Systematic mapping of functional enhancer-promoter connections with CRISPR interference

Regulatory variants in a novel distal enhancer regulate the expression of CYP3A4 and CYP3A5

The cytochrome P450 3As (CYP3As) are abundantly expressed in the liver and metabolize many commonly prescribed medications. Their expression is highly variable between individuals with little known genetic cause. Despite extensive investigation, cis-acting genetic elements that control the expression of the CYP3As remain uncharacterized. Using chromatin conformation capture (4C assays), we detected reciprocal interaction between a distal regulatory region (DRR) and the CYP3A4 promoter. The DRR colocalizes with a variety of enhancer marks and was found to promote transcription in reporter assays. CRISPR-mediated deletion of the DRR decreased expression of CYP3A4, CYP3A5, and CYP3A7, supporting its role as a shared enhancer regulating the expression of three CYP3A genes. Using reporter gene assays, we identified two single-nucleotide polymorphisms (rs115025140 and rs776744/rs776742) that increased DRR-driven luciferase reporter expression. In a liver cohort (n = 246), rs115025140 was associated with increased expression of CYP3A4 mRNA (1.8-fold) and protein (1.6-fold) and rs776744/rs776742 was associated with 1.39-fold increased expression of CYP3A5 mRNA. The rs115025140 is unique to the African population and in a clinical cohort of African Americans taking statins for lipid control rs115025140 carriers showed a trend toward reduced statin-mediated lipid reduction. In addition, using a published cohort of Chinese patients who underwent renal transplantation taking tacrolimus, rs776744/rs776742 carriers were associated with reduced tacrolimus concentration after adjusting for CYP3A5*3. Our results elucidate a complex regulatory network controlling expression of three CYP3A genes and identify two novel regulatory variants with potential clinical relevance for predicting CYP3A4 and CYP3A5 expression.

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.

Multiplexed functional genomic assays to decipher the noncoding genome

Linkage disequilibrium and the incomplete regulatory annotation of the noncoding genome complicates the identification of functional noncoding genetic variants and their causal association with disease. Current computational methods for variant prioritization have limited predictive value, necessitating the application of highly parallelized experimental assays to efficiently identify functional noncoding variation. Here, we summarize two distinct approaches, massively parallel reporter assays and CRISPR-based pooled screens and describe their flexible implementation to characterize human noncoding genetic variation at unprecedented scale. Each approach provides unique advantages and limitations, highlighting the importance of multimodal methodological integration. These multiplexed assays of variant effects are undoubtedly poised to play a key role in the experimental characterization of noncoding genetic risk, informing our understanding of the underlying mechanisms of disease-associated loci and the development of more robust predictive classification algorithms.

High-throughput CRISPRi and CRISPRa technologies in 3D genome regulation for neuropsychiatric diseases

Advances in genomics have led to the identification of many risk loci with hundreds of genes and thousands of DNA variants associated with neuropsychiatric disorders. A significant barrier to understanding the genetic underpinnings of complex diseases is the lack of functional characterization of risk genes and variants in biological systems relevant to human health and connecting disease-associated variants to pathological phenotypes. Characterizing gene and DNA variant functions requires genetic perturbations followed by molecular and cellular assays of neurobiological phenotypes. However, generating null or mutant alleles is low throughput, making it impossible to characterize disease-associated variants in large quantities efficiently. CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa) screens can be leveraged to dissect the biological consequences of the tested genes and variants in their native context. Nevertheless, testing non-coding variants associated with complex diseases remains non-trivial. In this review, we first discuss the current challenges of interpreting the function of the non-coding genome and approaches to prioritizing disease-associated variants in the context of the 3D epigenome. Second, we provide a brief overview of high-throughput CRISPRi and CRISPRa screening strategies applicable for characterizing non-coding sequences in appropriate biological systems. Lastly, we discuss the promising prospects of using CRISPR-based technologies to dissect DNA sequences associated with neuropsychiatric diseases.

Highly sensitive single-cell chromatin accessibility assay and transcriptome coassay with METATAC

The thriving field of single-cell genomics allows researchers to dissect the complexity and heterogeneity of tissues at single-cell resolution at large scale, involving transcriptome and epigenome. However, single-cell chromatin accessibility profiling methods exhibit low sensitivity. Here, we increased accessible chromatin detection sensitivity in single cells with METATAC, a single-cell ATAC-seq technique, with the help of META amplification strategy and other biochemical modifications. METATAC reached the highest accessible chromatin region detection efficiency compared with existing techniques, allowing more accurate cis-regulatory element coaccessibility measurement and allele-specific chromatin accessibility analysis in complex tissue samples. In combination with a high-resolution single-cell RNA sequencing assay, we further developed a high-sensitivity joint single-cell ATAC–RNA strategy, which helps us to better resolve gene regulatory programs.

Recent advances in single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) and its coassays have transformed the field of single-cell epigenomics and transcriptomics. However, the low detection efficiency of current methods has limited our understanding of the true complexity of chromatin accessibility and its relationship with gene expression in single cells. Here, we report a high-sensitivity scATAC-seq method, termed multiplexed end-tagging amplification of transposase accessible chromatin (METATAC), which detects a large number of accessible sites per cell and is compatible with automation. Our high detectability and statistical framework allowed precise linking of enhancers to promoters without merging single cells. We systematically investigated allele-specific accessibility in the mouse cerebral cortex, revealing allele-specific accessibility of promotors of certain imprinted genes but biallelic accessibility of their enhancers. Finally, we combined METATAC with our high-sensitivity single-cell RNA sequencing (scRNA-seq) method, multiple annealing and looping based amplification cycles for digital transcriptomics (MALBAC-DT), to develop a joint ATAC–RNA assay, termed METATAC and MALBAC-DT coassay by sequencing (M2C-seq). M2C-seq achieved significant improvements for both ATAC and RNA compared with previous methods, with consistent performance across cell lines and early mouse embryos.

Decoding the spatial chromatin organization and dynamic epigenetic landscapes of macrophage cells during differentiation and immune activation

Immunocytes dynamically reprogram their gene expression profiles during differentiation and immunoresponse. However, the underlying mechanism remains elusive. Here, we develop a single-cell Hi-C method and systematically delineate the 3D genome and dynamic epigenetic atlas of macrophages during these processes. We propose "degree of disorder" to measure genome organizational patterns inside topologically-associated domains, which is correlated with the chromatin epigenetic states, gene expression, and chromatin structure variability in individual cells. Furthermore, we identify that NF-κB initiates systematic chromatin conformation reorganization upon Mycobacterium tuberculosis infection. The integrated Hi-C, eQTL, and GWAS analysis depicts the atlas of the long-range target genes of mycobacterial disease susceptible loci. Among these, the SNP rs1873613 is located in the anchor of a dynamic chromatin loop with LRRK2, whose inhibitor AdoCbl could be an anti-tuberculosis drug candidate. Our study provides comprehensive resources for the 3D genome structure of immunocytes and sheds insights into the order of genome organization and the coordinated gene transcription during immunoresponse.

Dissection of the MEF2D-IRF8 transcriptional circuit dependency in acute myeloid leukemia

Transcriptional dysregulation is a prominent feature in leukemia. Here, we systematically surveyed transcription factor (TF) vulnerabilities in leukemia and uncovered TF clusters that exhibit context-specific vulnerabilities within and between different subtypes of leukemia. Among these TF clusters, we demonstrated that acute myeloid leukemia (AML) with high IRF8 expression was addicted to MEF2D. MEF2D and IRF8 form an autoregulatory loop via direct binding to mutual enhancer elements. One important function of this circuit in AML is to sustain PU.1/MEIS1 co-regulated transcriptional outputs via stabilizing PU.1’s chromatin occupancy. We illustrated that AML could acquire dependency on this circuit through various oncogenic mechanisms that results in the activation of their enhancers. In addition to forming a circuit, MEF2D and IRF8 can also separately regulate gene expression, and dual perturbation of these two TFs leads to a more robust inhibition of AML proliferation. Collectively, our results revealed a TF circuit essential for AML survival.

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MEF2D is a context-specific vulnerability in IRF8^hi AML

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MEF2D and IRF8 form a transcriptional circuit via binding to each other’s enhancers

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MEF2D-IRF8 circuit supports PU.1’s chromatin occupancy and transcriptional output

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MEF2D and IRF8 can regulate separate gene expression programs alongside the circuit

Functional inference of gene regulation using single-cell multi-omics

Cells require coordinated control over gene expression when responding to environmental stimuli. Here we apply scATAC-seq and single-cell RNA sequencing (scRNA-seq) in resting and stimulated human blood cells. Collectively, we generate ~91,000 single-cell profiles, allowing us to probe the cis-regulatory landscape of the immunological response across cell types, stimuli, and time. Advancing tools to integrate multi-omics data, we develop functional inference of gene regulation (FigR), a framework to computationally pair scA-TAC-seq with scRNA-seq cells, connect distal cis-regulatory elements to genes, and infer gene-regulatory networks (GRNs) to identify candidate transcription factor (TF) regulators. Utilizing these paired multi-omics data, we define domains of regulatory chromatin (DORCs) of immune stimulation and find that cells alter chromatin accessibility and gene expression at timescales of minutes. Construction of the stimulation GRN elucidates TF activity at disease-associated DORCs. Overall, FigR enables elucidation of regulatory interactions across single-cell data, providing new opportunities to understand the function of cells within tissues.

Small hairpin inhibitory RNA delivery in the metanephric organ culture identifies long noncoding RNA Pvt1 as a modulator of cyst growth

Autosomal dominant polycystic kidney disease (ADPKD) is a monogenic disorder characterized by the formation of kidney cysts that originate from the epithelial tubules of the nephron and primarily results from mutations in polycystin-1 (PKD1) and polycystin-2 (PKD2). The metanephric organ culture (MOC) is an ex vivo system in which explanted embryonic kidneys undergo tubular differentiation and kidney development. MOC has been previously used to study polycystic kidney disease as treatment with 8-bromo-cAMP induces the formation of kidney cysts. However, the inefficiency of manipulating gene expression in MOC has limited its utility for identifying genes and pathways that are involved in cystogenesis. Here, we used a lentivirus and three serotypes of self-complementary adeno-associated viral (scAAV) plasmids that express green fluorescent protein and found that scAAV serotype D/J transduces the epithelial compartment of MOC at an efficiency of 68%. We used scAAV/DJ to deliver shRNA to knockdown Pvt1, a long noncoding RNA, which was upregulated in kidneys from Pkd1 and Pkd2 mutant mice and humans with ADPKD. shRNA delivery by scAAV/DJ downregulated expression of Pvt1 by 45% and reduced the cyst index by 53% in wild-type MOCs and 32% in Pkd1-null MOCs. Knockdown of Pvt1 decreased the level of c-MYC protein by 60% without affecting Myc mRNA, indicating that Pvt1 regulation of c-MYC was posttranscriptional. These results identify Pvt1 as a long noncoding RNA that modulates cyst progression in MOC.NEW & NOTEWORTHY This study identified scAAV/DJ as effective in transducing epithelial cells of the metanephric organ culture (MOC). We used scAAV/DJ shRNA to knockdown Pvt1 in cystic MOCs derived from Pkd1-null embryos. Downregulation of Pvt1 reduced cyst growth and decreased levels of c-MYC protein. These data suggest that suppression of Pvt1 activity in autosomal dominant polycystic kidney disease might reduce cyst growth.

The role of single-cell genomics in human genetics

Single-cell sequencing is a powerful approach that can detect genetic alterations and their phenotypic consequences in the context of human development, with cellular resolution. Humans start out as single-cell zygotes and undergo fission and differentiation to develop into multicellular organisms. Before fertilisation and during development, the cellular genome acquires hundreds of mutations that propagate down the cell lineage. Whether germline or somatic in nature, some of these mutations may have significant genotypic impact and lead to diseased cellular phenotypes, either systemically or confined to a tissue. Single-cell sequencing enables the detection and monitoring of the genotype and the consequent molecular phenotypes at a cellular resolution. It offers powerful tools to compare the cellular lineage between 'normal' and 'diseased' conditions and to establish genotype-phenotype relationships. By preserving cellular heterogeneity, single-cell sequencing, unlike bulk-sequencing, allows the detection of even small, diseased subpopulations of cells within an otherwise normal tissue. Indeed, the characterisation of biopsies with cellular resolution can provide a mechanistic view of the disease. While single-cell approaches are currently used mainly in basic research, it can be expected that applications of these technologies in the clinic may aid the detection, diagnosis and eventually the treatment of rare genetic diseases as well as cancer. This review article provides an overview of the single-cell sequencing technologies in the context of human genetics, with an aim to empower clinicians to understand and interpret the single-cell sequencing data and analyses. We discuss the state-of-the-art experimental and analytical workflows and highlight current challenges/limitations. Notably, we focus on two prospective applications of the technology in human genetics, namely the annotation of the non-coding genome using single-cell functional genomics and the use of single-cell sequencing data for in silico variant prioritisation.

EPI-Mind: Identifying Enhancer-Promoter Interactions Based on Transformer Mechanism

MOTIVATION

Enhancer-Promoter Interactions (EPIs) is an essential step in the gene regulation process. However, the detection of EPIs by traditional wet experimental techniques is time-consuming and expensive. Thus, computational methods would be very useful for understanding the mechanism of EPIs. A number of approaches have been proposed to address this problem. Nevertheless, there is room for exploration and improvement for the existing research methods.

METHODS

In this study, a novel deep-learning model named EPI-Mind was proposed to predict EPIs with sequences features. First, we encoded enhancers and promoters sequences with pre-trained DNA vectors. Then, the Convolutional Neural Network (CNN) was utilized to rough extract the global and local features. Finally, the transformer mechanism was introduced to further extract the feature. We first trained a model named EPI-Mind_spe which can predict EPIs in one cell line. To achieve general prediction across different cell lines and further improve the performance of the model, a second-time training was carried on. The redivided dataset were used to train a new model called EPI-Mind_gen which can predict EPIs across different cell lines. To further improve the accuracy, a new model named EPI-Mind_best was trained which used the EPI-Mind_gen as a pre-trained model.

RESULTS

EPI-Mind_spe has the ability of predict EPIs with average AUROC above 90% and average AUPR above 70% but with cell lines specificity. EPI-Mind_gen can predict EPIs across different cell lines and its average AUROC is higher than the EPI-Mind_spe about 4.8%. EPI-Mind_best is superior to the state-of-the-art predictors on benchmarking datasets. EPI-Mind_best achieved best in 5 indicators within 12 indicators consists of AUPR and AUROC which is better than pioneers.

CONCLUSION

This research proposed a method, which was called EPI-Mind, to predict EPIs only with enhancer and promoters sequences, the framework of which was based on deep learning. This manuscript may provide a new route to solve the problem.

Natural and Experimental Rewiring of Gene Regulatory Regions

The successful development and ongoing functioning of complex organisms depend on the faithful execution of the genetic code. A critical step in this process is the correct spatial and temporal expression of genes. The highly orchestrated transcription of genes is controlled primarily by cis-regulatory elements: promoters, enhancers, and insulators. The medical importance of this key biological process can be seen by the frequency with which mutations and inherited variants that alter cis-regulatory elements lead to monogenic and complex diseases and cancer. Here, we provide an overview of the methods available to characterize and perturb gene regulatory circuits. We then highlight mechanisms through which regulatory rewiring contributes to disease, and conclude with a perspective on how our understanding of gene regulation can be used to improve human health.

Understanding the function of regulatory DNA interactions in the interpretation of non-coding GWAS variants

Genome-wide association studies (GWAS) have identified a vast number of variants associated with various complex human diseases and traits. However, most of these GWAS variants reside in non-coding regions producing no proteins, making the interpretation of these variants a daunting challenge. Prior evidence indicates that a subset of non-coding variants detected within or near cis-regulatory elements (e.g., promoters, enhancers, silencers, and insulators) might play a key role in disease etiology by regulating gene expression. Advanced sequencing- and imaging-based technologies, together with powerful computational methods, enabling comprehensive characterization of regulatory DNA interactions, have substantially improved our understanding of the three-dimensional (3D) genome architecture. Recent literature witnesses plenty of examples where using chromosome conformation capture (3C)-based technologies successfully links non-coding variants to their target genes and prioritizes relevant tissues or cell types. These examples illustrate the critical capability of 3D genome organization in annotating non-coding GWAS variants. This review discusses how 3D genome organization information contributes to elucidating the potential roles of non-coding GWAS variants in disease etiology.

Nested epistasis enhancer networks for robust genome regulation

Mammalian genomes possess multiple enhancers spanning an ultralong distance (>megabases) to modulate important genes, yet it is unclear how these enhancers coordinate to achieve this task. Here, we combine multiplexed CRISPRi screening with machine learning to define quantitative enhancer-enhancer interactions. We find that the ultralong distance enhancer network possesses a nested multi-layer architecture that confers functional robustness of gene expression. Experimental characterization reveals that enhancer epistasis is maintained by three-dimensional chromosomal interactions and BRD4 condensation. Machine learning prediction of synergistic enhancers provides an effective strategy to identify non-coding variant pairs associated with pathogenic genes in diseases beyond Genome-Wide Association Studies (GWAS) analysis. Our work unveils nested epistasis enhancer networks, which can better explain enhancer functions within cells and in diseases.

Predicting which genes will respond to transcription factor perturbations

The ability to predict which genes will respond to the perturbation of a transcription factor serves as a benchmark for our systems-level understanding of transcriptional regulatory networks. In previous work, machine learning models have been trained to predict static gene expression levels in a biological sample by using data from the same or similar samples, including data on their transcription factor binding locations, histone marks, or DNA sequence. We report on a different challenge-training machine learning models to predict which genes will respond to the perturbation of a transcription factor without using any data from the perturbed cells. We find that existing transcription factor location data (ChIP-seq) from human cells have very little detectable utility for predicting which genes will respond to perturbation of a transcription factor. Features of genes, including their preperturbation expression level and expression variation, are very useful for predicting responses to perturbation of any transcription factor. This shows that some genes are poised to respond to transcription factor perturbations and others are resistant, shedding light on why it has been so difficult to predict responses from binding locations. Certain histone marks, including H3K4me1 and H3K4me3, have some predictive power when located downstream of the transcription start site. However, the predictive power of histone marks is much less than that of gene expression level and expression variation. Sequence-based or epigenetic properties of genes strongly influence their tendency to respond to direct transcription factor perturbations, partially explaining the oft-noted difficulty of predicting responsiveness from transcription factor binding location data. These molecular features are largely reflected in and summarized by the gene's expression level and expression variation. Code is available at https://github.com/BrentLab/TFPertRespExplainer.

A review of deep learning applications in human genomics using next-generation sequencing data

Genomics is advancing towards data-driven science. Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data. To extract knowledge and pattern out of this genomic data, artificial intelligence especially deep learning methods has been instrumental. In the current review, we address development and application of deep learning methods/models in different subarea of human genomics. We assessed over- and under-charted area of genomics by deep learning techniques. Deep learning algorithms underlying the genomic tools have been discussed briefly in later part of this review. Finally, we discussed briefly about the late application of deep learning tools in genomic. Conclusively, this review is timely for biotechnology or genomic scientists in order to guide them why, when and how to use deep learning methods to analyse human genomic data.

A multi-omic dissection of super-enhancer driven oncogenic gene expression programs in ovarian cancer

The human genome contains regulatory elements, such as enhancers, that are often rewired by cancer cells for the activation of genes that promote tumorigenesis and resistance to therapy. This is especially true for cancers that have little or no known driver mutations within protein coding genes, such as ovarian cancer. Herein, we utilize an integrated set of genomic and epigenomic datasets to identify clinically relevant super-enhancers that are preferentially amplified in ovarian cancer patients. We systematically probe the top 86 super-enhancers, using CRISPR-interference and CRISPR-deletion assays coupled to RNA-sequencing, to nominate two salient super-enhancers that drive proliferation and migration of cancer cells. Utilizing Hi-C, we construct chromatin interaction maps that enable the annotation of direct target genes for these super-enhancers and confirm their activity specifically within the cancer cell compartment of human tumors using single-cell genomics data. Together, our multi-omic approach examines a number of fundamental questions about how regulatory information encoded into super-enhancers drives gene expression networks that underlie the biology of ovarian cancer.

EPIXplorer: A web server for prediction, analysis and visualization of enhancer-promoter interactions

Long distance enhancers can physically interact with promoters to regulate gene expression through formation of enhancer-promoter (E-P) interactions. Identification of E-P interactions is also important for profound understanding of normal developmental and disease-associated risk variants. Although the state-of-art predictive computation methods facilitate the identification of E-P interactions to a certain extent, currently there is no efficient method that can meet various requirements of usage. Here we developed EPIXplorer, a user-friendly web server for efficient prediction, analysis and visualization of E-P interactions. EPIXplorer integrates 9 robust predictive algorithms, supports multiple types of 3D contact data and multi-omics data as input. The output from EPIXplorer is scored, fully annotated by regulatory elements and risk single-nucleotide polymorphisms (SNPs). In addition, the Visualization and Downstream module provide further functional analysis, all the output files and high-quality images are available for download. Together, EPIXplorer provides a user-friendly interface to predict the E-P interactions in an acceptable time, as well as understand how the genome-wide association study (GWAS) variants influence disease pathology by altering DNA looping between enhancers and the target gene promoters. EPIXplorer is available at https://www.csuligroup.com/EPIXplorer.

Genome-wide identification of functional enhancers and their potential roles in pig breeding

Background

The pig is an economically important livestock species and is a widely applied large animal model in medical research. Enhancers are critical regulatory elements that have fundamental functions in evolution, development and disease. Genome-wide quantification of functional enhancers in the pig is needed.

Results

We performed self-transcribing active regulatory region sequencing (STARR-seq) in the porcine kidney epithelial PK15 and testicular ST cell lines, and reliably identified 2576 functional enhancers. Most of these enhancers were located in repetitive sequences and were enriched within silent and lowly expressed genes. Enhancers poorly overlapped with chromatin accessibility regions and were highly enriched in chromatin with the repressive histone modification H3K9me3, which is different from predicted pig enhancers detected using ChIP-seq for H3K27ac or/and H3K4me1 modified histones. This suggests that most pig enhancers identified with STARR-seq are endogenously repressed at the chromatin level and may function during cell type-specific development or at specific developmental stages. Additionally, the PPP3CA gene is associated with the loin muscle area trait and the QKI gene is associated with alkaline phosphatase activity that may be regulated by distal functional enhancers.

Conclusions

In summary, we generated the first functional enhancer map in PK15 and ST cells for the pig genome and highlight its potential roles in pig breeding.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40104-022-00726-y.

A comparison of experimental assays and analytical methods for genome-wide identification of active enhancers

Mounting evidence supports the idea that transcriptional patterns serve as more specific identifiers of active enhancers than histone marks; however, the optimal strategy to identify active enhancers both experimentally and computationally has not been determined. Here, we compared 13 genome-wide RNA sequencing (RNA-seq) assays in K562 cells and show that nuclear run-on followed by cap-selection assay (GRO/PRO-cap) has advantages in enhancer RNA detection and active enhancer identification. We also introduce a tool, peak identifier for nascent transcript starts (PINTS), to identify active promoters and enhancers genome wide and pinpoint the precise location of 5' transcription start sites. Finally, we compiled a comprehensive enhancer candidate compendium based on the detected enhancer RNA (eRNA) transcription start sites (TSSs) available in 120 cell and tissue types, which can be accessed at https://pints.yulab.org . With knowledge of the best available assays and pipelines, this large-scale annotation of candidate enhancers will pave the way for selection and characterization of their functions in a time- and labor-efficient manner.

Compatibility rules of human enhancer and promoter sequences

Gene regulation in the human genome is controlled by distal enhancers that activate specific nearby promoters1. A proposed model for this specificity is that promoters have sequence-encoded preferences for certain enhancers, for example, mediated by interacting sets of transcription factors or cofactors2. This 'biochemical compatibility' model has been supported by observations at individual human promoters and by genome-wide measurements in Drosophila3-9. However, the degree to which human enhancers and promoters are intrinsically compatible has not yet been systematically measured, and how their activities combine to control RNA expression remains unclear. Here we design a high-throughput reporter assay called enhancer × promoter self-transcribing active regulatory region sequencing (ExP STARR-seq) and applied it to examine the combinatorial compatibilities of 1,000 enhancer and 1,000 promoter sequences in human K562 cells. We identify simple rules for enhancer-promoter compatibility, whereby most enhancers activate all promoters by similar amounts, and intrinsic enhancer and promoter activities multiplicatively combine to determine RNA output (R2 = 0.82). In addition, two classes of enhancers and promoters show subtle preferential effects. Promoters of housekeeping genes contain built-in activating motifs for factors such as GABPA and YY1, which decrease the responsiveness of promoters to distal enhancers. Promoters of variably expressed genes lack these motifs and show stronger responsiveness to enhancers. Together, this systematic assessment of enhancer-promoter compatibility suggests a multiplicative model tuned by enhancer and promoter class to control gene transcription in the human genome.

Epigenetic regulation of T cell exhaustion

Chronic antigen stimulation during viral infections and cancer can lead to T cell exhaustion, which is characterized by reduced effector function and proliferation, and the expression of inhibitory immune checkpoint receptors. Recent studies have demonstrated that T cell exhaustion results in wholescale epigenetic remodeling that confers phenotypic stability to these cells and prevents T cell reinvigoration by checkpoint blockade. Here, we review foundational technologies to profile the epigenome at multiple scales, including mapping the locations of transcription factors and histone modifications, DNA methylation and three-dimensional genome conformation. We discuss how these technologies have elucidated the development and epigenetic regulation of exhausted T cells and functional implications across viral infection, cancer, autoimmunity and engineered T cell therapies. Finally, we cover emerging multi-omic and genome engineering technologies, current and upcoming opportunities to apply these to T cell exhaustion, and therapeutic opportunities for T cell engineering in the clinic.

Chromatin interaction maps identify Wnt responsive cis-regulatory elements coordinating Paupar-Pax6 expression in neuronal cells

Central nervous system-expressed long non-coding RNAs (lncRNAs) are often located in the genome close to protein coding genes involved in transcriptional control. Such lncRNA-protein coding gene pairs are frequently temporally and spatially co-expressed in the nervous system and are predicted to act together to regulate neuronal development and function. Although some of these lncRNAs also bind and modulate the activity of the encoded transcription factors, the regulatory mechanisms controlling co-expression of neighbouring lncRNA-protein coding genes remain unclear. Here, we used high resolution NG Capture-C to map the cis-regulatory interaction landscape of the key neuro-developmental Paupar-Pax6 lncRNA-mRNA locus. The results define chromatin architecture changes associated with high Paupar-Pax6 expression in neurons and identify both promoter selective as well as shared cis-regulatory-promoter interactions involved in regulating Paupar-Pax6 co-expression. We discovered that the TCF7L2 transcription factor, a regulator of chromatin architecture and major effector of the Wnt signalling pathway, binds to a subset of these candidate cis-regulatory elements to coordinate Paupar and Pax6 co-expression. We describe distinct roles for Paupar in Pax6 expression control and show that the Paupar DNA locus contains a TCF7L2 bound transcriptional silencer whilst the Paupar transcript can act as an activator of Pax6. Our work provides important insights into the chromatin interactions, signalling pathways and transcription factors controlling co-expression of adjacent lncRNAs and protein coding genes in the brain.

A synthetic synthesis to explore animal evolution and development

Identifying the general principles by which genotypes are converted into phenotypes remains a challenge in the post-genomic era. We still lack a predictive understanding of how genes shape interactions among cells and tissues in response to signalling and environmental cues, and hence how regulatory networks generate the phenotypic variation required for adaptive evolution. Here, we discuss how techniques borrowed from synthetic biology may facilitate a systematic exploration of evolvability across biological scales. Synthetic approaches permit controlled manipulation of both endogenous and fully engineered systems, providing a flexible platform for investigating causal mechanisms in vivo. Combining synthetic approaches with multi-level phenotyping (phenomics) will supply a detailed, quantitative characterization of how internal and external stimuli shape the morphology and behaviour of living organisms. We advocate integrating high-throughput experimental data with mathematical and computational techniques from a variety of disciplines in order to pursue a comprehensive theory of evolution.

This article is part of the theme issue ‘Genetic basis of adaptation and speciation: from loci to causative mutations’.

Understanding astrocyte differentiation: Clinical relevance, technical challenges, and new opportunities in the omics era

Astrocytes are a major type of glial cells that have essential functions in development and homeostasis of the central nervous system (CNS). Immature astrocytes in the developing CNS support neuronal maturation and possess neural-stem-cell-like properties. Mature astrocytes partially lose these functions but gain new functions essential for adult CNS homeostasis. In pathological conditions, astrocytes become "reactive", which disrupts their mature homeostatic functions and reactivates some immature astrocyte-like properties, suggesting a partial reversal of astrocyte maturation. The loss of homeostatic astrocyte functions contributes to the pathogenesis of various neurological conditions, and therefore activating maturation-promoting mechanisms may be a promising therapeutic strategy to restore homeostasis. Manipulating the mechanisms underlying astrocyte maturation might also allow to facilitate CNS regeneration by enhancing developmental functions of adult astrocytes. However, such therapeutic strategies are still some distance away because of our limited understanding of astrocyte differentiation and maturation, due to biological and technical challenges, including the high degree of similarity of astrocytes with neural stem cells and the shortcomings of astrocyte markers. Current advances in systems biology have a huge potential to overcome these challenges. Recent transcriptomic analyses have already revealed new astrocyte markers and new regulators of astrocyte differentiation. However, the epigenomic changes that presumably occur during astrocyte differentiation remain an important, largely unexplored area for future research. Emerging technologies such as CRISPR/Cas9-based functional screens will further improve our understanding of the mechanisms underlying astrocyte differentiation. This may open up new clinical approaches to restore homeostasis in neurological disorders and/or promote CNS regeneration. This article is categorized under: Neurological Diseases > Genetics/Genomics/Epigenetics Neurological Diseases > Stem Cells and Development Neurological Diseases > Molecular and Cellular Physiology.

A CRISPR view of hematopoietic stem cells: Moving innovative bioengineering into the clinic

Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas genome engineering has emerged as a powerful tool to modify precise genomic sequences with unparalleled accuracy and efficiency. Major advances in CRISPR technologies over the last 5 years have fueled the development of novel techniques in hematopoiesis research to interrogate the complexities of hematopoietic stem cell (HSC) biology. In particular, high throughput CRISPR based screens using various "flavors" of Cas coupled with sequencing and/or functional outputs are becoming increasingly efficient and accessible. In this review, we discuss recent achievements in CRISPR-mediated genomic engineering and how these new tools have advanced the understanding of HSC heterogeneity and function throughout life. Additionally, we highlight how these techniques can be used to answer previously inaccessible questions and the challenges to implement them. Finally, we focus on their translational potential to both model and treat hematological diseases in the clinic.

CRISPR in cancer biology and therapy

Over the past decade, CRISPR has become as much a verb as it is an acronym, transforming biomedical research and providing entirely new approaches for dissecting all facets of cell biology. In cancer research, CRISPR and related tools have offered a window into previously intractable problems in our understanding of cancer genetics, the noncoding genome and tumour heterogeneity, and provided new insights into therapeutic vulnerabilities. Here, we review the progress made in the development of CRISPR systems as a tool to study cancer, and the emerging adaptation of these technologies to improve diagnosis and treatment.

The oncogene-dependent resistance to reprogramming unveils cancer therapeutic targets

The resistance to transcription factor-mediated reprogramming into pluripotent stem cells is one of the distinctive features of cancer cells. Here we dissect the profiles of reprogramming factor binding and the subsequent transcriptional response in cancer cells to reveal its underlying mechanisms. Using clear cell sarcomas (CCSs), we show that the driver oncogene EWS/ATF1 misdirects the reprogramming factors to cancer-specific enhancers and thereby impairs the transcriptional response toward pluripotency that is otherwise provoked. Sensitization to the reprogramming cue is observed in other cancer types when the corresponding oncogenic signals are pharmacologically inhibited. Exploiting this oncogene dependence of the transcriptional "stiffness," we identify mTOR signaling pathways downstream of EWS/ATF1 and discover that inhibiting mTOR activity substantially attenuates the propagation of CCS cells in vitro and in vivo. Our results demonstrate that the early transcriptional response to cell fate perturbations can be a faithful readout to identify effective therapeutics targets in cancer cells.

Connecting high-resolution 3D chromatin organization with epigenomics

The resolution of chromatin conformation capture technologies keeps increasing, and the recent nucleosome resolution chromatin contact maps allow us to explore how fine-scale 3D chromatin organization is related to epigenomic states in human cells. Using publicly available Micro-C datasets, we develop a deep learning model, CAESAR, to learn a mapping function from epigenomic features to 3D chromatin organization. The model accurately predicts fine-scale structures, such as short-range chromatin loops and stripes, that Hi-C fails to detect. With existing epigenomic datasets from ENCODE and Roadmap Epigenomics Project, we successfully impute high-resolution 3D chromatin contact maps for 91 human tissues and cell lines. In the imputed high-resolution contact maps, we identify the spatial interactions between genes and their experimentally validated regulatory elements, demonstrating CAESAR's potential in coupling transcriptional regulation with 3D chromatin organization at high resolution.

CRISPR-Cas9 library screening approach for anti-cancer drug discovery: overview and perspectives

CRISPR-Cas9 is a Nobel Prize-winning robust gene-editing tool developed in the last decade. This technique enables a stable genetic engineering method with high precision on the genomes of all organisms. The latest advances in the technology include a genome library screening approach, which can detect survival-essential and drug resistance genes via gain or loss of function. The versatile machinery allows genomic screening for gene activation or inhibition, and targets non-coding sequences, such as promoters, miRNAs, and lncRNAs. In this review, we introduce the emerging high-throughput CRISPR-Cas9 library genome screening technology and its working principles to detect survival and drug resistance genes through positive and negative selection. The technology is compared with other existing approaches while focusing on the advantages of its variable applications in anti-cancer drug discovery, including functions and target identification, non-coding RNA information, actions of small molecules, and drug target discoveries. The combination of the CRISPR-Cas9 system with multi-omic platforms represents a dynamic field expected to advance anti-cancer drug discovery and precision medicine in the clinic.

Genome-wide analysis of somatic noncoding mutation patterns in cancer

We established a genome-wide compendium of somatic mutation events in 3949 whole cancer genomes representing 19 tumor types. Protein-coding events captured well-established drivers. Noncoding events near tissue-specific genes, such as ALB in the liver or KLK3 in the prostate, characterized localized passenger mutation patterns and may reflect tumor-cell-of-origin imprinting. Noncoding events in regulatory promoter and enhancer regions frequently involved cancer-relevant genes such as BCL6, FGFR2, RAD51B, SMC6, TERT, and XBP1 and represent possible drivers. Unlike most noncoding regulatory events, XBP1 mutations primarily accumulated outside the gene's promoter, and we validated their effect on gene expression using CRISPR-interference screening and luciferase reporter assays. Broadly, our study provides a blueprint for capturing mutation events across the entire genome to guide advances in biological discovery, therapies, and diagnostics.

Lupus enhancer risk variant causes dysregulation of IRF8 through cooperative lncRNA and DNA methylation machinery

Despite strong evidence that human genetic variants affect the expression of many key transcription factors involved in autoimmune diseases, establishing biological links between non-coding risk variants and the gene targets they regulate remains a considerable challenge. Here, we combine genetic, epigenomic, and CRISPR activation approaches to screen for functional variants that regulate IRF8 expression. We demonstrate that the locus containing rs2280381 is a cell-type-specific enhancer for IRF8 that spatially interacts with the IRF8 promoter. Further, rs2280381 mediates IRF8 expression through enhancer RNA AC092723.1, which recruits TET1 to the IRF8 promoter regulating IRF8 expression by affecting methylation levels. The alleles of rs2280381 modulate PU.1 binding and chromatin state to regulate AC092723.1 and IRF8 expression differentially. Our work illustrates an integrative strategy to define functional genetic variants that regulate the expression of critical genes in autoimmune diseases and decipher the mechanisms underlying the dysregulation of IRF8 expression mediated by lupus risk variants.

Gene editing and its applications in biomedicine

The steady progress in genome editing, especially genome editing based on the use of clustered regularly interspaced short palindromic repeats (CRISPR) and programmable nucleases to make precise modifications to genetic material, has provided enormous opportunities to advance biomedical research and promote human health. The application of these technologies in basic biomedical research has yielded significant advances in identifying and studying key molecular targets relevant to human diseases and their treatment. The clinical translation of genome editing techniques offers unprecedented biomedical engineering capabilities in the diagnosis, prevention, and treatment of disease or disability. Here, we provide a general summary of emerging biomedical applications of genome editing, including open challenges. We also summarize the tools of genome editing and the insights derived from their applications, hoping to accelerate new discoveries and therapies in biomedicine.

Understanding Regulatory Mechanisms of Brain Function and Disease through 3D Genome Organization

The human genome has a complex and dynamic three-dimensional (3D) organization, which plays a critical role for gene regulation and genome function. The importance of 3D genome organization in brain development and function has been well characterized in a region- and cell-type-specific fashion. Recent technological advances in chromosome conformation capture (3C)-based techniques, imaging approaches, and ligation-free methods, along with computational methods to analyze the data generated, have revealed 3D genome features at different scales in the brain that contribute to our understanding of genetic mechanisms underlying neuropsychiatric diseases and other brain-related traits. In this review, we discuss how these advances aid in the genetic dissection of brain-related traits.

Human epigenetic and transcriptional T cell differentiation atlas for identifying functional T cell-specific enhancers

The clinical benefit of T cell immunotherapies remains limited by incomplete understanding of T cell differentiation and dysfunction. We generated an epigenetic and transcriptional atlas of T cell differentiation from healthy humans that included exhausted CD8 T cells and applied this resource in three ways. First, we identified modules of gene expression and chromatin accessibility, revealing molecular coordination of differentiation after activation and between central memory and effector memory. Second, we applied this healthy molecular framework to three settings-a neoadjuvant anti-PD1 melanoma trial, a basal cell carcinoma scATAC-seq dataset, and autoimmune disease-associated SNPs-yielding insights into disease-specific biology. Third, we predicted genome-wide cis-regulatory elements and validated this approach for key effector genes using CRISPR interference, providing functional annotation and demonstrating the ability to identify targets for non-coding cellular engineering. These studies define epigenetic and transcriptional regulation of human T cells and illustrate the utility of interrogating disease in the context of a healthy T cell atlas.

Fetal Liver-Derived Alveolar-like Macrophages: A Self-Replicating Ex Vivo Model of Alveolar Macrophages for Functional Genetic Studies

Alveolar macrophages (AMs) are tissue-resident cells in the lungs derived from the fetal liver that maintain lung homeostasis and respond to inhaled stimuli. Although the importance of AMs is undisputed, they remain refractory to standard experimental approaches and high-throughput functional genetics, as they are challenging to isolate and rapidly lose AM properties in standard culture. This limitation hinders our understanding of key regulatory mechanisms that control AM maintenance and function. In this study, we describe the development of a new model, fetal liver-derived alveolar-like macrophages (FLAMs), which maintains cellular morphologies, expression profiles, and functional mechanisms similar to murine AMs. FLAMs combine treatment with two key cytokines for AM maintenance, GM-CSF and TGF-β. We leveraged the long-term stability of FLAMs to develop functional genetic tools using CRISPR-Cas9-mediated gene editing. Targeted editing confirmed the role of AM-specific gene Marco and the IL-1 receptor Il1r1 in modulating the AM response to crystalline silica. Furthermore, a genome-wide knockout library using FLAMs identified novel genes required for surface expression of the AM marker Siglec-F, most notably those related to the peroxisome. Taken together, our results suggest that FLAMs are a stable, self-replicating model of AM function that enables previously impossible global genetic approaches to define the underlying mechanisms of AM maintenance and function.

Enhancer RNA: What we know and what we can achieve

Enhancers are important cis-acting elements that can regulate gene transcription and cell fate alongside promoters. In fact, many human cancers and diseases are associated with the malfunction of enhancers. Recent studies have shown that enhancers can produce enhancer RNAs (eRNAs) by RNA polymerase II. In this review, we discuss eRNA production, characteristics, functions and mechanics. eRNAs can determine chromatin accessibility, histone modification and gene expression by constructing a 'chromatin loop', thereby bringing enhancers to their target gene. eRNA can also be involved in the phase separation with enhancers and other proteins. eRNAs are abundant, and importantly, tissue-specific in tumours, various diseases and stem cells; thus, eRNAs can be a potential target for disease diagnosis and treatment. As eRNA is produced from the active transcription of enhancers and is involved in the regulation of cell fate, its manipulation will influence cell function, and therefore, it can be a new target for biological therapy.

Screening of ETO2-GLIS2-induced Super Enhancers identifies targetable cooperative dependencies in acute megakaryoblastic leukemia

Super Enhancers (SEs) are clusters of regulatory elements associated with cell identity and disease. However, whether these elements are induced by oncogenes and can regulate gene modules cooperating for cancer cell transformation or maintenance remains elusive. To address this question, we conducted a genome-wide CRISPRi-based screening of SEs in ETO2-GLIS2⁺ acute megakaryoblastic leukemia. This approach revealed SEs essential for leukemic cell growth and survival that are induced by ETO2-GLIS2 expression. In particular, we identified a de novo SE specific of this leukemia subtype and regulating expression of tyrosine kinase-associated receptors KIT and PDGFRA. Combined expression of these two receptors was required for leukemic cell growth, and CRISPRi-mediated inhibition of this SE or treatment with tyrosine kinase inhibitors impaired progression of leukemia in vivo in patient-derived xenografts experiments. Our results show that fusion oncogenes, such as ETO2-GLIS2, can induce activation of SEs regulating essential gene modules synergizing for leukemia progression.

CRISPR activation and interference screens decode stimulation responses in primary human T cells

Regulation of cytokine production in stimulated T cells can be disrupted in autoimmunity, immunodeficiencies, and cancer. Systematic discovery of stimulation-dependent cytokine regulators requires both loss-of-function and gain-of-function studies, which have been challenging in primary human cells. We now report genome-wide CRISPR activation (CRISPRa) and interference (CRISPRi) screens in primary human T cells to identify gene networks controlling interleukin-2 (IL-2) and interferon-γ (IFN-γ) production. Arrayed CRISPRa confirmed key hits and enabled multiplexed secretome characterization, revealing reshaped cytokine responses. Coupling CRISPRa screening with single-cell RNA sequencing enabled deep molecular characterization of screen hits, revealing how perturbations tuned T cell activation and promoted cell states characterized by distinct cytokine expression profiles. These screens reveal genes that reprogram critical immune cell functions, which could inform the design of immunotherapies.

Bacon: a comprehensive computational benchmarking framework for evaluating targeted chromatin conformation capture-specific methodologies

Chromatin conformation capture (3C)-based technologies have enabled the accurate detection of topological genomic interactions, and the adoption of ChIP techniques to 3C-based protocols makes it possible to identify long-range interactions. To analyze these large and complex datasets, computational methods are undergoing rapid and expansive evolution. Thus, a thorough evaluation of these analytical pipelines is necessary to identify which commonly used algorithms and processing pipelines need to be improved. Here we present a comprehensive benchmark framework, Bacon, to evaluate the performance of several computational methods. Finally, we provide practical recommendations for users working with HiChIP and/or ChIA-PET analyses.

Distal and proximal cis-regulatory elements sense X chromosome dosage and developmental state at the Xist locus

Developmental genes such as Xist, which initiates X chromosome inactivation, are controlled by complex cis-regulatory landscapes, which decode multiple signals to establish specific spatiotemporal expression patterns. Xist integrates information on X chromosome dosage and developmental stage to trigger X inactivation in the epiblast specifically in female embryos. Through a pooled CRISPR screen in differentiating mouse embryonic stem cells, we identify functional enhancer elements of Xist at the onset of random X inactivation. Chromatin profiling reveals that X-dosage controls the promoter-proximal region, while differentiation cues activate several distal enhancers. The strongest distal element lies in an enhancer cluster associated with a previously unannotated Xist-enhancing regulatory transcript, which we named Xert. Developmental cues and X-dosage are thus decoded by distinct regulatory regions, which cooperate to ensure female-specific Xist upregulation at the correct developmental time. With this study, we start to disentangle how multiple, functionally distinct regulatory elements interact to generate complex expression patterns in mammals.

CRISPRi links COVID-19 GWAS loci to LZTFL1 and RAVER1

Background

To identify host genetic variants (SNPs) associated with COVID-19 disease severity, a number of genome-wide association studies (GWAS) have been conducted. Since most of the identified variants are located at non-coding regions, such variants are presumed to affect the expression of neighbouring genes, thereby influencing COVID-19 disease severity. However, it remains largely unknown which genes are influenced by such COVID-19 GWAS loci.

Methods

CRISPRi (interference)-mediated gene expression analysis was performed to identify genes functionally regulated by COVID-19 GWAS loci by targeting regions near the loci (SNPs) in lung epithelial cell lines. The expression of CRISPRi-identified genes was investigated using COVID-19-contracted human and monkey lung single-nucleus/cell (sn/sc) RNA-seq datasets.

Findings

CRISPRi analysis indicated that a region near rs11385942 at chromosome 3p21.31 (locus of highest significance with COVID-19 disease severity at intron 5 of LZTFL1) significantly affected the expression of LZTFL1 (P<0.05), an airway cilia regulator. A region near rs74956615 at chromosome 19p13.2 (locus located at the 3’ untranslated exonic region of RAVER1), which is associated with critical illness in COVID-19, affected the expression of RAVER1 (P<0.05), a coactivator of MDA5 (IFIH1), which induces antiviral response genes, including ICAM1. The sn/scRNA-seq datasets indicated that the MDA5/RAVER1-ICAM1 pathway was activated in lung epithelial cells of COVID-19-resistant monkeys but not those of COVID-19-succumbed humans.

Interpretation

Patients with risk alleles of rs11385942 and rs74956615 may be susceptible to critical illness in COVID-19 in part through weakened airway viral clearance via LZTFL1-mediated ciliogenesis and diminished antiviral immune response via the MDA5/RAVER1 pathway, respectively.

Funding

NIH.

Functional interrogation of autoimmune disease genetics using CRISPR/Cas9 technologies and massively parallel reporter assays

Genetic studies, including genome-wide association studies, have identified many common variants that are associated with autoimmune diseases. Strikingly, in addition to being frequently observed in healthy individuals, a number of these variants are shared across diseases with diverse clinical presentations. This highlights the potential for improved autoimmune disease understanding which could be achieved by characterising the mechanism by which variants lead to increased risk of disease. Of particular interest is the potential for identifying novel drug targets or of repositioning drugs currently used in other diseases. The majority of autoimmune disease variants do not alter coding regions and it is often difficult to generate a plausible hypothetical mechanism by which variants affect disease-relevant genes and pathways. Given the interest in this area, considerable effort has been invested in developing and applying appropriate methodologies. Two of the most important technologies in this space include both low- and high-throughput genomic perturbation using the CRISPR/Cas9 system and massively parallel reporter assays. In this review, we introduce the field of autoimmune disease functional genomics and use numerous examples to demonstrate the recent and potential future impact of these technologies.

Systematic identification of genomic elements that regulate FCGR2A expression and harbor variants linked with autoimmune disease

BACKGROUND

FCGR2A binds antibody-antigen complexes to regulate the abundance of circulating and deposited complexes along with downstream immune and autoimmune responses. Although the abundance of FCRG2A may be critical in immune-mediated diseases, little is known about whether its surface expression is regulated through cis genomic elements and non-coding variants. In the current study, we aimed to characterize the regulation of FCGR2A expression, the impact of genetic variation and its association with autoimmune disease.

METHODS

We applied CRISPR-based interference and editing to scrutinize 1.7 Mb of open chromatin surrounding the FCGR2A gene to identify regulatory elements. Relevant transcription factors (TFs) binding to these regions were defined through public databases. Genetic variants affecting regulation were identified using luciferase reporter assays and were verified in a cohort of 1996 genotyped healthy individuals using flow cytometry.

RESULTS

We identified a complex proximal region and five distal enhancers regulating FCGR2A. The proximal region split into subregions upstream and downstream of the transcription start site, was enriched in binding of inflammation-regulated TFs, and harbored a variant associated with FCGR2A expression in primary myeloid cells. One distal enhancer region was occupied by CCCTC-binding factor (CTCF) whose binding site was disrupted by a rare genetic variant, altering gene expression.

CONCLUSIONS

The FCGR2A gene is regulated by multiple proximal and distal genomic regions, with links to autoimmune disease. These findings may open up novel therapeutic avenues where fine-tuning of FCGR2A levels may constitute a part of treatment strategies for immune-mediated diseases.

Genome-wide identification of enhancers and transcription factors regulating the myogenic differentiation of bovine satellite cells

BACKGROUND

Satellite cells are the myogenic precursor cells in adult skeletal muscle. The objective of this study was to identify enhancers and transcription factors that regulate gene expression during the differentiation of bovine satellite cells into myotubes.

RESULTS

Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) was performed to identify genomic regions where lysine 27 of H3 histone is acetylated (H3K27ac), i.e., active enhancers, from bovine satellite cells before and during differentiation into myotubes. A total of 19,027 and 47,669 H3K27ac-marked enhancers were consistently identified from two biological replicates of before- and during-differentiation bovine satellite cells, respectively. Of these enhancers, 5882 were specific to before-differentiation, 35,723 to during-differentiation, and 13,199 common to before- and during-differentiation bovine satellite cells. Whereas most of the before- or during-differentiation-specific H3K27ac-marked enhancers were located distally to the transcription start site, the enhancers common to before- and during-differentiation were located both distally and proximally to the transcription start site. The three sets of H3K27ac-marked enhancers were associated with functionally different genes and enriched with different transcription factor binding sites. Specifically, many of the H3K27ac-marked enhancers specific to during-differentiation bovine satellite cells were associated with genes involved in muscle structure and development, and were enriched with binding sites for the MyoD, AP-1, KLF, TEAD, and MEF2 families of transcription factors. A positive role was validated for Fos and FosB, two AP-1 family transcription factors, in the differentiation of bovine satellite cells into myotubes by siRNA-mediated knockdown.

CONCLUSIONS

Tens of thousands of H3K27ac-marked active enhancers have been identified from bovine satellite cells before or during differentiation. These enhancers contain binding sites not only for transcription factors whose role in satellite cell differentiation is well known but also for transcription factors whose role in satellite cell differentiation is unknown. These enhancers and transcription factors are valuable resources for understanding the complex mechanism that mediates gene expression during satellite cell differentiation. Because satellite cell differentiation is a key step in skeletal muscle growth, the enhancers, the transcription factors, and their target genes identified in this study are also valuable resources for identifying and interpreting skeletal muscle trait-associated DNA variants in cattle.

CRISPR-Based Approaches for the High-Throughput Characterization of Long Non-Coding RNAs

Over the last decade, tens of thousands of new long non-coding RNAs (lncRNAs) have been identified in the human genome. Nevertheless, except for a handful of genes, the genetic characteristics and functions of most of these lncRNAs remain elusive; this is partially due to their relatively low expression, high tissue specificity, and low conservation across species. A major limitation for determining the function of lncRNAs was the lack of methodologies suitable for studying these genes. The recent development of CRISPR/Cas9 technology has opened unprecedented opportunities to uncover the genetic and functional characteristics of the non-coding genome via targeted and high-throughput approaches. Specific CRISPR/Cas9-based approaches were developed to target lncRNA loci. Some of these approaches involve modifying the sequence, but others were developed to study lncRNAs by inducing transcriptional and epigenetic changes. The discovery of other programable Cas proteins broaden our possibilities to target RNA molecules with greater precision and accuracy. These approaches allow for the knock-down and characterization of lncRNAs. Here, we review how various CRISPR-based strategies have been used to characterize lncRNAs with important functions in different biological contexts and how these approaches can be further utilized to improve our understanding of the non-coding genome.

Intronic elements associated with insomnia and restless legs syndrome exhibit cell type-specific epigenetic features contributing to MEIS1 regulation

A highly evolutionarily conserved MEIS1 intronic region is strongly associated with restless legs syndrome (RLS) and insomnia. To understand its regulatory function, we dissected the region by analyzing chromatin accessibility, enhancer-promoter contacts, DNA methylation, and eQTLs in different human neural cell types and tissues. We observed specific activity with respect to cell type and developmental maturation, indicating a prominent role for distinct highly conserved intronic elements in forebrain inhibitory neuron differentiation. Two elements were hypomethylated in neural cells with higher MEIS1 expression, suggesting a role of enhancer demethylation in gene regulation. MEIS1 eQTLs showed a striking modular chromosomal distribution, with forebrain eQTLs clustering in intron 8/9. CRISPR interference targeting of individual elements in this region attenuated MEIS1 expression, revealing a complex regulatory interplay of distinct elements. In summary, we found that MEIS1 regulation is organized in a modular pattern. Disease-associated intronic regulatory elements control MEIS1 expression with cell type and maturation stage specificity, particularly in the inhibitory neuron lineage. The precise spatiotemporal activity of these elements likely contributes to the pathogenesis of insomnia and RLS.

Interrogating lncRNA functions via CRISPR/Cas systems

Long noncoding RNAs (lncRNAs) are an increasing focus of investigation due to their implications in diverse biological processes and disease. Nevertheless, the majority of lncRNAs are low in abundance and poorly conserved, posing challenges to functional studies. The CRISPR/Cas system, an innovative technology that has emerged over the last decade, can be utilized to further understand lncRNA function. The system targets specific DNA and/or RNA sequences via a guide RNA (gRNA) and Cas nuclease complex. We and others have utilized this technology in various applications such as lncRNA knockout, knockdown, overexpression, and imaging. In this review, we summarize how the CRISPR/Cas technology provides new tools to investigate the roles and therapeutic implications of lncRNAs.