, PISD; p = 0.0051, and TRAF2; p = 0.0075) closely predicted a poorer prognosis for OSCC patients according to log-rank tests. Increased expression of the four genes (mRNA Z-score ≥ 0) frequently co-occurred in TCGA-OSCC analyses. The high and low expression groups of the four genes showed significant differences in prognosis, suggesting that there are clear differences in the pathways based on the underlying gene expression profiles. These data indicate that potential stratified therapeutic strategies could be used to overcome resistance to drugs (including cetuximab) and further improve responses in drug-sensitive patients.

Systematic comparison of CRISPR-based transcriptional activators uncovers gene-regulatory features of enhancer-promoter interactions

Nuclease-inactivated CRISPR/Cas-based (dCas-based) systems have emerged as powerful technologies to synthetically reshape the human epigenome and gene expression. Despite the increasing adoption of these platforms, their relative potencies and mechanistic differences are incompletely characterized, particularly at human enhancer-promoter pairs. Here, we systematically compared the most widely adopted dCas9-based transcriptional activators, as well as an activator consisting of dCas9 fused to the catalytic core of the human CBP protein, at human enhancer-promoter pairs. We find that these platforms display variable relative expression levels in different human cell types and that their transactivation efficacies vary based upon the effector domain, effector recruitment architecture, targeted locus and cell type. We also show that each dCas9-based activator can induce the production of enhancer RNAs (eRNAs) and that this eRNA induction is positively correlated with downstream mRNA expression from a cognate promoter. Additionally, we use dCas9-based activators to demonstrate that an intrinsic transcriptional and epigenetic reciprocity can exist between human enhancers and promoters and that enhancer-mediated tracking and engagement of a downstream promoter can be synthetically driven by targeting dCas9-based transcriptional activators to an enhancer. Collectively, our study provides new insights into the enhancer-mediated control of human gene expression and the use of dCas9-based activators.

Coming full circle: On the origin and evolution of the looping model for enhancer-promoter communication

In mammalian organisms, enhancers can regulate transcription from great genomic distances. How enhancers affect distal gene expression has been a major question in the field of gene regulation. One model to explain how enhancers communicate with their target promoters, the chromatin looping model, posits that enhancers and promoters come in close spatial proximity to mediate communication. Chromatin looping has been broadly accepted as a means for enhancer-promoter communication, driven by accumulating in vitro and in vivo evidence. The genome is now known to be folded into a complex 3D arrangement, created and maintained in part by the interplay of the Cohesin complex and the DNA-binding protein CTCF. In the last few years, however, doubt over the relationship between looping and transcriptional activation has emerged, driven by studies finding that only a modest number of genes are perturbed with acute degradation of looping machinery components. In parallel, newer models describing distal enhancer action have also come to prominence. In this article, we explore the emergence and development of the looping model as a means for enhancer-promoter communication and review the contrasting evidence between historical gene-specific and current global data for the role of chromatin looping in transcriptional regulation. We also discuss evidence for alternative models to chromatin looping and their support in the literature. We suggest that, while there is abundant evidence for chromatin looping as a major mechanism for enhancer function, enhancer-promoter communication is likely mediated by more than one mechanism in an enhancer- and context-dependent manner.

Normal and Pathological NRF2 Signalling in the Central Nervous System

The nuclear factor erythroid 2-related factor 2 (NRF2) was originally described as a master regulator of antioxidant cellular response, but in the time since, numerous important biological functions linked to cell survival, cellular detoxification, metabolism, autophagy, proteostasis, inflammation, immunity, and differentiation have been attributed to this pleiotropic transcription factor that regulates hundreds of genes. After 40 years of in-depth research and key discoveries, NRF2 is now at the center of a vast regulatory network, revealing NRF2 signalling as increasingly complex. It is widely recognized that reactive oxygen species (ROS) play a key role in human physiological and pathological processes such as ageing, obesity, diabetes, cancer, and neurodegenerative diseases. The high oxygen consumption associated with high levels of free iron and oxidizable unsaturated lipids make the brain particularly vulnerable to oxidative stress. A good stability of NRF2 activity is thus crucial to maintain the redox balance and therefore brain homeostasis. In this review, we have gathered recent data about the contribution of the NRF2 pathway in the healthy brain as well as during metabolic diseases, cancer, ageing, and ageing-related neurodegenerative diseases. We also discuss promising therapeutic strategies and the need for better understanding of cell-type-specific functions of NRF2 in these different fields.

Epigenomics of conventional type-I dendritic cells depicted preferential control of TLR9 versus TLR3 response by NCoR1 through differential IRF3 activation

Tight control of gene regulation in dendritic cells (DCs) is important to mount pathogen specific immune responses. Apart from transcription factor binding, dynamic regulation of enhancer activity through global transcriptional repressors like Nuclear Receptor Co-repressor 1 (NCoR1) plays a major role in fine-tuning of DC responses. However, how NCoR1 regulates enhancer activity and gene expression in individual or multiple Toll-like receptor (TLR) activation in DCs is largely unknown. In this study, we did a comprehensive epigenomic analysis of murine conventional type-I DCs (cDC1) across different TLR ligation conditions. We profiled gene expression changes along with H3K27ac active enhancers and NCoR1 binding in the TLR9, TLR3 and combined TLR9 + TLR3 activated cDC1. We observed spatio-temporal activity of TLR9 and TLR3 specific enhancers regulating signal specific target genes. Interestingly, we found that NCoR1 differentially controls the TLR9 and TLR3-specific responses. NCoR1 depletion specifically enhanced TLR9 responses as evident from increased enhancer activity as well as TLR9-specific gene expression, whereas TLR3-mediated antiviral response genes were negatively regulated. We validated that NCoR1 KD cDC1 showed significantly decreased TLR3 specific antiviral responses through decreased IRF3 activation. In addition, decreased IRF3 binding was observed at selected ISGs leading to their decreased expression upon NCoR1 depletion. Consequently, the NCoR1 depleted cDC1 showed reduced Sendai Virus (SeV) clearance and cytotoxic potential of CD8+ T cells upon TLR3 activation. NCoR1 directly controls the majority of these TLR specific enhancer activity and the gene expression. Overall, for the first time, we revealed NCoR1 mediates transcriptional control towards TLR9 as compared to TLR3 in cDC1.

Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition

Enhancers are short DNA segments that play a key role in biological processes, such as accelerating transcription of target genes. Since the enhancer resides anywhere in a genome sequence, it is difficult to precisely identify enhancers. We presented a bi-directional long-short term memory (Bi-LSTM) and attention-based deep learning method (Enhancer-LSTMAtt) for enhancer recognition. Enhancer-LSTMAtt is an end-to-end deep learning model that consists mainly of deep residual neural network, Bi-LSTM, and feed-forward attention. We extensively compared the Enhancer-LSTMAtt with 19 state-of-the-art methods by 5-fold cross validation, 10-fold cross validation and independent test. Enhancer-LSTMAtt achieved competitive performances, especially in the independent test. We realized Enhancer-LSTMAtt into a user-friendly web application. Enhancer-LSTMAtt is applicable not only to recognizing enhancers, but also to distinguishing strong enhancer from weak enhancers. Enhancer-LSTMAtt is believed to become a promising tool for identifying enhancers.

Prognostic Ability of Enhancer RNAs in Metastasis of Non-Small Cell Lung Cancer

(1) Background: Non-small cell lung cancer (NSCLC) is the most common lung cancer. Enhancer RNA (eRNA) has potential utility in the diagnosis, prognosis and treatment of cancer, but the role of eRNAs in NSCLC metastasis is not clear; (2) Methods: Differentially expressed transcription factors (DETFs), enhancer RNAs (DEEs), and target genes (DETGs) between primary NSCLC and metastatic NSCLC were identified. Prognostic DEEs (PDEEs) were screened by Cox regression analyses and a predicting model for metastatic NSCLC was constructed. We identified DEE interactions with DETFs, DETGs, reverse phase protein arrays (RPPA) protein chips, immunocytes, and pathways to construct a regulation network using Pearson correlation. Finally, the mechanisms and clinical significance were explained using multi-dimensional validation unambiguously; (3) Results: A total of 255 DEEs were identified, and 24 PDEEs were selected into the multivariate Cox regression model (AUC = 0.699). Additionally, the NSCLC metastasis-specific regulation network was constructed, and six key PDEEs were defined (ANXA8L1, CASTOR2, CYP4B1, GTF2H2C, PSMF1 and TNS4); (4) Conclusions: This study focused on the exploration of the prognostic value of eRNAs in the metastasis of NSCLC. Finally, six eRNAs were identified as potential markers for the prediction of metastasis of NSCLC.