Extensive promoter-centered chromatin interactions provide a topological basis for transcription regulation

The MVPs (masterful versatile players): Chromatin factors as pivotal mediators between 3D genome organization and the response to environment

In recent years, the study of genome dynamics has become a prominent research field due to its influence on understanding the control of gene expression. The study of 3D genome organization has unveiled multiple mechanisms in orchestrating chromosome folding. Growing evidence reveals that these mechanisms are not only important for genome organization, but play a pivotal role in enabling plants to adapt to environmental stimuli. In this review, we provide an overview of the current knowledge concerning epigenetic factors and regulatory elements driving 3D genome dynamics and their responses to external stimuli. We discuss the most recent findings, previous evidence, and explore their implications for future research.

Enhancer selectivity in space and time: from enhancer-promoter interactions to promoter activation

The primary regulators of metazoan gene expression are enhancers, originally functionally defined as DNA sequences that can activate transcription at promoters in an orientation-independent and distance-independent manner. Despite being crucial for gene regulation in animals, what mechanisms underlie enhancer selectivity for promoters, and more fundamentally, how enhancers interact with promoters and activate transcription, remain poorly understood. In this Review, we first discuss current models of enhancer-promoter interactions in space and time and how enhancers affect transcription activation. Next, we discuss different mechanisms that mediate enhancer selectivity, including repression, biochemical compatibility and regulation of 3D genome structure. Through 3D polymer simulations, we illustrate how the ability of 3D genome folding mechanisms to mediate enhancer selectivity strongly varies for different enhancer-promoter interaction mechanisms. Finally, we discuss how recent technical advances may provide new insights into mechanisms of enhancer-promoter interactions and how technical biases in methods such as Hi-C and Micro-C and imaging techniques may affect their interpretation.

Transcription regulates the spatio-temporal dynamics of genes through micro-compartmentalization

Although our understanding of the involvement of heterochromatin architectural factors in shaping nuclear organization is improving, there is still ongoing debate regarding the role of active genes in this process. In this study, we utilize publicly-available Micro-C data from mouse embryonic stem cells to investigate the relationship between gene transcription and 3D gene folding. Our analysis uncovers a nonmonotonic - globally positive - correlation between intragenic contact density and Pol II occupancy, independent of cohesin-based loop extrusion. Through the development of a biophysical model integrating the role of transcription dynamics within a polymer model of chromosome organization, we demonstrate that Pol II-mediated attractive interactions with limited valency between transcribed regions yield quantitative predictions consistent with chromosome-conformation-capture and live-imaging experiments. Our work provides compelling evidence that transcriptional activity shapes the 4D genome through Pol II-mediated micro-compartmentalization.

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

BACKGROUND

Recognition of enhancer-promoter Interactions (EPIs) is crucial for human development. EPIs in the genome play a key role in regulating transcription. However, experimental approaches for classifying EPIs are too expensive in terms of effort, time, and resources. Therefore, more and more studies are being done on developing computational techniques, particularly using deep learning and other machine learning techniques, to address such problems. Unfortunately, the majority of current computational methods are based on convolutional neural networks, recurrent neural networks, or a combination of them, which don't take into consideration contextual details and the long-range interactions between the enhancer and promoter sequences. A new transformer-based model called EPI-Trans is presented in this study to overcome the aforementioned limitations. The multi-head attention mechanism in the transformer model automatically learns features that represent the long interrelationships between enhancer and promoter sequences. Furthermore, a generic model is created with transferability that can be utilized as a pre-trained model for various cell lines. Moreover, the parameters of the generic model are fine-tuned using a particular cell line dataset to improve performance.

RESULTS

Based on the results obtained from six benchmark cell lines, the average AUROC for the specific, generic, and best models is 94.2%, 95%, and 95.7%, while the average AUPR is 80.5%, 66.1%, and 79.6% respectively.

CONCLUSIONS

This study proposed a transformer-based deep learning model for EPI prediction. The comparative results on certain cell lines show that EPI-Trans outperforms other cutting-edge techniques and can provide superior performance on the challenge of recognizing EPI.

Estrogen-induced chromatin looping changes identify a subset of functional regulatory elements

Transcriptional enhancers can regulate individual or multiple genes through long-range three-dimensional (3D) genome interactions, and these interactions are commonly altered in cancer. Yet, the functional relationship between changes in 3D interactions associated with regulatory regions and differential gene expression appears context-dependent. In this study, we used HiChiP to capture changes in 3D genome interactions between active regulatory regions of endometrial cancer cells in response to estrogen treatment and uncovered significant differential long-range interactions that are strongly enriched for estrogen receptor α (ER) bound sites (ERBS). The ERBS anchoring differential loops with either a gene's promoter or distal regions were correlated with larger transcriptional responses to estrogen compared to ERBS not involved in differential interactions. To functionally test this observation, CRISPR-based Enhancer-i was used to deactivate specific ERBS, which revealed a wide range of effects on the transcriptional response to estrogen. However, these effects are only subtly and not significantly stronger for ERBS in differential loops. In addition, we observed an enrichment of 3D interactions between the promoters of estrogen up-regulated genes and found that looped promoters can work together cooperatively. Overall, our work suggests that changes in 3D genome structure upon estrogen treatment identify some functionally important regulatory regions; however, these changes aren't required for a transcriptional response to E2 in endometrial cancer cells.

Mechanistic analysis of enhancer sequences in the estrogen receptor transcriptional program

Estrogen Receptor α (ERα) is a major lineage determining transcription factor (TF) in mammary gland development. Dysregulation of ERα-mediated transcriptional program results in cancer. Transcriptomic and epigenomic profiling of breast cancer cell lines has revealed large numbers of enhancers involved in this regulatory program, but how these enhancers encode function in their sequence remains poorly understood. A subset of ERα-bound enhancers are transcribed into short bidirectional RNA (enhancer RNA or eRNA), and this property is believed to be a reliable marker of active enhancers. We therefore analyze thousands of ERα-bound enhancers and build quantitative, mechanism-aware models to discriminate eRNAs from non-transcribing enhancers based on their sequence. Our thermodynamics-based models provide insights into the roles of specific TFs in ERα-mediated transcriptional program, many of which are supported by the literature. We use in silico perturbations to predict TF-enhancer regulatory relationships and integrate these findings with experimentally determined enhancer-promoter interactions to construct a gene regulatory network. We also demonstrate that the model can prioritize breast cancer-related sequence variants while providing mechanistic explanations for their function. Finally, we experimentally validate the model-proposed mechanisms underlying three such variants.

LINE-1 transcription activates long-range gene expression

Long interspersed nuclear element-1 (LINE-1 or L1) is a retrotransposon group that constitutes 17% of the human genome and shows variable expression across cell types. However, the control of L1 expression and its function in gene regulation are incompletely understood. Here we show that L1 transcription activates long-range gene expression. Genome-wide CRISPR-Cas9 screening using a reporter driven by the L1 5' UTR in human cells identifies functionally diverse genes affecting L1 expression. Unexpectedly, altering L1 expression by knockout of regulatory genes impacts distant gene expression. L1s can physically contact their distal target genes, with these interactions becoming stronger upon L1 activation and weaker when L1 is silenced. Remarkably, L1s contact and activate genes essential for zygotic genome activation (ZGA), and L1 knockdown impairs ZGA, leading to developmental arrest in mouse embryos. These results characterize the regulation and function of L1 in long-range gene activation and reveal its importance in mammalian ZGA.

Prediction of cell-type-specific cohesin-mediated chromatin loops based on chromatin state

Chromatin loop is of crucial importance for the regulation of gene transcription. Cohesin is a type of chromatin-associated protein that mediates the interaction of chromatin through the loop extrusion. Cohesin-mediated chromatin interactions have strong cell-type specificity, posing a challenge for predicting chromatin loops. Existing computational methods perform poorly in predicting cell-type-specific chromatin loops. To address this issue, we propose a random forest model to predict cell-type-specific cohesin-mediated chromatin loops based on chromatin states identified by ChromHMM and the occupancy of related factors. Our results show that chromatin state is responsible for cell-type-specificity of loops. Using only chromatin states as features, the model achieved high accuracy in predicting cell-type-specific loops between two cell types and can be applied to different cell types. Furthermore, when chromatin states are combined with the occurrence frequency of CTCF, RAD21, YY1, and H3K27ac ChIP-seq peaks, more accurate prediction can be achieved. Our feature extraction method provides novel insights into predicting cell-type-specific chromatin loops and reveals the relationship between chromatin state and chromatin loop formation.

Statistical modeling and significance estimation of multi-way chromatin contacts with HyperloopFinder

Recent advances in chromatin conformation capture technologies, such as SPRITE and Pore-C, have enabled the detection of simultaneous contacts among multiple chromatin loci. This has made it possible to investigate the cooperative transcriptional regulation involving multiple genes and regulatory elements at the resolution of a single molecule. However, these technologies are unavoidably subject to the random polymer looping effect and technical biases, making it challenging to distinguish genuine regulatory relationships directly from random polymer interactions. Here, we present HyperloopFinder, a method for identifying regulatory multi-way chromatin contacts (hyperloops) by jointly modeling the random polymer looping effect and technical biases to estimate the statistical significance of multi-way contacts. The results show that our model can accurately estimate the expected interaction frequency of multi-way contacts based on the distance distribution of pairwise contacts, revealing that most multi-way contacts can be formed by randomly linking the pairwise contacts adjacent to each other. Moreover, we observed the spatial colocalization of the interaction sites of hyperloops from image-based data. Our results also revealed that hyperloops can function as scaffolds for the cooperation among multiple genes and regulatory elements. In summary, our work contributes novel insights into higher-order chromatin structures and functions and has the potential to enhance our understanding of transcriptional regulation and other cellular processes.

Light control of three-dimensional chromatin organization in soybean

Higher-order chromatin structure is critical for regulation of gene expression. In plants, light profoundly affects the morphogenesis of emerging seedlings as well as global gene expression to ensure optimal adaptation to environmental conditions. However, the changes and functional significance of chromatin organization in response to light during seedling development are not well documented. We constructed Hi-C contact maps for the cotyledon, apical hook and hypocotyl of soybean subjected to dark and light conditions. The resulting high-resolution Hi-C contact maps identified chromosome territories, A/B compartments, A/B sub-compartments, TADs (Topologically Associated Domains) and chromatin loops in each organ. We observed increased chromatin compaction under light and we found that domains that switched from B sub-compartments in darkness to A sub-compartments under light contained genes that were activated during photomorphogenesis. At the local scale, we identified a group of TADs constructed by gene clusters consisting of different numbers of Small Auxin-Upregulated RNAs (SAURs), which exhibited strict co-expression in the hook and hypocotyl in response to light stimulation. In the hypocotyl, RNA polymerase II (RNAPII) regulated the transcription of a SAURs cluster under light via TAD condensation. Our results suggest that the 3D genome is involved in the regulation of light-related gene expression in a tissue-specific manner.

Interpretable online network dictionary learning for inferring long-range chromatin interactions

Dictionary learning (DL), implemented via matrix factorization (MF), is commonly used in computational biology to tackle ubiquitous clustering problems. The method is favored due to its conceptual simplicity and relatively low computational complexity. However, DL algorithms produce results that lack interpretability in terms of real biological data. Additionally, they are not optimized for graph-structured data and hence often fail to handle them in a scalable manner. In order to address these limitations, we propose a novel DL algorithm called online convex network dictionary learning (online cvxNDL). Unlike classical DL algorithms, online cvxNDL is implemented via MF and designed to handle extremely large datasets by virtue of its online nature. Importantly, it enables the interpretation of dictionary elements, which serve as cluster representatives, through convex combinations of real measurements. Moreover, the algorithm can be applied to data with a network structure by incorporating specialized subnetwork sampling techniques. To demonstrate the utility of our approach, we apply cvxNDL on 3D-genome RNAPII ChIA-Drop data with the goal of identifying important long-range interaction patterns (long-range dictionary elements). ChIA-Drop probes higher-order interactions, and produces data in the form of hypergraphs whose nodes represent genomic fragments. The hyperedges represent observed physical contacts. Our hypergraph model analysis has the objective of creating an interpretable dictionary of long-range interaction patterns that accurately represent global chromatin physical contact maps. Through the use of dictionary information, one can also associate the contact maps with RNA transcripts and infer cellular functions. To accomplish the task at hand, we focus on RNAPII-enriched ChIA-Drop data from Drosophila Melanogaster S2 cell lines. Our results offer two key insights. First, we demonstrate that online cvxNDL retains the accuracy of classical DL (MF) methods while simultaneously ensuring unique interpretability and scalability. Second, we identify distinct collections of proximal and distal interaction patterns involving chromatin elements shared by related processes across different chromosomes, as well as patterns unique to specific chromosomes. To associate the dictionary elements with biological properties of the corresponding chromatin regions, we employ Gene Ontology (GO) enrichment analysis and perform multiple RNA coexpression studies.

An integrated omics approach highlights how epigenetic events can explain and predict response to neoadjuvant chemotherapy and bevacizumab in breast cancer

Treatment with the anti-angiogenic drug bevacizumab in addition to chemotherapy has shown efficacy for breast cancer in some clinical trials, but better biomarkers are needed to optimally select patients for treatment. Here, we present an omics approach where DNA methylation profiles are integrated with gene expression and results from proteomic data in breast cancer patients to predict response to therapy and pinpoint response-related epigenetic events. Fresh-frozen tumor biopsies taken before, during, and after treatment from human epidermal growth factor receptor 2 negative non-metastatic patients receiving neoadjuvant chemotherapy with or without bevacizumab were subjected to molecular profiling. Here, we report that DNA methylation at enhancer CpGs related to cell cycle regulation can predict response to chemotherapy and bevacizumab for the estrogen receptor positive subset of patients (AUC = 0.874), and we validated this observation in an independent patient cohort with a similar treatment regimen (AUC = 0.762). Combining the DNA methylation scores with the scores from a previously published protein signature resulted in a slight increase in the prediction performance (AUC = 0.784). We also show that tumors receiving the combination treatment underwent more extensive epigenetic alterations. Finally, we performed an integrative expression-methylation quantitative trait loci analysis on alterations in DNA methylation and gene expression levels, showing that the epigenetic alterations that occur during treatment are different between responders and non-responders and that these differences may be explained by the proliferation-epithelial-to-mesenchymal transition axis through the activity of grainyhead like transcription factor 2. Using tumor purity computed from copy number data, we developed a method for estimating cancer cell-specific methylation to confirm that the association to response reflects DNA methylation in cancer cells. Taken together, these results support the potential for clinical benefit of the addition of bevacizumab to chemotherapy when administered to the correct patients.

A Bidirectional Non-Coding RNA Promoter Mediates Long-Range Gene Expression Regulation

Recent evidence suggests that human gene promoters display gene expression regulatory mechanisms beyond the typical single gene local transcription modulation. In mammalian genomes, genes with an associated bidirectional promoter are abundant; bidirectional promoter architecture serves as a regulatory hub for a gene pair expression. However, it has been suggested that its contribution to transcriptional regulation might exceed local transcription initiation modulation. Despite their abundance, the functional consequences of bidirectional promoter architecture remain largely unexplored. This work studies the long-range gene expression regulatory role of a long non-coding RNA gene promoter using chromosome conformation capture methods. We found that this particular bidirectional promoter contributes to distal gene expression regulation in a target-specific manner by establishing promoter-promoter interactions. In particular, we validated that the promoter-promoter interactions of this regulatory element with the promoter of distal gene BBX contribute to modulating the transcription rate of this gene; removing the bidirectional promoter from its genomic context leads to a rearrangement of BBX promoter-enhancer interactions and to increased gene expression. Moreover, long-range regulatory functionality is not directly dependent on its associated non-coding gene pair expression levels.

A fine-scale Arabidopsis chromatin landscape reveals chromatin conformation-associated transcriptional dynamics

Plants, as sessile organisms, deploy transcriptional dynamics for adapting to extreme growth conditions such as cold stress. Emerging evidence suggests that chromatin architecture contributes to transcriptional regulation. However, the relationship between chromatin architectural dynamics and transcriptional reprogramming in response to cold stress remains unclear. Here, we apply a chemical-crosslinking assisted proximity capture (CAP-C) method to elucidate the fine-scale chromatin landscape, revealing chromatin interactions within gene bodies closely associated with RNA polymerase II (Pol II) densities across initiation, pausing, and termination sites. We observe dynamic changes in chromatin interactions alongside Pol II activity alterations during cold stress, suggesting local chromatin dynamics may regulate Pol II activity. Notably, cold stress does not affect large-scale chromatin conformations. We further identify a comprehensive promoter-promoter interaction (PPI) network across the genome, potentially facilitating co-regulation of gene expression in response to cold stress. Our study deepens the understanding of chromatin conformation-associated gene regulation in plant response to cold.

Increased enhancer-promoter interactions during developmental enhancer activation in mammals

Remote enhancers are thought to interact with their target promoters via physical proximity, yet the importance of this proximity for enhancer function remains unclear. Here we investigate the three-dimensional (3D) conformation of enhancers during mammalian development by generating high-resolution tissue-resolved contact maps for nearly a thousand enhancers with characterized in vivo activities in ten murine embryonic tissues. Sixty-one percent of developmental enhancers bypass their neighboring genes, which are often marked by promoter CpG methylation. The majority of enhancers display tissue-specific 3D conformations, and both enhancer-promoter and enhancer-enhancer interactions are moderately but consistently increased upon enhancer activation in vivo. Less than 14% of enhancer-promoter interactions form stably across tissues; however, these invariant interactions form in the absence of the enhancer and are likely mediated by adjacent CTCF binding. Our results highlight the general importance of enhancer-promoter physical proximity for developmental gene activation in mammals.

A systematic analyses of different bioinformatics pipelines for genomic data and its impact on deep learning models for chromatin loop prediction

Genomic data analysis has witnessed a surge in complexity and volume, primarily driven by the advent of high-throughput technologies. In particular, studying chromatin loops and structures has become pivotal in understanding gene regulation and genome organization. This systematic investigation explores the realm of specialized bioinformatics pipelines designed specifically for the analysis of chromatin loops and structures. Our investigation incorporates two protein (CTCF and Cohesin) factor-specific loop interaction datasets from six distinct pipelines, amassing a comprehensive collection of 36 diverse datasets. Through a meticulous review of existing literature, we offer a holistic perspective on the methodologies, tools and algorithms underpinning the analysis of this multifaceted genomic feature. We illuminate the vast array of approaches deployed, encompassing pivotal aspects such as data preparation pipeline, preprocessing, statistical features and modelling techniques. Beyond this, we rigorously assess the strengths and limitations inherent in these bioinformatics pipelines, shedding light on the interplay between data quality and the performance of deep learning models, ultimately advancing our comprehension of genomic intricacies.

Multifaceted roles of cohesin in regulating transcriptional loops

Cohesin is required for chromatin loop formation. However, its precise role in regulating gene transcription remains largely unknown. We investigated the relationship between cohesin and RNA Polymerase II (RNAPII) using single-molecule mapping and live-cell imaging methods in human cells. Cohesin-mediated transcriptional loops were highly correlated with those of RNAPII and followed the direction of gene transcription. Depleting RAD21, a subunit of cohesin, resulted in the loss of long-range (>100 kb) loops between distal (super-)enhancers and promoters of cell-type-specific genes. By contrast, the short-range (<50 kb) loops were insensitive to RAD21 depletion and connected genes that are mostly housekeeping. This result explains why only a small fraction of genes are affected by the loss of long-range chromatin interactions due to cohesin depletion. Remarkably, RAD21 depletion appeared to up-regulate genes located in early initiation zones (EIZ) of DNA replication, and the EIZ signals were amplified drastically without RAD21. Our results revealed new mechanistic insights of cohesin's multifaceted roles in establishing transcriptional loops, preserving long-range chromatin interactions for cell-specific genes, and maintaining timely order of DNA replication.

Machine and deep learning methods for predicting 3D genome organization

Three-Dimensional (3D) chromatin interactions, such as enhancer-promoter interactions (EPIs), loops, Topologically Associating Domains (TADs), and A/B compartments play critical roles in a wide range of cellular processes by regulating gene expression. Recent development of chromatin conformation capture technologies has enabled genome-wide profiling of various 3D structures, even with single cells. However, current catalogs of 3D structures remain incomplete and unreliable due to differences in technology, tools, and low data resolution. Machine learning methods have emerged as an alternative to obtain missing 3D interactions and/or improve resolution. Such methods frequently use genome annotation data (ChIP-seq, DNAse-seq, etc.), DNA sequencing information (k-mers, Transcription Factor Binding Site (TFBS) motifs), and other genomic properties to learn the associations between genomic features and chromatin interactions. In this review, we discuss computational tools for predicting three types of 3D interactions (EPIs, chromatin interactions, TAD boundaries) and analyze their pros and cons. We also point out obstacles of computational prediction of 3D interactions and suggest future research directions.

Promoter-centred chromatin interactions associated with EVI1 expression in EVI1+3q- myeloid leukaemia cells

EVI1 expression is associated with poor prognosis in myeloid leukaemia, which can result from Chr.3q alterations that juxtapose enhancers to induce EVI1 expression via long-range chromatin interactions. More often, however, EVI1 expression occurs unrelated to 3q alterations, and it remained unclear if, in these cases, EVI1 expression is similarly caused by aberrant enhancer activation. Here, we report that, in EVI1+3q- myeloid leukaemia cells, the EVI1 promoter interacts via long-range chromatin interactions with promoters of distally located, active genes, rather than with enhancer elements. Unlike in 3q+ cells, EVI1 expression and long-range interactions appear to not depend on CTCF/cohesin, though EVI1+3q- cells utilise an EVI1 promoter-proximal site to enhance its expression that is also involved in CTCF-mediated looping in 3q+ cells. Long-range interactions in 3q- cells connect EVI1 to promoters of multiple genes, whose transcription correlates with EVI1 in EVI1+3q- cell lines, suggesting a shared mechanism of transcriptional regulation. In line with this, CRISPR interference-induced silencing of two of these sites minimally, but consistently reduced EVI1 expression. Together, we provide novel evidence of features associated with EVI1 expression in 3q- leukaemia and consolidate the view that EVI1 in 3q- leukaemia is largely promoter-driven, potentially involving long-distance promoter clustering.

An intronic enhancer of Cebpa regulates adipocyte differentiation and adipose tissue development via long-range loop formation

Cebpa is a master transcription factor gene for adipogenesis. However, the mechanisms of enhancer-promoter chromatin interactions controlling Cebpa transcriptional regulation during adipogenic differentiation remain largely unknown. To reveal how the three-dimensional structure of Cebpa changes during adipogenesis, we generated high-resolution chromatin interactions of Cebpa in 3T3-L1 preadipocytes and 3T3-L1 adipocytes using circularized chromosome conformation capture sequencing (4C-seq). We revealed dramatic changes in chromatin interactions and chromatin status at interaction sites during adipogenic differentiation. Based on this, we identified five active enhancers of Cebpa in 3T3-L1 adipocytes through epigenomic data and luciferase reporter assays. Next, epigenetic repression of Cebpa-L1-AD-En2 or -En3 by the dCas9-KRAB system significantly down-regulated Cebpa expression and inhibited adipocyte differentiation. Furthermore, experimental depletion of cohesin decreased the interaction intensity between Cebpa-L1-AD-En2 and the Cebpa promoter and down-regulated Cebpa expression, indicating that long-range chromatin loop formation was mediated by cohesin. Two transcription factors, RXRA and PPARG, synergistically regulate the activity of Cebpa-L1-AD-En2. To test whether Cebpa-L1-AD-En2 plays a role in adipose tissue development, we injected dCas9-KRAB-En2 lentivirus into the inguinal white adipose tissue (iWAT) of mice to suppress the activity of Cebpa-L1-AD-En2. Repression of Cebpa-L1-AD-En2 significantly decreased Cebpa expression and adipocyte size, altered iWAT transcriptome, and affected iWAT development. We identified functional enhancers regulating Cebpa expression and clarified the crucial roles of Cebpa-L1-AD-En2 and Cebpa promoter interaction in adipocyte differentiation and adipose tissue development.

Characterization of enhancer activity in early human neurodevelopment using Massively Parallel Reporter Assay (MPRA) and forebrain organoids

Regulation of gene expression through enhancers is one of the major processes shaping the structure and function of the human brain during development. High-throughput assays have predicted thousands of enhancers involved in neurodevelopment, and confirming their activity through orthogonal functional assays is crucial. Here, we utilized Massively Parallel Reporter Assays (MPRAs) in stem cells and forebrain organoids to evaluate the activity of ~ 7000 gene-linked enhancers previously identified in human fetal tissues and brain organoids. We used a Gaussian mixture model to evaluate the contribution of background noise in the measured activity signal to confirm the activity of ~ 35% of the tested enhancers, with most showing temporal-specific activity, suggesting their evolving role in neurodevelopment. The temporal specificity was further supported by the correlation of activity with gene expression. Our findings provide a valuable gene regulatory resource to the scientific community.

Epigenetic characterization of housekeeping core promoters and their importance in tumor suppression

In this research, we elucidate the presence of around 11,000 housekeeping cis-regulatory elements (HK-CREs) and describe their main characteristics. Besides the trivial promoters of housekeeping genes, most HK-CREs reside in promoter regions and are involved in a broader role beyond housekeeping gene regulation. HK-CREs are conserved regions rich in unmethylated CpG sites. Their distribution highly correlates with that of protein-coding genes, and they interact with many genes over long distances. We observed reduced activity of a subset of HK-CREs in diverse cancer subtypes due to aberrant methylation, particularly those located in chromosome 19 and associated with zinc finger genes. Further analysis of samples from 17 cancer subtypes showed a significantly increased survival probability of patients with higher expression of these genes, suggesting them as housekeeping tumor suppressor genes. Overall, our work unravels the presence of housekeeping CREs indispensable for the maintenance and stability of cells.

Transcription Factor Condensates Mediate Clustering of MET Regulon and Enhancement in Gene Expression

Some transcription factors (TFs) can form liquid-liquid phase separated (LLPS) condensates. However, the functions of these TF condensates in 3D genome organization and gene regulation remain elusive. In response to methionine (met) starvation, budding yeast TF Met4 and a few co-activators, including Met32, induce a set of genes involved in met biosynthesis. Here, we show that the endogenous Met4 and Met32 form co-localized puncta-like structures in yeast nuclei upon met depletion. Recombinant Met4 and Met32 form mixed droplets with LLPS properties in vitro. In relation to chromatin, Met4 puncta co-localize with target genes, and at least a subset of these target genes are clustered in 3D in a Met4-dependent manner. A MET3pr-GFP reporter inserted near several native Met4 binding sites becomes co-localized with Met4 puncta and displays enhanced transcriptional activity. A Met4 variant with a partial truncation of an intrinsically disordered region (IDR) shows less puncta formation, and this mutant selectively reduces the reporter activity near Met4 binding sites to the basal level. Overall, these results support a model where Met4 and co-activators form condensates to bring multiple target genes into a vicinity with higher local TF concentrations, which facilitates a strong response to methionine depletion.

Locus folding mechanisms determine modes of antigen receptor gene assembly

The dynamic folding of genomes regulates numerous biological processes, including antigen receptor (AgR) gene assembly. We show that, unlike other AgR loci, homotypic chromatin interactions and bidirectional chromosome looping both contribute to structuring Tcrb for efficient long-range V(D)J recombination. Inactivation of the CTCF binding element (CBE) or promoter at the most 5'Vβ segment (Trbv1) impaired loop extrusion originating locally and extending to DβJβ CBEs at the opposite end of Tcrb. Promoter or CBE mutation nearly eliminated Trbv1 contacts and decreased RAG endonuclease-mediated Trbv1 recombination. Importantly, Trbv1 rearrangement can proceed independent of substrate orientation, ruling out scanning by DβJβ-bound RAG as the sole mechanism of Vβ recombination, distinguishing it from Igh. Our data indicate that CBE-dependent generation of loops cooperates with promoter-mediated activation of chromatin to juxtapose Vβ and DβJβ segments for recombination through diffusion-based synapsis. Thus, the mechanisms that fold a genomic region can influence molecular processes occurring in that space, which may include recombination, repair, and transcriptional programming.

An in silico procedure for generating protein-mediated chromatin interaction data and comparison of significant interaction calling methods

The ability to simulate high-throughput data with high fidelity to real experimental data is fundamental for benchmarking methods used to detect true long-range chromatin interactions mediated by a specific protein. Yet, such tools are not currently available. To fill this gap, we develop an in silico experimental procedure, ChIA-Sim, which imitates the experimental procedures that produce real ChIA-PET, Hi-ChIP, or PLAC-seq data. We show the fidelity of ChIA-Sim to real data by using guiding characteristics of several real datasets to generate data using the simulation procedure. We also used ChIA-Sim data to demonstrate the use of our in silico procedure in benchmarking methods for significant interactions analysis by evaluating four methods for significant interaction calling (SIC). In particular, we assessed each method's performance in terms of correct identification of long-range interactions. We further analyzed four experimental datasets from publicly available databases and shew that the trend of the results are consistent with those seen in data generated from ChIA-Sim. This serves as additional evidence that ChIA-Sim closely resembles data produced from the experimental protocols it models after.

3D genome structural variations play important roles in regulating seed oil content of Brassica napus

Dissecting the complex regulatory mechanism of seed oil content (SOC) is one of the main research goals in Brassica napus. Increasing evidence suggests that genome architecture is linked to multiple biological functions. However, the effect of genome architecture on SOC regulation remains unclear. Here, we used high-throughput chromatin conformation capture to characterize differences in the three-dimensional (3D) landscape of genome architecture of seeds from two B. napus lines, N53-2 (with high SOC) and Ken-C8 (with low SOC). Bioinformatics analysis demonstrated that differentially accessible regions and differentially expressed genes between N53-2 and Ken-C8 were preferentially enriched in regions with quantitative trait loci (QTLs)/associated genomic regions (AGRs) for SOC. A multi-omics analysis demonstrated that expression of SOC-related genes was tightly correlated with genome structural variations in QTLs/AGRs of B. napus. The candidate gene BnaA09g48250D, which showed structural variation in a QTL/AGR on chrA09, was identified by fine-mapping of a KN double-haploid population derived from hybridization of N53-2 and Ken-C8. Overexpression and knockout of BnaA09g48250D led to significant increases and decreases in SOC, respectively, in the transgenic lines. Taken together, our results reveal the 3D genome architecture of B. napus seeds and the roles of genome structural variations in SOC regulation, enriching our understanding of the molecular mechanisms of SOC regulation from the perspective of spatial chromatin structure.

A chromatin-regulated biphasic circuit coordinates IL-1β-mediated inflammation

Inflammation is characterized by a biphasic cycle consisting initially of a proinflammatory phase that is subsequently resolved by anti-inflammatory processes. Interleukin-1β (IL-1β) is a master regulator of proinflammation and is encoded within the same topologically associating domain (TAD) as IL-37, which is an anti-inflammatory cytokine that opposes the function of IL-1β. Within this TAD, we identified a long noncoding RNA called AMANZI, which negatively regulates IL-1β expression and trained immunity through the induction of IL37 transcription. We found that the activation of IL37 occurs through the formation of a dynamic long-range chromatin contact that leads to the temporal delay of anti-inflammatory responses. The common variant rs16944 present in AMANZI augments this regulatory circuit, predisposing individuals to enhanced proinflammation or immunosuppression. Our work illuminates a chromatin-mediated biphasic circuit coordinating expression of IL-1β and IL-37, thereby regulating two functionally opposed states of inflammation from within a single TAD.

Exploring testicular miRNA profiles during developmental stages in Dezhou donkeys: A preliminary insight

Testicular development and spermatogenesis are complex phenomena controlled by various genetic factors, including miRNA-based post-transcriptional gene expression regulation. Exploring the miRNA expression patterns during testicular development in Dezhou donkeys would enhance our understanding of equine fertility and spermatogenesis. In this investigation, we examined the testicular miRNA profiles at various stages of development. The experimental animals were divided into three groups based on their developmental stages: 2 months old (juvenile: n = 3), 12 months old (adolescent; n = 3) and 24 months old (adult; n = 3) donkeys. Total RNA was extracted from dissected testicles for miRNA sequencing and analysis. In total, 586 miRNAs, including 451 known miRNAs and 135 novel miRNAs, were identified. Among identified miRNAs, 315 displayed age-dependent expression differences. The levels of miRNA expression in the juvenile group were significantly higher than in the adolescent or adult groups. The MiR-483 exhibited the maximum fold change between juvenile and adolescent groups. Several screened genes, including SLC45A4 and TFCP2L1, have been linked to male reproductive pathways in donkeys. In addition, miR-744 was predicted to regulate SPIN2B, a gene implicated in spermatocyte cell cycle progression and genomic integrity of spermatozoa. These results contribute to our comprehension of microRNA regulation during testicular development and spermatogenesis in Dezhou donkeys. The identified microRNAs and their target genes have the potential to serve as biomarkers for evaluating the reproductive capacity of stud donkeys.

3D Enhancer-promoter networks provide predictive features for gene expression and coregulation in early embryonic lineages

Mammalian embryogenesis commences with two pivotal and binary cell fate decisions that give rise to three essential lineages: the trophectoderm, the epiblast and the primitive endoderm. Although key signaling pathways and transcription factors that control these early embryonic decisions have been identified, the non-coding regulatory elements through which transcriptional regulators enact these fates remain understudied. Here, we characterize, at a genome-wide scale, enhancer activity and 3D connectivity in embryo-derived stem cell lines that represent each of the early developmental fates. We observe extensive enhancer remodeling and fine-scale 3D chromatin rewiring among the three lineages, which strongly associate with transcriptional changes, although distinct groups of genes are irresponsive to topological changes. In each lineage, a high degree of connectivity, or 'hubness', positively correlates with levels of gene expression and enriches for cell-type specific and essential genes. Genes within 3D hubs also show a significantly stronger probability of coregulation across lineages compared to genes in linear proximity or within the same contact domains. By incorporating 3D chromatin features, we build a predictive model for transcriptional regulation (3D-HiChAT) that outperforms models using only 1D promoter or proximal variables to predict levels and cell-type specificity of gene expression. Using 3D-HiChAT, we identify, in silico, candidate functional enhancers and hubs in each cell lineage, and with CRISPRi experiments, we validate several enhancers that control gene expression in their respective lineages. Our study identifies 3D regulatory hubs associated with the earliest mammalian lineages and describes their relationship to gene expression and cell identity, providing a framework to comprehensively understand lineage-specific transcriptional behaviors.

Therapeutic potential of natural antisense transcripts and various mechanisms involved for clinical applications and disease prevention

Antisense transcription, a prevalent occurrence in mammalian genomes, gives rise to natural antisense transcripts (NATs) as RNA molecules. These NATs serve as agents of diverse transcriptional and post-transcriptional regulatory mechanisms, playing crucial roles in various biological processes vital for cell function and immune response. However, when their normal functions are disrupted, they can contribute to human diseases. This comprehensive review aims to establish the molecular foundation linking NATs to the development of disorders like cancer, neurodegenerative conditions, and cardiovascular ailments. Additionally, we evaluate the potential of oligonucleotide-based therapies targeting NATs, presenting both their advantages and limitations, while also highlighting the latest advancements in this promising realm of clinical investigation.Abbreviations: NATs- Natural antisense transcripts, PRC1- Polycomb Repressive Complex 1, PRC2- Polycomb Repressive Complex 2, ADARs- Adenosine deaminases acting on RNA, BDNF-AS- Brain-derived neurotrophic factor antisense transcript, ASOs- Antisense oligonucleotides, SINEUPs- Inverted SINEB2 sequence-mediated upregulating molecules, PTBP1- Polypyrimidine tract binding protein-1, HNRNPK- heterogeneous nuclear ribonucleoprotein K, MAPT-AS1- microtubule-associated protein tau antisense 1, KCNQ1OT- (KCNQ1 opposite strand/antisense transcript 1, ERK- extracellular signal-regulated kinase 1, USP14- ubiquitin-specific protease 14, EGF- Epidermal growth factor, LSD1- Lysine Specific Demethylase 1, ANRIL- Antisense Noncoding RNA in the INK4 Locus, BWS- Beckwith-Wiedemann syndrome, VEGFA- Vascular Endothelial Growth component A.

Regulatory architecture of cell identity genes and housekeeping genes

Gene regulation and chromosome architecture are intimately linked. Genes with prominent roles in cell identity are often regulated by clusters of enhancer elements. By contrast, a recent study shows housekeeping genes are often regulated through clustering of promoters. We discuss here new regulatory insights for these two types of genes.

Ex vivo mapping of enhancer networks that define the transcriptional program driving melanoma metastasis

Mortality from vmelanoma is associated with metastatic disease, but the mechanisms leading to spreading of the cancer cells remain obscure. Spatial profiling revealed that melanoma is characterized by a high degree of heterogeneity, which is established by the ability of melanoma cells to switch between different phenotypical stages. This plasticity, likely a heritage from embryonic pathways, accounts for a relevant part of the metastatic potential of these lesions, and requires the rapid and efficient reorganization of the transcriptional landscape of melanoma cells. A large part of the non-coding genome cooperates to control gene expression, specifically through the activity of enhancers (ENHs). In this study, we aimed to identify ex vivo the network of active ENHs and to outline their cooperative interactions in supporting transcriptional adaptation during melanoma metastatic progression. We conducted a genome-wide analysis to map active ENHs distribution in a retrospective cohort of 39 melanoma patients, comparing the profiles obtained in primary (N = 19) and metastatic (N = 20) melanoma lesions. Unsupervised clustering showed that the profile for acetylated histone H3 at lysine 27 (H3K27ac) efficiently segregates lesions into three different clusters corresponding to progressive stages of the disease. We reconstructed the map of super-ENHs (SEs) and cooperative ENHs that associate with metastatic progression in melanoma, which showed that cooperation among regulatory elements is a mandatory requirement for transcriptional plasticity. We also showed that these elements carry out specialized and non-redundant functions, and indicated the existence of a hierarchical organization, with SEs on top as masterminds of the entire transcriptional program and classical ENHs as executors. By providing an innovative vision of how the chromatin landscape of melanoma works during metastatic spreading, our data also point out the need to integrate functional profiling in the analysis of cancer lesions to increase definition and improve interpretation of tumor heterogeneity.

An encyclopedia of enhancer-gene regulatory interactions in the human genome

Identifying transcriptional enhancers and their target genes is essential for understanding gene regulation and the impact of human genetic variation on disease1-6. Here we create and evaluate a resource of >13 million enhancer-gene regulatory interactions across 352 cell types and tissues, by integrating predictive models, measurements of chromatin state and 3D contacts, and largescale genetic perturbations generated by the ENCODE Consortium7. We first create a systematic benchmarking pipeline to compare predictive models, assembling a dataset of 10,411 elementgene pairs measured in CRISPR perturbation experiments, >30,000 fine-mapped eQTLs, and 569 fine-mapped GWAS variants linked to a likely causal gene. Using this framework, we develop a new predictive model, ENCODE-rE2G, that achieves state-of-the-art performance across multiple prediction tasks, demonstrating a strategy involving iterative perturbations and supervised machine learning to build increasingly accurate predictive models of enhancer regulation. Using the ENCODE-rE2G model, we build an encyclopedia of enhancer-gene regulatory interactions in the human genome, which reveals global properties of enhancer networks, identifies differences in the functions of genes that have more or less complex regulatory landscapes, and improves analyses to link noncoding variants to target genes and cell types for common, complex diseases. By interpreting the model, we find evidence that, beyond enhancer activity and 3D enhancer-promoter contacts, additional features guide enhancerpromoter communication including promoter class and enhancer-enhancer synergy. Altogether, these genome-wide maps of enhancer-gene regulatory interactions, benchmarking software, predictive models, and insights about enhancer function provide a valuable resource for future studies of gene regulation and human genetics.

PRO-IP-seq tracks molecular modifications of engaged Pol II complexes at nucleotide resolution

RNA Polymerase II (Pol II) is a multi-subunit complex that undergoes covalent modifications as transcription proceeds through genes and enhancers. Rate-limiting steps of transcription control Pol II recruitment, site and degree of initiation, pausing duration, productive elongation, nascent transcript processing, transcription termination, and Pol II recycling. Here, we develop Precision Run-On coupled to Immuno-Precipitation sequencing (PRO-IP-seq), which double-selects nascent RNAs and transcription complexes, and track phosphorylation of Pol II C-terminal domain (CTD) at nucleotide-resolution. We uncover precise positional control of Pol II CTD phosphorylation as transcription proceeds from the initiating nucleotide (+1 nt), through early (+18 to +30 nt) and late (+31 to +60 nt) promoter-proximal pause, and into productive elongation. Pol II CTD is predominantly unphosphorylated from initiation until the early pause-region, whereas serine-2- and serine-5-phosphorylations are preferentially deposited in the later pause-region. Upon pause-release, serine-7-phosphorylation rapidly increases and dominates over the region where Pol II assembles elongation factors and accelerates to its full elongational speed. Interestingly, tracking CTD modifications upon heat-induced transcriptional reprogramming demonstrates that Pol II with phosphorylated CTD remains paused on thousands of heat-repressed genes. These results uncover dynamic Pol II regulation at rate-limiting steps of transcription and provide a nucleotide-resolution technique for tracking composition of engaged transcription complexes.

Never a dull enzyme, RNA polymerase II

RNA polymerase II (Pol II) is composed of 12 subunits that collaborate to synthesize mRNA within the nucleus. Pol II is widely recognized as a passive holoenzyme, with the molecular functions of its subunits largely ignored. Recent studies employing auxin-inducible degron (AID) and multi-omics techniques have revealed that the functional diversity of Pol II is achieved through the differential contributions of its subunits to various transcriptional and post-transcriptional processes. By regulating these processes in a coordinated manner through its subunits, Pol II can optimize its activity for diverse biological functions. Here, we review recent progress in understanding Pol II subunits and their dysregulation in diseases, Pol II heterogeneity, Pol II clusters and the regulatory roles of RNA polymerases.

EVRC: reconstruction of chromosome 3D structure models using error-vector resultant algorithm with clustering coefficient

MOTIVATION

Reconstruction of 3D structure models is of great importance for the study of chromosome function. Software tools for this task are highly needed.

RESULTS

We present a novel reconstruction algorithm, called EVRC, which utilizes co-clustering coefficients and error-vector resultant for chromosome 3D structure reconstruction. As an update of our previous EVR algorithm, EVRC now can deal with both single and multiple chromosomes in structure modeling. To evaluate the effectiveness and accuracy of the EVRC algorithm, we applied it to simulation datasets and real Hi-C datasets. The results show that the reconstructed structures have high similarity to the original/real structures, indicating the effectiveness and robustness of the EVRC algorithm. Furthermore, we applied the algorithm to the 3D conformation reconstruction of the wild-type and mutant Arabidopsis thaliana chromosomes and demonstrated the differences in structural characteristics between different chromosomes. We also accurately showed the conformational change in the centromere region of the mutant compared with the wild-type of Arabidopsis chromosome 1. Our EVRC algorithm is a valuable software tool for the field of chromatin structure reconstruction, and holds great promise for advancing our understanding on the chromosome functions.

AVAILABILITY AND IMPLEMENTATION

The software is available at https://github.com/mbglab/EVRC.

International Union of Basic and Clinical Pharmacology CXIII: Nuclear Receptor Superfamily-Update 2023

The NR superfamily comprises 48 transcription factors in humans that control a plethora of gene network programs involved in a wide range of physiologic processes. This review will summarize and discuss recent progress in NR biology and drug development derived from integrating various approaches, including biophysical techniques, structural studies, and translational investigation. We also highlight how defective NR signaling results in various diseases and disorders and how NRs can be targeted for therapeutic intervention via modulation via binding to synthetic lipophilic ligands. Furthermore, we also review recent studies that improved our understanding of NR structure and signaling. SIGNIFICANCE STATEMENT: Nuclear receptors (NRs) are ligand-regulated transcription factors that are critical regulators of myriad physiological processes. NRs serve as receptors for an array of drugs, and in this review, we provide an update on recent research into the roles of these drug targets.

Tackling redundancy: genetic mechanisms underlying paralog compensation in plants

Gene duplication is a powerful source of biological innovation giving rise to paralogous genes that undergo diverse fates. Redundancy between paralogous genes is an intriguing outcome of duplicate gene evolution, and its maintenance over evolutionary time has long been considered a paradox. Redundancy can also be dubbed 'a geneticist's nightmare': It hinders the predictability of genome editing outcomes and limits our ability to link genotypes to phenotypes. Genetic studies in yeast and plants have suggested that the ability of ancient redundant duplicates to compensate for dosage perturbations resulting from a loss of function depends on the reprogramming of gene expression, a phenomenon known as active compensation. Starting from considerations on the stoichiometric constraints that drive the evolutionary stability of redundancy, this review aims to provide insights into the mechanisms of active compensation between duplicates that could be targeted for breaking paralog dependencies - the next frontier in plant functional studies.

Understanding fundamental principles of enhancer biology at a model locus: Analysing the structure and function of an enhancer cluster at the α-globin locus

Despite ever-increasing accumulation of genomic data, the fundamental question of how individual genes are switched on during development, lineage-specification and differentiation is not fully answered. It is widely accepted that this involves the interaction between at least three fundamental regulatory elements: enhancers, promoters and insulators. Enhancers contain transcription factor binding sites which are bound by transcription factors (TFs) and co-factors expressed during cell fate decisions and maintain imposed patterns of activation, at least in part, via their epigenetic modification. This information is transferred from enhancers to their cognate promoters often by coming into close physical proximity to form a 'transcriptional hub' containing a high concentration of TFs and co-factors. The mechanisms underlying these stages of transcriptional activation are not fully explained. This review focuses on how enhancers and promoters are activated during differentiation and how multiple enhancers work together to regulate gene expression. We illustrate the currently understood principles of how mammalian enhancers work and how they may be perturbed in enhanceropathies using expression of the α-globin gene cluster during erythropoiesis, as a model.

Bioinformatics tools for the sequence complexity estimates

We review current methods and bioinformatics tools for the text complexity estimates (information and entropy measures). The search DNA regions with extreme statistical characteristics such as low complexity regions are important for biophysical models of chromosome function and gene transcription regulation in genome scale. We discuss the complexity profiling for segmentation and delineation of genome sequences, search for genome repeats and transposable elements, and applications to next-generation sequencing reads. We review the complexity methods and new applications fields: analysis of mutation hotspots loci, analysis of short sequencing reads with quality control, and alignment-free genome comparisons. The algorithms implementing various numerical measures of text complexity estimates including combinatorial and linguistic measures have been developed before genome sequencing era. The series of tools to estimate sequence complexity use compression approaches, mainly by modification of Lempel-Ziv compression. Most of the tools are available online providing large-scale service for whole genome analysis. Novel machine learning applications for classification of complete genome sequences also include sequence compression and complexity algorithms. We present comparison of the complexity methods on the different sequence sets, the applications for gene transcription regulatory regions analysis. Furthermore, we discuss approaches and application of sequence complexity for proteins. The complexity measures for amino acid sequences could be calculated by the same entropy and compression-based algorithms. But the functional and evolutionary roles of low complexity regions in protein have specific features differing from DNA. The tools for protein sequence complexity aimed for protein structural constraints. It was shown that low complexity regions in protein sequences are conservative in evolution and have important biological and structural functions. Finally, we summarize recent findings in large scale genome complexity comparison and applications for coronavirus genome analysis.

Epromoters are new players in the regulatory landscape with potential pleiotropic roles

Precise spatiotemporal control of gene expression during normal development and cell differentiation is achieved by the combined action of proximal (promoters) and distal (enhancers) cis-regulatory elements. Recent studies have reported that a subset of promoters, termed Epromoters, works also as enhancers to regulate distal genes. This new paradigm opened novel questions regarding the complexity of our genome and raises the possibility that genetic variation within Epromoters has pleiotropic effects on various physiological and pathological traits by differentially impacting multiple proximal and distal genes. Here, we discuss the different observations pointing to an important role of Epromoters in the regulatory landscape and summarize the evidence supporting a pleiotropic impact of these elements in disease. We further hypothesize that Epromoter might represent a major contributor to phenotypic variation and disease.

IChrom-Deep: An Attention-Based Deep Learning Model for Identifying Chromatin Interactions

Identification of chromatin interactions is crucial for advancing our knowledge of gene regulation. However, due to the limitations of high-throughput experimental techniques, there is an urgent need to develop computational methods for predicting chromatin interactions. In this study, we propose a novel attention-based deep learning model, termed IChrom-Deep, to identify chromatin interactions using sequence features and genomic features. The experimental results based on the datasets of three cell lines demonstrate that the IChrom-Deep achieves satisfactory performance and is superior to the previous methods. We also investigate the effect of DNA sequence and associated features and genomic features on chromatin interactions, and highlight the applicable scenarios of some features, such as sequence conservation and distance. Moreover, we identify a few genomic features that are extremely important across different cell lines, and IChrom-Deep achieves comparable performance with only these significant genomic features versus using all genomic features. It is believed that IChrom-Deep can serve as a useful tool for future studies that seek to identify chromatin interactions.

Characterization of enhancer activity in early human neurodevelopment using Massively parallel reporter assay (MPRA) and forebrain organoids

Regulation of gene expression through enhancers is one of the major processes shaping the structure and function of the human brain during development. High-throughput assays have predicted thousands of enhancers involved in neurodevelopment, and confirming their activity through orthogonal functional assays is crucial. Here, we utilized Massively Parallel Reporter Assays (MPRAs) in stem cells and forebrain organoids to evaluate the activity of ~7,000 gene-linked enhancers previously identified in human fetal tissues and brain organoids. We used a Gaussian mixture model to evaluate the contribution of background noise in the measured activity signal to confirm the activity of ~35% of the tested enhancers, with most showing temporal-specific activity, suggesting their evolving role in neurodevelopment. The temporal specificity was further supported by the correlation of activity with gene expression. Our findings provide a valuable gene regulatory resource to the scientific community.

3D organization of regulatory elements for transcriptional regulation in Arabidopsis

BACKGROUND

Although spatial organization of compartments and topologically associating domains at large scale is relatively well studied, the spatial organization of regulatory elements at fine scale is poorly understood in plants.

RESULTS

Here we perform high-resolution chromatin interaction analysis using paired-end tag sequencing approach. We map chromatin interactions tethered with RNA polymerase II and associated with heterochromatic, transcriptionally active, and Polycomb-repressive histone modifications in Arabidopsis. Analysis of the regulatory repertoire shows that distal active cis-regulatory elements are linked to their target genes through long-range chromatin interactions with increased expression of the target genes, while poised cis-regulatory elements are linked to their target genes through long-range chromatin interactions with depressed expression of the target genes. Furthermore, we demonstrate that transcription factor MYC2 is critical for chromatin spatial organization, and propose that MYC2 occupancy and MYC2-mediated chromatin interactions coordinately facilitate transcription within the framework of 3D chromatin architecture. Analysis of functionally related gene-defined chromatin connectivity networks reveals that genes implicated in flowering-time control are functionally compartmentalized into separate subdomains via their spatial activity in the leaf or shoot apical meristem, linking active mark- or Polycomb-repressive mark-associated chromatin conformation to coordinated gene expression.

CONCLUSION

The results reveal that the regulation of gene transcription in Arabidopsis is not only by linear juxtaposition, but also by long-range chromatin interactions. Our study uncovers the fine scale genome organization of Arabidopsis and the potential roles of such organization in orchestrating transcription and development.

RNA polymerase II dynamics shape enhancer-promoter interactions

How enhancers control target gene expression over long genomic distances remains an important unsolved problem. Here we investigated enhancer-promoter communication by integrating data from nucleosome-resolution genomic contact maps, nascent transcription and perturbations affecting either RNA polymerase II (Pol II) dynamics or the activity of thousands of candidate enhancers. Integration of new Micro-C experiments with published CRISPRi data demonstrated that enhancers spend more time in close proximity to their target promoters in functional enhancer-promoter pairs compared to nonfunctional pairs, which can be attributed in part to factors unrelated to genomic position. Manipulation of the transcription cycle demonstrated a key role for Pol II in enhancer-promoter interactions. Notably, promoter-proximal paused Pol II itself partially stabilized interactions. We propose an updated model in which elements of transcriptional dynamics shape the duration or frequency of interactions to facilitate enhancer-promoter communication.

Large-scale long terminal repeat insertions produced a significant set of novel transcripts in cotton

Genomic analysis has revealed that the 1,637-Mb Gossypium arboreum genome contains approximately 81% transposable elements (TEs), while only 57% of the 735-Mb G. raimondii genome is occupied by TEs. In this study, we investigated whether there were unknown transcripts associated with TE or TE fragments and, if so, how these new transcripts were evolved and regulated. As sequence depths increased from 4 to 100 G, a total of 10,284 novel intergenic transcripts (intergenic genes) were discovered. On average, approximately 84% of these intergenic transcripts possibly overlapped with the long terminal repeat (LTR) insertions in the otherwise untranscribed intergenic regions and were expressed at relatively low levels. Most of these intergenic transcripts possessed no transcription activation markers, while the majority of the regular genic genes possessed at least one such marker. Genes without transcription activation markers formed their+1 and -1 nucleosomes more closely (only (117±1.4)bp apart), while twice as big spaces (approximately (403.5±46.0) bp apart) were detected for genes with the activation markers. The analysis of 183 previously assembled genomes across three different kingdoms demonstrated systematically that intergenic transcript numbers in a given genome correlated positively with its LTR content. Evolutionary analysis revealed that genic genes originated during one of the whole-genome duplication events around 137.7 million years ago (MYA) for all eudicot genomes or 13.7 MYA for the Gossypium family, respectively, while the intergenic transcripts evolved around 1.6 MYA, resultant of the last LTR insertion. The characterization of these low-transcribed intergenic transcripts can facilitate our understanding of the potential biological roles played by LTRs during speciation and diversifications.

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.

Systematic mapping and modeling of 3D enhancer-promoter interactions in early mouse embryonic lineages reveal regulatory principles that determine the levels and cell-type specificity of gene expression

Mammalian embryogenesis commences with two pivotal and binary cell fate decisions that give rise to three essential lineages, the trophectoderm (TE), the epiblast (EPI) and the primitive endoderm (PrE). Although key signaling pathways and transcription factors that control these early embryonic decisions have been identified, the non-coding regulatory elements via which transcriptional regulators enact these fates remain understudied. To address this gap, we have characterized, at a genome-wide scale, enhancer activity and 3D connectivity in embryo-derived stem cell lines that represent each of the early developmental fates. We observed extensive enhancer remodeling and fine-scale 3D chromatin rewiring among the three lineages, which strongly associate with transcriptional changes, although there are distinct groups of genes that are irresponsive to topological changes. In each lineage, a high degree of connectivity or "hubness" positively correlates with levels of gene expression and enriches for cell-type specific and essential genes. Genes within 3D hubs also show a significantly stronger probability of coregulation across lineages, compared to genes in linear proximity or within the same contact domains. By incorporating 3D chromatin features, we build a novel predictive model for transcriptional regulation (3D-HiChAT), which outperformed models that use only 1D promoter or proximal variables in predicting levels and cell-type specificity of gene expression. Using 3D-HiChAT, we performed genome-wide in silico perturbations to nominate candidate functional enhancers and hubs in each cell lineage, and with CRISPRi experiments we validated several novel enhancers that control expression of one or more genes in their respective lineages. Our study comprehensively identifies 3D regulatory hubs associated with the earliest mammalian lineages and describes their relationship to gene expression and cell identity, providing a framework to understand lineage-specific transcriptional behaviors.

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

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

Complementary Alu sequences mediate enhancer-promoter selectivity

Enhancers determine spatiotemporal gene expression programs by engaging with long-range promoters^1-4. However, it remains unknown how enhancers find their cognate promoters. We recently developed a RNA in situ conformation sequencing technology to identify enhancer-promoter connectivity using pairwise interacting enhancer RNAs and promoter-derived noncoding RNAs^5,6. Here we apply this technology to generate high-confidence enhancer-promoter RNA interaction maps in six additional cell lines. Using these maps, we discover that 37.9% of the enhancer-promoter RNA interaction sites are overlapped with Alu sequences. These pairwise interacting Alu and non-Alu RNA sequences tend to be complementary and potentially form duplexes. Knockout of Alu elements compromises enhancer-promoter looping, whereas Alu insertion or CRISPR-dCasRx-mediated Alu tethering to unregulated promoter RNAs can create new loops to homologous enhancers. Mapping 535,404 noncoding risk variants back to the enhancer-promoter RNA interaction maps enabled us to construct variant-to-function maps for interpreting their molecular functions, including 15,318 deletions or insertions in 11,677 Alu elements that affect 6,497 protein-coding genes. We further demonstrate that polymorphic Alu insertion at the PTK2 enhancer can promote tumorigenesis. Our study uncovers a principle for determining enhancer-promoter pairing specificity and provides a framework to link noncoding risk variants to their molecular functions.